---
license: mit
language:
 - ja
base_model: microsoft/mdeberta-v3-base
tags:
 - generated_from_trainer
 - bert
 - zero-shot-classification
 - text-classification
datasets:
 - MoritzLaurer/multilingual-NLI-26lang-2mil7
 - shunk031/JGLUE
metrics:
 - accuracy
 - f1
model-index:
 - name: mDeBERTa-v3-base-finetuned-nli-jnli
   results: []
pipeline_tag: zero-shot-classification
widget:
- text: 今日の予定を教えて
  candidate_labels: 天気,ニュース,金融,予定
---

<!-- 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. -->

# mDeBERTa-v3-base-finetuned-nli-jnli

This model is a fine-tuned version of [microsoft/mdeberta-v3-base](https://huggingface.co/microsoft/mdeberta-v3-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7739
- Accuracy: 0.6808
- F1: 0.6742

## Model description

More information needed

## Intended uses & limitations
#### zero-shot classification

```python
from transformers import pipeline

model_name = "thkkvui/mDeBERTa-v3-base-finetuned-nli-jnli"
classifier = pipeline("zero-shot-classification", model=model_name)

text = ["今日の天気を教えて", "ニュースある？", "予定をチェックして", "ドル円は？"]
labels = ["天気", "ニュース", "金融", "予定"]

for t in text:
    output = classifier(t, labels, multi_label=False)
    print(output)
```

#### NLI use-case
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

device = torch.device("mps" if torch.backends.mps.is_available() else "cpu")
model_name = "thkkvui/mDeBERTa-v3-base-finetuned-nli-jnli"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)

premise = "NY Yankees is the professional baseball team in America."
hypothesis = "メジャーリーグのチームは、日本ではニューヨークヤンキースが有名だ。"

inputs = tokenizer(premise, hypothesis, truncation=True, return_tensors="pt")

with torch.no_grad():
    output = model(**inputs)
    
preds = torch.softmax(output["logits"][0], -1).tolist()
label_names = ["entailment", "neutral", "contradiction"]
result = {name: round(float(pred) * 100, 1) for pred, name in zip(preds, label_names)}
print(result)
```

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 3e-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
- lr_scheduler_warmup_ratio: 0.06
- num_epochs: 2

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy | F1     |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|
| 0.753         | 0.53  | 5000  | 0.8758          | 0.6105   | 0.6192 |
| 0.5947        | 1.07  | 10000 | 0.6619          | 0.7054   | 0.7035 |
| 0.5791        | 1.6   | 15000 | 0.7739          | 0.6808   | 0.6742 |


### Framework versions

- Transformers 4.33.2
- Pytorch 2.0.1
- Datasets 2.14.5
- Tokenizers 0.13.3