Training complete

README.md

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+---
+license: apache-2.0
+base_model: bert-base-cased
+tags:
+- generated_from_trainer
+metrics:
+- precision
+- recall
+- f1
+- accuracy
+model-index:
+- name: assignment2_meher_test3
+  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. -->
+
+# assignment2_meher_test3
+
+This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.5370
+- Precision: 0.1642
+- Recall: 0.4158
+- F1: 0.2354
+- Accuracy: 0.8892
+
+## 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   | 149  | 0.3231          | 0.1406    | 0.2405 | 0.1774 | 0.9098   |
+| No log        | 2.0   | 298  | 0.2897          | 0.1711    | 0.3505 | 0.2300 | 0.9103   |
+| No log        | 3.0   | 447  | 0.3376          | 0.1715    | 0.3849 | 0.2373 | 0.9029   |
+| 0.3658        | 4.0   | 596  | 0.3870          | 0.1669    | 0.4261 | 0.2398 | 0.8887   |
+| 0.3658        | 5.0   | 745  | 0.4245          | 0.1542    | 0.3952 | 0.2218 | 0.8884   |
+| 0.3658        | 6.0   | 894  | 0.4291          | 0.1815    | 0.3986 | 0.2495 | 0.9024   |
+| 0.0735        | 7.0   | 1043 | 0.5257          | 0.1530    | 0.4296 | 0.2256 | 0.8820   |
+| 0.0735        | 8.0   | 1192 | 0.5211          | 0.1680    | 0.4261 | 0.2410 | 0.8900   |
+| 0.0735        | 9.0   | 1341 | 0.5810          | 0.1560    | 0.4502 | 0.2317 | 0.8784   |
+| 0.0735        | 10.0  | 1490 | 0.5370          | 0.1642    | 0.4158 | 0.2354 | 0.8892   |
+
+
+### Framework versions
+
+- Transformers 4.34.1
+- Pytorch 2.1.0+cu118
+- Datasets 2.14.6
+- Tokenizers 0.14.1

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