Token Classification
Transformers
PyTorch
TensorBoard
xlm-roberta
Generated from Trainer
Eval Results (legacy)
Instructions to use cfilt/HiNER-original-xlm-roberta-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cfilt/HiNER-original-xlm-roberta-large with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="cfilt/HiNER-original-xlm-roberta-large")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("cfilt/HiNER-original-xlm-roberta-large") model = AutoModelForTokenClassification.from_pretrained("cfilt/HiNER-original-xlm-roberta-large", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "_name_or_path": "../HiNER/cfilt_original/cfilt_original-xlm-roberta-large_ner_16_3e-05_10_1/", | |
| "architectures": [ | |
| "XLMRobertaForTokenClassification" | |
| ], | |
| "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": 1024, | |
| "id2label": { | |
| "0": "B-FESTIVAL", | |
| "1": "B-GAME", | |
| "2": "B-LANGUAGE", | |
| "3": "B-LITERATURE", | |
| "4": "B-LOCATION", | |
| "5": "B-MISC", | |
| "6": "B-NUMEX", | |
| "7": "B-ORGANIZATION", | |
| "8": "B-PERSON", | |
| "9": "B-RELIGION", | |
| "10": "B-TIMEX", | |
| "11": "I-FESTIVAL", | |
| "12": "I-GAME", | |
| "13": "I-LANGUAGE", | |
| "14": "I-LITERATURE", | |
| "15": "I-LOCATION", | |
| "16": "I-MISC", | |
| "17": "I-NUMEX", | |
| "18": "I-ORGANIZATION", | |
| "19": "I-PERSON", | |
| "20": "I-RELIGION", | |
| "21": "I-TIMEX", | |
| "22": "O" | |
| }, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 4096, | |
| "label2id": { | |
| "B-FESTIVAL": 0, | |
| "B-GAME": 1, | |
| "B-LANGUAGE": 2, | |
| "B-LITERATURE": 3, | |
| "B-LOCATION": 4, | |
| "B-MISC": 5, | |
| "B-NUMEX": 6, | |
| "B-ORGANIZATION": 7, | |
| "B-PERSON": 8, | |
| "B-RELIGION": 9, | |
| "B-TIMEX": 10, | |
| "I-FESTIVAL": 11, | |
| "I-GAME": 12, | |
| "I-LANGUAGE": 13, | |
| "I-LITERATURE": 14, | |
| "I-LOCATION": 15, | |
| "I-MISC": 16, | |
| "I-NUMEX": 17, | |
| "I-ORGANIZATION": 18, | |
| "I-PERSON": 19, | |
| "I-RELIGION": 20, | |
| "I-TIMEX": 21, | |
| "O": 22 | |
| }, | |
| "layer_norm_eps": 1e-05, | |
| "max_position_embeddings": 514, | |
| "model_type": "xlm-roberta", | |
| "num_attention_heads": 16, | |
| "num_hidden_layers": 24, | |
| "output_past": true, | |
| "pad_token_id": 1, | |
| "position_embedding_type": "absolute", | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.14.0", | |
| "type_vocab_size": 1, | |
| "use_cache": true, | |
| "vocab_size": 250002 | |
| } | |