Instructions to use wietsedv/bert-base-dutch-cased-finetuned-udlassy-pos with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use wietsedv/bert-base-dutch-cased-finetuned-udlassy-pos with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="wietsedv/bert-base-dutch-cased-finetuned-udlassy-pos")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("wietsedv/bert-base-dutch-cased-finetuned-udlassy-pos") model = AutoModelForTokenClassification.from_pretrained("wietsedv/bert-base-dutch-cased-finetuned-udlassy-pos") - Notebooks
- Google Colab
- Kaggle
| { | |
| "_num_labels": 17, | |
| "architectures": [ | |
| "BertForTokenClassification" | |
| ], | |
| "attention_probs_dropout_prob": 0.2, | |
| "bad_words_ids": null, | |
| "bos_token_id": null, | |
| "decoder_start_token_id": null, | |
| "do_sample": false, | |
| "early_stopping": false, | |
| "eos_token_id": null, | |
| "finetuning_task": null, | |
| "hidden_act": "gelu", | |
| "hidden_dropout_prob": 0.3, | |
| "hidden_size": 768, | |
| "id2label": { | |
| "0": "O", | |
| "1": "adj", | |
| "2": "adp", | |
| "3": "adv", | |
| "4": "aux", | |
| "5": "cconj", | |
| "6": "det", | |
| "7": "intj", | |
| "8": "noun", | |
| "9": "num", | |
| "10": "pron", | |
| "11": "propn", | |
| "12": "punct", | |
| "13": "sconj", | |
| "14": "sym", | |
| "15": "verb", | |
| "16": "x" | |
| }, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 3072, | |
| "is_decoder": false, | |
| "is_encoder_decoder": false, | |
| "label2id": { | |
| "O": 0, | |
| "adj": 1, | |
| "adp": 2, | |
| "adv": 3, | |
| "aux": 4, | |
| "cconj": 5, | |
| "det": 6, | |
| "intj": 7, | |
| "noun": 8, | |
| "num": 9, | |
| "pron": 10, | |
| "propn": 11, | |
| "punct": 12, | |
| "sconj": 13, | |
| "sym": 14, | |
| "verb": 15, | |
| "x": 16 | |
| }, | |
| "layer_norm_eps": 1e-12, | |
| "length_penalty": 1.0, | |
| "max_length": 20, | |
| "max_position_embeddings": 512, | |
| "min_length": 0, | |
| "model_type": "bert", | |
| "no_repeat_ngram_size": 0, | |
| "num_attention_heads": 12, | |
| "num_beams": 1, | |
| "num_hidden_layers": 12, | |
| "num_return_sequences": 1, | |
| "output_attentions": false, | |
| "output_hidden_states": false, | |
| "output_past": true, | |
| "pad_token_id": 0, | |
| "prefix": null, | |
| "pruned_heads": {}, | |
| "repetition_penalty": 1.0, | |
| "task_specific_params": null, | |
| "temperature": 1.0, | |
| "top_k": 50, | |
| "top_p": 1.0, | |
| "torchscript": false, | |
| "type_vocab_size": 2, | |
| "use_bfloat16": false, | |
| "vocab_size": 30000 | |
| } | |