- model: roberta-base
- datasets: ag_news
- train_set: 120K -> train_set: 100K, valid_set: 20K
- test_Set: 7.6K
- label: World(0), Sports(1), Business(2), Sci/Tech(3)
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained('dhtocks/Topic-Classification')
tokenizer = AutoTokenizer.from_pretrained('roberta-base') - model: roberta-base
- datasets: conll2003_noMISC
- train_set: 14K
- valid_set: 3.2K
- test_set: 3.4K
- label:
- O (0)
- B-PER (1)
- I-PER (2)
- B-ORG (3)
- I-ORG (4)
- B-LOC (5)
- I-LOC (6)
from transformers import AutoModelForTokenClassification, AutoTokenizer
model = AutoModelForTokenClassification.from_pretrained('dhtocks/Named-Entity-Recognition')
tokenizer = AutoTokenizer.from_pretrained('roberta-base')