Training completed

README.md

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+---
+license: apache-2.0
+base_model: distilroberta-base
+tags:
+- generated_from_trainer
+datasets:
+- conll2003
+metrics:
+- precision
+- recall
+- f1
+- accuracy
+model-index:
+- name: RoBERTa_conll_epoch_10
+  results:
+  - task:
+      name: Token Classification
+      type: token-classification
+    dataset:
+      name: conll2003
+      type: conll2003
+      config: conll2003
+      split: validation
+      args: conll2003
+    metrics:
+    - name: Precision
+      type: precision
+      value: 0.9443059019118869
+    - name: Recall
+      type: recall
+      value: 0.9559071019858634
+    - name: F1
+      type: f1
+      value: 0.9500710880655683
+    - name: Accuracy
+      type: accuracy
+      value: 0.9882329477463103
+---
+
+<!-- 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. -->
+
+# RoBERTa_conll_epoch_10
+
+This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the conll2003 dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.0906
+- Precision: 0.9443
+- Recall: 0.9559
+- F1: 0.9501
+- Accuracy: 0.9882
+
+## 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: 5e-05
+- train_batch_size: 8
+- eval_batch_size: 8
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 10
+
+### Training results
+
+| Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
+|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| 0.0839        | 1.0   | 1756  | 0.0705          | 0.9055    | 0.9303 | 0.9177 | 0.9827   |
+| 0.0454        | 2.0   | 3512  | 0.0690          | 0.9257    | 0.9431 | 0.9343 | 0.9853   |
+| 0.0272        | 3.0   | 5268  | 0.0590          | 0.9310    | 0.9495 | 0.9402 | 0.9865   |
+| 0.0183        | 4.0   | 7024  | 0.0803          | 0.9324    | 0.9515 | 0.9419 | 0.9862   |
+| 0.0129        | 5.0   | 8780  | 0.0747          | 0.9433    | 0.9517 | 0.9475 | 0.9872   |
+| 0.0079        | 6.0   | 10536 | 0.0792          | 0.9359    | 0.9534 | 0.9446 | 0.9874   |
+| 0.0055        | 7.0   | 12292 | 0.0785          | 0.9457    | 0.9549 | 0.9503 | 0.9879   |
+| 0.003         | 8.0   | 14048 | 0.0881          | 0.9438    | 0.9561 | 0.9499 | 0.9879   |
+| 0.001         | 9.0   | 15804 | 0.0875          | 0.9448    | 0.9562 | 0.9505 | 0.9879   |
+| 0.0008        | 10.0  | 17560 | 0.0906          | 0.9443    | 0.9559 | 0.9501 | 0.9882   |
+
+
+### Framework versions
+
+- Transformers 4.40.2
+- Pytorch 2.3.0+cu121
+- Datasets 2.19.1
+- Tokenizers 0.19.1