Instructions to use stdnan/rubert-tiny-antispam with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use stdnan/rubert-tiny-antispam with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="stdnan/rubert-tiny-antispam")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("stdnan/rubert-tiny-antispam") model = AutoModelForSequenceClassification.from_pretrained("stdnan/rubert-tiny-antispam", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download config.json from stdnan/rubert-tiny-antispam: direct link, hf CLI and curl.
- Browser
- Download file 751 Bytes
-
https://huggingface.co/stdnan/rubert-tiny-antispam/resolve/main/config.json
- Command line
-
hf download hf://stdnan/rubert-tiny-antispam/config.json
-
curl -L -o config.json https://huggingface.co/stdnan/rubert-tiny-antispam/resolve/main/config.json
751 Bytes
| { | |
| "_name_or_path": "cointegrated/rubert-tiny", | |
| "architectures": [ | |
| "BertForSequenceClassification" | |
| ], | |
| "attention_probs_dropout_prob": 0.1, | |
| "classifier_dropout": null, | |
| "emb_size": 312, | |
| "gradient_checkpointing": false, | |
| "hidden_act": "gelu", | |
| "hidden_dropout_prob": 0.1, | |
| "hidden_size": 312, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 600, | |
| "layer_norm_eps": 1e-12, | |
| "max_position_embeddings": 512, | |
| "model_type": "bert", | |
| "num_attention_heads": 12, | |
| "num_hidden_layers": 3, | |
| "pad_token_id": 0, | |
| "position_embedding_type": "absolute", | |
| "problem_type": "single_label_classification", | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.46.2", | |
| "type_vocab_size": 2, | |
| "use_cache": true, | |
| "vocab_size": 29564 | |
| } | |