update model card README.md

README.md

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+---
+license: apache-2.0
+base_model: bert-base-uncased
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+- f1
+- precision
+- recall
+model-index:
+- name: w266_model2_BERT_LSTM_1
+  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. -->
+
+# w266_model2_BERT_LSTM_1
+
+This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 2.6673
+- Accuracy: {'accuracy': 0.586}
+- F1: {'f1': 0.5941271393567649}
+- Precision: {'precision': 0.6305594263991693}
+- Recall: {'recall': 0.586}
+
+## 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: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 5.0
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Accuracy            | F1                         | Precision                         | Recall                         |
+|:-------------:|:-----:|:----:|:---------------:|:-------------------:|:--------------------------:|:---------------------------------:|:------------------------------:|
+| No log        | 1.0   | 125  | 2.7886          | {'accuracy': 0.563} | {'f1': 0.5737642190234387} | {'precision': 0.6070380044002861} | {'recall': 0.563}              |
+| No log        | 2.0   | 250  | 3.2762          | {'accuracy': 0.567} | {'f1': 0.5732065475023022} | {'precision': 0.6124992011023714} | {'recall': 0.567}              |
+| No log        | 3.0   | 375  | 3.1370          | {'accuracy': 0.57}  | {'f1': 0.5799666523302439} | {'precision': 0.6122839339063632} | {'recall': 0.57}               |
+| 0.0465        | 4.0   | 500  | 3.3590          | {'accuracy': 0.569} | {'f1': 0.5796357806282344} | {'precision': 0.6093440842818532} | {'recall': 0.5689999999999998} |
+| 0.0465        | 5.0   | 625  | 3.4285          | {'accuracy': 0.57}  | {'f1': 0.580483223593091}  | {'precision': 0.618976915416096}  | {'recall': 0.57}               |
+
+
+### Framework versions
+
+- Transformers 4.31.0
+- Pytorch 2.0.1+cu118
+- Datasets 2.14.2
+- Tokenizers 0.13.3