Text Classification
Transformers
PyTorch
TensorBoard
xlm-roberta
Generated from Trainer
text-embeddings-inference
Instructions to use RogerB/kin-sentiC with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use RogerB/kin-sentiC with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="RogerB/kin-sentiC")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("RogerB/kin-sentiC") model = AutoModelForSequenceClassification.from_pretrained("RogerB/kin-sentiC") - Notebooks
- Google Colab
- Kaggle
Librarian Bot: Add base_model information to model
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by librarian-bot - opened
README.md
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- generated_from_trainer
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metrics:
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- f1
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model-index:
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- name: kin-sentiC
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results: []
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- generated_from_trainer
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metrics:
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- f1
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base_model: RogerB/afro-xlmr-large-finetuned-kintweetsD
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model-index:
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- name: kin-sentiC
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results: []
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