metadata

license: apache-2.0
tags:
  - generated_from_trainer
language: en
widget:
  - text: >-
      i have been in contact today with the insurance at least 3 times the
      person i had originally spoken to eileen i believe her name was she was
      very helpful. the two reps i got after her didnaaaTMt help me solve the
      issue i was calling about at all. i have been playing middle man between
      my insurance and doctors office and i have been getting told two different
      things. all i wanted was for them to get in contact with each other about
      the issue simple as that. the last person i just spoke to was extremely
      rude. iaaaTMm very disappointed with the service provided today. iaaaTMm
      the one paying for the insurance i just wanted to be led in the right
      direction on what to do.
  - text: The agent on the phone was very helpful and nice to me.
metrics:
  - accuracy
  - f1
model-index:
  - name: bert-base-uncased-finetuned-surveyclassification
    results: []

bert-base-uncased-finetuned-surveyclassification

This model is a fine-tuned version of bert-base-uncased on a custom survey dataset. It achieves the following results on the evaluation set:

Loss: 0.2818
Accuracy: 0.9097
F1: 0.9097

Model description

More information needed

Limitations and bias

This model is limited by its training dataset of survey results for a particular customer service domain. This may not generalize well for all use cases in different domains.

How to use

You can use this model with Transformers pipeline for Text Classification.

from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
tokenizer = AutoTokenizer.from_pretrained("Jorgeutd/bert-base-uncased-finetuned-surveyclassification")
model = AutoModelForSequenceClassification.from_pretrained("Jorgeutd/bert-base-uncased-finetuned-surveyclassification")
text_classifier = pipeline("text-classification", model=model,tokenizer=tokenizer, device=0)
example = "The agent on the phone was very helpful and nice to me."
results = text_classifier(example)
print(results)

Training and evaluation data

Custom survey dataset.

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 3e-05
train_batch_size: 16
eval_batch_size: 16
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 100
num_epochs: 10
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1
0.4136	1.0	902	0.2818	0.9097	0.9097
0.2213	2.0	1804	0.2990	0.9077	0.9077
0.1548	3.0	2706	0.3507	0.9026	0.9026
0.1034	4.0	3608	0.4692	0.9011	0.9011

Framework versions

Transformers 4.16.2
Pytorch 1.8.1+cu111
Datasets 1.18.3
Tokenizers 0.11.0