Text Classification
Transformers
PyTorch
TensorFlow
English
bert
financial-sentiment-analysis
sentiment-analysis
Instructions to use ldeb/solved-finbert-tone with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ldeb/solved-finbert-tone with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ldeb/solved-finbert-tone")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ldeb/solved-finbert-tone") model = AutoModelForSequenceClassification.from_pretrained("ldeb/solved-finbert-tone", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download config.json from ldeb/solved-finbert-tone: direct link, hf CLI and curl.
- Browser
- Download file 557 Bytes
-
https://huggingface.co/ldeb/solved-finbert-tone/resolve/main/config.json
- Command line
-
hf download hf://ldeb/solved-finbert-tone/config.json
-
curl -L -o config.json https://huggingface.co/ldeb/solved-finbert-tone/resolve/main/config.json
557 Bytes
| { | |
| "architectures": [ | |
| "BertForSequenceClassification" | |
| ], | |
| "id2label": { | |
| "0": "Neutral", | |
| "1": "Positive", | |
| "2": "Negative" | |
| }, | |
| "label2id": { | |
| "Positive": 1, | |
| "Negative": 2, | |
| "Neutral": 0 | |
| }, | |
| "attention_probs_dropout_prob": 0.1, | |
| "hidden_act": "gelu", | |
| "model_type": "bert", | |
| "hidden_dropout_prob": 0.1, | |
| "hidden_size": 768, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 3072, | |
| "max_position_embeddings": 512, | |
| "num_attention_heads": 12, | |
| "num_hidden_layers": 12, | |
| "type_vocab_size": 2, | |
| "vocab_size": 30873 | |
| } | |