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- ---
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- license: apache-2.0
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- base_model: distilbert/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: birthday-detector
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- results: []
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- ---
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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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-
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- # birthday-detector
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-
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- This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on an unknown dataset.
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- It achieves the following results on the evaluation set:
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- - Loss: 0.1084
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- - Accuracy: 0.9796
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-
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- ## Model description
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-
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- More information needed
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-
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- ## Intended uses & limitations
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-
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- More information needed
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-
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- ## Training and evaluation data
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-
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- More information needed
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-
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- ## Training procedure
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-
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- ### Training hyperparameters
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-
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- The following hyperparameters were used during training:
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- - learning_rate: 2e-05
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- - train_batch_size: 16
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- - eval_batch_size: 16
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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: 4
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-
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- ### Training results
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-
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- | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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- |:-------------:|:-----:|:----:|:---------------:|:--------:|
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- | No log | 1.0 | 58 | 0.1070 | 0.9796 |
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- | No log | 2.0 | 116 | 0.1378 | 0.9592 |
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- | No log | 3.0 | 174 | 0.1139 | 0.9592 |
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- | No log | 4.0 | 232 | 0.1084 | 0.9796 |
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-
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-
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- ### Framework versions
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-
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- - Transformers 4.40.2
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- - Pytorch 2.3.0+cpu
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- - Datasets 2.19.1
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- - Tokenizers 0.19.1
 
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+ ---
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+ license: mit
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+ base_model: distilbert/distilbert-base-uncased
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+ metrics:
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+ - accuracy
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+ model-index:
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+ - name: birthday-detector
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+ results: []
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+ datasets:
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+ - nroggendorff/doug
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+ language:
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+ - en
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+ pipeline_tag: text-classification
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+ ---
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+
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+ # Birthday Detector
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+
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+ This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased).
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+ It achieves the following results on the evaluation set:
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+
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+ - Loss: 0.0101
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+ - Accuracy: 1.0
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+
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+ You can easily run it with inference, or run it in python with:
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+
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+ ```
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+ from transformers import pipeline
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+ classifier = pipeline("sentiment-analysis", model="nroggendorff/birthday-detector")
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+
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+ isBirthday = classifier("happy birthday doug")[0]["label"] == "POSITIVE"
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+ ```
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+
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+ Make sure you have the necessary dependencies installed.
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+
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+ [This models inspiration](https://youtu.be/Q6fjwHPVqjQ)