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--- |
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license: apache-2.0 |
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tags: |
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- generated_from_trainer |
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language: en |
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widget: |
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- 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." |
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- text: "The agent on the phone was very helpful and nice to me." |
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metrics: |
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- accuracy |
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- f1 |
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model-index: |
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- name: bert-base-uncased-finetuned-surveyclassification |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# bert-base-uncased-finetuned-surveyclassification |
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This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co./bert-base-uncased) on a custom survey dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2818 |
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- Accuracy: 0.9097 |
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- F1: 0.9097 |
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## Model description |
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More information needed |
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#### Limitations and bias |
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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. |
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#### How to use |
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You can use this model with Transformers *pipeline* for Text Classification. |
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```python |
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from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline |
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tokenizer = AutoTokenizer.from_pretrained("Jorgeutd/bert-base-uncased-finetuned-surveyclassification") |
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model = AutoModelForSequenceClassification.from_pretrained("Jorgeutd/bert-base-uncased-finetuned-surveyclassification") |
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text_classifier = pipeline("text-classification", model=model,tokenizer=tokenizer, device=0) |
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example = "The agent on the phone was very helpful and nice to me." |
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results = text_classifier(example) |
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print(results) |
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``` |
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## Training and evaluation data |
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Custom survey dataset. |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 3e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_steps: 100 |
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- num_epochs: 10 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| |
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| 0.4136 | 1.0 | 902 | 0.2818 | 0.9097 | 0.9097 | |
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| 0.2213 | 2.0 | 1804 | 0.2990 | 0.9077 | 0.9077 | |
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| 0.1548 | 3.0 | 2706 | 0.3507 | 0.9026 | 0.9026 | |
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| 0.1034 | 4.0 | 3608 | 0.4692 | 0.9011 | 0.9011 | |
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### Framework versions |
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- Transformers 4.16.2 |
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- Pytorch 1.8.1+cu111 |
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- Datasets 1.18.3 |
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- Tokenizers 0.11.0 |
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