Hey there how is it going??! I'm at ai major bit I'm not into programming "I don't like it " I was thinking how it could be better specializing at prompt engineering, I need to know somethings about this position, is it available these days??! Is it wanted on the future?? The last and not the least... what about salaries and best countries needs this service , thank you all and have a great day

I have released a revised edition of my machine learning book "Feature Engineering & Selection for Explainable Models: A Second Course for Data Scientists"

I am giving away free Kindle copies between 2nd, 3rd, and 4th December. Grab yours now

USA: https://www.amazon.com/dp/B0DP5DSFH4/

Canada: https://www.amazon.ca/dp/B0DP5DSFH4/

India: https://www.amazon.in/dp/B0DP5DSFH4/

UK: https://www.amazon.co.uk/dp/B0DP5DSFH4/

Germany: https://www.amazon.de/dp/B0DP5DSFH4/

Australia: https://www.amazon.com.au/dp/B0DP5DSFH4/

Nederlands: https://www.amazon.nl/dp/B0DP5DSFH4/

Since non-English LLMs are increasing, i was wondering if companies who hire developers may look into those that have developed non-English models?

I live in a Turkish speaking country and Turkish has a lot of suffixes with a lot of edge cases. As a school project I made an algorithm that can seperate the suffixes from the base word. It also can add suffixes to another word. The algorithm relies solely on the Turkish grammar and does not use AI. Does this count as NLP? If it does it would be a significant advantage for the project

Hi everyone,

I’m having a perplexing issue with the Qwen VL 7B 4bit model sourced from Unsloth. Before fine-tuning, the model's performance was already questionable—it’s making bizarre predictions like identifying a mobile phone as an Accord car. Despite this, I proceeded to fine-tune it using over 100,000+ images, but the fine-tuned model still performs terribly. It struggles to detect even basic elements in images.

For context, my goal with fine-tuning was to train the model to extract structured information from images, specifically:

• Description
• Title
• Brand
• Model
• Price
• Discount price

I chose the 4-bit quantized model from Unsloth because I have an RTX 4070 Ti Super GPU with 16GB VRAM, and I needed a version that would fit within my hardware constraints. However, the results have been disappointing.

To compare, I tested the base Qwen VL 7B model downloaded directly from Hugging Face (8-bit quantization with bitsandbytes) without fine-tuning, and it worked significantly better. The Hugging Face version feels far more robust, while the Unsloth version seems… lobotomized, for lack of a better term.

Here’s my setup:

• Fine-tuned model: Qwen VL 7B (4-bit quantized), sourced from Unsloth
• Base model: Qwen VL 7B (8-bit quantized), downloaded from Hugging Face
• Data: 100,000+ images, preprocessed for training
• Performance issues:
  • Unsloth model (4bit): Poor predictions even before fine-tuning (e.g., misidentifying objects)
  • Hugging Face model (8bit): Performs significantly better without fine-tuning

I’m a beginner in fine-tuning LLMs and vision-language models, so I could be missing something obvious here. Could this issue be related to:

• The quality of the Unsloth version of the model?
• The impact of using a 4-bit quantized model for fine-tuning versus an 8-bit model?
• My fine-tuning setup, hyperparameters, or data preprocessing?

I’d love to understand what’s going on here and how I can fix it. If anyone has insights, guidance, or has faced similar issues, your help would be greatly appreciated. Thanks in advance!

Here is the code sample I used for fine-tuning!

# Step 2: Import Libraries and Load Model
from unsloth import FastVisionModel
import torch
from PIL import Image as PILImage
import os

import logging

# Configure logging
logging.basicConfig(
    level=logging.INFO,  # Set to DEBUG to see all messages
    format='%(asctime)s - %(levelname)s - %(message)s',
    handlers=[
        logging.FileHandler("preprocessing.log"),  # Log to a file
        logging.StreamHandler()  # Also log to console
    ]
)

logger = logging.getLogger(__name__)

# Define the model name
model_name = "unsloth/Qwen2-VL-7B-Instruct"

# Initialize the model and tokenizer
model, tokenizer = FastVisionModel.from_pretrained(
    model_name,
    load_in_4bit=True,  # Use 4-bit quantization to reduce memory usage
    use_gradient_checkpointing="unsloth",  # Enable gradient checkpointing for longer contexts

