transformers tensorboard safetensors distilbert text-classification generated_from_trainer dataset:ZephyrUtopia/ratemyprofessors-reviews-2-labels base_model:distilbert/distilbert-base-uncased base_model:finetune:distilbert/distilbert-base-uncased license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
---
base_model: distilbert-base-uncased
datasets:
- ZephyrUtopia/ratemyprofessors-reviews-2-labels
library_name: transformers
license: apache-2.0
metrics:
- accuracy
- f1
tags:
- generated_from_trainer
model-index:
- name: ratemyprofessors-reviews-sentiment-analysis-finetuning-5000-samples
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: ZephyrUtopia/ratemyprofessors-reviews-2-labels
      type: arrow
      config: default
      split: train
      args: default
    metrics:
    - type: accuracy
      value: 0.974
      name: Accuracy
    - type: f1
      value: 0.9823609226594301
      name: F1
---

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# ratemyprofessors-reviews-sentiment-analysis-5000


## Model description

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on 
the [ratemyprofessors-reviews-2-labels](https://huggingface.co/datasets/ZephyrUtopia/ratemyprofessors-reviews-2-labels) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1183
- Accuracy: 0.974
- F1: 0.9824

## Base model description

DistilBERT is a transformers model, smaller and faster than BERT, which was pretrained on the same corpus in a self-supervised fashion, using the BERT base model as a teacher. This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts using the BERT base model. More precisely, it was pretrained with three objectives:

Distillation loss: the model was trained to return the same probabilities as the BERT base model.
Masked language modeling (MLM): this is part of the original training loss of the BERT base model. When taking a sentence, the model randomly masks 15% of the words in the input then run the entire masked sentence through the model and has to predict the masked words. This is different from traditional recurrent neural networks (RNNs) that usually see the words one after the other, or from autoregressive models like GPT which internally mask the future tokens. It allows the model to learn a bidirectional representation of the sentence.
Cosine embedding loss: the model was also trained to generate hidden states as close as possible as the BERT base model.
This way, the model learns the same inner representation of the English language than its teacher model, while being faster for inference or downstream tasks.

## Training and evaluation data

The [ratemyprofessors-reviews-2-labels](https://huggingface.co/datasets/ZephyrUtopia/ratemyprofessors-reviews-2-labels) dataset
is a collection of reviews on RateMyProfessors gathered via webscraping. This model's finetuning used 5000 random rows of that dataset as
training data and 500 other random rows as test data.

## Intended uses & limitations

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-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
- num_epochs: 2

### Training results



### Framework versions

- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.1
- Tokenizers 0.19.1