Text Classification
Transformers
PyTorch
TensorBoard
distilbert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use sunilkumardash9/finetuning-sentiment-model-3000-samples with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sunilkumardash9/finetuning-sentiment-model-3000-samples with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="sunilkumardash9/finetuning-sentiment-model-3000-samples")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("sunilkumardash9/finetuning-sentiment-model-3000-samples") model = AutoModelForSequenceClassification.from_pretrained("sunilkumardash9/finetuning-sentiment-model-3000-samples", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from sunilkumardash9/finetuning-sentiment-model-3000-samples: direct link, hf CLI and curl.
- Browser
- Download file 268 MB
-
https://huggingface.co/sunilkumardash9/finetuning-sentiment-model-3000-samples/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://sunilkumardash9/finetuning-sentiment-model-3000-samples/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/sunilkumardash9/finetuning-sentiment-model-3000-samples/resolve/main/pytorch_model.bin
268 MB
- Xet hash:
- 5c44a361e24825def6abc79cd3aa3ce2021aba5fae8af98f294f3e98c66ca316
- Size of remote file:
- 268 MB
- SHA256:
- 74aaec1178e200fc1327a20dd506adf76cf3f34ac10726a74d179d5d6dfb3e1e
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