Text Classification
Transformers
Safetensors
English
bert
token-classification
Generated from Trainer
text-embeddings-inference
Instructions to use Prience91/ner_model_output with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Prience91/ner_model_output with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Prience91/ner_model_output")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("Prience91/ner_model_output") model = AutoModelForTokenClassification.from_pretrained("Prience91/ner_model_output", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update README.md
Browse files
README.md
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model-index:
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- name: ner_model_output
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results: []
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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## Intended uses & limitations
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## Training and evaluation data
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- Transformers 5.12.1
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- Pytorch 2.11.0+cu128
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- Datasets 4.0.0
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- Tokenizers 0.22.2
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model-index:
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- name: ner_model_output
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results: []
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datasets:
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- lhoestq/conll2003
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language:
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- en
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pipeline_tag: text-classification
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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## Intended uses & limitations
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Free Experiment
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## Training and evaluation data
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- Transformers 5.12.1
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- Pytorch 2.11.0+cu128
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- Datasets 4.0.0
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- Tokenizers 0.22.2
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