stanfordnlp/imdb
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How to use ManavDhayeCoder/sentiment-bert with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="ManavDhayeCoder/sentiment-bert") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("ManavDhayeCoder/sentiment-bert")
model = AutoModelForSequenceClassification.from_pretrained("ManavDhayeCoder/sentiment-bert")This model is a fine-tuned version of google-bert/bert-base-uncased, trained on the IMDB movie reviews dataset for binary sentiment classification.
It predicts whether text expresses negative or positive sentiment.
This model is hosted by @ManavDhayeCoder.
| Property | Value |
|---|---|
| Base model | google-bert/bert-base-uncased |
| Task | Sentiment Analysis (Sequence Classification) |
| Labels | negative / positive |
| Dataset | IMDB |
| Library | Hugging Face Transformers |
| Format | model.safetensors |
The model has two classes:
LABEL_0 β negative LABEL_1 β positivefrom transformers import pipeline
clf = pipeline("text-classification", model="ManavDhayeCoder/sentiment-bert")
print(clf("This movie was amazing!"))
Base model
google-bert/bert-base-uncased