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import collections
from huggingface_hub import list_models
def get_sorted_models(hub_models):
"""
Filters and sorts a list of models based on their download count.
Args:
hub_models (list): A list of model objects. Each model object must have the attributes 'id', 'downloads', and 'private'.
Returns:
list: A list of model IDs sorted by their download count in descending order. Only includes models that are not private.
"""
hub_models = [{"id": m.id, "downloads": m.downloads} for m in hub_models if m.private is False]
hub_models = sorted(hub_models, key=lambda x: x["downloads"], reverse=True)
hub_models = [m["id"] for m in hub_models]
return hub_models
def _fetch_text_classification_models():
"""
Fetches and sorts text classification models from the Hugging Face model hub.
This function retrieves models for the tasks "fill-mask" and "text-classification"
from the Hugging Face model hub, sorts them by the number of downloads, and combines
them into a single list. Additionally, it fetches trending models based on the number
of likes in the past 7 days, sorts them, and places them at the beginning of the list
if they are not already included.
Returns:
list: A sorted list of model identifiers from the Hugging Face model hub.
"""
hub_models1 = list(
list_models(
task="fill-mask",
library="transformers",
sort="downloads",
direction=-1,
limit=100,
full=False,
)
)
hub_models2 = list(
list_models(
task="text-classification",
library="transformers",
sort="downloads",
direction=-1,
limit=100,
full=False,
)
)
hub_models = list(hub_models1) + list(hub_models2)
hub_models = get_sorted_models(hub_models)
trending_models = list(
list_models(
task="fill-mask",
library="transformers",
sort="likes7d",
direction=-1,
limit=30,
full=False,
)
)
if len(trending_models) > 0:
trending_models = get_sorted_models(trending_models)
hub_models = [m for m in hub_models if m not in trending_models]
hub_models = trending_models + hub_models
return hub_models
def _fetch_llm_models():
hub_models = list(
list_models(
task="text-generation",
library="transformers",
sort="downloads",
direction=-1,
limit=100,
full=False,
)
)
hub_models = get_sorted_models(hub_models)
trending_models = list(
list_models(
task="text-generation",
library="transformers",
sort="likes7d",
direction=-1,
limit=30,
full=False,
)
)
if len(trending_models) > 0:
trending_models = get_sorted_models(trending_models)
hub_models = [m for m in hub_models if m not in trending_models]
hub_models = trending_models + hub_models
return hub_models
def _fetch_image_classification_models():
hub_models = list(
list_models(
task="image-classification",
library="transformers",
sort="downloads",
direction=-1,
limit=100,
full=False,
)
)
hub_models = get_sorted_models(hub_models)
trending_models = list(
list_models(
task="image-classification",
library="transformers",
sort="likes7d",
direction=-1,
limit=30,
full=False,
)
)
if len(trending_models) > 0:
trending_models = get_sorted_models(trending_models)
hub_models = [m for m in hub_models if m not in trending_models]
hub_models = trending_models + hub_models
return hub_models
def _fetch_image_object_detection_models():
hub_models = list(
list_models(
task="object-detection",
library="transformers",
sort="downloads",
direction=-1,
limit=100,
full=False,
pipeline_tag="object-detection",
)
)
hub_models = get_sorted_models(hub_models)
trending_models = list(
list_models(
task="object-detection",
library="transformers",
sort="likes7d",
direction=-1,
limit=30,
full=False,
pipeline_tag="object-detection",
)
)
if len(trending_models) > 0:
trending_models = get_sorted_models(trending_models)
hub_models = [m for m in hub_models if m not in trending_models]
hub_models = trending_models + hub_models
return hub_models
def _fetch_seq2seq_models():
hub_models = list(
list_models(
task="text2text-generation",
library="transformers",
sort="downloads",
direction=-1,
limit=100,
full=False,
)
)
hub_models = get_sorted_models(hub_models)
