This model is a fine-tuned version of xlm-roberta-base on the None dataset. It achieves the following results on the evaluation set: The following hyperparameters were used during training: - learningrate: 3e-05 - trainbatchsize: 16 - evalbatchsize: 16 - lrschedulertype: linear - trainingsteps: 1800 - mixedprecisiontraining: Native AMP - Transformers 4.37.2 - Pytorch 2.1.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2
Open-weight model · Text classification
xlm-roberta-base-language-detection
by Luca Papariello papluca/xlm-roberta-base-language-detection
This model is a fine-tuned version of xlm-roberta-base on the Language Identification dataset. This model is an XLM-RoBERTa transformer model with a classification head on top (i.e. a linear layer on top of the pooled output).
Parameters278M
Context514
Weights3.3 GB
Licensemit
AccessOpen weights
Monthly Downloads579k
Runs On
What it takes to serve xlm-roberta-base-language-detection (278M parameters): the memory its weights need at each precision, and the cheapest way to rent enough data-center GPUs to hold them.
| Precision | Weights | Memory needed | Cheapest setup | Per hour | Also fits |
|---|---|---|---|---|---|
| 16-bit | 0.6 GB | 0.7 GB | 1x MI300X (192 GB) Vultr |
$1.85 | 1x H100 $1.99 · 1x MI325X $2.00 |
| 8-bit | 0.3 GB | 0.3 GB | 1x MI300X (192 GB) Vultr |
$1.85 | 1x H100 $1.99 · 1x MI325X $2.00 |
| 4-bit | 0.1 GB | 0.2 GB | 1x MI300X (192 GB) Vultr |
$1.85 | 1x H100 $1.99 · 1x MI325X $2.00 |
Memory is the weights at that precision plus 20% for the runtime and a short context; a long context needs more. Prices are the lowest on-demand hourly rates in the SAVRN Index, read Sep 18, 2026.
SAVRN's Notes on xlm-roberta-base-language-detection
Sorting inbound text by language before it reaches a larger model is the job this one does, with a 278M-parameter XLM-RoBERTa classifier covering 20 languages. At 16-bit the weights take 0.6 GB and the run needs 0.7 GB; at 8-bit it fits in 0.3 GB. It never gets its own accelerator in our facilities. It rides beside whatever else is on the card, and the cheapest Index host, one MI300X with 192 GB at $1.85 per hour, would give it a fraction of one percent of its memory.
MIT is the license, so a commercial deployment only has to carry the copyright and permission notices. Two checks: the context window is 514 tokens, so long documents get chunked first, and the model is a fine-tune of FacebookAI/xlm-roberta-base on the papluca/language-identification dataset, worth reading if your traffic includes languages outside those 20.
Model Card
By Luca Papariello, published under mit, revision 9865598389ca.
This model is a fine-tuned version of xlm-roberta-base on the Language Identification dataset.
Model description
This model is an XLM-RoBERTa transformer model with a classification head on top (i.e. a linear layer on top of the pooled output). For additional information please refer to the xlm-roberta-base model card or to the paper Unsupervised Cross-lingual Representation Learning at Scale by Conneau et al.
Intended uses & limitations
You can directly use this model as a language detector, i.e. for sequence classification tasks. Currently, it supports the following 20 languages:
arabic (ar), bulgarian (bg), german (de), modern greek (el), english (en), spanish (es), french (fr), hindi (hi), italian (it), japanese (ja), dutch (nl), polish (pl), portuguese (pt), russian (ru), swahili (sw), thai (th), turkish (tr), urdu (ur), vietnamese (vi), and chinese (zh)
Training and evaluation data
Configuration
- Architecture
- XLMRobertaForSequenceClassification
- Context length (tokens)
- 514
- Layers
- 12
- Hidden size
- 768
- Feed-forward size
- 3,072
- Attention heads
- 12
- Vocabulary size
- 250,002
- Stored precision
- float32
- Model type
- xlm-roberta
Identity and Version
- Repository
- papluca/xlm-roberta-base-language-detection
- Publisher
- Luca Papariello
- Task
- Text classification
- Modality
- Text
- Library
- transformers
- Parameters
- 278M parameters
- Languages
- ar, bg, de, el, en, es, fr, hi
- Revision
- 9865598389ca9d95637462f743f683b51d75b87b
- First published
- 2022-03-02
- Last updated
- 2023-12-28
Files and Weights
10 files, 3.4 GB in total. The weights are 3 files totalling 3.3 GB in bin, h5, safetensors.
Weights3 files · 3.3 GB
Configuration2 files · 1.7 KB
Tokenizer2 files · 9.1 MB
Documentation1 file · 7.2 KB
Other1 file · 5.1 MB
Repository1 file · 1.2 KB
Every file
| File | Type | Size | SHA-256 |
|---|---|---|---|
| model.safetensors | Weights | 1.1 GB | a835d6e8ed50 |
| pytorch_model.bin | Weights | 1.1 GB | eb6bded160fd |
| tf_model.h5 | Weights | 1.1 GB | d6417044a145 |
| config.json | Configuration | 1.4 KB | — |
| special_tokens_map.json | Configuration | 239 B | — |
| README.md | Documentation | 7.2 KB | — |
| sentencepiece.bpe.model | Other | 5.1 MB | cfc8146abe2a |
| .gitattributes | Repository | 1.2 KB | — |
| tokenizer.json | Tokenizer | 9.1 MB | — |
| tokenizer_config.json | Tokenizer | 502 B | — |
License and Download
- License
- mit
- Access
- Open weights, no gate
- Download size
- 3.3 GB
Download from Luca Papariello
Released by Luca Papariello through its official repository on Hugging Face. Read the license.
Built From
- Derived from FacebookAI/xlm-roberta-base
- Described by arXiv:1911.02116
- Trained on (disclosed) papluca/language-identification
Memory Requirements
| Precision | Weights in memory |
|---|---|
| As published | 3.3 GB |
| 16-bit | 0.6 GB |
| 8-bit | 0.3 GB |
| 4-bit | 0.1 GB |
Weights only, from the published parameter count; the key-value cache and runtime add to this.
Compare xlm-roberta-base-language-detection
Questions About xlm-roberta-base-language-detection
How much GPU memory does xlm-roberta-base-language-detection need?
About 0.7 GB at 16-bit and 0.2 GB at 4-bit: the weights (278M parameters) plus a working margin. A long context needs more.
What is the cheapest GPU to run xlm-roberta-base-language-detection on?
At 16-bit, 1x MI300X from $1.85 an hour; at 4-bit, 1x MI300X from $1.85 an hour, at the lowest on-demand prices the SAVRN Index lists.
Can I use xlm-roberta-base-language-detection commercially?
Yes. xlm-roberta-base-language-detection is released under MIT License. The MIT License is a short permissive license. It permits commercial use, modification and redistribution, provided the copyright notice and permission notice are included.
What is xlm-roberta-base-language-detection's context length?
514 tokens, from the maximum position embeddings in its published configuration.
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