Training completed

README.md

@@ -0,0 +1,98 @@
+---
+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_9
+  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: 0.9447027565592826
+    - name: Recall
+      type: recall
+      value: 0.9574217435207001
+    - name: F1
+      type: f1
+      value: 0.9510197258441992
+    - name: Accuracy
+      type: accuracy
+      value: 0.9884323893099322
+---
+
+<!-- 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_9
+
+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: 0.0841
+- Precision: 0.9447
+- Recall: 0.9574
+- F1: 0.9510
+- Accuracy: 0.9884
+
+## 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: 9
+
+### Training results
+
+| Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
+|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| 0.0779        | 1.0   | 1756  | 0.0640          | 0.9142    | 0.9359 | 0.9249 | 0.9836   |
+| 0.0448        | 2.0   | 3512  | 0.0867          | 0.9220    | 0.9364 | 0.9291 | 0.9836   |
+| 0.03          | 3.0   | 5268  | 0.0580          | 0.9263    | 0.9482 | 0.9371 | 0.9865   |
+| 0.018         | 4.0   | 7024  | 0.0760          | 0.9330    | 0.9490 | 0.9409 | 0.9864   |
+| 0.0108        | 5.0   | 8780  | 0.0733          | 0.9363    | 0.9544 | 0.9452 | 0.9873   |
+| 0.0096        | 6.0   | 10536 | 0.0773          | 0.9413    | 0.9534 | 0.9473 | 0.9879   |
+| 0.0039        | 7.0   | 12292 | 0.0755          | 0.9442    | 0.9561 | 0.9501 | 0.9885   |
+| 0.0024        | 8.0   | 14048 | 0.0834          | 0.9425    | 0.9567 | 0.9496 | 0.9884   |
+| 0.0006        | 9.0   | 15804 | 0.0841          | 0.9447    | 0.9574 | 0.9510 | 0.9884   |
+
+
+### Framework versions
+
+- Transformers 4.40.2
+- Pytorch 2.3.0+cu121
+- Datasets 2.19.1
+- Tokenizers 0.19.1