Acc0.8545568039950062, F10.8538288440438083 , Augmented with bert-base-uncased.csv, finetuned on SALT-NLP/FLANG-ELECTRA

README.md

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+---
+base_model: SALT-NLP/FLANG-ELECTRA
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+- f1
+- precision
+- recall
+model-index:
+- name: FLANG-ELECTRA_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. -->
+
+# FLANG-ELECTRA_bert-base-uncased
+
+This model is a fine-tuned version of [SALT-NLP/FLANG-ELECTRA](https://huggingface.co/SALT-NLP/FLANG-ELECTRA) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.4748
+- Accuracy: 0.8705
+- F1: 0.8705
+- Precision: 0.8705
+- 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: 32
+- eval_batch_size: 32
+- 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 |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
+| 0.6775        | 1.0   | 181  | 0.5462          | 0.7972   | 0.7894 | 0.7973    | 0.7972 |
+| 0.4966        | 2.0   | 362  | 0.3989          | 0.8612   | 0.8612 | 0.8633    | 0.8612 |
+| 0.2509        | 3.0   | 543  | 0.3791          | 0.8612   | 0.8620 | 0.8645    | 0.8612 |
+| 0.2241        | 4.0   | 724  | 0.5297          | 0.8471   | 0.8471 | 0.8501    | 0.8471 |
+| 0.2248        | 5.0   | 905  | 0.4748          | 0.8705   | 0.8705 | 0.8705    | 0.8705 |
+| 1.1108        | 6.0   | 1086 | 1.1042          | 0.3245   | 0.1590 | 0.1053    | 0.3245 |
+| 1.1122        | 7.0   | 1267 | 1.1028          | 0.3245   | 0.1590 | 0.1053    | 0.3245 |
+| 1.102         | 8.0   | 1448 | 1.0987          | 0.3510   | 0.1824 | 0.1232    | 0.3510 |
+| 1.1015        | 9.0   | 1629 | 1.1069          | 0.3245   | 0.1590 | 0.1053    | 0.3245 |
+| 1.0908        | 10.0  | 1810 | 1.1022          | 0.3510   | 0.1824 | 0.1232    | 0.3510 |
+
+
+### 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": "SALT-NLP/FLANG-ELECTRA",
+  "architectures": [
+    "ElectraForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "embedding_size": 1024,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 1024,
+  "id2label": {
+    "0": "LABEL_0",
+    "1": "LABEL_1",
+    "2": "LABEL_2"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 4096,
+  "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": 16,
+  "num_hidden_layers": 24,
+  "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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