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
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