Model Card for meinvirgos/aina-translator-es-ast-onnx
Translator spanish - asturian
version of: projecte-aina/aina-translator-es-ast to 4 bits
Model Details
Model Description
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- Developed by: [email protected]
- Funded by [optional]:
- Shared by [optional]:
- Model type: M2M100
- Language(s) (NLP): spanish, asturian
- License: cc-by-nc-4.0
- Finetuned from model [optional]: projecte-aina/aina-translator-es-ast
Model Sources [optional]
- Repository: projecte-aina/aina-translator-es-ast
- Paper [optional]:
- Demo [optional]:
Uses
Translation from spanish to asturian
Direct Use
The model is intended to be used as a intermediate step to other formats
Out-of-Scope Use
Bias, Risks, and Limitations
Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
How to Get Started with the Model
Use the code below to get started with the model.
from optimum.onnxruntime import ORTModelForSeq2SeqLM
model = ORTModelForSeq2SeqLM.from_pretrained("meinvirgos/aina-translator-es-ast-onnx")
print ("leido modelo")
from transformers import NllbTokenizer
tokenizer_name = "meinvirgos/aina-translator-es-ast-onnx"
tokenizer = NllbTokenizer.from_pretrained(tokenizer_name, token=True, src_lang="spa_Latn")
print ("leido tokenizer")
encoded_hi = tokenizer("Hola papá", return_tensors="pt")
# Generate the translation
generated_tokens = model.generate(**encoded_hi, forced_bos_token_id=tokenizer.convert_tokens_to_ids("ast_Latn"))
# Decode the output
output_text = tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)
print(output_text) # Output: "Hola pá"
How the model was obtained
Inicialization
!pip install optimum[exporters]
Reading model as ONNX
from optimum.onnxruntime import ORTModelForSeq2SeqLM
model = ORTModelForSeq2SeqLM.from_pretrained("projecte-aina/aina-translator-es-ast", export = True)
print ("leido modelo")
Testing and saving
from transformers import NllbTokenizer
tokenizer_name = "facebook/nllb-200-distilled-600M"
tokenizer = NllbTokenizer.from_pretrained(tokenizer_name, token=True, src_lang="spa_Latn")
print ("leido tokenizer")
encoded_hi = tokenizer("Hola papá", return_tensors="pt")
# Generate the translation
generated_tokens = model.generate(**encoded_hi, forced_bos_token_id=tokenizer.convert_tokens_to_ids("ast_Latn"))
# Decode the output
output_text = tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)
print(output_text) # Output: "Hola pá"
model.save_pretrained("save_dir")
Uploading to Huggingface
from kaggle_secrets import UserSecretsClient
miToken = UserSecretsClient().get_secret("HF_TOKEN")
from huggingface_hub import login
login(token=miToken)
from huggingface_hub import HfApi
api = HfApi()
# Upload all the content from the local folder to your remote Space.
# By default, files are uploaded at the root of the repo
#api.create_repo(
# repo_id="meinvirgos/aina-translator-es-ast-onnx",
# repo_type="model",
# private=False,
#)
api.upload_folder(
folder_path="./save_dir",
repo_id="meinvirgos/aina-translator-es-ast-onnx",
repo_type="model",
)
Hardware
Software
optimum
Model Card Authors [optional]
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