Add model card

README.md

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-library_name: transformers
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-# Model Card for Model ID
+# Model Card for DistilBertForSequenceClassification_6h_768dim
 
-<!-- Provide a quick summary of what the model is/does. -->
+## Model Description
+- **Purpose**: This model is designed for sentiment analysis tasks, aimed at classifying text into positive or negative sentiment categories.
+- **Model architecture**: The model is based on the DistilBERT architecture, which is a distilled version of BERT that maintains most of the original model's performance while being more efficient. Specifically, it uses 6 attention heads, a hidden dimension size of 768, and an intermediate (hidden) layer size of 4*768.
+- **Training data**: The model was fine-tuned on a dataset compiled from various sources, including social media posts, product reviews, and movie reviews. The data was preprocessed to remove usernames, URLs, and any identifiable information. Texts were lowercased, and stopwords were removed to focus on meaningful content.
 
+## Intended Use
+- **Intended users**: This model is intended for developers and data scientists looking to integrate sentiment analysis into their applications, such as customer feedback analysis or content moderation.
+- **Use cases**: Potential use cases include analyzing customer reviews to gauge overall sentiment about products or services, monitoring social media for brand sentiment, and filtering content based on sentiment for moderation purposes.
 
+## Limitations
+- **Known limitations**: The model may exhibit biases present in the training data, potentially leading to inaccuracies in certain contexts or for specific demographic groups. Its performance has not been extensively tested across all possible domains, so results may vary for texts outside of the training distribution.
 
-## Model Details
+## Hardware 
+- **Training Platform**: The model was trained on a cloud computing platform using NVIDIA Tesla V100 GPUs. Training involved multiple epochs over the dataset with careful monitoring for overfitting.
 
-### Model Description
+## Software Optimizations
+- **Known Optimizations**: During training, techniques such as gradient accumulation and mixed-precision training were employed to enhance performance and reduce memory usage. The AdamW optimizer was used for its effective learning rate adjustments.
 
-<!-- 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.
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-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
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-### Model Sources [optional]
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-<!-- Provide the basic links for the model. -->
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-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
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-## 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. -->
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-### Direct Use
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-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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-[More Information Needed]
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-### Downstream Use [optional]
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-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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-[More Information Needed]
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-### Out-of-Scope Use
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-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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-[More Information Needed]
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-## Bias, Risks, and Limitations
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-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
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-[More Information Needed]
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-### Recommendations
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-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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-## How to Get Started with the Model
-
-Use the code below to get started with the model.
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-[More Information Needed]
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-## Training Details
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-### Training Data
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-<!-- 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. -->
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-[More Information Needed]
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-### Training Procedure 
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-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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-#### Preprocessing [optional]
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-[More Information Needed]
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-#### Training Hyperparameters
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-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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-#### Speeds, Sizes, Times [optional]
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-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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-[More Information Needed]
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-## Evaluation
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-<!-- This section describes the evaluation protocols and provides the results. -->
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-### Testing Data, Factors & Metrics
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-#### Testing Data
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-<!-- This should link to a Dataset Card if possible. -->
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-[More Information Needed]
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-#### Factors
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-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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-[More Information Needed]
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-#### Metrics
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-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
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-[More Information Needed]
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-### Results
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-[More Information Needed]
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-#### Summary
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-## Model Examination [optional]
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-<!-- Relevant interpretability work for the model goes here -->
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-[More Information Needed]
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-## Environmental Impact
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-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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-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).
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-- **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]
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-## Technical Specifications [optional]
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-### Model Architecture and Objective
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-[More Information Needed]
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-### Compute Infrastructure
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-[More Information Needed]
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-#### Hardware
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-[More Information Needed]
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-#### Software
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-[More Information Needed]
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-## Citation [optional]
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-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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-**BibTeX:**
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-[More Information Needed]
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-**APA:**
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-[More Information Needed]
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-## Glossary [optional]
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-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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-[More Information Needed]
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-## More Information [optional]
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-[More Information Needed]
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-## Model Card Authors [optional]
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-[More Information Needed]
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-## Model Card Contact
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-[More Information Needed]
+## Ethical Considerations
+- **Ethical concerns**: There is a risk of the model reinforcing or amplifying biases present in the training data, leading to potentially unfair outcomes. Users are encouraged to thoroughly test the model in their specific contexts and consider bias mitigation strategies.
 
+## More Information
+- For more details on the DistilBERT model architecture and its implementation, please refer to the original paper and documentation available on the Hugging Face model hub.