End of training

README.md

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+---
+license: apache-2.0
+base_model: cross-encoder/ms-marco-MiniLM-L-6-v2
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+- precision
+- recall
+model-index:
+- name: ce-MiniLM-L6layer
+  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. -->
+
+# ce-MiniLM-L6layer
+
+This model is a fine-tuned version of [cross-encoder/ms-marco-MiniLM-L-6-v2](https://huggingface.co/cross-encoder/ms-marco-MiniLM-L-6-v2) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.1559
+- Accuracy: 0.7273
+- Precision: 0.9091
+- Recall: 0.6349
+
+## 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: 5e-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: linear
+- lr_scheduler_warmup_steps: 500
+- num_epochs: 10
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|
+| 12.9679       | 1.0   | 56   | 20.2827         | 0.6970   | 0.7797    | 0.7302 |
+| 9.2483        | 2.0   | 112  | 12.1491         | 0.6465   | 0.7188    | 0.7302 |
+| 1.9612        | 3.0   | 168  | 1.7406          | 0.6667   | 0.8409    | 0.5873 |
+| 0.5046        | 4.0   | 224  | 0.4060          | 0.6061   | 0.8158    | 0.4921 |
+| 0.3575        | 5.0   | 280  | 0.2410          | 0.6667   | 0.7885    | 0.6508 |
+| 0.244         | 6.0   | 336  | 0.1860          | 0.6263   | 0.9062    | 0.4603 |
+| 0.2324        | 7.0   | 392  | 0.1706          | 0.6970   | 0.9231    | 0.5714 |
+| 0.1958        | 8.0   | 448  | 0.1873          | 0.7172   | 0.7869    | 0.7619 |
+| 0.1687        | 9.0   | 504  | 0.1742          | 0.7778   | 0.8868    | 0.7460 |
+| 0.1581        | 10.0  | 560  | 0.1559          | 0.7273   | 0.9091    | 0.6349 |
+
+
+### Framework versions
+
+- Transformers 4.37.2
+- Pytorch 2.2.1
+- Datasets 2.14.6
+- Tokenizers 0.15.1

config.json

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+{
+  "_name_or_path": "cross-encoder/ms-marco-MiniLM-L-6-v2",
+  "architectures": [
+    "BertForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 384,
+  "id2label": {
+    "0": "LABEL_0"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 1536,
+  "label2id": {
+    "LABEL_0": 0
+  },
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 6,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "problem_type": "regression",
+  "sbert_ce_default_activation_function": "torch.nn.modules.linear.Identity",
+  "torch_dtype": "float32",
+  "transformers_version": "4.37.2",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

model.safetensors

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+oid sha256:eff0bcb9127d1b42eedb94643b9e8116ff1de66e9c4c740a4b53a5ed3052720a
+size 90866412

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,
+      "special": true
+    },
+    "100": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "101": {
+      "content": "[CLS]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
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+    "102": {
+      "content": "[SEP]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "103": {
+      "content": "[MASK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "[CLS]",
+  "do_basic_tokenize": true,
+  "do_lower_case": true,
+  "mask_token": "[MASK]",
+  "model_max_length": 512,
+  "never_split": null,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "unk_token": "[UNK]"
+}

training_args.bin

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