distilroberta-finetuned-financial-news-sentiment-analysis
This model is a fine-tuned version of distilroberta-base on the financialphrasebank dataset. It achieves the following results on the evaluation set: - Loss: 0.1116 - Accuracy: 0.9823
Params
80 M
Context
512
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Likes
471
Download history
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Can you run it?
Estimated VRAM at 8K context unless noted. Pick your hardware to see the verdict per quantization.
| File | Quant | Size | Est. VRAM | Verdict on RTX 4090 · 24 GB |
|---|---|---|---|---|
| model.safetensors | f32 | 0.3 GB | 0.9 GB | ✅ Runs comfortably |
Estimate: file size × 1.1 + KV cache at 8K + 0.5 GB overhead. Not a benchmark — how we calculate this.
Specifications
- Architecture
- RobertaForSequenceClassification
- Parameters
- 80 M
- Tensor type
- F32
- Context length
- 512
- Vocabulary
- 50,265
- Layers / heads
- 6 / 12
- Licence
- apache-2.0
- First seen on the Hub
- 2022-03-02
- Training datasets
- financial_phrasebank
- financial_phrasebank (reported)
- 0.98230088495575
- Added to our catalog
- 2026-07-31
Compare with any text-classification model