Check out this example project on how to find transcripts of audio recordings with positive emotions. A good example of a project demonstrating of extract actionable insights from audio! It takes common voice dataset of audio files from hagging face, applies emotion recognition model and whisper-tiny model for the transcripts. All is organized in a nice looking batch pipeline. An interesting detail - No need to extract archives! This pipeline analyzes audio files directly from tar archives, saving you extra steps. Video: https://www.youtube.com/watch?v=OCm5W0L5BTU Colab notebook: https://colab.research.google.com/github/iterative/datachain-examples/blob/main/audio/hf_common_voice.ipynb Jupyter Notebook: https://github.com/iterative/datachain-examples/blob/main/audio/hf_common_voice.ipynb submitted by /u/dmpetrov [link] [comments]

There was a paper released along with a GitHub repository of an extremely well-made transformer designed for testing out new components. But I can't find it! It's not one of the ones that has existed like HuggingFace ones. Any clue? submitted by /u/Breck_Emert [link] [comments]

Hello folks, I've been working on an agentic solution where you can have an autonomous agent taking live calls. We're using a pipeline of Speech to Text, LLM for generating responses and then Text to Speech. In this pipeline, Speech to text is causing some issues because it's difficult to determine when exactly a sentence is over since the user can take pauses. Moreover, when multiple inputs go into LLM, multiple responses are generated and they queue up for Text to speech. How would you solve this problem? How would you also handle cases where the user interrupts the agent? submitted by /u/Leo2000Immortal [link] [comments]

We all saw in class the trade off between bias and variance, that we don't want our train loss to keep going down and our test loss go up. But in practice I feel like doing hyperparameter tuning for classic ML models with GridSearchCV / BayesSearchCV is not enough. Even though I do cross validation, the search.best_model obtained at the end is almost always overfitting. How can you actually perform a search that will give you a robust generalized model with higher chances ? submitted by /u/desslyie [link] [comments]

submitted by /u/BotherBubbly5096 [link] [comments]

Head to head of meme-interpretability with the same image and text prompt! Anecdotal but interesting responses. Also clear winner! submitted by /u/No_Cartoonist8629 [link] [comments]

In my academic field (social sciences) I deal with the problem of bias in SA models. My previous work showed that deep learning SA systems inherit bias (e.g. nonrepresentative of the population political bias) from annotators: https://arxiv.org/abs/2407.13891 Now I devised a solution that used a technique I call semantic blinding to provide only the bare necessary information for the model to predict emotions in text, leaving no signal for the model to overfit and produce bias from: https://arxiv.org/abs/2411.12493 Interested to hear your thoughts before I publish the SProp Gnn. Do you think it could be useful beyond the academia? submitted by /u/Hub_Pli [link] [comments]

When would the phase 2 decision come out? I know the date is December 9th, but would there be chances for the result to come out earlier than the announced date? or did it open the result at exact time in previous years? (i.e., 2024, 2023, 2022 ....) Kinda make me sick to keep waiting. submitted by /u/No-Style-7975 [link] [comments]

Few months ago, I migrated from TF 2.0 to Jax. I found that jax is significantly faster than Tf. I noticed in the official documentation that it relies on XLA default that uses JIT compilation which makes execution faster. I also noticed that TF graphs also have option to enable JIT compilation with XLA. But still jax dominates TF with XLA. I just want to know why. submitted by /u/Odd-Detective289 [link] [comments]

Multimodal AI is changing the game by combining text, images, and even video into a single, cohesive system. It’s being talked about as a major leap in AI capabilities. What industries do you think will benefit the most from this tech? And are there any challenges you see in integrating these models into everyday use? Would love to hear everyone's thoughts! submitted by /u/Frosty_Programmer672 [link] [comments]

Hello everyone, I am looking for methods that can automatically categorize and select layers from for transfer learning. If you know any such methods or research please let me know or share. Thanks submitted by /u/reshail_raza [link] [comments]

