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")# 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
- Xet hash:
- 4d380d5d1463c9d775af5957446a5108a1bdd6a7340579120a98eb32cdc735c8
- Size of remote file:
- 268 MB
- SHA256:
- 4a0da51e962804c08dae32a9a8fcbecb0124fe008e70a0e3ded22274abd41c3b
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