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--- |
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license: mit |
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tags: |
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- generated_from_trainer |
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datasets: |
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- go_emotions |
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metrics: |
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- f1 |
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model-index: |
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- name: text-classification-goemotions |
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results: |
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- task: |
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name: Text Classification |
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type: text-classification |
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dataset: |
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name: go_emotions |
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type: multilabel_classification |
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config: simplified |
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split: test |
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args: simplified |
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metrics: |
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- name: F1 |
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type: f1 |
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value: 0.5072 |
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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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# Text Classification GoEmotions |
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This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the [go_emotions](https://huggingface.co/datasets/go_emotions) dataset. |
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## Model description |
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At first, 4 epochs of training with a learning rate of 5e-5 was performed on the `roberta-large` model. |
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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). |
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As the performance decreased in the fifth epoch, further training was discontinued. |
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After the 4th epoch, the model achieved a macro-F1 score of 53% on the test set, but the fifth epoch reduced the performance. |
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The model on commit "5b532728cef22ca9e9bacc8ff9f5687654d36bf3" attains the following scores on the test set: |
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- Accuracy: 0.4271236410539893 |
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- Precision: 0.5101494353184485 |
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- Recall: 0.5763722014150806 |
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- macro-F1: 0.5297380709491947 |
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Load this specific version of the model using the syntax below: |
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```py |
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import os |
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from transformers import AutoTokenizer, AutoModelForSequenceClassification |
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os.environ["TOKENIZERS_PARALLELISM"] = "FALSE" |
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model_name = "tasinhoque/text-classification-goemotions" |
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commit = "5b532728cef22ca9e9bacc8ff9f5687654d36bf3" |
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tokenizer = AutoTokenizer.from_pretrained(model_name, revision=commit) |
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model = AutoModelForSequenceClassification.from_pretrained( |
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model_name, |
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num_labels=n_emotion, |
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problem_type="multi_label_classification", |
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revision=commit |
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) |
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``` |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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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: 5e-05 (2e-5 in the 5th epoch) |
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- train_batch_size: 128 |
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- eval_batch_size: 128 |
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- seed: 42 (only in the 5th epoch) |
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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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- num_epochs: 5 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| |
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| No log | 1.0 | 340 | 0.0884 | 0.3782 | 0.4798 | 0.4643 | 0.4499 | |
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| 0.1042 | 2.0 | 680 | 0.0829 | 0.4093 | 0.4766 | 0.5272 | 0.4879 | |
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| 0.1042 | 3.0 | 1020 | 0.0821 | 0.4202 | 0.5103 | 0.5531 | 0.5092 | |
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| 0.0686 | 4.0 | 1360 | 0.0830 | 0.4327 | 0.5160 | 0.5556 | 0.5226 | |
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| No log | 5.0 | 1700 | 0.0961 | 0.4521 | 0.5190 | 0.5359 | 0.5218 | |
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### Framework versions |
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- Transformers 4.20.1 |
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- Pytorch 1.12.0 |
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- Datasets 2.1.0 |
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- Tokenizers 0.12.1 |
