
# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 838426740
- CO2 Emissions (in grams): 0.0911766483095575

## Validation Metrics

- Loss: 0.3866589665412903
- Accuracy: 0.9137332672285573
- Macro F1: 0.6518117007658014
- Micro F1: 0.9137332672285573
- Weighted F1: 0.9110993117549759
- Macro Precision: 0.649358664024301
- Micro Precision: 0.9137332672285573
- Weighted Precision: 0.9091854625539633
- Macro Recall: 0.6551854233645032
- Micro Recall: 0.9137332672285573
- Weighted Recall: 0.9137332672285573


## 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/pier297/autotrain-chemprot-re-838426740
```

Or Python API:

```
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("pier297/autotrain-chemprot-re-838426740", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("pier297/autotrain-chemprot-re-838426740", use_auth_token=True)

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

outputs = model(**inputs)
```
{"language": "en", "tags": "autotrain", "datasets": ["pier297/autotrain-data-chemprot-re"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 0.0911766483095575}
transformers pytorch bert text-classification autotrain en dataset:pier297/autotrain-data-chemprot-re co2_eq_emissions autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #text-classification #autotrain #en #dataset-pier297/autotrain-data-chemprot-re #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 


# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 838426740
- CO2 Emissions (in grams): 0.0911766483095575

## Validation Metrics

- Loss: 0.3866589665412903
- Accuracy: 0.9137332672285573
- Macro F1: 0.6518117007658014
- Micro F1: 0.9137332672285573
- Weighted F1: 0.9110993117549759
- Macro Precision: 0.649358664024301
- Micro Precision: 0.9137332672285573
- Weighted Precision: 0.9091854625539633
- Macro Recall: 0.6551854233645032
- Micro Recall: 0.9137332672285573
- Weighted Recall: 0.9137332672285573


## Usage

You can use cURL to access this model:



Or Python API:


# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 838426740
- CO2 Emissions (in grams): 0.0911766483095575 ## Validation Metrics

- Loss: 0.3866589665412903
- Accuracy: 0.9137332672285573
- Macro F1: 0.6518117007658014
- Micro F1: 0.9137332672285573
- Weighted F1: 0.9110993117549759
- Macro Precision: 0.649358664024301
- Micro Precision: 0.9137332672285573
- Weighted Precision: 0.9091854625539633
- Macro Recall: 0.6551854233645032
- Micro Recall: 0.9137332672285573
- Weighted Recall: 0.9137332672285573 ## Usage

You can use cURL to access this model:



Or Python API:
TAGS
#transformers #pytorch #bert #text-classification #autotrain #en #dataset-pier297/autotrain-data-chemprot-re #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 
 # Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 838426740
- CO2 Emissions (in grams): 0.0911766483095575 ## Validation Metrics

- Loss: 0.3866589665412903
- Accuracy: 0.9137332672285573
- Macro F1: 0.6518117007658014
- Micro F1: 0.9137332672285573
- Weighted F1: 0.9110993117549759
- Macro Precision: 0.649358664024301
- Micro Precision: 0.9137332672285573
- Weighted Precision: 0.9091854625539633
- Macro Recall: 0.6551854233645032
- Micro Recall: 0.9137332672285573
- Weighted Recall: 0.9137332672285573 ## Usage

You can use cURL to access this model:



Or Python API:
passage: TAGS
#transformers #pytorch #bert #text-classification #autotrain #en #dataset-pier297/autotrain-data-chemprot-re #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 
# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 838426740
- CO2 Emissions (in grams): 0.0911766483095575## Validation Metrics

- Loss: 0.3866589665412903
- Accuracy: 0.9137332672285573
- Macro F1: 0.6518117007658014
- Micro F1: 0.9137332672285573
- Weighted F1: 0.9110993117549759
- Macro Precision: 0.649358664024301
- Micro Precision: 0.9137332672285573
- Weighted Precision: 0.9091854625539633
- Macro Recall: 0.6551854233645032
- Micro Recall: 0.9137332672285573
- Weighted Recall: 0.9137332672285573## Usage

You can use cURL to access this model:



Or Python API: