text-classification-goemotions

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Text Classification GoEmotions

This model is a fine-tuned version of roberta-large on the go_emotions dataset.

Model description

At first, 4 epochs of training with a learning rate of 5e-5 was performed on the roberta-large model. After that, the weights were loaded in a new environment and another epoch of training was done (this time with a learning rate of 2e-5). As the performance decreased in the fifth epoch, further training was discontinued.

After the 4th epoch, the model achieved a macro-F1 score of 53% on the test set, but the fifth epoch reduced the performance. The model on commit "5b532728cef22ca9e9bacc8ff9f5687654d36bf3" attains the following scores on the test set:

• Accuracy: 0.4271236410539893
• Precision: 0.5101494353184485
• Recall: 0.5763722014150806
• macro-F1: 0.5297380709491947

Load this specific version of the model using the syntax below:

import os
from transformers import AutoTokenizer, AutoModelForSequenceClassification

os.environ["TOKENIZERS_PARALLELISM"] = "FALSE"

model_name = "tasinhoque/text-classification-goemotions"
commit = "5b532728cef22ca9e9bacc8ff9f5687654d36bf3"
tokenizer = AutoTokenizer.from_pretrained(model_name, revision=commit)

model = AutoModelForSequenceClassification.from_pretrained(
    model_name, 
    num_labels=n_emotion, 
    problem_type="multi_label_classification", 
    revision=commit
)

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 5e-05 (2e-5 in the 5th epoch)
• train_batch_size: 128
• eval_batch_size: 128
• seed: 42 (only in the 5th epoch)
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 5

Training results

Framework versions

• Transformers 4.20.1
• Pytorch 1.12.0
• Datasets 2.1.0
• Tokenizers 0.12.1