Text Classification
Transformers
PyTorch
Safetensors
Arabic
bert
hate-speech
gender-based-violence
arabic
binary-classification
pilot
Eval Results (legacy)
text-embeddings-inference
Instructions to use thejosango/nuha-ajp-binary with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use thejosango/nuha-ajp-binary with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="thejosango/nuha-ajp-binary")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("thejosango/nuha-ajp-binary") model = AutoModelForSequenceClassification.from_pretrained("thejosango/nuha-ajp-binary") - Notebooks
- Google Colab
- Kaggle
binary-19
Browse files- README.md +15 -12
- config.toml +1 -1
- pytorch_model.bin +1 -1
- training_args.bin +1 -1
README.md
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metrics:
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- name: F1
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type: f1
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value: 0.
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- name: Precision
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type: precision
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- name: Recall
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type: recall
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value: 0.
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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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This model is a fine-tuned version of [thejosango/nuha-mlm](https://huggingface.co/thejosango/nuha-mlm) on the nuha-dataset dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.
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- F1: 0.
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- Precision: 0.
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- Support: None
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## Model description
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: constant
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- lr_scheduler_warmup_steps: 1000.0
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- num_epochs:
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- label_smoothing_factor: 0.1
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### Training results
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| Training Loss | Epoch | Step | Validation Loss |
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| 2.4774 | 1.96 | 500 | 0.
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| 1.4697 | 3.92 | 1000 | 0.
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### Framework versions
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metrics:
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- name: F1
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type: f1
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value: 0.6647587898609976
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- name: Precision
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type: precision
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value: 0.5703262013328657
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- name: Recall
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type: recall
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value: 0.7966682998530132
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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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This model is a fine-tuned version of [thejosango/nuha-mlm](https://huggingface.co/thejosango/nuha-mlm) on the nuha-dataset dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.8563
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- F1: 0.6648
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- Precision: 0.5703
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- Recall: 0.7967
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- Support: None
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## Model description
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: constant
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- lr_scheduler_warmup_steps: 1000.0
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- num_epochs: 10
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- label_smoothing_factor: 0.1
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### Training results
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| Training Loss | Epoch | Step | F1 | Validation Loss | Precision | Recall | Support |
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|:-------------:|:-----:|:----:|:------:|:---------------:|:---------:|:------:|:-------:|
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| 2.4774 | 1.96 | 500 | 0.6241 | 0.8233 | 0.6118 | 0.6369 | None |
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| 1.4697 | 3.92 | 1000 | 0.6530 | 0.8029 | 0.6085 | 0.7046 | None |
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| 1.35 | 5.88 | 1500 | 0.8659 | 0.6592 | 0.5726 | 0.7766 | None |
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| 1.2811 | 7.84 | 2000 | 0.8426 | 0.6305 | 0.6559 | 0.6071 | None |
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| 1.1341 | 9.8 | 2500 | 0.8563 | 0.6648 | 0.5703 | 0.7967 | None |
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### Framework versions
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config.toml
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[training]
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num_train_epochs =
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warmup_steps = 1e3
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lr_scheduler_type = "constant"
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learning_rate = 2e-5
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[training]
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num_train_epochs = 10
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warmup_steps = 1e3
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lr_scheduler_type = "constant"
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learning_rate = 2e-5
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pytorch_model.bin
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training_args.bin
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