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# Turkish Texts for Toxic Language Detection
## Dataset Description
### Dataset Summary
This text dataset is a collection of Turkish texts that have been merged from various existing offensive language datasets found online. The dataset contains a total of 77,800 instances, each labeled as either offensive or not offensive.
To ensure the dataset's completeness, we utilized multiple transformer models to augment the dataset with pseudo labels. The resulting dataset is designed to be a comprehensive resource for Turkish offensive language detection.
The dataset is provided in CSV format, for more details on the merged datasets used, please refer to the reference section.
## Loading Dataset
To use dataset by using Huggingface dataset use snippet below:
```python
from datasets import load_dataset
# If the dataset is gated/private, make sure you have run huggingface-cli login
dataset = load_dataset("Overfit-GM/turkish-toxic-language")
```
## Dataset Structure
| Dataset Information | |
|---------------------|-------|
| Number of instances | 77,800|
| Target label distribution | |
|OTHER | 37,663|
|PROFANITY | 18,252|
|INSULT | 10,777|
|RACIST | 10,163|
|SEXIST | 945|
| Number of offensive instances | 40,137 |
| Number of non-offensive instances | 37,663 |
| Data source distribution | |
|Jigsaw Multilingual Toxic Comments | 35,624 |
|Turkish Offensive Language Detection Dataset | 39,551 |
|Turkish Cyberbullying Dataset | 2,525 |
### Source Data & References
- https://www.kaggle.com/c/jigsaw-multilingual-toxic-comment-classification
- https://huggingface.co/datasets/offenseval2020_tr
- https://github.com/imayda/turkish-hate-speech-dataset-2
- https://www.kaggle.com/datasets/kbulutozler/5k-turkish-tweets-with-incivil-content
- https://www.kaggle.com/abozyigit/turkish-cyberbullying
|
Overfit-GM/turkish-toxic-language
|
[
"task_categories:text-classification",
"size_categories:10K<n<100K",
"language:tr",
"license:apache-2.0",
"region:us"
] |
2023-04-04T13:06:44+00:00
|
{"language": ["tr"], "license": "apache-2.0", "size_categories": ["10K<n<100K"], "task_categories": ["text-classification"], "pretty_name": "Turkish Toxic Language Dataset"}
|
2023-04-04T13:15:02+00:00
|
[] |
[
"tr"
] |
TAGS
#task_categories-text-classification #size_categories-10K<n<100K #language-Turkish #license-apache-2.0 #region-us
|
Turkish Texts for Toxic Language Detection
==========================================
Dataset Description
-------------------
### Dataset Summary
This text dataset is a collection of Turkish texts that have been merged from various existing offensive language datasets found online. The dataset contains a total of 77,800 instances, each labeled as either offensive or not offensive.
To ensure the dataset's completeness, we utilized multiple transformer models to augment the dataset with pseudo labels. The resulting dataset is designed to be a comprehensive resource for Turkish offensive language detection.
The dataset is provided in CSV format, for more details on the merged datasets used, please refer to the reference section.
Loading Dataset
---------------
To use dataset by using Huggingface dataset use snippet below:
Dataset Structure
-----------------
### Source Data & References
* URL
* URL
* URL
* URL
* URL
|
[
"### Dataset Summary\n\n\nThis text dataset is a collection of Turkish texts that have been merged from various existing offensive language datasets found online. The dataset contains a total of 77,800 instances, each labeled as either offensive or not offensive.\n\n\nTo ensure the dataset's completeness, we utilized multiple transformer models to augment the dataset with pseudo labels. The resulting dataset is designed to be a comprehensive resource for Turkish offensive language detection.\n\n\nThe dataset is provided in CSV format, for more details on the merged datasets used, please refer to the reference section.\n\n\nLoading Dataset\n---------------\n\n\nTo use dataset by using Huggingface dataset use snippet below:\n\n\nDataset Structure\n-----------------",
"### Source Data & References\n\n\n* URL\n* URL\n* URL\n* URL\n* URL"
] |
[
"TAGS\n#task_categories-text-classification #size_categories-10K<n<100K #language-Turkish #license-apache-2.0 #region-us \n",
"### Dataset Summary\n\n\nThis text dataset is a collection of Turkish texts that have been merged from various existing offensive language datasets found online. The dataset contains a total of 77,800 instances, each labeled as either offensive or not offensive.\n\n\nTo ensure the dataset's completeness, we utilized multiple transformer models to augment the dataset with pseudo labels. The resulting dataset is designed to be a comprehensive resource for Turkish offensive language detection.\n\n\nThe dataset is provided in CSV format, for more details on the merged datasets used, please refer to the reference section.\n\n\nLoading Dataset\n---------------\n\n\nTo use dataset by using Huggingface dataset use snippet below:\n\n\nDataset Structure\n-----------------",
"### Source Data & References\n\n\n* URL\n* URL\n* URL\n* URL\n* URL"
] |
[
43,
169,
17
] |
[
"passage: TAGS\n#task_categories-text-classification #size_categories-10K<n<100K #language-Turkish #license-apache-2.0 #region-us \n### Dataset Summary\n\n\nThis text dataset is a collection of Turkish texts that have been merged from various existing offensive language datasets found online. The dataset contains a total of 77,800 instances, each labeled as either offensive or not offensive.\n\n\nTo ensure the dataset's completeness, we utilized multiple transformer models to augment the dataset with pseudo labels. The resulting dataset is designed to be a comprehensive resource for Turkish offensive language detection.\n\n\nThe dataset is provided in CSV format, for more details on the merged datasets used, please refer to the reference section.\n\n\nLoading Dataset\n---------------\n\n\nTo use dataset by using Huggingface dataset use snippet below:\n\n\nDataset Structure\n-----------------### Source Data & References\n\n\n* URL\n* URL\n* URL\n* URL\n* URL"
] |
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] |
a09e79a521218713d12bcc41a5313540e57c65f4
|
# Dataset Card for "informes_discriminacion_gitana"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
rwheel/informes_discriminacion_gitana
|
[
"region:us"
] |
2023-04-04T13:18:41+00:00
|
{"dataset_info": {"features": [{"name": "sintetico", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "intervencion", "dtype": "string"}, {"name": "tipo_discriminacion", "dtype": "string"}, {"name": "resultado", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1569183.3, "num_examples": 1791}, {"name": "test", "num_bytes": 87614.92462311558, "num_examples": 100}, {"name": "valid", "num_bytes": 86738.77537688443, "num_examples": 99}], "download_size": 936705, "dataset_size": 1743537.0000000002}}
|
2023-04-04T13:18:52+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "informes_discriminacion_gitana"
More Information needed
|
[
"# Dataset Card for \"informes_discriminacion_gitana\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"informes_discriminacion_gitana\"\n\nMore Information needed"
] |
[
6,
19
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"informes_discriminacion_gitana\"\n\nMore Information needed"
] |
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d5620d1430f7abcc0ec86ee5c9388e4a95051c17
|
### Resumen del dataset
Se trata de un dataset en español, extraído del centro de documentación de la Fundación Secretariado Gitano, en el que se presentan distintas situaciones discriminatorias acontecidas por el pueblo gitano. Puesto que el objetivo del modelo es crear un sistema de generación de actuaciones que permita minimizar el impacto de una situación discriminatoria, se hizo un scrappeo y se extrajeron todos los PDFs que contuvieron casos de discriminación con el formato (HECHOS, INTERVENCIÓN, RESULTADO). Para extraer la información se hizo un scrappeo de la página, a continuación se limpió y se unificó todo el dataset con un script de preprocesamiento para que todo el dataset tuviera el mismo formato.
