End of training

README.md

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+---
+license: mit
+base_model: Supabase/gte-small
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+model-index:
+- name: v_best_model
+ 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. -->
+
+# v_best_model
+
+This model is a fine-tuned version of [Supabase/gte-small](https://huggingface.co/Supabase/gte-small) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.0734
+- Accuracy: 0.9990
+
+## 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: 16
+- eval_batch_size: 16
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 4
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|
+| 1.2837 | 1.0 | 62 | 0.6846 | 0.9231 |
+| 0.448 | 2.0 | 124 | 0.2268 | 0.9808 |
+| 0.1566 | 3.0 | 186 | 0.1397 | 0.9808 |
+| 0.0879 | 4.0 | 248 | 0.1302 | 0.9808 |
+
+
+### Framework versions
+
+- Transformers 4.36.2
+- Pytorch 2.0.1+cu117
+- Datasets 2.17.1
+- Tokenizers 0.15.0

config.json

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+{
+ "_name_or_path": "Supabase/gte-small",
+ "architectures": [
+ "BertForSequenceClassification"
+ ],
+ "attention_probs_dropout_prob": 0.1,
+ "classifier_dropout": null,
+ "hidden_act": "gelu",
+ "hidden_dropout_prob": 0.1,
+ "hidden_size": 384,
+ "id2label": {
+ "0": "LABEL_0",
+ "1": "LABEL_1",
+ "2": "LABEL_2",
+ "3": "LABEL_3",
+ "4": "LABEL_4",
+ "5": "LABEL_5"
+ },
+ "initializer_range": 0.02,
+ "intermediate_size": 1536,
+ "label2id": {
+ "LABEL_0": 0,
+ "LABEL_1": 1,
+ "LABEL_2": 2,
+ "LABEL_3": 3,
+ "LABEL_4": 4,
+ "LABEL_5": 5
+ },
+ "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": "4.36.2",
+ "type_vocab_size": 2,
+ "use_cache": true,
+ "vocab_size": 30522
+}

model.safetensors

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runs/Mar08_15-59-20_sdc2-bdi-analytic-gpu1/events.out.tfevents.1709888360.sdc2-bdi-analytic-gpu1

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runs/Mar08_15-59-20_sdc2-bdi-analytic-gpu1/events.out.tfevents.1709888393.sdc2-bdi-analytic-gpu1

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special_tokens_map.json

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+{
+ "cls_token": {
+ "content": "[CLS]",
+ "lstrip": false,
+ "normalized": false,
+ "rstrip": false,
+ "single_word": false
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+ "content": "[MASK]",
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+ "rstrip": false,
+ "single_word": false
+ },
+ "pad_token": {
+ "content": "[PAD]",
+ "lstrip": false,
+ "normalized": false,
+ "rstrip": false,
+ "single_word": false
+ },
+ "sep_token": {
+ "content": "[SEP]",
+ "lstrip": false,
+ "normalized": false,
+ "rstrip": false,
+ "single_word": false
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+ "unk_token": {
+ "content": "[UNK]",
+ "lstrip": false,
+ "normalized": false,
+ "rstrip": false,
+ "single_word": false
+ }
+}

tokenizer_config.json

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+{
+ "added_tokens_decoder": {
+ "0": {
+ "content": "[PAD]",
+ "lstrip": false,
+ "normalized": false,
+ "rstrip": false,
+ "single_word": false,
+ "special": true
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+ "lstrip": false,
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+ "special": true
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+ "101": {
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+ "single_word": false,
+ "special": true
+ }
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+ "clean_up_tokenization_spaces": true,
+ "cls_token": "[CLS]",
+ "do_basic_tokenize": true,
+ "do_lower_case": true,
+ "mask_token": "[MASK]",
+ "max_length": 128,
+ "model_max_length": 512,
+ "never_split": null,
+ "pad_to_multiple_of": null,
+ "pad_token": "[PAD]",
+ "pad_token_type_id": 0,
+ "padding_side": "right",
+ "sep_token": "[SEP]",
+ "stride": 0,
+ "strip_accents": null,
+ "tokenize_chinese_chars": true,
+ "tokenizer_class": "BertTokenizer",
+ "truncation_side": "right",
+ "truncation_strategy": "longest_first",
+ "unk_token": "[UNK]"
+}

training_args.bin

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