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Upload model
Browse files- README.md +133 -0
- added_tokens.json +4 -0
- config.json +139 -0
- merges.txt +0 -0
- pytorch_model.bin +3 -0
- special_tokens_map.json +51 -0
- tokenizer.json +0 -0
- tokenizer_config.json +65 -0
- vocab.json +0 -0
README.md
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---
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library_name: span-marker
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tags:
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- span-marker
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- token-classification
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- ner
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- named-entity-recognition
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- generated_from_span_marker_trainer
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metrics:
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- precision
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- recall
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- f1
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widget: []
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pipeline_tag: token-classification
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---
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# SpanMarker
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This is a [SpanMarker](https://github.com/tomaarsen/SpanMarkerNER) model that can be used for Named Entity Recognition.
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## Model Details
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### Model Description
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- **Model Type:** SpanMarker
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<!-- - **Encoder:** [Unknown](https://huggingface.co/models/unknown) -->
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- **Maximum Sequence Length:** 256 tokens
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- **Maximum Entity Length:** 8 words
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<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
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<!-- - **Language:** Unknown -->
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<!-- - **License:** Unknown -->
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### Model Sources
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- **Repository:** [SpanMarker on GitHub](https://github.com/tomaarsen/SpanMarkerNER)
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- **Thesis:** [SpanMarker For Named Entity Recognition](https://raw.githubusercontent.com/tomaarsen/SpanMarkerNER/main/thesis.pdf)
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## Uses
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### Direct Use for Inference
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```python
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from span_marker import SpanMarkerModel
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# Download from the 🤗 Hub
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model = SpanMarkerModel.from_pretrained("span_marker_model_id")
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# Run inference
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entities = model.predict("Amelia Earhart flew her single engine Lockheed Vega 5B across the Atlantic to Paris.")
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```
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### Downstream Use
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You can finetune this model on your own dataset.
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<details><summary>Click to expand</summary>
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```python
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from span_marker import SpanMarkerModel, Trainer
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# Download from the 🤗 Hub
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model = SpanMarkerModel.from_pretrained("span_marker_model_id")
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# Specify a Dataset with "tokens" and "ner_tag" columns
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dataset = load_dataset("conll2003") # For example CoNLL2003
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# Initialize a Trainer using the pretrained model & dataset
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trainer = Trainer(
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model=model,
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train_dataset=dataset["train"],
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eval_dataset=dataset["validation"],
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)
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trainer.train()
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trainer.save_model("span_marker_model_id-finetuned")
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```
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</details>
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<!--
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### Out-of-Scope Use
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*List how the model may foreseeably be misused and address what users ought not to do with the model.*
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-->
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<!--
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## Bias, Risks and Limitations
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*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
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-->
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<!--
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### Recommendations
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*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
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-->
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## Training Details
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### Framework Versions
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- Python: 3.10.12
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- SpanMarker: 1.3.1.dev
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- Transformers: 4.33.2
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- PyTorch: 2.0.1+cu118
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- Datasets: 2.14.5
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- Tokenizers: 0.13.3
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## Citation
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### BibTeX
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```
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@software{Aarsen_SpanMarker,
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author = {Aarsen, Tom},
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license = {Apache-2.0},
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title = {{SpanMarker for Named Entity Recognition}},
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url = {https://github.com/tomaarsen/SpanMarkerNER}
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}
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```
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<!--
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## Glossary
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*Clearly define terms in order to be accessible across audiences.*
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-->
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<!--
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## Model Card Authors
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*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
