mDeBERTa-v3-base-finetuned-nli-jnli

This model is a fine-tuned version of 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

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

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