Imagine a customer support chatbot for an e-commerce platform that retrieves relevant product details from its knowledge base and performs web searches for additional information. Furthermore, it remembers past conversations to deliver a seamless and personalized experience for returning users. Here is how it works: - Store your own data in the knowledge base—in our case, a Website URL. - Convert the data into embeddings and save it in the Qdrant Vector Database. - Use phidata Agentic Workflow to combine Tools, LLM, Memory, and the Knowledge Base. Code Implementation Video: https://www.youtube.com/watch?v=CDC3GOuJyZ0 submitted by /u/External_Ad_11 [link] [comments]

New paper and code for the scale-wise transformer for fast text-to-image generation from our team at Yandex Research Switti outperforms existing T2I AR models and competes with state-of-the-art T2I diffusion models while being faster than distilled diffusion models. Code with checkpoints: https://github.com/yandex-research/switti Generation examples submitted by /u/_puhsu [link] [comments]

Hello everyone, I've been working on a project for some time now and wanted to share a concept I'm exploring. As we know, decision tree-based models typically split the feature space using certain metrics like MSE, entropy, etc. I started thinking about an alternative approach: instead of splitting individual features, what if we could split the entire space directly? However, this seemed quite difficult, as determining boundaries and regions in the space is challenging. Then I had an idea—what if I project the data onto a line within the feature space, and then split that line, like how trees are typically built on individual features? In essence, I’m thinking of projecting points onto a line and then using tree-based methods to split them progressively. Here's a high-level view of the algorithm: Fit a linear regression model to the dataset (normalized values). Project the data onto the line defined by the regression. Apply a decision tree on this projection, effectively splitting one feature (the projection axis). Calculate the residuals and fit another linear model on the residuals, applying boosting in the process. Since the new linear regressions fitted on the residuals will define separate lines, I assume that through boosting, the model will gradually divide the data in the desired manner over time. You can read a more detailed description of the algorithm here: Algorithm PDF. To visualize how the decision boundaries are formed in a 2D dataset: SpaceBoostingRegressor Note: If you want to see a visual example, uploading high-dimensional GIFs can sometimes be an issue. You can check out the example here: Gif on GitHub. Also you can check the code in the repository: Repository This approach is simple because it assumes linearity, and it works in scenarios where there is a high linear correlation between the target and features while also allowing for some non-linear relationships. You can see an example in the repo,example.ipynb file. However, I’m not sure how well it would perform on real-world datasets, as the linear assumption may not always hold. I want to take this algorithm further, but speed is important for scaling. Techniques like PCA don't seem to help because I need the line to reflect the variance in both the target and feature space, rather than just feature variance. I tried using MLPs and extracting the embeddings from a hidden layer before the output layer, which works better since we're evaluating the target in a larger space, but this approach becomes too slow and isn’t feasible in practice. I think this project has great potential, and I’m looking for feedback, ideas, or anyone interested in collaborating. Any comments or suggestions are welcome! submitted by /u/zedeleyici3401 [link] [comments]

Edit: I have misworded the title as if OpenAI would confirm how O1 was implemented. I have changed the text to reflect what I meant say. I really want to deep dive into the technicals of how the O1 models perform better than previous models. Have researchers come to any definitive agreement as to what OpenAI could have possible done to achieve O1? From reading online I hear about MCTS, COT... etc, but are any of these methods in large agreement by researhers? submitted by /u/Daveboi7 [link] [comments]

Hey everyone, I'm working on a pipeline to encode over 100 million rows into embeddings using SentenceTransformers, PySpark, and Pandas UDF on Dataproc Serverless. Currently, it takes several hours to process everything. I only have one column containing sentences, each under 30 characters long. These are encoded into 64-dimensional vectors using a custom model in a Docker image. At the moment, the job has been running for over 12 hours with 57 executors (each with 24GB of memory and 4 cores). I’ve partitioned the data into 2000 partitions, hoping to speed up the process, but it's still slow. Here’s the core part of my code: F.pandas_udf(returnType=ArrayType(FloatType())) def encode_pd(x: pd.Series) -> pd.Series: try: model = load_model() return pd.Series(model.encode(x, batch_size=512).tolist()) except Exception as e: logger.error(f"Error in encode_pd function: {str(e)}") raise The load_model function is as follows: def load_model() -> SentenceTransformer: model = SentenceTransformer( "custom_model", device="cpu", cache_folder=os.environ['SENTENCE_TRANSFORMERS_HOME'], truncate_dim=64 ) return model I tried broadcasting the model, but I couldn't refer to it inside the Pandas UDF. Does anyone have suggestions to optimize this? Perhaps ways to load the model more efficiently, reduce execution time, or better utilize resources? submitted by /u/nidalap24 [link] [comments]

