Fearao/RoBERTa_based_on_eastmoney_guba_comments

Model description

This model is based on uer/roberta-base-finetuned-dianping-chinese, fine-tuned using comment data from Eastmoney stock bar, and I used the original tokenizer. Thanks a lot to the authors of the model for all the help they gave me.

How to use

You can use this model directly with a pipeline for text classification (take the case of RoBERTa_based_on_eastmoney_guba_comments):

>>> from transformers import AutoModelForSequenceClassification,AutoTokenizer,pipeline
>>> model = AutoModelForSequenceClassification.from_pretrained('Fearao/RoBERTa_based_on_eastmoney_guba_comments')
>>> tokenizer = AutoTokenizer.from_pretrained('uer/roberta-base-finetuned-chinanews-chinese')
>>> text_classification = pipeline('sentiment-analysis', model=model, tokenizer=tokenizer)
>>> text_classification("又跳水了")
   [{'label': 'negative (stars 1, 2 and 3)', 'score': 0.9989427924156189}]

Training data

Eastmoney stock bar comments datasets are used Fearao/guba_eastmoney。

Training procedure

Num examples = 7087
Num Epochs = 3
Instantaneous batch size per device = 8
Total train batch size (w. parallel, distributed & accumulation) = 8
Gradient Accumulation steps = 1
Total optimization steps = 2658
Number of trainable parameters = 102269186