library_name: transformers license: apache-2.0 language:

am
ti This is a RoBERTa-base model trained on ~124M tweets from January hugging face and finetuned for sentiment analysis with the TweetEval benchmark. The original Twitter-based RoBERTa model can be found here and the original reference paper is TweetEval. This model is suitable for Amharic and Tigriyna.

Model Card for Model ID

Model Card Summary: Hailay/FT_EXLMR Model Name: Hailay/FT_EXLMR Type: XLM-Roberta model for sequence classification Language(s): [Languages supported by the model] License: [License type, e.g., Apache 2.0] Pre-trained Model: xlm-roberta-base Uses:

Primary: Text classification (e.g., sentiment analysis) Additional: Can be fine-tuned for specific tasks Key Features:

Trained Data: Custom dataset with text and labels Training Details: 3 epochs, learning rate of 1e-5 Evaluation: Accuracy and loss metrics Code Example: Load the model and tokenizer, then use them for text classification. Considerations:

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