)

# Step 3: Prepare the Dataset
from datasets import load_dataset, Features, Value

# Define the dataset features
features = Features({
    'local_image_path': Value('string'),
    'main_category': Value('string'),
    'sub_category': Value('string'),
    'description': Value('string'),
    'price': Value('string'),
    'was_price': Value('string'),
    'brand': Value('string'),
    'model': Value('string'),
})

# Load the dataset
dataset = load_dataset(
    'csv',
    data_files='/home/nabeel/Documents/go-test/finetune_qwen/output_filtered.csv',
    split='train',
    features=features,
)
# dataset = dataset.select(range(5000))  # Adjust the number as needed

from collections import defaultdict
# Initialize a dictionary to count drop reasons
drop_reasons = defaultdict(int)

import base64
from io import BytesIO

def convert_to_conversation(sample):
    # Define the target text
    target_text = (
        f"Main Category: {sample['main_category']}\n"
        f"Sub Category: {sample['sub_category']}\n"
        f"Description: {sample['description']}\n"
        f"Price: {sample['price']}\n"
        f"Was Price: {sample['was_price']}\n"
        f"Brand: {sample['brand']}\n"
        f"Model: {sample['model']}"
    )

    # Get the image path
    image_path = sample['local_image_path']

    # Convert to absolute path if necessary
    if not os.path.isabs(image_path):
        image_path = os.path.join('/home/nabeel/Documents/go-test/finetune_qwen/', image_path)
        logger.debug(f"Converted to absolute path: {image_path}")

    # Check if the image file exists
    if not os.path.exists(image_path):
        logger.warning(f"Dropping example due to missing image: {image_path}")
        drop_reasons['missing_image'] += 1
        return None  # Skip this example

    # Instead of loading the image, store the image path
    messages = [
        {
            "role": "user",
            "content": [
                {"type": "text", "text": "You are a expert data entry staff that aims to Extract accurate product information from the given image like Main Category, Sub Category, Description, Price, Was Price, Brand and Model."},
                {"type": "image", "image": image_path}  # Store the image path
            ]
        },
        {
            "role": "assistant",
            "content": [
                {"type": "text", "text": target_text}
            ]
        },
    ]

    return {"messages": messages}

converted_dataset = [convert_to_conversation(sample) for sample in dataset]

print(converted_dataset[2])

# Log the drop reasons
for reason, count in drop_reasons.items():
    logger.info(f"Number of examples dropped due to {reason}: {count}")

# Step 4: Prepare for Fine-tuning
model = FastVisionModel.get_peft_model(
    model,
    finetune_vision_layers=True,     # Finetune vision layers
    finetune_language_layers=True,   # Finetune language layers
    finetune_attention_modules=True, # Finetune attention modules
    finetune_mlp_modules=True,       # Finetune MLP modules

    r=32,           # Rank for LoRA
    lora_alpha=32,  # LoRA alpha
    lora_dropout=0.1,
    bias="none",
    random_state=3407,
    use_rslora=False,  # Disable Rank Stabilized LoRA
    loftq_config=None, # No LoftQ configuration
)

# Enable training mode
FastVisionModel.for_training(model)

# Verify the number of trainable parameters
trainable_params = sum(p.numel() for p in model.parameters() if p.requires_grad)
print(f"Number of trainable parameters: {trainable_params}")

# Step 5: Fine-tune the Model
from unsloth import is_bf16_supported
from unsloth.trainer import UnslothVisionDataCollator
from trl import SFTTrainer, SFTConfig

# Initialize the data collator
data_collator = UnslothVisionDataCollator(model, tokenizer)

# Define the training configuration
training_config = SFTConfig(
    per_device_train_batch_size=1,       # Reduced batch size
    gradient_accumulation_steps=8,       # Effective batch size remains the same
    warmup_steps=5,
    num_train_epochs = 1,                        # Set to a higher value for full training
    learning_rate=1e-5,
    fp16=False,                           # Use FP16 to reduce memory usage
    bf16=True,                          # Ensure bf16 is False if not supported
    logging_steps=1,
    optim="adamw_8bit",
    weight_decay=0.01,
    lr_scheduler_type="linear",
    seed=3407,
    output_dir="outputs",
    report_to="none",                     # Disable reporting to external services
    remove_unused_columns=False,
    dataset_text_field="",
    dataset_kwargs={"skip_prepare_dataset": True},
    dataset_num_proc=1,                   # Match num_proc in mapping
    max_seq_length=2048,
    dataloader_num_workers=0,             # Avoid multiprocessing in DataLoader
    dataloader_pin_memory=True,
)

