Instructions to use yujiepan/openai-privacy-filter-tiny-random with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use yujiepan/openai-privacy-filter-tiny-random with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="yujiepan/openai-privacy-filter-tiny-random")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("yujiepan/openai-privacy-filter-tiny-random") model = AutoModelForTokenClassification.from_pretrained("yujiepan/openai-privacy-filter-tiny-random") - Notebooks
- Google Colab
- Kaggle
| { | |
| "architectures": [ | |
| "OpenAIPrivacyFilterForTokenClassification" | |
| ], | |
| "attention_bias": true, | |
| "attention_dropout": 0.0, | |
| "bos_token_id": null, | |
| "classifier_dropout": 0.0, | |
| "default_n_ctx": 128000, | |
| "dtype": "bfloat16", | |
| "eos_token_id": 199999, | |
| "head_dim": 32, | |
| "hidden_act": "silu", | |
| "hidden_size": 8, | |
| "id2label": { | |
| "0": "O", | |
| "1": "B-account_number", | |
| "2": "I-account_number", | |
| "3": "E-account_number", | |
| "4": "S-account_number", | |
| "5": "B-private_address", | |
| "6": "I-private_address", | |
| "7": "E-private_address", | |
| "8": "S-private_address", | |
| "9": "B-private_date", | |
| "10": "I-private_date", | |
| "11": "E-private_date", | |
| "12": "S-private_date", | |
| "13": "B-private_email", | |
| "14": "I-private_email", | |
| "15": "E-private_email", | |
| "16": "S-private_email", | |
| "17": "B-private_person", | |
| "18": "I-private_person", | |
| "19": "E-private_person", | |
| "20": "S-private_person", | |
| "21": "B-private_phone", | |
| "22": "I-private_phone", | |
| "23": "E-private_phone", | |
| "24": "S-private_phone", | |
| "25": "B-private_url", | |
| "26": "I-private_url", | |
| "27": "E-private_url", | |
| "28": "S-private_url", | |
| "29": "B-secret", | |
| "30": "I-secret", | |
| "31": "E-secret", | |
| "32": "S-secret" | |
| }, | |
| "initial_context_length": 4096, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 32, | |
| "label2id": { | |
| "B-account_number": 1, | |
| "B-private_address": 5, | |
| "B-private_date": 9, | |
| "B-private_email": 13, | |
| "B-private_person": 17, | |
| "B-private_phone": 21, | |
| "B-private_url": 25, | |
| "B-secret": 29, | |
| "E-account_number": 3, | |
| "E-private_address": 7, | |
| "E-private_date": 11, | |
| "E-private_email": 15, | |
| "E-private_person": 19, | |
| "E-private_phone": 23, | |
| "E-private_url": 27, | |
| "E-secret": 31, | |
| "I-account_number": 2, | |
| "I-private_address": 6, | |
| "I-private_date": 10, | |
| "I-private_email": 14, | |
| "I-private_person": 18, | |
| "I-private_phone": 22, | |
| "I-private_url": 26, | |
| "I-secret": 30, | |
| "O": 0, | |
| "S-account_number": 4, | |
| "S-private_address": 8, | |
| "S-private_date": 12, | |
| "S-private_email": 16, | |
| "S-private_person": 20, | |
| "S-private_phone": 24, | |
| "S-private_url": 28, | |
| "S-secret": 32 | |
| }, | |
| "max_position_embeddings": 131072, | |
| "model_type": "openai_privacy_filter", | |
| "num_attention_heads": 8, | |
| "num_experts_per_tok": 4, | |
| "num_hidden_layers": 4, | |
| "num_key_value_heads": 4, | |
| "num_local_experts": 128, | |
| "output_router_logits": false, | |
| "pad_token_id": 199999, | |
| "rms_norm_eps": 1e-05, | |
| "rope_parameters": { | |
| "beta_fast": 32.0, | |
| "beta_slow": 1.0, | |
| "factor": 32.0, | |
| "original_max_position_embeddings": 4096, | |
| "rope_theta": 150000.0, | |
| "rope_type": "yarn", | |
| "truncate": false | |
| }, | |
| "router_aux_loss_coef": 0.001, | |
| "sliding_window": 128, | |
| "tie_word_embeddings": false, | |
| "transformers_version": "5.7.0.dev0", | |
| "use_cache": true, | |
| "vocab_size": 200064 | |
| } | |