Instructions to use Souvikcmsa/SentimentAnalysisDistillBERT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Souvikcmsa/SentimentAnalysisDistillBERT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Souvikcmsa/SentimentAnalysisDistillBERT")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Souvikcmsa/SentimentAnalysisDistillBERT") model = AutoModelForSequenceClassification.from_pretrained("Souvikcmsa/SentimentAnalysisDistillBERT", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Souvikcmsa/SentimentAnalysisDistillBERT: direct link, hf CLI and curl.
- Browser
- Download file 268 MB
-
https://huggingface.co/Souvikcmsa/SentimentAnalysisDistillBERT/resolve/refs%2Fpr%2F1/pytorch_model.bin
- Command line
-
hf download hf://Souvikcmsa/SentimentAnalysisDistillBERT@refs/pr/1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Souvikcmsa/SentimentAnalysisDistillBERT/resolve/refs%2Fpr%2F1/pytorch_model.bin
268 MB
- Xet hash:
- 1306606d5390013af8310e6664f2ed84d7cf401fbb514743b2d113bb9281c378
- Size of remote file:
- 268 MB
- SHA256:
- 235a36b6deafcae2f9a76f081aa54ad0422e452a924d3b449158e5d1d0adcab3
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.