update model card README.md

README.md

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+---
+license: cc-by-sa-4.0
+tags:
+- generated_from_trainer
+datasets:
+- fin
+metrics:
+- precision
+- recall
+- f1
+- accuracy
+model-index:
+- name: fin3
+  results:
+  - task:
+      name: Token Classification
+      type: token-classification
+    dataset:
+      name: fin
+      type: fin
+      config: default
+      split: train
+      args: default
+    metrics:
+    - name: Precision
+      type: precision
+      value: 0.944
+    - name: Recall
+      type: recall
+      value: 0.9402390438247012
+    - name: F1
+      type: f1
+      value: 0.9421157684630739
+    - name: Accuracy
+      type: accuracy
+      value: 0.9921209540034072
+---
+
+<!-- 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. -->
+
+# fin3
+
+This model is a fine-tuned version of [nlpaueb/sec-bert-base](https://huggingface.co/nlpaueb/sec-bert-base) on the fin dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.0748
+- Precision: 0.944
+- Recall: 0.9402
+- F1: 0.9421
+- Accuracy: 0.9921
+
+## 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: 10
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| No log        | 1.0   | 129  | 0.0669          | 0.8821    | 0.9243 | 0.9027 | 0.9883   |
+| No log        | 2.0   | 258  | 0.0568          | 0.9289    | 0.9363 | 0.9325 | 0.9913   |
+| No log        | 3.0   | 387  | 0.0565          | 0.9141    | 0.9323 | 0.9231 | 0.9904   |
+| 0.0556        | 4.0   | 516  | 0.0617          | 0.9237    | 0.9163 | 0.92   | 0.9904   |
+| 0.0556        | 5.0   | 645  | 0.0658          | 0.9243    | 0.9243 | 0.9243 | 0.9904   |
+| 0.0556        | 6.0   | 774  | 0.0695          | 0.944     | 0.9402 | 0.9421 | 0.9921   |
+| 0.0556        | 7.0   | 903  | 0.0731          | 0.932     | 0.9283 | 0.9301 | 0.9917   |
+| 0.0016        | 8.0   | 1032 | 0.0750          | 0.9283    | 0.9283 | 0.9283 | 0.9917   |
+| 0.0016        | 9.0   | 1161 | 0.0737          | 0.944     | 0.9402 | 0.9421 | 0.9921   |
+| 0.0016        | 10.0  | 1290 | 0.0748          | 0.944     | 0.9402 | 0.9421 | 0.9921   |
+
+
+### Framework versions
+
+- Transformers 4.25.1
+- Pytorch 1.13.0+cu116
+- Datasets 2.7.1
+- Tokenizers 0.13.2