Text Classification
Transformers
PyTorch
TensorBoard
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use crystalw3636/fin_sentiment with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use crystalw3636/fin_sentiment with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="crystalw3636/fin_sentiment")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("crystalw3636/fin_sentiment") model = AutoModelForSequenceClassification.from_pretrained("crystalw3636/fin_sentiment", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from crystalw3636/fin_sentiment: direct link, hf CLI and curl.
- Browser
- Download file 3.44 kB
-
https://huggingface.co/crystalw3636/fin_sentiment/resolve/main/training_args.bin
- Command line
-
hf download hf://crystalw3636/fin_sentiment/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/crystalw3636/fin_sentiment/resolve/main/training_args.bin
3.44 kB
- Xet hash:
- fdc76c216d668b051f25b6ad91e6d70812435f9561e971640235e1b7d7e017bf
- Size of remote file:
- 3.44 kB
- SHA256:
- fc4513004990afc1ebee961798af2993e426f86378ab9821b991a77ad0674f49
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