RuPERTa-base (uncased) is a RoBERTa model trained on a uncased verison of big Spanish corpus.
RoBERTa iterates on BERT's pretraining procedure, including training the model longer, with bigger batches over more data; removing the next sentence prediction objective; training on longer sequences; and dynamically changing the masking pattern applied to the training data.
The architecture is the same as roberta-base:
roberta.base: RoBERTa using the BERT-base architecture 125M params
WIP (I continue working on it) 🚧
| Task/Dataset | F1 | Precision | Recall | Fine-tuned model | Reproduce it |
|---|---|---|---|---|---|
| POS | 97.39 | 97.47 | 97.32 | RuPERTa-base-finetuned-pos |  |
| NER | 77.55 | 75.53 | 79.68 | RuPERTa-base-finetuned-ner | |
| SQUAD-es v1 | to-do | RuPERTa-base-finetuned-squadv1 | |||
| SQUAD-es v2 | to-do | RuPERTa-base-finetuned-squadv2 |
import torch
from transformers import AutoModelForTokenClassification, AutoTokenizer
id2label = {
"0": "B-LOC",
"1": "B-MISC",
"2": "B-ORG",
"3": "B-PER",
"4": "I-LOC",
"5": "I-MISC",
"6": "I-ORG",
"7": "I-PER",
"8": "O"
}
tokenizer = AutoTokenizer.from_pretrained('mrm8488/RuPERTa-base-finetuned-ner')
model = AutoModelForTokenClassification.from_pretrained('mrm8488/RuPERTa-base-finetuned-ner')
text ="Julien, CEO de HF, nació en Francia."
input_ids = torch.tensor(tokenizer.encode(text)).unsqueeze(0)
outputs = model(input_ids)
last_hidden_states = outputs[0]
for m in last_hidden_states:
for index, n in enumerate(m):
if(index > 0 and index <= len(text.split(" "))):
print(text.split(" ")[index-1] + ": " + id2label[str(torch.argmax(n).item())])
# Output:
'''
Julien,: I-PER
CEO: O
de: O
HF,: B-ORG
nació: I-PER
en: I-PER
Francia.: I-LOC
'''For POS just change the id2label dictionary and the model path to mrm8488/RuPERTa-base-finetuned-pos
from transformers import AutoModelWithLMHead, AutoTokenizer
model = AutoModelWithLMHead.from_pretrained('mrm8488/RuPERTa-base')
tokenizer = AutoTokenizer.from_pretrained("mrm8488/RuPERTa-base", do_lower_case=True)
from transformers import pipeline
pipeline_fill_mask = pipeline("fill-mask", model=model, tokenizer=tokenizer)
pipeline_fill_mask("España es un país muy <mask> en la UE")[
{
"score": 0.1814306527376175,
"sequence": "<s> españa es un país muy importante en la ue</s>",
"token": 1560
},
{
"score": 0.024842597544193268,
"sequence": "<s> españa es un país muy fuerte en la ue</s>",
"token": 2854
},
{
"score": 0.02473250962793827,
"sequence": "<s> españa es un país muy pequeño en la ue</s>",
"token": 2948
},
{
"score": 0.023991240188479424,
"sequence": "<s> españa es un país muy antiguo en la ue</s>",
"token": 5240
},
{
"score": 0.0215945765376091,
"sequence": "<s> españa es un país muy popular en la ue</s>",
"token": 5782
}
]I thank 🤗/transformers team for answering my doubts and Google for helping me with the TensorFlow Research Cloud program.
Created by Manuel Romero/@mrm8488
Made with ♥ in Spain