Text Classification
Transformers
PyTorch
English
bert
financial-sentiment-analysis
sentiment-analysis
text-embeddings-inference
Instructions to use ahmedrachid/FinancialBERT-Sentiment-Analysis with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ahmedrachid/FinancialBERT-Sentiment-Analysis with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ahmedrachid/FinancialBERT-Sentiment-Analysis")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ahmedrachid/FinancialBERT-Sentiment-Analysis") model = AutoModelForSequenceClassification.from_pretrained("ahmedrachid/FinancialBERT-Sentiment-Analysis", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from ahmedrachid/FinancialBERT-Sentiment-Analysis: direct link, hf CLI and curl.
- Browser
- Download file 439 MB
-
https://huggingface.co/ahmedrachid/FinancialBERT-Sentiment-Analysis/resolve/refs%2Fpr%2F5/pytorch_model.bin
- Command line
-
hf download hf://ahmedrachid/FinancialBERT-Sentiment-Analysis@refs/pr/5/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/ahmedrachid/FinancialBERT-Sentiment-Analysis/resolve/refs%2Fpr%2F5/pytorch_model.bin
439 MB
- Xet hash:
- c74d8f27b0a559b2eff83dfc32711ca59a4eb7c9f7468aae041e02cc9a879846
- Size of remote file:
- 439 MB
- SHA256:
- aa62f8baa25b9530ccf34944da817cc1eb73c20e3b021e33db754302b49ae4bf
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