update model card README.md

README.md

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+---
+license: cc-by-sa-4.0
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+- precision
+- recall
+- f1
+model-index:
+- name: legal-bert-small-NER
+  results: []
+---
+
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+# legal-bert-small-NER
+
+This model is a fine-tuned version of [nlpaueb/legal-bert-small-uncased](https://huggingface.co/nlpaueb/legal-bert-small-uncased) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.2334
+- Accuracy: 0.9558
+- Precision: 0.7587
+- Recall: 0.7950
+- F1: 0.7764
+- Classification Report:               precision    recall  f1-score   support
+
+         LOC       0.85      0.86      0.86      1668
+        MISC       0.56      0.67      0.61       702
+         ORG       0.68      0.67      0.68      1661
+         PER       0.83      0.91      0.87      1617
+
+   micro avg       0.76      0.79      0.78      5648
+   macro avg       0.73      0.78      0.75      5648
+weighted avg       0.76      0.79      0.78      5648
+
+
+## Model description
+
+More information needed
+
+## Intended uses & limitations
+
+More information needed
+
+## Training and evaluation data
+
+More information needed
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 2e-05
+- train_batch_size: 16
+- eval_batch_size: 32
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 2
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     | Classification Report                                                                                                                                                                                                                                                                                                                                                                                                                              |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|
+| 0.0289        | 1.0   | 434  | 0.2151          | 0.9555   | 0.7592    | 0.7890 | 0.7738 |               precision    recall  f1-score   support
+
+         LOC       0.86      0.85      0.86      1668
+        MISC       0.57      0.67      0.62       702
+         ORG       0.69      0.64      0.67      1661
+         PER       0.81      0.92      0.86      1617
+
+   micro avg       0.76      0.79      0.77      5648
+   macro avg       0.73      0.77      0.75      5648
+weighted avg       0.76      0.79      0.77      5648
+ |
+| 0.0193        | 2.0   | 868  | 0.2334          | 0.9558   | 0.7587    | 0.7950 | 0.7764 |               precision    recall  f1-score   support
+
+         LOC       0.85      0.86      0.86      1668
+        MISC       0.56      0.67      0.61       702
+         ORG       0.68      0.67      0.68      1661
+         PER       0.83      0.91      0.87      1617
+
+   micro avg       0.76      0.79      0.78      5648
+   macro avg       0.73      0.78      0.75      5648
+weighted avg       0.76      0.79      0.78      5648
+ |
+
+
+### Framework versions
+
+- Transformers 4.30.2
+- Pytorch 2.0.0
+- Datasets 2.1.0
+- Tokenizers 0.13.3