### Tareas admitidas y tablas de clasificación
- `task-generation`: Dado el hecho generar la intervención y la etiqueta de resultado, para dar métodos para hacer la intervección y que sea efectiva. ([PAG-BERT](https://huggingface.co/hackathon-somos-nlp-2023/PAG-BERT))
- `task-classication`: Se puede entrenar un modelo de clasificación, dejamos a los usarios, predecir el tipo de descriminación de dependiedo del hecho
### Idioma
Es un dataset con la variante español de España, el estilo empleado es formal y objetivo, limitándose a describir los hechos descritos por las personas afectadas.
## Estructura de los datos
### Instancias
A continuación se muestra una instancia de ejemplo del dataset:
```
{
'sintetico': '0',
'text': 'Una joven gitana comenzó a trabajar en una tienda de ropa, hace dos años, con contrato indefinido. Al mes de comenzar a trabajar, una compañera le preguntó, en presencia de su encargada, si era gitana, ella respondió que sí; desde entonces el trato de la encargada hacia la joven cambió, comenzó a tirar al suelo perchas, tierra, para luego acusarla de que no limpiaba el suelo, además de hacer continuamente comentarios generalizados refiriéndose a las mujeres gitanas, del tipo “¿Pero te dejan trabajar?” “¿Y estudiar?”, “tú tienes que saber cómo trabajar en la tienda porque como aprendéis en los mercadillos...” La víctima comentó que desde que la encargada se enteró de que era gitana le hizo la vida imposible, se sintió muy humillada. No aguantó más y presentó la baja voluntaria, aun siendo consciente de que perdía su derecho a la prestación por desempleo.',
'intervencion': 'Se entrevistó a la joven. Se comprobó a través del testimonio de la víctima que desde que su encargada se enteró de que es mujer gitana, al mes de comenzar a trabajar aproximadamente, comenzó a sufrir discriminación. Se informó a la víctima del Servicio, del trabajo que realizamos y de sus derechos.\xa0',
'tipo_discriminacion': 'Discriminación directa',
'resultado': 'Negativo.'
}
```
### Campos de los datos
- `sintetico`: indica si los datos son relacionados con la intervención y el resultado son originales, es decir, proceden de la fuente "Fundación Secretariado Gitano" (valor 0); o si, por el contrario, los hemos generado sintéticamente (valor 1).
- `text`: expone los hechos descritos por la persona afectada.
- `intervencion`: presenta las medidas que se tomaron desde la Fundación para evitar que los hechos descritos en "text" se volvieran a repetir.
- `tipo_discriminacion`: etiqueta que identifica el tipo de discriminación. Puede tomar los valores **Acoso discriminatorio**, **Discriminación directa**, **Discriminación indirecta**, **Discriminación interseccional**, **Discurso de odio**, **Orden de discriminar**,, **Sin especificar**.
- `resultado`: presenta la repercusión que tuvo la intervención adoptada. Sus posibles valores son **Positivo**, **Negativo** y **Neutro**.
### División de los datos
El dataset cuenta con un total de 1990 instancias, repartidas del siguiente modo:
| | train | validation | test |
|-------------------------|----------:|-------------:|----------:|
| Input Sentences | 90% | 5% | 5% |
| Average Sentence Length | 94.71 | 90.94 | 98.07 |
Cabe destacar que, teniendo en cuenta el resultado de las intervenciones (positivo, negativo o neutro), el dataset no está balanceado. En concreto, hay un total de 280 muestras positivas, 939 negativas y 771 neutras. En próximas actualizaciones del dataset trabajaremos para incrementar el tamaño del dataset de forma balanceada.
## Creación del dataset
### Justificación de la curación
El motivo por el que se creó este dataset es para conocer de una forma objetiva, si las medidas actuales que se están adoptando por parte de la Fundación han surtido efecto (en cuyo caso sería positivo), no ha surtido ningún efecto (negativo), o si por el contrario, las medidas propuestas no han incentivado al usuario a llevar a cabo ninguna acción.
Se ha optado por este dataset por el volumen de datos que contiene relativos a distintos escenarios, y por el formato que todos comparten de: HECHOS, INTERVENCIÓN Y RESULTADO.
### Fuente de los datos
Los datos utilizados para construir el modelo fueron extraídos de la página web de la Fundación Secretariado Gitano (<a href="https://informesdiscriminacion.gitanos.org">FSM</a>). El FSM tiene una base de datos que contiene actos de discriminación que han sido reportados a la organización. Estos actos de discriminación fueron seleccionados para entrenar y evaluar el modelo.
#### Recogida inicial de datos y normalización
Los datos fueron extraídos de la sección de <a href = "https://informesdiscriminacion.gitanos.org/buscar-casos" >Buscador de casos</a>, donde se lleva un registro de todo los casos de descriminación.
Los campos que ofrece la página web para estetipo de informes son:
* `Hecho` que hace referencia al acto de descriminación.
* `Intervención` qué medidas tomo la FSG para solucionar el problema.
* `Resultado`: Descripción del resultado.
* Año que ocurrió el caso.
* Año del informe.
* Ámbito: Dado el caso de que la discrimnación haya sido una empresa gubernamenta, en cual derecho fundamental se presentó.
* Provincia: Lugar donde ocurrió el acto.
* Tipo de discriminación.
En la extracción de datos solo tuvimos en cuenta los campos **hechos**, **intervención**, **resultados** y **tipo de discriminación**. El lenguaje usado en los informes es formal.
Originalmente, una elevado número de Hechos no contaban con una intervención y resultado (los campos estaban vacíos).
#### Limpieza de los datos
En la página web, el campo resultado contiene un breve explicación del los efectos obtenidos tras llevar a cabo la intervección. Usando la librería <a href="https://github.com/pysentimiento/pysentimiento">pysentimiento</a>, se clasificó el resultado entre negativo, neutro y positivo.
