Text Classification
Transformers
PyTorch
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use Jedida/sentence_sentiments_analysis_distilbert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jedida/sentence_sentiments_analysis_distilbert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Jedida/sentence_sentiments_analysis_distilbert")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Jedida/sentence_sentiments_analysis_distilbert") model = AutoModelForSequenceClassification.from_pretrained("Jedida/sentence_sentiments_analysis_distilbert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Jedida/sentence_sentiments_analysis_distilbert: direct link, hf CLI and curl.
- Browser
- Download file 263 MB
-
https://huggingface.co/Jedida/sentence_sentiments_analysis_distilbert/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Jedida/sentence_sentiments_analysis_distilbert/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Jedida/sentence_sentiments_analysis_distilbert/resolve/main/pytorch_model.bin
263 MB
- Xet hash:
- baf398720b658fd0b9bd4f5d1b10a031fdd69fc1b97835e3e0fba40c49cc8d41
- Size of remote file:
- 263 MB
- SHA256:
- d41c26185fd4119042de805acb0dbcdca9800d49af465ea922ae48071e8ed202
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