Instructions to use csocsci/xlm-roberta-large-binary-cs-iib with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use csocsci/xlm-roberta-large-binary-cs-iib with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="csocsci/xlm-roberta-large-binary-cs-iib")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("csocsci/xlm-roberta-large-binary-cs-iib") model = AutoModel.from_pretrained("csocsci/xlm-roberta-large-binary-cs-iib") - Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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<!-- Provide the basic links for the model. -->
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- **Repository:** https://github.com/
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- **Paper:** Stay tuned!
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## Usage
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# Load the model and tokenizer
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model = AutoModelForSequenceClassification.from_pretrained(
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tokenizer = AutoTokenizer.from_pretrained(
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use_fast=False, truncation_side='left')
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assert tokenizer.truncation_side == 'left'
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<!-- Provide the basic links for the model. -->
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- **Repository:** https://github.com/csocsci/supportive-interactions
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- **Paper:** Stay tuned!
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## Usage
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# Load the model and tokenizer
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model = AutoModelForSequenceClassification.from_pretrained(
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'csocsci/xlm-roberta-large-binary-cs-iib', num_labels=2).to("cuda")
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tokenizer = AutoTokenizer.from_pretrained(
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'csocsci/xlm-roberta-large-binary-cs-iib',
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use_fast=False, truncation_side='left')
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assert tokenizer.truncation_side == 'left'
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