End of training

README.md

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+---
+base_model: textattack/albert-base-v2-imdb
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+- f1
+model-index:
+- name: albert-tune
+  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. -->
+
+# albert-tune
+
+This model is a fine-tuned version of [textattack/albert-base-v2-imdb](https://huggingface.co/textattack/albert-base-v2-imdb) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.8791
+- Accuracy: 0.6857
+- F1: 0.7039
+
+## 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: 5e-05
+- 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: 7
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
+| 1.8887        | 1.0   | 53   | 1.8444          | 0.1929   | 0.1619 |
+| 1.6055        | 2.0   | 106  | 1.5226          | 0.5929   | 0.5821 |
+| 1.2048        | 3.0   | 159  | 1.1546          | 0.5857   | 0.5779 |
+| 0.7243        | 4.0   | 212  | 0.9967          | 0.6214   | 0.6173 |
+| 0.6455        | 5.0   | 265  | 0.9122          | 0.6857   | 0.6941 |
+| 0.9166        | 6.0   | 318  | 0.8791          | 0.6857   | 0.7039 |
+| 0.505         | 7.0   | 371  | 1.0556          | 0.6643   | 0.6665 |
+
+
+### Framework versions
+
+- Transformers 4.43.3
+- Pytorch 2.4.0+cu121
+- Datasets 2.20.0
+- Tokenizers 0.19.1

config.json

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+{
+  "_name_or_path": "textattack/albert-base-v2-imdb",
+  "architectures": [
+    "AlbertForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0,
+  "bos_token_id": 2,
+  "classifier_dropout_prob": 0.1,
+  "down_scale_factor": 1,
+  "embedding_size": 128,
+  "eos_token_id": 3,
+  "finetuning_task": "imdb",
+  "gap_size": 0,
+  "hidden_act": "gelu_new",
+  "hidden_dropout_prob": 0,
+  "hidden_size": 768,
+  "id2label": {
+    "0": "fees_and_charges",
+    "1": "fees_and_charges/loans",
+    "2": "fees_and_charges/loans/payday",
+    "3": "fees_and_charges/service_fee",
+    "4": "income/paycheck",
+    "5": "transfer",
+    "6": "transfer/email"
+  },
+  "initializer_range": 0.02,
+  "inner_group_num": 1,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "albert",
+  "net_structure_type": 0,
+  "num_attention_heads": 12,
+  "num_hidden_groups": 1,
+  "num_hidden_layers": 12,
+  "num_memory_blocks": 0,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "problem_type": "single_label_classification",
+  "torch_dtype": "float32",
+  "transformers_version": "4.43.3",
+  "type_vocab_size": 2,
+  "vocab_size": 30000
+}

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special_tokens_map.json

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tokenizer_config.json

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+}

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