Update README.md

README.md

@@ -21,114 +21,93 @@ using the [F-Coref](https://arxiv.org/abs/2209.04280) library. The model was tra
 
 <!-- Provide a longer summary of what this model is. -->
 
-
-
 - **Developed by:** [Artem Kramov](https://www.linkedin.com/in/artem-kramov-0b3731100/), Andrii Kursin ([email protected]).
 - **Languages:** Ukrainian
 - **Finetuned from model:** [XML-Roberta-base](https://huggingface.co/ukr-models/xlm-roberta-base-uk) 
 
-### Model Sources [optional]
+### Model Sources
 
 <!-- Provide the basic links for the model. -->
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-
-## Uses
-
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-
-### Direct Use
-
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-
-[More Information Needed]
-
-### Downstream Use [optional]
-
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-
-[More Information Needed]
+- **Repository:** https://github.com/artemkramov/fastcoref-ua/blob/main/README.md
+- **Demo:** [Google Colab](https://colab.research.google.com/drive/1vsaH15DFDrmKB4aNsQ-9TCQGTW73uk1y?usp=sharing)
 
 ### Out-of-Scope Use
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+According to the metrics retrieved from the evaluation dataset, the model is more precision-oriented. Also, there is a high level of granularity of mentions. 
+E.g., the mention "Головний виконавчий директор Андрій Сидоренко" can be divided into the following coreferent groups: ["Головний виконавчий директор Андрій Сидоренко", "Головний виконавчий директор", "Андрій Сидоренко"].
+Such a feature can also be used to extract some positions, roles, or other features of entities in the text.
 
-[More Information Needed]
-
-## Bias, Risks, and Limitations
-
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
-
-### Recommendations
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
 
 ## How to Get Started with the Model
 
 Use the code below to get started with the model.
 
-[More Information Needed]
+```python
+from fastcoref import FCoref
+import spacy
 
-## Training Details
+nlp = spacy.load('uk_core_news_md')
 
-### Training Data
+model_path = "artemkramov/coref-ua"
+model = FCoref(model_name_or_path=model_path, device='cuda:0', nlp=nlp)
 
-<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+preds = model.predict(
+   texts=["""Мій друг дав мені свою машину та ключі до неї; крім того, він дав мені його книгу. Я з радістю її читаю."""]
+)
 
-[More Information Needed]
-
-### Training Procedure 
-
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-#### Preprocessing [optional]
-
-[More Information Needed]
+preds[0].get_clusters(as_strings=False)
+> [[(0, 3), (13, 17), (66, 70), (83, 84)],
+ [(0, 8), (18, 22), (58, 61), (71, 75)],
+ [(18, 29), (42, 45)],
+ [(71, 81), (95, 97)]]
 
+preds[0].get_clusters()
+> [['Мій', 'мені', 'мені', 'Я'], ['Мій друг', 'свою', 'він', 'його'], ['свою машину', 'неї'], ['його книгу', 'її']]
 
-#### Training Hyperparameters
+preds[0].get_logit(
+   span_i=(13, 17), span_j=(42, 45)
+)
 
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+> -6.867196
+```
 
-#### Speeds, Sizes, Times [optional]
+## Training Details
 
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+### Training Data
 
-[More Information Needed]
+The model was trained on the silver coreference resolution dataset: https://huggingface.co/datasets/artemkramov/coreference-dataset-ua.
 
 ## Evaluation
 
 <!-- This section describes the evaluation protocols and provides the results. -->
 
-### Testing Data, Factors & Metrics
-
-#### Testing Data
-
-<!-- This should link to a Data Card if possible. -->
-
-[More Information Needed]
-
-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
-
 #### Metrics
 
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+Two types of metrics were considered: mention-based and the coreference resolution metrics themselves.
 
-[More Information Needed]
+Mention-based metrics:
+- mention precision
+- mention recall
+- mention F1
+
+Coreference resolution metrics were calculated as the average values across the following metrics: MUC, BCubed, CEAFE:
+- coreference precision
+- coreference recall
+- coreference F1
 
 ### Results
 
-[More Information Needed]
+The metrics for the validation dataset:
+
+|         Metric        | Value |
+|:---------------------:|-------|
+| Mention precision     | 0.850 |
+| Mention recall        | 0.798 |
+| Mention F1            | 0.824 |
+| Coreference precision | 0.758 |
+| Coreference recall    | 0.706 |
+| Coreference F1        | 0.731 |
 
 #### Summary