Posterior mente se revisó la etiqueta y se ajustó para lo que se consideraba neutral, negativo o positivo
El 17% de los actos de discriminación en el dataset no contaban con intervención ni resultado. Para completar estos campos se aplicó la técnica few-show learning usando el modelo Bloom. De modo que dado algunos ejemplos de **hechos**, **intervención** y **resultado**, seríamos capaces de generar **intervenciones** y **resultados** de forma automática. El output del modelo Bloom se revisó manualmente para corregir errores.
El 41% de los textos del campo **hechos** eran demasiado largos para ser utilizado en BLOOM aplicando la técnica de few-shot learning. Para resolver este problema, se decidió resumirlos, para esto se utilizó la función `segmenter.split_single` de la librería <a href="https://github.com/fnl/segtok" >segtok</a>, que divide el texto en oraciones y las separa por caracteres de nueva línea.
Se usaron dos modelos pre-etrenados para resumir cada sub-texto. El primero fue <a href="https://huggingface.co/mrm8488/bert2bert_shared-spanish-finetuned-summarization">mrm8488/bert2bert_shared-spanish-finetuned-summarization</a> y el segundo fue el <a href="https://huggingface.co/Narrativa/bsc_roberta2roberta_shared-spanish-finetuned-mlsum-summarization">Narrativa/bsc_roberta2roberta_shared-spanish-finetuned-mlsum-summarization</a>
En el repositorio https://github.com/Frorozcoloa/somos_nlp_hackaton se encuentran los scripts originales usados para el preprocesamiento. También puedes encontrar una copia de los mismos en este mismo repositorio.
### Anotación
Las anotaciones que se ralizaron fueron verificaciones a los datos de sintéticos generados con few-shot learning (intervenciones y resultados):
* Se rellenaron los valores nulos.
* Se hicieron resumenes de algunos textos (Hehos) aplicando modelos pre-entrenados.
* Se cambió el texto de resultado por etiquetas de POS, NEU, NEG.
#### Proceso de anotación
Para el proceso de etiquetado se utilizó Argilla para etiquetar la categoría de "Resultado", para ello se emplearon las siguientes etiquetas: "Positivo", "Negativo" y "Neutro". En el proceso de etiquetado lo que nos interesaba era etiquetar el resultado de las intervenciones para que el modelo aprendiera y pudiera generar texto para dar respuesta a la situación expuesta por el usuario, además de predecir con los datos etiquetados si la repercusión que pudiera tener la medida que propone el modelo sería "positiva"(surtiría efecto), "negativa"(no tendría ningún efecto) o "neutra"(si es posible que el usuario no llevara a cabo ninguna acción).
En concreto, tras descargar todos los datos disponibles en la web, los preprocesamos y unimos en un solo dataset que fue subido a Argilla. Una vez aquí, validamos cada una de las instancias del siguiente modo:
* Si la intervención y/o resultado están vacías, se anota como tal.
* Se comprueba que el resultado positivo, negativo o neutro es correcto. La mayoría de las incongruencias surgen entre los pares positivo/neutro y negativo/neutro.
Una vez validado el dataset con argilla, seleccionamos las muestras que fueron anotadas como "vacías" para proceder a completarlas. Para ello, hemos aplicado Few-Shot Learning usando el modelo [BLOOM](https://huggingface.co/bigscience/bloom).
Cabe destacar que algunos hechos del dataset eran demasiado largos y no podían ser procesados por BLOOM (generaba un error que indicaba que habíamos superado el número máximo de tokens), para solucionarlo, utilizamos los modelos <a href="https://huggingface.co/mrm8488/bert2bert_shared-spanish-finetuned-summarization">mrm8488/bert2bert_shared-spanish-finetuned-summarization</a> y <a href="https://huggingface.co/Narrativa/bsc_roberta2roberta_shared-spanish-finetuned-mlsum-summarization">Narrativa/bsc_roberta2roberta_shared-spanish-finetuned-mlsum-summarization</a> para resumir dichos hechos y minimizar así su tamaño.
### Información personal y sensible
En este caso no se ha necesitado utilizar ningún proceso de anonimización, ya que los datos procedentes de esta fuente no contienen ninguna información que vulnere los derechos de los afectados.
## Consideraciones sobre el uso de los datos
### Consideraciones sobre el uso de los datos
El impacto social de este dataset se dirige a ser una herramienta que sirva para implementar acciones que ayuden a combatir el racismo hacia la población gitana, además este dataset se podría utilizar para evaluar la repercusión de las distintas medidas adoptadas durante un período de tiempo, y aquellas medidas con una repercusión "negativa" o "neutra" investigarlas y mejorarlas con un trato más concienzudo hacia la población gitana.
### Debate sobre los prejuicios
Sé realizó un analisís exploratorio de los datos, para eso hemos realizado una nube de palabras para analizar los datos sintéticos y no sintéticos.
#### Datos no sintéticos
<img src="https://huggingface.co/datasets/hackathon-somos-nlp-2023/informes_discriminacion_gitana/resolve/main/imagenes/Hechos_normales.png">
Aquí podemos ver que muchos de los hechos se generaron en noticias, en mujeres, temas de vivienda, con la policia y la familia.
<img src="https://huggingface.co/datasets/hackathon-somos-nlp-2023/informes_discriminacion_gitana/resolve/main/imagenes/Intervenci%C3%B3n_normal.png">
Las intervenciones hablan de derechos, de cartas, de igualdad, asesorar a la persona y de presentar quejas.
<img src="https://huggingface.co/datasets/hackathon-somos-nlp-2023/informes_discriminacion_gitana/resolve/main/imagenes/etiqueta_normal.png">
Muchos de los resultados de las intervenciones fueron negativos o neutrales (Posiblemente sin respuesta) o de que no se logró lo propuesto (Negativo). Se puede observar el desbalance en los datos.
Por medio de la librería *pysentimiento* y usando el modelo `pysentimiento/pt_hate_speech`, se realizó una métrica para medir el discurso de odio en el `Hecho`.
Para eso análizaremos hateful, targeted y aggressive. La métrica va de 0 a 1, para cada una. Siendo la probabilidad de que esa caracteristica esté en el texto.
Se encotró lo siguiente
<img src="https://huggingface.co/datasets/hackathon-somos-nlp-2023/informes_discriminacion_gitana/resolve/main/imagenes/hate_normal.png">
<img src="https://huggingface.co/datasets/hackathon-somos-nlp-2023/informes_discriminacion_gitana/resolve/main/imagenes/hate_2_normal.png">
La distribución de los valores de hateful, targeted y aggressive presentan una cola alargada hacia la derecha, lo que indica que hay pocos casos en los que se detecta un mensaje de odio en los hechos.
