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1_Pooling/config.json

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+{
+  "word_embedding_dimension": 768,
+  "pooling_mode_cls_token": false,
+  "pooling_mode_mean_tokens": true,
+  "pooling_mode_max_tokens": false,
+  "pooling_mode_mean_sqrt_len_tokens": false,
+  "pooling_mode_weightedmean_tokens": false,
+  "pooling_mode_lasttoken": false,
+  "include_prompt": true
+}

README.md

@@ -0,0 +1,566 @@
+---
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:10
+- loss:MatryoshkaLoss
+- loss:MultipleNegativesRankingLoss
+base_model: nreimers/albert-small-v2
+widget:
+- source_sentence: What processes are used to separate the raw liquid mix from natural
+    gas in a gas recycling plant?
+  sentences:
+  - '2016 ►M43 (*1) ◄ 21 September 2017 ►M43 (*2) ◄ [▼M28](./../../../legal-content/EN/AUTO/?uri=celex:32014R0895
+    "32014R0895: INSERTED") 23. Formaldehyde, oligomeric reaction products with aniline
+    (technical MDA) EC No: 500-036-1 CAS No: 25214-70-4 Carcinogenic (category 1B)
+    22 February 2016 ►M43 (*1) ◄ 22 August 2017 ►M43 (*2) ◄ — 24. Arsenic acid EC
+    No: 231-901-9 CAS No: 7778-39-4 Carcinogenic (category 1A) 22 February 2016 22
+    August 2017 — 25. Bis(2-methoxyethyl) ether (diglyme) EC No: 203-924-4 CAS No:
+    111-96-6 Toxic for reproduction (category 1B) 22 February 2016 ►M43 (*1) ◄ 22
+    August 2017 ►M43 (*2) ◄ — 26. 1,2-dichloroethane (EDC) EC No: 203-458-1 CAS No:
+    107-06-2 Carcinogenic (category 1B) 22 May 2016 22 November 2017 — 27.'
+  - '1. Member States shall ensure that their competent authorities establish at least
+    one AI regulatory sandbox at national level, which shall be operational by 2 August
+    2026. That sandbox may also be established jointly with the competent authorities
+    of other Member States. The Commission may provide technical support, advice and
+    tools for the establishment and operation of AI regulatory sandboxes.
+
+
+    The obligation under the first subparagraph may also be fulfilled by participating
+    in an existing sandbox in so far as that participation provides an equivalent
+    level of national coverage for the participating Member States.'
+  - and that boils in a range of approximately 149 °C to 205 °C.) 649-345-00-4 232-489-3
+    8052-41-3 P Natural gas condensates (petroleum); Low boiling point naphtha — unspecified
+    (A complex combination of hydrocarbons separated as a liquid from natural gas
+    in a surface separator by retrograde condensation. It consists mainly of hydrocarbons
+    having carbon numbers predominantly in the range of C2 to C20. It is a liquid
+    at atmospheric temperature and pressure.) 649-346-00-X 265-047-3 64741-47-5 P
+    Natural gas (petroleum), raw liquid mix; Low boiling point naphtha — unspecified
+    (A complex combination of hydrocarbons separated as a liquid from natural gas
+    in a gas recycling plant by processes such as refrigeration or absorption. It
+    consists mainly of
+- source_sentence: What should the report on income tax information include as per
+    Article 48c?
+  sentences:
+  - '(d)
+
+
+    seal any business premises and books or records for the period of time of, and
+    to the extent necessary for, the inspection.
+
+
+    3.
+
+
+    The undertaking or association of undertakings shall submit to inspections ordered
+    by decision of the Commission. The officials and other accompanying persons authorised
+    by the Commission to conduct an inspection shall exercise their powers upon production
+    of a Commission decision:
+
+
+    (a)
+
+
+    specifying the subject matter and purpose of the inspection;
+
+
+    (b)
+
+
+    containing a statement that, pursuant to Article 16, a lack of cooperation allows
+    the Commission to take a decision on the basis of the facts that are available
+    to it;
+
+
+    (c)'
+  - 'By way of derogation from Article 10c, the Member States concerned may only give
+    transitional free allocation to installations in accordance with that Article
+    for investments carried out until 31 December 2024. Any allowances available to
+    the Member States concerned in accordance with Article 10c for the period from
+    2021 to 2030 that are not used for such investments shall, in the proportion determined
+    by the respective Member State:
+
+
+    (a)
+
+
+    be added to the total quantity of allowances that the Member State concerned is
+    to auction pursuant to Article 10(2); or
+
+
+    (b)'
+  - '7.
+
+
+    Member States shall require subsidiary undertakings or branches not subject to
+    the provisions of paragraphs 4 and 5 of this Article to publish and make accessible
+    a report on income tax information where such subsidiary undertakings or branches
+    serve no other objective than to circumvent the reporting requirements set out
+    in this Chapter.
+
+
+    Article 48c
+
+
+    Content of the report on income tax information
+
+
+    1.
+
+
+    The report on income tax information required under Article 48b shall include
+    information relating to all the activities of the standalone undertaking or ultimate
+    parent undertaking, including those of all affiliated undertakings consolidated
+    in the financial statements in respect of the relevant financial year.
+
+
+    2.'
