Instructions to use Mahmoud8/small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Mahmoud8/small with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Mahmoud8/small")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Mahmoud8/small") model = AutoModelForSequenceClassification.from_pretrained("Mahmoud8/small", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download config.json from Mahmoud8/small: direct link, hf CLI and curl.
- Browser
- Download file 880 Bytes
-
https://huggingface.co/Mahmoud8/small/resolve/main/config.json
- Command line
-
hf download hf://Mahmoud8/small/config.json
-
curl -L -o config.json https://huggingface.co/Mahmoud8/small/resolve/main/config.json
880 Bytes
| { | |
| "_name_or_path": "funnel-transformer/small", | |
| "activation_dropout": 0.0, | |
| "architectures": [ | |
| "FunnelForSequenceClassification" | |
| ], | |
| "attention_dropout": 0.1, | |
| "attention_type": "relative_shift", | |
| "block_repeats": [ | |
| 1, | |
| 1, | |
| 1 | |
| ], | |
| "block_sizes": [ | |
| 4, | |
| 4, | |
| 4 | |
| ], | |
| "d_head": 64, | |
| "d_inner": 3072, | |
| "d_model": 768, | |
| "hidden_act": "gelu_new", | |
| "hidden_dropout": 0.1, | |
| "initializer_range": 0.1, | |
| "initializer_std": null, | |
| "layer_norm_eps": 1e-09, | |
| "max_position_embeddings": 512, | |
| "model_type": "funnel", | |
| "n_head": 12, | |
| "num_decoder_layers": 2, | |
| "pool_q_only": true, | |
| "pooling_type": "mean", | |
| "problem_type": "single_label_classification", | |
| "rel_attn_type": "factorized", | |
| "separate_cls": true, | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.31.0", | |
| "truncate_seq": true, | |
| "type_vocab_size": 3, | |
| "vocab_size": 30522 | |
| } | |