Update README.md

README.md

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+---
 library_name: transformers
 tags:
+- art
+- reading
+- reviews
+- sentiment
+license: apache-2.0
+datasets:
+- McAuley-Lab/Amazon-Reviews-2023
+language:
+- en
+metrics:
+- accuracy
+base_model:
+- google-bert/bert-base-uncased
+pipeline_tag: text-classification
+---
 
-text-classification
-bert
-pytorch
-tasks:
-text-classification
-
-
-Model Card for Book Reviews Classification Model
-Model Details
-Model Description
-This is a BERT-based text classification model that has been fine-tuned on the Amazon book reviews dataset to classify book reviews into star ratings (1-5).
-
-Developed by: Anthropic
-Funded by: N/A
-Shared by: Newviel
-Model type: Text Classification
-Language(s): English
-License: Apache 2.0
-Finetuned from model: bert-base-uncased
-
-Model Sources
-
-Repository: https://huggingface.co/newviel/book_reviews_model
-Paper: N/A
-Demo: N/A
-
-Uses
-Direct Use
-This model can be used to classify book reviews into star ratings (1-5) based on the review text. It could be used as a standalone model or integrated into a larger application that deals with book reviews.
-Downstream Use
-The model could be fine-tuned further on additional book review data to improve its performance. It could also be used as a starting point for building other text classification models, such as for product reviews, movie reviews, or any other text-based categorization task.
-Out-of-Scope Use
-This model is trained specifically on book reviews and may not perform well on other types of text classification tasks, such as sentiment analysis, topic classification, or language translation. Users should be cautious about applying the model outside of the book reviews domain.
-Bias, Risks, and Limitations
-The model was trained on the Amazon book reviews dataset, which may have inherent biases based on the demographics and preferences of Amazon customers. The model's performance may also be limited to the specific writing styles and vocabulary found in book reviews.
-Recommendations
-Users should be aware of the potential biases and limitations of the model, and carefully evaluate its performance on their specific use case before deploying it. It is recommended to fine-tune the model on additional data or customize the model architecture if the use case differs significantly from the original training data.
-How to Get Started with the Model
-To use the model, you can follow the example code below:
-pythonCopyfrom transformers import pipeline
-
-classifier = pipeline("text-classification", model="newviel/book_reviews_model")
-result = classifier("This book was an incredible read!")
-print(result)
-Training Details
-Training Data
-The model was trained on the Amazon book reviews dataset, which contains customer reviews for books sold on Amazon. The dataset contains over 130 million reviews spanning a wide range of book genres and topics.
-Training Procedure
-Preprocessing
-The text data was preprocessed by tokenizing the reviews using the BERT tokenizer, and converting the star ratings to a numerical label (1-5).
-Training Hyperparameters
-
-Training regime: FP32 mixed precision
-Learning rate: 5e-05
-Train batch size: 8
-Eval batch size: 8
-Epochs: 3
-Optimizer: AdamW with betas=(0.9, 0.999) and epsilon=1e-08
-LR Scheduler: Linear
-
-Speeds, Sizes, Times
-
-Model size: 438 MB
-Training time: Approximately 40 minutes
-
-Evaluation
-Testing Data, Factors & Metrics
-Testing Data
-The model was evaluated on a held-out portion of the Amazon book reviews dataset, which was not used for training.
-Factors
-The evaluation focused on the model's performance in classifying book reviews into the correct star rating (1-5).
-Metrics
-
-Accuracy: 0.7537
-Evaluation Loss: 0.7654
-
-Results
-The model achieved an accuracy of 0.7537 and an evaluation loss of 0.7654 on the held-out test set, indicating a reasonably strong performance on the book review classification task.
-Summary
-The model is capable of classifying book reviews into star ratings with a high degree of accuracy, making it suitable for use in applications that deal with book reviews. However, users should be aware of the potential biases and limitations of the model, and consider further fine-tuning or customization if necessary.
-Environmental Impact
-Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
-
-Hardware Type: NVIDIA V100 GPU
-Hours used: 0.67 hours
-Cloud Provider: Google Cloud Platform
-Compute Region: us-east1
-Carbon Emitted: 0.25 kgCO2eq
-
-Technical Specifications
-Model Architecture and Objective
-The model architecture is based on the BERT-base-uncased model, with a linear classification head added on top to perform the star rating prediction task.
-Compute Infrastructure
-Hardware
-The model was trained on a single NVIDIA V100 GPU.
-Software
-
-Framework: PyTorch 2.5.1+cu121
-Transformers Version: 4.46.2
-Datasets Version: 3.1.0
-Tokenizers Version: 0.20.3
-
-Citation
-No published paper or blog post to cite.
-Glossary
-N/A
-More Information
-N/A
-Model Card Authors
-This model card was generated by Anthropic.
-Model Card Contact
-For more information or issues, please contact newvel-website@hotmail.com
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+
+## Model Details
+
+### 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:** [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]
+
+### 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. -->
+
+[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]