Instructions to use subhasisj/de-TAPT-MLM-MiniLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use subhasisj/de-TAPT-MLM-MiniLM with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="subhasisj/de-TAPT-MLM-MiniLM")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("subhasisj/de-TAPT-MLM-MiniLM") model = AutoModelForMaskedLM.from_pretrained("subhasisj/de-TAPT-MLM-MiniLM", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "_name_or_path": "subhasisj/MiniLMv2-qa-encoder", | |
| "architectures": [ | |
| "BertForMaskedLM" | |
| ], | |
| "attention_probs_dropout_prob": 0.1, | |
| "classifier_dropout": null, | |
| "hidden_act": "gelu", | |
| "hidden_dropout_prob": 0.1, | |
| "hidden_size": 384, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 1536, | |
| "layer_norm_eps": 1e-12, | |
| "max_position_embeddings": 512, | |
| "model_type": "bert", | |
| "num_attention_heads": 12, | |
| "num_hidden_layers": 12, | |
| "pad_token_id": 0, | |
| "position_embedding_type": "absolute", | |
| "tokenizer_class": "XLMRobertaTokenizer", | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.18.0", | |
| "type_vocab_size": 2, | |
| "use_cache": true, | |
| "vocab_size": 250037 | |
| } | |