Instructions to use somukandula/maskara-tiny-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use somukandula/maskara-tiny-v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="somukandula/maskara-tiny-v2")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("somukandula/maskara-tiny-v2") model = AutoModelForTokenClassification.from_pretrained("somukandula/maskara-tiny-v2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
End of training
Browse files- README.md +73 -0
- model.safetensors +1 -1
README.md
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---
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library_name: transformers
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license: mit
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base_model: somukandula/maskara-tiny
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tags:
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- generated_from_trainer
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metrics:
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- precision
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- recall
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- f1
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model-index:
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- name: maskara-tiny-v2
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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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# maskara-tiny-v2
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This model is a fine-tuned version of [somukandula/maskara-tiny](https://huggingface.co/somukandula/maskara-tiny) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 1.0869
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- Precision: 0.0225
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- Recall: 0.4320
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- F1: 0.0428
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## Model description
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More information needed
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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: 8e-06
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- train_batch_size: 32
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- eval_batch_size: 64
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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_ratio: 0.06
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- num_epochs: 2
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 |
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|:-------------:|:------:|:----:|:---------------:|:---------:|:------:|:------:|
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| 1.2055 | 0.0936 | 1000 | 1.4326 | 0.0151 | 0.3009 | 0.0288 |
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| 1.0776 | 0.1873 | 2000 | 1.2621 | 0.0191 | 0.3655 | 0.0364 |
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| 0.9771 | 0.2809 | 3000 | 1.1981 | 0.0211 | 0.4012 | 0.0400 |
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| 0.9773 | 0.3745 | 4000 | 1.1648 | 0.0215 | 0.4185 | 0.0408 |
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| 0.942 | 0.4682 | 5000 | 1.1355 | 0.0228 | 0.4359 | 0.0434 |
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| 0.9314 | 0.5618 | 6000 | 1.1103 | 0.0230 | 0.4359 | 0.0437 |
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| 0.9201 | 0.6554 | 7000 | 1.1048 | 0.0221 | 0.4301 | 0.0420 |
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| 0.9042 | 0.7491 | 8000 | 1.0869 | 0.0225 | 0.4320 | 0.0428 |
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### Framework versions
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- Transformers 4.44.2
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- Pytorch 2.4.0+cu121
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- Datasets 2.21.0
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- Tokenizers 0.19.1
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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-
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size 90327228
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version https://git-lfs.github.com/spec/v1
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size 90327228
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