Training in progress, epoch 3, checkpoint

checkpoint-11800/1_Pooling/config.json

@@ -0,0 +1,10 @@
+{
+    "word_embedding_dimension": 384,
+    "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-11800/README.md

@@ -0,0 +1,424 @@
+---
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- dense
+- generated_from_trainer
+- dataset_size:1006385
+- loss:MultipleNegativesSymmetricRankingLoss
+base_model: sentence-transformers/multi-qa-MiniLM-L6-cos-v1
+widget:
+- source_sentence: essence multi task concealer 15 natural nude
+  sentences:
+  - tarte 4 in 1 mini mascara
+  - essence
+  - face make-up
+- source_sentence: granville original one bite original rice crispy squares
+  sentences:
+  - rice crispy sweetened
+  - sweet
+  - roasted hazelnut syrup
+- source_sentence: sand eel shad soft lure combo eelo 150 25 g ayu/blue
+  sentences:
+  - fishing
+  - fast fishing fishing lure
+  - black marble plant pot
+- source_sentence: apple cinnamon greek yoghurt
+  sentences:
+  - dairy
+  - gluten free yogurt
+  - 1 kg coffee frappe base
+- source_sentence: golden olive pouch
+  sentences:
+  - trio kaftan
+  - pouch
+  - bag
+pipeline_tag: sentence-similarity
+library_name: sentence-transformers
+metrics:
+- cosine_accuracy
+model-index:
+- name: SentenceTransformer based on sentence-transformers/multi-qa-MiniLM-L6-cos-v1
+  results:
+  - task:
+      type: triplet
+      name: Triplet
+    dataset:
+      name: Unknown
+      type: unknown
+    metrics:
+    - type: cosine_accuracy
+      value: 0.9703438878059387
+      name: Cosine Accuracy
+---
+
+# SentenceTransformer based on sentence-transformers/multi-qa-MiniLM-L6-cos-v1
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/multi-qa-MiniLM-L6-cos-v1](https://huggingface.co/sentence-transformers/multi-qa-MiniLM-L6-cos-v1). It maps sentences & paragraphs to a 384-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:** [sentence-transformers/multi-qa-MiniLM-L6-cos-v1](https://huggingface.co/sentence-transformers/multi-qa-MiniLM-L6-cos-v1) <!-- at revision b207367332321f8e44f96e224ef15bc607f4dbf0 -->
+- **Maximum Sequence Length:** 512 tokens
+- **Output Dimensionality:** 384 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/huggingface/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, 'architecture': 'BertModel'})
+  (1): Pooling({'word_embedding_dimension': 384, '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})
+  (2): Normalize()
+)
+```
+
+## 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("LamaDiab/MultiMiniLM-V25Data-256BATCH-SemanticEngine")
+# Run inference
+sentences = [
+    'golden olive pouch',
+    'pouch',
+    'trio kaftan',
+]
+embeddings = model.encode(sentences)
+print(embeddings.shape)
+# [3, 384]
+
+# Get the similarity scores for the embeddings
+similarities = model.similarity(embeddings, embeddings)
+print(similarities)
+# tensor([[1.0000, 0.8017, 0.1199],
+#         [0.8017, 1.0000, 0.0611],
+#         [0.1199, 0.0611, 1.0000]])
+```
+
+<!--
+### 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
+
+#### Triplet
+
+* Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator)
+
+| Metric              | Value      |
+|:--------------------|:-----------|
+| **cosine_accuracy** | **0.9703** |
+
+<!--
+## 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: 1,006,385 training samples
+* Columns: <code>anchor</code>, <code>positive</code>, and <code>itemCategory</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | anchor                                                                            | positive                                                                         | itemCategory                                                                    |
+  |:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:--------------------------------------------------------------------------------|
+  | type    | string                                                                            | string                                                                           | string                                                                          |
