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: CONDITIONAL-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. -->
+
+# CONDITIONAL-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: 0.5295
+- Precision-micro: 0.5138
+- Precision-samples: 0.1866
+- Precision-weighted: 0.5169
+- Recall-micro: 0.7378
+- Recall-samples: 0.1874
+- Recall-weighted: 0.7378
+- F1-micro: 0.6058
+- F1-samples: 0.1852
+- F1-weighted: 0.6065
+
+## 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: 4.02e-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: 6
+
+### 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 |
+|:-------------:|:-----:|:----:|:---------------:|:---------------:|:-----------------:|:------------------:|:------------:|:--------------:|:---------------:|:--------:|:----------:|:-----------:|
+| 0.5361        | 1.0   | 369  | 0.4405          | 0.3405          | 0.1655            | 0.4102             | 0.6311       | 0.1622         | 0.6311          | 0.4423   | 0.1622     | 0.4503      |
+| 0.3692        | 2.0   | 738  | 0.3437          | 0.4631          | 0.1794            | 0.4929             | 0.6890       | 0.1761         | 0.6890          | 0.5539   | 0.1762     | 0.5604      |
+| 0.182         | 3.0   | 1107 | 0.3915          | 0.4702          | 0.1857            | 0.4871             | 0.7470       | 0.1891         | 0.7470          | 0.5771   | 0.1854     | 0.5800      |
+| 0.0757        | 4.0   | 1476 | 0.4713          | 0.4960          | 0.1882            | 0.4986             | 0.7530       | 0.1908         | 0.7530          | 0.5981   | 0.1877     | 0.5987      |
+| 0.0298        | 5.0   | 1845 | 0.4971          | 0.5161          | 0.1840            | 0.5184             | 0.7317       | 0.1857         | 0.7317          | 0.6053   | 0.1829     | 0.6058      |
+| 0.0152        | 6.0   | 2214 | 0.5295          | 0.5138          | 0.1866            | 0.5169             | 0.7378       | 0.1874         | 0.7378          | 0.6058   | 0.1852     | 0.6065      |
+
+
+### 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": "ConditionalLabel",
+    "1": "UnconditionalLabel"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "ConditionalLabel": 0,
+    "UnconditionalLabel": 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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model.safetensors

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runs/Mar05_18-42-35_0e7496e21892/events.out.tfevents.1709664168.0e7496e21892.163.0

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special_tokens_map.json

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tokenizer_config.json

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+  "tokenizer_class": "BertTokenizer",
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+}

training_args.bin

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