abb animal body city code color count country cremat currency date def desc dismed dist event exp food gr ind instru lang letter manner money mount ord other perc period plant product reason religion speed sport state substance symbol techmeth temp termeq title veh volsize weight word
---
tags: autotrain
language: en
widget:
- text: "I love AutoTrain 🤗"
datasets:
- ndavid/autotrain-data-trec-fine-bert
co2_eq_emissions: 0.02238820299105448
---

# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 739422530
- CO2 Emissions (in grams): 0.02238820299105448

## Validation Metrics

- Loss: 0.36623290181159973
- Accuracy: 0.9321753515301903
- Macro F1: 0.9066706944656866
- Micro F1: 0.9321753515301903
- Weighted F1: 0.9314858667247282
- Macro Precision: 0.9489233194839841
- Micro Precision: 0.9321753515301903
- Weighted Precision: 0.9347346558570125
- Macro Recall: 0.8842587178845419
- Micro Recall: 0.9321753515301903
- Weighted Recall: 0.9321753515301903


## 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/ndavid/autotrain-trec-fine-bert-739422530
```

Or Python API:

```
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("ndavid/autotrain-trec-fine-bert-739422530", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("ndavid/autotrain-trec-fine-bert-739422530", use_auth_token=True)

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

outputs = model(**inputs)
```