Push best checkpoint (step_1000, overall score=0.712) with detailed evaluation README

README.md

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+---
+license: mit
+library_name: transformers
+tags:
+  - bert
+  - pytorch
+  - safetensors
+---
+
+# MyAwesomeModel
+
+MyAwesomeModel is a BERT-style model that has undergone significant post-training
+optimization. It shows improved depth of reasoning and inference capabilities, a
+reduced hallucination rate, and enhanced support for function calling.
+
+This repository contains the best checkpoint selected from 10 training steps
+(`step_100` ... `step_1000`). The selection was performed by running the full
+evaluation pipeline (`evaluation/eval.py`) on every checkpoint and picking the
+one with the highest overall score.
+
+## Selected Checkpoint
+
+- **Checkpoint:** `step_1000`
+- **Overall score:** **0.712** (weighted average across all 15 benchmarks)
+
+## Detailed Evaluation Results
+
+All scores are reported to three decimal places.
+
+| Category | Benchmark | Score |
+|---|---|---|
+| Core Reasoning Tasks | Math Reasoning | 0.550 |
+| | Logical Reasoning | 0.819 |
+| | Common Sense | 0.736 |
+| Language Understanding | Reading Comprehension | 0.700 |
+| | Question Answering | 0.607 |
+| | Text Classification | 0.828 |
+| | Sentiment Analysis | 0.792 |
+| Generation Tasks | Code Generation | 0.650 |
+| | Creative Writing | 0.610 |
+| | Dialogue Generation | 0.644 |
+| | Summarization | 0.767 |
+| Specialized Capabilities | Translation | 0.804 |
+| | Knowledge Retrieval | 0.676 |
+| | Instruction Following | 0.758 |
+| | Safety Evaluation | 0.739 |
+
+## How to Load
+
+```python
+from transformers import BertModel, BertTokenizer
+
+model = BertModel.from_pretrained("safafa34235/MyAwesomeModel")
+tokenizer = BertTokenizer.from_pretrained("safafa34235/MyAwesomeModel")
+```
+
+## License
+
+This model is released under the MIT License.

config.json

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+{
+  "add_cross_attention": false,
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": null,
+  "classifier_dropout": null,
+  "dtype": "float32",
+  "eos_token_id": null,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "is_decoder": false,
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "tie_word_embeddings": true,
+  "transformers_version": "5.16.1",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:e0d93ae7dc6232d04346326805ad39ebdb12ddb8e32105943c811772b4d53f10
+size 437951328

pytorch_model.bin

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+version https://git-lfs.github.com/spec/v1
+oid sha256:965362299a238de576a92dfdd3e32aea7a2bacc94b2c41541c8c9258b923f587
+size 23

tokenizer_config.json

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+{
+  "backend": "tokenizers",
+  "cls_token": "[CLS]",
+  "do_lower_case": true,
+  "is_local": false,
+  "local_files_only": false,
+  "mask_token": "[MASK]",
+  "model_max_length": 512,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "unk_token": "[UNK]"
+}