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-->
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<!--
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## Model Card Contact
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*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
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-->
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added_tokens.json
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{
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"<end>": 50263,
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"<start>": 50262
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}
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config.json
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{
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"_name_or_path": "/content/models/span_marker_roberta_base_bne_conll_2002_es/checkpoint-final",
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"architectures": [
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"SpanMarkerModel"
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],
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"encoder": {
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"_name_or_path": "PlanTL-GOB-ES/roberta-base-bne",
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"add_cross_attention": false,
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"architectures": [
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"RobertaForMaskedLM"
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],
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"attention_probs_dropout_prob": 0.0,
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"bad_words_ids": null,
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"begin_suppress_tokens": null,
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"bos_token_id": 0,
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"chunk_size_feed_forward": 0,
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"classifier_dropout": null,
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"cross_attention_hidden_size": null,
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"decoder_start_token_id": null,
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"diversity_penalty": 0.0,
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"do_sample": false,
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"early_stopping": false,
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"encoder_no_repeat_ngram_size": 0,
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"eos_token_id": 2,
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"exponential_decay_length_penalty": null,
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"finetuning_task": null,
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"forced_bos_token_id": null,
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"forced_eos_token_id": null,
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"gradient_checkpointing": false,
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"hidden_act": "gelu",
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"hidden_dropout_prob": 0.0,
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"hidden_size": 768,
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"id2label": {
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"0": "O",
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"1": "B-PER",
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"2": "I-PER",
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"3": "B-ORG",
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"4": "I-ORG",
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"5": "B-LOC",
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"6": "I-LOC",
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"7": "B-MISC",
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"8": "I-MISC"
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},
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"initializer_range": 0.02,
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"intermediate_size": 3072,
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"is_decoder": false,
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"is_encoder_decoder": false,
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"label2id": {
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"B-LOC": 5,
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"B-MISC": 7,
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"B-ORG": 3,
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"B-PER": 1,
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"I-LOC": 6,
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"I-MISC": 8,
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"I-ORG": 4,
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"I-PER": 2,
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"O": 0
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},
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"layer_norm_eps": 1e-05,
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"length_penalty": 1.0,
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"max_length": 20,
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"max_position_embeddings": 514,
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"min_length": 0,
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"model_type": "roberta",
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"no_repeat_ngram_size": 0,
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"num_attention_heads": 12,
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"num_beam_groups": 1,
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"num_beams": 1,
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"num_hidden_layers": 12,
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"num_return_sequences": 1,
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"output_attentions": false,
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"output_hidden_states": false,
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"output_scores": false,
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"pad_token_id": 1,
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"position_embedding_type": "absolute",
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"prefix": null,
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"problem_type": null,
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"pruned_heads": {},
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"remove_invalid_values": false,
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"repetition_penalty": 1.0,
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"return_dict": true,
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"return_dict_in_generate": false,
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"sep_token_id": null,
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"suppress_tokens": null,
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"task_specific_params": null,
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"temperature": 1.0,
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"tf_legacy_loss": false,
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"tie_encoder_decoder": false,
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"tie_word_embeddings": true,
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"tokenizer_class": null,
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"top_k": 50,
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"top_p": 1.0,
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"torch_dtype": null,
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"torchscript": false,
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"transformers_version": "4.33.2",
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"type_vocab_size": 1,
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"typical_p": 1.0,
