arincon/ia-detection-bert-tiny

README.md

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+---
+license: mit
+tags:
+- generated_from_trainer
+datasets:
+- autextification2023
+metrics:
+- accuracy
+- f1
+- precision
+- recall
+model-index:
+- name: ia-detection-bert-tiny
+  results:
+  - task:
+      name: Text Classification
+      type: text-classification
+    dataset:
+      name: autextification2023
+      type: autextification2023
+      config: detection_en
+      split: train
+      args: detection_en
+    metrics:
+    - name: Accuracy
+      type: accuracy
+      value: 0.699019787467937
+    - name: F1
+      type: f1
+      value: 0.7522153927372828
+    - name: Precision
+      type: precision
+      value: 0.6506621436492922
+    - name: Recall
+      type: recall
+      value: 0.891331546023235
+---
+
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+# ia-detection-bert-tiny
+
+This model is a fine-tuned version of [prajjwal1/bert-tiny](https://huggingface.co/prajjwal1/bert-tiny) on the autextification2023 dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.9775
+- Accuracy: 0.6990
+- F1: 0.7522
+- Precision: 0.6507
+- Recall: 0.8913
+
+## Model description
+
+More information needed
+
+## Intended uses & limitations
+
+More information needed
+
+## Training and evaluation data
+
+More information needed
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 0.0001
+- train_batch_size: 8
+- eval_batch_size: 8
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_steps: 500
+- num_epochs: 10
+
+### Training results
+
+| Training Loss | Epoch | Step  | Validation Loss | Accuracy | F1     | Precision | Recall |
+|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:|
+| 0.3606        | 1.0   | 3808  | 0.4135          | 0.8068   | 0.8126 | 0.7795    | 0.8486 |
+| 0.39          | 2.0   | 7616  | 0.4197          | 0.8213   | 0.8147 | 0.8344    | 0.7959 |
+| 0.386         | 3.0   | 11424 | 0.5145          | 0.8210   | 0.8249 | 0.7977    | 0.8540 |
+| 0.277         | 4.0   | 15232 | 0.7962          | 0.8080   | 0.7887 | 0.8633    | 0.7259 |
+| 0.1913        | 5.0   | 19040 | 0.8833          | 0.8115   | 0.8001 | 0.8396    | 0.7642 |
+| 0.2053        | 6.0   | 22848 | 0.9313          | 0.8180   | 0.8070 | 0.8468    | 0.7708 |
+
+
+### Framework versions
+
+- Transformers 4.26.1
+- Pytorch 2.1.0+cu118
+- Datasets 2.14.6
+- Tokenizers 0.13.3

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special_tokens_map.json

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+  "unk_token": "[UNK]"
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tokenizer_config.json

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+  "do_lower_case": true,
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+  "name_or_path": "prajjwal1/bert-tiny",
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+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "unk_token": "[UNK]"
+}