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--- |
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license: apache-2.0 |
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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: infoquality |
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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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# infoquality |
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This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on a custom dataset curated by the model engineer. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0015 |
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- Accuracy: 0.9999 |
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## Model description |
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A binary classifier of text inputs (messages) designed to represent the quality of information with `"High"` and `"Low"` categories. |
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- **`High`** represents meaningful natural language |
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- **`Low`** represents cliché or otherwise meaningless natural language |
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## Intended uses & limitations |
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Designed for natural language detection and/or weighting of natural language messages. |
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## Training and evaluation data |
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Algorithmically curated from millions of publicly available social messages and, in some cases, programatically generated to reflect |
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theoretical design principles. |
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## Training procedure |
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```py |
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# label maps |
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id2label = {0: "low", 1: "high"} |
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label2id = {"low": 0, "high": 1} |
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# auto model |
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model = AutoModelForSequenceClassification.from_pretrained( |
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"distilbert-base-uncased", |
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num_labels=2, |
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id2label=id2label, |
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label2id=label2id, |
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) |
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``` |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 5e-05 |
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- train_batch_size: 10 |
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- eval_batch_size: 10 |
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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: 0.2 |
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### Training results |
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| Epoch | Step | Val. Loss | Accuracy | |
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|:-----:|:----:|:---------:|:--------:| |
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| 0.01 | 10 | 0.4780 | 0.96 | |
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| 0.02 | 20 | 0.1759 | 0.965 | |
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| 0.03 | 30 | 0.0477 | 0.995 | |
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| 0.04 | 40 | 0.1199 | 0.95 | |
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| 0.05 | 50 | 0.0413 | 0.99 | |
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| 0.06 | 60 | 0.0068 | 1.0 | |
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| 0.07 | 70 | 0.0056 | 1.0 | |
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| 0.08 | 80 | 0.0220 | 0.995 | |
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| 0.09 | 90 | 0.0081 | 1.0 | |
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| 0.1 | 100 | 0.0074 | 0.995 | |
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| 0.11 | 110 | 0.0035 | 1.0 | |
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| 0.12 | 120 | 0.0030 | 1.0 | |
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| 0.13 | 130 | 0.0022 | 1.0 | |
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| 0.14 | 140 | 0.0024 | 1.0 | |
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| 0.15 | 150 | 0.0021 | 1.0 | |
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| 0.16 | 160 | 0.0016 | 1.0 | |
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| 0.17 | 170 | 0.0016 | 1.0 | |
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| 0.18 | 180 | 0.0016 | 1.0 | |
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| 0.19 | 190 | 0.0015 | 1.0 | |
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| 0.2 | 200 | 0.0015 | 1.0 | |
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### Framework versions |
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- Transformers 4.32.1 |
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- Pytorch 2.0.1 |
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- Datasets 2.14.4 |
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- Tokenizers 0.13.3 |
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