ACTIVATOR AGONIST AGONIST-ACTIVATOR AGONIST-INHIBITOR ANTAGONIST DOWNREGULATOR INDIRECT-DOWNREGULATOR INDIRECT-UPREGULATOR INHIBITOR PRODUCT-OF SUBSTRATE SUBSTRATE_PRODUCT-OF UPREGULATOR
---
tags: autotrain
language: en
widget:
- text: "I love AutoTrain 🤗"
datasets:
- pier297/autotrain-data-chemprot-re
co2_eq_emissions: 0.0911766483095575
---

# 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)
```