Acc0.8520599250936329, F10.8500507249833522 , Augmented with bert-base-uncased.csv, finetuned on google/electra-base-discriminator

README.md

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+---
+license: apache-2.0
+base_model: google/electra-base-discriminator
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+- f1
+- precision
+- recall
+model-index:
+- name: electra-base-discriminator_bert-base-uncased
+  results: []
+---
+
+<!-- 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. -->
+
+# electra-base-discriminator_bert-base-uncased
+
+This model is a fine-tuned version of [google/electra-base-discriminator](https://huggingface.co/google/electra-base-discriminator) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.5398
+- Accuracy: 0.8705
+- F1: 0.8691
+- Precision: 0.8729
+- Recall: 0.8705
+
+## 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: 64
+- eval_batch_size: 64
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_steps: 1000
+- num_epochs: 25
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
+| 0.9217        | 1.0   | 91   | 0.8648          | 0.6459   | 0.5998 | 0.6333    | 0.6459 |
+| 0.5726        | 2.0   | 182  | 0.5369          | 0.8066   | 0.8064 | 0.8238    | 0.8066 |
+| 0.3522        | 3.0   | 273  | 0.4095          | 0.8440   | 0.8415 | 0.8477    | 0.8440 |
+| 0.2589        | 4.0   | 364  | 0.5367          | 0.8097   | 0.8069 | 0.8258    | 0.8097 |
+| 0.2718        | 5.0   | 455  | 0.4216          | 0.8612   | 0.8621 | 0.8670    | 0.8612 |
+| 0.164         | 6.0   | 546  | 0.5346          | 0.8612   | 0.8602 | 0.8616    | 0.8612 |
+| 0.1075        | 7.0   | 637  | 0.5398          | 0.8705   | 0.8691 | 0.8729    | 0.8705 |
+| 0.1461        | 8.0   | 728  | 0.6163          | 0.8362   | 0.8368 | 0.8442    | 0.8362 |
+| 0.132         | 9.0   | 819  | 0.4933          | 0.8674   | 0.8675 | 0.8701    | 0.8674 |
+| 0.1359        | 10.0  | 910  | 0.7141          | 0.8424   | 0.8416 | 0.8489    | 0.8424 |
+| 0.0971        | 11.0  | 1001 | 0.5662          | 0.8596   | 0.8578 | 0.8623    | 0.8596 |
+| 0.1148        | 12.0  | 1092 | 0.5685          | 0.8612   | 0.8609 | 0.8610    | 0.8612 |
+
+
+### Framework versions
+
+- Transformers 4.37.0
+- Pytorch 2.1.2
+- Datasets 2.1.0
+- Tokenizers 0.15.1

config.json

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+{
+  "_name_or_path": "google/electra-base-discriminator",
+  "architectures": [
+    "ElectraForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "embedding_size": 768,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "id2label": {
+    "0": "LABEL_0",
+    "1": "LABEL_1",
+    "2": "LABEL_2"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "LABEL_0": 0,
+    "LABEL_1": 1,
+    "LABEL_2": 2
+  },
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "electra",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "problem_type": "single_label_classification",
+  "summary_activation": "gelu",
+  "summary_last_dropout": 0.1,
+  "summary_type": "first",
+  "summary_use_proj": true,
+  "torch_dtype": "float32",
+  "transformers_version": "4.37.0",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

model.safetensors

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training_args.bin

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