# Initialize the trainer
trainer = SFTTrainer(
    model=model,
    tokenizer=tokenizer,
    data_collator=data_collator,
    train_dataset=converted_dataset,  # Use the Dataset object directly
    args=training_config,
)

save_directory = "fine_tuned_model_28"

# Save the fine-tuned model
trainer.save_model(save_directory)

# Optionally, save the tokenizer separately (if not already saved by save_model)
tokenizer.save_pretrained(save_directory)

logger.info(f"Model and tokenizer saved to {save_directory}")

# Show current GPU memory stats
gpu_stats = torch.cuda.get_device_properties(0)
start_gpu_memory = round(torch.cuda.max_memory_reserved() / 1024 / 1024 / 1024, 3)
max_memory = round(gpu_stats.total_memory / 1024 / 1024 / 1024, 3)
print(f"GPU = {gpu_stats.name}. Max memory = {max_memory} GB.")
print(f"{start_gpu_memory} GB of memory reserved.")

# Start training
trainer_stats = trainer.train()


# Enable inference mode
FastVisionModel.for_inference(model)

# Example inference
# Define the path to the image for inference
inference_image_path = '/home/nabeel/Documents/go-test/finetune_qwen/test2.jpg'  

# Check if the image exists
if not os.path.exists(inference_image_path):
    logger.error(f"Inference image not found at: {inference_image_path}")
else:
    # Load the image using PIL
    image = PILImage.open(inference_image_path).convert("RGB")
    
    instruction = "You are a expert data entry staff that aims to Extract accurate product information from the given image like Main Category, Sub Category, Description, Price, Was Price, Brand and Model."
    
    messages = [
        {"role": "user", "content": [
            {"type": "image", "image": inference_image_path},  # Provide image path
            {"type": "text", "text": instruction}
        ]}
    ]
    
    # Apply the chat template
    input_text = tokenizer.apply_chat_template(messages, add_generation_prompt=True)
    
    # Tokenize the inputs
    inputs = tokenizer(
        image,
        input_text,
        add_special_tokens=False,
        return_tensors="pt",
    ).to("cuda")
    
    from transformers import TextStreamer
    text_streamer = TextStreamer(tokenizer, skip_prompt=True)
    
    # Generate the response
    _ = model.generate(
        **inputs,
        streamer=text_streamer,
        max_new_tokens=128,
        use_cache=True,
        temperature=1.5,
        min_p=0.1
    )

Needing some advice, maybe this sub will help me. I'm a 24 yo Brazilian with an undergrad degree in Linguistics and Literature at a Brazilian University. My thesis involved NLP by LLMs.

I'm planning on applying for a master's program on Europe. I want to keep studying NLP and, preferably, get a job on this field instead of following an academic path.

I found many Computational Linguistics masters, some NLP ones focused on AI, and some AI ones focused on NLP that accepted Linguistics undergrads.

What should I look for when deciding between the master programs I found in the area?

Please, if my question is too vague, let me know what is missing, I'll give any information needed. I'd appreciate any help.

So I am implementing a feature that automatically extracts information from a document using Pre-Trained LLMs (specifically the recent Llama 3.2 3b models). The two main things I want to extract are the title of the document and a list of names involved mentioned in it. Basically, this is for a document management system, so having those two pieces of information automatically extracted makes organization easier.

The system in theory should be very simple, it is basically just: Document Text + Prompt -> LLM -> Extracted data. The extracted data would either be the title or an empty string if it could not identify a title. The same goes for the list of names, a JSON array of names or an empty array if it doesn't identify any names.

Since what I am trying to extract is the title and a list of names involved I am planning to just process the first 3-5 pages (most of the documents are just 1-3 pages, so it really does not matter), which means I think it should fit within a small context window. I have tested this manually through the chat interface of Open WebUI and it seems to work quite well.

