Instructions to use HJ-AILAB/FinTree with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use HJ-AILAB/FinTree with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="HJ-AILAB/FinTree")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("HJ-AILAB/FinTree") model = AutoModel.from_pretrained("HJ-AILAB/FinTree", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download config.json from HJ-AILAB/FinTree: direct link, hf CLI and curl.
- Browser
- Download file 897 Bytes
-
https://huggingface.co/HJ-AILAB/FinTree/resolve/main/config.json
- Command line
-
hf download hf://HJ-AILAB/FinTree/config.json
-
curl -L -o config.json https://huggingface.co/HJ-AILAB/FinTree/resolve/main/config.json
897 Bytes
| { | |
| "_name_or_path": "/data/r7play/sigir_pretrain/financial-deberta-drop15-epochs2", | |
| "architectures": [ | |
| "DebertaV2Model" | |
| ], | |
| "attention_probs_dropout_prob": 0.1, | |
| "hidden_act": "gelu", | |
| "hidden_dropout_prob": 0.1, | |
| "hidden_size": 1024, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 4096, | |
| "layer_norm_eps": 1e-07, | |
| "max_position_embeddings": 512, | |
| "max_relative_positions": -1, | |
| "model_type": "deberta-v2", | |
| "norm_rel_ebd": "layer_norm", | |
| "num_attention_heads": 16, | |
| "num_hidden_layers": 24, | |
| "pad_token_id": 0, | |
| "pooler_dropout": 0, | |
| "pooler_hidden_act": "gelu", | |
| "pooler_hidden_size": 1024, | |
| "pos_att_type": [ | |
| "p2c", | |
| "c2p" | |
| ], | |
| "position_biased_input": false, | |
| "position_buckets": 256, | |
| "relative_attention": true, | |
| "share_att_key": true, | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.26.1", | |
| "type_vocab_size": 0, | |
| "vocab_size": 128100 | |
| } | |