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Admin: @Raminmousa ID: @Machine_learn link: https://t.me/Machine_learn
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👩💻 Python Notes for Professionals book
🔗 Book
@Machine_learn
💠Title:BERTCaps: BERT Capsule for persian Multi-domain Sentiment Analysis.
🔺Abstract:
Sentiment classification is widely known as a domain-dependent problem. In order to learn an accurate domain-specific sentiment classifier, a large number of labeled samples are needed, which are expensive and time-consuming to annotate. Multi-domain sentiment analysis based on multi-task learning can leverage labeled samples in each single domain, which can alleviate the need for large amount of labeled data in all domains. In this article, the purpose is BERTCaps to provide a multi-domain classifier. In this model, BERT was used for Instance Representation and Capsule was used for instance learning. In the evaluation dataset, the model was able to achieve an accuracy of 0.9712 in polarity classification and an accuracy of 0.8509 in domain classification.
journal: https://www.sciencedirect.com/journal/array
If:2.3
جايگاه ٢ و ٤ اين مقاله رو نياز داريم.
دوستاني كه مايل به شركت هستن مي تونن به ايدي بنده پيام بدن.
@Raminmousa
@Paper4money
@Machine_learn
📑A Survey of Deep Learning Methods for Estimating the Accuracy of Protein Quaternary Structure Models
📎 Study the paper
@Machine_learn
💠Title:BERTCaps: BERT Capsule for persian Multi-domain Sentiment Analysis.
🔺Abstract:
Sentiment classification is widely known as a domain-dependent problem. In order to learn an accurate domain-specific sentiment classifier, a large number of labeled samples are needed, which are expensive and time-consuming to annotate. Multi-domain sentiment analysis based on multi-task learning can leverage labeled samples in each single domain, which can alleviate the need for large amount of labeled data in all domains. In this article, the purpose is BERTCaps to provide a multi-domain classifier. In this model, BERT was used for Instance Representation and Capsule was used for instance learning. In the evaluation dataset, the model was able to achieve an accuracy of 0.9712 in polarity classification and an accuracy of 0.8509 in domain classification.
journal: https://www.sciencedirect.com/journal/array
If:2.3
جايگاه ٢ و ٤ اين مقاله رو نياز داريم.
دوستاني كه مايل به شركت هستن مي تونن به ايدي بنده پيام بدن.
@Raminmousa
@Paper4money
@Machine_learn
SAM2Long: Enhancing SAM 2 for Long Video Segmentation with a Training-Free Memory Tree
🖥 Github: https://github.com/mark12ding/sam2long
📕 Paper: https://arxiv.org/abs/2410.16268v1
🤗 HF: https://huggingface.co/papers/2410.16268
@Machine_learn
🌟 Aya Expanse
🟢Aya Expanse 32B
🟢Aya Expanse 8B
🟠Aya Expanse 32B-GGUF
🟠Aya Expanse 8B-GGUF
Expanse 8B Transformers :from transformers import AutoTokenizer, AutoModelForCausalLM
model_id = "CohereForAI/aya-expanse-8b"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)
# Format the message with the chat template
messages = [{"role": "user", "content": " %prompt% "}]
input_ids = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt")
## <BOS_TOKEN><|START_OF_TURN_TOKEN|><|USER_TOKEN|>%prompt%<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>
gen_tokens = model.generate(
input_ids,
max_new_tokens=100,
do_sample=True,
temperature=0.3,
)
gen_text = tokenizer.decode(gen_tokens[0])
print(gen_text)
🟡GGUF 32B
🟡GGUF 8B
🟡Demo
@Machine_learn
با عرض سلام امروز اخرين وقت براي مشاركت در اين مقاله مي باشد...!
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THINKING LLMS: GENERAL INSTRUCTION FOLLOWING WITH THOUGHT GENERATION
📚 Reed
@Machine_learn
با عرض سلام نيازمند co-author براي مقاله زیر هستيم.
