Text Classification
Transformers
PyTorch
TensorFlow
JAX
Safetensors
English
bert
financial-sentiment-analysis
sentiment-analysis
text-embeddings-inference
Instructions to use Narsil/finbert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Narsil/finbert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Narsil/finbert")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Narsil/finbert") model = AutoModelForSequenceClassification.from_pretrained("Narsil/finbert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer_config.json from Narsil/finbert: direct link, hf CLI and curl.
- Browser
- Download file 252 Bytes
-
https://huggingface.co/Narsil/finbert/resolve/main/tokenizer_config.json
- Command line
-
hf download hf://Narsil/finbert/tokenizer_config.json
-
curl -L -o tokenizer_config.json https://huggingface.co/Narsil/finbert/resolve/main/tokenizer_config.json
252 Bytes
| {"do_lower_case": true, "unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]", "tokenize_chinese_chars": true, "strip_accents": null, "model_max_length": 512, "name_or_path": "bert-base-uncased"} |