Instructions to use kunley2/FinBERT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kunley2/FinBERT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="kunley2/FinBERT")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("kunley2/FinBERT") model = AutoModelForMaskedLM.from_pretrained("kunley2/FinBERT", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download config.json from kunley2/FinBERT: direct link, hf CLI and curl.
- Browser
- Download file 674 Bytes
-
https://huggingface.co/kunley2/FinBERT/resolve/main/config.json
- Command line
-
hf download hf://kunley2/FinBERT/config.json
-
curl -L -o config.json https://huggingface.co/kunley2/FinBERT/resolve/main/config.json
674 Bytes
| { | |
| "add_cross_attention": false, | |
| "architectures": [ | |
| "BertForMaskedLM" | |
| ], | |
| "attention_probs_dropout_prob": 0.1, | |
| "bos_token_id": null, | |
| "classifier_dropout": null, | |
| "dtype": "float32", | |
| "eos_token_id": null, | |
| "hidden_act": "gelu", | |
| "hidden_dropout_prob": 0.1, | |
| "hidden_size": 128, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 512, | |
| "is_decoder": false, | |
| "layer_norm_eps": 1e-12, | |
| "max_position_embeddings": 266, | |
| "model_type": "bert", | |
| "num_attention_heads": 4, | |
| "num_hidden_layers": 4, | |
| "pad_token_id": 0, | |
| "tie_word_embeddings": true, | |
| "transformers_version": "5.16.1", | |
| "type_vocab_size": 2, | |
| "use_cache": false, | |
| "vocab_size": 22223 | |
| } | |