---
library_name: transformers
language:
- en
base_model:
- google-bert/bert-base-cased
datasets:
- zeroshot/twitter-financial-news-sentiment
metrics:
- accuracy
---

# Model Card for Model ID

<!-- Provide a quick summary of what the model is/does. -->
Description for the model on Hugging Face:
A model for analyzing the tone of financial messages can be used to classify messages into three categories: bullish, bearish, and neutral tone. Here's how it might work:

▎1. Bullish Sentiment.
Bullish sentiment messages usually contain positive words and phrases that indicate expectations of rising prices or improving economic conditions. Examples of such messages might include:
- “Shares of XYZ Company are expected to rise after a successful quarterly report.”
- “Analysts forecast oil prices to rise due to increased demand.”

▎2. Bearish Sentiment.
Bearish sentiment messages, on the other hand, contain negative words and phrases that indicate expectations of falling prices or deteriorating economic conditions. Examples of such messages might include:
- “Economic data points to a possible recession, which could negatively impact the markets.”
- “ABC Company is facing losses and the stock may fall.”

▎3. Neutral Sentiment (Neutral Sentiment)
Neutral messages do not express clear optimism or pessimism. They may contain factual information without assessing future changes. Examples of such messages might include:
- “There was little price movement in the market today.”
- “XYZ Company announced the launch of a new product but did not provide sales projections.”

Classification results can be used to analyze market sentiment and make investment decisions.

"LABEL_0": "Bearish" 
"LABEL_1": "Bullish" 
"LABEL_2": "Neutral" 


## Model Details

"LABEL_0": "Bearish" 
"LABEL_1": "Bullish" 
"LABEL_2": "Neutral" 

### Model Description

<!-- Provide a longer summary of what this model is. -->

This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.

- **Developed by:** Mikhail Luk
- **Model type:** Financial Model
- **Language(s) (NLP):** English
- **License:** Free


### Model Sources [optional]

<!-- Provide the basic links for the model. -->

- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]

## Uses

<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->

### Direct Use

<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
Classification results can be used to analyze market sentiment and make investment decisions.


[More Information Needed]

### Downstream Use [optional]

<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->

[More Information Needed]

### Out-of-Scope Use

<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->

[More Information Needed]

## Bias, Risks, and Limitations

<!-- This section is meant to convey both technical and sociotechnical limitations. -->

[More Information Needed]

### Recommendations

<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

## How to Get Started with the Model

Use the code below to get started with the model.

[More Information Needed]

## Training Details

### Training Data

<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->

[More Information Needed]

### Training Procedure

<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->

#### Preprocessing [optional]

[More Information Needed]


#### Training Hyperparameters

- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->

#### Speeds, Sizes, Times [optional]

<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->

[More Information Needed]

## Evaluation

<!-- This section describes the evaluation protocols and provides the results. -->

### Testing Data, Factors & Metrics

#### Testing Data

<!-- This should link to a Dataset Card if possible. -->

[More Information Needed]

#### Factors

<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->

[More Information Needed]

#### Metrics

<!-- These are the evaluation metrics being used, ideally with a description of why. -->

[More Information Needed]

### Results

[More Information Needed]

#### Summary



## Model Examination [optional]

<!-- Relevant interpretability work for the model goes here -->

[More Information Needed]

## Environmental Impact

<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->

Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).

- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]

## Technical Specifications [optional]

### Model Architecture and Objective

[More Information Needed]

### Compute Infrastructure

[More Information Needed]

#### Hardware

[More Information Needed]

#### Software

[More Information Needed]

## Citation [optional]

<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->

**BibTeX:**

[More Information Needed]

**APA:**

[More Information Needed]

## Glossary [optional]

<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->

[More Information Needed]

## More Information [optional]

[More Information Needed]

## Model Card Authors [optional]

[More Information Needed]

## Model Card Contact

[More Information Needed]