Para el caso, donde no se generó la intervección y resultado se presenta un crecimiento en el tercer cuartil, esto quiere decir que hay mensajes que muestra un discurso de odio. Por ejemplo el hateful es de 0.4, targeted de 0.02 y aggresive de 0.03. En conclusión, como está escrito el hecho y como fue entrenado el modelo de *pysentimiento*, en general los hechos no tienen un mensaje de odio.
#### Datos sintéticos.
Se realizó el mismo análisis para los datos sintéticos
<img src="https://huggingface.co/datasets/hackathon-somos-nlp-2023/informes_discriminacion_gitana/resolve/main/imagenes/Hechos_sinteticos.png"/>
Cabe resltar que el hecho no fue generado.
Es claro que el dataset está más sesgado a contener las palabras gitano, gitana, comunidad gitana, etnia gitana, familia, discriminación.
<img src="https://huggingface.co/datasets/hackathon-somos-nlp-2023/informes_discriminacion_gitana/resolve/main/imagenes/Intervenci%C3%B3n_sintetica.png"/>
Esta parte fue generada por el modelo *Bloom*. Puede comprobarse que con *few-shot* se logra captar más que todo la palabra `derecho`.
<img src="https://huggingface.co/datasets/hackathon-somos-nlp-2023/informes_discriminacion_gitana/resolve/main/imagenes/Etiquetas%20sinteticas.png">
Tambien hay un desbalance en las etiquetas generadas.
Por medio de la librería *pysentimiento* y usando el modelo `pysentimiento/pt_hate_speech` ,se realizó una métrica para medir el discurso de odio en el `Hecho`
Para eso análizaremos hateful, targeted y aggressive. La métrica va de 0 a 1, para cada una. Siendo la probabilidad de que esa caracteristica esté en el texto.
Se encotró lo siguiente
<img src="https://huggingface.co/datasets/hackathon-somos-nlp-2023/informes_discriminacion_gitana/resolve/main/imagenes/hate_sintetico.png">
<img src="https://huggingface.co/datasets/hackathon-somos-nlp-2023/informes_discriminacion_gitana/resolve/main/imagenes/hate_2_sintetico.png">
La distribución de los valores de hateful, targeted y aggressive presentan una cola alargada hacia la derecha, lo que indica que hay pocos casos en los que se detecta un mensaje de odio en los hechos.
Tanto la mediana como la media de los valores de hateful, targeted y aggressive están muy cerca de cero, lo que indica que la mayoría de los hechos no contienen mensajes de odio. Además, se observó que en el tercer cuartil, el 75% de los datos en la métrica de hateful es 0.3, para targeted es de 0.0089 y para aggressive es de 0.06, lo que refuerza la conclusión de que la mayoría de los datos no contienen un mensaje de odio en la descripción de los hechos.
## Información adicional
### Curadores del dataset
* <a href="https://www.linkedin.com/in/frorozcol/">Fredy Orozco</a>
* <a href="https://www.linkedin.com/in/mariajesusgs">María Jesús García</a>
* <a href="https://www.linkedin.com/in/ramonruedadelgado/">Ramón Rueda</a>
|
hackathon-somos-nlp-2023/informes_discriminacion_gitana
|
[
"task_categories:text-classification",
"task_categories:text2text-generation",
"size_categories:n<1K",
"language:es",
"license:apache-2.0",
"hate",
"region:us"
] |
2023-04-04T13:19:40+00:00
|
{"language": ["es"], "license": "apache-2.0", "size_categories": ["n<1K"], "task_categories": ["text-classification", "text2text-generation"], "dataset_info": {"features": [{"name": "sintetico", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "intervencion", "dtype": "string"}, {"name": "tipo_discriminacion", "dtype": "string"}, {"name": "resultado", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1569183.3, "num_examples": 1791}, {"name": "test", "num_bytes": 87614.92462311558, "num_examples": 100}, {"name": "valid", "num_bytes": 86738.77537688443, "num_examples": 99}], "download_size": 936705, "dataset_size": 1743537.0000000002}, "tags": ["hate"]}
|
2023-04-11T08:29:14+00:00
|
[] |
[
"es"
] |
TAGS
#task_categories-text-classification #task_categories-text2text-generation #size_categories-n<1K #language-Spanish #license-apache-2.0 #hate #region-us
|
### Resumen del dataset
Se trata de un dataset en español, extraído del centro de documentación de la Fundación Secretariado Gitano, en el que se presentan distintas situaciones discriminatorias acontecidas por el pueblo gitano. Puesto que el objetivo del modelo es crear un sistema de generación de actuaciones que permita minimizar el impacto de una situación discriminatoria, se hizo un scrappeo y se extrajeron todos los PDFs que contuvieron casos de discriminación con el formato (HECHOS, INTERVENCIÓN, RESULTADO). Para extraer la información se hizo un scrappeo de la página, a continuación se limpió y se unificó todo el dataset con un script de preprocesamiento para que todo el dataset tuviera el mismo formato.
### Tareas admitidas y tablas de clasificación
* 'task-generation': Dado el hecho generar la intervención y la etiqueta de resultado, para dar métodos para hacer la intervección y que sea efectiva. (PAG-BERT)
* 'task-classication': Se puede entrenar un modelo de clasificación, dejamos a los usarios, predecir el tipo de descriminación de dependiedo del hecho
### Idioma
Es un dataset con la variante español de España, el estilo empleado es formal y objetivo, limitándose a describir los hechos descritos por las personas afectadas.
Estructura de los datos
-----------------------
### Instancias
A continuación se muestra una instancia de ejemplo del dataset:
### Campos de los datos
* 'sintetico': indica si los datos son relacionados con la intervención y el resultado son originales, es decir, proceden de la fuente "Fundación Secretariado Gitano" (valor 0); o si, por el contrario, los hemos generado sintéticamente (valor 1).
* 'text': expone los hechos descritos por la persona afectada.
* 'intervencion': presenta las medidas que se tomaron desde la Fundación para evitar que los hechos descritos en "text" se volvieran a repetir.
* 'tipo\_discriminacion': etiqueta que identifica el tipo de discriminación. Puede tomar los valores Acoso discriminatorio, Discriminación directa, Discriminación indirecta, Discriminación interseccional, Discurso de odio, Orden de discriminar,, Sin especificar.
* 'resultado': presenta la repercusión que tuvo la intervención adoptada. Sus posibles valores son Positivo, Negativo y Neutro.
