SAVRN Model Hub · Comparisons
distilbert-base-uncased-finetuned-sst-2-english vs distilroberta-finetuned-financial-news-sentiment-analysis
Distilbert-base-uncased-finetuned-sst-2-english has 67M parameters and distilroberta-finetuned-financial-news-sentiment-analysis has 82M parameters; both are released under Apache License 2.0; at 16-bit, distilbert-base-uncased-finetuned-sst-2-english needs about 0.2 GB (1x MI300X from $1.85 an hour) and distilroberta-finetuned-financial-news-sentiment-analysis about 0.2 GB (1x MI300X from $1.85 an hour).
| Field | distilbert-base-uncased-finetuned-sst-2-english distilbert/distilbert-base-uncased-finetuned-sst-2-english | distilroberta-finetuned-financial-news-sentiment-analysis mrm8488/distilroberta-finetuned-financial-news-sentiment-analysis |
|---|---|---|
| Publisher | DistilBERT community | Manuel Romero |
| Task | Text classification | Text classification |
| Modality | Text | Text |
| Parameters, as reported | 67M parameters | 82M parameters |
| Architecture | DistilBertForSequenceClassification | RobertaForSequenceClassification |
| Library | transformers | transformers |
| Context length | 512 tokens | 514 tokens |
| Repository size | 1.3 GB | 659.8 MB |
| Artifact formats | safetensors, onnx, pytorch, rust, tf | safetensors, pytorch, tensorboard |
| License | apache-2.0 | apache-2.0 |
| Access | Open weights, no gate | Open weights, no gate |
| Memory at 16-bit (weights and margin) | 0.2 GB | 0.2 GB |
| Cheapest GPUs at 16-bit, per hour | 1x MI300X, $1.85 | 1x MI300X, $1.85 |
| Memory at 4-bit (weights and margin) | 0 GB | 0 GB |
| Cheapest GPUs at 4-bit, per hour | 1x MI300X, $1.85 | 1x MI300X, $1.85 |
| Revision viewed | 714eb0fa89d2 | ae0eab9ad336 |
| Downloads reported by the hub | 3.7M | 346.6k |
| Last observed | 2026-09-18 | 2026-09-18 |
An evaluation row appears only where at least two of these models report the same benchmark with the same stated configuration, metric, unit and setup. Different evaluators stay named in each cell. Values are shown as reported: no unit conversion, no ranking.
Other Reported Results
These results are listed for each model on its own, because the conditions needed to compare them are not stated or do not match. Two results that leave a condition blank are not assumed to share it.
distilbert-base-uncased-finetuned-sst-2-english
| Benchmark | Conditions | Result | Reported by | Revision | Date |
|---|---|---|---|---|---|
| glue | Configuration sst2Task Text ClassificationMetric AUCComparison conditions not established | 0.971663 | distilbert Publisher reported |
Evaluated revision not stated | — |
| glue | Configuration sst2Task Text ClassificationMetric AccuracyComparison conditions not established | 0.91055 | distilbert Publisher reported |
Evaluated revision not stated | — |
| glue | Configuration sst2Task Text ClassificationMetric F1Comparison conditions not established | 0.913717 | distilbert Publisher reported |
Evaluated revision not stated | — |
| glue | Configuration sst2Task Text ClassificationMetric PrecisionComparison conditions not established | 0.897826 | distilbert Publisher reported |
Evaluated revision not stated | — |
| glue | Configuration sst2Task Text ClassificationMetric RecallComparison conditions not established | 0.93018 | distilbert Publisher reported |
Evaluated revision not stated | — |
| glue | Configuration sst2Task Text ClassificationMetric lossComparison conditions not established | 0.390139 | distilbert Publisher reported |
Evaluated revision not stated | — |
| sst2 | Configuration defaultTask Text ClassificationMetric AccuracyComparison conditions not established | 0.988552 | distilbert Publisher reported |
Evaluated revision not stated | — |
| sst2 | Configuration defaultTask Text ClassificationMetric F1 MacroComparison conditions not established | 0.988402 | distilbert Publisher reported |
Evaluated revision not stated | — |
| sst2 | Configuration defaultTask Text ClassificationMetric F1 MicroComparison conditions not established | 0.988552 | distilbert Publisher reported |
Evaluated revision not stated | — |
| sst2 | Configuration defaultTask Text ClassificationMetric F1 WeightedComparison conditions not established | 0.988555 | distilbert Publisher reported |
