File size: 4,248 Bytes
e81d91a | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 | ---
pipeline_tag: fill-mask
license: mit
base_model: FacebookAI/xlm-roberta-base
library_name: kerasformers
tags:
- keras
- kerasformers
- xlm-roberta
- fill-mask
- multilingual
- text-encoder
- arxiv:1911.02116
- pytorch
- jax
- tf
---
## ***See [our collection](https://huggingface.co/collections/kerasformers/xlm-roberta-6a6e8fd0a258b1a8991cf608) for all versions of XLM-RoBERTa.***
# Run XLM-RoBERTa with Keras 3: JAX, PyTorch, or TensorFlow
[](https://github.com/IMvision12/KerasFormers) [](https://imvision12.github.io/KerasFormers/xlm_roberta/) [](https://huggingface.co/collections/kerasformers/xlm-roberta-6a6e8fd0a258b1a8991cf608)
# kerasformers/xlm_roberta_base
Paper: [Unsupervised Cross-lingual Representation Learning at Scale (arXiv:1911.02116)](https://arxiv.org/abs/1911.02116) · [HF Papers](https://huggingface.co/papers/1911.02116)
XLM-RoBERTa is the **multilingual** RoBERTa: same encoder architecture, pretrained on 2.5TB CommonCrawl across **100 languages**, with a 250k SentencePiece vocabulary (mask token `<mask>`).
For more details on the model, please go to the upstream [model card](https://huggingface.co/FacebookAI/xlm-roberta-base).
Pure-**Keras 3** conversion of [`FacebookAI/xlm-roberta-base`](https://huggingface.co/FacebookAI/xlm-roberta-base) for [kerasformers](https://github.com/IMvision12/KerasFormers). One implementation runs unmodified on **TensorFlow / Torch / JAX**.
This is a **fill-mask / encoder** checkpoint (`XLMRobertaMaskedLM`, base). Task heads load via `hf:` fine-tunes.
## ✨ Quick start (multilingual fill-mask)
```python
import os
os.environ["KERAS_BACKEND"] = "torch" # or "jax" / "tensorflow"
from kerasformers.models.xlm_roberta import (
XLMRobertaMaskedLM,
XLMRobertaTokenizer,
)
mlm = XLMRobertaMaskedLM.from_weights("kerasformers/xlm_roberta_base")
tokenizer = XLMRobertaTokenizer.from_weights("kerasformers/xlm_roberta_base")
# Multilingual: same <mask> API as RoBERTa, 100-language SentencePiece vocab.
inputs = tokenizer("La capitale de la France est <mask>.")
logits = mlm(inputs) # (1, L, vocab_size)
mask = int((inputs["input_ids"][0] == tokenizer.mask_token_id).argmax())
print(tokenizer.decode([int(logits[0, mask].argmax())]))
```
Load any XLM-RoBERTa variant the same way with `from_weights("kerasformers/<variant>")`:
| Variant | Hub |
|---|---|
| `xlm_roberta_base` | [`kerasformers/xlm_roberta_base`](https://huggingface.co/kerasformers/xlm_roberta_base) |
| `xlm_roberta_large` | [`kerasformers/xlm_roberta_large`](https://huggingface.co/kerasformers/xlm_roberta_large) |
## Available classes
Load any of these from this repo with `from_weights("kerasformers/xlm_roberta_base")` (or on the fly via the `hf:` prefix). The pretrained backbone is shared; task heads not stored in this checkpoint start randomly initialized, ready for fine-tuning (or load a `hf:` fine-tune).
| Class | Task |
|---|---|
| `XLMRobertaModel` | Encoder backbone |
| `XLMRobertaMaskedLM` | Masked language modeling (fill-mask) |
| `XLMRobertaSequenceClassify` | Sequence classification |
| `XLMRobertaTokenClassify` | Token classification (NER / POS) |
| `XLMRobertaQnA` | Extractive question answering |
| `XLMRobertaMultipleChoice` | Multiple choice |
```python
from kerasformers.models.xlm_roberta import XLMRobertaSequenceClassify
model = XLMRobertaSequenceClassify.from_weights("kerasformers/xlm_roberta_base")
```
## Tips
- Set `KERAS_BACKEND` **before** importing Keras / kerasformers.
- Prefer `XLMRobertaTokenizer.from_weights(...)` so the SentencePiece vocab matches.
- Use `<mask>` (not `[MASK]`).
- See [XLM-RoBERTa docs](https://imvision12.github.io/KerasFormers/xlm_roberta/) and [Loading Weights](https://imvision12.github.io/KerasFormers/loading_weights/).
- Community / upstream safetensors still work via the `hf:` prefix, e.g. `XLMRobertaMaskedLM.from_weights("hf:FacebookAI/xlm-roberta-base")`.
## Special Thanks
A huge thank you to the Facebook AI XLM-RoBERTa authors for creating and releasing these models.
License: MIT.
|