+pipeline_tag: sentence-similarity
+library_name: sentence-transformers
+metrics:
+- cosine_accuracy@1
+- cosine_accuracy@3
+- cosine_accuracy@5
+- cosine_accuracy@10
+- cosine_precision@1
+- cosine_precision@3
+- cosine_precision@5
+- cosine_precision@10
+- cosine_recall@1
+- cosine_recall@3
+- cosine_recall@5
+- cosine_recall@10
+- cosine_ndcg@10
+- cosine_mrr@10
+- cosine_map@100
+model-index:
+- name: SentenceTransformer based on nreimers/albert-small-v2
+  results:
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: Unknown
+      type: unknown
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.7
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.7
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.8
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.9
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.7
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.23333333333333334
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.16
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.09
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.7
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.7
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.8
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.9
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.7675917633552429
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.7300000000000001
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.739090909090909
+      name: Cosine Map@100
+---
+
+# SentenceTransformer based on nreimers/albert-small-v2
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [nreimers/albert-small-v2](https://huggingface.co/nreimers/albert-small-v2). It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
+
+## Model Details
+
+### Model Description
+- **Model Type:** Sentence Transformer
+- **Base model:** [nreimers/albert-small-v2](https://huggingface.co/nreimers/albert-small-v2) <!-- at revision 18045fa83de53fd7d4548fdc2473862914cbc7d5 -->
+- **Maximum Sequence Length:** 512 tokens
+- **Output Dimensionality:** 768 dimensions
+- **Similarity Function:** Cosine Similarity
+<!-- - **Training Dataset:** Unknown -->
+<!-- - **Language:** Unknown -->
+<!-- - **License:** Unknown -->
+
+### Model Sources
+
+- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
+- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
+- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
+
+### Full Model Architecture
+
+```
+SentenceTransformer(
+  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: AlbertModel 
+  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
+)
+```
+
+## Usage
+
+### Direct Usage (Sentence Transformers)
+
+First install the Sentence Transformers library:
+
+```bash
+pip install -U sentence-transformers
+```
+
+Then you can load this model and run inference.
+```python
+from sentence_transformers import SentenceTransformer
+
+# Download from the 🤗 Hub
+model = SentenceTransformer("sentence_transformers_model_id")
+# Run inference
+sentences = [
+    'What should the report on income tax information include as per Article 48c?',
+    '7.\n\nMember States shall require subsidiary undertakings or branches not subject to the provisions of paragraphs 4 and 5 of this Article to publish and make accessible a report on income tax information where such subsidiary undertakings or branches serve no other objective than to circumvent the reporting requirements set out in this Chapter.\n\nArticle 48c\n\nContent of the report on income tax information\n\n1.\n\nThe report on income tax information required under Article 48b shall include information relating to all the activities of the standalone undertaking or ultimate parent undertaking, including those of all affiliated undertakings consolidated in the financial statements in respect of the relevant financial year.\n\n2.',
+    '(d)\n\nseal any business premises and books or records for the period of time of, and to the extent necessary for, the inspection.\n\n3.\n\nThe undertaking or association of undertakings shall submit to inspections ordered by decision of the Commission. The officials and other accompanying persons authorised by the Commission to conduct an inspection shall exercise their powers upon production of a Commission decision:\n\n(a)\n\nspecifying the subject matter and purpose of the inspection;\n\n(b)\n\ncontaining a statement that, pursuant to Article 16, a lack of cooperation allows the Commission to take a decision on the basis of the facts that are available to it;\n\n(c)',
+]
+embeddings = model.encode(sentences)
+print(embeddings.shape)
+# [3, 768]
+
+# Get the similarity scores for the embeddings
+similarities = model.similarity(embeddings, embeddings)
+print(similarities.shape)
+# [3, 3]
+```
+
+<!--
+### Direct Usage (Transformers)
+
+<details><summary>Click to see the direct usage in Transformers</summary>
+
+</details>
+-->
+
+<!--
+### Downstream Usage (Sentence Transformers)
+
+You can finetune this model on your own dataset.
