metadata

language:
  - en
tags:
  - autotrain
  - text-classification
datasets:
  - davanstrien/autotrain-data-dataset-mentions
widget:
  - text: ' frases-bertimbau-v0.4 This model is a fine-tuned version of [neuralmind/bert-base-portuguese-cased](https://huggingface.co/neuralmind/bert-base-portuguese-cased) on an unknown dataset.'
  - text: >-
      Model description BERTa is a transformer-based masked language model for
      the Catalan language. It is based on the
      [RoBERTA](https://github.com/pytorch/fairseq/tree/master/examples/roberta)
      base model and has been trained on a medium-size corpus collected from
      publicly available corpora and crawlers
  - text: Model description More information needed
co2_eq_emissions:
  emissions: 0.008999666562870793
base_model: neuralmind/bert-base-portuguese-cased

Model Trained Using AutoTrain

Problem type: Binary Classification
Model ID: 3390592983
CO2 Emissions (in grams): 0.0090

Validation Metrics

Loss: 0.014
Accuracy: 0.997
Precision: 0.998
Recall: 0.997
AUC: 1.000
F1: 0.998

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/davanstrien/autotrain-dataset-mentions-3390592983

Or Python API:

from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("davanstrien/autotrain-dataset-mentions-3390592983", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("davanstrien/autotrain-dataset-mentions-3390592983", use_auth_token=True)

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

outputs = model(**inputs)