### División de los datos
El dataset cuenta con un total de 1990 instancias, repartidas del siguiente modo:
Cabe destacar que, teniendo en cuenta el resultado de las intervenciones (positivo, negativo o neutro), el dataset no está balanceado. En concreto, hay un total de 280 muestras positivas, 939 negativas y 771 neutras. En próximas actualizaciones del dataset trabajaremos para incrementar el tamaño del dataset de forma balanceada.
Creación del dataset
--------------------
### Justificación de la curación
El motivo por el que se creó este dataset es para conocer de una forma objetiva, si las medidas actuales que se están adoptando por parte de la Fundación han surtido efecto (en cuyo caso sería positivo), no ha surtido ningún efecto (negativo), o si por el contrario, las medidas propuestas no han incentivado al usuario a llevar a cabo ninguna acción.
Se ha optado por este dataset por el volumen de datos que contiene relativos a distintos escenarios, y por el formato que todos comparten de: HECHOS, INTERVENCIÓN Y RESULTADO.
### Fuente de los datos
Los datos utilizados para construir el modelo fueron extraídos de la página web de la Fundación Secretariado Gitano ([FSM](URL)). El FSM tiene una base de datos que contiene actos de discriminación que han sido reportados a la organización. Estos actos de discriminación fueron seleccionados para entrenar y evaluar el modelo.
#### Recogida inicial de datos y normalización
Los datos fueron extraídos de la sección de <a href = "URL >Buscador de casos, donde se lleva un registro de todo los casos de descriminación.
Los campos que ofrece la página web para estetipo de informes son:
* 'Hecho' que hace referencia al acto de descriminación.
* 'Intervención' qué medidas tomo la FSG para solucionar el problema.
* 'Resultado': Descripción del resultado.
* Año que ocurrió el caso.
* Año del informe.
* Ámbito: Dado el caso de que la discrimnación haya sido una empresa gubernamenta, en cual derecho fundamental se presentó.
* Provincia: Lugar donde ocurrió el acto.
* Tipo de discriminación.
En la extracción de datos solo tuvimos en cuenta los campos hechos, intervención, resultados y tipo de discriminación. El lenguaje usado en los informes es formal.
Originalmente, una elevado número de Hechos no contaban con una intervención y resultado (los campos estaban vacíos).
#### Limpieza de los datos
En la página web, el campo resultado contiene un breve explicación del los efectos obtenidos tras llevar a cabo la intervección. Usando la librería <a href="URL se clasificó el resultado entre negativo, neutro y positivo.
Posterior mente se revisó la etiqueta y se ajustó para lo que se consideraba neutral, negativo o positivo
El 17% de los actos de discriminación en el dataset no contaban con intervención ni resultado. Para completar estos campos se aplicó la técnica few-show learning usando el modelo Bloom. De modo que dado algunos ejemplos de hechos, intervención y resultado, seríamos capaces de generar intervenciones y resultados de forma automática. El output del modelo Bloom se revisó manualmente para corregir errores.
El 41% de los textos del campo hechos eran demasiado largos para ser utilizado en BLOOM aplicando la técnica de few-shot learning. Para resolver este problema, se decidió resumirlos, para esto se utilizó la función 'segmenter.split\_single' de la librería <a href="URL >segtok, que divide el texto en oraciones y las separa por caracteres de nueva línea.
Se usaron dos modelos pre-etrenados para resumir cada sub-texto. El primero fue <a href="URL y el segundo fue el <a href="URL
En el repositorio URL se encuentran los scripts originales usados para el preprocesamiento. También puedes encontrar una copia de los mismos en este mismo repositorio.
### Anotación
Las anotaciones que se ralizaron fueron verificaciones a los datos de sintéticos generados con few-shot learning (intervenciones y resultados):
* Se rellenaron los valores nulos.
* Se hicieron resumenes de algunos textos (Hehos) aplicando modelos pre-entrenados.
* Se cambió el texto de resultado por etiquetas de POS, NEU, NEG.
#### Proceso de anotación
Para el proceso de etiquetado se utilizó Argilla para etiquetar la categoría de "Resultado", para ello se emplearon las siguientes etiquetas: "Positivo", "Negativo" y "Neutro". En el proceso de etiquetado lo que nos interesaba era etiquetar el resultado de las intervenciones para que el modelo aprendiera y pudiera generar texto para dar respuesta a la situación expuesta por el usuario, además de predecir con los datos etiquetados si la repercusión que pudiera tener la medida que propone el modelo sería "positiva"(surtiría efecto), "negativa"(no tendría ningún efecto) o "neutra"(si es posible que el usuario no llevara a cabo ninguna acción).
En concreto, tras descargar todos los datos disponibles en la web, los preprocesamos y unimos en un solo dataset que fue subido a Argilla. Una vez aquí, validamos cada una de las instancias del siguiente modo:
* Si la intervención y/o resultado están vacías, se anota como tal.
* Se comprueba que el resultado positivo, negativo o neutro es correcto. La mayoría de las incongruencias surgen entre los pares positivo/neutro y negativo/neutro.
Una vez validado el dataset con argilla, seleccionamos las muestras que fueron anotadas como "vacías" para proceder a completarlas. Para ello, hemos aplicado Few-Shot Learning usando el modelo BLOOM.
Cabe destacar que algunos hechos del dataset eran demasiado largos y no podían ser procesados por BLOOM (generaba un error que indicaba que habíamos superado el número máximo de tokens), para solucionarlo, utilizamos los modelos <a href="URL y <a href="URL para resumir dichos hechos y minimizar así su tamaño.
### Información personal y sensible
En este caso no se ha necesitado utilizar ningún proceso de anonimización, ya que los datos procedentes de esta fuente no contienen ninguna información que vulnere los derechos de los afectados.
Consideraciones sobre el uso de los datos
-----------------------------------------
### Consideraciones sobre el uso de los datos
El impacto social de este dataset se dirige a ser una herramienta que sirva para implementar acciones que ayuden a combatir el racismo hacia la población gitana, además este dataset se podría utilizar para evaluar la repercusión de las distintas medidas adoptadas durante un período de tiempo, y aquellas medidas con una repercusión "negativa" o "neutra" investigarlas y mejorarlas con un trato más concienzudo hacia la población gitana.
### Debate sobre los prejuicios
Sé realizó un analisís exploratorio de los datos, para eso hemos realizado una nube de palabras para analizar los datos sintéticos y no sintéticos.
#### Datos no sintéticos
<img src="URL
Aquí podemos ver que muchos de los hechos se generaron en noticias, en mujeres, temas de vivienda, con la policia y la familia.
<img src="URL
Las intervenciones hablan de derechos, de cartas, de igualdad, asesorar a la persona y de presentar quejas.
<img src="URL
Muchos de los resultados de las intervenciones fueron negativos o neutrales (Posiblemente sin respuesta) o de que no se logró lo propuesto (Negativo). Se puede observar el desbalance en los datos.
