trainind complete

README.md

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+---
+license: cc-by-nc-sa-4.0
+base_model: Babelscape/wikineural-multilingual-ner
+tags:
+- generated_from_trainer
+datasets:
+- wnut_17
+metrics:
+- precision
+- recall
+- f1
+- accuracy
+model-index:
+- name: bert-finetuned-ner-Colab
+  results:
+  - task:
+      name: Token Classification
+      type: token-classification
+    dataset:
+      name: wnut_17
+      type: wnut_17
+      config: wnut_17
+      split: validation
+      args: wnut_17
+    metrics:
+    - name: Precision
+      type: precision
+      value: 0.6477093206951027
+    - name: Recall
+      type: recall
+      value: 0.4904306220095694
+    - name: F1
+      type: f1
+      value: 0.5582028590878148
+    - name: Accuracy
+      type: accuracy
+      value: 0.9344202521095948
+---
+
+<!-- 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. -->
+
+# bert-finetuned-ner-Colab
+
+This model is a fine-tuned version of [Babelscape/wikineural-multilingual-ner](https://huggingface.co/Babelscape/wikineural-multilingual-ner) on the wnut_17 dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.4102
+- Precision: 0.6477
+- Recall: 0.4904
+- F1: 0.5582
+- Accuracy: 0.9344
+
+## 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: 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: 3
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| No log        | 1.0   | 425  | 0.3037          | 0.5963    | 0.5072 | 0.5482 | 0.9321   |
+| 0.0672        | 2.0   | 850  | 0.3751          | 0.6604    | 0.4653 | 0.5460 | 0.9316   |
+| 0.0451        | 3.0   | 1275 | 0.4102          | 0.6477    | 0.4904 | 0.5582 | 0.9344   |
+
+
+### Framework versions
+
+- Transformers 4.32.1
+- Pytorch 2.0.1+cu118
+- Datasets 2.14.4
+- Tokenizers 0.13.3