+
+<details><summary>Click to expand</summary>
+
+</details>
+-->
+
+<!--
+### Out-of-Scope Use
+
+*List how the model may foreseeably be misused and address what users ought not to do with the model.*
+-->
+
+## Evaluation
+
+### Metrics
+
+#### Information Retrieval
+
+* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
+
+| Metric              | Value      |
+|:--------------------|:-----------|
+| cosine_accuracy@1   | 0.7        |
+| cosine_accuracy@3   | 0.7        |
+| cosine_accuracy@5   | 0.8        |
+| cosine_accuracy@10  | 0.9        |
+| cosine_precision@1  | 0.7        |
+| cosine_precision@3  | 0.2333     |
+| cosine_precision@5  | 0.16       |
+| cosine_precision@10 | 0.09       |
+| cosine_recall@1     | 0.7        |
+| cosine_recall@3     | 0.7        |
+| cosine_recall@5     | 0.8        |
+| cosine_recall@10    | 0.9        |
+| **cosine_ndcg@10**  | **0.7676** |
+| cosine_mrr@10       | 0.73       |
+| cosine_map@100      | 0.7391     |
+
+<!--
+## Bias, Risks and Limitations
+
+*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
+-->
+
+<!--
+### Recommendations
+
+*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
+-->
+
+## Training Details
+
+### Training Dataset
+
+#### Unnamed Dataset
+
+* Size: 10 training samples
+* Columns: <code>query_text</code> and <code>doc_text</code>
+* Approximate statistics based on the first 10 samples:
+  |         | query_text                                                                        | doc_text                                                                             |
+  |:--------|:----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
+  | type    | string                                                                            | string                                                                               |
+  | details | <ul><li>min: 17 tokens</li><li>mean: 38.6 tokens</li><li>max: 89 tokens</li></ul> | <ul><li>min: 113 tokens</li><li>mean: 237.7 tokens</li><li>max: 512 tokens</li></ul> |
+* Samples:
+  | query_text                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       | doc_text                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       |
+  |:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+  | <code>What are the requirements for Member States regarding the establishment of AI regulatory sandboxes, including the timeline for operational readiness and the possibility of joint establishment with other Member States?</code>                                                                                                                                                                                                                                                                                                           | <code>1. Member States shall ensure that their competent authorities establish at least one AI regulatory sandbox at national level, which shall be operational by 2 August 2026. That sandbox may also be established jointly with the competent authorities of other Member States. The Commission may provide technical support, advice and tools for the establishment and operation of AI regulatory sandboxes.<br><br>The obligation under the first subparagraph may also be fulfilled by participating in an existing sandbox in so far as that participation provides an equivalent level of national coverage for the participating Member States.</code>                                                                                                                                                                                                                                                                                                                                                                                            |
+  | <code>Member States must provide updates on their national energy and climate strategies, detailing the anticipated energy savings from 2021 to 2030. They are also obligated to report on the necessary energy savings and the policies intended to achieve these goals. If assessments reveal that a Member State's measures are inadequate to meet energy savings targets, the Commission may issue recommendations for improvement. Additionally, any shortfall in energy savings must be addressed in subsequent obligation periods.</code> | <code>9. Member States shall apply and calculate the effect of the options chosen under paragraph 8 for the period referred to in paragraph 1, first subparagraph, points (a) and (b)(i), separately:<br><br>(a) for the calculation of the amount of energy savings required for the obligation period referred to in paragraph 1, first subparagraph, point (a), Member States may make use of the options listed in paragraph 8, points (a) to (d). All the options chosen under paragraph 8 taken together shall amount to no more than 25 % of the amount of energy savings referred to in paragraph 1, first subparagraph, point (a); (b) for the calculation of the amount of energy savings required for the obligation period referred to in paragraph 1, first subparagraph, point (b)(i), Member States may make use of the options listed in paragraph 8, points (b) to (g), provided that the individual actions referred to in paragraph 8, point (d), continue to have a verifiable and measurable impact after 31 December 2020. All...</code> |
+  | <code>What is the functional definition of a remote biometric identification system, and how does it operate in terms of identifying individuals without their active participation?</code>                                                                                                                                                                                                                                                                                                                                                      | <code>(17) The notion of ‘remote biometric identification system’ referred to in this Regulation should be defined functionally, as an AI system intended for the identification of natural persons without their active involvement, typically at a distance, through the comparison of a person’s biometric data with the biometric data contained in a reference database, irrespectively of the particular technology, processes or types of biometric data used. Such remote biometric identification systems are typically used to perceive multiple persons or their behaviour simultaneously in order to facilitate significantly the identification of natural persons without their active involvement. This excludes AI systems intended to be used for biometric</code>                                                                                                                                                                                                                                                                            |
+* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
+  ```json
+  {
+      "loss": "MultipleNegativesRankingLoss",
+      "matryoshka_dims": [
+          768,
+          512,
+          256,
+          128,
+          64
+      ],
+      "matryoshka_weights": [
+          1,
+          1,
+          1,
+          1,
+          1
+      ],
+      "n_dims_per_step": -1
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `eval_strategy`: steps
+- `per_device_train_batch_size`: 3
+- `per_device_eval_batch_size`: 3
+- `learning_rate`: 2e-05
+- `num_train_epochs`: 1
+- `warmup_ratio`: 0.1
+- `fp16`: True
+- `load_best_model_at_end`: True
+
+#### All Hyperparameters
+<details><summary>Click to expand</summary>
+
+- `overwrite_output_dir`: False
+- `do_predict`: False
+- `eval_strategy`: steps
+- `prediction_loss_only`: True
+- `per_device_train_batch_size`: 3
+- `per_device_eval_batch_size`: 3
+- `per_gpu_train_batch_size`: None
+- `per_gpu_eval_batch_size`: None
+- `gradient_accumulation_steps`: 1
+- `eval_accumulation_steps`: None
+- `torch_empty_cache_steps`: None
+- `learning_rate`: 2e-05
+- `weight_decay`: 0.0
+- `adam_beta1`: 0.9
+- `adam_beta2`: 0.999
+- `adam_epsilon`: 1e-08
+- `max_grad_norm`: 1.0
+- `num_train_epochs`: 1
+- `max_steps`: -1
+- `lr_scheduler_type`: linear
+- `lr_scheduler_kwargs`: {}
+- `warmup_ratio`: 0.1
+- `warmup_steps`: 0
+- `log_level`: passive
+- `log_level_replica`: warning
+- `log_on_each_node`: True
+- `logging_nan_inf_filter`: True
+- `save_safetensors`: True
+- `save_on_each_node`: False
+- `save_only_model`: False
+- `restore_callback_states_from_checkpoint`: False
+- `no_cuda`: False
+- `use_cpu`: False
+- `use_mps_device`: False
+- `seed`: 42
+- `data_seed`: None
+- `jit_mode_eval`: False
+- `use_ipex`: False
+- `bf16`: False
+- `fp16`: True
+- `fp16_opt_level`: O1
+- `half_precision_backend`: auto
+- `bf16_full_eval`: False
+- `fp16_full_eval`: False
+- `tf32`: None
+- `local_rank`: 0
+- `ddp_backend`: None
+- `tpu_num_cores`: None
+- `tpu_metrics_debug`: False
+- `debug`: []
+- `dataloader_drop_last`: False
+- `dataloader_num_workers`: 0
+- `dataloader_prefetch_factor`: None
+- `past_index`: -1
+- `disable_tqdm`: False
+- `remove_unused_columns`: True
+- `label_names`: None
+- `load_best_model_at_end`: True
+- `ignore_data_skip`: False
+- `fsdp`: []
+- `fsdp_min_num_params`: 0
+- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
+- `fsdp_transformer_layer_cls_to_wrap`: None
+- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
+- `deepspeed`: None
+- `label_smoothing_factor`: 0.0
+- `optim`: adamw_torch
+- `optim_args`: None
+- `adafactor`: False
+- `group_by_length`: False
+- `length_column_name`: length
+- `ddp_find_unused_parameters`: None
+- `ddp_bucket_cap_mb`: None
+- `ddp_broadcast_buffers`: False
+- `dataloader_pin_memory`: True
+- `dataloader_persistent_workers`: False
+- `skip_memory_metrics`: True
+- `use_legacy_prediction_loop`: False
+- `push_to_hub`: False
+- `resume_from_checkpoint`: None
+- `hub_model_id`: None
+- `hub_strategy`: every_save
+- `hub_private_repo`: None
+- `hub_always_push`: False
+- `gradient_checkpointing`: False
+- `gradient_checkpointing_kwargs`: None
+- `include_inputs_for_metrics`: False
+- `include_for_metrics`: []
+- `eval_do_concat_batches`: True
+- `fp16_backend`: auto
+- `push_to_hub_model_id`: None
+- `push_to_hub_organization`: None
+- `mp_parameters`: 
+- `auto_find_batch_size`: False
+- `full_determinism`: False
+- `torchdynamo`: None
+- `ray_scope`: last
+- `ddp_timeout`: 1800
+- `torch_compile`: False
+- `torch_compile_backend`: None
+- `torch_compile_mode`: None
+- `dispatch_batches`: None
+- `split_batches`: None
+- `include_tokens_per_second`: False
+- `include_num_input_tokens_seen`: False
+- `neftune_noise_alpha`: None
+- `optim_target_modules`: None
+- `batch_eval_metrics`: False
+- `eval_on_start`: False
+- `use_liger_kernel`: False
+- `eval_use_gather_object`: False
+- `average_tokens_across_devices`: False
+- `prompts`: None
+- `batch_sampler`: batch_sampler
+- `multi_dataset_batch_sampler`: proportional
+
+</details>
+
+### Training Logs
+| Epoch | Step | cosine_ndcg@10 |
+|:-----:|:----:|:--------------:|
+| -1    | -1   | 0.7676         |
+
+
+### Framework Versions
+- Python: 3.11.10
+- Sentence Transformers: 4.0.2
+- Transformers: 4.49.0
+- PyTorch: 2.6.0+cu124
+- Accelerate: 0.26.0
+- Datasets: 3.1.0
+- Tokenizers: 0.21.2
+
+## Citation
+
+### BibTeX
+
+#### Sentence Transformers
+```bibtex
+@inproceedings{reimers-2019-sentence-bert,
+    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
+    author = "Reimers, Nils and Gurevych, Iryna",
+    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
+    month = "11",
+    year = "2019",
+    publisher = "Association for Computational Linguistics",
+    url = "https://arxiv.org/abs/1908.10084",
+}
+```
+
+#### MatryoshkaLoss
+```bibtex
+@misc{kusupati2024matryoshka,
+    title={Matryoshka Representation Learning},
+    author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi},
+    year={2024},
+    eprint={2205.13147},
+    archivePrefix={arXiv},
+    primaryClass={cs.LG}
+}
+```
+
+#### MultipleNegativesRankingLoss
+```bibtex
+@misc{henderson2017efficient,
+    title={Efficient Natural Language Response Suggestion for Smart Reply},
+    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
+    year={2017},
+    eprint={1705.00652},
+    archivePrefix={arXiv},
+    primaryClass={cs.CL}
+}
+```
+
+<!--
+## Glossary
+
+*Clearly define terms in order to be accessible across audiences.*
+-->
+
+<!--
+## Model Card Authors
+
+*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
+-->
+
+<!--
+## Model Card Contact
+
+*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
+-->

checkpoint-4/1_Pooling/config.json

@@ -0,0 +1,10 @@
+{
+  "word_embedding_dimension": 768,
+  "pooling_mode_cls_token": false,
+  "pooling_mode_mean_tokens": true,
+  "pooling_mode_max_tokens": false,
+  "pooling_mode_mean_sqrt_len_tokens": false,
+  "pooling_mode_weightedmean_tokens": false,
+  "pooling_mode_lasttoken": false,
+  "include_prompt": true
+}

checkpoint-4/README.md

@@ -0,0 +1,566 @@
+---
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:10
+- loss:MatryoshkaLoss
+- loss:MultipleNegativesRankingLoss
+base_model: nreimers/albert-small-v2
+widget:
+- source_sentence: What processes are used to separate the raw liquid mix from natural
+    gas in a gas recycling plant?
