Instructions to use laiBatool/tiny-bert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use laiBatool/tiny-bert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="laiBatool/tiny-bert")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("laiBatool/tiny-bert") model = AutoModelForSequenceClassification.from_pretrained("laiBatool/tiny-bert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download config.json from laiBatool/tiny-bert: direct link, hf CLI and curl.
- Browser
- Download file 753 Bytes
-
https://huggingface.co/laiBatool/tiny-bert/resolve/main/config.json
- Command line
-
hf download hf://laiBatool/tiny-bert/config.json
-
curl -L -o config.json https://huggingface.co/laiBatool/tiny-bert/resolve/main/config.json
753 Bytes
| { | |
| "architectures": [ | |
| "AlbertForSequenceClassification" | |
| ], | |
| "attention_probs_dropout_prob": 0.1, | |
| "bos_token_id": 2, | |
| "classifier_dropout_prob": 0.1, | |
| "embedding_size": 128, | |
| "eos_token_id": 3, | |
| "hidden_act": "gelu", | |
| "hidden_dropout_prob": 0.1, | |
| "hidden_size": 128, | |
| "initializer_range": 0.02, | |
| "inner_group_num": 1, | |
| "intermediate_size": 512, | |
| "layer_norm_eps": 1e-12, | |
| "max_position_embeddings": 512, | |
| "model_type": "albert", | |
| "num_attention_heads": 2, | |
| "num_hidden_groups": 1, | |
| "num_hidden_layers": 2, | |
| "pad_token_id": 0, | |
| "position_embedding_type": "absolute", | |
| "problem_type": "single_label_classification", | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.51.3", | |
| "type_vocab_size": 2, | |
| "vocab_size": 30522 | |
| } | |