Now what I am struggling with is how this feature can be evaluated and if it is considered Named Entity Recognition, if not what would it be considered/categorized as (So I could do further research). What I'm planning to use is a confusion matrix and the related metrics like Accuracy, Recall, Precision, and F-Measure (F1).

I'm really sorry I was going to explain my confusion further but I am struggling to write a coherent explanation 😅

Okay so my confusion is about accuracy. It seems like all the resources I've read about evaluating NER or Information Retrieval say that Accuracy isn't useful because of class imbalance where the negative class is probably going to make up a big majority and thus the accuracy would be very high due to the amount of true negatives skewing the accuracy in a way that isn't useful. At least this is how I am understanding it so far.

Now in my case, True Positive would be extracting the real title, True Negative would be extracting no title because there isn't any title, False Positive would extracting a title incorrectly, and False Negatives would be falsely extracting no title even though there is a title.

But in my case I think there isn't a class imbalance? Like getting a a True Positive is just as important as getting a False Negative and thus accuracy would be a valid metric? But I think that sort of highlights a difference between this Information Extraction vs Named Entity Recognition/Information Retrieval, which makes me unsure if this fits those categories. Does that make sense?

So in this information extraction I'm doing, finding and extracting a title (True Positive) or not finding a title thus returning an empty string (True Negative) are both important output and thus I think having the accuracy metric is a valid way to evaluate the feature.

I think in a way extraction is a step you do after recognition. While doing NER you go through every word in a document and label them as an entity or not, so the output of that is a list of those words with a label for each. Now with extraction, you're taking that list and filtering it by ones labeled by a specific class and then returning those words/entities.

What this means is that the positive and negative classes are different. From what I understand in NER, the positive class would be an entity that is recognized while the negative class would be one that is not a recognized entity. But in extraction, the positive class is if it was found and extracted and the negative class is fit it was not found and thus nothing was extracted.

Honestly I don't know if this makes any sense, I've been trying to wrap my head around this since noon and it is midnight now lol

Here I made a document that shows how I imagine Named Entity Recognition, Text Classification, and my method would work: https://docs.google.com/document/d/e/2PACX-1vTfgySSyn52eEmkYrVEAQt8bp3ZbDRFf_ry1xDBVF77s0DetWr1mSjN9UPGpYnMc6HgfitpZ3Uye5gq/pub

Also, one thing I haven't mentioned is that this is for my final project at my University. I'm working with one of the organizations in my University to use their software as a case study to implement a feature using LLM. So for the report I need to have proper evaluations and also proper references/sources for everything. Which is why I'm making this post trying to figure out what my method would be classified as so I can get more info to help with me finding more related literature/books.

Hello all - this problem has been bothering me for a long time. I dont think there is a quick and easy answer but i thought i may as well ask the experts. I had to deduplicate a dataset containing merchant names. I've cleaned the data to a good extent and achieved a reasonably standardized format for the merchant names (though it's still not perfect). For example:

I want to build a model that, given an uncleaned name, outputs the cleaned version. However, the problem I’m facing with using RNNs and CNNs is that when the model encounters an out-of-vocabulary (OOV) term, the predictions are extremely poor, I want the model to learn the cleaning and cluster patterns, rather than learning embedding representation in the training data. My dataset is large, with around half a million observations.

I considered building a Named Entity Recognition (NER) model, but it would be difficult to annotate representative data due to the significant geographical variation in the merchant names. FastText isn't ideal for entity recognition in this case, so I'm currently using Sentence-BERT.

I'm looking for a robust model that can generalise well to other similar datasets, using transfer learning. Any ideas on how to approach this?

Hello all - this problem has been bothering me for a long time. I dont think there is a quick and easy answer but i thought i may as well ask the experts.

In public sector research there's often massive spreadsheets with proper nouns taking up one of the columns. These are usually public entities, companies, or people. Much of the time these are free text entries.

This means for proper analysis one needs to standardise. Whilst fuzzy matching can take you some of the way, its not specifically for this kind of use case and has limitations. It cant deal with abbreviations, often different sequences of words etc.

brute forcing with llms is one way, the most thorough approach I think ive got to is something like:

1. cleaning low value but common words
2. fingerprint
3. levenshtein
4. soundex

but this seems so messy! I was just hoping i'd missed something or if anyone has any other advice!