+  sentences:
+  - '2016 ►M43 (*1) ◄ 21 September 2017 ►M43 (*2) ◄ [▼M28](./../../../legal-content/EN/AUTO/?uri=celex:32014R0895
+    "32014R0895: INSERTED") 23. Formaldehyde, oligomeric reaction products with aniline
+    (technical MDA) EC No: 500-036-1 CAS No: 25214-70-4 Carcinogenic (category 1B)
+    22 February 2016 ►M43 (*1) ◄ 22 August 2017 ►M43 (*2) ◄ — 24. Arsenic acid EC
+    No: 231-901-9 CAS No: 7778-39-4 Carcinogenic (category 1A) 22 February 2016 22
+    August 2017 — 25. Bis(2-methoxyethyl) ether (diglyme) EC No: 203-924-4 CAS No:
+    111-96-6 Toxic for reproduction (category 1B) 22 February 2016 ►M43 (*1) ◄ 22
+    August 2017 ►M43 (*2) ◄ — 26. 1,2-dichloroethane (EDC) EC No: 203-458-1 CAS No:
+    107-06-2 Carcinogenic (category 1B) 22 May 2016 22 November 2017 — 27.'
+  - '1. Member States shall ensure that their competent authorities establish at least
+    one AI regulatory sandbox at national level, which shall be operational by 2 August
+    2026. That sandbox may also be established jointly with the competent authorities
+    of other Member States. The Commission may provide technical support, advice and
+    tools for the establishment and operation of AI regulatory sandboxes.
+
+
+    The obligation under the first subparagraph may also be fulfilled by participating
+    in an existing sandbox in so far as that participation provides an equivalent
+    level of national coverage for the participating Member States.'
+  - and that boils in a range of approximately 149 °C to 205 °C.) 649-345-00-4 232-489-3
+    8052-41-3 P Natural gas condensates (petroleum); Low boiling point naphtha — unspecified
+    (A complex combination of hydrocarbons separated as a liquid from natural gas
+    in a surface separator by retrograde condensation. It consists mainly of hydrocarbons
+    having carbon numbers predominantly in the range of C2 to C20. It is a liquid
+    at atmospheric temperature and pressure.) 649-346-00-X 265-047-3 64741-47-5 P
+    Natural gas (petroleum), raw liquid mix; Low boiling point naphtha — unspecified
+    (A complex combination of hydrocarbons separated as a liquid from natural gas
+    in a gas recycling plant by processes such as refrigeration or absorption. It
+    consists mainly of
+- source_sentence: What should the report on income tax information include as per
+    Article 48c?
+  sentences:
+  - '(d)
+
+
+    seal any business premises and books or records for the period of time of, and
+    to the extent necessary for, the inspection.
+
+
+    3.
+
+
+    The undertaking or association of undertakings shall submit to inspections ordered
+    by decision of the Commission. The officials and other accompanying persons authorised
+    by the Commission to conduct an inspection shall exercise their powers upon production
+    of a Commission decision:
+
+
+    (a)
+
+
+    specifying the subject matter and purpose of the inspection;
+
+
+    (b)
+
+
+    containing a statement that, pursuant to Article 16, a lack of cooperation allows
+    the Commission to take a decision on the basis of the facts that are available
+    to it;
+
+
+    (c)'
+  - 'By way of derogation from Article 10c, the Member States concerned may only give
+    transitional free allocation to installations in accordance with that Article
+    for investments carried out until 31 December 2024. Any allowances available to
+    the Member States concerned in accordance with Article 10c for the period from
+    2021 to 2030 that are not used for such investments shall, in the proportion determined
+    by the respective Member State:
+
+
+    (a)
+
+
+    be added to the total quantity of allowances that the Member State concerned is
+    to auction pursuant to Article 10(2); or
+
+
+    (b)'
+  - '7.
+
+
+    Member States shall require subsidiary undertakings or branches not subject to
+    the provisions of paragraphs 4 and 5 of this Article to publish and make accessible
+    a report on income tax information where such subsidiary undertakings or branches
+    serve no other objective than to circumvent the reporting requirements set out
+    in this Chapter.
+
+
+    Article 48c
+
+
+    Content of the report on income tax information
+
+
+    1.
+
+
+    The report on income tax information required under Article 48b shall include
+    information relating to all the activities of the standalone undertaking or ultimate
+    parent undertaking, including those of all affiliated undertakings consolidated
+    in the financial statements in respect of the relevant financial year.
+
+
+    2.'
