arincon/ia-detection-distilbert-base-cased

README.md

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+---
+license: apache-2.0
+tags:
+- generated_from_trainer
+datasets:
+- autextification2023
+metrics:
+- accuracy
+- f1
+- precision
+- recall
+model-index:
+- name: ia-detection-distilbert-base-cased
+  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: 0.6757969952363503
+    - name: F1
+      type: f1
+      value: 0.7481855699444998
+    - name: Precision
+      type: precision
+      value: 0.6215273673010995
+    - name: Recall
+      type: recall
+      value: 0.9396782841823056
+---
+
+<!-- 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-distilbert-base-cased
+
+This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on the autextification2023 dataset.
+It achieves the following results on the evaluation set:
+- Loss: 1.1147
+- Accuracy: 0.6758
+- F1: 0.7482
+- Precision: 0.6215
+- Recall: 0.9397
+
+## 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 |
+|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:|
+| 0.4298        | 1.0   | 3808  | 0.5010          | 0.7725   | 0.8114 | 0.6964    | 0.9718 |
+| 0.4464        | 2.0   | 7616  | 0.4737          | 0.8514   | 0.8531 | 0.8493    | 0.8568 |
+| 0.4296        | 3.0   | 11424 | 0.4870          | 0.8402   | 0.8424 | 0.8363    | 0.8486 |
+| 0.2034        | 4.0   | 15232 | 0.5404          | 0.8493   | 0.8510 | 0.8475    | 0.8545 |
+| 0.0803        | 5.0   | 19040 | 0.6954          | 0.8520   | 0.8491 | 0.8724    | 0.8269 |
+
+
+### 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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+{
+  "cls_token": "[CLS]",
+  "mask_token": "[MASK]",
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "unk_token": "[UNK]"
+}

tokenizer_config.json

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+{
+  "cls_token": "[CLS]",
+  "do_lower_case": false,
+  "mask_token": "[MASK]",
+  "model_max_length": 512,
+  "name_or_path": "distilbert-base-cased",
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "special_tokens_map_file": null,
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "DistilBertTokenizer",
+  "unk_token": "[UNK]"
+}