"""
This file contains the templating for model cards prior to the v3.0 release. It still exists to be used alongside
SentenceTransformer.old_fit for backwards compatibility, but will be removed in a future release.
"""
from __future__ import annotations
import logging
from .util import fullname
class ModelCardTemplate:
__TAGS__ = ["sentence-transformers", "feature-extraction", "sentence-similarity"]
__DEFAULT_VARS__ = {
"{PIPELINE_TAG}": "sentence-similarity",
"{MODEL_DESCRIPTION}": "<!--- Describe your model here -->",
"{TRAINING_SECTION}": "",
"{USAGE_TRANSFORMERS_SECTION}": "",
"{EVALUATION}": "<!--- Describe how your model was evaluated -->",
"{CITING}": "<!--- Describe where people can find more information -->",
}
__MODEL_CARD__ = """
---
library_name: sentence-transformers
pipeline_tag: {PIPELINE_TAG}
tags:
{TAGS}
{DATASETS}
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a {NUM_DIMENSIONS} dimensional dense vector space and can be used for tasks like clustering or semantic search.
{MODEL_DESCRIPTION}
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
{USAGE_TRANSFORMERS_SECTION}
## Evaluation Results
{EVALUATION}
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
{TRAINING_SECTION}
## Full Model Architecture
```
{FULL_MODEL_STR}
```
## Citing & Authors
{CITING}
"""
__TRAINING_SECTION__ = """
## Training
The model was trained with the parameters:
{LOSS_FUNCTIONS}
Parameters of the fit()-Method:
```
{FIT_PARAMETERS}
```
"""
__USAGE_TRANSFORMERS__ = """\n
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
{POOLING_FUNCTION}
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
model = AutoModel.from_pretrained('{MODEL_NAME}')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, {POOLING_MODE} pooling.
sentence_embeddings = {POOLING_FUNCTION_NAME}(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
"""
@staticmethod
def model_card_get_pooling_function(pooling_mode):
if pooling_mode == "max":
return (
"max_pooling",
"""
# Max Pooling - Take the max value over time for every dimension.
def max_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
token_embeddings[input_mask_expanded == 0] = -1e9 # Set padding tokens to large negative value
return torch.max(token_embeddings, 1)[0]
""",
)
elif pooling_mode == "mean":
return (
"mean_pooling",
"""
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
""",
)
elif pooling_mode == "cls":
return (
"cls_pooling",
"""
def cls_pooling(model_output, attention_mask):
return model_output[0][:,0]
""",
)
@staticmethod
def get_train_objective_info(dataloader, loss):
try:
if hasattr(dataloader, "get_config_dict"):
loader_params = dataloader.get_config_dict()
else:
loader_params = {}
loader_params["batch_size"] = dataloader.batch_size if hasattr(dataloader, "batch_size") else "unknown"
if hasattr(dataloader, "sampler"):
loader_params["sampler"] = fullname(dataloader.sampler)
if hasattr(dataloader, "batch_sampler"):
loader_params["batch_sampler"] = fullname(dataloader.batch_sampler)
dataloader_str = f"""**DataLoader**:\n\n`{fullname(dataloader)}` of length {len(dataloader)} with parameters:
```
{loader_params}
```"""
loss_str = "**Loss**:\n\n`{}` {}".format(
fullname(loss),
f"""with parameters:
```
{loss.get_config_dict()}
```"""
if hasattr(loss, "get_config_dict")
else "",
)
return [dataloader_str, loss_str]
except Exception as e:
logging.WARN(f"Exception when creating get_train_objective_info: {str(e)}")
return ""