Update README.md

README.md

@@ -1,6 +1,9 @@
 ---
 library_name: transformers
-tags: []
+license: apache-2.0
+language:
+- am
+- ti
 ---
 
 # Model Card for Model ID
@@ -17,88 +20,79 @@ tags: []
 
 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]
+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]
 
-<!-- Provide the basic links for the model. -->
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+### Model Sources [optional]
 
+Repository: [soon will be available]
+Paper: [soon will be available]
+Demo: [soon will be available]
 ## 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]
+This model can be used for sequence classification tasks, such as sentiment analysis or text classification.
 
 ### 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]
+Can be fine-tuned further for specific classification tasks or domains.
 
 ### Out-of-Scope Use
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+Ensure not to use this model for tasks where biased or sensitive language handling is crucial without further validation.
+
 
-[More Information Needed]
 
 ## Bias, Risks, and Limitations
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+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.
 
-[More Information Needed]
 
 ### Recommendations
 
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+Users should assess the model's performance in their specific use case, especially considering any potential biases or 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.
+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.
+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.
 
-[More Information Needed]
+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.
 
-<!-- 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]
+####PreprocessingThe dataset was tokenized using the XLM-RoBERTa tokenizer. The text was padded and truncated to a fixed length of 128 tokens.
 
 
 #### 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 -->
+- **Training regime:** Fine-tuned for 3 epochs with a learning rate of 1e-5.
 
 #### Speeds, Sizes, Times [optional]
 
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
 
 ## Evaluation
 
@@ -108,39 +102,22 @@ Use the code below to get started with the model.
 
 #### Testing Data
 
-<!-- This should link to a Dataset Card if possible. -->
+Evaluated on a separate test dataset using the same preprocessing as the training data.
 
 [More Information Needed]
 
 #### Factors
 
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
+Factors such as text length and class imbalance were considered during evaluation.
 [More Information Needed]
 
 #### Metrics
 
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
+Metrics include accuracy and loss during training and evaluation.
 
 ### 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).
 
@@ -169,12 +146,19 @@ Carbon emissions can be estimated using the [Machine Learning Impact calculator]
 [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}},
+}
 
-<!-- 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]
+Hailay. (2024). *Hailay/FT_EXLMR*. Hugging Face. Retrieved from https://huggingface.co/Hailay/FT_EXLMR
+
 
 **APA:**