Evaluated revision not stated | — |
| sst2 | Configuration defaultTask Text ClassificationMetric Precision MacroComparison conditions not established | 0.988197 | distilbert Publisher reported |
Evaluated revision not stated | — |
| sst2 | Configuration defaultTask Text ClassificationMetric Precision MicroComparison conditions not established | 0.988552 | distilbert Publisher reported |
Evaluated revision not stated | — |
| sst2 | Configuration defaultTask Text ClassificationMetric Precision WeightedComparison conditions not established | 0.988564 | distilbert Publisher reported |
Evaluated revision not stated | — |
| sst2 | Configuration defaultTask Text ClassificationMetric Recall MacroComparison conditions not established | 0.988615 | distilbert Publisher reported |
Evaluated revision not stated | — |
| sst2 | Configuration defaultTask Text ClassificationMetric Recall MicroComparison conditions not established | 0.988552 | distilbert Publisher reported |
Evaluated revision not stated | — |
| sst2 | Configuration defaultTask Text ClassificationMetric Recall WeightedComparison conditions not established | 0.988552 | distilbert Publisher reported |
Evaluated revision not stated | — |
| sst2 | Configuration defaultTask Text ClassificationMetric lossComparison conditions not established | 0.0406526 | distilbert Publisher reported |
Evaluated revision not stated | — |
distilroberta-finetuned-financial-news-sentiment-analysis
| Benchmark | Conditions | Result | Reported by | Revision | Date |
|---|---|---|---|---|---|
| financial_phrasebank | Task Text ClassificationMetric AccuracyComparison conditions not established | 0.982301 | mrm8488 Publisher reported |
Evaluated revision not stated | — |
SAVRN's Notes on distilbert-base-uncased-finetuned-sst-2-english
Sixty-seven million parameters, 512 tokens of context, one label per input: a classifier, not a generator, and the publisher's suggested use is topic classification. Quantizing is beside the point: 16-bit needs 0.2 GB, 8-bit 0.1 GB, the 4-bit row rounds to 0.0 GB, so run it at full precision. The cheapest Index setup is still a 192 GB MI300X at $1.85 an hour on-demand, a warehouse for a shoebox; in our facilities it shares a card with whatever else is running.
Apache 2.0 covers commercial use, modification and redistribution, notices kept, changes stated. The 512-token context means anything longer gets split upstream. It was trained on sst2 and glue, and the publisher reports 91.3 on the dev set against 92.7 for bert-base-uncased, the price of distillation. The repository holds 17 files in five formats, so pull only the one your stack loads.
Questions
Which is larger, distilbert-base-uncased-finetuned-sst-2-english or distilroberta-finetuned-financial-news-sentiment-analysis?
distilroberta-finetuned-financial-news-sentiment-analysis (82M parameters) is larger than distilbert-base-uncased-finetuned-sst-2-english (67M parameters), by the parameter counts their publishers report.
Which is cheaper to run, distilbert-base-uncased-finetuned-sst-2-english or distilroberta-finetuned-financial-news-sentiment-analysis?
At 4-bit, distilbert-base-uncased-finetuned-sst-2-english fits on 1x MI300X from $1.85 an hour and distilroberta-finetuned-financial-news-sentiment-analysis on 1x MI300X from $1.85 an hour, at the lowest on-demand prices the SAVRN Index lists.
Can I use distilbert-base-uncased-finetuned-sst-2-english commercially?
Yes. distilbert-base-uncased-finetuned-sst-2-english is released under Apache License 2.0. The Apache License 2.0 is a permissive open-source license. It permits commercial use, modification and redistribution. It requires keeping the license and copyright notices and any NOTICE file, stating significant changes, and it includes an express patent grant from contributors.
Can I use distilroberta-finetuned-financial-news-sentiment-analysis commercially?
Yes. distilroberta-finetuned-financial-news-sentiment-analysis is released under Apache License 2.0. The Apache License 2.0 is a permissive open-source license. It permits commercial use, modification and redistribution. It requires keeping the license and copyright notices and any NOTICE file, stating significant changes, and it includes an express patent grant from contributors.