metadata

language:
  - ja
license: cc-by-sa-4.0
tags:
  - zero-shot-classification
  - text-classification
  - nli
  - pytorch
metrics:
  - accuracy
datasets:
  - JSNLI
pipeline_tag: text-classification
widget:
  - text: あなた が 好きです 。 　 あなた を 愛して い ます 。
model-index:
  - name: roberta-base-japanese-jsnli
    results:
      - task:
          type: text-classification
          name: Natural Language Inference
        dataset:
          type: snli
          name: JSNLI
          split: dev
        metrics:
          - type: accuracy
            value: 0.9328
            verified: false

roberta-base-japanese-jsnli

This model is a fine-tuned version of nlp-waseda/roberta-base-japanese on the JSNLI dataset. It achieves the following results on the evaluation set:

Loss: 0.2039
Accuracy: 0.9328

How to use the model

The input text should be segmented into words by Juman++ in advance.

Simple zero-shot classification pipeline

from transformers import pipeline
from pyknp import Juman

juman = Juman()

classifier = pipeline("zero-shot-classification", model="Formzu/roberta-base-japanese-jsnli")

sequence_to_classify = " ".join([tok.midasi for tok in juman.analysis("いつか世界を見る。").mrph_list()])

candidate_labels = ['旅行', '料理', '踊り']
out = classifier(sequence_to_classify, candidate_labels, hypothesis_template="この 例 は {} です 。")
print(out)
#{'sequence': 'いつか 世界 を 見る 。', 
# 'labels': ['旅行', '踊り', '料理'], 
# 'scores': [0.8998081684112549, 0.06059670448303223, 0.03959512338042259]}

NLI use-case

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
from pyknp import Juman

juman = Juman()

device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")

model_name = "Formzu/roberta-base-japanese-jsnli"
model = AutoModelForSequenceClassification.from_pretrained(model_name).to(device)
tokenizer = AutoTokenizer.from_pretrained(model_name)

premise = " ".join([tok.midasi for tok in juman.analysis("いつか世界を見る。").mrph_list()])
label = '旅行'
hypothesis = f'この 例 は {label} です 。'

input = tokenizer.encode(premise, hypothesis, return_tensors='pt').to(device)
with torch.no_grad():
    logits = model(input)["logits"][0]
    probs = logits.softmax(dim=-1)
    print(probs.cpu().numpy(), logits.cpu().numpy())
#[0.82168734 0.1744363  0.00387629] [ 2.3362164   0.78641605 -3.0202653 ]

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 2e-05
train_batch_size: 32
eval_batch_size: 32
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 3.0

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.4067	1.0	16657	0.2224	0.9201
0.3397	2.0	33314	0.2152	0.9208
0.2775	3.0	49971	0.2039	0.9328

Framework versions

Transformers 4.21.2
Pytorch 1.12.1+cu116
Datasets 2.4.0
Tokenizers 0.12.1