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: ADAPMIT-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. -->
+
+# ADAPMIT-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: 0.3101
+- Precision-micro: 0.9058
+- Precision-samples: 0.8647
+- Precision-weighted: 0.9058
+- Recall-micro: 0.9305
+- Recall-samples: 0.8693
+- Recall-weighted: 0.9305
+- F1-micro: 0.9180
+- F1-samples: 0.8622
+- F1-weighted: 0.9180
+
+## 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: 4.08e-05
+- 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: cosine
+- lr_scheduler_warmup_steps: 300
+- num_epochs: 4
+
+### 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 |
+|:-------------:|:-----:|:----:|:---------------:|:---------------:|:-----------------:|:------------------:|:------------:|:--------------:|:---------------:|:--------:|:----------:|:-----------:|
+| 0.3368        | 1.0   | 784  | 0.2917          | 0.8651          | 0.8450            | 0.8664             | 0.9138       | 0.8542         | 0.9138          | 0.8888   | 0.8437     | 0.8890      |
+| 0.1807        | 2.0   | 1568 | 0.2549          | 0.9092          | 0.8643            | 0.9094             | 0.9156       | 0.8571         | 0.9156          | 0.9124   | 0.8571     | 0.9123      |
+| 0.0955        | 3.0   | 2352 | 0.2988          | 0.9069          | 0.8660            | 0.9072             | 0.9252       | 0.8655         | 0.9252          | 0.9160   | 0.8613     | 0.9160      |
+| 0.0495        | 4.0   | 3136 | 0.3101          | 0.9058          | 0.8647            | 0.9058             | 0.9305       | 0.8693         | 0.9305          | 0.9180   | 0.8622     | 0.9180      |
+
+
+### Framework versions
+
+- Transformers 4.38.1
+- Pytorch 2.1.0+cu121
+- Datasets 2.18.0
+- Tokenizers 0.15.2

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": "AdaptationLabel",
+    "1": "MitigationLabel"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "AdaptationLabel": 0,
+    "MitigationLabel": 1
+  },
+  "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": "4.38.1",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

emissions.csv

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+2024-03-05T16:21:19,codecarbon,87d2cba8-3879-463b-8c13-47446347f1a9,3581.484291791916,0.040517430302682955,1.1313027505255638e-05,42.5,45.313087583201835,4.753043174743652,0.04226476030035151,0.069033904949302,0.004721923368884577,0.11602058861853813,United States,USA,nevada,,,Linux-6.1.58+-x86_64-with-glibc2.35,3.10.12,2.3.4,2,Intel(R) Xeon(R) CPU @ 2.00GHz,1,1 x Tesla T4,-115.1164,36.1685,12.674781799316406,machine,N,1.0

model.safetensors

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runs/Mar05_15-21-35_502e8c615647/events.out.tfevents.1709652097.502e8c615647.701.0

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special_tokens_map.json

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tokenizer_config.json

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+      "normalized": false,
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+  "problem_type": "multi_label_classification",
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+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "unk_token": "[UNK]"
+}

training_args.bin

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