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2025-06-25 00:32:52
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1231czx/llama3_star_ep2_lr2e6_tmp0 | 1231czx | 2024-12-22T02:50:26Z | 17 | 0 | [
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vlinhd11/viVoice-v1-5-desc_tokened | vlinhd11 | 2025-04-09T03:47:44Z | 15 | 0 | [
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MadvaAparna/KannadaVibhaktiSamples | MadvaAparna | 2025-02-06T10:32:25Z | 14 | 0 | [
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task_categories:
- token-classification
language:
- kn
---
# Dataset Card for Dataset Name
This is a dataset for the Named Entity Recognition (NER) task in Kannada language. Each instance represents one of the vibhakti cases.
## Dataset Details
### Dataset Description
Kannada language supports eight (grammatical) cases [Refer https://kannadakalike.org/grammar/cases]. The cases are called vibhakti and corresponding suffixes, pratyaya. Noun words are inflected with the suffix corresponding to these cases to create another grammatically meaningful word.
In this dataset, 12 sentences are composed for each case, thus resulting in a total of 96 sentences. Each sentence contains at least one word representing any named entity which is inflected with the suffix for the corresponding case.
We include examples for PER, LOC and ORG class of entities.
For PER class, we also include entities which span across multiple words such as caMdragupta maurya.
This vibhakti dataset can be used to analyse how the presence of case specific suffixes in a named entity affects the prediction by the model.
- **Curated by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
### Dataset Sources [optional]
<!-- Provide the basic links for the dataset. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
Dataset can be used in case of the NER task.
## Dataset Structure
<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
[More Information Needed]
### Source Data
Manually created
#### Who are the annotators?
<!-- This section describes the people or systems who created the annotations. -->
[More Information Needed] |
1231czx/llama3_star_ep2_lr2e6_tmp10 | 1231czx | 2024-12-22T02:25:20Z | 44 | 0 | [
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YanAdjeNole/Document_ranking6_test | YanAdjeNole | 2025-02-11T21:45:10Z | 37 | 0 | [
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Luongdzung/2280_math_exams_dataset_seed_42 | Luongdzung | 2024-11-13T10:06:26Z | 19 | 0 | [
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noma1999/pad-gemma-2-n4 | noma1999 | 2025-03-28T09:36:12Z | 15 | 0 | [
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guanghao/openr1_math_220k_qwen_cot | guanghao | 2025-02-20T23:10:09Z | 17 | 0 | [
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shoyimobloqulov/law | shoyimobloqulov | 2025-03-11T20:03:26Z | 13 | 0 | [
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license: apache-2.0
---
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fawazahmed0/quran-data | fawazahmed0 | 2024-10-30T12:45:44Z | 44 | 0 | [
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license: cc0-1.0
source: https://github.com/fawazahmed0/quran-api
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MusYW/test3 | MusYW | 2025-06-10T08:34:57Z | 0 | 0 | [
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Ki1n/urillm_vanilla | Ki1n | 2025-01-13T07:30:09Z | 17 | 0 | [
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math-extraction-comp/microsoft__Phi-3-small-128k-instruct | math-extraction-comp | 2025-01-12T21:14:58Z | 18 | 0 | [
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leo-step/arxiv-papers-10k | leo-step | 2025-04-15T04:07:03Z | 26 | 0 | [
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---
This dataset consists of 9,776 cs.ai papers retrieved from ArXiv on April 14th, 2025. Each sample has the title, abstract, url, and text of the paper. The text was extracted using pymupdf and preprocessed by removing lines with less than 9 characters and those that did not start with an alphanumeric character. The lines were then joined together without any newline characters and word breaks. Useful for training small scale language models, embeddings, and autocomplete systems.
If you find this dataset useful in your work, feel free to cite the following:
```
@misc{stepanewk2025arxivpapers10k,
author = {Leo Stepanewk},
title = {ArXiv Papers 10k Dataset},
year = {2025},
howpublished = {\url{https://huggingface.co/datasets/leo-step/arxiv-papers-10k}},
}
```
|
takara-ai/MovieStills_Captioned_SmolVLM | takara-ai | 2025-02-25T15:22:12Z | 29 | 0 | [
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---
<img src="https://takara.ai/images/logo-24/TakaraAi.svg" width="200" alt="Takara.ai Logo" />
From the Frontier Research Team at **Takara.ai** we present **MovieStills_Captioned_SmolVLM**, a dataset of 75,000 movie stills with high-quality synthetic captions generated using SmolVLM.
---
## Dataset Description
This dataset contains 75,000 movie stills, each paired with a high-quality synthetic caption. It was generated using the **HuggingFaceTB/SmolVLM-256M-Instruct** model, designed for instruction-tuned multimodal tasks. The dataset aims to support image captioning tasks, particularly for machine learning research and application development in the domain of movie scenes and visual storytelling.
**Languages:** The dataset captions are in English (ISO 639-1: `en`).
**Domain:** Movie stills with general, descriptive captions for each image.
## Dataset Structure
### Data Fields
Each dataset instance consists of:
- **image:** A PIL image object representing a single movie still.
- **caption:** A descriptive caption for the corresponding image.
### Example Instance
```json
{
"image": "<PIL.Image.Image image mode=RGB size=640x360>",
"caption": "A man standing on a rainy street looking at a distant figure."
}
```
### Data Splits
The dataset currently has no predefined splits (train/test/validation). Users can create custom splits as needed.
## Dataset Creation
### Process
The dataset captions were generated using the **HuggingFaceTB/SmolVLM-256M-Instruct** model. The process involved:
1. Processing 75,000 movie stills with the ONNX Runtime (ONNXRT) for efficient inference.
2. Running inference on an **RTX 2080 Ti** GPU, which took approximately **25 hours** to complete.
### Source Data
- **Source:** The dataset uses stills from the `killah-t-cell/movie_stills_captioned_dataset_local` dataset.
### Preprocessing
- Images were provided in their original formats and converted into PIL objects.
- Captions were generated using an instruction-tuned multimodal model to enhance descriptive quality.
## Considerations for Using the Data
### Potential Biases
The dataset captions may reflect biases present in the source model (HuggingFaceTB/SmolVLM-256M-Instruct). As synthetic captions are generated from a single model, there may be limitations in diversity and linguistic nuance.
### Ethical Considerations
This dataset is intended for research purposes. Users should be aware that captions might not fully reflect context or cultural sensitivities present in the movie stills.
### Limitations
- No human verification was performed for caption accuracy.
- The dataset is limited to English captions and may not generalise well to other languages or contexts.
## Additional Information
**License:** The dataset is licensed under [Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/).
**Citation:** Please cite the dataset using its Hugging Face repository citation format.
## Sample Usage
Here's an example code snippet to load and use the dataset:
```python
from datasets import load_dataset
from PIL import Image
# Load the dataset
dataset = load_dataset("takara-ai/MovieStills_Captioned_SmolVLM")
# Display a sample
sample = dataset["train"][0]
image = sample["image"]
caption = sample["caption"]
# Show the image and caption
image.show()
print(f"Caption: {caption}")
```
---
For research inquiries and press, please reach out to [email protected]
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