Instructions to use devrishi/roberta-retrained with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use devrishi/roberta-retrained with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="devrishi/roberta-retrained")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("devrishi/roberta-retrained") model = AutoModelForTokenClassification.from_pretrained("devrishi/roberta-retrained") - Notebooks
- Google Colab
- Kaggle
| { | |
| "_name_or_path": "kunalr63/roberta-retrained", | |
| "architectures": [ | |
| "RobertaForTokenClassification" | |
| ], | |
| "attention_probs_dropout_prob": 0.1, | |
| "bos_token_id": 0, | |
| "classifier_dropout": null, | |
| "eos_token_id": 2, | |
| "hidden_act": "gelu", | |
| "hidden_dropout_prob": 0.1, | |
| "hidden_size": 768, | |
| "id2label": { | |
| "0": "O", | |
| "1": "B-NAME", | |
| "2": "L-NAME", | |
| "3": "B-C_DESIG", | |
| "4": "I-C_DESIG", | |
| "5": "L-C_DESIG", | |
| "6": "B-CANDIDATE_ADDRESS", | |
| "7": "I-CANDIDATE_ADDRESS", | |
| "8": "L-CANDIDATE_ADDRESS", | |
| "9": "B-PHONE", | |
| "10": "I-PHONE", | |
| "11": "L-PHONE", | |
| "12": "U-EMAIL", | |
| "13": "U-DOB", | |
| "14": "B-T_EXP", | |
| "15": "I-T_EXP", | |
| "16": "L-T_EXP", | |
| "17": "B-EXP_COMPANY_WISE", | |
| "18": "I-EXP_COMPANY_WISE", | |
| "19": "L-EXP_COMPANY_WISE", | |
| "20": "B-PROJECTS", | |
| "21": "I-PROJECTS", | |
| "22": "L-PROJECTS", | |
| "23": "B-SKILLS", | |
| "24": "L-SKILLS", | |
| "25": "U-SKILLS", | |
| "26": "I-SKILLS", | |
| "27": "B-CERTIFICATE", | |
| "28": "I-CERTIFICATE", | |
| "29": "L-CERTIFICATE", | |
| "30": "B-G_IN", | |
| "31": "I-G_IN", | |
| "32": "L-G_IN", | |
| "33": "B-G_CLG", | |
| "34": "I-G_CLG", | |
| "35": "L-G_CLG", | |
| "36": "U-G_YEAR", | |
| "37": "U-PHONE", | |
| "38": "B-HIGH_FROM", | |
| "39": "L-HIGH_FROM", | |
| "40": "B-HIGH_PER", | |
| "41": "L-HIGH_PER", | |
| "42": "B-INTER_FROM", | |
| "43": "L-INTER_FROM", | |
| "44": "B-INTER_PER", | |
| "45": "L-INTER_PER", | |
| "46": "B-GRAD_PER", | |
| "47": "L-GRAD_PER", | |
| "48": "B-DOB", | |
| "49": "I-DOB", | |
| "50": "L-DOB", | |
| "51": "U-NAME", | |
| "52": "B-EMAIL", | |
| "53": "I-EMAIL", | |
| "54": "L-EMAIL", | |
| "55": "I-INTER_FROM", | |
| "56": "I-HIGH_FROM", | |
| "57": "I-NAME", | |
| "58": "B-PG_FROM", | |
| "59": "L-PG_FROM", | |
| "60": "B-PG_IN", | |
| "61": "I-PG_IN", | |
| "62": "L-PG_IN", | |
| "63": "U-PG_YEAR", | |
| "64": "U-G_IN", | |
| "65": "I-PG_FROM", | |
| "66": "U-CANDIDATE_ADDRESS", | |
| "67": "U-G_CLG", | |
| "68": "U-PG_IN", | |
| "69": "B-PG_YEAR", | |
| "70": "I-PG_YEAR", | |
| "71": "L-PG_YEAR", | |
| "72": "B-G_YEAR", | |
| "73": "I-G_YEAR", | |
| "74": "L-G_YEAR", | |
| "75": "U-PG_FROM", | |
| "76": "U-INTER_FROM", | |
| "77": "U-HIGH_FROM", | |
| "78": "U-GRAD_PER", | |
