Model save

README.md

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+---
+license: apache-2.0
+base_model: distilbert-base-uncased
+tags:
+- generated_from_trainer
+datasets:
+- conll2003
+metrics:
+- precision
+- recall
+- f1
+- accuracy
+model-index:
+- name: distilbert-base-uncased-finetuned-ner
+  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: 0.9306140545333629
+    - name: Recall
+      type: recall
+      value: 0.9392549502181452
+    - name: F1
+      type: f1
+      value: 0.9349145370525026
+    - name: Accuracy
+      type: accuracy
+      value: 0.9842248240583348
+---
+
+<!-- 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. -->
+
+# distilbert-base-uncased-finetuned-ner
+
+This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the conll2003 dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.0664
+- Precision: 0.9306
+- Recall: 0.9393
+- F1: 0.9349
+- Accuracy: 0.9842
+
+## 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
+- 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 | Precision | Recall | F1     | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| 0.0525        | 1.0   | 878  | 0.0671          | 0.9121    | 0.9308 | 0.9213 | 0.9820   |
+| 0.0287        | 2.0   | 1756 | 0.0640          | 0.9281    | 0.9361 | 0.9321 | 0.9838   |
+| 0.0169        | 3.0   | 2634 | 0.0664          | 0.9306    | 0.9393 | 0.9349 | 0.9842   |
+
+
+### Framework versions
+
+- Transformers 4.32.1
+- Pytorch 2.2.1+cpu
+- Datasets 2.21.0
+- Tokenizers 0.13.2

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