Instructions to use Hartunka/bert_base_rand_10_v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Hartunka/bert_base_rand_10_v1 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, DistilBertForLDAMaskedLM tokenizer = AutoTokenizer.from_pretrained("Hartunka/bert_base_rand_10_v1") model = DistilBertForLDAMaskedLM.from_pretrained("Hartunka/bert_base_rand_10_v1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download config.json from Hartunka/bert_base_rand_10_v1: direct link, hf CLI and curl.
- Browser
- Download file 504 Bytes
-
https://huggingface.co/Hartunka/bert_base_rand_10_v1/resolve/main/config.json
- Command line
-
hf download hf://Hartunka/bert_base_rand_10_v1/config.json
-
curl -L -o config.json https://huggingface.co/Hartunka/bert_base_rand_10_v1/resolve/main/config.json
504 Bytes
| { | |
| "activation": "gelu", | |
| "architectures": [ | |
| "DistilBertForLDAMaskedLM" | |
| ], | |
| "attention_dropout": 0.1, | |
| "dim": 768, | |
| "dropout": 0.1, | |
| "hidden_dim": 3072, | |
| "initializer_range": 0.02, | |
| "max_position_embeddings": 512, | |
| "model_type": "distilbert", | |
| "n_heads": 12, | |
| "n_layers": 12, | |
| "n_topics": 10, | |
| "pad_token_id": 0, | |
| "qa_dropout": 0.1, | |
| "seq_classif_dropout": 0.2, | |
| "sinusoidal_pos_embds": false, | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.50.2", | |
| "vocab_size": 30522 | |
| } | |