+pipeline_tag: sentence-similarity
+library_name: sentence-transformers
+metrics:
+- cosine_accuracy@1
+- cosine_accuracy@3
+- cosine_accuracy@5
+- cosine_accuracy@10
+- cosine_precision@1
+- cosine_precision@3
+- cosine_precision@5
+- cosine_precision@10
+- cosine_recall@1
+- cosine_recall@3
+- cosine_recall@5
+- cosine_recall@10
+- cosine_ndcg@10
+- cosine_mrr@10
+- cosine_map@100
+model-index:
+- name: SentenceTransformer based on nreimers/albert-small-v2
+  results:
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: Unknown
+      type: unknown
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.7
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.7
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.8
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.9
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.7
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.23333333333333334
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.16
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.09
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.7
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.7
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.8
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.9
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.7675917633552429
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.7300000000000001
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.739090909090909
+      name: Cosine Map@100
+---
+
+# SentenceTransformer based on nreimers/albert-small-v2
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [nreimers/albert-small-v2](https://huggingface.co/nreimers/albert-small-v2). It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
+
+## Model Details
+
+### Model Description
+- **Model Type:** Sentence Transformer
+- **Base model:** [nreimers/albert-small-v2](https://huggingface.co/nreimers/albert-small-v2) <!-- at revision 18045fa83de53fd7d4548fdc2473862914cbc7d5 -->
+- **Maximum Sequence Length:** 512 tokens
+- **Output Dimensionality:** 768 dimensions
+- **Similarity Function:** Cosine Similarity
+<!-- - **Training Dataset:** Unknown -->
+<!-- - **Language:** Unknown -->
+<!-- - **License:** Unknown -->
+
+### Model Sources
+
+- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
+- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
+- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
+
+### Full Model Architecture
+
+```
+SentenceTransformer(
+  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: AlbertModel 
+  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
+)
+```
+
+## Usage
+
+### Direct Usage (Sentence Transformers)
+
+First install the Sentence Transformers library:
+
+```bash
+pip install -U sentence-transformers
+```
+
+Then you can load this model and run inference.
+```python
+from sentence_transformers import SentenceTransformer
+
+# Download from the 🤗 Hub
+model = SentenceTransformer("sentence_transformers_model_id")
+# Run inference
+sentences = [
+    'What should the report on income tax information include as per Article 48c?',
+    '7.\n\nMember States shall require subsidiary undertakings or branches not subject to the provisions of paragraphs 4 and 5 of this Article to publish and make accessible a report on income tax information where such subsidiary undertakings or branches serve no other objective than to circumvent the reporting requirements set out in this Chapter.\n\nArticle 48c\n\nContent of the report on income tax information\n\n1.\n\nThe report on income tax information required under Article 48b shall include information relating to all the activities of the standalone undertaking or ultimate parent undertaking, including those of all affiliated undertakings consolidated in the financial statements in respect of the relevant financial year.\n\n2.',
+    '(d)\n\nseal any business premises and books or records for the period of time of, and to the extent necessary for, the inspection.\n\n3.\n\nThe undertaking or association of undertakings shall submit to inspections ordered by decision of the Commission. The officials and other accompanying persons authorised by the Commission to conduct an inspection shall exercise their powers upon production of a Commission decision:\n\n(a)\n\nspecifying the subject matter and purpose of the inspection;\n\n(b)\n\ncontaining a statement that, pursuant to Article 16, a lack of cooperation allows the Commission to take a decision on the basis of the facts that are available to it;\n\n(c)',
+]
+embeddings = model.encode(sentences)
+print(embeddings.shape)
+# [3, 768]
+
+# Get the similarity scores for the embeddings
+similarities = model.similarity(embeddings, embeddings)
+print(similarities.shape)
+# [3, 3]
+```
+
+<!--
+### Direct Usage (Transformers)
+
+<details><summary>Click to see the direct usage in Transformers</summary>
+
+</details>
+-->
+
+<!--
+### Downstream Usage (Sentence Transformers)
+
+You can finetune this model on your own dataset.
+
+<details><summary>Click to expand</summary>
+
+</details>
+-->
+
+<!--
+### Out-of-Scope Use
+
+*List how the model may foreseeably be misused and address what users ought not to do with the model.*
+-->
+
+## Evaluation
+
+### Metrics
+
+#### Information Retrieval
+
+* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
+
+| Metric              | Value      |
+|:--------------------|:-----------|
+| cosine_accuracy@1   | 0.7        |
+| cosine_accuracy@3   | 0.7        |
+| cosine_accuracy@5   | 0.8        |
+| cosine_accuracy@10  | 0.9        |
+| cosine_precision@1  | 0.7        |
+| cosine_precision@3  | 0.2333     |
+| cosine_precision@5  | 0.16       |
+| cosine_precision@10 | 0.09       |
+| cosine_recall@1     | 0.7        |
+| cosine_recall@3     | 0.7        |
+| cosine_recall@5     | 0.8        |
+| cosine_recall@10    | 0.9        |
+| **cosine_ndcg@10**  | **0.7676** |
+| cosine_mrr@10       | 0.73       |
+| cosine_map@100      | 0.7391     |
+
+<!--
+## Bias, Risks and Limitations
+
+*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
+-->
+
+<!--
+### Recommendations
+
+*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
+-->
+
+## Training Details
+
+### Training Dataset
+
+#### Unnamed Dataset
+
+* Size: 10 training samples
