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: TAPP-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. -->
+
+# TAPP-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: 0.9217
+- Precision-micro: 0.7772
+- Precision-samples: 0.7644
+- Precision-weighted: 0.7756
+- Recall-micro: 0.8329
+- Recall-samples: 0.7920
+- Recall-weighted: 0.8329
+- F1-micro: 0.8041
+- F1-samples: 0.7609
+- F1-weighted: 0.8029
+
+## 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: 7.4e-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: 200
+- num_epochs: 7
+
+### 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 |
+|:-------------:|:-----:|:----:|:---------------:|:---------------:|:-----------------:|:------------------:|:------------:|:--------------:|:---------------:|:--------:|:----------:|:-----------:|
+| 0.7161        | 1.0   | 627  | 0.6322          | 0.5931          | 0.6373            | 0.6274             | 0.8219       | 0.7833         | 0.8219          | 0.6890   | 0.6728     | 0.7000      |
+| 0.4549        | 2.0   | 1254 | 0.5420          | 0.6639          | 0.6891            | 0.7049             | 0.8090       | 0.7684         | 0.8090          | 0.7293   | 0.7048     | 0.7409      |
+| 0.2599        | 3.0   | 1881 | 0.6966          | 0.7354          | 0.7396            | 0.7346             | 0.8219       | 0.7845         | 0.8219          | 0.7762   | 0.7425     | 0.7713      |
+| 0.1405        | 4.0   | 2508 | 0.7530          | 0.7569          | 0.7494            | 0.7569             | 0.8292       | 0.7899         | 0.8292          | 0.7914   | 0.7505     | 0.7905      |
+| 0.0681        | 5.0   | 3135 | 0.8234          | 0.7596          | 0.7535            | 0.7599             | 0.8356       | 0.7945         | 0.8356          | 0.7958   | 0.7546     | 0.7953      |
+| 0.0291        | 6.0   | 3762 | 0.8849          | 0.7773          | 0.7640            | 0.7776             | 0.8301       | 0.7890         | 0.8301          | 0.8028   | 0.7597     | 0.8027      |
+| 0.0147        | 7.0   | 4389 | 0.9217          | 0.7772          | 0.7644            | 0.7756             | 0.8329       | 0.7920         | 0.8329          | 0.8041   | 0.7609     | 0.8029      |
+
+
+### 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": "ActionLabel",
+    "1": "PlansLabel",
+    "2": "PolicyLabel",
+    "3": "TargetLabel"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "ActionLabel": 0,
+    "PlansLabel": 1,
+    "PolicyLabel": 2,
+    "TargetLabel": 3
+  },
+  "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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runs/Mar05_09-21-54_f0a18e2da3f8/events.out.tfevents.1709630528.f0a18e2da3f8.1538.0

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special_tokens_map.json

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+{
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+}

tokenizer_config.json

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+      "lstrip": false,
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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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