Add exported onnx model 'model_O4.onnx'

Hello!

*This pull request has been automatically generated from the [`export_optimized_onnx_model`](https://sbert.net/docs/package_reference/util.html#sentence_transformers.backend.export_optimized_onnx_model) function from the Sentence Transformers library.*

## Config
```python
OptimizationConfig(
optimization_level=2,
optimize_for_gpu=True,
fp16=True,
optimize_with_onnxruntime_only=None,
enable_transformers_specific_optimizations=True,
disable_gelu=None,
disable_gelu_fusion=False,
disable_layer_norm=None,
disable_layer_norm_fusion=False,
disable_attention=None,
disable_attention_fusion=False,
disable_skip_layer_norm=None,
disable_skip_layer_norm_fusion=False,
disable_bias_skip_layer_norm=None,
disable_bias_skip_layer_norm_fusion=False,
disable_bias_gelu=None,
disable_bias_gelu_fusion=False,
disable_embed_layer_norm=True,
disable_embed_layer_norm_fusion=True,
enable_gelu_approximation=True,
use_mask_index=False,
no_attention_mask=False,
disable_shape_inference=False,
use_multi_head_attention=False,
enable_gemm_fast_gelu_fusion=False,
use_raw_attention_mask=False,
disable_group_norm_fusion=True,
disable_packed_kv=True,
disable_rotary_embeddings=False
)
```

## Tip:
Consider testing this pull request before merging by loading the model from this PR with the `revision` argument:
```python
from sentence_transformers import SentenceTransformer

# TODO: Fill in the PR number
pr_number = 2
model = SentenceTransformer(
"shibing624/text2vec-base-chinese",
revision=f"refs/pr/{pr_number}",
backend="onnx",
model_kwargs={"file_name": "model_O4.onnx"},
)

# Verify that everything works as expected
embeddings = model.encode(["The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium."])
print(embeddings.shape)

similarities = model.similarity(embeddings, embeddings)
print(similarities)
```

onnx/model_O4.onnx

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