Thanks so much

How impossible is it for a humanities student (specifically English) to get a job in the world of computational linguistics?

To give you some background: I graduated with a degree in English Studies in 2021 and since then I have not known how to fit my studies into real job without having to be an English teacher. A year ago I found an approved UDIMA course (Universidad a Distancia de Madrid) on Natural Language Processing at a school aimed at humanistic profiles (philology, translation, editing, proofreading, etc.) to introduce them to the world of NLP. I understand that the course serves as a basis and that from there I would have to continue studying on my own. This course also gives the option of doing an internship in a company, so I could at least get some experience in the sector. The problem is that I am still trying to understand what Natural Language Processing is and why we need it, and from what I have seen there is a lot of statistics and mathematics, which I have never been good at. It is quite a leap, going from analyzing old texts to programming. I am 27 years old and I feel like I am running out of time. I do not know if this field is too saturated or if (especially in Spain) profiles like mine are needed: people from with a humanities background who are training to acquire technical skills.

I ask for help from people who have followed a similar path to mine or directly from people who are working in this field and can share with me their opinion and perspective on all this.

Thank you very much in advance.

¿Cómo de imposible es que una persona de humanidades consiga un trabajo dentro del mundo de la lingüística computacional?

Por orientar un poco: me gradué de la carrera de Estudios Ingleses en 2021 y desde entonces no he sabido cómo encajar mi formación en el mundo laboral sin tener que ser profesora de inglés. Hace un año encontré un curso homologado de la UDIMA (Universidad a Distancia de Madrid) de Procesamiento del Lenguaje Natural en una escuela dirigida a perfiles humanísticos (filología, traducción, edición, corrección, etc) para introducirles en el mundo de PLN. Entiendo que el curso sirve como base y que a partir de ahí yo tendría que seguir formándome. Este curso también da la opción de hacer unas prácticas en una empresa, por lo que por lo menos podría conseguir un poco de experiencia en el sector. El problema es que aún estoy intentando entender qué es y para qué necesitamos el Procesamiento del Lenguaje Natural, y por lo que he visto hay mucha estadística y matemáticas, que nunca se me han dado nada bien. Es un salto bastante fuerte, pasar de analizar textos antiguos a ponerme a programar. Tengo 27 años y siento que me estoy quedando sin tiempo. No sé si este campo está muy saturado o si se necesitan (sobre todo en España) perfiles como el mío: gente de humanidades que se esté formando para adquirir habilidades técnicas.

Pido la ayuda de gente que haya seguido un camino parecido al mío o directamente a gente que estéis trabajando en este ámbito y podáis compartir conmigo vuestra opinión y perspectiva de todo esto.

Muchísimas gracias de antemano.

I have an upcoming onsite interview for a Language Engineer position at Amazon. I'm trying to get a sense of what kinds of NLP/Linguistic concepts they might ask about during the interview (aside from the behavioral questions and leadership principles). Ling is obviously very broad, so I was hoping for some suggestions on what specifically to focus on reviewing. I've searched for older posts on Reddit, but the few I found on this are several years old, so I was hoping to get more recent info. Can anyone who has some insights share their advice?

Thanks!

Hello, I would appreciate any answers ! I’m a PhD student rn in a language department with a focus on linguistics. I have MA in the same field as well. I want to however try and apply to Masters in computational linguistics. What are my chances? Is it even possible after my basically arts major.

I need dataset from IEEE Dataport. My institution does not have subscription. If anyone is willing to share please let me know. I will send you the link.

Hello everyone. I have scraped forum posts of adolescents, in which they talk about their emotional problems. I want to extract cause, effect / emotion, cause pairs. For ex "I am sad because I was bullied at school" should return "sad, bullied" for example. This is not the exact format I expect it to be in btw. However, keep in mind that I dont have annotated data. How can I go forward with this in an unsupervised manner. Many thanks!

What’s a good translator app that doesn’t speak out loud and just fills it in by text when someone speaks? And works offline too would be a bonus. Google translate speaks out loud and trying to find alternative apps on your suggestions. Let me know in comments please

I'm a little skeptical that this exists, but does there happen to be something like a pre-trained sentence transformer that generate embeddings which provide information about sentiment?