+  | details | <ul><li>min: 3 tokens</li><li>mean: 11.94 tokens</li><li>max: 72 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 4.57 tokens</li><li>max: 83 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 3.91 tokens</li><li>max: 9 tokens</li></ul> |
+* Samples:
+  | anchor                                                                                           | positive                         | itemCategory            |
+  |:-------------------------------------------------------------------------------------------------|:---------------------------------|:------------------------|
+  | <code>lice repellent serum</code>                                                                | <code>hair serum</code>          | <code>hair serum</code> |
+  | <code>vanilla sponge cake with fresh moisturizer and strawberry pieces.<br>serve  person.</code> | <code>vanilla tres leches</code> | <code>sweet</code>      |
+  | <code>wyl chips - kettle cooked sea salt & balsamic vinegar potato chips -  gr</code>            | <code>snacks</code>              | <code>snacks</code>     |
+* Loss: [<code>MultipleNegativesSymmetricRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativessymmetricrankingloss) with these parameters:
+  ```json
+  {
+      "scale": 20.0,
+      "similarity_fct": "cos_sim",
+      "gather_across_devices": false
+  }
+  ```
+
+### Evaluation Dataset
+
+#### Unnamed Dataset
+
+* Size: 9,509 evaluation samples
+* Columns: <code>anchor</code>, <code>positive</code>, <code>negative</code>, and <code>itemCategory</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | anchor                                                                           | positive                                                                         | negative                                                                        | itemCategory                                                                     |
+  |:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:--------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
+  | type    | string                                                                           | string                                                                           | string                                                                          | string                                                                           |
+  | details | <ul><li>min: 3 tokens</li><li>mean: 9.63 tokens</li><li>max: 43 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 6.3 tokens</li><li>max: 150 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 9.5 tokens</li><li>max: 35 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 3.88 tokens</li><li>max: 10 tokens</li></ul> |
+* Samples:
+  | anchor                                                               | positive                                 | negative                                                           | itemCategory                        |
+  |:---------------------------------------------------------------------|:-----------------------------------------|:-------------------------------------------------------------------|:------------------------------------|
+  | <code>pilot mechanical pencil progrex h-127 - 0.7 mm</code>          | <code> pilot pencil </code>              | <code>lunch bag colors 22 × 16 × 28 cm must shark 000586181</code> | <code>pencil</code>                 |
+  | <code>superior drawing marker -pen - set of 12 colors - 2 nib</code> | <code> marker pen </code>                | <code>staedtler triplus fineliner 10 + 3 pack</code>               | <code>marker</code>                 |
+  | <code>first person singular author: haruki murakami</code>           | <code> first person singular book</code> | <code>misty grater stainless steel</code>                          | <code>literature and fiction</code> |
+* Loss: [<code>MultipleNegativesSymmetricRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativessymmetricrankingloss) with these parameters:
+  ```json
+  {
+      "scale": 20.0,
+      "similarity_fct": "cos_sim",
+      "gather_across_devices": false
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `eval_strategy`: steps
