Update README.md

README.md

@@ -6,54 +6,27 @@ tags:
 model-index:
 - name: distilroberta-base-finetuned-wikitext2
   results: []
+datasets:
+- wikitext
+language:
+- en
+pipeline_tag: fill-mask
 ---
 
-<!-- This model card has been generated automatically according to the information the Trainer had access to. You
-should probably proofread and complete it, then remove this comment. -->
+## Overview
++ **Model Name**: FILL-MAsk-RoBERTa-base
++ **Task**: Masked Language Modeling (FILL-MAsk)
++ **Dataset**: WikiText2
 
-# distilroberta-base-finetuned-wikitext2
 
-This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the None dataset.
-It achieves the following results on the evaluation set:
-- Loss: 1.8538
+## Model Description
+**FILL-MAsk-RoBERTa-base** is a distilled version of the RoBERTa-base model, designed for the Masked Language Modeling task. This model follows a similar training procedure as DistilBERT, resulting in a smaller model with 6 layers, 768 dimensions, and 12 attention heads. It contains a total of 82 million parameters, making it more lightweight compared to the original RoBERTa-base, which has 125 million parameters. On average, DistilRoBERTa is approximately twice as fast as RoBERTa-base.
 
-## Model description
+## Usage
+**FILL-MAsk-RoBERTa-base** can be used for both direct and downstream tasks. It is suitable for masked language modeling tasks, where tokens are masked, and the model must predict the masked tokens. It is also intended to be fine-tuned on downstream tasks such as sequence classification, token classification, or question answering. Users can explore the Hugging Face Model Hub to find fine-tuned versions of this model for specific tasks of interest.
 
-More information needed
+## Limitations
 
-## Intended uses & limitations
-
-More information needed
-
-## Training and evaluation data
-
-More information needed
-
-## Training procedure
-
-### Training hyperparameters
-
-The following hyperparameters were used during training:
-- learning_rate: 2e-05
-- train_batch_size: 16
-- eval_batch_size: 16
-- seed: 42
-- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
-- lr_scheduler_type: linear
-- num_epochs: 3.0
-
-### Training results
-
-| Training Loss | Epoch | Step | Validation Loss |
-|:-------------:|:-----:|:----:|:---------------:|
-| 2.101         | 1.0   | 1203 | 1.9475          |
-| 2.0265        | 2.0   | 2406 | 1.8914          |
-| 1.9672        | 3.0   | 3609 | 1.8538          |
-
-
-### Framework versions
-
-- Transformers 4.33.0
-- Pytorch 2.0.0
-- Datasets 2.1.0
-- Tokenizers 0.13.3
++ **Bias**: Significant research has explored bias and fairness issues with language models. Predictions generated by this model may contain biases, including harmful stereotypes related to protected classes, identity characteristics, and sensitive social and occupational groups.
++ **Fairness**: It's essential to be aware of fairness considerations when using this model and to ensure that its predictions do not contribute to unfair or harmful outcomes.
++ **Ethical Use**: Users are encouraged to use this model ethically and responsibly, taking into account the potential for bias and ensuring that it does not generate harmful or offensive content.