# Model Card for Model ID


## Model Details
The technique of marking the words in a phrase to their appropriate POS
tags is known as part-of-speech tagging (POS tagging or POST). There are
two sorts of POS tagging algorithms: rule-based and stochastic, and
monolingual and multilingual are different types from a modelling
standpoint. POS tags provide grammatical context to a sentence, which can
be employed in NLP tasks such as NER, NLU and QNA systems.
In this research field, a lot of researchers had already tried to propose
various novel approaches, tags and models like Weightless Artificial
Neural Network (WANN), different forms of CRF, Bi-LSTM CRF, and
transformers, various techniques for language tag mixed POS tags to
handle mixed languages. All this research work leads to the enhancement
or creating a benchmark for different popular and low resource languages,
In the state of monolingual or multilingual context. In this model
we are trying to achieve state-of-the-art model for the Indian language
context in both native and its Romanised format. 

### Model Description

The model has been trained on the romanized forms of the Indian languages as well as English, Hindi, Gujarati, and Marathi.i.e(en,gu,mr,hi,gu_romanised,mr_romanised,hi_romanised)

To use this model you have import this class

```commandline
rom transformers import BertPreTrainedModel, BertModel
from transformers.modeling_outputs import  TokenClassifierOutput
from torch import nn
from torch.nn import CrossEntropyLoss
import torch

from torchcrf import CRF
from transformers import BertTokenizerFast
from transformers import BertTokenizerFast, Trainer, TrainingArguments
from transformers.trainer_utils import IntervalStrategy

class BertCRF(BertPreTrainedModel):

    _keys_to_ignore_on_load_unexpected = [r"pooler"]

    def __init__(self, config):
        super().__init__(config)
        self.num_labels = config.num_labels

        self.bert = BertModel(config, add_pooling_layer=False)
        self.dropout = nn.Dropout(config.hidden_dropout_prob)
        self.classifier = nn.Linear(config.hidden_size, config.num_labels)
        self.crf = CRF(num_tags=config.num_labels, batch_first=True)
        self.init_weights()

    def forward(
        self,
        input_ids=None,
        attention_mask=None,
        token_type_ids=None,
        position_ids=None,
        head_mask=None,
        inputs_embeds=None,
        labels=None,
        output_attentions=None,
        output_hidden_states=None,
        return_dict=None,
    ):
        r"""
        labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
            Labels for computing the token classification loss. Indices should be in ``[0, ..., config.num_labels -
            1]``.
        """
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        outputs = self.bert(
            input_ids,
            attention_mask=attention_mask,
            token_type_ids=token_type_ids,
            position_ids=position_ids,
            head_mask=head_mask,
            inputs_embeds=inputs_embeds,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )

        sequence_output = outputs[0]
        sequence_output = self.dropout(sequence_output)
        logits = self.classifier(sequence_output)

        loss = None
        if labels is not None:
            log_likelihood, tags = self.crf(logits, labels), self.crf.decode(logits)
            loss = 0 - log_likelihood
        else:
            tags = self.crf.decode(logits)
        tags = torch.Tensor(tags)

        if not return_dict:
            output = (tags,) + outputs[2:]
            return ((loss,) + output) if loss is not None else output

        return loss, tags
```
Some sample output from the model

| Types              | Output                                                                                 |
|--------------------|----------------------------------------------------------------------------------------|
| English            | [{'words': ['my', 'name', 'is', 'swagat'], 'labels': ['DET', 'NN', 'VB', 'NN']}]       |
| Hindi              | [{'words': ['मेरा', 'नाम', 'स्वागत', 'है'], 'labels': ['PRP', 'NN', 'NNP', 'VM']}]        |
| Hindi Romanised    | [{'words': ['mera', 'naam', 'swagat', 'hai'], 'labels': [‘PRP', 'NN', 'NNP', 'VM']}]   |
| Gujarati           | [{'words': ['મારું', 'નામ', 'સ્વગત', 'છે'], 'labels': ['PRP', 'NN', 'NNP', 'VAUX']}]       |
| Gujarati Romanised | [{'words': ['maru', 'naam', 'swagat', 'che'], 'labels': ['PRP', 'NN', 'NNP', 'VAUX']}] |




- **Developed by:** Swagat Panda
- **Finetuned from model :** google/muril-base-cased

### Model Sources [optional]
- **Paper :** https://www.academia.edu/87916386/MULTILINGUAL_APPROACH_TOWARDS_THE_NATIVE_AND_ROMANISED_SCRIPTS_FOR_INDIAN_LANGUGE_CONTEXT_ON_POS_TAGGING?source=swp_share


{"language": ["en", "gu", "mr", "hi"], "license": "apache-2.0"}
transformers pytorch bert en gu mr hi license:apache-2.0 endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #en #gu #mr #hi #license-apache-2.0 #endpoints_compatible #region-us 

Model Card for Model ID
=======================


Model Details
-------------


The technique of marking the words in a phrase to their appropriate POS
tags is known as part-of-speech tagging (POS tagging or POST). There are
two sorts of POS tagging algorithms: rule-based and stochastic, and
monolingual and multilingual are different types from a modelling
standpoint. POS tags provide grammatical context to a sentence, which can
be employed in NLP tasks such as NER, NLU and QNA systems.
In this research field, a lot of researchers had already tried to propose
various novel approaches, tags and models like Weightless Artificial
Neural Network (WANN), different forms of CRF, Bi-LSTM CRF, and
transformers, various techniques for language tag mixed POS tags to
handle mixed languages. All this research work leads to the enhancement
or creating a benchmark for different popular and low resource languages,
In the state of monolingual or multilingual context. In this model
we are trying to achieve state-of-the-art model for the Indian language
context in both native and its Romanised format.


### Model Description


The model has been trained on the romanized forms of the Indian languages as well as English, Hindi, Gujarati, and Marathi.i.e(en,gu,mr,hi,gu\_romanised,mr\_romanised,hi\_romanised)


To use this model you have import this class


Some sample output from the model



* Developed by: Swagat Panda
* Finetuned from model : google/muril-base-cased


### Model Sources [optional]


* Paper : URL



### Model Description


The model has been trained on the romanized forms of the Indian languages as well as English, Hindi, Gujarati, and Marathi.i.e(en,gu,mr,hi,gu\_romanised,mr\_romanised,hi\_romanised)


To use this model you have import this class


Some sample output from the model



* Developed by: Swagat Panda
* Finetuned from model : google/muril-base-cased ### Model Sources [optional]


* Paper : URL
TAGS
#transformers #pytorch #bert #en #gu #mr #hi #license-apache-2.0 #endpoints_compatible #region-us 
 ### Model Description


The model has been trained on the romanized forms of the Indian languages as well as English, Hindi, Gujarati, and Marathi.i.e(en,gu,mr,hi,gu\_romanised,mr\_romanised,hi\_romanised)


To use this model you have import this class


Some sample output from the model



* Developed by: Swagat Panda
* Finetuned from model : google/muril-base-cased ### Model Sources [optional]


* Paper : URL
passage: TAGS
#transformers #pytorch #bert #en #gu #mr #hi #license-apache-2.0 #endpoints_compatible #region-us 
### Model Description


The model has been trained on the romanized forms of the Indian languages as well as English, Hindi, Gujarati, and Marathi.i.e(en,gu,mr,hi,gu\_romanised,mr\_romanised,hi\_romanised)


To use this model you have import this class


Some sample output from the model



* Developed by: Swagat Panda
* Finetuned from model : google/muril-base-cased### Model Sources [optional]


* Paper : URL