Text Classification
Transformers
PyTorch
TensorBoard
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use Jedida/tweet_sentiments_analysis_distilbert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jedida/tweet_sentiments_analysis_distilbert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Jedida/tweet_sentiments_analysis_distilbert")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Jedida/tweet_sentiments_analysis_distilbert") model = AutoModelForSequenceClassification.from_pretrained("Jedida/tweet_sentiments_analysis_distilbert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Jedida/tweet_sentiments_analysis_distilbert: direct link, hf CLI and curl.
- Browser
- Download file 263 MB
-
https://huggingface.co/Jedida/tweet_sentiments_analysis_distilbert/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Jedida/tweet_sentiments_analysis_distilbert/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Jedida/tweet_sentiments_analysis_distilbert/resolve/main/pytorch_model.bin
263 MB
- Xet hash:
- 785ad9c15701568e99a1e46d432935a04558a937dbe0c971f5dc2efd9273e381
- Size of remote file:
- 263 MB
- SHA256:
- 93a3aa5446e26fbbc2624f773bcfa8307ecf08dc765a4a7ba66a84b64e9a96ac
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.