+- `per_device_train_batch_size`: 256
+- `per_device_eval_batch_size`: 256
+- `learning_rate`: 3e-05
+- `weight_decay`: 0.001
+- `num_train_epochs`: 5
+- `warmup_ratio`: 0.1
+- `fp16`: True
+- `dataloader_num_workers`: 1
+- `dataloader_prefetch_factor`: 2
+- `dataloader_persistent_workers`: True
+- `push_to_hub`: True
+- `hub_model_id`: LamaDiab/MultiMiniLM-V25Data-256BATCH-SemanticEngine
+- `hub_strategy`: all_checkpoints
+
+#### 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`: 256
+- `per_device_eval_batch_size`: 256
+- `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`: 3e-05
+- `weight_decay`: 0.001
+- `adam_beta1`: 0.9
+- `adam_beta2`: 0.999
+- `adam_epsilon`: 1e-08
+- `max_grad_norm`: 1.0
+- `num_train_epochs`: 5
+- `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`: 1
+- `dataloader_prefetch_factor`: 2
+- `past_index`: -1
+- `disable_tqdm`: False
+- `remove_unused_columns`: True
+- `label_names`: None
+- `load_best_model_at_end`: False
+- `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`: True
+- `skip_memory_metrics`: True
+- `use_legacy_prediction_loop`: False
+- `push_to_hub`: True
+- `resume_from_checkpoint`: None
+- `hub_model_id`: LamaDiab/MultiMiniLM-V25Data-256BATCH-SemanticEngine
+- `hub_strategy`: all_checkpoints
+- `hub_private_repo`: None
+- `hub_always_push`: False
+- `hub_revision`: None
+- `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
+- `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
+- `liger_kernel_config`: None
+- `eval_use_gather_object`: False
+- `average_tokens_across_devices`: False
+- `prompts`: None
+- `batch_sampler`: batch_sampler
+- `multi_dataset_batch_sampler`: proportional
+- `router_mapping`: {}
+- `learning_rate_mapping`: {}
+
+</details>
+
+### Training Logs
+| Epoch  | Step  | Training Loss | Validation Loss | cosine_accuracy |
+|:------:|:-----:|:-------------:|:---------------:|:---------------:|
+| 0.0003 | 1     | 3.7722        | -               | -               |
+| 0.2543 | 1000  | 2.8712        | 0.5578          | 0.9445          |
+| 0.5086 | 2000  | 1.8052        | 0.5062          | 0.9498          |
+| 0.7630 | 3000  | 1.2908        | 0.4583          | 0.9575          |
+| 1.0173 | 4000  | 1.0201        | 0.4365          | 0.9621          |
+| 1.2715 | 5000  | 1.2224        | 0.4258          | 0.9627          |
+| 1.5257 | 6000  | 1.1341        | 0.4155          | 0.9645          |
+| 1.7799 | 7000  | 1.0798        | 0.4215          | 0.9657          |
+| 2.0341 | 8000  | 1.0249        | 0.4113          | 0.9681          |
+| 2.2883 | 9000  | 0.9443        | 0.4053          | 0.9673          |
+| 2.5425 | 10000 | 0.9178        | 0.3976          | 0.9688          |
+| 2.7966 | 11000 | 0.8991        | 0.4033          | 0.9703          |
+
+
+### Framework Versions
+- Python: 3.11.13
+- Sentence Transformers: 5.1.2
+- Transformers: 4.53.3
+- PyTorch: 2.6.0+cu124
+- Accelerate: 1.9.0
+- Datasets: 4.4.1
+- 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",
+}
+```
+
+<!--
+## 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-11800/config.json

@@ -0,0 +1,25 @@
+{
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 384,
+  "initializer_range": 0.02,
+  "intermediate_size": 1536,
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 6,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.53.3",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

checkpoint-11800/config_sentence_transformers.json

@@ -0,0 +1,14 @@
+{
+  "__version__": {
+    "sentence_transformers": "5.1.2",
+    "transformers": "4.53.3",
+    "pytorch": "2.6.0+cu124"
+  },
+  "model_type": "SentenceTransformer",
+  "prompts": {
+    "query": "",
+    "document": ""
+  },
+  "default_prompt_name": null,
+  "similarity_fn_name": "cosine"
+}

checkpoint-11800/model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6038d08cbee876788c5fa32ed07bfd30fd1770bf473a58fa264328d409009ea2
+size 90864192

checkpoint-11800/modules.json

@@ -0,0 +1,20 @@
+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  },
+  {
+    "idx": 2,
+    "name": "2",
+    "path": "2_Normalize",
+    "type": "sentence_transformers.models.Normalize"
