Token Classification
Transformers
Safetensors
Spanish
bert
ner
spanish
emergencies
ecu-911
Eval Results (legacy)
Instructions to use dannyLeo16/ner_model_bert_base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dannyLeo16/ner_model_bert_base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="dannyLeo16/ner_model_bert_base")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("dannyLeo16/ner_model_bert_base") model = AutoModelForTokenClassification.from_pretrained("dannyLeo16/ner_model_bert_base") - Notebooks
- Google Colab
- Kaggle
Update README.md
Browse files
README.md
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@@ -88,8 +88,7 @@ This model is a Spanish BERT fine-tuned to identify **persons** and **locations*
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- `evaluation_strategy = "epoch"`, `save_strategy = "epoch"`
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- `load_best_model_at_end = true` *(por `eval_loss`)*
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- **Data collator:** `DataCollatorForTokenClassification` (padding a `max_length`)
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- **Seed:** <si fijaste semilla, indícala>
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- `evaluation_strategy = "epoch"`, `save_strategy = "epoch"`
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- `load_best_model_at_end = true` *(por `eval_loss`)*
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- **Data collator:** `DataCollatorForTokenClassification` (padding a `max_length`)
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