Model reference · open weights
reranker-bert-tiny-gooaq-bce is an open-weight embedding model from cross-encoder-testing, listed in the AxForge catalogue. AxForge can bring it up on EU-owned hardware for you on request — with the licence handled where one is required.
About
BERT-tiny trained on GooAQ This is a Cross Encoder model finetuned from prajjwal1/bert-tiny using the sentence-transformers library. It computes scores for pairs of texts, which can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. This model was trained using trainscript.py. Model Details Model Description - Model Type: Cross Encoder - Base model: prajjwal1/bert-tiny <!-- at revision 6f75de8b60a9f8a2fdf7b69cbd86d9e64bcb3837 -- - Maximum Sequence Length: 512 tokens - Number of Output Labels: 1 label - Language: en - License: apache-2.0 Model Sources - Documentation: Sentence Transformers Documentation - Documentation: Cross Encoder Documentation - Repository: Sentence Transformers on GitHub - Hugging Face: Cross Encoders on Hugging Face Usage Direct Usage (Sentence Transformers) First install the Sentence Transformers library: Then you can load this model and run inference. Direct Usage (Transformers) -- Downstream Usage (Sentence Transformers) You can finetune this model on your own dataset. -- Out-of-Scope Use List how the model may foreseeably be misused and address what users ought not to do with the model. -- Evaluation Metrics Cross Encoder Reranking Datasets: gooaq-dev, NanoMSMARCO, NanoNFCorpus and NanoNQ Evaluated with <codeCrossEncoderRerankingEvaluator</code Cross Encoder Nano BEIR Dataset: NanoBEIRR100mean Evaluated with <codeCrossEncoderNanoBEIREvaluator</code Bias, Risks and Limitations What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model. -- Recommendations What are recommendations with respect to the foreseeable issues? For example, filtering explicit content. -- Training Details Training Dataset Unnamed Dataset Size: 578,402 training samples Columns: <codequestion</code, <codeanswer</code, and <codelabel</code Approximate statistics based on the first 1000 samples: Samples: Loss: <codeBinaryCrossEntropyLoss</code with these parameters: Training Hyperparameters Non-Default Hyperparameters - evalstrategy: steps - perdevicetrainbatchsize: 2048 - perdeviceevalbatchsize: 2048 - learningrate: 0.000
Summarised from the published model card. Read the full card on the HuggingFace links below.
Specifications
| Maker | cross-encoder-testing |
|---|---|
| Type | Embedding models |
| Parameters (lead) | 4M |
| Context | 512 tokens |
| Variants | 1 |
| Runs with | sentence-transformers |
| Based on | prajjwal1/bert-tiny |
| Released | 2025-02-26 |
| Popularity | 276k downloads / month |
| Licence | Open weights |
How it works
Variants
Open weights ship in several sizes and precisions. One page, all the variants — pick the one that fits your GPU. VRAM figures are estimates from model size.
| Variant | Params | Precision | VRAM | Fits 16 GB | Weights |
|---|---|---|---|---|---|
| reranker-bert-tiny-gooaq-bce | 4M | BF16 | ~0 GB | ✓ | Weights ↗ |
Using it via the API
Once AxForge deploys reranker-bert-tiny-gooaq-bce for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (reranker-bert-tiny-gooaq-bce below is illustrative; you get the exact model name on deployment.)
$ curl -sS https://api.axforge.ai/v1/embeddings \
-H "Authorization: Bearer $AXFORGE_API_KEY" \
-H "Content-Type: application/json" \
-d '{"model":"reranker-bert-tiny-gooaq-bce","input":"text to embed"}'
Licence
Open weights under apache-2.0 — commercial use is permitted. Deploy it on AxForge EU hardware on request. Read the licence ↗
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