---
language:
- sv
license: unknown
tags:
- onnx
- swedish
- bert
- fill-mask
- feature-extraction
- megatron-bert
library_name: onnxruntime
base_model: KBLab/megatron-bert-base-swedish-cased-600k
---

# megatron-bert-base-swedish-cased-600k - ONNX

ONNX export of [`KBLab/megatron-bert-base-swedish-cased-600k`](https://huggingface.co/KBLab/megatron-bert-base-swedish-cased-600k).

This package contains two task-specific exports:

- `fill-mask/onnx/model.onnx` for masked language modeling logits
- `feature-extraction/onnx/model.onnx` for `last_hidden_state` and `cls_embedding`

## Available variants

Each task folder contains:

- `model.onnx` - FP32 baseline
- `model_fp16.onnx` - FP16, recommended
- `model_int8.onnx` - dynamic INT8
- `model_uint8.onnx` - dynamic UINT8
- `model_q4.onnx` - 4-bit MatMul quantization

## File sizes

### Fill-mask

- `model.onnx`: 738.6 MB
- `model_fp16.onnx`: 369.4 MB
- `model_int8.onnx`: 185.6 MB
- `model_uint8.onnx`: 185.6 MB
- `model_q4.onnx`: 283.8 MB

### Feature-extraction

- `model.onnx`: 539.0 MB
- `model_fp16.onnx`: 269.6 MB
- `model_int8.onnx`: 135.4 MB
- `model_uint8.onnx`: 135.4 MB
- `model_q4.onnx`: 252.4 MB

## Accuracy summary

### FP32 parity vs PyTorch

- Fill-mask max logit diff: `0.000069`
- Fill-mask top-5 tokens: exact match
- Feature `last_hidden_state` max diff: `0.000006`
- Feature `cls_embedding` cosine similarity: `1.0`

### Quantized variants vs FP32 ONNX

#### Fill-mask

- `fp16`: top-5 exact match, max diff `0.0131`
- `int8`: top-5 drift after rank 2
- `uint8`: top-5 drift after rank 2
- `q4`: top-5 drift after rank 2

#### Feature-extraction

- `fp16`: CLS cosine `0.9999997`
- `q4`: CLS cosine `0.7404`
- `int8`: CLS cosine `0.9731`
- `uint8`: CLS cosine `0.9717`

## Recommendation

- Use `model_fp16.onnx` by default.
- For feature extraction, prefer `fp16`; `int8`/`uint8` preserve embeddings better than `q4` on this model.
- Avoid `int8` and `uint8` for fill-mask if token ranking fidelity matters.

## Layout

```text
megatron-bert-base-swedish-cased-600k/
├── fill-mask/
│   ├── config.json
│   ├── tokenizer.json
│   ├── tokenizer_config.json
│   ├── special_tokens_map.json
│   └── onnx/
│       ├── model.onnx
│       ├── model_fp16.onnx
│       ├── model_int8.onnx
│       ├── model_uint8.onnx
│       └── model_q4.onnx
└── feature-extraction/
    ├── config.json
    ├── tokenizer.json
    ├── tokenizer_config.json
    ├── special_tokens_map.json
    └── onnx/
        ├── model.onnx
        ├── model_fp16.onnx
        ├── model_int8.onnx
        ├── model_uint8.onnx
        └── model_q4.onnx
```

## Export notes

- Native `optimum 2.1.0` task export does not support `megatron-bert`.
- These graphs were exported with direct `torch.onnx.export` wrappers using legacy TorchScript exporter (`dynamo=False`).
- Feature extraction export uses `cls_embedding = last_hidden_state[:, 0, :]`.
- No pooler output included, because loading `MegatronBertModel` from this MLM checkpoint would introduce randomly initialized pooler weights.