

# Finetuned distilBERT model for stock news classification

This distilbert model was fine-tuned on 50.000 stock news articles using the HuggingFace adapter from Kern AI refinery. The articles consisted of the headlines plus abstract of the article.
For the finetuning, a single NVidia K80 was used for about four hours. 

Join our Discord if you have questions about this model: https://discord.gg/MdZyqSxKbe

DistilBERT is a smaller, faster and lighter version of BERT. It was trained by distilling BERT base and has 40% less parameters than bert-base-uncased. 
It runs 60% faster while preserving over 95% of BERT’s performances as measured on the GLUE language understanding benchmark. 
DistilBERT does not have token-type embeddings, pooler and retains only half of the layers from Google’s BERT.
## Features

- The model can handle various text classification tasks, especially when it comes to stock and finance news sentiment classification.
- The output of the model are the three classes "positive", "neutral" and "negative" plus the models respective confidence score of the class. 
- The model was fine-tuned on a custom datasets that was curated by Kern AI and labeled in our tool refinery.
- The model is currently supported by the PyTorch framework and can be easily deployed on various platforms using the HuggingFace Pipeline API.

## Usage

To use the model, you need to install the HuggingFace Transformers library:

```bash
pip install transformers
```
Then you can load the model and the tokenizer from the HuggingFace Hub:

```python
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("KernAI/stock-news-distilbert")
tokenizer = AutoTokenizer.from_pretrained("KernAI/stock-news-distilbert")
```
To classify a single sentence or a sentence pair, you can use the HuggingFace Pipeline API:

```python
from transformers import pipeline

classifier = pipeline("text-classification", model=model, tokenizer=tokenizer)
result = classifier("This is a positive sentence.")
print(result)
# [{'label': 'POSITIVE', 'score': 0.9998656511306763}]
```
{"license": "mit", "widget": [{"text": "NEW YORK (TheStreet) -- Microsoft (MSFT) - Get Free Report had its price target raised to $39 from $38 by analysts at Jefferies who maintained their 'underperform' rating. In Thursday's pre-market trading session shares are advancing 1.24% to $44.79. This action comes as Microsoft said yesterday that it will eliminate up to 7,800 jobs mostly in its phone unit as it looks to restructure its phone hardware business that has been struggling, the New York Times reports.", "example_title": "MSFT news (positive)"}, {"text": "Adobe Brings Major New Innovations to Video Tools SAN JOSE, Calif.--(BUSINESS WIRE)--Today, ahead of the 2023 NAB Show \u2013 the preeminent conference and exhibition driving the evolution of broadcast, media and entertainment \u2013 Adobe (Nasdaq:ADBE) announced industry-first innovations across its family of video applications, including AI-powered text-based video editing and automated color tone-mapping capabilities in Premiere Pro. SAN JOSE, Calif.--(BUSINESS WIRE).", "example_title": "ADBE news (neutral)"}, {"text": "Unilever PLC (NYSE: UL)\u2019s stock price has gone decline by -0.61 in comparison to its previous close of 54.27, however, the company has experienced a -1.61% decrease in its stock price over the last five trading days. The Wall Street Journal reported on 10/24/22 that Dry Shampoo Recalled Due to Potential Cancer-Causing Ingredient.", "example_title": "UL news (negative)"}]}
transformers pytorch distilbert text-classification license:mit autotrain_compatible endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #distilbert #text-classification #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us 



# Finetuned distilBERT model for stock news classification

This distilbert model was fine-tuned on 50.000 stock news articles using the HuggingFace adapter from Kern AI refinery. The articles consisted of the headlines plus abstract of the article.
For the finetuning, a single NVidia K80 was used for about four hours. 

Join our Discord if you have questions about this model: URL

DistilBERT is a smaller, faster and lighter version of BERT. It was trained by distilling BERT base and has 40% less parameters than bert-base-uncased. 
It runs 60% faster while preserving over 95% of BERT’s performances as measured on the GLUE language understanding benchmark. 
DistilBERT does not have token-type embeddings, pooler and retains only half of the layers from Google’s BERT.
## Features

- The model can handle various text classification tasks, especially when it comes to stock and finance news sentiment classification.
- The output of the model are the three classes "positive", "neutral" and "negative" plus the models respective confidence score of the class. 
- The model was fine-tuned on a custom datasets that was curated by Kern AI and labeled in our tool refinery.
- The model is currently supported by the PyTorch framework and can be easily deployed on various platforms using the HuggingFace Pipeline API.

## Usage

To use the model, you need to install the HuggingFace Transformers library:


Then you can load the model and the tokenizer from the HuggingFace Hub:


To classify a single sentence or a sentence pair, you can use the HuggingFace Pipeline API:


# Finetuned distilBERT model for stock news classification

This distilbert model was fine-tuned on 50.000 stock news articles using the HuggingFace adapter from Kern AI refinery. The articles consisted of the headlines plus abstract of the article.
For the finetuning, a single NVidia K80 was used for about four hours. 

Join our Discord if you have questions about this model: URL

DistilBERT is a smaller, faster and lighter version of BERT. It was trained by distilling BERT base and has 40% less parameters than bert-base-uncased. 
It runs 60% faster while preserving over 95% of BERT’s performances as measured on the GLUE language understanding benchmark. 
DistilBERT does not have token-type embeddings, pooler and retains only half of the layers from Google’s BERT. ## Features

- The model can handle various text classification tasks, especially when it comes to stock and finance news sentiment classification.
- The output of the model are the three classes "positive", "neutral" and "negative" plus the models respective confidence score of the class. 
- The model was fine-tuned on a custom datasets that was curated by Kern AI and labeled in our tool refinery.
- The model is currently supported by the PyTorch framework and can be easily deployed on various platforms using the HuggingFace Pipeline API. ## Usage

To use the model, you need to install the HuggingFace Transformers library:


Then you can load the model and the tokenizer from the HuggingFace Hub:


To classify a single sentence or a sentence pair, you can use the HuggingFace Pipeline API:
TAGS
#transformers #pytorch #distilbert #text-classification #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us 
 # Finetuned distilBERT model for stock news classification

This distilbert model was fine-tuned on 50.000 stock news articles using the HuggingFace adapter from Kern AI refinery. The articles consisted of the headlines plus abstract of the article.
For the finetuning, a single NVidia K80 was used for about four hours. 

Join our Discord if you have questions about this model: URL

DistilBERT is a smaller, faster and lighter version of BERT. It was trained by distilling BERT base and has 40% less parameters than bert-base-uncased. 
It runs 60% faster while preserving over 95% of BERT’s performances as measured on the GLUE language understanding benchmark. 
DistilBERT does not have token-type embeddings, pooler and retains only half of the layers from Google’s BERT. ## Features

- The model can handle various text classification tasks, especially when it comes to stock and finance news sentiment classification.
- The output of the model are the three classes "positive", "neutral" and "negative" plus the models respective confidence score of the class. 
- The model was fine-tuned on a custom datasets that was curated by Kern AI and labeled in our tool refinery.
- The model is currently supported by the PyTorch framework and can be easily deployed on various platforms using the HuggingFace Pipeline API. ## Usage

To use the model, you need to install the HuggingFace Transformers library:


Then you can load the model and the tokenizer from the HuggingFace Hub:


To classify a single sentence or a sentence pair, you can use the HuggingFace Pipeline API: