Text Classification
Transformers
Safetensors
English
distilbert
document-classification
document-ai
pii-detection
redaction
text-embeddings-inference
Instructions to use FahrenheitResearch/FR-Docs-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use FahrenheitResearch/FR-Docs-v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="FahrenheitResearch/FR-Docs-v1")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("FahrenheitResearch/FR-Docs-v1") model = AutoModelForSequenceClassification.from_pretrained("FahrenheitResearch/FR-Docs-v1") - Notebooks
- Google Colab
- Kaggle
| { | |
| "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": "contract", | |
| "1": "nda", | |
| "2": "court_filing", | |
| "3": "patent", | |
| "4": "compliance_filing", | |
| "5": "invoice", | |
| "6": "receipt", | |
| "7": "purchase_order", | |
| "8": "financial_statement", | |
| "9": "tax_document", | |
| "10": "insurance_document", | |
| "11": "cv_resume", | |
| "12": "offer_letter", | |
| "13": "policy_document", | |
| "14": "product_spec", | |
| "15": "datasheet", | |
| "16": "user_manual", | |
| "17": "proposal", | |
| "18": "case_study", | |
| "19": "press_release", | |
| "20": "marketing_collateral", | |
| "21": "sop", | |
| "22": "report", | |
| "23": "meeting_minutes", | |
| "24": "form", | |
| "25": "presentation", | |
| "26": "email_thread", | |
| "27": "letter", | |
| "28": "memo", | |
| "29": "research_paper", | |
| "30": "other" | |
| }, | |
| "initializer_range": 0.02, | |
| "label2id": { | |
| "case_study": 18, | |
| "compliance_filing": 4, | |
| "contract": 0, | |
| "court_filing": 2, | |
| "cv_resume": 11, | |
| "datasheet": 15, | |
| "email_thread": 26, | |
| "financial_statement": 8, | |
| "form": 24, | |
| "insurance_document": 10, | |
| "invoice": 5, | |
| "letter": 27, | |
| "marketing_collateral": 20, | |
| "meeting_minutes": 23, | |
| "memo": 28, | |
| "nda": 1, | |
| "offer_letter": 12, | |
| "other": 30, | |
| "patent": 3, | |
| "policy_document": 13, | |
| "presentation": 25, | |
| "press_release": 19, | |
| "product_spec": 14, | |
| "proposal": 17, | |
| "purchase_order": 7, | |
| "receipt": 6, | |
| "report": 22, | |
| "research_paper": 29, | |
| "sop": 21, | |
| "tax_document": 9, | |
| "user_manual": 16 | |
| }, | |
| "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": "5.6.2", | |
| "use_cache": false, | |
| "vocab_size": 30522 | |
| } | |