Hugging Face typo (#275)

huggingface -> Hugging Face

README.md

@@ -248,7 +248,7 @@ python -m training.main \
 
 ### Training with pre-trained language models as text encoder:
 
-If you wish to use different language models as the text encoder for CLIP you can do so by using one of the huggingface model configs in ```src/open_clip/model_configs``` and passing in it's tokenizer as the ```--model``` and ```--hf-tokenizer-name``` parameters respectively. Currently we only support RoBERTa ("test-roberta" config), however adding new models should be trivial. You can also determine how many layers, from the end, to leave unfrozen with the ```--lock-text-unlocked-layers``` parameter. Here's an example command to train CLIP with the RoBERTa LM that has it's last 10 layers unfrozen:
+If you wish to use different language models as the text encoder for CLIP you can do so by using one of the Hugging Face model configs in ```src/open_clip/model_configs``` and passing in it's tokenizer as the ```--model``` and ```--hf-tokenizer-name``` parameters respectively. Currently we only support RoBERTa ("test-roberta" config), however adding new models should be trivial. You can also determine how many layers, from the end, to leave unfrozen with the ```--lock-text-unlocked-layers``` parameter. Here's an example command to train CLIP with the RoBERTa LM that has it's last 10 layers unfrozen:
 ```bash
 python -m training.main \
          --train-data="pipe:aws s3 cp s3://s-mas/cc3m/{00000..00329}.tar -" \