+* Columns: <code>query_text</code> and <code>doc_text</code>
+* Approximate statistics based on the first 10 samples:
+  |         | query_text                                                                        | doc_text                                                                             |
+  |:--------|:----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
+  | type    | string                                                                            | string                                                                               |
+  | details | <ul><li>min: 17 tokens</li><li>mean: 38.6 tokens</li><li>max: 89 tokens</li></ul> | <ul><li>min: 113 tokens</li><li>mean: 237.7 tokens</li><li>max: 512 tokens</li></ul> |
+* Samples:
+  | query_text                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       | doc_text                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       |
+  |:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+  | <code>What are the requirements for Member States regarding the establishment of AI regulatory sandboxes, including the timeline for operational readiness and the possibility of joint establishment with other Member States?</code>                                                                                                                                                                                                                                                                                                           | <code>1. Member States shall ensure that their competent authorities establish at least one AI regulatory sandbox at national level, which shall be operational by 2 August 2026. That sandbox may also be established jointly with the competent authorities of other Member States. The Commission may provide technical support, advice and tools for the establishment and operation of AI regulatory sandboxes.<br><br>The obligation under the first subparagraph may also be fulfilled by participating in an existing sandbox in so far as that participation provides an equivalent level of national coverage for the participating Member States.</code>                                                                                                                                                                                                                                                                                                                                                                                            |
+  | <code>Member States must provide updates on their national energy and climate strategies, detailing the anticipated energy savings from 2021 to 2030. They are also obligated to report on the necessary energy savings and the policies intended to achieve these goals. If assessments reveal that a Member State's measures are inadequate to meet energy savings targets, the Commission may issue recommendations for improvement. Additionally, any shortfall in energy savings must be addressed in subsequent obligation periods.</code> | <code>9. Member States shall apply and calculate the effect of the options chosen under paragraph 8 for the period referred to in paragraph 1, first subparagraph, points (a) and (b)(i), separately:<br><br>(a) for the calculation of the amount of energy savings required for the obligation period referred to in paragraph 1, first subparagraph, point (a), Member States may make use of the options listed in paragraph 8, points (a) to (d). All the options chosen under paragraph 8 taken together shall amount to no more than 25 % of the amount of energy savings referred to in paragraph 1, first subparagraph, point (a); (b) for the calculation of the amount of energy savings required for the obligation period referred to in paragraph 1, first subparagraph, point (b)(i), Member States may make use of the options listed in paragraph 8, points (b) to (g), provided that the individual actions referred to in paragraph 8, point (d), continue to have a verifiable and measurable impact after 31 December 2020. All...</code> |
+  | <code>What is the functional definition of a remote biometric identification system, and how does it operate in terms of identifying individuals without their active participation?</code>                                                                                                                                                                                                                                                                                                                                                      | <code>(17) The notion of ‘remote biometric identification system’ referred to in this Regulation should be defined functionally, as an AI system intended for the identification of natural persons without their active involvement, typically at a distance, through the comparison of a person’s biometric data with the biometric data contained in a reference database, irrespectively of the particular technology, processes or types of biometric data used. Such remote biometric identification systems are typically used to perceive multiple persons or their behaviour simultaneously in order to facilitate significantly the identification of natural persons without their active involvement. This excludes AI systems intended to be used for biometric</code>                                                                                                                                                                                                                                                                            |
+* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
+  ```json
+  {
+      "loss": "MultipleNegativesRankingLoss",
+      "matryoshka_dims": [
+          768,
+          512,
+          256,
+          128,
+          64
+      ],
+      "matryoshka_weights": [
+          1,
+          1,
+          1,
+          1,
+          1
+      ],
+      "n_dims_per_step": -1
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `eval_strategy`: steps
+- `per_device_train_batch_size`: 3
+- `per_device_eval_batch_size`: 3
+- `learning_rate`: 2e-05
+- `num_train_epochs`: 1
+- `warmup_ratio`: 0.1
+- `fp16`: True