Target Journal: International Journal of Media and Networks | Opast Publishing Group (opastpublishers.com)
if: 1.2
Paper link: A Survey of Generative Adversarial Network on Next Generation Network[v1] | Preprints.org
تغييرات كامل نسخه نهايي تا يك هفته اينده اعمال ميشه كسي از دوستان تمايل به همكاري داشت به ايدي بنده پيام بدن.
@Raminmousa
@Machine_learn
/channel/+SP9l58Ta_zZmYmY0
An Infinite Descent into Pure Mathematics
📚 Book
@Machine_learn
NotebookLlama: An Open Source version of NotebookLM
📚 Book
@Machine_learn
با عرض سلام
در حال نوشتن مقاله اي تحت عنوان
title:A Comparative Survey on Large Language Models for Biological Data and Knowledge Graph systems
هستيم كه ژورنال هدف Nature ميباشد. ٢ نفر از دوستان به دليل مشغله كاري نتونستن همكاري كنن. نفر ٤ و نفر ٦ از اين ليست رو تصمیم به جايگذيني كرديم. دوستاني كه توانايي كار دارن لطفا به بنده پيام بدن. تسك ها كامل مشخص شده و هزينه هر شخص هم تعيين شده.
@Raminmousa
@Machine_learn
/channel/+SP9l58Ta_zZmYmY0
💡 SAM2Long, a training-free enhancement to SAM 2 for long-term video segmentation
🟡Technical Report: https://huggingface.co/papers/2410.16268
🟡Github: https://github.com/Mark12Ding/SAM2Long
🟡Homepage: https://mark12ding.github.io/project/SAM2Long/
@Machine_learn
private link:
/channel/+SP9l58Ta_zZmYmY0
الحمدالله تو اين بازه ٣ ماه تونستيم مقالات مشاركتي رو تحت وظايف زير انجام بديم:
🔹ثبت ٤ مقاله در حوزه Multi-modal wond classification
🔹ارائه ی دو مقاله در حوزه ی breast cancer segmentation
🔹 ارائه ی سه مقاله در حوزه ی cancer detection
که ۸۰٪ مراحل این مقالات هم تموم شده.
به زودی پس از اتمام این مقالات لیستی از مقالات مشارکتی رو خواهیم داشت .
/channel/+SP9l58Ta_zZmYmY0
📖 LLM-Agent-Paper-List is a repository of papers on the topic of agents based on large language models (LLM)! The papers are divided into categories such as LLM agent architectures, autonomous LLM agents, reinforcement learning (RL), natural language processing methods, multimodal approaches and tools for developing LLM agents, and more.
🖥 Github
/channel/deep_learning_proj
Data Pipelines with Apache Airflow
📘 book
@Machine_learn
Ms - SmolLM2 1.7B - beats Qwen 2.5 1.5B & Llama 3.21B, Apache 2.0 licensed, trained on 11 Trillion tokens 🔥
> 135M, 360M, 1.7B parameter model
> Trained on FineWeb-Edu, DCLM, The Stack, along w/ new mathematics and coding datasets
> Specialises in Text rewriting, Summarization & Function Calling
> Integrated with transformers & model on the hub!
You can run the 1.7B in less than 2GB VRAM on a Q4 👑
Fine-tune, run inference, test, train, repeat - intelligence is just 5 lines of code away!
https://huggingface.co/collections/HuggingFaceTB/smollm2-6723884218bcda64b34d7db9
@Machine_learn
تخفيف ٥٠٪🔹
دو پكيچ كدنويسي پايه يادگيري ماشين و يادگيري عميق به همراه ٣٦ بروژه عملي با پشتيباني ٢ ماهه .
جهت سفارش به ايدي بنده پيام بدين.
🔺
هزینه هر دو پک با تخفيف ۱۵۰۰ هزار ميباشد.
