Masked-Language-Modelling-for-Telugu_Language

license: mit tags:

• generated_from_trainer model-index:
• name: xlm-roberta-base-finetuned-wikitext2 results: [] language:
• en metrics:
• accuracy
• code_eval pipeline_tag: fill-mask

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xlm-roberta-base-finetuned-wikitext2

This model is a fine-tuned version of xlm-roberta-base on the None dataset. It achieves the following results on the evaluation set:

• Loss: 2.0384

Model description

We developed a language model for Telugu using the dataset called Telugu_books, which is from the Kaggle platform, and the dataset contains Telugu data, there are only a few language models are developed for regional languages like Telugu, Hindi, Kannada...etc, so we built a dedicated language model especially for the Telugu language. The model aim is to predict a Telugu word that is masked in a given Telugu sentence by using Masked Language Modeling of BERT [Bidirectional Encoder Representation from Transformers] and we achieved state-of-the-art performance in it.

Intended uses & limitations

Using this model we can predict the exact and contextual word which is already masked in a given Telugu sentence and we achieved state-of-the-art performance in it.

Training and evaluation data

Training data: Required libraries like Trainer and Training arguments are imported from transformers library. The after giving the Training arguments with our data we train the model using train() method which takes 1 to 1 ½ hour depending upon the size of our input data

Evaluating data: In the hugging face after opening our model page there is an API in which We give a Telugu Sentence as input with <mask> keyword and click the compute button then the predicted words with their probabilities are displayed. Then we check that words with the actual words and evaluated

Training procedure

Step-1: Collecting Data From the Kaggle Telugu dataset is collected. It contains Telugu paragraphs from different books.

Step2: Pre-processing Data The collected data is pre-processed using different pre-processing techniques and splitting the large Telugu Sentence into small sentences.

Step-3: Connecting to Hugging Face Hugging Face provides a token with which we can log in using a notebook function and the rest of the work we do will be exported to the platform automatically.

Step-4: Loading pre-trained model and tokenizer The pre-trained model and tokenizer from xlm-roberta-base are loaded for training our Telugu data

Step-5: Training the model Required libraries like Trainer and Training arguments are imported from Transformers library. The after giving the Training arguments with our data we train the model using the train() method which takes 1 to 1 ½ hours depending upon the size of our input data

Step-6: Pushing model and tokenizer Then trainer.push_to_hub() and tokenizer.push_to_hub() methods are used to export our trained model and its tokenizers which are used for the mapping of words in prediction.

Step-7: Testing In the hugging face after opening our model page there is an API in which We give a Telugu Sentence as input with <mask> keyword and click the compute button then the predicted words with their probabilities are displayed. Then we check that words with the actual words and evaluated

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 2e-05
• train_batch_size: 8
• eval_batch_size: 8
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 3.0

Training results

Framework versions

• Transformers 4.24.0
• Pytorch 1.12.1+cu113
• Datasets 2.7.1
• Tokenizers 0.13.2