Update README.md

README.md

@@ -4,180 +4,40 @@ license: apache-2.0
 language:
 - am
 - ti
----
-
-# 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: [Your Name or Organization]
-Funded by: [Optional: Funding Information]
-Shared by: [Optional: Sharing Information]
-Model type: XLM-RoBERTa for Sequence Classification
-Language(s) (NLP): [Language(s) of the dataset, e.g., Tigrinya, Amharic]
-License: [ Apache 2.0]
-Finetuned from model: xlm-roberta-base
-
-
-
-### Model Sources [optional]
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-Repository: [soon will be available]
-Paper: [soon will be available]
-Demo: [soon will be available]
-## Uses
-
-
-### Direct Use
-
-This model can be used for sequence classification tasks, such as sentiment analysis or text classification.
-
-### Downstream Use [optional]
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-Can be fine-tuned further for specific classification tasks or domains.
-
-### Out-of-Scope Use
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-Ensure not to use this model for tasks where biased or sensitive language handling is crucial without further validation.
-
-
-
-## Bias, Risks, and Limitations
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-The model may exhibit biases present in the training data. Users should evaluate its performance carefully in their specific application to avoid reinforcing unwanted biases.
-
-
-### Recommendations
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-Users should assess the model's performance in their specific use case, especially considering any potential biases or 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.
-
-## How to Get Started with the Model
-
-Use the provided tokenizer and model to load and use the model for sequence classification tasks. Fine-tuning on your dataset can be achieved using the provided code snippet.
-
-from transformers import XLMRobertaTokenizer, XLMRobertaForSequenceClassification
+---from transformers import XLMRobertaTokenizer, XLMRobertaForSequenceClassification
 
 model_name = "Hailay/FT_EXLMR"
 tokenizer = XLMRobertaTokenizer.from_pretrained(model_name)
 model = XLMRobertaForSequenceClassification.from_pretrained(model_name)
 
-# Example usage
 inputs = tokenizer("Your text here", return_tensors="pt")
 outputs = model(**inputs)
+------
 
-## Training Details
-
-### Training Data
-First, the model was extended the original tokenizer scaling to handle low resource languages then, The model was fine-tuned using a custom dataset consisting of text and labels in a CSV format. Data includes sentences labeled for binary classification.
-
-
-### Training Procedure
-####PreprocessingThe dataset was tokenized using the XLM-RoBERTa tokenizer. The text was padded and truncated to a fixed length of 128 tokens.
-
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-#### Training Hyperparameters
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-- **Training regime:** Fine-tuned for 3 epochs with a learning rate of 1e-5.
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-#### Speeds, Sizes, Times [optional]
-
-
-## Evaluation
-
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
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-#### Testing Data
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-Evaluated on a separate test dataset using the same preprocessing as the training data.
-
-[More Information Needed]
-
-#### Factors
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-Factors such as text length and class imbalance were considered during evaluation.
-[More Information Needed]
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-#### Metrics
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-Metrics include accuracy and loss during training and evaluation.
-
-### Results
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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).
-
-- **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]
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-### Model Architecture and Objective
-
-[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
-
-[More Information Needed]
-
-## Citation [optional]
-**BibTeX:**
-@misc{hailay_ft_exlm,
-  author = {Your Name},
-  title = {Hailay/FT_EXLMR},
-  year = {2024},
-  publisher = {Hugging Face},
-  how published = {\url{https://huggingface.co/Hailay/FT_EXLMR}},
-}
-
-
-
-Hailay. (2024). *Hailay/FT_EXLMR*. Hugging Face. Retrieved from https://huggingface.co/Hailay/FT_EXLMR
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-**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]
+# Model Card for Model ID
+Model Card Summary: Hailay/FT_EXLMR
+Model Name: Hailay/FT_EXLMR
+Type: XLM-RoBERTa model for sequence classification
+Language(s): [Languages supported by the model]
+License: [License type, e.g., Apache 2.0]
+Pre-trained Model: xlm-roberta-base
+Uses:
 
-## More Information [optional]
+Primary: Text classification (e.g., sentiment analysis)
+Additional: Can be fine-tuned for specific tasks
+Key Features:
 
-[More Information Needed]
+Trained Data: Custom dataset with text and labels
+Training Details: 3 epochs, learning rate of 1e-5
+Evaluation: Accuracy and loss metrics
+Getting Started:
 
-## Model Card Authors [optional]
+Code Example: Load the model and tokenizer, then use them for text classification.
+Considerations:
 
-[More Information Needed]
+Bias & Risks: Assess for biases; evaluate suitability for specific applications
+Environmental Impact: [Details about hardware and training time]
+Citation:
 
-## Model Card Contact
+BibTeX & APA formats available
 
-[More Information Needed]