Token Classification
Transformers
PyTorch
TensorBoard
xlm-roberta
Generated from Trainer
Eval Results (legacy)
Instructions to use rootacess/xlm-roberta-base-finetuned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rootacess/xlm-roberta-base-finetuned with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="rootacess/xlm-roberta-base-finetuned")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("rootacess/xlm-roberta-base-finetuned") model = AutoModelForTokenClassification.from_pretrained("rootacess/xlm-roberta-base-finetuned", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Librarian Bot: Add base_model information to model
#2
by librarian-bot - opened
README.md
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@@ -6,12 +6,13 @@ datasets:
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- xtreme
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metrics:
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- f1
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model-index:
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- name: xlm-roberta-base-finetuned
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results:
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- task:
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name: Token Classification
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type: token-classification
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dataset:
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name: xtreme
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type: xtreme
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split: validation
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args: PAN-X.de
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metrics:
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type: f1
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value: 0.8638300289723342
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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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- xtreme
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metrics:
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- f1
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base_model: xlm-roberta-base
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model-index:
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- name: xlm-roberta-base-finetuned
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results:
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- task:
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type: token-classification
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name: Token Classification
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dataset:
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name: xtreme
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type: xtreme
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split: validation
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args: PAN-X.de
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metrics:
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- type: f1
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value: 0.8638300289723342
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name: F1
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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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