Instructions to use FacebookAI/xlm-roberta-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use FacebookAI/xlm-roberta-large with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="FacebookAI/xlm-roberta-large")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("FacebookAI/xlm-roberta-large") model = AutoModelForMaskedLM.from_pretrained("FacebookAI/xlm-roberta-large", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download src/utils/mapper.py from FacebookAI/xlm-roberta-large: direct link, hf CLI and curl.
- Browser
- Download file 1.54 kB
-
https://huggingface.co/FacebookAI/xlm-roberta-large/resolve/refs%2Fpr%2F8/src/utils/mapper.py
- Command line
-
hf download hf://FacebookAI/xlm-roberta-large@refs/pr/8/src/utils/mapper.py
-
curl -L -o mapper.py https://huggingface.co/FacebookAI/xlm-roberta-large/resolve/refs%2Fpr%2F8/src/utils/mapper.py
1.54 kB
| class ConfigMapper: | |
| """Class for creating ConfigMapper objects. | |
| This class can be used to create custom configuration names using YAML files. | |
| For each class or object instantiated in any modules, | |
| the ConfigMapper object can be used either with the functions, | |
| or as a decorator to store the mapping in the function. | |
| Attributes | |
| ---------- | |
| Methods | |
| ------- | |
| """ | |
| dicts = { | |
| "models": {}, | |
| "trainers": {}, | |
| "metrics": {}, | |
| "losses": {}, | |
| "optimizers": {}, | |
| "schedulers": {}, | |
| "devices": {}, | |
| "embeddings": {}, | |
| "params": {}, | |
| "datasets": {}, | |
| "preprocessors": {}, | |
| "tokenizers": {}, | |
| } | |
| def map(cls, key, name): | |
| """ | |
| Map a particular name to an object, in the specified key | |
| Parameters | |
| ---------- | |
| name : str | |
| The name of the object which will be used. | |
| key : str | |
| The key of the mapper to be used. | |
| """ | |
| def wrap(obj): | |
| if key in cls.dicts.keys(): | |
| cls.dicts[key][name] = obj | |
| else: | |
| cls.dicts[key] = {} | |
| cls.dicts[key][name] = obj | |
| return obj | |
| return wrap | |
| def get_object(cls, key, name): | |
| """""" | |
| try: | |
| return cls.dicts[key][name] | |
| except: | |
| raise NotImplementedError("Key:{name} Undefined".format(name=name)) | |
| configmapper = ConfigMapper() | |