# Pretrained model used for the NASDAQ_news dataset

Model for Binary classification on headlines from the NASDAQ_news dataset.
Label_0 = Downward movement
Label_1 = Upward movement

The target_variable is the return 10-minutes after an article has been published

## Model Info:

This model is a fine-tuned DistilBertForSequenceClassification on random samples of the NASDAQ_news dataset.
It uses the DistilBertTokenizerFast as a tokenizer.

### Model Data:

The model is finetuned on headlines
{"language": ["en"], "tags": ["Economics"], "pipeline_tag": "text-classification"}
transformers pytorch distilbert text-classification Economics en autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #distilbert #text-classification #Economics #en #autotrain_compatible #endpoints_compatible #region-us 

# Pretrained model used for the NASDAQ_news dataset

Model for Binary classification on headlines from the NASDAQ_news dataset.
Label_0 = Downward movement
Label_1 = Upward movement

The target_variable is the return 10-minutes after an article has been published

## Model Info:

This model is a fine-tuned DistilBertForSequenceClassification on random samples of the NASDAQ_news dataset.
It uses the DistilBertTokenizerFast as a tokenizer.

### Model Data:

The model is finetuned on headlines
# Pretrained model used for the NASDAQ_news dataset

Model for Binary classification on headlines from the NASDAQ_news dataset.
Label_0 = Downward movement
Label_1 = Upward movement

The target_variable is the return 10-minutes after an article has been published ## Model Info:

This model is a fine-tuned DistilBertForSequenceClassification on random samples of the NASDAQ_news dataset.
It uses the DistilBertTokenizerFast as a tokenizer. ### Model Data:

The model is finetuned on headlines
TAGS
#transformers #pytorch #distilbert #text-classification #Economics #en #autotrain_compatible #endpoints_compatible #region-us 
 # Pretrained model used for the NASDAQ_news dataset

Model for Binary classification on headlines from the NASDAQ_news dataset.
Label_0 = Downward movement
Label_1 = Upward movement

The target_variable is the return 10-minutes after an article has been published ## Model Info:

This model is a fine-tuned DistilBertForSequenceClassification on random samples of the NASDAQ_news dataset.
It uses the DistilBertTokenizerFast as a tokenizer. ### Model Data:

The model is finetuned on headlines
TAGS
#transformers #pytorch #distilbert #text-classification #Economics #en #autotrain_compatible #endpoints_compatible #region-us 
# Pretrained model used for the NASDAQ_news dataset

Model for Binary classification on headlines from the NASDAQ_news dataset.
Label_0 = Downward movement
Label_1 = Upward movement

The target_variable is the return 10-minutes after an article has been published## Model Info:

This model is a fine-tuned DistilBertForSequenceClassification on random samples of the NASDAQ_news dataset.
It uses the DistilBertTokenizerFast as a tokenizer.### Model Data:

The model is finetuned on headlines