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https://api-inference.huggingface.co/models/rohanrajpal/bert-base-en-hi-codemix-cased
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rohanrajpal/bert-base-en-hi-codemix-cased rohanrajpal/bert-base-en-hi-codemix-cased
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pytorch

tf

Contributed by

rohanrajpal Rohan Rajpal
4 models

How to use this model directly from the πŸ€—/transformers library:

			
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from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("rohanrajpal/bert-base-en-hi-codemix-cased") model = AutoModelForSequenceClassification.from_pretrained("rohanrajpal/bert-base-en-hi-codemix-cased")

BERT codemixed base model for Hinglish (cased)

This model was built using lingualytics, an open-source library that supports code-mixed analytics.

Model description

Input for the model: Any codemixed Hinglish text Output for the model: Sentiment. (0 - Negative, 1 - Neutral, 2 - Positive)

I took a bert-base-multilingual-cased model from Huggingface and finetuned it on SAIL 2017 dataset.

Eval results

Performance of this model on the dataset

metric score
acc 0.55873
f1 0.558369
acc_and_f1 0.558549
precision 0.558075
recall 0.55873

How to use

Here is how to use this model to get the features of a given text in PyTorch:

# You can include sample code which will be formatted
from transformers import BertTokenizer, BertModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained('rohanrajpal/bert-base-en-es-codemix-cased')
model = AutoModelForSequenceClassification.from_pretrained('rohanrajpal/bert-base-en-es-codemix-cased')
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)

and in TensorFlow:

from transformers import BertTokenizer, TFBertModel
tokenizer = BertTokenizer.from_pretrained('rohanrajpal/bert-base-en-es-codemix-cased')
model = TFBertModel.from_pretrained('rohanrajpal/bert-base-en-es-codemix-cased')
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='tf')
output = model(encoded_input)

Preprocessing

Followed standard preprocessing techniques:

  • removed digits
  • removed punctuation
  • removed stopwords
  • removed excess whitespace Here's the snippet
from pathlib import Path
import pandas as pd
from lingualytics.preprocessing import remove_lessthan, remove_punctuation, remove_stopwords
from lingualytics.stopwords import hi_stopwords,en_stopwords
from texthero.preprocessing import remove_digits, remove_whitespace

root = Path('<path-to-data>')

for file in 'test','train','validation':
  tochange = root / f'{file}.txt'
  df = pd.read_csv(tochange,header=None,sep='\t',names=['text','label'])
  df['text'] = df['text'].pipe(remove_digits) \
                                    .pipe(remove_punctuation) \
                                    .pipe(remove_stopwords,stopwords=en_stopwords.union(hi_stopwords)) \
                                    .pipe(remove_whitespace)
  df.to_csv(tochange,index=None,header=None,sep='\t')

Training data

The dataset and annotations are not good, but this is the best dataset I could find. I am working on procuring my own dataset and will try to come up with a better model!

Training procedure

I trained on the dataset on the bert-base-multilingual-cased model.