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--- |
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license: apache-2.0 |
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base_model: HooshvareLab/bert-fa-zwnj-base |
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tags: |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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model-index: |
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- name: ParsBERT-nli-FarsTail-FarSick |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# ParsBERT-nli-FarsTail-FarSick |
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This model is a fine-tuned version of [HooshvareLab/bert-fa-zwnj-base](https://huggingface.co/HooshvareLab/bert-fa-zwnj-base) on the [FarsTail](https://github.com/dml-qom/FarsTail/tree/master) |
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and [FarSick](https://github.com/ZahraGhasemi-AI/FarSick/tree/main) datasets. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.8730 |
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- Accuracy: 0.8055 |
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- Precision (macro): 0.7900 |
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- Precision (micro): 0.8055 |
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- Recall (macro): 0.7926 |
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- Recall (micro): 0.7926 |
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- F1 (macro): 0.7909 |
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- F1 (micro): 0.8055 |
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## How to use |
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``` python |
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import torch |
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import transformers |
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model_name_or_path = "parsi-ai-nlpclass/ParsBERT-nli-FarsTail-FarSick" |
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config = transformers.AutoConfig.from_pretrained(model_name_or_path) |
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tokenizer_pb = transformers.AutoTokenizer.from_pretrained(model_name_or_path) |
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model_pb = transformers.AutoModelForSequenceClassification.from_pretrained(model_name_or_path, |
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num_labels=3) |
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premise = "سلام خوبی؟" |
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hypothesis = "آره خوبم" |
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print(model_pb(**tokenizer_pb(premise, hypothesis, return_tensors='pt'))) |
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``` |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 2e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 5 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision (macro) | Precision (micro) | Recall (macro) | Recall (micro) | F1 (macro) | F1 (micro) | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:-----------------:|:-----------------:|:--------------:|:--------------:|:----------:|:----------:| |
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| 0.6248 | 1.0 | 1137 | 0.5391 | 0.7768 | 0.7677 | 0.7768 | 0.7728 | 0.7728 | 0.7647 | 0.7768 | |
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| 0.4449 | 2.0 | 2274 | 0.5017 | 0.8055 | 0.7909 | 0.8055 | 0.7963 | 0.7963 | 0.7932 | 0.8055 | |
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| 0.304 | 3.0 | 3411 | 0.5851 | 0.8125 | 0.8006 | 0.8125 | 0.7979 | 0.7979 | 0.7985 | 0.8125 | |
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| 0.1844 | 4.0 | 4548 | 0.7549 | 0.8140 | 0.8010 | 0.8140 | 0.7982 | 0.7982 | 0.7993 | 0.8140 | |
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| 0.1224 | 5.0 | 5685 | 0.8730 | 0.8055 | 0.7900 | 0.8055 | 0.7926 | 0.7926 | 0.7909 | 0.8055 | |
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### Framework versions |
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- Transformers 4.37.2 |
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- Pytorch 2.1.0+cu121 |
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- Datasets 2.17.1 |
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- Tokenizers 0.15.2 |
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