I am trying to build an efficient algorithm for finding word groups within a corpus made of online posts but the various methods I have tried have caveats in different aspects making this a rather difficult nut to crack.

to give a snippet of the data, here are some phrases that can be found in the dataset

Japan has lots of fun environments to visit
The best shows come from Nippon
Nihon is where again

Do you watch anime
jap animation is taking over entertainment
japanese animation is more serious than cartoons

In these,

Japan = Nippon = Nihon

Anime = Jap Animation = Japanese Animation

I want to know what conversational topics are being discussed within the corpus and my first approach was to tokenize everything and perform counts. This did ok but quickly common non-stop words rose above the more meaningful words and phrases.

The several attempts tried to perform calculations on ngrams, phrases, highly processed sentences (lamentized, etc) and all usually result in similar troubles.

One potential solution I have thought of was to try and identify these overlapping words and combine them into word groups. This way the word groupings would be tracked which should theoretically aid in increasing visibility of the topics in questions.

However this is quite laborious as generating these groupings requires a lot of similarity calculations.

I have thought about using umap to convert the embeddings into coordinates and through plotting on a graph, this would aid in finding similar words. this paper performed a similar methodology that I am trying to implement. Implementing it though has run into some issues where I am now stuck.

The embeddings of 768 layers to 3 feels random as words that should be next to each other (tested with cosine similarity) usually end up on the opposite sides of the figure.

Is there something I am missing?

Im looking to get into nlp and computational linguistics. What would be a good framework for starting out with python?

Came across this paper and GitHub project called Precision Knowledge Editing (PKE), and it seemed like something worth sharing here to get others’ thoughts. The idea is to reduce toxicity in large language models by identifying specific parts of the model (they call them "toxic hotspots") and tweaking them without breaking the model's overall performance.

Here’s the paper: https://arxiv.org/pdf/2410.03772
And the GitHub: https://github.com/HydroXai/Enhancing-Safety-in-Large-Language-Models

I’m curious what others think about this kind of approach. Is focusing on specific neurons/layers in a model a good way to address toxicity, or are there bigger trade-offs I’m missing? Would something like this scale to larger, more complex models?

Haven't tried it out too much yet myself but just been getting more into AI Safety recently. Would love to hear any thoughts or critiques from people who are deeper into AI safety or LLMs.

Hi, everyone! I am currently focusing on constructing a domain-specific benchmark and I would like to ask for some advice.

In order to enhance the benchmark, I want to incorporate several modules from the pipeline of one of the domain-specific sota models. These modules form the foundation of my benchmark construction pipeline, in the sense that they do the great "language modeling". All questions and answers are built upon the output of these modules(as well as the original raw text, etc).

However, since benchmarks are used for evaluation purpose, will it cause "contamination" so that the evaluation results will become unreliable because of the usage of domain-specific models? And will it be mitigated if I simply avoid directly evaluating the sota model itself as well as models those are based on it? (Given that quality assurance is carefully conducted)

Indeed, I haven't found any previous work(not constrained to any domain) that are doing this kind of stuff for benchmark construction. If any previous benchmarks are doing this, please provide me with the references. Thanks in advance!

Recently, unsloth has added support to fine-tune multi-modal LLMs as well starting off with Llama3.2 Vision. This post explains the codes on how to fine-tune Llama 3.2 Vision in Google Colab free tier : https://youtu.be/KnMRK4swzcM?si=GX14ewtTXjDczZtM

Hi , I'm fine tuning mBART-50-many-to-many-mt on a language that is unseen in its pre training.

I did a lot of background research and found that many papers discuss that fine tuning NMT models on high quality unseen data works and it gives good results. (Bleu : 10)

When I'm trying to replicate the same. This doesn't work at all (Bleu:0.1, 5epochs) I don't know what I'm doing wrong . I've basically followed hugging face's documentation to write the code , which I verified was right after cross checking from a GitHub repo of someone who fine tuned the same model.

