End of training

README.md

@@ -0,0 +1,55 @@
+---
+base_model: gunkaynar/distilbert-base-uncased
+tags:
+- generated_from_trainer
+model-index:
+- name: distilbert-base-uncased
+  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. -->
+
+# distilbert-base-uncased
+
+This model is a fine-tuned version of [gunkaynar/distilbert-base-uncased](https://huggingface.co/gunkaynar/distilbert-base-uncased) on the None dataset.
+It achieves the following results on the evaluation set:
+- eval_loss: 0.4930
+- eval_accuracy: 0.7391
+- eval_f1: 0.7946
+- eval_runtime: 63.7885
+- eval_samples_per_second: 878.073
+- eval_steps_per_second: 54.884
+- step: 0
+
+## 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: 16
+- eval_batch_size: 16
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 2
+
+### Framework versions
+
+- Transformers 4.33.3
+- Pytorch 2.1.1
+- Datasets 2.14.7
+- Tokenizers 0.11.0

config.json

@@ -1,5 +1,5 @@
 {
-  "_name_or_path": "distilbert-base-uncased",
+  "_name_or_path": "gunkaynar/distilbert-base-uncased",
   "activation": "gelu",
   "architectures": [
     "DistilBertForSequenceClassification"

tokenizer_config.json

@@ -3,11 +3,18 @@
   "cls_token": "[CLS]",
   "do_lower_case": true,
   "mask_token": "[MASK]",
+  "max_length": 512,
   "model_max_length": 512,
+  "pad_to_multiple_of": null,
   "pad_token": "[PAD]",
+  "pad_token_type_id": 0,
+  "padding_side": "right",
   "sep_token": "[SEP]",
+  "stride": 0,
   "strip_accents": null,
   "tokenize_chinese_chars": true,
   "tokenizer_class": "DistilBertTokenizer",
+  "truncation_side": "right",
+  "truncation_strategy": "longest_first",
   "unk_token": "[UNK]"
 }

training_args.bin

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