Instructions to use FlowRank/mailSort with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use FlowRank/mailSort with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="FlowRank/mailSort")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("FlowRank/mailSort", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model/config.json from FlowRank/mailSort: direct link, hf CLI and curl.
- Browser
- Download file 1.91 kB
-
https://huggingface.co/FlowRank/mailSort/resolve/main/model/config.json
- Command line
-
hf download hf://FlowRank/mailSort/model/config.json
-
curl -L -o config.json https://huggingface.co/FlowRank/mailSort/resolve/main/model/config.json
1.91 kB
| { | |
| "activation": "gelu", | |
| "architectures": [ | |
| "DistilBertForSequenceClassification" | |
| ], | |
| "attention_dropout": 0.1, | |
| "bos_token_id": null, | |
| "dim": 768, | |
| "dropout": 0.1, | |
| "dtype": "float32", | |
| "eos_token_id": null, | |
| "hidden_dim": 3072, | |
| "id2label": { | |
| "0": "family_not_urgent", | |
| "1": "family_urgent", | |
| "2": "finance_not_urgent", | |
| "3": "finance_urgent", | |
| "4": "games_not_urgent", | |
| "5": "games_urgent", | |
| "6": "human resources_not_urgent", | |
| "7": "human resources_urgent", | |
| "8": "medical_not_urgent", | |
| "9": "medical_urgent", | |
| "10": "pets_not_urgent", | |
| "11": "pets_urgent", | |
| "12": "school_not_urgent", | |
| "13": "school_urgent", | |
| "14": "software engineering_not_urgent", | |
| "15": "software engineering_urgent", | |
| "16": "sport_not_urgent", | |
| "17": "sport_urgent", | |
| "18": "work/airbus_not_urgent", | |
| "19": "work/airbus_urgent" | |
| }, | |
| "initializer_range": 0.02, | |
| "label2id": { | |
| "family_not_urgent": 0, | |
| "family_urgent": 1, | |
| "finance_not_urgent": 2, | |
| "finance_urgent": 3, | |
| "games_not_urgent": 4, | |
| "games_urgent": 5, | |
| "human resources_not_urgent": 6, | |
| "human resources_urgent": 7, | |
| "medical_not_urgent": 8, | |
| "medical_urgent": 9, | |
| "pets_not_urgent": 10, | |
| "pets_urgent": 11, | |
| "school_not_urgent": 12, | |
| "school_urgent": 13, | |
| "software engineering_not_urgent": 14, | |
| "software engineering_urgent": 15, | |
| "sport_not_urgent": 16, | |
| "sport_urgent": 17, | |
| "work/airbus_not_urgent": 18, | |
| "work/airbus_urgent": 19 | |
| }, | |
| "max_position_embeddings": 512, | |
| "model_type": "distilbert", | |
| "n_heads": 12, | |
| "n_layers": 6, | |
| "pad_token_id": 0, | |
| "problem_type": "single_label_classification", | |
| "qa_dropout": 0.1, | |
| "seq_classif_dropout": 0.2, | |
| "sinusoidal_pos_embds": false, | |
| "tie_weights_": true, | |
| "tie_word_embeddings": true, | |
| "transformers_version": "5.8.0", | |
| "use_cache": false, | |
| "vocab_size": 30522 | |
| } | |