trust_remote_code not passed in properly

The flag requires trust_remote_code=True doesn't seem to be passed by SetFitModel.from_pretrained properly. The following code results in an error:

```
from setfit import SetFitModel

model_id = './nomic-embed-text-v1'
model = SetFitModel.from_pretrained(model_id, labels=['negative','positive'], trust_remote_code=True)
```

Results in:
```
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
Cell In[5], line 5
3 #config = AutoConfig.from_pretrained(model_id,trust_remote_code=True)
4 model_id = './nomic-embed-text-v1'
----> 5 model = SetFitModel.from_pretrained(model_id, labels=['negative','positive'], trust_remote_code=True)

File ~/Library/Python/3.11/lib/python/site-packages/huggingface_hub/utils/_validators.py:118, in validate_hf_hub_args.._inner_fn(*args, **kwargs)
115 if check_use_auth_token:
116 kwargs = smoothly_deprecate_use_auth_token(fn_name=fn.__name__, has_token=has_token, kwargs=kwargs)
--> 118 return fn(*args, **kwargs)

File ~/Library/Python/3.11/lib/python/site-packages/huggingface_hub/hub_mixin.py:157, in ModelHubMixin.from_pretrained(cls, pretrained_model_name_or_path, force_download, resume_download, proxies, token, cache_dir, local_files_only, revision, **model_kwargs)
154 config = json.load(f)
155 model_kwargs.update({"config": config})
--> 157 return cls._from_pretrained(
158 model_id=str(model_id),
159 revision=revision,
160 cache_dir=cache_dir,
161 force_download=force_download,
162 proxies=proxies,
163 resume_download=resume_download,
164 local_files_only=local_files_only,
165 token=token,
166 **model_kwargs,
...
624 " set the option `trust_remote_code=True` to remove this error."
625 )
627 return trust_remote_code

ValueError: Loading ./nomic-embed-text-v1 requires you to execute the configuration file in that repo on your local machine. Make sure you have read the code there to avoid malicious use, then set the option `trust_remote_code=True` to remove this error.
```

However if I use the huggingface automodel class it appears to load the model correctly:
```
from transformers import AutoModel

model_id = './nomic-embed-text-v1'
AutoModel.from_pretrained(model_id, trust_remote_code=True)

NomicBertModel(
(embeddings): NomicBertEmbeddings(
(word_embeddings): Embedding(30528, 768)
(token_type_embeddings): Embedding(2, 768)
)
(emb_drop): Dropout(p=0.0, inplace=False)
(emb_ln): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
(encoder): NomicBertEncoder(
(layers): ModuleList(
(0-11): 12 x NomicBertBlock(
(attn): NomicBertAttention(
(rotary_emb): NomicBertRotaryEmbedding()
(Wqkv): Linear(in_features=768, out_features=2304, bias=False)
(out_proj): Linear(in_features=768, out_features=768, bias=False)
(drop): Dropout(p=0.0, inplace=False)
)
(mlp): NomciBertGatedMLP(
(fc11): Linear(in_features=768, out_features=3072, bias=False)
(fc12): Linear(in_features=768, out_features=3072, bias=False)
(fc2): Linear(in_features=3072, out_features=768, bias=False)
)
(dropout1): Dropout(p=0.0, inplace=False)
(norm1): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
(norm2): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
(dropout2): Dropout(p=0.0, inplace=False)
)
)
)
)
```