Por medio de la librería *pysentimiento* y usando el modelo 'pysentimiento/pt\_hate\_speech', se realizó una métrica para medir el discurso de odio en el 'Hecho'.
Para eso análizaremos hateful, targeted y aggressive. La métrica va de 0 a 1, para cada una. Siendo la probabilidad de que esa caracteristica esté en el texto.
Se encotró lo siguiente
<img src="URL
<img src="URL
La distribución de los valores de hateful, targeted y aggressive presentan una cola alargada hacia la derecha, lo que indica que hay pocos casos en los que se detecta un mensaje de odio en los hechos.
Para el caso, donde no se generó la intervección y resultado se presenta un crecimiento en el tercer cuartil, esto quiere decir que hay mensajes que muestra un discurso de odio. Por ejemplo el hateful es de 0.4, targeted de 0.02 y aggresive de 0.03. En conclusión, como está escrito el hecho y como fue entrenado el modelo de *pysentimiento*, en general los hechos no tienen un mensaje de odio.
#### Datos sintéticos.
Se realizó el mismo análisis para los datos sintéticos
<img src="URL
Cabe resltar que el hecho no fue generado.
Es claro que el dataset está más sesgado a contener las palabras gitano, gitana, comunidad gitana, etnia gitana, familia, discriminación.
<img src="URL
Esta parte fue generada por el modelo *Bloom*. Puede comprobarse que con *few-shot* se logra captar más que todo la palabra 'derecho'.
<img src="URL
Tambien hay un desbalance en las etiquetas generadas.
Por medio de la librería *pysentimiento* y usando el modelo 'pysentimiento/pt\_hate\_speech' ,se realizó una métrica para medir el discurso de odio en el 'Hecho'
Para eso análizaremos hateful, targeted y aggressive. La métrica va de 0 a 1, para cada una. Siendo la probabilidad de que esa caracteristica esté en el texto.
Se encotró lo siguiente
<img src="URL
<img src="URL
La distribución de los valores de hateful, targeted y aggressive presentan una cola alargada hacia la derecha, lo que indica que hay pocos casos en los que se detecta un mensaje de odio en los hechos.
Tanto la mediana como la media de los valores de hateful, targeted y aggressive están muy cerca de cero, lo que indica que la mayoría de los hechos no contienen mensajes de odio. Además, se observó que en el tercer cuartil, el 75% de los datos en la métrica de hateful es 0.3, para targeted es de 0.0089 y para aggressive es de 0.06, lo que refuerza la conclusión de que la mayoría de los datos no contienen un mensaje de odio en la descripción de los hechos.
Información adicional
---------------------
### Curadores del dataset
* <a href="URL Orozco
* <a href="URL>María Jesús García
* <a href="URL>Ramón Rueda
|
[
"### Resumen del dataset\n\n\nSe trata de un dataset en español, extraído del centro de documentación de la Fundación Secretariado Gitano, en el que se presentan distintas situaciones discriminatorias acontecidas por el pueblo gitano. Puesto que el objetivo del modelo es crear un sistema de generación de actuaciones que permita minimizar el impacto de una situación discriminatoria, se hizo un scrappeo y se extrajeron todos los PDFs que contuvieron casos de discriminación con el formato (HECHOS, INTERVENCIÓN, RESULTADO). Para extraer la información se hizo un scrappeo de la página, a continuación se limpió y se unificó todo el dataset con un script de preprocesamiento para que todo el dataset tuviera el mismo formato.",
"### Tareas admitidas y tablas de clasificación\n\n\n* 'task-generation': Dado el hecho generar la intervención y la etiqueta de resultado, para dar métodos para hacer la intervección y que sea efectiva. (PAG-BERT)\n* 'task-classication': Se puede entrenar un modelo de clasificación, dejamos a los usarios, predecir el tipo de descriminación de dependiedo del hecho",
"### Idioma\n\n\nEs un dataset con la variante español de España, el estilo empleado es formal y objetivo, limitándose a describir los hechos descritos por las personas afectadas.\n\n\nEstructura de los datos\n-----------------------",
"### Instancias\n\n\nA continuación se muestra una instancia de ejemplo del dataset:",
"### Campos de los datos\n\n\n* 'sintetico': indica si los datos son relacionados con la intervención y el resultado son originales, es decir, proceden de la fuente \"Fundación Secretariado Gitano\" (valor 0); o si, por el contrario, los hemos generado sintéticamente (valor 1).\n* 'text': expone los hechos descritos por la persona afectada.\n* 'intervencion': presenta las medidas que se tomaron desde la Fundación para evitar que los hechos descritos en \"text\" se volvieran a repetir.\n* 'tipo\\_discriminacion': etiqueta que identifica el tipo de discriminación. Puede tomar los valores Acoso discriminatorio, Discriminación directa, Discriminación indirecta, Discriminación interseccional, Discurso de odio, Orden de discriminar,, Sin especificar.\n* 'resultado': presenta la repercusión que tuvo la intervención adoptada. Sus posibles valores son Positivo, Negativo y Neutro.",
"### División de los datos\n\n\nEl dataset cuenta con un total de 1990 instancias, repartidas del siguiente modo:\n\n\n\nCabe destacar que, teniendo en cuenta el resultado de las intervenciones (positivo, negativo o neutro), el dataset no está balanceado. En concreto, hay un total de 280 muestras positivas, 939 negativas y 771 neutras. En próximas actualizaciones del dataset trabajaremos para incrementar el tamaño del dataset de forma balanceada.\n\n\nCreación del dataset\n--------------------",
"### Justificación de la curación\n\n\nEl motivo por el que se creó este dataset es para conocer de una forma objetiva, si las medidas actuales que se están adoptando por parte de la Fundación han surtido efecto (en cuyo caso sería positivo), no ha surtido ningún efecto (negativo), o si por el contrario, las medidas propuestas no han incentivado al usuario a llevar a cabo ninguna acción.\nSe ha optado por este dataset por el volumen de datos que contiene relativos a distintos escenarios, y por el formato que todos comparten de: HECHOS, INTERVENCIÓN Y RESULTADO.",
"### Fuente de los datos\n\n\nLos datos utilizados para construir el modelo fueron extraídos de la página web de la Fundación Secretariado Gitano ([FSM](URL)). El FSM tiene una base de datos que contiene actos de discriminación que han sido reportados a la organización. Estos actos de discriminación fueron seleccionados para entrenar y evaluar el modelo.",