A little more context

1. The dataset consists of En->Xx sentnce pairs

2. I used the auto tokenizer and used hugging face's trainer to train the model.

3. As for arguments, the important ones are LR:0.0005 , Epoch : 5 (runtime constraints) , batch :16 (memory constraints) , optim : adamW . Basically these. The loss improved from 3.3 to 0.8 after 5 epochs and Bleu 0.04 to 0.1 (don't know if this is improvement)

I even tried looking into majority reasons why this could happen but I've made sure to not overlook things. The dataset quality is high. Tokenizing is proper, arguments are proper . So I'm very lost as to why this is happening. Can someone help me please.

Reviews are to be released in less than 24 hours. Nervous

Just sharing our paper presented at EMNLP 2024 main conference, which introduces a sentence embedding model that captures both the semantics and communicative intention of utterances. This allows for the modeling of conversational "steps" and thus the automatic extraction of dialog flows.

We hope some of you find it useful! :)

Resources:

• Paper: here
• Github repo: here (including code to replicate paper and generate also the interactive 3D Voronoi plots for sentence embeddings and to generate the graphs from any colleciton of dialogues provided by the user)
• Hugging Face models: here
• Hugging Face dataset: here
• License: MIT License

Paper Key Contributions:

• Intent-Aware Embeddings: The model encodes utterances with a richer representation that includes their intended communicative purpose (available in Hugging Face).
• Dialog Flow Extraction: By clustering utterance embeddings, the model can automatically identify the "steps" or transitions within a conversation, effectively generating a dialog flow graph (Github code available).
• Soft-Contrastive Loss: The paper introduces a new supervised contrastive loss function that can be beneficial for representation learning tasks with numerous labels (implementation available).
• Dataset: A collection of 3.4 million utterances annotated with ground truth intent (available in Hugging Face).

Have a nice day everyone! :)

Hi everyone ,

I am a beginner at NLP , I am trying to train mBART-50 for translation on an unseen language. I have referred a lot of docx , a hell lot of discussions but nobody seems to address this fact. So I am confused if my issue is valid or is it just in my head.

As i know BART has a pre defined vocabulary where each token is defined. With that understanding if I am training the model on an unseen language, do I have to extend the vocabulary by adding tokens from the new language? Or the model extends its vocabulary on its own ?

If i had to provide a little more context , I can tokenize the English sentences using the pretrained tokenizer , but for the unseen language I do have a tokenizer which was trained for indic languages and it indeed does tokenize sentences properly. But what i am confused is if i do pass them to the model wouldn't it just classify as <unk> (unknown token?) since they're not present in its vocab?

Kindly help me with this , If someone can guide me about this I'd appreciate it!

So, I am currently about to graduate in about a month with a bachelors in Linguistics (with a 4.0 if that matters?) and I am trying to makes se of what to do after. I really would love to work in NLP, but unfortunately I didn’t have the time to complete more than a single python text processing class before my time has ended. (Though I’ve done other things on my own like cs50 and really loved it and picked up the content fast, so me not liking cs is not a concern) I’d really love to pursue a master’s degree in comp ling like through uni of washington, but i don’t have $50k ready to go for that, nor do i have the math basics to be admitted.

So, my thought is that I’ll do something like getting a job that will take any degree, then use that to pay for a second bachelors in comp sci through something affordable for me like wgu and use both degrees together to to get me into a position i’d really love, which i could then decide to pursue a masters once i’m more stable.

Does this sound ridiculous? Essentially what I’m asking before I actually try to go through with it is, would getting a second bachelors in comp sci after my first in linguistics be enough to break into nlp?

I am very new to NLP and the project I am working on is a chatbot, where the pipeline takes in the user query, identifies some unique value the user is asking about and performs a lookup. For example, here is a sample query "How many people work under Nancy Drew?". Currently we are performing windowing to extract chunks of words and performing look-up using FAISS embeddings and indexing. It works perfectly fine when the user asks for values exactly the way it is stored in the dataset. The problem arises when they misspell names. For example, "How many people work under nincy draw?" does not work. How can we go about handling this?

I’ve been working on a project designed to make audio transcription, translation, and content summarization (like interviews, cases, meetings, etc.) faster and more efficient.

Do you think something like this would be useful in your work or daily tasks? If so, what features or capabilities would you find most helpful?”

Let me know your thoughts 💭 💭

Pd: DM if you want to try it out

The proyect