trending_models = list(
list_models(
task="text2text-generation",
library="transformers",
sort="likes7d",
direction=-1,
limit=30,
full=False,
)
)
if len(trending_models) > 0:
trending_models = get_sorted_models(trending_models)
hub_models = [m for m in hub_models if m not in trending_models]
hub_models = trending_models + hub_models
return hub_models
def _fetch_token_classification_models():
hub_models1 = list(
list_models(
task="fill-mask",
library="transformers",
sort="downloads",
direction=-1,
limit=100,
full=False,
)
)
hub_models2 = list(
list_models(
task="token-classification",
library="transformers",
sort="downloads",
direction=-1,
limit=100,
full=False,
)
)
hub_models = list(hub_models1) + list(hub_models2)
hub_models = get_sorted_models(hub_models)
trending_models = list(
list_models(
task="fill-mask",
library="transformers",
sort="likes7d",
direction=-1,
limit=30,
full=False,
)
)
if len(trending_models) > 0:
trending_models = get_sorted_models(trending_models)
hub_models = [m for m in hub_models if m not in trending_models]
hub_models = trending_models + hub_models
return hub_models
def _fetch_st_models():
hub_models1 = list(
list_models(
task="sentence-similarity",
library="sentence-transformers",
sort="downloads",
direction=-1,
limit=30,
full=False,
)
)
hub_models2 = list(
list_models(
task="fill-mask",
library="transformers",
sort="downloads",
direction=-1,
limit=30,
full=False,
)
)
hub_models = list(hub_models1) + list(hub_models2)
hub_models = get_sorted_models(hub_models)
trending_models = list(
list_models(
task="sentence-similarity",
library="sentence-transformers",
sort="likes7d",
direction=-1,
limit=30,
full=False,
)
)
if len(trending_models) > 0:
trending_models = get_sorted_models(trending_models)
hub_models = [m for m in hub_models if m not in trending_models]
hub_models = trending_models + hub_models
return hub_models
def _fetch_vlm_models():
hub_models1 = list(
list_models(
task="image-text-to-text",
sort="downloads",
direction=-1,
limit=100,
full=False,
filter=["paligemma"],
)
)
# hub_models2 = list(
# list_models(
# task="image-text-to-text",
# sort="downloads",
# direction=-1,
# limit=100,
# full=False,
# filter=["florence2"],
# )
# )
hub_models2 = []
hub_models = list(hub_models1) + list(hub_models2)
hub_models = get_sorted_models(hub_models)
trending_models1 = list(
list_models(
task="image-text-to-text",
sort="likes7d",
direction=-1,
limit=30,
full=False,
filter=["paligemma"],
)
)
# trending_models2 = list(
# list_models(
# task="image-text-to-text",
# sort="likes7d",
# direction=-1,
# limit=30,
# full=False,
# filter=["florence2"],
# )
# )
trending_models2 = []
trending_models = list(trending_models1) + list(trending_models2)
if len(trending_models) > 0:
trending_models = get_sorted_models(trending_models)
hub_models = [m for m in hub_models if m not in trending_models]
hub_models = trending_models + hub_models
return hub_models
def fetch_models():
_mc = collections.defaultdict(list)
_mc["text-classification"] = _fetch_text_classification_models()
_mc["llm"] = _fetch_llm_models()
_mc["image-classification"] = _fetch_image_classification_models()
_mc["image-regression"] = _fetch_image_classification_models()
_mc["seq2seq"] = _fetch_seq2seq_models()
_mc["token-classification"] = _fetch_token_classification_models()
_mc["text-regression"] = _fetch_text_classification_models()
_mc["image-object-detection"] = _fetch_image_object_detection_models()
_mc["sentence-transformers"] = _fetch_st_models()
_mc["vlm"] = _fetch_vlm_models()
_mc["extractive-qa"] = _fetch_text_classification_models()
# tabular-classification
_mc["tabular-classification"] = [
"xgboost",
"random_forest",
"ridge",
"logistic_regression",
"svm",
"extra_trees",
"adaboost",
"decision_tree",
"knn",
]
# tabular-regression
_mc["tabular-regression"] = [
"xgboost",
"random_forest",
"ridge",
"svm",
"extra_trees",
"adaboost",
"decision_tree",
"knn",
]
return _mc
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