End of training

README.md

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+---
+license: mit
+base_model: BAAI/bge-base-en-v1.5
+tags:
+- generated_from_trainer
+model-index:
+- name: IKI-Category-multilabel_bge
+  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. -->
+
+# IKI-Category-multilabel_bge
+
+This model is a fine-tuned version of [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.4541
+- Precision-micro: 0.75
+- Precision-samples: 0.7708
+- Precision-weighted: 0.7517
+- Recall-micro: 0.7880
+- Recall-samples: 0.7858
+- Recall-weighted: 0.7880
+- F1-micro: 0.7685
+- F1-samples: 0.7537
+- F1-weighted: 0.7615
+
+## 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: 4.5e-05
+- train_batch_size: 8
+- eval_batch_size: 8
+- seed: 42
+- gradient_accumulation_steps: 2
+- total_train_batch_size: 16
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_steps: 200
+- num_epochs: 15
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Precision-micro | Precision-samples | Precision-weighted | Recall-micro | Recall-samples | Recall-weighted | F1-micro | F1-samples | F1-weighted |
+|:-------------:|:-----:|:----:|:---------------:|:---------------:|:-----------------:|:------------------:|:------------:|:--------------:|:---------------:|:--------:|:----------:|:-----------:|
+| 0.8999        | 0.99  | 94   | 0.8742          | 0.3889          | 0.0272            | 0.1308             | 0.0169       | 0.0188         | 0.0169          | 0.0323   | 0.0202     | 0.0280      |
+| 0.7377        | 2.0   | 189  | 0.6770          | 0.4727          | 0.4996            | 0.5333             | 0.5639       | 0.5782         | 0.5639          | 0.5143   | 0.4883     | 0.4998      |
+| 0.5582        | 2.99  | 283  | 0.5552          | 0.5111          | 0.5585            | 0.5685             | 0.7229       | 0.7357         | 0.7229          | 0.5988   | 0.5959     | 0.6175      |
+| 0.3943        | 4.0   | 378  | 0.4713          | 0.5616          | 0.6397            | 0.5869             | 0.7904       | 0.8071         | 0.7904          | 0.6567   | 0.6761     | 0.6611      |
+| 0.2883        | 4.99  | 472  | 0.4555          | 0.6384          | 0.6969            | 0.6444             | 0.7446       | 0.7641         | 0.7446          | 0.6874   | 0.6901     | 0.6854      |
+| 0.2112        | 6.0   | 567  | 0.4459          | 0.6443          | 0.6968            | 0.6637             | 0.7855       | 0.7942         | 0.7855          | 0.7079   | 0.7123     | 0.7068      |
+| 0.1608        | 6.99  | 661  | 0.4212          | 0.6508          | 0.7071            | 0.6586             | 0.7904       | 0.7931         | 0.7904          | 0.7138   | 0.7161     | 0.7116      |
+| 0.1247        | 8.0   | 756  | 0.4177          | 0.6633          | 0.7145            | 0.6650             | 0.7976       | 0.8006         | 0.7976          | 0.7243   | 0.7193     | 0.7195      |
+| 0.1031        | 8.99  | 850  | 0.4435          | 0.7277          | 0.7523            | 0.7306             | 0.7855       | 0.7875         | 0.7855          | 0.7555   | 0.7425     | 0.7487      |
+| 0.0851        | 10.0  | 945  | 0.4522          | 0.7380          | 0.7623            | 0.7465             | 0.7807       | 0.7795         | 0.7807          | 0.7588   | 0.7432     | 0.7516      |
+| 0.074         | 10.99 | 1039 | 0.4548          | 0.7359          | 0.7663            | 0.7368             | 0.7855       | 0.7910         | 0.7855          | 0.7599   | 0.7490     | 0.7521      |
+| 0.0648        | 12.0  | 1134 | 0.4430          | 0.7425          | 0.7676            | 0.7437             | 0.7783       | 0.7781         | 0.7783          | 0.76     | 0.7461     | 0.7540      |
+| 0.0605        | 12.99 | 1228 | 0.4478          | 0.7366          | 0.7651            | 0.7379             | 0.7952       | 0.7948         | 0.7952          | 0.7648   | 0.7545     | 0.7579      |
+| 0.0566        | 14.0  | 1323 | 0.4574          | 0.7506          | 0.7708            | 0.7519             | 0.7904       | 0.7893         | 0.7904          | 0.7700   | 0.7546     | 0.7625      |
+| 0.0546        | 14.92 | 1410 | 0.4541          | 0.75            | 0.7708            | 0.7517             | 0.7880       | 0.7858         | 0.7880          | 0.7685   | 0.7537     | 0.7615      |
+
+
+### Framework versions
+
+- Transformers 4.35.2
+- Pytorch 2.1.0+cu121
+- Datasets 2.17.0
+- Tokenizers 0.15.1

config.json

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+{
+  "_name_or_path": "BAAI/bge-base-en-v1.5",
+  "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": "Active mobility",
+    "1": "Alternative fuels",
+    "2": "Aviation improvements",
+    "3": "Comprehensive transport planning",
+    "4": "Digital solutions",
+    "5": "Economic instruments",
+    "6": "Education and behavioral change",
+    "7": "Electric mobility",
+    "8": "Freight efficiency improvements",
+    "9": "Improve infrastructure",
+    "10": "Land use",
+    "11": "Other Transport Category",
+    "12": "Public transport improvement",
+    "13": "Shipping improvements",
+    "14": "Transport demand management",
+    "15": "Vehicle improvements"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "Active mobility": 0,
+    "Alternative fuels": 1,
+    "Aviation improvements": 2,
+    "Comprehensive transport planning": 3,
+    "Digital solutions": 4,
+    "Economic instruments": 5,
+    "Education and behavioral change": 6,
+    "Electric mobility": 7,
+    "Freight efficiency improvements": 8,
+    "Improve infrastructure": 9,
+    "Land use": 10,
+    "Other Transport Category": 11,
+    "Public transport improvement": 12,
+    "Shipping improvements": 13,
+    "Transport demand management": 14,
+    "Vehicle improvements": 15
+  },
+  "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": "multi_label_classification",
+  "torch_dtype": "float32",
+  "transformers_version": "4.35.2",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

emissions.csv

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+2024-02-13T23:25:59,codecarbon,fe3751ff-c8bd-4ab3-aab4-d58b1339aedd,1735.1764719486237,0.02489628814029973,1.434798623816107e-05,42.5,40.35687700653838,4.753046035766602,0.020473451719350298,0.032251047189706,0.002280241278595821,0.055004740187652126,United States,USA,iowa,,,Linux-6.1.58+-x86_64-with-glibc2.35,3.10.12,2.3.4,2,Intel(R) Xeon(R) CPU @ 2.30GHz,1,1 x Tesla T4,-95.8517,41.2591,12.674789428710938,machine,N,1.0

model.safetensors

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runs/Feb13_22-36-37_78ddadfe6cfb/events.out.tfevents.1707863822.78ddadfe6cfb.1216.0

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special_tokens_map.json

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+{
+  "cls_token": "[CLS]",
+  "mask_token": "[MASK]",
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "unk_token": "[UNK]"
+}

tokenizer_config.json

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+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
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+  "clean_up_tokenization_spaces": true,
+  "cls_token": "[CLS]",
+  "do_basic_tokenize": true,
+  "do_lower_case": true,
+  "mask_token": "[MASK]",
+  "model_max_length": 512,
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+  "problem_type": "multi_label_classification",
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+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "unk_token": "[UNK]"
+}

training_args.bin

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