
# Model Trained Using AutoTrain

- Problem type: Multi-class Classification (3-class Sentiment Classification)

## Validation Metrics
If you search sentiment analysis model in huggingface you find a model from finiteautomata. Their model provides micro and macro F1 score around 67%. Check out this model with around 80% of macro and micro F1 score. 
- Loss: 0.4992932379245758
- Accuracy: 0.799017824663514
- Macro F1: 0.8021508522962549
- Micro F1: 0.799017824663514
- Weighted F1: 0.7993775463659935
- Macro Precision: 0.80406197665167
- Micro Precision: 0.799017824663514
- Weighted Precision: 0.8000374433849405
- Macro Recall: 0.8005261994732908
- Micro Recall: 0.799017824663514
- Weighted Recall: 0.799017824663514


## Usage

You can use cURL to access this model:

```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/Souvikcmsa/autotrain-sentiment_analysis-762923428
```

Or Python API:

```
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("Souvikcmsa/autotrain-sentiment_analysis-762923428", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("Souvikcmsa/autotrain-sentiment_analysis-762923428", use_auth_token=True)

inputs = tokenizer("I love AutoTrain", return_tensors="pt")

outputs = model(**inputs)
```
OR
```
from transformers import pipeline

classifier = pipeline("text-classification", model = "Souvikcmsa/BERT_sentiment_analysis")
classifier("I loved Star Wars so much!")# Positive
classifier("A soccer game with multiple males playing. Some men are playing a sport.")# Neutral
```
{"language": "en", "tags": "autotrain", "datasets": ["Souvikcmsa/autotrain-data-sentiment_analysis"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}, {"Output": "Positive"}], "co2_eq_emissions": 0.029363397844935534}
transformers pytorch bert text-classification autotrain en dataset:Souvikcmsa/autotrain-data-sentiment_analysis co2_eq_emissions autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #text-classification #autotrain #en #dataset-Souvikcmsa/autotrain-data-sentiment_analysis #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 


# Model Trained Using AutoTrain

- Problem type: Multi-class Classification (3-class Sentiment Classification)

## Validation Metrics
If you search sentiment analysis model in huggingface you find a model from finiteautomata. Their model provides micro and macro F1 score around 67%. Check out this model with around 80% of macro and micro F1 score. 
- Loss: 0.4992932379245758
- Accuracy: 0.799017824663514
- Macro F1: 0.8021508522962549
- Micro F1: 0.799017824663514
- Weighted F1: 0.7993775463659935
- Macro Precision: 0.80406197665167
- Micro Precision: 0.799017824663514
- Weighted Precision: 0.8000374433849405
- Macro Recall: 0.8005261994732908
- Micro Recall: 0.799017824663514
- Weighted Recall: 0.799017824663514


## Usage

You can use cURL to access this model:



Or Python API:


OR

# Model Trained Using AutoTrain

- Problem type: Multi-class Classification (3-class Sentiment Classification) ## Validation Metrics
If you search sentiment analysis model in huggingface you find a model from finiteautomata. Their model provides micro and macro F1 score around 67%. Check out this model with around 80% of macro and micro F1 score. 
- Loss: 0.4992932379245758
- Accuracy: 0.799017824663514
- Macro F1: 0.8021508522962549
- Micro F1: 0.799017824663514
- Weighted F1: 0.7993775463659935
- Macro Precision: 0.80406197665167
- Micro Precision: 0.799017824663514
- Weighted Precision: 0.8000374433849405
- Macro Recall: 0.8005261994732908
- Micro Recall: 0.799017824663514
- Weighted Recall: 0.799017824663514 ## Usage

You can use cURL to access this model:



Or Python API:


OR
TAGS
#transformers #pytorch #bert #text-classification #autotrain #en #dataset-Souvikcmsa/autotrain-data-sentiment_analysis #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 
 # Model Trained Using AutoTrain

- Problem type: Multi-class Classification (3-class Sentiment Classification) ## Validation Metrics
If you search sentiment analysis model in huggingface you find a model from finiteautomata. Their model provides micro and macro F1 score around 67%. Check out this model with around 80% of macro and micro F1 score. 
- Loss: 0.4992932379245758
- Accuracy: 0.799017824663514
- Macro F1: 0.8021508522962549
- Micro F1: 0.799017824663514
- Weighted F1: 0.7993775463659935
- Macro Precision: 0.80406197665167
- Micro Precision: 0.799017824663514
- Weighted Precision: 0.8000374433849405
- Macro Recall: 0.8005261994732908
- Micro Recall: 0.799017824663514
- Weighted Recall: 0.799017824663514 ## Usage

You can use cURL to access this model:



Or Python API:


OR
passage: TAGS
#transformers #pytorch #bert #text-classification #autotrain #en #dataset-Souvikcmsa/autotrain-data-sentiment_analysis #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 
# Model Trained Using AutoTrain

- Problem type: Multi-class Classification (3-class Sentiment Classification)## Validation Metrics
If you search sentiment analysis model in huggingface you find a model from finiteautomata. Their model provides micro and macro F1 score around 67%. Check out this model with around 80% of macro and micro F1 score. 
- Loss: 0.4992932379245758
- Accuracy: 0.799017824663514
- Macro F1: 0.8021508522962549
- Micro F1: 0.799017824663514
- Weighted F1: 0.7993775463659935
- Macro Precision: 0.80406197665167
- Micro Precision: 0.799017824663514
- Weighted Precision: 0.8000374433849405
- Macro Recall: 0.8005261994732908
- Micro Recall: 0.799017824663514
- Weighted Recall: 0.799017824663514## Usage

You can use cURL to access this model:



Or Python API:


OR