@Raminmousa
Intermediate Python
📖 Book
@Machine_learn
⚡️ Stable Diffusion 3.5 Large.# install Diffusers
pip install -U diffusers
# Inference
import torch
from diffusers import StableDiffusion3Pipeline
pipe = StableDiffusion3Pipeline.from_pretrained("stabilityai/stable-diffusion-3.5-large", torch_dtype=torch.bfloat16)
pipe = pipe.to("cuda")
image = pipe(
"A happy woman laying on a grass",
num_inference_steps=28,
guidance_scale=3.5,
).images[0]
image.save("woman.png")
🟡Arxiv
@Machine_learn
Title:
Advanced Classification of Drug-Drug Interactions for Assessing Adverse Effect Risks of Fluvoxamine and Curcumin Using Deep Learning in COVID-19
———————————————————————
Keywords:
Drug–Drug Interactions; Deep Neural Network; Fluvoxamine; Curcumin; Machine Learning.
———————————————————————
Journal of Infrastructure, Policy and Development
نفر اول پرشده
نفر دوم و سوم و چهارم خالی هست.
مقاله در اخرین ریوایزد خود می باشد.
@Raminmousa
@Machine_learn
/channel/+SP9l58Ta_zZmYmY0
📕 Applied Causal #Inference Powered by #MachineLearning
📌Book
@Machine_learn
🌟 Zamba2-Instruct
В семействе 2 модели:
🟢Zamba2-1.2B-instruct;
🟠Zamba2-2.7B-instruct.# Clone repo
git clone https://github.com/Zyphra/transformers_zamba2.git
cd transformers_zamba2
# Install the repository & accelerate:
pip install -e .
pip install accelerate
# Inference:
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
tokenizer = AutoTokenizer.from_pretrained("Zyphra/Zamba2-2.7B-instruct")
model = AutoModelForCausalLM.from_pretrained("Zyphra/Zamba2-2.7B-instruct", device_map="cuda", torch_dtype=torch.bfloat16)
user_turn_1 = "user_prompt1."
assistant_turn_1 = "assistant_prompt."
user_turn_2 = "user_prompt2."
sample = [{'role': 'user', 'content': user_turn_1}, {'role': 'assistant', 'content': assistant_turn_1}, {'role': 'user', 'content': user_turn_2}]
chat_sample = tokenizer.apply_chat_template(sample, tokenize=False)
input_ids = tokenizer(chat_sample, return_tensors='pt', add_special_tokens=False).to("cuda")
outputs = model.generate(**input_ids, max_new_tokens=150, return_dict_in_generate=False, output_scores=False, use_cache=True, num_beams=1, do_sample=False)
print((tokenizer.decode(outputs[0])))
🖥GitHub
/channel/deep_learning_proj
Tutorial on Diffusion Models for Imaging and Vision
📚 Book
@Machine_learn
The State of AI Report
📚 Report
@Machine_learn
📑 A guide to RNA sequencing and functional analysis
📎 Study the paper
@Machine_learn
فقط نفر ۲ و ۴ از این باقی مونده ....!
Читать полностью…
Title: BERTCaps: BERT Capsule for persian Multi-domain Sentiment Analysis.
Abstract:
Sentiment classification is widely known as a domain-dependent problem. In order to learn an accurate domain-specific sentiment classifier, a large number of labeled samples are needed, which are expensive and time-consuming to annotate. Multi-domain sentiment analysis based on multi-task learning can leverage labeled samples in each single domain, which can alleviate the need for large amount of labeled data in all domains. In this article, the purpose is BERTCaps to provide a multi-domain classifier. In this model, BERT was used for Instance Representation and Capsule was used for instance learning. In the evaluation dataset, the model was able to achieve an accuracy of 0.9712 in polarity classification and an accuracy of 0.8509 in domain classification.
journal: https://www.sciencedirect.com/journal/array
If: 2.3
نفرات ٢ تا ٤ اين مقاله رو نياز داريم.
دوستاني كه مايل به شركت هستن مي تونن به ايدي بنده پيام بدن.
@Raminmousa
@Paper4money
@Machine_learn