Token Classification
Transformers
PyTorch
Safetensors
English
French
German
stacked_bert
v1.0.0
custom_code
Instructions to use impresso-project/ner-stacked-bert-multilingual-v1.1.0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use impresso-project/ner-stacked-bert-multilingual-v1.1.0 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="impresso-project/ner-stacked-bert-multilingual-v1.1.0", trust_remote_code=True)# Load model directly from transformers import AutoModelForTokenClassification model = AutoModelForTokenClassification.from_pretrained("impresso-project/ner-stacked-bert-multilingual-v1.1.0", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "_name_or_path": "experiments/model_dbmdz_bert_medium_historic_multilingual_cased_max_sequence_length_512_epochs_5_run_multitask.baseline.False2025/", | |
| "architectures": [ | |
| "ExtendedMultitaskTimeModelForTokenClassification" | |
| ], | |
| "attention_probs_dropout_prob": 0.1, | |
| "auto_map": { | |
| "AutoConfig": "configuration_stacked.ImpressoConfig", | |
| "AutoModelForTokenClassification": "modeling_stacked.ExtendedMultitaskTimeModelForTokenClassification" | |
| }, | |
| "classifier_dropout": null, | |
| "custom_pipelines": { | |
| "generic-ner": { | |
| "impl": "generic_ner.ExtendedMultitaskTimeModelForTokenClassificationPipeline", | |
| "pt": "AutoModelForTokenClassification" | |
| } | |
| }, | |
| "hidden_act": "gelu", | |
| "hidden_dropout_prob": 0.1, | |
| "hidden_size": 512, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 2048, | |
| "label_map": { | |
| "NE-COARSE-LIT": { | |
| "I-pers": 0, | |
| "I-prod": 1, | |
| "B-prod": 2, | |
| "B-loc": 3, | |
| "I-time": 4, | |
| "B-pers": 5, | |
| "B-org": 6, | |
| "B-time": 7, | |
| "I-loc": 8, | |
| "O": 9, | |
| "I-org": 10 | |
| }, | |
| "NE-FINE-COMP": { | |
| "I-comp.title": 0, | |
| "B-comp.title": 1, | |
| "I-comp.function": 2, | |
| "I-comp.name": 3, | |
| "B-comp.function": 4, | |
| "O": 5, | |
| "B-comp.name": 6 | |
| } | |
| }, | |
| "layer_norm_eps": 1e-12, | |
| "max_position_embeddings": 512, | |
| "model_type": "stacked_bert", | |
| "num_attention_heads": 8, | |
| "num_hidden_layers": 8, | |
| "pad_token_id": 0, | |
| "position_embedding_type": "absolute", | |
| "pretrained_config": { | |
| "_name_or_path": "dbmdz/bert-medium-historic-multilingual-cased", | |
| "add_cross_attention": false, | |
| "architectures": [ | |
| "BertForMaskedLM" | |
| ], | |
| "attention_probs_dropout_prob": 0.1, | |
| "bad_words_ids": null, | |
| "begin_suppress_tokens": null, | |
| "bos_token_id": null, | |
| "chunk_size_feed_forward": 0, | |
| "classifier_dropout": null, | |
| "cross_attention_hidden_size": null, | |
| "decoder_start_token_id": null, | |
| "diversity_penalty": 0.0, | |
| "do_sample": false, | |
| "early_stopping": false, | |
| "encoder_no_repeat_ngram_size": 0, | |
| "eos_token_id": null, | |
| "exponential_decay_length_penalty": null, | |
| "finetuning_task": null, | |
| "forced_bos_token_id": null, | |
| "forced_eos_token_id": null, | |
| "hidden_act": "gelu", | |
| "hidden_dropout_prob": 0.1, | |
| "hidden_size": 512, | |
| "id2label": { | |
| "0": "LABEL_0", | |
| "1": "LABEL_1" | |
| }, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 2048, | |
| "is_decoder": false, | |
| "is_encoder_decoder": false, | |
| "label2id": { | |
| "LABEL_0": 0, | |
| "LABEL_1": 1 | |
| }, | |
| "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": 8, | |
| "num_beam_groups": 1, | |
| "num_beams": 1, | |
| "num_hidden_layers": 8, | |
| "num_return_sequences": 1, | |
| "output_attentions": false, | |
| "output_hidden_states": false, | |
| "output_scores": false, | |
| "pad_token_id": 0, | |
| "position_embedding_type": "absolute", | |
| "prefix": null, | |
| "problem_type": null, | |
| "pruned_heads": {}, | |
| "remove_invalid_values": false, | |
| "repetition_penalty": 1.0, | |
| "return_dict": true, | |
| "return_dict_in_generate": false, | |
| "sep_token_id": null, | |
| "suppress_tokens": null, | |
| "task_specific_params": null, | |
| "temperature": 1.0, | |
| "tf_legacy_loss": false, | |
| "tie_encoder_decoder": false, | |
| "tie_word_embeddings": true, | |
| "tokenizer_class": null, | |
| "top_k": 50, | |
| "top_p": 1.0, | |
| "torch_dtype": null, | |
| "torchscript": false, | |
| "type_vocab_size": 2, | |
| "typical_p": 1.0, | |
| "use_bfloat16": false, | |
| "use_cache": true, | |
| "vocab_size": 32000 | |
| }, | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.40.0.dev0", | |
| "type_vocab_size": 2, | |
| "use_cache": true, | |
| "vocab_size": 32000 | |
| } | |