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