Instructions to use tner/roberta-base-tweetner7-continuous with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tner/roberta-base-tweetner7-continuous with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="tner/roberta-base-tweetner7-continuous")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("tner/roberta-base-tweetner7-continuous") model = AutoModelForTokenClassification.from_pretrained("tner/roberta-base-tweetner7-continuous") - Notebooks
- Google Colab
- Kaggle
add model
Browse files- config.json +1 -1
- pytorch_model.bin +2 -2
config.json
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{
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"_name_or_path": "cner_output/model/baseline_2021/roberta_base_continuous/
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"architectures": [
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"RobertaForTokenClassification"
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],
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{
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"_name_or_path": "cner_output/model/baseline_2021/roberta_base_continuous/best_model",
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"architectures": [
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"RobertaForTokenClassification"
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],
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pytorch_model.bin
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version https://git-lfs.github.com/spec/v1
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version https://git-lfs.github.com/spec/v1
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oid sha256:b52666d8e832b7c7c7972ddf0716fcc94a68c124c1b6ee267cd9172ba5e99b58
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size 496349169
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