| "79": "U-INTER_PER", | |
| "80": "U-HIGH_PER", | |
| "81": "U-T_EXP", | |
| "82": "I-GRAD_PER", | |
| "83": "U-PROJECTS", | |
| "84": "U-CERTIFICATE", | |
| "85": "U-EXP_COMPANY_WISE", | |
| "86": "I-HIGH_PER", | |
| "87": "I-INTER_PER" | |
| }, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 3072, | |
| "label2id": { | |
| "B-CANDIDATE_ADDRESS": 6, | |
| "B-CERTIFICATE": 27, | |
| "B-C_DESIG": 3, | |
| "B-DOB": 48, | |
| "B-EMAIL": 52, | |
| "B-EXP_COMPANY_WISE": 17, | |
| "B-GRAD_PER": 46, | |
| "B-G_CLG": 33, | |
| "B-G_IN": 30, | |
| "B-G_YEAR": 72, | |
| "B-HIGH_FROM": 38, | |
| "B-HIGH_PER": 40, | |
| "B-INTER_FROM": 42, | |
| "B-INTER_PER": 44, | |
| "B-NAME": 1, | |
| "B-PG_FROM": 58, | |
| "B-PG_IN": 60, | |
| "B-PG_YEAR": 69, | |
| "B-PHONE": 9, | |
| "B-PROJECTS": 20, | |
| "B-SKILLS": 23, | |
| "B-T_EXP": 14, | |
| "I-CANDIDATE_ADDRESS": 7, | |
| "I-CERTIFICATE": 28, | |
| "I-C_DESIG": 4, | |
| "I-DOB": 49, | |
| "I-EMAIL": 53, | |
| "I-EXP_COMPANY_WISE": 18, | |
| "I-GRAD_PER": 82, | |
| "I-G_CLG": 34, | |
| "I-G_IN": 31, | |
| "I-G_YEAR": 73, | |
| "I-HIGH_FROM": 56, | |
| "I-HIGH_PER": 86, | |
| "I-INTER_FROM": 55, | |
| "I-INTER_PER": 87, | |
| "I-NAME": 57, | |
| "I-PG_FROM": 65, | |
| "I-PG_IN": 61, | |
| "I-PG_YEAR": 70, | |
| "I-PHONE": 10, | |
| "I-PROJECTS": 21, | |
| "I-SKILLS": 26, | |
| "I-T_EXP": 15, | |
| "L-CANDIDATE_ADDRESS": 8, | |
| "L-CERTIFICATE": 29, | |
| "L-C_DESIG": 5, | |
| "L-DOB": 50, | |
| "L-EMAIL": 54, | |
| "L-EXP_COMPANY_WISE": 19, | |
| "L-GRAD_PER": 47, | |
| "L-G_CLG": 35, | |
| "L-G_IN": 32, | |
| "L-G_YEAR": 74, | |
| "L-HIGH_FROM": 39, | |
| "L-HIGH_PER": 41, | |
| "L-INTER_FROM": 43, | |
| "L-INTER_PER": 45, | |
| "L-NAME": 2, | |
| "L-PG_FROM": 59, | |
| "L-PG_IN": 62, | |
| "L-PG_YEAR": 71, | |
| "L-PHONE": 11, | |
| "L-PROJECTS": 22, | |
| "L-SKILLS": 24, | |
| "L-T_EXP": 16, | |
| "O": 0, | |
| "U-CANDIDATE_ADDRESS": 66, | |
| "U-CERTIFICATE": 84, | |
| "U-DOB": 13, | |
| "U-EMAIL": 12, | |
| "U-EXP_COMPANY_WISE": 85, | |
| "U-GRAD_PER": 78, | |
| "U-G_CLG": 67, | |
| "U-G_IN": 64, | |
| "U-G_YEAR": 36, | |
| "U-HIGH_FROM": 77, | |
| "U-HIGH_PER": 80, | |
| "U-INTER_FROM": 76, | |
| "U-INTER_PER": 79, | |
| "U-NAME": 51, | |
| "U-PG_FROM": 75, | |
| "U-PG_IN": 68, | |
| "U-PG_YEAR": 63, | |
| "U-PHONE": 37, | |
| "U-PROJECTS": 83, | |
| "U-SKILLS": 25, | |
| "U-T_EXP": 81 | |
| }, | |
| "layer_norm_eps": 1e-05, | |
| "max_position_embeddings": 514, | |
| "model_type": "roberta", | |
| "num_attention_heads": 12, | |
| "num_hidden_layers": 12, | |
| "pad_token_id": 1, | |
| "position_embedding_type": "absolute", | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.35.2", | |
| "type_vocab_size": 1, | |
| "use_cache": true, | |
| "vocab_size": 50265 | |
| } | |