# `BERT-tweet-eval-emotion` trained using autoNLP
- Problem type: Multi-class Classification

## Validation Metrics

- Loss: 0.5408923625946045
- Accuracy: 0.8099929627023223
- Macro F1: 0.7737195387641751
- Micro F1: 0.8099929627023222
- Weighted F1: 0.8063100677512649
- Macro Precision: 0.8083955817268176
- Micro Precision: 0.8099929627023223
- Weighted Precision: 0.8104009668394634
- Macro Recall: 0.7529197049888299
- Micro Recall: 0.8099929627023223
- Weighted Recall: 0.8099929627023223

## 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": "Worry is a down payment on a problem you may never have'. Joyce Meyer.  #motivation #leadership #worry"}' https://api-inference.huggingface.co/models/philschmid/BERT-tweet-eval-emotion
```

Or Python API:

```py
from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline

model_id = 'philschmid/BERT-tweet-eval-emotion'

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForSequenceClassification.from_pretrained(model_id)

classifier = pipeline('text-classification', tokenizer=tokenizer, model=model)
classifier("Worry is a down payment on a problem you may never have'. Joyce Meyer.  #motivation #leadership #worry")
```
{"language": "en", "tags": "autonlp", "datasets": ["tweet_eval"], "widget": [{"text": "Worry is a down payment on a problem you may never have'. Joyce Meyer.  #motivation #leadership #worry"}], "model-index": [{"name": "BERT-tweet-eval-emotion", "results": [{"task": {"type": "sentiment-analysis", "name": "Sentiment Analysis"}, "dataset": {"name": "tweeteval", "type": "tweet-eval"}, "metrics": [{"type": "accuracy", "value": 81.0, "name": "Accuracy"}, {"type": "macro-f1", "value": 77.37, "name": "Macro F1"}, {"type": "weighted-f1", "value": 80.63, "name": "Weighted F1"}]}]}]}
transformers pytorch bert text-classification autonlp en dataset:tweet_eval model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #text-classification #autonlp #en #dataset-tweet_eval #model-index #autotrain_compatible #endpoints_compatible #region-us 

# 'BERT-tweet-eval-emotion' trained using autoNLP
- Problem type: Multi-class Classification

## Validation Metrics

- Loss: 0.5408923625946045
- Accuracy: 0.8099929627023223
- Macro F1: 0.7737195387641751
- Micro F1: 0.8099929627023222
- Weighted F1: 0.8063100677512649
- Macro Precision: 0.8083955817268176
- Micro Precision: 0.8099929627023223
- Weighted Precision: 0.8104009668394634
- Macro Recall: 0.7529197049888299
- Micro Recall: 0.8099929627023223
- Weighted Recall: 0.8099929627023223

## Usage

You can use cURL to access this model:



Or Python API:


# 'BERT-tweet-eval-emotion' trained using autoNLP
- Problem type: Multi-class Classification ## Validation Metrics

- Loss: 0.5408923625946045
- Accuracy: 0.8099929627023223
- Macro F1: 0.7737195387641751
- Micro F1: 0.8099929627023222
- Weighted F1: 0.8063100677512649
- Macro Precision: 0.8083955817268176
- Micro Precision: 0.8099929627023223
- Weighted Precision: 0.8104009668394634
- Macro Recall: 0.7529197049888299
- Micro Recall: 0.8099929627023223
- Weighted Recall: 0.8099929627023223 ## Usage

You can use cURL to access this model:



Or Python API:
TAGS
#transformers #pytorch #bert #text-classification #autonlp #en #dataset-tweet_eval #model-index #autotrain_compatible #endpoints_compatible #region-us 
 # 'BERT-tweet-eval-emotion' trained using autoNLP
- Problem type: Multi-class Classification ## Validation Metrics

- Loss: 0.5408923625946045
- Accuracy: 0.8099929627023223
- Macro F1: 0.7737195387641751
- Micro F1: 0.8099929627023222
- Weighted F1: 0.8063100677512649
- Macro Precision: 0.8083955817268176
- Micro Precision: 0.8099929627023223
- Weighted Precision: 0.8104009668394634
- Macro Recall: 0.7529197049888299
- Micro Recall: 0.8099929627023223
- Weighted Recall: 0.8099929627023223 ## Usage

You can use cURL to access this model:



Or Python API:
passage: TAGS
#transformers #pytorch #bert #text-classification #autonlp #en #dataset-tweet_eval #model-index #autotrain_compatible #endpoints_compatible #region-us 
# 'BERT-tweet-eval-emotion' trained using autoNLP
- Problem type: Multi-class Classification## Validation Metrics

- Loss: 0.5408923625946045
- Accuracy: 0.8099929627023223
- Macro F1: 0.7737195387641751
- Micro F1: 0.8099929627023222
- Weighted F1: 0.8063100677512649
- Macro Precision: 0.8083955817268176
- Micro Precision: 0.8099929627023223
- Weighted Precision: 0.8104009668394634
- Macro Recall: 0.7529197049888299
- Micro Recall: 0.8099929627023223
- Weighted Recall: 0.8099929627023223## Usage

You can use cURL to access this model:



Or Python API: