full KG from HF + simple Graph Search
This commit is contained in:
DaniG2106
parent
d447b8f3ea
commit
a295bc6657
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@ -0,0 +1,189 @@
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|
||||
sc:mlgoal sc:ObjectDetection ;
|
||||
sc:output sc:BoundingBox,
|
||||
sc:Labels .
|
||||
|
||||
sc:DETR a sc:Transformer ;
|
||||
sc:hasParameters sc:41524768 ;
|
||||
sc:input sc:ImageData ;
|
||||
sc:mlgoal sc:ObjectDetection ;
|
||||
sc:output sc:BoundingBox,
|
||||
sc:Labels .
|
||||
|
||||
sc:EEGData a sc:TemporalData .
|
||||
|
||||
sc:FullyConnected a sc:Model ;
|
||||
sc:input sc:Features ;
|
||||
sc:mlgoal sc:Classification ;
|
||||
sc:output sc:Labels .
|
||||
|
||||
sc:IMUData a sc:TemporalData .
|
||||
|
||||
sc:InceptionV4 a sc:Model ;
|
||||
sc:hasParameters sc:42679816 ;
|
||||
sc:input sc:ImageData ;
|
||||
sc:mlgoal sc:ImageClassification ;
|
||||
sc:output sc:Labels .
|
||||
|
||||
sc:OneFormer a sc:Transformer ;
|
||||
sc:hasParameters sc:218781179 ;
|
||||
sc:input sc:ImageData ;
|
||||
sc:mlgoal sc:ImageSegmentation ;
|
||||
sc:output sc:Labels .
|
||||
|
||||
sc:Open13 a sc:Model ;
|
||||
sc:input sc:Spectrogram ;
|
||||
sc:mlgoal sc:InformationEmbedding ;
|
||||
sc:output sc:Features .
|
||||
|
||||
sc:SAM a sc:Transformer ;
|
||||
sc:hasParameters sc:93735472 ;
|
||||
sc:input sc:ImageData ;
|
||||
sc:mlgoal sc:ImageSegmentation ;
|
||||
sc:output sc:Labels .
|
||||
|
||||
sc:STFT a sc:Operation ;
|
||||
sc:input sc:TemporalData ;
|
||||
sc:output sc:Spectrogram .
|
||||
|
||||
sc:TemporalConvolution a sc:Model ;
|
||||
sc:input sc:TemporalData ;
|
||||
sc:mlgoal sc:InformationEmbedding ;
|
||||
sc:output sc:Features .
|
||||
|
||||
sc:VIT a sc:Model ;
|
||||
sc:hasParameters sc:86567656 ;
|
||||
sc:input sc:ImageData ;
|
||||
sc:mlgoal sc:ImageClassification ;
|
||||
sc:output sc:Labels .
|
||||
|
||||
sc:VideoData a sc:CompositeData ;
|
||||
sc:TemporalSeq sc:ImageData ;
|
||||
sc:is sc:SpatialData,
|
||||
sc:TemporalData .
|
||||
|
||||
sc:YOLO a sc:Model ;
|
||||
sc:hasParameters sc:68229648 ;
|
||||
sc:input sc:ImageData ;
|
||||
sc:mlgoal sc:ObjectDetection ;
|
||||
sc:output sc:BoundingBox,
|
||||
sc:Labels .
|
||||
|
||||
sc:YOLOS a sc:Transformer ;
|
||||
sc:hasParameters sc:6488736 ;
|
||||
sc:input sc:ImageData ;
|
||||
sc:mlgoal sc:ObjectDetection ;
|
||||
sc:output sc:BoundingBox,
|
||||
sc:Labels .
|
||||
|
||||
sc:distilbert a sc:Transformer ;
|
||||
sc:hasParameters sc:135326979 ;
|
||||
sc:input sc:TextData ;
|
||||
sc:mlgoal sc:NaturalLanguageUnderstanding,
|
||||
sc:SentimentAnalysis ;
|
||||
sc:output sc:Labels .
|
||||
|
||||
sc:135326979 a sc:numberOfParameters .
|
||||
|
||||
sc:167360261 a sc:numberOfParameters .
|
||||
|
||||
sc:218781179 a sc:numberOfParameters .
|
||||
|
||||
sc:41524768 a sc:numberOfParameters .
|
||||
|
||||
sc:42679816 a sc:numberOfParameters .
|
||||
|
||||
sc:48438489 a sc:numberOfParameters .
|
||||
|
||||
sc:6488736 a sc:numberOfParameters .
|
||||
|
||||
sc:68229648 a sc:numberOfParameters .
|
||||
|
||||
sc:86567656 a sc:numberOfParameters .
|
||||
|
||||
sc:93735472 a sc:numberOfParameters .
|
||||
|
||||
sc:SpectralDensity a sc:DiscreteData .
|
||||
|
||||
sc:CompositeData a sc:Datatype .
|
||||
|
||||
sc:ContinuousData a sc:Datatype .
|
||||
|
||||
sc:ImageClassification a sc:Classification ;
|
||||
sc:input sc:ImageData ;
|
||||
sc:output sc:Labels .
|
||||
|
||||
sc:ImageSegmentation a sc:Classification ;
|
||||
sc:input sc:ImageData ;
|
||||
sc:output sc:Labels .
|
||||
|
||||
sc:InformationEmbedding a sc:MLGoal ;
|
||||
sc:output sc:Features .
|
||||
|
||||
sc:SpatialData a sc:ContinuousData .
|
||||
|
||||
sc:Spectrogram a sc:CompositeData ;
|
||||
sc:TemporalSeq sc:SpectralDensity ;
|
||||
sc:islike sc:ImageData .
|
||||
|
||||
sc:Classification a sc:MLGoal ;
|
||||
sc:input sc:Features ;
|
||||
sc:output sc:Labels .
|
||||
|
||||
sc:NaturalLanguageUnderstanding a sc:MLGoal ;
|
||||
sc:input sc:TextData ;
|
||||
sc:output sc:Features .
|
||||
|
||||
sc:SentimentAnalysis a sc:NaturalLanguageUnderstanding ;
|
||||
sc:output sc:Labels .
|
||||
|
||||
sc:TextData a sc:DiscreteData ;
|
||||
sc:input sc:SentimentAnalysis .
|
||||
|
||||
sc:ObjectDetection a sc:MLGoal ;
|
||||
sc:input sc:ImageData ;
|
||||
sc:output sc:BoundingBox .
|
||||
|
||||
sc:DiscreteData a sc:Datatype .
|
||||
|
||||
sc:BoundingBox a sc:DiscreteData .
|
||||
|
||||
sc:Features a sc:DiscreteData .
|
||||
|
||||
sc:TemporalData a sc:ContinuousData .
|
||||
|
||||
sc:Transformer a sc:Model .
|
||||
|
||||
sc:numberOfParameters a rdfs:Class .
|
||||
|
||||
sc:ImageData a sc:SpatialData .
|
||||
|
||||
sc:Labels a sc:DiscreteData .
|
||||
|
||||
|
|
@ -13,7 +13,7 @@ tags = json.loads(response.content)["pipeline_tag"]
|
|||
print(tags)
|
||||
|
||||
|
||||
conn = sqlite3.connect('huggingface_data.db')
|
||||
conn = sqlite3.connect('huggingface.db')
|
||||
cursor = conn.cursor()
|
||||
|
||||
|
||||
|
|
@ -21,9 +21,9 @@ cursor.execute('''
|
|||
CREATE TABLE IF NOT EXISTS Models (
|
||||
model_id TEXT PRIMARY KEY,
|
||||
model_name TEXT,
|
||||
tag TEXT,
|
||||
customTags TEXT,
|
||||
subType TEXT,
|
||||
problem TEXT,
|
||||
tags TEXT,
|
||||
coverTag TEXT,
|
||||
library TEXT,
|
||||
downloads INTEGER,
|
||||
likes INTEGER,
|
||||
|
|
@ -33,14 +33,14 @@ CREATE TABLE IF NOT EXISTS Models (
|
|||
conn.commit()
|
||||
|
||||
for tag in tags:
|
||||
tag_name = tag["id"]
|
||||
subType = tag["subType"]
|
||||
problem = tag["id"]
|
||||
coverTag = tag["subType"]
|
||||
|
||||
print(tag_name)
|
||||
print(problem)
|
||||
|
||||
response = requests.get(
|
||||
"https://huggingface.co/api/models",
|
||||
params={"limit": "unlimited", "full": "True", "config": "True", "sort": "downloads", "filter": tag_name},
|
||||
params={"limit": "unlimited", "full": "True", "config": "True", "sort": "downloads", "filter": problem},
|
||||
headers={}
|
||||
)
|
||||
data = json.loads(response.content)
|
||||
|
|
@ -49,16 +49,16 @@ for tag in tags:
|
|||
try:
|
||||
model_id = model['_id']
|
||||
model_name = model['id']
|
||||
customTags = json.dumps(model['tags'])
|
||||
tags = json.dumps(model['tags'])
|
||||
library = model["library_name"]
|
||||
downloads = model['downloads']
|
||||
likes = model['downloads']
|
||||
modified = model['lastModified']
|
||||
|
||||
cursor.execute('''
|
||||
INSERT INTO Models (model_id, model_name, tag, customTags, subType, library, downloads, likes, lastModified)
|
||||
INSERT INTO Models (model_id, model_name, problem, tags, coverTag, library, downloads, likes, lastModified)
|
||||
VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?)
|
||||
''', (model_id, model_name, tag_name, customTags, subType, library, downloads, likes, modified))
|
||||
''', (model_id, model_name, problem, tags, coverTag, library, downloads, likes, modified))
|
||||
except sqlite3.IntegrityError:
|
||||
# print(model)
|
||||
pass
|
||||
|
|
|
|||
File diff suppressed because it is too large
Load Diff
Binary file not shown.
|
|
@ -0,0 +1,153 @@
|
|||
from rdflib import Graph, Namespace, RDF, Literal
|
||||
from rdflib.namespace import XSD
|
||||
|
||||
|
||||
def load_graph(file_path):
|
||||
g = Graph()
|
||||
g.parse(file_path, format="turtle")
|
||||
return g
|
||||
|
||||
def get_cover_tags(graph):
|
||||
query = """
|
||||
PREFIX conn: <http://example.org/conn/>
|
||||
SELECT ?coverTag
|
||||
WHERE {
|
||||
?coverTag a conn:CoverTag .
|
||||
}
|
||||
"""
|
||||
results = graph.query(query)
|
||||
cover_tags = [row[0] for row in results]
|
||||
return cover_tags
|
||||
|
||||
def get_problems(graph):
|
||||
query = """
|
||||
PREFIX conn: <http://example.org/conn/>
|
||||
SELECT ?problem
|
||||
WHERE {
|
||||
?problem a conn:Problem .
|
||||
}
|
||||
"""
|
||||
results = graph.query(query)
|
||||
problems = [row[0] for row in results]
|
||||
return problems
|
||||
|
||||
def get_problems_for_cover_tag(graph, cover_tag_literal_text):
|
||||
cover_tag_literal = Literal(cover_tag_literal_text, datatype=XSD.string)
|
||||
|
||||
query = """
|
||||
PREFIX conn: <http://example.org/conn/>
|
||||
PREFIX problem: <http://example.org/problem/>
|
||||
PREFIX tag: <http://example.org/tag/>
|
||||
SELECT ?problem
|
||||
WHERE {
|
||||
?problem a conn:Problem .
|
||||
?problem conn:hasCoverTag ?coverTag .
|
||||
FILTER (?coverTag = ?cover_tag_literal)
|
||||
}
|
||||
"""
|
||||
|
||||
results = graph.query(query, initBindings={'cover_tag_literal': cover_tag_literal})
|
||||
problems = [row[0] for row in results]
|
||||
return problems
|
||||
|
||||
def get_models_for_problem(graph, problem_literal_text):
|
||||
problem_literal = Literal(problem_literal_text, datatype=XSD.string)
|
||||
|
||||
query = """
|
||||
PREFIX conn: <http://example.org/conn/>
|
||||
PREFIX model: <http://example.org/model/>
|
||||
SELECT ?model ?downloads
|
||||
WHERE {
|
||||
?model a conn:Model .
|
||||
?model conn:hasProblem ?problem .
|
||||
?model conn:downloads ?downloads .
|
||||
FILTER (?problem = ?problem_literal)
|
||||
}
|
||||
ORDER BY DESC(?downloads)
|
||||
"""
|
||||
|
||||
results = graph.query(query, initBindings={'problem_literal': problem_literal})
|
||||
models = [(row[0], row[1]) for row in results]
|
||||
return models
|
||||
|
||||
def get_model_details(graph, model_name):
|
||||
model_literal = Literal(model_name, datatype=XSD.string)
|
||||
|
||||
query = """
|
||||
PREFIX conn: <http://example.org/conn/>
|
||||
PREFIX model: <http://example.org/model/>
|
||||
PREFIX xsd: <http://www.w3.org/2001/XMLSchema#>
|
||||
SELECT ?model ?id ?name ?problem ?coverTag ?library ?downloads ?likes ?lastModified
|
||||
WHERE {
|
||||
?model a conn:Model .
|
||||
?model conn:model_name ?name .
|
||||
?model conn:model_id ?id .
|
||||
?model conn:hasProblem ?problem .
|
||||
?model conn:hasCoverTag ?coverTag .
|
||||
?model conn:usesLibrary ?library .
|
||||
?model conn:downloads ?downloads .
|
||||
?model conn:likes ?likes .
|
||||
?model conn:lastModified ?lastModified .
|
||||
FILTER (?name = ?model_literal)
|
||||
}
|
||||
"""
|
||||
|
||||
results = graph.query(query, initBindings={'model_literal': model_literal})
|
||||
details = {}
|
||||
for row in results:
|
||||
details = {
|
||||
'model_uri': row[0],
|
||||
'id': row[1],
|
||||
'name': row[2],
|
||||
'problem': row[3],
|
||||
'coverTag': row[4],
|
||||
'library': row[5],
|
||||
'downloads': row[6],
|
||||
'likes': row[7],
|
||||
'lastModified': row[8]
|
||||
}
|
||||
return details
|
||||
|
||||
def print_results(literals, label):
|
||||
print(f"List of available {label}:")
|
||||
for literal in literals:
|
||||
print(literal)
|
||||
print()
|
||||
|
||||
def print_models(models):
|
||||
print("Models ordered by downloads:")
|
||||
for model, downloads in models:
|
||||
print(f"Model: {model}, Downloads: {downloads}")
|
||||
print()
|
||||
|
||||
def print_model_details(details):
|
||||
print("Model details:")
|
||||
for key, value in details.items():
|
||||
print(f"{key}: {value}")
|
||||
print()
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
graph = load_graph("graph.ttl")
|
||||
|
||||
# Get Modality (Cover Tags)
|
||||
cover_tags = get_cover_tags(graph)
|
||||
print_results(cover_tags, "cover tags")
|
||||
|
||||
# Get Problem List
|
||||
problems = get_problems(graph)
|
||||
print_results(problems, "problems")
|
||||
|
||||
cover_tag = "audio"
|
||||
problems = get_problems_for_cover_tag(graph, cover_tag)
|
||||
print_results(problems, "problems connected to the cover tag")
|
||||
|
||||
# Get Model by Problem to solve
|
||||
problem_literal_text = "image-to-text"
|
||||
models = get_models_for_problem(graph, problem_literal_text)
|
||||
print_models(models)
|
||||
|
||||
# Get Model Details (Example)
|
||||
model_name = "OleehyO/TexTeller"
|
||||
model_details = get_model_details(graph, model_name)
|
||||
print_model_details(model_details)
|
||||
|
|
@ -0,0 +1,89 @@
|
|||
import sqlite3
|
||||
from rdflib import Graph, Literal, Namespace, RDF
|
||||
from rdflib.namespace import XSD
|
||||
import ast
|
||||
|
||||
# Namespaces
|
||||
CONN = Namespace("http://example.org/conn/")
|
||||
MODEL = Namespace("http://example.org/model/")
|
||||
PROBLEM = Namespace("http://example.org/problem/")
|
||||
TAG = Namespace("http://example.org/tag/")
|
||||
LIBRARY = Namespace("http://example.org/library/")
|
||||
|
||||
# Extract Tags from Stringified List
|
||||
allowed_tags = set()
|
||||
with open('topTags.txt', 'r', encoding='utf-8') as file:
|
||||
for line in file:
|
||||
tag = line.strip()
|
||||
if tag:
|
||||
allowed_tags.add(tag)
|
||||
|
||||
conn = sqlite3.connect('huggingface.db')
|
||||
cursor = conn.cursor()
|
||||
cursor.execute("SELECT * FROM Models")
|
||||
rows = cursor.fetchall()
|
||||
|
||||
g = Graph()
|
||||
|
||||
# Namespaces
|
||||
g.bind("conn", CONN)
|
||||
g.bind("model", MODEL)
|
||||
g.bind("problem", PROBLEM)
|
||||
g.bind("tag", TAG)
|
||||
g.bind("library", LIBRARY)
|
||||
|
||||
# Add triples to the graph
|
||||
for row in rows:
|
||||
model_id = row[0]
|
||||
model_name = row[1]
|
||||
problem = row[2]
|
||||
tags = row[3]
|
||||
coverTag = row[4]
|
||||
library = row[5]
|
||||
downloads = row[6]
|
||||
likes = row[7]
|
||||
lastModified = row[8]
|
||||
|
||||
# Literals for string based KG
|
||||
model_node = Literal(model_name)
|
||||
problem_node = Literal(problem)
|
||||
coverTag_node = Literal(coverTag)
|
||||
library_node = Literal(library)
|
||||
|
||||
# Add Types
|
||||
g.add((model_node, RDF.type, CONN.Model))
|
||||
g.add((problem_node, RDF.type, CONN.Problem))
|
||||
g.add((coverTag_node, RDF.type, CONN.CoverTag))
|
||||
g.add((library_node, RDF.type, CONN.Library))
|
||||
|
||||
# Add Model Details
|
||||
g.add((model_node, CONN.model_name, Literal(model_name, datatype=XSD.string)))
|
||||
g.add((model_node, CONN.model_id, Literal(model_id, datatype=XSD.string)))
|
||||
g.add((model_node, CONN.downloads, Literal(downloads, datatype=XSD.integer)))
|
||||
g.add((model_node, CONN.likes, Literal(likes, datatype=XSD.integer)))
|
||||
g.add((model_node, CONN.lastModified, Literal(lastModified, datatype=XSD.dateTime)))
|
||||
|
||||
# Relationships
|
||||
g.add((model_node, CONN.hasProblem, problem_node))
|
||||
g.add((model_node, CONN.hasCoverTag, coverTag_node))
|
||||
g.add((model_node, CONN.usesLibrary, library_node))
|
||||
g.add((problem_node, CONN.hasCoverTag, coverTag_node))
|
||||
|
||||
try:
|
||||
tags_list = ast.literal_eval(tags)
|
||||
if isinstance(tags_list, list):
|
||||
for tag in tags_list:
|
||||
tag = tag.strip()
|
||||
if tag in allowed_tags:
|
||||
tag_literal = Literal(tag, datatype=XSD.string)
|
||||
g.add((model_node, CONN.hasTag, tag_literal))
|
||||
g.add((tag_literal, RDF.type, CONN.Tag))
|
||||
else:
|
||||
print(f"Tags for model {model_name} are not in a list format")
|
||||
except Exception as e:
|
||||
print(f"Error parsing tags for model {model_name}: {e}")
|
||||
|
||||
# TO FILE
|
||||
g.serialize("graph.ttl", format="turtle")
|
||||
|
||||
conn.close()
|
||||
|
|
@ -0,0 +1,44 @@
|
|||
import sqlite3
|
||||
import ast
|
||||
from collections import Counter
|
||||
|
||||
|
||||
conn = sqlite3.connect('huggingface.db')
|
||||
cursor = conn.cursor()
|
||||
cursor.execute('SELECT tags FROM Models')
|
||||
rows = cursor.fetchall()
|
||||
|
||||
tag_counter = Counter()
|
||||
|
||||
for id,row in enumerate(rows):
|
||||
try:
|
||||
tags_str = row[0]
|
||||
tags = ast.literal_eval(tags_str)
|
||||
|
||||
if isinstance(tags, list):
|
||||
tag_counter.update(tags)
|
||||
else:
|
||||
print(f"Unexpected format, not a list: {tags_str}")
|
||||
except (ValueError, SyntaxError, UnicodeEncodeError) as e:
|
||||
# Some Strings are broken from Huggingface
|
||||
print(f"Error encountered, skipping row: {e}")
|
||||
print(row)
|
||||
conn.close()
|
||||
|
||||
ranked_tags = tag_counter.most_common()
|
||||
|
||||
|
||||
def safe_format(tag, count):
|
||||
try:
|
||||
# return f"{tag}; {count}".encode('utf-8', errors='replace').decode('utf-8')
|
||||
return f"{tag}".encode('utf-8', errors='replace').decode('utf-8')
|
||||
except UnicodeEncodeError:
|
||||
return f"Encoding error for tag {tag}: {count}"
|
||||
|
||||
|
||||
output_file = 'topTags.txt'
|
||||
occurence = 50
|
||||
with open(output_file, 'w', encoding='utf-8') as file:
|
||||
for tag, count in ranked_tags:
|
||||
if count >=occurence and len(tag)>2:
|
||||
file.write(safe_format(tag, count) + '\n')
|
||||
|
|
@ -0,0 +1,267 @@
|
|||
region:us
|
||||
endpoints_compatible
|
||||
transformers
|
||||
pytorch
|
||||
safetensors
|
||||
autotrain_compatible
|
||||
license:apache-2.0
|
||||
tensorboard
|
||||
license:mit
|
||||
generated_from_trainer
|
||||
text2text-generation
|
||||
diffusers
|
||||
bert
|
||||
model-index
|
||||
text-generation-inference
|
||||
feature-extraction
|
||||
onnx
|
||||
license:cc-by-nc-4.0
|
||||
text-generation
|
||||
text-classification
|
||||
vision
|
||||
text-to-audio
|
||||
jax
|
||||
text-to-image
|
||||
license:other
|
||||
sentence-similarity
|
||||
fill-mask
|
||||
image-classification
|
||||
automatic-speech-recognition
|
||||
token-classification
|
||||
object-detection
|
||||
reinforcement-learning
|
||||
audio-classification
|
||||
unconditional-image-generation
|
||||
translation
|
||||
audio-to-audio
|
||||
text-to-speech
|
||||
question-answering
|
||||
summarization
|
||||
video-classification
|
||||
stable-diffusion
|
||||
deep-reinforcement-learning
|
||||
sentence-transformers
|
||||
diffusers:DDPMPipeline
|
||||
diffusion-models-class
|
||||
ml-agents
|
||||
text-embeddings-inference
|
||||
rvc
|
||||
sail-rvc
|
||||
roberta
|
||||
videomae
|
||||
wav2vec2
|
||||
marian
|
||||
timm
|
||||
arxiv:1910.09700
|
||||
image-segmentation
|
||||
audio
|
||||
dataset:imagenet-1k
|
||||
license:creativeml-openrail-m
|
||||
diffusers:StableDiffusionPipeline
|
||||
custom_code
|
||||
vits
|
||||
image-to-text
|
||||
detr
|
||||
ML-Agents-Huggy
|
||||
xlm-roberta
|
||||
speecht5
|
||||
zero-shot-image-classification
|
||||
stable-diffusion-diffusers
|
||||
llama
|
||||
whisper
|
||||
mms
|
||||
arxiv:2305.13516
|
||||
multilingual
|
||||
license:cc-by-4.0
|
||||
segformer
|
||||
clip
|
||||
image-text-to-text
|
||||
zero-shot-classification
|
||||
unity-ml-agents
|
||||
visual-question-answering
|
||||
distilbert
|
||||
image-to-image
|
||||
conversational
|
||||
bart
|
||||
Huggy
|
||||
hubert
|
||||
transformers.js
|
||||
speech
|
||||
image-feature-extraction
|
||||
base_model:MCG-NJU/videomae-base
|
||||
base_model:microsoft/speecht5_tts
|
||||
mteb
|
||||
gguf
|
||||
arxiv:2010.11929
|
||||
tabular-classification
|
||||
diffusers:StableDiffusionXLPipeline
|
||||
dataset:marsyas/gtzan
|
||||
co2_eq_emissions
|
||||
vision-encoder-decoder
|
||||
hf-asr-leaderboard
|
||||
deberta-v2
|
||||
license:bsd-3-clause
|
||||
autotrain
|
||||
arxiv:1908.10084
|
||||
pretraining
|
||||
mt5
|
||||
art
|
||||
stable-diffusion-xl
|
||||
license:cc-by-sa-4.0
|
||||
license:cc-by-nc-sa-4.0
|
||||
mistral
|
||||
open_clip
|
||||
document-question-answering
|
||||
image-captioning
|
||||
license:openrail
|
||||
license:gemma
|
||||
ML-Agents-SnowballTarget
|
||||
audio-spectrogram-transformer
|
||||
dataset:wikipedia
|
||||
vit
|
||||
dataset:facebook/voxpopuli
