Acc0.8445692883895131, F10.8449084579706567 , Augmented with flang-bert.csv, finetuned on SALT-NLP/FLANG-BERT

README.md

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+---
+base_model: SALT-NLP/FLANG-BERT
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+- f1
+- precision
+- recall
+model-index:
+- name: FLANG-BERT_flang-bert
+  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-BERT_flang-bert
+
+This model is a fine-tuned version of [SALT-NLP/FLANG-BERT](https://huggingface.co/SALT-NLP/FLANG-BERT) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.4908
+- Accuracy: 0.8487
+- F1: 0.8486
+- Precision: 0.8489
+- Recall: 0.8487
+
+## 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 |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
+| 0.8968        | 1.0   | 91   | 0.8147          | 0.6256   | 0.5775 | 0.6185    | 0.6256 |
+| 0.5694        | 2.0   | 182  | 0.4893          | 0.8097   | 0.8101 | 0.8124    | 0.8097 |
+| 0.3393        | 3.0   | 273  | 0.4303          | 0.8471   | 0.8472 | 0.8474    | 0.8471 |
+| 0.2442        | 4.0   | 364  | 0.5042          | 0.8487   | 0.8462 | 0.8512    | 0.8487 |
+| 0.1807        | 5.0   | 455  | 0.4908          | 0.8487   | 0.8486 | 0.8489    | 0.8487 |
+| 0.1061        | 6.0   | 546  | 0.5168          | 0.8409   | 0.8396 | 0.8405    | 0.8409 |
+| 0.1287        | 7.0   | 637  | 0.6537          | 0.8440   | 0.8438 | 0.8470    | 0.8440 |
+| 0.1079        | 8.0   | 728  | 0.6641          | 0.8315   | 0.8319 | 0.8345    | 0.8315 |
+| 0.0693        | 9.0   | 819  | 0.8833          | 0.8346   | 0.8344 | 0.8363    | 0.8346 |
+| 0.1084        | 10.0  | 910  | 0.8721          | 0.7941   | 0.7947 | 0.7972    | 0.7941 |
+
+
+### 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-BERT",
+  "architectures": [
+    "BertForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "gradient_checkpointing": false,
+  "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": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "problem_type": "single_label_classification",
+  "torch_dtype": "float32",
+  "transformers_version": "4.37.0",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

model.safetensors

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+size 437961724

training_args.bin

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