lakshaywadhwa1993
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README.md
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datasets:
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- wikiann
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example_title: "Sentence_1"
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- text: "विराट कोहली भारताकडून खेळतो."
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example_title: "Sentence_2"
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- text: "नवी दिल्ली ही भारताची राजधानी आहे"
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example_title: "Sentence_3"
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```py
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from transformers import AutoTokenizer, AutoModelForTokenClassification
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from transformers import pipeline
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---
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license: apache-2.0
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tags:
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- generated_from_trainer
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datasets:
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- wikiann
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model-index:
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- name: ner_marathi_bert
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results: []
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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should probably proofread and complete it, then remove this comment. -->
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# ner_marathi_bert
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This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on the wikiann dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.3606
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- Overall Precision: 0.8939
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- Overall Recall: 0.9030
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- Overall F1: 0.8984
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- Overall Accuracy: 0.9347
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- Loc F1: 0.8823
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- Org F1: 0.8555
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- Per F1: 0.9435
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 2e-05
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- train_batch_size: 16
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- eval_batch_size: 16
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- seed: 42
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: linear
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- num_epochs: 7
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy | Loc F1 | Org F1 | Per F1 |
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|:-------------:|:-----:|:----:|:---------------:|:-----------------:|:--------------:|:----------:|:----------------:|:------:|:------:|:------:|
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| 0.2961 | 3.19 | 1000 | 0.3496 | 0.8720 | 0.8841 | 0.8780 | 0.9229 | 0.8599 | 0.8210 | 0.9343 |
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| 0.0613 | 6.39 | 2000 | 0.3606 | 0.8939 | 0.9030 | 0.8984 | 0.9347 | 0.8823 | 0.8555 | 0.9435 |
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### Framework versions
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- Transformers 4.21.0
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- Pytorch 1.12.0+cu113
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- Datasets 2.4.0
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- Tokenizers 0.12.1
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