|
||||
sklearn
|
||||
espnet
|
||||
ML-Agents-SoccerTwos
|
||||
arxiv:2303.15343
|
||||
layoutlmv2
|
||||
base_model:stabilityai/stable-diffusion-xl-base-1.0
|
||||
code
|
||||
arxiv:1908.04913
|
||||
arxiv:1804.00015
|
||||
paligemma
|
||||
medical
|
||||
lora
|
||||
anime
|
||||
arxiv:2209.06794
|
||||
dataset:squad
|
||||
mask-generation
|
||||
dataset:common_voice
|
||||
yolo
|
||||
4-bit
|
||||
base_model:nvidia/mit-b0
|
||||
SnowballTarget
|
||||
sam
|
||||
blip
|
||||
table-transformer
|
||||
arxiv:1905.13648
|
||||
arxiv:1706.03762
|
||||
arxiv:2103.01913
|
||||
arxiv:2310.09199
|
||||
arxiv:2403.08295
|
||||
arxiv:2209.04372
|
||||
arxiv:2205.12522
|
||||
arxiv:2110.11624
|
||||
arxiv:2108.03353
|
||||
arxiv:2010.04295
|
||||
arxiv:2401.06209
|
||||
arxiv:2305.10355
|
||||
arxiv:2203.10244
|
||||
arxiv:1810.12440
|
||||
arxiv:1608.00272
|
||||
joblib
|
||||
arxiv:2407.07726
|
||||
ceb
|
||||
biology
|
||||
dataset:mozilla-foundation/common_voice_11_0
|
||||
llava
|
||||
controlnet
|
||||
electra
|
||||
gpt2
|
||||
yolos
|
||||
albert
|
||||
skops
|
||||
whisper-event
|
||||
dataset:voxpopuli
|
||||
rust
|
||||
vilt
|
||||
depth-estimation
|
||||
arxiv:1512.03385
|
||||
camembert
|
||||
table-question-answering
|
||||
text-to-video
|
||||
tabular-regression
|
||||
base_model:ntu-spml/distilhubert
|
||||
license:gpl-3.0
|
||||
blip-2
|
||||
haw
|
||||
mpnet
|
||||
dataset:imagenet-22k
|
||||
license:openrail++
|
||||
generated_from_keras_callback
|
||||
dataset:imagenet-21k
|
||||
ultralytics
|
||||
xlsr-fine-tuning-week
|
||||
yolov8
|
||||
ultra-realistic
|
||||
dataset:squad_v2
|
||||
ultralyticsplus
|
||||
base_model:facebook/detr-resnet-50
|
||||
stable-diffusion-api
|
||||
arxiv:2110.00476
|
||||
adapter-transformers
|
||||
mbart
|
||||
arxiv:2103.00020
|
||||
arxiv:2201.03545
|
||||
license:llama2
|
||||
dataset:superb
|
||||
fairseq
|
||||
vivit
|
||||
dataset:multi_nli
|
||||
classification
|
||||
tabular
|
||||
deberta
|
||||
base_model:finetune:microsoft/speecht5_tts
|
||||
pony
|
||||
tf-keras
|
||||
dataset:coco
|
||||
base_model:runwayml/stable-diffusion-v1-5
|
||||
arxiv:1705.00652
|
||||
peft
|
||||
loss:MultipleNegativesRankingLoss
|
||||
Anime
|
||||
bitsandbytes
|
||||
unk
|
||||
ner
|
||||
PyTorch
|
||||
base_model:finetune:MCG-NJU/videomae-base
|
||||
license:llama3
|
||||
pegasus
|
||||
robust-speech-event
|
||||
template:sd-lora
|
||||
stable-baselines3
|
||||
arxiv:1910.10683
|
||||
speechbrain
|
||||
base_model:openai/whisper-small
|
||||
timesformer
|
||||
arxiv:1907.11692
|
||||
Transformer
|
||||
arxiv:1810.04805
|
||||
license:unknown
|
||||
nso
|
||||
arxiv:2112.10752
|
||||
spacy
|
||||
qwen2
|
||||
nemo
|
||||
fon
|
||||
dataset:imagefolder
|
||||
awesome-yolov8-models
|
||||
big_vision
|
||||
llama-2
|
||||
facebook
|
||||
dataset:cnn_dailymail
|
||||
ML-Agents-Pyramids
|
||||
war
|
||||
yue
|
||||
sequence-tagger-model
|
||||
flair
|
||||
chemistry
|
||||
quantized
|
||||
dataset:Lin-Chen/ShareGPT4V
|
||||
license:agpl-3.0
|
||||
base_model:MCG-NJU/videomae-base-finetuned-kinetics
|
||||
exbert
|
||||
gpt_neox
|
||||
hmn
|
||||
longt5
|
||||
arxiv:2302.05543
|
||||
Loading…
Reference in New Issue