+  }
+]

checkpoint-11800/optimizer.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2e911adf3dc1d324ae635025accae8b98f14dc1e317c55f65ccfaefb04a75310
+size 180607738

checkpoint-11800/rng_state.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7ca744b749fa6d90e9caec2cb8f544482def2ca4025736dad507477773745ec0
+size 14244

checkpoint-11800/scaler.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:03d4009edce1e0cad37659ad894764721b7236b7435f1dfc1c48c47416533671
+size 988

checkpoint-11800/scheduler.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:395b95106083eecdb772d8e4e67886417d0cc60d53f88a10c8f4c007d32d2104
+size 1064

checkpoint-11800/sentence_bert_config.json

@@ -0,0 +1,4 @@
+{
+    "max_seq_length": 512,
+    "do_lower_case": false
+}

checkpoint-11800/special_tokens_map.json

@@ -0,0 +1,37 @@
+{
+  "cls_token": {
+    "content": "[CLS]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "mask_token": {
+    "content": "[MASK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "[PAD]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sep_token": {
+    "content": "[SEP]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "[UNK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

checkpoint-11800/tokenizer_config.json

@@ -0,0 +1,65 @@
+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "101": {
+      "content": "[CLS]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "102": {
+      "content": "[SEP]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "103": {
+      "content": "[MASK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "clean_up_tokenization_spaces": false,
+  "cls_token": "[CLS]",
+  "do_basic_tokenize": true,
+  "do_lower_case": true,
+  "extra_special_tokens": {},
+  "mask_token": "[MASK]",
+  "max_length": 250,
+  "model_max_length": 512,
+  "never_split": null,
+  "pad_to_multiple_of": null,
+  "pad_token": "[PAD]",
+  "pad_token_type_id": 0,
+  "padding_side": "right",
+  "sep_token": "[SEP]",
+  "stride": 0,
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "truncation_side": "right",
+  "truncation_strategy": "longest_first",
+  "unk_token": "[UNK]"
+}

checkpoint-11800/trainer_state.json

@@ -0,0 +1,217 @@
+{
+  "best_global_step": null,
+  "best_metric": null,
+  "best_model_checkpoint": null,
+  "epoch": 3.0,
+  "eval_steps": 1000,
+  "global_step": 11800,
+  "is_hyper_param_search": false,
+  "is_local_process_zero": true,
+  "is_world_process_zero": true,
+  "log_history": [
+    {
+      "epoch": 0.000254323499491353,
+      "grad_norm": 6.461288928985596,
+      "learning_rate": 0.0,
+      "loss": 3.7722,
+      "step": 1
+    },
+    {
+      "epoch": 0.254323499491353,
+      "grad_norm": 5.289657115936279,
+      "learning_rate": 1.5244150559511698e-05,
+      "loss": 2.8712,
+      "step": 1000
+    },
+    {
+      "epoch": 0.254323499491353,
+      "eval_cosine_accuracy": 0.9444736838340759,
+      "eval_loss": 0.5577611327171326,
+      "eval_runtime": 35.3135,
+      "eval_samples_per_second": 269.274,
+      "eval_steps_per_second": 1.076,
+      "step": 1000
+    },
+    {
+      "epoch": 0.508646998982706,
+      "grad_norm": 6.285799026489258,
+      "learning_rate": 2.9944048830111903e-05,
+      "loss": 1.8052,
+      "step": 2000
+    },
+    {
+      "epoch": 0.508646998982706,
+      "eval_cosine_accuracy": 0.9498369693756104,
+      "eval_loss": 0.5062179565429688,
+      "eval_runtime": 34.9468,
+      "eval_samples_per_second": 272.099,
+      "eval_steps_per_second": 1.087,
+      "step": 2000
+    },
+    {
+      "epoch": 0.762970498474059,
+      "grad_norm": 6.981422424316406,
+      "learning_rate": 2.8250254323499494e-05,
+      "loss": 1.2908,
+      "step": 3000
+    },
+    {
+      "epoch": 0.762970498474059,
+      "eval_cosine_accuracy": 0.9575139284133911,
+      "eval_loss": 0.4583439528942108,
+      "eval_runtime": 34.7917,
+      "eval_samples_per_second": 273.312,
+      "eval_steps_per_second": 1.092,
+      "step": 3000
+    },
+    {
+      "epoch": 1.0172852058973056,
+      "grad_norm": 6.195133209228516,
+      "learning_rate": 2.6554764326890472e-05,
+      "loss": 1.0201,
