Instructions to use NasimB/bert-concat with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NasimB/bert-concat with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="NasimB/bert-concat")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("NasimB/bert-concat") model = AutoModelForMaskedLM.from_pretrained("NasimB/bert-concat", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download config.json from NasimB/bert-concat: direct link, hf CLI and curl.
- Browser
- Download file 589 Bytes
-
https://huggingface.co/NasimB/bert-concat/resolve/main/config.json
- Command line
-
hf download hf://NasimB/bert-concat/config.json
-
curl -L -o config.json https://huggingface.co/NasimB/bert-concat/resolve/main/config.json
589 Bytes
| { | |
| "architectures": [ | |
| "BertForMaskedLM" | |
| ], | |
| "attention_probs_dropout_prob": 0.1, | |
| "classifier_dropout": null, | |
| "hidden_act": "gelu", | |
| "hidden_dropout_prob": 0.1, | |
| "hidden_size": 768, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 3072, | |
| "layer_norm_eps": 1e-12, | |
| "max_position_embeddings": 128, | |
| "model_type": "bert", | |
| "num_attention_heads": 12, | |
| "num_hidden_layers": 12, | |
| "pad_token_id": 0, | |
| "position_embedding_type": "absolute", | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.26.1", | |
| "type_vocab_size": 2, | |
| "use_cache": true, | |
| "vocab_size": 30522 | |
| } | |