datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
Fatima_Fellowship
Bengali Quote Error Analysis Dataset
This repository contains an error-analysis dataset for Bengali quote understanding.
Model outputs: Fatima_Fellowship.csv
The goal is to document diverse model mistakes and propose a fine-tuning direction.
Model Tested
Model: Qwen/Qwen3.5-0.8B
Framework: transformers
Prompt format: chat template with a system instruction and one few-shot example
How the Model Was Loaded
The following code was used in the notebook:
from… See the full description on the dataset page: https://huggingface.co/datasets/SumitKumarKar01/Fatima_Fellowship.qwen-3-5-blindspots-fatima-fellowship
Qwen3.5-4B-Base Blindspots Dataset
A collection of 11 prompts where Qwen/Qwen3.5-4B-Base produces incorrect, incomplete, or degenerate outputs. Each row records the input prompt, the raw model output, the extracted model response, the expected (correct) response, and a label for the failure category.
Dataset Summary
Field
Value
Model tested
Qwen/Qwen3.5-4B-Base
Number of examples
11
Columns
prompt, raw_output, thinking, output, expected_output, blindspot… See the full description on the dataset page: https://huggingface.co/datasets/dawaawawa/qwen-3-5-blindspots-fatima-fellowship.fatimaFellowship2026Model: https://huggingface.co/CohereLabs/tiny-aya-base
Model Loading
I loaded the model using the transformers' library pipeline helper function and kept the default model parameters provided by the model card. The only thing I would occasionally
change was max_new_tokens, to adjust it so that it makes sense with the prompt I'm giving. I also chose to keep temperature low for more determinism:
from transformers import pipeline
def predict(
input: str,
max_new_tokens=50… See the full description on the dataset page: https://huggingface.co/datasets/akhanafer/fatimaFellowship2026.QA_Low_Resource_FatimaFellowship
Model Experimentation and Analysis
1. Model Experimentations
I chose the task of Q&A, branching into two categories: general and specific. I tested the output, i.e., the LLM's response against the expected output. I have created the dataset in a CSV file. The model used is : https://huggingface.co/Andron00e/YetAnother_Open-Llama-3B-LoRA-OpenOrca
2. "Blind spots"
In this dataset, model is not able to predict well on very-specific information, like dates or years… See the full description on the dataset page: https://huggingface.co/datasets/KushieBoi/QA_Low_Resource_FatimaFellowship.Dimeji-Fatima-Fellowship
Evaluating Causal and Reasoning Blind Spots in Base LLMs
Model Tested
Model Name: Qwen/Qwen2.5-3B
Model Link: https://huggingface.co/Qwen/Qwen2.5-3B
How the Model Was Loaded
The model was evaluated using a Google Colab instance with a free T4 GPU. To accommodate the VRAM constraints of the hardware while maintaining inference fidelity, I utilized the transformers library alongside bitsandbytes to load the model using 8-bit quantization.
Here is the exact code… See the full description on the dataset page: https://huggingface.co/datasets/Dimeji12/Dimeji-Fatima-Fellowship.fatima-fellowship-qwen35-eval
Technical Challenge: Blind Spots of Qwen3.5-4B
Model Tested
Qwen/Qwen3.5-4B. This is a foundation model released in early 2026.
Loading Methodology
The model was loaded using the transformers library on a Google Colab T4 GPU instance.
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = "Qwen/Qwen3.5-4B"
tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model =… See the full description on the dataset page: https://huggingface.co/datasets/Alaa-Abdelkader/fatima-fellowship-qwen35-eval.