
---
  <b>BYRD'S I - ROBERTA BASED TWEET/REVIEW/TEXT ANALYSIS</b>
---

This is ro<b>BERT</b>a-base model fine tuned on 8 datasets with ~20 M tweets this model is suitable for english while can do a fine job on other languages.

<b>Git Repo:</b><a href = "https://github.com/Caffeine-Coders/Sentiment-Analysis-Project"> SENTIMENTANALYSIS-PROJECT</a>

<b>Demo:</b><a href = "https://byrdi.netlify.app/"> BYRD'S I</a>

<b>labels: </b> 
 0 -> Negative; 
 1 -> Neutral; 
 2 -> Positive;
 
<b>Model Metrics</b><br/>
<b>Accuracy: </b> ~96% <br/>
<b>Sparse Categorical Accuracy: </b> 0.9597 <br/>
<b>Loss: </b> 0.1144 <br/>
<b>val_loss -- [onLast_train] : </b> 0.1482 <br/>
<b>Note: </b> 
Due to dataset discrepencies of Neutral data we published another model <a href = "https://huggingface.co/AK776161/birdseye_roberta-base-18">
Byrd's I only positive_negative model</a> to find only neutral data and have used 
<b>AdaBoot</b> method to get the accurate output. 
# Example of Classification:
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification, AutoModelForSeq2SeqLM
from transformers import TFAutoModelForSequenceClassification
import pandas as pd
import numpy as np
import tensorflow

# model 0
tokenizer = AutoTokenizer.from_pretrained("AK776161/birdseye_roberta-base-18", use_fast = True)
model = AutoModelForSequenceClassification.from_pretrained("AK776161/birdseye_roberta-base-18", from_tf=True)
# model1 
tokenizer1 = AutoTokenizer.from_pretrained("AK776161/birdseye_roberta-base-tweet-eval", use_fast = True)
model1 = AutoModelForSequenceClassification.from_pretrained("AK776161/birdseye_roberta-base-tweet-eval",from_tf=True)

#-----------------------Adaboot technique---------------------------
def nparraymeancalc(arr1, arr2):
  returner = []
  for i in range(0,len(arr1)):
    if(arr1[i][1] < -7):
      arr1[i][1] = 0
    returner.append(np.mean([arr1[i],arr2[i]], axis = 0))
    
  return np.array(returner)

def predictions(tokenizedtext):
  output1 = model(**tokenizedtext)
  output2 = model1(**tokenizedtext)

  logits1 = output1.logits
  logits1 = logits1.detach().numpy()

  logits2 = output2.logits
  logits2 = logits2.detach().numpy()
  
  # print(logits1, logits2)
  predictionresult = nparraymeancalc(logits1,logits2)

  return np.array(predictionresult)

def labelassign(predictionresult):
  labels = []
  for i in predictionresult:
    label_id = i.argmax()
    labels.append(label_id)
  return labels

tokenizeddata = tokenizer("----YOUR_TEXT---", return_tensors = 'pt', padding = True, truncation = True)
result = predictions(tokenizeddata)

print(labelassign(result))
```
Output for "I LOVE YOU":
```
1) Positive: 0.994
2) Negative: 0.000
3) Neutral: 0.006
```




{"language": ["en"], "license": "afl-3.0", "library_name": "transformers", "tags": ["textclassisification", "roberta", "robertabase", "sentimentanalysis", "nlp", "tweetanalysis", "tweet", "analysis", "sentiment", "positive", "newsanalysis"], "datasets": ["tweet_eval", "sentiment140", "mteb/tweet_sentiment_extraction", "yelp_review_full", "amazon_polarity"], "metrics": ["accuracy", "sparse_val accuracy", "sparse_val categorical accuracy"], "pipeline_tag": "text-classification"}
transformers tf roberta text-classification textclassisification robertabase sentimentanalysis nlp tweetanalysis tweet analysis sentiment positive newsanalysis en dataset:tweet_eval dataset:sentiment140 dataset:mteb/tweet_sentiment_extraction dataset:yelp_review_full dataset:amazon_polarity license:afl-3.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #tf #roberta #text-classification #textclassisification #robertabase #sentimentanalysis #nlp #tweetanalysis #tweet #analysis #sentiment #positive #newsanalysis #en #dataset-tweet_eval #dataset-sentiment140 #dataset-mteb/tweet_sentiment_extraction #dataset-yelp_review_full #dataset-amazon_polarity #license-afl-3.0 #autotrain_compatible #endpoints_compatible #region-us 


---
  <b>BYRD'S I - ROBERTA BASED TWEET/REVIEW/TEXT ANALYSIS</b>
---

This is ro<b>BERT</b>a-base model fine tuned on 8 datasets with ~20 M tweets this model is suitable for english while can do a fine job on other languages.

<b>Git Repo:</b><a href = "URL SENTIMENTANALYSIS-PROJECT</a>

<b>Demo:</b><a href = "URL BYRD'S I</a>

<b>labels: </b> 
 0 -> Negative; 
 1 -> Neutral; 
 2 -> Positive;
 
<b>Model Metrics</b><br/>
<b>Accuracy: </b> ~96% <br/>
<b>Sparse Categorical Accuracy: </b> 0.9597 <br/>
<b>Loss: </b> 0.1144 <br/>
<b>val_loss -- [onLast_train] : </b> 0.1482 <br/>
<b>Note: </b> 
Due to dataset discrepencies of Neutral data we published another model <a href = "URL
Byrd's I only positive_negative model</a> to find only neutral data and have used 
<b>AdaBoot</b> method to get the accurate output. 
# Example of Classification:

Output for "I LOVE YOU":





# Example of Classification:

Output for "I LOVE YOU":
TAGS
#transformers #tf #roberta #text-classification #textclassisification #robertabase #sentimentanalysis #nlp #tweetanalysis #tweet #analysis #sentiment #positive #newsanalysis #en #dataset-tweet_eval #dataset-sentiment140 #dataset-mteb/tweet_sentiment_extraction #dataset-yelp_review_full #dataset-amazon_polarity #license-afl-3.0 #autotrain_compatible #endpoints_compatible #region-us 
 # Example of Classification:

Output for "I LOVE YOU":
TAGS
#transformers #tf #roberta #text-classification #textclassisification #robertabase #sentimentanalysis #nlp #tweetanalysis #tweet #analysis #sentiment #positive #newsanalysis #en #dataset-tweet_eval #dataset-sentiment140 #dataset-mteb/tweet_sentiment_extraction #dataset-yelp_review_full #dataset-amazon_polarity #license-afl-3.0 #autotrain_compatible #endpoints_compatible #region-us 
# Example of Classification:

Output for "I LOVE YOU":