update model card README.md

README.md

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+---
+base_model: vinai/bertweet-large
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+- f1
+- precision
+- recall
+model-index:
+- name: bertweet-large_epoch6_batch4_lr2e-05_w0.005
+  results: []
+---
+
+<!-- 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. -->
+
+# bertweet-large_epoch6_batch4_lr2e-05_w0.005
+
+This model is a fine-tuned version of [vinai/bertweet-large](https://huggingface.co/vinai/bertweet-large) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.5716
+- Accuracy: 0.6274
+- F1: 0.0
+- Precision: 0.0
+- Recall: 0.0
+
+## 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: 4
+- eval_batch_size: 4
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 6
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1  | Precision | Recall |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:---:|:---------:|:------:|
+| 0.7185        | 1.0   | 788  | 0.6708          | 0.6274   | 0.0 | 0.0       | 0.0    |
+| 0.6791        | 2.0   | 1576 | 0.6699          | 0.6274   | 0.0 | 0.0       | 0.0    |
+| 0.6781        | 3.0   | 2364 | 0.8322          | 0.6274   | 0.0 | 0.0       | 0.0    |
+| 0.6609        | 4.0   | 3152 | 0.7472          | 0.6274   | 0.0 | 0.0       | 0.0    |
+| 0.6673        | 5.0   | 3940 | 0.6602          | 0.6274   | 0.0 | 0.0       | 0.0    |
+| 0.6166        | 6.0   | 4728 | 0.5716          | 0.6274   | 0.0 | 0.0       | 0.0    |
+
+
+### Framework versions
+
+- Transformers 4.31.0
+- Pytorch 2.0.1+cu118
+- Datasets 2.14.3
+- Tokenizers 0.13.3