| --- |
| license: mit |
| base_model: cardiffnlp/twitter-roberta-base |
| datasets: |
| - cardiffnlp/tweet_eval |
| language: |
| - en |
| pipeline_tag: text-classification |
| tags: |
| - sentiment-analysis |
| - twitter |
| - social-media |
| metrics: |
| - accuracy |
| - f1 |
| --- |
| |
| # twitter-roberta-base → TweetEval Sentiment |
|
|
| Small LLM fine-tuned for **social-media (tweet) sentiment analysis**. 3 classes: negative / neutral / positive. |
|
|
| - **Base model:** [cardiffnlp/twitter-roberta-base](https://huggingface.co/cardiffnlp/twitter-roberta-base) (~125M, RoBERTa pretrained on tweets) |
| - **Dataset:** [cardiffnlp/tweet_eval](https://huggingface.co/datasets/cardiffnlp/tweet_eval) (`sentiment` config, 45.6K train) |
| - **Training:** 3 epochs, lr 2e-5, batch 32, max_len 128, warmup 0.1, weight decay 0.01 |
| |
| ## Test-set results (TweetEval sentiment, 12,284 tweets) |
| |
| | Metric | Score | |
| |---|---| |
| | Accuracy | **0.7155** | |
| | Macro-F1 | **0.7155** | |
| | Macro-Recall | **0.7268** | |
| | Speed (T4) | ~1600 tweets/s | |
| |
| ## Comparison vs distilbert-base |
| |
| | Model | Size | Accuracy | Macro-F1 | tweets/s | |
| |---|---|---|---|---| |
| | **twitter-roberta-base (this)** | 125M | **0.7155** | **0.7155** | 1600 | |
| | distilbert-base | 67M | 0.6888 | 0.6877 | 2897 | |
| |
| Domain pretraining on tweets gives **+2.7 pts accuracy / +2.8 pts macro-F1** over generic DistilBERT, at ~1.8× the inference cost. |
| |
| ## Usage |
| |
| ```python |
| from transformers import pipeline |
| clf = pipeline("text-classification", model="Ido-shraga/twitter-roberta-base-tweeteval-sentiment") |
| clf("I can't believe how good this is 🔥") |
| ``` |
| |
