End of training

README.md

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+---
+license: mit
+base_model: microsoft/xtremedistil-l6-h256-uncased
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+model-index:
+- name: xtremedistil-l6-h256-uncased-zeroshot-v1.1-none
+  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. -->
+
+# xtremedistil-l6-h256-uncased-zeroshot-v1.1-none
+
+This model is a fine-tuned version of [microsoft/xtremedistil-l6-h256-uncased](https://huggingface.co/microsoft/xtremedistil-l6-h256-uncased) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.1992
+- F1 Macro: 0.5455
+- F1 Micro: 0.6194
+- Accuracy Balanced: 0.5960
+- Accuracy: 0.6194
+- Precision Macro: 0.5566
+- Recall Macro: 0.5960
+- Precision Micro: 0.6194
+- Recall Micro: 0.6194
+
+## 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: 2e-05
+- train_batch_size: 32
+- eval_batch_size: 128
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_ratio: 0.06
+- num_epochs: 3
+
+### Training results
+
+| Training Loss | Epoch | Step  | Validation Loss | F1 Macro | F1 Micro | Accuracy Balanced | Accuracy | Precision Macro | Recall Macro | Precision Micro | Recall Micro |
+|:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:|:-----------------:|:--------:|:---------------:|:------------:|:---------------:|:------------:|
+| 0.3056        | 1.0   | 30790 | 0.4634          | 0.7791   | 0.8013   | 0.7757            | 0.8013   | 0.7832          | 0.7757       | 0.8013          | 0.8013       |
+| 0.2847        | 2.0   | 61580 | 0.4656          | 0.7826   | 0.8040   | 0.7797            | 0.8040   | 0.7859          | 0.7797       | 0.8040          | 0.8040       |
+| 0.2618        | 3.0   | 92370 | 0.4774          | 0.7848   | 0.8045   | 0.7841            | 0.8045   | 0.7856          | 0.7841       | 0.8045          | 0.8045       |
+
+
+### Framework versions
+
+- Transformers 4.33.3
+- Pytorch 2.1.2+cu121
+- Datasets 2.14.7
+- Tokenizers 0.13.3

config.json

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+{
+  "_name_or_path": "microsoft/xtremedistil-l6-h256-uncased",
+  "architectures": [
+    "BertForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 256,
+  "id2label": {
+    "0": "entailment",
+    "1": "not_entailment"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 1024,
+  "label2id": {
+    "entailment": 0,
+    "not_entailment": 1
+  },
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 8,
+  "num_hidden_layers": 6,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "problem_type": "single_label_classification",
+  "torch_dtype": "float16",
+  "transformers_version": "4.33.3",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

pytorch_model.bin

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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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+{
+  "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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