"#### Recogida inicial de datos y normalización\n\n\nLos datos fueron extraídos de la sección de <a href = \"URL >Buscador de casos, donde se lleva un registro de todo los casos de descriminación.\n\n\nLos campos que ofrece la página web para estetipo de informes son:\n\n\n* 'Hecho' que hace referencia al acto de descriminación.\n* 'Intervención' qué medidas tomo la FSG para solucionar el problema.\n* 'Resultado': Descripción del resultado.\n* Año que ocurrió el caso.\n* Año del informe.\n* Ámbito: Dado el caso de que la discrimnación haya sido una empresa gubernamenta, en cual derecho fundamental se presentó.\n* Provincia: Lugar donde ocurrió el acto.\n* Tipo de discriminación.\n\n\nEn la extracción de datos solo tuvimos en cuenta los campos hechos, intervención, resultados y tipo de discriminación. El lenguaje usado en los informes es formal.\n\n\nOriginalmente, una elevado número de Hechos no contaban con una intervención y resultado (los campos estaban vacíos).",
"#### Limpieza de los datos\n\n\nEn la página web, el campo resultado contiene un breve explicación del los efectos obtenidos tras llevar a cabo la intervección. Usando la librería <a href=\"URL se clasificó el resultado entre negativo, neutro y positivo.\nPosterior mente se revisó la etiqueta y se ajustó para lo que se consideraba neutral, negativo o positivo\n\n\nEl 17% de los actos de discriminación en el dataset no contaban con intervención ni resultado. Para completar estos campos se aplicó la técnica few-show learning usando el modelo Bloom. De modo que dado algunos ejemplos de hechos, intervención y resultado, seríamos capaces de generar intervenciones y resultados de forma automática. El output del modelo Bloom se revisó manualmente para corregir errores.\n\n\nEl 41% de los textos del campo hechos eran demasiado largos para ser utilizado en BLOOM aplicando la técnica de few-shot learning. Para resolver este problema, se decidió resumirlos, para esto se utilizó la función 'segmenter.split\\_single' de la librería <a href=\"URL >segtok, que divide el texto en oraciones y las separa por caracteres de nueva línea.\nSe usaron dos modelos pre-etrenados para resumir cada sub-texto. El primero fue <a href=\"URL y el segundo fue el <a href=\"URL\n\n\nEn el repositorio URL se encuentran los scripts originales usados para el preprocesamiento. También puedes encontrar una copia de los mismos en este mismo repositorio.",
"### Anotación\n\n\nLas anotaciones que se ralizaron fueron verificaciones a los datos de sintéticos generados con few-shot learning (intervenciones y resultados):\n\n\n* Se rellenaron los valores nulos.\n* Se hicieron resumenes de algunos textos (Hehos) aplicando modelos pre-entrenados.\n* Se cambió el texto de resultado por etiquetas de POS, NEU, NEG.",
"#### Proceso de anotación\n\n\nPara el proceso de etiquetado se utilizó Argilla para etiquetar la categoría de \"Resultado\", para ello se emplearon las siguientes etiquetas: \"Positivo\", \"Negativo\" y \"Neutro\". En el proceso de etiquetado lo que nos interesaba era etiquetar el resultado de las intervenciones para que el modelo aprendiera y pudiera generar texto para dar respuesta a la situación expuesta por el usuario, además de predecir con los datos etiquetados si la repercusión que pudiera tener la medida que propone el modelo sería \"positiva\"(surtiría efecto), \"negativa\"(no tendría ningún efecto) o \"neutra\"(si es posible que el usuario no llevara a cabo ninguna acción).\nEn concreto, tras descargar todos los datos disponibles en la web, los preprocesamos y unimos en un solo dataset que fue subido a Argilla. Una vez aquí, validamos cada una de las instancias del siguiente modo:\n\n\n* Si la intervención y/o resultado están vacías, se anota como tal.\n* Se comprueba que el resultado positivo, negativo o neutro es correcto. La mayoría de las incongruencias surgen entre los pares positivo/neutro y negativo/neutro.\n\n\nUna vez validado el dataset con argilla, seleccionamos las muestras que fueron anotadas como \"vacías\" para proceder a completarlas. Para ello, hemos aplicado Few-Shot Learning usando el modelo BLOOM.\n\n\nCabe destacar que algunos hechos del dataset eran demasiado largos y no podían ser procesados por BLOOM (generaba un error que indicaba que habíamos superado el número máximo de tokens), para solucionarlo, utilizamos los modelos <a href=\"URL y <a href=\"URL para resumir dichos hechos y minimizar así su tamaño.",
"### Información personal y sensible\n\n\nEn este caso no se ha necesitado utilizar ningún proceso de anonimización, ya que los datos procedentes de esta fuente no contienen ninguna información que vulnere los derechos de los afectados.\n\n\nConsideraciones sobre el uso de los datos\n-----------------------------------------",
"### Consideraciones sobre el uso de los datos\n\n\nEl impacto social de este dataset se dirige a ser una herramienta que sirva para implementar acciones que ayuden a combatir el racismo hacia la población gitana, además este dataset se podría utilizar para evaluar la repercusión de las distintas medidas adoptadas durante un período de tiempo, y aquellas medidas con una repercusión \"negativa\" o \"neutra\" investigarlas y mejorarlas con un trato más concienzudo hacia la población gitana.",
"### Debate sobre los prejuicios\n\n\nSé realizó un analisís exploratorio de los datos, para eso hemos realizado una nube de palabras para analizar los datos sintéticos y no sintéticos.",
"#### Datos no sintéticos\n\n\n<img src=\"URL\nAquí podemos ver que muchos de los hechos se generaron en noticias, en mujeres, temas de vivienda, con la policia y la familia.\n<img src=\"URL\nLas intervenciones hablan de derechos, de cartas, de igualdad, asesorar a la persona y de presentar quejas.\n<img src=\"URL\nMuchos de los resultados de las intervenciones fueron negativos o neutrales (Posiblemente sin respuesta) o de que no se logró lo propuesto (Negativo). Se puede observar el desbalance en los datos.\n\n\nPor medio de la librería *pysentimiento* y usando el modelo 'pysentimiento/pt\\_hate\\_speech', se realizó una métrica para medir el discurso de odio en el 'Hecho'.\nPara eso análizaremos hateful, targeted y aggressive. La métrica va de 0 a 1, para cada una. Siendo la probabilidad de que esa caracteristica esté en el texto.\n\n\nSe encotró lo siguiente\n<img src=\"URL\n<img src=\"URL\nLa distribución de los valores de hateful, targeted y aggressive presentan una cola alargada hacia la derecha, lo que indica que hay pocos casos en los que se detecta un mensaje de odio en los hechos.\nPara el caso, donde no se generó la intervección y resultado se presenta un crecimiento en el tercer cuartil, esto quiere decir que hay mensajes que muestra un discurso de odio. Por ejemplo el hateful es de 0.4, targeted de 0.02 y aggresive de 0.03. En conclusión, como está escrito el hecho y como fue entrenado el modelo de *pysentimiento*, en general los hechos no tienen un mensaje de odio.",
