Instructions to use ElMad/adaptable-fly-271 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ElMad/adaptable-fly-271 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ElMad/adaptable-fly-271")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ElMad/adaptable-fly-271") model = AutoModelForSequenceClassification.from_pretrained("ElMad/adaptable-fly-271", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download config.json from ElMad/adaptable-fly-271: direct link, hf CLI and curl.
- Browser
- Download file 2.07 kB
-
https://huggingface.co/ElMad/adaptable-fly-271/resolve/main/config.json
- Command line
-
hf download hf://ElMad/adaptable-fly-271/config.json
-
curl -L -o config.json https://huggingface.co/ElMad/adaptable-fly-271/resolve/main/config.json
2.07 kB
| { | |
| "_name_or_path": "microsoft/deberta-v3-xsmall", | |
| "architectures": [ | |
| "DebertaV2ForSequenceClassification" | |
| ], | |
| "attention_probs_dropout_prob": 0.1, | |
| "hidden_act": "gelu", | |
| "hidden_dropout_prob": 0.1, | |
| "hidden_size": 384, | |
| "id2label": { | |
| "0": "python-3.x", | |
| "1": "windows", | |
| "2": "django", | |
| "3": "list", | |
| "4": "csv", | |
| "5": "json", | |
| "6": "numpy", | |
| "7": "dictionary", | |
| "8": "matplotlib", | |
| "9": "flask", | |
| "10": "beautifulsoup", | |
| "11": "django-models", | |
| "12": "multithreading", | |
| "13": "python-2.7", | |
| "14": "sqlalchemy", | |
| "15": "arrays", | |
| "16": "tkinter", | |
| "17": "google-app-engine", | |
| "18": "regex", | |
| "19": "mysql", | |
| "20": "scipy", | |
| "21": "string", | |
| "22": "pandas", | |
| "23": "html", | |
| "24": "linux" | |
| }, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 1536, | |
| "label2id": { | |
| "arrays": 15, | |
| "beautifulsoup": 10, | |
| "csv": 4, | |
| "dictionary": 7, | |
| "django": 2, | |
| "django-models": 11, | |
| "flask": 9, | |
| "google-app-engine": 17, | |
| "html": 23, | |
| "json": 5, | |
| "linux": 24, | |
| "list": 3, | |
| "matplotlib": 8, | |
| "multithreading": 12, | |
| "mysql": 19, | |
| "numpy": 6, | |
| "pandas": 22, | |
| "python-2.7": 13, | |
| "python-3.x": 0, | |
| "regex": 18, | |
| "scipy": 20, | |
| "sqlalchemy": 14, | |
| "string": 21, | |
| "tkinter": 16, | |
| "windows": 1 | |
| }, | |
| "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": 6, | |
| "num_hidden_layers": 12, | |
| "pad_token_id": 0, | |
| "pooler_dropout": 0, | |
| "pooler_hidden_act": "gelu", | |
| "pooler_hidden_size": 384, | |
| "pos_att_type": [ | |
| "p2c", | |
| "c2p" | |
| ], | |
| "position_biased_input": false, | |
| "position_buckets": 256, | |
| "problem_type": "multi_label_classification", | |
| "relative_attention": true, | |
| "share_att_key": true, | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.46.2", | |
| "type_vocab_size": 0, | |
| "vocab_size": 128100 | |
| } | |