update model card README.md

README.md

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+---
+license: apache-2.0
+tags:
+- generated_from_trainer
+datasets:
+- inspec
+metrics:
+- f1
+- precision
+- recall
+model-index:
+- name: bert-finetuned-inspec-3-epochs
+  results:
+  - task:
+      name: Token Classification
+      type: token-classification
+    dataset:
+      name: inspec
+      type: inspec
+      args: extraction
+    metrics:
+    - name: F1
+      type: f1
+      value: 0.28328008519701814
+    - name: Precision
+      type: precision
+      value: 0.26594090202177295
+    - name: Recall
+      type: recall
+      value: 0.3030379746835443
+---
+
+<!-- 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-inspec-3-epochs
+
+This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the inspec dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.2728
+- F1: 0.2833
+- Precision: 0.2659
+- Recall: 0.3030
+
+## 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: 0
+- 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 | F1     | Precision | Recall |
+|:-------------:|:-----:|:----:|:---------------:|:------:|:---------:|:------:|
+| 0.3338        | 1.0   | 125  | 0.2837          | 0.1401 | 0.1510    | 0.1306 |
+| 0.2575        | 2.0   | 250  | 0.2658          | 0.2183 | 0.2519    | 0.1927 |
+| 0.2259        | 3.0   | 375  | 0.2728          | 0.2833 | 0.2659    | 0.3030 |
+
+
+### Framework versions
+
+- Transformers 4.19.2
+- Pytorch 1.11.0+cu113
+- Datasets 2.2.1
+- Tokenizers 0.12.1