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--- |
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license: apache-2.0 |
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base_model: google/mt5-small |
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tags: |
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- summarization |
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- generated_from_trainer |
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- music |
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- song-lyrics |
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metrics: |
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- rouge |
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model-index: |
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- name: mt5-small-finetuned-genius |
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results: [] |
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pipeline_tag: summarization |
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datasets: |
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- miscjose/genius-music |
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widget: |
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- text: > |
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Thought I'd end up with Sean |
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But he wasn't a match \n |
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Wrote some songs about Ricky |
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Now I listen and laugh |
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Even almost got married |
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And for Pete, I'm so thankful |
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Wish I could say, "Thank you" to Malcolm |
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'Cause he was an angel |
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One taught me love |
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One taught me patience |
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And one taught me pain |
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Now, I'm so amazing |
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Say I've loved and I've lost |
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But that's not what I see |
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So, look what I got |
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Look what you taught me |
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And for that, I say |
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Thank you, next (Next) |
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Thank you, next (Next) |
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Thank you, next |
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I'm so fuckin' grateful for my ex |
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Thank you, next (Next) |
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Thank you, next (Next) |
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Thank you, next (Next) |
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I'm so fuckin'— |
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Spend more time with my friends |
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I ain't worried 'bout nothin' |
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Plus, I met someone else |
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We havin' better discussions |
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I know they say I move on too fast |
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But this one gon' last |
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'Cause her name is Ari |
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And I'm so good with that (So good with that) |
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She taught me love (Love) |
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She taught me patience (Patience) |
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How she handles pain (Pain) |
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That shit's amazing (Yeah, she's amazing) |
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I've loved and I've lost (Yeah, yeah) |
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But that's not what I see (Yeah, yeah) |
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'Cause look what I've found (Yeah, yeah, I've found) |
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Ain't no need for searching, and for that, I say |
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Thank you, next (Thank you, next) |
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Thank you, next (Thank you, next) |
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Thank you, next (Thank you) |
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I'm so fuckin' grateful for my ex |
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Thank you, next (Thank you, next) |
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Thank you, next (Said thank you, next) |
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Thank you, next (Next) |
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I'm so fuckin' grateful for my ex |
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Thank you, next |
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Thank you, next |
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Thank you, next |
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I'm so fuckin'— |
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One day I'll walk down the aisle |
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Holding hands with my mama |
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I'll be thanking my dad |
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'Cause she grew from the drama |
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Only wanna do it once, real bad |
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Gon' make that shit last |
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God forbid something happens |
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Least this song is a smash (Song is a smash) |
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I've got so much love (Love) |
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Got so much patience (Patience) |
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And I've learned from the pain (Pain) |
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I turned out amazing (Turned out amazing) |
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Say I've loved and I've lost (Yeah, yeah) |
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But that's not what I see (Yeah, yeah) |
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'Cause look what I've found (Yeah, yeah) |
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Ain't no need for searching |
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And for that, I say |
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Thank you, next (Thank you, next) |
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Thank you, next (Thank you, next) |
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Thank you, next |
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I'm so fuckin' grateful for my ex |
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Thank you, next (Thank you, next) |
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Thank you, next (Said thank you, next) |
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Thank you, next (Next) |
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I'm so fuckin' grateful for my ex |
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Thank you, next |
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Thank you, next |
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Thank you, next |
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Yeah, yee |
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Thank you, next |
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Thank you, next |
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Thank you, next |
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Yeah, yee |
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--- |
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# mt5-small-finetuned-genius |
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This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the [Genius](https://genius.com/) Music dataset found [here](https://www.cs.cornell.edu/~arb/data/genius-expertise/). |
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The song lyrics and song titles were preprocessed and used for fine-tuning. |
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You can view more examples of this model's inference on the following [Space](https://huggingface.co/spaces/miscjose/song-title-generation). |
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## Model description |
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Please visit: [google/mt5-small](https://huggingface.co/google/mt5-small) |
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## Intended uses & limitations |
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- Intended Uses: Given song lyrics, generate a summary. |
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- Limitations: Due to the nature of music, the model can generate summaries containing hate speech. |
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## Training and evaluation data |
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- 27.6K Training Samples |
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- 3.45 Validation Samples |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 4e-05 |
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- train_batch_size: 32 |
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- eval_batch_size: 32 |
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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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- num_epochs: 5 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | |
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|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:| |
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| 7.9304 | 1.0 | 863 | 3.5226 | 14.235 | 6.78 | 14.206 | 14.168 | |
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| 3.8394 | 2.0 | 1726 | 3.0382 | 22.97 | 13.166 | 22.981 | 22.944 | |
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| 3.3799 | 3.0 | 2589 | 2.9010 | 24.932 | 14.54 | 24.929 | 24.919 | |
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| 3.2204 | 4.0 | 3452 | 2.8441 | 26.678 | 15.587 | 26.624 | 26.665 | |
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| 3.1498 | 5.0 | 4315 | 2.8363 | **26.827** | **15.696** | **26.773** | **26.793** | |
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### Framework versions |
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- Transformers 4.31.0 |
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- Pytorch 2.0.1+cu117 |
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- Datasets 2.14.1 |
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- Tokenizers 0.13.3 |
