# embeddings
## Utils
## Example Data
``` python
df = pd.DataFrame(
{
"sequence": [
"MKWVTFISLLLLFSSAYSRGVFRRDAHKSEVAHRFKDLGE",
"GSSHHHHHHSSGLVPRGSHMNNIRRVAILAALVAGQALA",
]
}
)
df.shape
```
``` python
df.head()
```
``` python
run_model_examples = False
```
## ESM2
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source
### get_esm
``` python
def get_esm(
df:DataFrame, # dataframe containing protein sequences
col:str, # column containing amino acid sequences
model_name:str='esm2_t33_650M_UR50D', batch_size:int=1, # number of sequences per inference batch
)->DataFrame:
```
*Extract ESM2 embeddings (mean pooled per sequence).*
``` python
if run_model_examples:
get_esm(df, "sequence").head()
else:
print("Skipped get_esm example: set run_model_examples=True to allow model downloads.")
```
## ProtT5 Uniref50
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source
### get_t5
``` python
def get_t5(
df:DataFrame, # dataframe containing protein sequences
col:str='sequence', # column containing amino acid sequences
)->DataFrame:
```
*Extract ProtT5-XL-uniref50 embeddings from protein sequences in a
dataframe.*
``` python
if run_model_examples:
get_t5(df, "sequence").head()
else:
print("Skipped get_t5 example: set run_model_examples=True to allow model downloads.")
```
## ProtT5 BFD
------------------------------------------------------------------------
source
### get_t5_bfd
``` python
def get_t5_bfd(
df:DataFrame, # dataframe containing protein sequences
col:str='sequence', # column containing amino acid sequences
)->DataFrame:
```
*Extract ProtT5-XL-BFD embeddings from protein sequences in a
dataframe.*
``` python
if run_model_examples:
get_t5_bfd(df, "sequence").head()
else:
print("Skipped get_t5_bfd example: set run_model_examples=True to allow model downloads.")
```