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"use_bfloat16": false,
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"use_cache": true,
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"vocab_size": 50264
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},
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"entity_max_length": 8,
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"id2label": {
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"0": "O",
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"1": "LOC",
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"2": "MISC",
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"3": "ORG",
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"4": "PER"
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},
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"id2reduced_id": {
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"0": 0,
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"1": 4,
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"2": 4,
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"3": 3,
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"4": 3,
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"5": 1,
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"6": 1,
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"7": 2,
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"8": 2
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},
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"label2id": {
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"LOC": 1,
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"MISC": 2,
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"O": 0,
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"ORG": 3,
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"PER": 4
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},
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"marker_max_length": 128,
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"max_next_context": null,
|
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"max_prev_context": null,
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"model_max_length": 256,
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"model_max_length_default": 512,
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"model_type": "span-marker",
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"span_marker_version": "1.3.1.dev",
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"torch_dtype": "float32",
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"trained_with_document_context": false,
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"transformers_version": "4.33.2",
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"vocab_size": 50264
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}
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merges.txt
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pytorch_model.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:e7662a097d32ea1d5d24f15efd8fd0376766d62f571c7dc4a2458b5755e63f54
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size 498676337
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special_tokens_map.json
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{
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"bos_token": {
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"content": "<s>",
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"lstrip": false,
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"normalized": true,
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"rstrip": false,
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"single_word": false
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},
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"cls_token": {
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"content": "<s>",
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"lstrip": false,
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"normalized": true,
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"rstrip": false,
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"single_word": false
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},
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"eos_token": {
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"content": "</s>",
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"lstrip": false,
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"normalized": true,
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"rstrip": false,
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"single_word": false
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},
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"mask_token": {
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"content": "<mask>",
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"lstrip": true,
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"normalized": true,
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"rstrip": false,
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"single_word": false
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},
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"pad_token": {
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"content": "<pad>",
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"lstrip": false,
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33 |
+
"normalized": true,
|
34 |
+
"rstrip": false,
|
35 |
+
"single_word": false
|
36 |
+
},
|
37 |
+
"sep_token": {
|
38 |
+
"content": "</s>",
|
39 |
+
"lstrip": false,
|
40 |
+
"normalized": true,
|
41 |
+
"rstrip": false,
|
42 |
+
"single_word": false
|
43 |
+
},
|
44 |
+
"unk_token": {
|
45 |
+
"content": "<unk>",
|
46 |
+
"lstrip": false,
|
47 |
+
"normalized": true,
|
48 |
+
"rstrip": false,
|
49 |
+
"single_word": false
|
50 |
+
}
|
51 |
+
}
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tokenizer.json
ADDED
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tokenizer_config.json
ADDED
@@ -0,0 +1,65 @@
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|
1 |
+
{
|
2 |
+
"add_prefix_space": true,
|
3 |
+
"bos_token": {
|
4 |
+
"__type": "AddedToken",
|
5 |
+
"content": "<s>",
|
6 |
+
"lstrip": false,
|
7 |
+
"normalized": true,
|
8 |
+
"rstrip": false,
|
9 |
+
"single_word": false
|
10 |
+
},
|
11 |
+
"clean_up_tokenization_spaces": true,
|
12 |
+
"cls_token": {
|
13 |
+
"__type": "AddedToken",
|
14 |
+
"content": "<s>",
|
15 |
+
"lstrip": false,
|
16 |
+
"normalized": true,
|
17 |
+
"rstrip": false,
|
18 |
+
"single_word": false
|
19 |
+
},
|
20 |
+
"eos_token": {
|
21 |
+
"__type": "AddedToken",
|
22 |
+
"content": "</s>",
|
23 |
+
"lstrip": false,
|
24 |
+
"normalized": true,
|
25 |
+
"rstrip": false,
|
26 |
+
"single_word": false
|
27 |
+
},
|
28 |
+
"errors": "replace",
|
29 |
+
"mask_token": {
|
30 |
+
"__type": "AddedToken",
|
31 |
+
"content": "<mask>",
|
32 |
+
"lstrip": true,
|
33 |
+
"normalized": true,
|
34 |
+
"rstrip": false,
|
35 |
+
"single_word": false
|
36 |
+
},
|
37 |
+
"max_len": 512,
|
38 |
+
"model_max_length": 512,
|
39 |
+
"pad_token": {
|
40 |
+
"__type": "AddedToken",
|
41 |
+
"content": "<pad>",
|
42 |
+
"lstrip": false,
|
43 |
+
"normalized": true,
|
44 |
+
"rstrip": false,
|
45 |
+
"single_word": false
|
46 |
+
},
|
47 |
+
"sep_token": {
|
48 |
+
"__type": "AddedToken",
|
49 |
+
"content": "</s>",
|
50 |
+
"lstrip": false,
|
51 |
+
"normalized": true,
|
52 |
+
"rstrip": false,
|
53 |
+
"single_word": false
|
54 |
+
},
|
55 |
+
"tokenizer_class": "RobertaTokenizer",
|
56 |
+
"trim_offsets": true,
|
57 |
+
"unk_token": {
|
58 |
+
"__type": "AddedToken",
|
59 |
+
"content": "<unk>",
|
60 |
+
"lstrip": false,
|
61 |
+
"normalized": true,
|
62 |
+
"rstrip": false,
|
63 |
+
"single_word": false
|
64 |
+
}
|
65 |
+
}
|
vocab.json
ADDED
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