加载中…
加载中…
This model is bert-base-multilingual-cased (an encoder-only Transformer pre-trained on 104 languages), fine-tuned on the IMDB movie-review dataset to classify text as POSITIVE or NEGATIVE. It reads the whole input at once, using the words on both the left and the right, and returns a label with a confidence score.
Although it was fine-tuned on English reviews, it can also classify text in other languages, such as Spanish, French and Urdu.
| Item | Value |
|---|---|
| Architecture | Encoder-only Transformer (BERT) |
| Base model | bert-base-multilingual-cased |
| Language | Multilingual (104 languages) |
| Dataset | IMDB movie reviews |
| Task | Sentiment classification |
| Labels | 0 = NEGATIVE, 1 = POSITIVE |
from transformers import pipeline
clf = pipeline("sentiment-analysis", model="TahiraAbbas/bert-multilingual-imdb-sentiment")
print(clf("The movie was excellent."))
print(clf("Ce film était terrible."))
Trained on a small subset of IMDB reviews. Accuracy on other languages and other domains can be lower than on English movie reviews. Reviews longer than 512 tokens are truncated.