End of training

README.md

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+---
+base_model: allenai/scibert_scivocab_uncased
+tags:
+- generated_from_trainer
+model-index:
+- name: BC5CDR_SciBERT_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. -->
+
+# BC5CDR_SciBERT_NER
+
+This model is a fine-tuned version of [allenai/scibert_scivocab_uncased](https://huggingface.co/allenai/scibert_scivocab_uncased) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.0818
+- Seqeval classification report:               precision    recall  f1-score   support
+
+    Chemical       0.92      0.94      0.93      7079
+     Disease       0.98      0.98      0.98    103426
+
+   micro avg       0.98      0.97      0.98    110505
+   macro avg       0.95      0.96      0.95    110505
+weighted avg       0.98      0.97      0.98    110505
+
+
+## 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: 16
+- seed: 42
+- gradient_accumulation_steps: 2
+- total_train_batch_size: 32
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 3
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Seqeval classification report                                                                                                                                                                                                                                                                                                          |
+|:-------------:|:-----:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|
+| No log        | 1.0   | 143  | 0.0891          |               precision    recall  f1-score   support
+
+    Chemical       0.91      0.93      0.92      7079
+     Disease       0.98      0.97      0.97    103426
+
+   micro avg       0.97      0.97      0.97    110505
+   macro avg       0.94      0.95      0.95    110505
+weighted avg       0.97      0.97      0.97    110505
+ |
+| No log        | 2.0   | 286  | 0.0830          |               precision    recall  f1-score   support
+
+    Chemical       0.93      0.93      0.93      7079
+     Disease       0.98      0.97      0.98    103426
+
+   micro avg       0.98      0.97      0.97    110505
+   macro avg       0.96      0.95      0.95    110505
+weighted avg       0.98      0.97      0.97    110505
+ |
+| No log        | 3.0   | 429  | 0.0818          |               precision    recall  f1-score   support
+
+    Chemical       0.92      0.94      0.93      7079
+     Disease       0.98      0.98      0.98    103426
+
+   micro avg       0.98      0.97      0.98    110505
+   macro avg       0.95      0.96      0.95    110505
+weighted avg       0.98      0.97      0.98    110505
+ |
+
+
+### Framework versions
+
+- Transformers 4.35.2
+- Pytorch 2.1.0+cu121
+- Datasets 2.15.0
+- Tokenizers 0.15.0