Instructions to use Politus/swa_eng_org_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Politus/swa_eng_org_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Politus/swa_eng_org_model")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Politus/swa_eng_org_model") model = AutoModelForSequenceClassification.from_pretrained("Politus/swa_eng_org_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download config.json from Politus/swa_eng_org_model: direct link, hf CLI and curl.
- Browser
- Download file 796 Bytes
-
https://huggingface.co/Politus/swa_eng_org_model/resolve/main/config.json
- Command line
-
hf download hf://Politus/swa_eng_org_model/config.json
-
curl -L -o config.json https://huggingface.co/Politus/swa_eng_org_model/resolve/main/config.json
796 Bytes
| { | |
| "add_cross_attention": false, | |
| "architectures": [ | |
| "XLMRobertaForSequenceClassification" | |
| ], | |
| "attention_probs_dropout_prob": 0.1, | |
| "bos_token_id": 0, | |
| "classifier_dropout": null, | |
| "dtype": "float32", | |
| "eos_token_id": 2, | |
| "gradient_checkpointing": false, | |
| "hidden_act": "gelu", | |
| "hidden_dropout_prob": 0.1, | |
| "hidden_size": 768, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 3072, | |
| "is_decoder": false, | |
| "layer_norm_eps": 1e-05, | |
| "max_position_embeddings": 514, | |
| "model_type": "xlm-roberta", | |
| "num_attention_heads": 12, | |
| "num_hidden_layers": 12, | |
| "output_past": true, | |
| "pad_token_id": 1, | |
| "position_embedding_type": "absolute", | |
| "tie_word_embeddings": true, | |
| "transformers_version": "5.9.0", | |
| "type_vocab_size": 1, | |
| "use_cache": true, | |
| "vocab_size": 250002 | |
| } | |