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_8
+  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: 0.9463544261750539
+    - name: Recall
+      type: recall
+      value: 0.9589363850555369
+    - name: F1
+      type: f1
+      value: 0.9526038619075483
+    - name: Accuracy
+      type: accuracy
+      value: 0.9888772974133964
+---
+
+<!-- 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_8
+
+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: 0.0813
+- Precision: 0.9464
+- Recall: 0.9589
+- F1: 0.9526
+- Accuracy: 0.9889
+
+## 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: 8
+
+### Training results
+
+| Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
+|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| 0.0799        | 1.0   | 1756  | 0.0700          | 0.9133    | 0.9320 | 0.9225 | 0.9827   |
+| 0.0449        | 2.0   | 3512  | 0.0661          | 0.9325    | 0.9440 | 0.9382 | 0.9865   |
+| 0.0283        | 3.0   | 5268  | 0.0707          | 0.9275    | 0.9456 | 0.9365 | 0.9852   |
+| 0.0203        | 4.0   | 7024  | 0.0622          | 0.9424    | 0.9586 | 0.9504 | 0.9882   |
+| 0.0111        | 5.0   | 8780  | 0.0758          | 0.9382    | 0.9549 | 0.9465 | 0.9878   |
+| 0.0067        | 6.0   | 10536 | 0.0761          | 0.9395    | 0.9546 | 0.9470 | 0.9880   |
+| 0.0031        | 7.0   | 12292 | 0.0821          | 0.9391    | 0.9546 | 0.9468 | 0.9878   |
+| 0.0021        | 8.0   | 14048 | 0.0813          | 0.9464    | 0.9589 | 0.9526 | 0.9889   |
+
+
+### Framework versions
+
+- Transformers 4.40.2
+- Pytorch 2.3.0+cu121
+- Datasets 2.19.1
+- Tokenizers 0.19.1