Text Classification
Transformers
Safetensors
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use cloudwoowoo/zooguide_bert_checkpoints with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cloudwoowoo/zooguide_bert_checkpoints with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="cloudwoowoo/zooguide_bert_checkpoints")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("cloudwoowoo/zooguide_bert_checkpoints") model = AutoModelForSequenceClassification.from_pretrained("cloudwoowoo/zooguide_bert_checkpoints") - Notebooks
- Google Colab
- Kaggle
cloudwoowoo/zooguide-bert-animal-fact-assistant
Browse files- README.md +1 -18
- training_args.bin +1 -1
README.md
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base_model: distilbert-base-uncased
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tags:
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- generated_from_trainer
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metrics:
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- accuracy
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model-index:
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- name: zooguide_bert_checkpoints
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results: []
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# zooguide_bert_checkpoints
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This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.0869
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- Accuracy: 0.9667
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- Macro F1: 0.9663
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## Model description
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- seed: 42
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- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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- lr_scheduler_type: linear
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- num_epochs:
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | Macro F1 |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|
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| 1.2897 | 1.0 | 132 | 0.2416 | 0.9644 | 0.9647 |
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| 0.1339 | 2.0 | 264 | 0.1100 | 0.9689 | 0.9692 |
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| 0.0744 | 3.0 | 396 | 0.0700 | 0.98 | 0.9798 |
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| 0.0595 | 4.0 | 528 | 0.0626 | 0.9667 | 0.9667 |
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| 0.0518 | 5.0 | 660 | 0.0591 | 0.9711 | 0.9710 |
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### Framework versions
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base_model: distilbert-base-uncased
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tags:
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- generated_from_trainer
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model-index:
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- name: zooguide_bert_checkpoints
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results: []
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# zooguide_bert_checkpoints
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This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
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## Model description
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- seed: 42
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- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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- lr_scheduler_type: linear
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- num_epochs: 7
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### Framework versions
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training_args.bin
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size 5201
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