update model card README.md

README.md

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+---
+license: mit
+tags:
+- text-classification
+- generated_from_trainer
+datasets:
+- paws
+metrics:
+- f1
+- precision
+- recall
+model-index:
+- name: deberta-v3-large-finetuned-paws-paraphrase-detector
+  results:
+  - task:
+      name: Text Classification
+      type: text-classification
+    dataset:
+      name: paws
+      type: paws
+      args: labeled_final
+    metrics:
+    - name: F1
+      type: f1
+      value: 0.9426698284279537
+    - name: Precision
+      type: precision
+      value: 0.9300853289292595
+    - name: Recall
+      type: recall
+      value: 0.9555995475113123
+---
+
+<!-- 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. -->
+
+# deberta-v3-large-finetuned-paws-paraphrase-detector
+
+This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on the paws dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.3046
+- F1: 0.9427
+- Precision: 0.9301
+- Recall: 0.9556
+
+## 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: 6e-06
+- 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
+- lr_scheduler_warmup_steps: 50
+- num_epochs: 3
+- mixed_precision_training: Native AMP
+
+### Training results
+
+| Training Loss | Epoch | Step  | Validation Loss | F1     | Precision | Recall |
+|:-------------:|:-----:|:-----:|:---------------:|:------:|:---------:|:------:|
+| 0.1492        | 1.0   | 6176  | 0.1650          | 0.9537 | 0.9385    | 0.9695 |
+| 0.1018        | 2.0   | 12352 | 0.1968          | 0.9544 | 0.9427    | 0.9664 |
+| 0.0482        | 3.0   | 18528 | 0.2419          | 0.9521 | 0.9388    | 0.9658 |
+
+
+### Framework versions
+
+- Transformers 4.20.1
+- Pytorch 1.11.0
+- Datasets 2.1.0
+- Tokenizers 0.12.1