+- `load_best_model_at_end`: True
+
+#### All Hyperparameters
+<details><summary>Click to expand</summary>
+
+- `overwrite_output_dir`: False
+- `do_predict`: False
+- `eval_strategy`: steps
+- `prediction_loss_only`: True
+- `per_device_train_batch_size`: 3
+- `per_device_eval_batch_size`: 3
+- `per_gpu_train_batch_size`: None
+- `per_gpu_eval_batch_size`: None
+- `gradient_accumulation_steps`: 1
+- `eval_accumulation_steps`: None
+- `torch_empty_cache_steps`: None
+- `learning_rate`: 2e-05
+- `weight_decay`: 0.0
+- `adam_beta1`: 0.9
+- `adam_beta2`: 0.999
+- `adam_epsilon`: 1e-08
+- `max_grad_norm`: 1.0
+- `num_train_epochs`: 1
+- `max_steps`: -1
+- `lr_scheduler_type`: linear
+- `lr_scheduler_kwargs`: {}
+- `warmup_ratio`: 0.1
+- `warmup_steps`: 0
+- `log_level`: passive
+- `log_level_replica`: warning
+- `log_on_each_node`: True
+- `logging_nan_inf_filter`: True
+- `save_safetensors`: True
+- `save_on_each_node`: False
+- `save_only_model`: False
+- `restore_callback_states_from_checkpoint`: False
+- `no_cuda`: False
+- `use_cpu`: False
+- `use_mps_device`: False
+- `seed`: 42
+- `data_seed`: None
+- `jit_mode_eval`: False
+- `use_ipex`: False
+- `bf16`: False
+- `fp16`: True
+- `fp16_opt_level`: O1
+- `half_precision_backend`: auto
+- `bf16_full_eval`: False
+- `fp16_full_eval`: False
+- `tf32`: None
+- `local_rank`: 0
+- `ddp_backend`: None
+- `tpu_num_cores`: None
+- `tpu_metrics_debug`: False
+- `debug`: []
+- `dataloader_drop_last`: False
+- `dataloader_num_workers`: 0
+- `dataloader_prefetch_factor`: None
+- `past_index`: -1
+- `disable_tqdm`: False
+- `remove_unused_columns`: True
+- `label_names`: None
+- `load_best_model_at_end`: True
+- `ignore_data_skip`: False
+- `fsdp`: []
+- `fsdp_min_num_params`: 0
+- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
+- `fsdp_transformer_layer_cls_to_wrap`: None
+- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
+- `deepspeed`: None
+- `label_smoothing_factor`: 0.0
+- `optim`: adamw_torch
+- `optim_args`: None
+- `adafactor`: False
+- `group_by_length`: False
+- `length_column_name`: length
+- `ddp_find_unused_parameters`: None
+- `ddp_bucket_cap_mb`: None
+- `ddp_broadcast_buffers`: False
+- `dataloader_pin_memory`: True
+- `dataloader_persistent_workers`: False
+- `skip_memory_metrics`: True
+- `use_legacy_prediction_loop`: False
+- `push_to_hub`: False
+- `resume_from_checkpoint`: None
+- `hub_model_id`: None
+- `hub_strategy`: every_save
+- `hub_private_repo`: None
+- `hub_always_push`: False
+- `gradient_checkpointing`: False
+- `gradient_checkpointing_kwargs`: None
+- `include_inputs_for_metrics`: False
+- `include_for_metrics`: []
+- `eval_do_concat_batches`: True
+- `fp16_backend`: auto
+- `push_to_hub_model_id`: None
+- `push_to_hub_organization`: None
+- `mp_parameters`: 
+- `auto_find_batch_size`: False
+- `full_determinism`: False
+- `torchdynamo`: None
+- `ray_scope`: last
+- `ddp_timeout`: 1800
+- `torch_compile`: False
+- `torch_compile_backend`: None
+- `torch_compile_mode`: None
+- `dispatch_batches`: None
+- `split_batches`: None
+- `include_tokens_per_second`: False
+- `include_num_input_tokens_seen`: False
+- `neftune_noise_alpha`: None
+- `optim_target_modules`: None
+- `batch_eval_metrics`: False
+- `eval_on_start`: False
+- `use_liger_kernel`: False
+- `eval_use_gather_object`: False
+- `average_tokens_across_devices`: False
+- `prompts`: None
+- `batch_sampler`: batch_sampler
+- `multi_dataset_batch_sampler`: proportional
+
+</details>
+
+### Training Logs
+| Epoch | Step | cosine_ndcg@10 |
+|:-----:|:----:|:--------------:|
+| -1    | -1   | 0.7676         |
+
+
+### Framework Versions
+- Python: 3.11.10
+- Sentence Transformers: 4.0.2
+- Transformers: 4.49.0
+- PyTorch: 2.6.0+cu124
+- Accelerate: 0.26.0
+- Datasets: 3.1.0
+- Tokenizers: 0.21.2
+
+## Citation
+
+### BibTeX
+
+#### Sentence Transformers
+```bibtex
+@inproceedings{reimers-2019-sentence-bert,
+    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
+    author = "Reimers, Nils and Gurevych, Iryna",
+    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
+    month = "11",
+    year = "2019",
+    publisher = "Association for Computational Linguistics",
+    url = "https://arxiv.org/abs/1908.10084",
+}
+```
+
+#### MatryoshkaLoss
+```bibtex
+@misc{kusupati2024matryoshka,
+    title={Matryoshka Representation Learning},
+    author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi},
+    year={2024},
+    eprint={2205.13147},
+    archivePrefix={arXiv},
+    primaryClass={cs.LG}
+}
+```
+
+#### MultipleNegativesRankingLoss
+```bibtex
+@misc{henderson2017efficient,
+    title={Efficient Natural Language Response Suggestion for Smart Reply},
+    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
+    year={2017},
+    eprint={1705.00652},
+    archivePrefix={arXiv},
+    primaryClass={cs.CL}
+}
+```
+
+<!--
+## Glossary
+
+*Clearly define terms in order to be accessible across audiences.*
+-->
+
+<!--
+## Model Card Authors
+
+*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
+-->
+
+<!--
+## Model Card Contact
+
+*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
+-->

checkpoint-4/config.json

@@ -0,0 +1,33 @@
+{
+  "_name_or_path": "nreimers/albert-small-v2",
+  "architectures": [
+    "AlbertModel"
+  ],
+  "attention_probs_dropout_prob": 0,
+  "bos_token_id": 2,
+  "classifier_dropout_prob": 0.1,
+  "down_scale_factor": 1,
+  "embedding_size": 128,
+  "eos_token_id": 3,
+  "gap_size": 0,
+  "hidden_act": "gelu_new",
+  "hidden_dropout_prob": 0,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "inner_group_num": 1,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "albert",
+  "net_structure_type": 0,
+  "num_attention_heads": 12,
+  "num_hidden_groups": 1,
+  "num_hidden_layers": 6,
+  "num_memory_blocks": 0,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.49.0",
+  "type_vocab_size": 2,
+  "vocab_size": 30000
+}

checkpoint-4/config_sentence_transformers.json

@@ -0,0 +1,10 @@
+{
+  "__version__": {
+    "sentence_transformers": "4.0.2",
+    "transformers": "4.49.0",
+    "pytorch": "2.6.0+cu124"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": "cosine"
+}

checkpoint-4/model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
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