Has anyone had experience using an ML model to recognize handwriting like this? The notebook contains important information that could help me decode a puzzle I’m solving. I have a total of five notebooks, all from the same person, with consistent handwriting patterns. My goal is to use ML to recognize and extract the notes, then convert them into a digital format. I was considering Google API after knowing that Tesseract might not work well with illegible samples like this. However, I’m not sure if Google API will be able to read it either. I read somewhere that OCR+ CNN might work, so I’m here asking for suggestions. Thanks! Any advice/suggestions are welcomed! submitted by /u/SpaceSheep23 [link] [comments]

Recent research has revealed that state-of-the-art LLMs often struggle with mathematical reasoning: The GSM-Symbolic benchmark highlights that LLMs frequently fail when numerical values or question wording change, suggesting reliance on memorization rather than true mathematical understanding (source). Logical reasoning studies, like the AIW problem, show inconsistent performance even for basic reasoning tasks (source). Furthermore, research indicates LLMs lack effective self-correction capabilities, with performance degrading after multiple iterations (source). Despite these challenges, OpenAI’s new O1 model reportedly exceeds all other models in math benchmarks. How does it address these known issues in mathematical reasoning, such as: Reliance on memorization instead of understanding? Inconsistencies in reasoning across problem variations? Inability to self-correct errors effectively? Would love to hear insights or hypotheses! submitted by /u/AImSamy [link] [comments]

I am finetuning Qwen2.5 instruct using qLoRA, for a instruction tuning like dataset with around 50k samples, and my training loss is looking weird. What might be the issue, and how can i possibly fix it? Finetuning details are as following, along with training loss graphs: Code: ``` model, tokenizer = FastLanguageModel.from_pretrained( model_name = "Qwen/Qwen2.5-32B-Instruct", max_seq_length = max_seq_length, dtype = None, load_in_4bit = True, ) # Do model patching and add fast LoRA weights model = FastLanguageModel.get_peft_model( model, r = 64, target_modules = ["q_proj", "k_proj", "v_proj", "o_proj", "gate_proj", "up_proj", "down_proj",], lora_alpha = 128, lora_dropout = 0, # Supports any, but = 0 is optimized bias = "none", # Supports any, but = "none" is optimized use_gradient_checkpointing = "unsloth", # True or "unsloth" for very long context random_state = 3407, max_seq_length = max_seq_length, use_rslora = True, # We support rank stabilized LoRA loftq_config = None, # And LoftQ ) from trl import SFTTrainer from transformers import TrainingArguments from unsloth import is_bfloat16_supported trainer = SFTTrainer( model = model, tokenizer = tokenizer, train_dataset = dataset['train'], dataset_text_field = "text", max_seq_length = max_seq_length, dataset_num_proc = 2, packing = False, args = TrainingArguments( per_device_train_batch_size = 4, gradient_accumulation_steps = 2, warmup_steps = 5, num_train_epochs = 3, learning_rate = 0.0002, fp16 = not is_bfloat16_supported(), bf16 = is_bfloat16_supported(), logging_steps = 10, optim = "adamw_8bit", weight_decay = 0.01, lr_scheduler_type = "linear", seed = 69, output_dir = "outputs", report_to = "wandb", save_strategy = "steps", save_steps = 50, save_total_limit=10 ), ) ``` Training Loss: https://preview.redd.it/s2vn2z44y55e1.png?width=2888&format=png&auto=webp&s=a4e1038e9c27dae96d7e25fcb5db852c794efd97 submitted by /u/Raise_Fickle [link] [comments]

submitted by /u/HydrousIt [link] [comments]