End of training

README.md

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+---
+license: mit
+base_model: BAAI/bge-m3-retromae
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+model-index:
+- name: bge-m3-retromae-zeroshot-v2.0-2024-04-02-09-26
+  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. -->
+
+# bge-m3-retromae-zeroshot-v2.0-2024-04-02-09-26
+
+This model is a fine-tuned version of [BAAI/bge-m3-retromae](https://huggingface.co/BAAI/bge-m3-retromae) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.1873
+- F1 Macro: 0.6439
+- F1 Micro: 0.7032
+- Accuracy Balanced: 0.6808
+- Accuracy: 0.7032
+- Precision Macro: 0.6635
+- Recall Macro: 0.6808
+- Precision Micro: 0.7032
+- Recall Micro: 0.7032
+
+## 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: 9e-06
+- train_batch_size: 4
+- eval_batch_size: 32
+- seed: 42
+- gradient_accumulation_steps: 8
+- total_train_batch_size: 32
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_ratio: 0.06
+- num_epochs: 2
+
+### Training results
+
+| Training Loss | Epoch | Step  | Validation Loss | F1 Macro | F1 Micro | Accuracy Balanced | Accuracy | Precision Macro | Recall Macro | Precision Micro | Recall Micro |
+|:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:|:-----------------:|:--------:|:---------------:|:------------:|:---------------:|:------------:|
+| 0.2461        | 1.0   | 33914 | 0.3735          | 0.8313   | 0.8456   | 0.8329            | 0.8456   | 0.8298          | 0.8329       | 0.8456          | 0.8456       |
+| 0.1947        | 2.0   | 67828 | 0.3949          | 0.8370   | 0.8515   | 0.8371            | 0.8515   | 0.8370          | 0.8371       | 0.8515          | 0.8515       |
+
+
+### Framework versions
+
+- Transformers 4.37.2
+- Pytorch 2.2.1+cu121
+- Datasets 2.17.1
+- Tokenizers 0.15.2

config.json

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+{
+  "_name_or_path": "BAAI/bge-m3-retromae",
+  "architectures": [
+    "XLMRobertaForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 0,
+  "classifier_dropout": null,
+  "eos_token_id": 2,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 1024,
+  "id2label": {
+    "0": "entailment",
+    "1": "not_entailment"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 4096,
+  "label2id": {
+    "entailment": 0,
+    "not_entailment": 1
+  },
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 8194,
+  "model_type": "xlm-roberta",
+  "num_attention_heads": 16,
+  "num_hidden_layers": 24,
+  "output_past": true,
+  "pad_token_id": 1,
+  "position_embedding_type": "absolute",
+  "problem_type": "single_label_classification",
+  "torch_dtype": "float16",
+  "transformers_version": "4.37.2",
+  "type_vocab_size": 1,
+  "use_cache": true,
+  "vocab_size": 250002
+}

model.safetensors

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sentencepiece.bpe.model

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special_tokens_map.json

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+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

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+{
+  "added_tokens_decoder": {
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+  "mask_token": "<mask>",
+  "model_max_length": 512,
+  "pad_token": "<pad>",
+  "sep_token": "</s>",
+  "sp_model_kwargs": {},
+  "tokenizer_class": "XLMRobertaTokenizer",
+  "unk_token": "<unk>"
+}

training_args.bin

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