Instructions to use hf-internal-testing/tiny-albert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-albert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="hf-internal-testing/tiny-albert")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-albert") model = AutoModelForMaskedLM.from_pretrained("hf-internal-testing/tiny-albert") - Notebooks
- Google Colab
- Kaggle
| { | |
| "architectures": [ | |
| "AlbertForMaskedLM" | |
| ], | |
| "attention_probs_dropout_prob": 0.1, | |
| "bos_token_id": 2, | |
| "classifier_dropout_prob": 0.1, | |
| "down_scale_factor": 1, | |
| "embedding_size": 64, | |
| "eos_token_id": 3, | |
| "gap_size": 0, | |
| "hidden_act": "gelu", | |
| "hidden_dropout_prob": 0.1, | |
| "hidden_size": 32, | |
| "initializer_range": 0.02, | |
| "inner_group_num": 1, | |
| "intermediate_size": 128, | |
| "layer_norm_eps": 1e-12, | |
| "max_position_embeddings": 256, | |
| "model_type": "albert", | |
| "net_structure_type": 0, | |
| "num_attention_heads": 2, | |
| "num_hidden_groups": 1, | |
| "num_hidden_layers": 2, | |
| "num_memory_blocks": 0, | |
| "pad_token_id": 0, | |
| "position_embedding_type": "absolute", | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.52.0.dev0", | |
| "type_vocab_size": 2, | |
| "vocab_size": 5000 | |
| } | |