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README.md

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+Quantization made by Richard Erkhov.
+
+[Github](https://github.com/RichardErkhov)
+
+[Discord](https://discord.gg/pvy7H8DZMG)
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+[Request more models](https://github.com/RichardErkhov/quant_request)
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+
+xlm-roberta-base-finetuned-squad2 - bnb 8bits
+- Model creator: https://huggingface.co/IProject-10/
+- Original model: https://huggingface.co/IProject-10/xlm-roberta-base-finetuned-squad2/
+
+
+
+
+Original model description:
+---
+license: mit
+base_model: xlm-roberta-base
+tags:
+- generated_from_trainer
+datasets:
+- squad_v2
+model-index:
+- name: xlm-roberta-base-finetuned-squad2
+  results: []
+language:
+- en
+- ar
+- de
+- el
+- es
+- hi
+- ro
+- ru
+- th
+- tr
+- vi
+- zh
+metrics:
+- exact_match
+- f1
+pipeline_tag: question-answering
+---
+
+<!-- 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. -->
+
+## Model description
+
+XLM-RoBERTa is a multilingual version of RoBERTa developed by Facebook AI. It is pre-trained on 2.5TB of filtered CommonCrawl data containing 100 languages.
+It is an extension of RoBERTa, which is itself a variant of the BERT model. XLM-RoBERTa is designed to handle multiple languages and demonstrate strong performance across a wide range of tasks, making it highly useful for multilingual natural language processing (NLP) applications.
+
+**Language model:** xlm-roberta-base  
+**Language:** English  
+**Downstream-task:** Question-Answering  
+**Training data:** Train-set SQuAD 2.0  
+**Evaluation data:** Evaluation-set SQuAD 2.0   
+**Hardware Accelerator used**: GPU Tesla T4
+
+## Intended uses & limitations
+
+Multilingual Question-Answering
+
+For Question-Answering in English- 
+
+```python
+!pip install transformers
+from transformers import pipeline
+model_checkpoint = "IProject-10/bert-base-uncased-finetuned-squad2"
+question_answerer = pipeline("question-answering", model=model_checkpoint)
+
+context = """
+The Statue of Unity is the world's tallest statue, with a height of 182 metres (597 feet), located near Kevadia in the state of Gujarat, India.
+"""
+
+question = "What is the height of statue of Unity?"
+question_answerer(question=question, context=context)
+```
+For Question-Answering in Hindi- 
+
+```python
+!pip install transformers
+from transformers import pipeline
+model_checkpoint = "IProject-10/bert-base-uncased-finetuned-squad2"
+question_answerer = pipeline("question-answering", model=model_checkpoint)
+
+context = """
+स्टैच्यू ऑफ यूनिटी दुनिया की सबसे ऊंची प्रतिमा है, जिसकी ऊंचाई 182 मीटर (597 फीट) है, जो भारत के गुजरात राज्य में केवडिया के पास स्थित है।
+"""
+
+question = "स्टैच्यू ऑफ यूनिटी की ऊंचाई कितनी है?"
+question_answerer(question=question, context=context)
+```
+
+For Question-Answering in Spanish- 
+
+```python
+!pip install transformers
+from transformers import pipeline
+model_checkpoint = "IProject-10/bert-base-uncased-finetuned-squad2"
+question_answerer = pipeline("question-answering", model=model_checkpoint)
+
+context = """
+La Estatua de la Unidad es la estatua más alta del mundo, con una altura de 182 metros (597 pies), ubicada cerca de Kevadia en el estado de Gujarat, India.
+"""
+
+question = "¿Cuál es la altura de la estatua de la Unidad?"
+question_answerer(question=question, context=context)
+```
+
+## Results
+
+Evaluation on SQuAD 2.0 validation dataset:
+
+```
+ exact: 75.51587635812348,
+ f1: 78.7328391907263,
+ total: 11873,
+ HasAns_exact: 73.00944669365722,
+ HasAns_f1: 79.45259779208723,
+ HasAns_total: 5928,
+ NoAns_exact: 78.01513877207738,
+ NoAns_f1: 78.01513877207738,
+ NoAns_total: 5945,
+ best_exact: 75.51587635812348,
+ best_exact_thresh: 0.999241054058075,
+ best_f1: 78.73283919072665,
+ best_f1_thresh: 0.999241054058075,
+ total_time_in_seconds: 218.97641910400125,
+ samples_per_second: 54.220450076686134,
+ latency_in_seconds: 0.018443225730986376
+```
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 3e-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
+
+### Training results
+
+| Training Loss | Epoch | Step  | Validation Loss |
+|:-------------:|:-----:|:-----:|:---------------:|
+| 1.0539        | 1.0   | 8333  | 0.9962          |
+| 0.8013        | 2.0   | 16666 | 0.8910          |
+| 0.5918        | 3.0   | 24999 | 0.9802          |
+
+This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the squad_v2 dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.9802
+  
+### Framework versions
+
+- Transformers 4.31.0
+- Pytorch 2.0.1+cu118
+- Datasets 2.14.3
+- Tokenizers 0.13.3
+