+      "step": 4000
+    },
+    {
+      "epoch": 1.0172852058973056,
+      "eval_cosine_accuracy": 0.9621411561965942,
+      "eval_loss": 0.4364590048789978,
+      "eval_runtime": 35.4237,
+      "eval_samples_per_second": 268.436,
+      "eval_steps_per_second": 1.073,
+      "step": 4000
+    },
+    {
+      "epoch": 1.2714794102694458,
+      "grad_norm": 6.555699825286865,
+      "learning_rate": 2.4859274330281453e-05,
+      "loss": 1.2224,
+      "step": 5000
+    },
+    {
+      "epoch": 1.2714794102694458,
+      "eval_cosine_accuracy": 0.962666928768158,
+      "eval_loss": 0.4258391261100769,
+      "eval_runtime": 36.1657,
+      "eval_samples_per_second": 262.928,
+      "eval_steps_per_second": 1.051,
+      "step": 5000
+    },
+    {
+      "epoch": 1.5256736146415861,
+      "grad_norm": 4.430365562438965,
+      "learning_rate": 2.316378433367243e-05,
+      "loss": 1.1341,
+      "step": 6000
+    },
+    {
+      "epoch": 1.5256736146415861,
+      "eval_cosine_accuracy": 0.964454710483551,
+      "eval_loss": 0.4155160188674927,
+      "eval_runtime": 35.2226,
+      "eval_samples_per_second": 269.969,
+      "eval_steps_per_second": 1.079,
+      "step": 6000
+    },
+    {
+      "epoch": 1.7798678190137265,
+      "grad_norm": 5.276082515716553,
+      "learning_rate": 2.146998982706002e-05,
+      "loss": 1.0798,
+      "step": 7000
+    },
+    {
+      "epoch": 1.7798678190137265,
+      "eval_cosine_accuracy": 0.9657166600227356,
+      "eval_loss": 0.42145127058029175,
+      "eval_runtime": 35.2976,
+      "eval_samples_per_second": 269.395,
+      "eval_steps_per_second": 1.077,
+      "step": 7000
+    },
+    {
+      "epoch": 2.0340620233858666,
+      "grad_norm": 4.906863689422607,
+      "learning_rate": 1.9774499830451e-05,
+      "loss": 1.0249,
+      "step": 8000
+    },
+    {
+      "epoch": 2.0340620233858666,
+      "eval_cosine_accuracy": 0.9681354761123657,
+      "eval_loss": 0.41128453612327576,
+      "eval_runtime": 35.164,
+      "eval_samples_per_second": 270.419,
+      "eval_steps_per_second": 1.081,
+      "step": 8000
+    },
+    {
+      "epoch": 2.288256227758007,
+      "grad_norm": 6.295294761657715,
+      "learning_rate": 1.808070532383859e-05,
+      "loss": 0.9443,
+      "step": 9000
+    },
+    {
+      "epoch": 2.288256227758007,
+      "eval_cosine_accuracy": 0.9672941565513611,
+      "eval_loss": 0.4052944779396057,
+      "eval_runtime": 35.2065,
+      "eval_samples_per_second": 270.092,
+      "eval_steps_per_second": 1.079,
+      "step": 9000
+    },
+    {
+      "epoch": 2.5424504321301473,
+      "grad_norm": 5.057905197143555,
+      "learning_rate": 1.6386910817226178e-05,
+      "loss": 0.9178,
+      "step": 10000
+    },
+    {
+      "epoch": 2.5424504321301473,
+      "eval_cosine_accuracy": 0.968766450881958,
+      "eval_loss": 0.39761021733283997,
+      "eval_runtime": 35.1256,
+      "eval_samples_per_second": 270.714,
+      "eval_steps_per_second": 1.082,
+      "step": 10000
+    },
+    {
+      "epoch": 2.796644636502288,
+      "grad_norm": 5.986538410186768,
+      "learning_rate": 1.4691420820617159e-05,
+      "loss": 0.8991,
+      "step": 11000
+    },
+    {
+      "epoch": 2.796644636502288,
+      "eval_cosine_accuracy": 0.9703438878059387,
+      "eval_loss": 0.40334755182266235,
+      "eval_runtime": 36.7674,
+      "eval_samples_per_second": 258.626,
+      "eval_steps_per_second": 1.034,
+      "step": 11000
+    }
+  ],
+  "logging_steps": 1000,
+  "max_steps": 19660,
+  "num_input_tokens_seen": 0,
+  "num_train_epochs": 5,
+  "save_steps": 500,
+  "stateful_callbacks": {
+    "TrainerControl": {
+      "args": {
+        "should_epoch_stop": false,
+        "should_evaluate": false,
+        "should_log": false,
+        "should_save": true,
+        "should_training_stop": false
+      },
+      "attributes": {}
+    }
+  },
+  "total_flos": 0.0,
+  "train_batch_size": 256,
+  "trial_name": null,
+  "trial_params": null
+}

checkpoint-11800/training_args.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b61ef0de5bcb7bee01b87b6bd644bcb28374d5ce6c476638cb500a99eb6d31cf
+size 5752