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_7
+  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: 0.944675195215152
+    - name: Recall
+      type: recall
+      value: 0.9569168630090878
+    - name: F1
+      type: f1
+      value: 0.9507566257001923
+    - name: Accuracy
+      type: accuracy
+      value: 0.9885704642385935
+---
+
+<!-- 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_7
+
+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: 0.0772
+- Precision: 0.9447
+- Recall: 0.9569
+- F1: 0.9508
+- Accuracy: 0.9886
+
+## 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: 7
+
+### Training results
+
+| Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
+|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| 0.078         | 1.0   | 1756  | 0.0745          | 0.9048    | 0.9310 | 0.9177 | 0.9831   |
+| 0.0424        | 2.0   | 3512  | 0.0702          | 0.9317    | 0.9451 | 0.9383 | 0.9851   |
+| 0.0254        | 3.0   | 5268  | 0.0722          | 0.9312    | 0.9498 | 0.9404 | 0.9857   |
+| 0.0173        | 4.0   | 7024  | 0.0678          | 0.9348    | 0.9505 | 0.9426 | 0.9867   |
+| 0.0086        | 5.0   | 8780  | 0.0798          | 0.9306    | 0.9498 | 0.9401 | 0.9859   |
+| 0.0058        | 6.0   | 10536 | 0.0786          | 0.9406    | 0.9562 | 0.9483 | 0.9881   |
+| 0.0033        | 7.0   | 12292 | 0.0772          | 0.9447    | 0.9569 | 0.9508 | 0.9886   |
+
+
+### Framework versions
+
+- Transformers 4.40.2
+- Pytorch 2.3.0+cu121
+- Datasets 2.19.1
+- Tokenizers 0.19.1