update model card README.md

README.md

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+---
+license: mit
+tags:
+- generated_from_trainer
+metrics:
+- precision
+- recall
+- f1
+model-index:
+- name: sentence_eval1
+  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. -->
+
+# sentence_eval1
+
+This model is a fine-tuned version of [roberta-large-mnli](https://huggingface.co/roberta-large-mnli) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.4766
+- Precision: {'precision': 0.863681451041519}
+- Recall: {'recall': 0.8702170188463735}
+- F1: {'f1': 0.8669369177156675}
+- Acc: {'accuracy': 0.8073120494335736}
+
+## 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: 48
+- eval_batch_size: 32
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 3.0
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Precision                         | Recall                         | F1                         | Acc                              |
+|:-------------:|:-----:|:----:|:---------------:|:---------------------------------:|:------------------------------:|:--------------------------:|:--------------------------------:|
+| 0.437         | 1.0   | 1771 | 0.4753          | {'precision': 0.9119431443924424} | {'recall': 0.7511422044545973} | {'f1': 0.8237688874970641} | {'accuracy': 0.7681771369721936} |
+| 0.367         | 2.0   | 3542 | 0.4342          | {'precision': 0.8658256880733946} | {'recall': 0.8623643632210166} | {'f1': 0.8640915593705294} | {'accuracy': 0.8043254376930999} |
+| 0.2915        | 3.0   | 5313 | 0.4766          | {'precision': 0.863681451041519}  | {'recall': 0.8702170188463735} | {'f1': 0.8669369177156675} | {'accuracy': 0.8073120494335736} |
+
+
+### Framework versions
+
+- Transformers 4.25.1
+- Pytorch 1.13.0+cu116
+- Datasets 2.7.1
+- Tokenizers 0.13.2