"#### Datos sintéticos.\n\n\nSe realizó el mismo análisis para los datos sintéticos\n<img src=\"URL\nCabe resltar que el hecho no fue generado.\nEs claro que el dataset está más sesgado a contener las palabras gitano, gitana, comunidad gitana, etnia gitana, familia, discriminación.\n<img src=\"URL\nEsta parte fue generada por el modelo *Bloom*. Puede comprobarse que con *few-shot* se logra captar más que todo la palabra 'derecho'.\n<img src=\"URL\nTambien hay un desbalance en las etiquetas generadas.\n\n\nPor medio de la librería *pysentimiento* y usando el modelo 'pysentimiento/pt\\_hate\\_speech' ,se realizó una métrica para medir el discurso de odio en el 'Hecho'\nPara eso análizaremos hateful, targeted y aggressive. La métrica va de 0 a 1, para cada una. Siendo la probabilidad de que esa caracteristica esté en el texto.\n\n\nSe encotró lo siguiente\n\n\n<img src=\"URL\n<img src=\"URL\nLa distribución de los valores de hateful, targeted y aggressive presentan una cola alargada hacia la derecha, lo que indica que hay pocos casos en los que se detecta un mensaje de odio en los hechos.\nTanto la mediana como la media de los valores de hateful, targeted y aggressive están muy cerca de cero, lo que indica que la mayoría de los hechos no contienen mensajes de odio. Además, se observó que en el tercer cuartil, el 75% de los datos en la métrica de hateful es 0.3, para targeted es de 0.0089 y para aggressive es de 0.06, lo que refuerza la conclusión de que la mayoría de los datos no contienen un mensaje de odio en la descripción de los hechos.\n\n\nInformación adicional\n---------------------",
"### Curadores del dataset\n\n\n* <a href=\"URL Orozco\n* <a href=\"URL>María Jesús García\n* <a href=\"URL>Ramón Rueda"
] |
[
"TAGS\n#task_categories-text-classification #task_categories-text2text-generation #size_categories-n<1K #language-Spanish #license-apache-2.0 #hate #region-us \n",
"### Resumen del dataset\n\n\nSe trata de un dataset en español, extraído del centro de documentación de la Fundación Secretariado Gitano, en el que se presentan distintas situaciones discriminatorias acontecidas por el pueblo gitano. Puesto que el objetivo del modelo es crear un sistema de generación de actuaciones que permita minimizar el impacto de una situación discriminatoria, se hizo un scrappeo y se extrajeron todos los PDFs que contuvieron casos de discriminación con el formato (HECHOS, INTERVENCIÓN, RESULTADO). Para extraer la información se hizo un scrappeo de la página, a continuación se limpió y se unificó todo el dataset con un script de preprocesamiento para que todo el dataset tuviera el mismo formato.",
"### Tareas admitidas y tablas de clasificación\n\n\n* 'task-generation': Dado el hecho generar la intervención y la etiqueta de resultado, para dar métodos para hacer la intervección y que sea efectiva. (PAG-BERT)\n* 'task-classication': Se puede entrenar un modelo de clasificación, dejamos a los usarios, predecir el tipo de descriminación de dependiedo del hecho",
"### Idioma\n\n\nEs un dataset con la variante español de España, el estilo empleado es formal y objetivo, limitándose a describir los hechos descritos por las personas afectadas.\n\n\nEstructura de los datos\n-----------------------",
"### Instancias\n\n\nA continuación se muestra una instancia de ejemplo del dataset:",
"### Campos de los datos\n\n\n* 'sintetico': indica si los datos son relacionados con la intervención y el resultado son originales, es decir, proceden de la fuente \"Fundación Secretariado Gitano\" (valor 0); o si, por el contrario, los hemos generado sintéticamente (valor 1).\n* 'text': expone los hechos descritos por la persona afectada.\n* 'intervencion': presenta las medidas que se tomaron desde la Fundación para evitar que los hechos descritos en \"text\" se volvieran a repetir.\n* 'tipo\\_discriminacion': etiqueta que identifica el tipo de discriminación. Puede tomar los valores Acoso discriminatorio, Discriminación directa, Discriminación indirecta, Discriminación interseccional, Discurso de odio, Orden de discriminar,, Sin especificar.\n* 'resultado': presenta la repercusión que tuvo la intervención adoptada. Sus posibles valores son Positivo, Negativo y Neutro.",
"### División de los datos\n\n\nEl dataset cuenta con un total de 1990 instancias, repartidas del siguiente modo:\n\n\n\nCabe destacar que, teniendo en cuenta el resultado de las intervenciones (positivo, negativo o neutro), el dataset no está balanceado. En concreto, hay un total de 280 muestras positivas, 939 negativas y 771 neutras. En próximas actualizaciones del dataset trabajaremos para incrementar el tamaño del dataset de forma balanceada.\n\n\nCreación del dataset\n--------------------",
"### Justificación de la curación\n\n\nEl motivo por el que se creó este dataset es para conocer de una forma objetiva, si las medidas actuales que se están adoptando por parte de la Fundación han surtido efecto (en cuyo caso sería positivo), no ha surtido ningún efecto (negativo), o si por el contrario, las medidas propuestas no han incentivado al usuario a llevar a cabo ninguna acción.\nSe ha optado por este dataset por el volumen de datos que contiene relativos a distintos escenarios, y por el formato que todos comparten de: HECHOS, INTERVENCIÓN Y RESULTADO.",
"### Fuente de los datos\n\n\nLos datos utilizados para construir el modelo fueron extraídos de la página web de la Fundación Secretariado Gitano ([FSM](URL)). El FSM tiene una base de datos que contiene actos de discriminación que han sido reportados a la organización. Estos actos de discriminación fueron seleccionados para entrenar y evaluar el modelo.",
"#### Recogida inicial de datos y normalización\n\n\nLos datos fueron extraídos de la sección de <a href = \"URL >Buscador de casos, donde se lleva un registro de todo los casos de descriminación.\n\n\nLos campos que ofrece la página web para estetipo de informes son:\n\n\n* 'Hecho' que hace referencia al acto de descriminación.\n* 'Intervención' qué medidas tomo la FSG para solucionar el problema.\n* 'Resultado': Descripción del resultado.\n* Año que ocurrió el caso.\n* Año del informe.\n* Ámbito: Dado el caso de que la discrimnación haya sido una empresa gubernamenta, en cual derecho fundamental se presentó.\n* Provincia: Lugar donde ocurrió el acto.\n* Tipo de discriminación.\n\n\nEn la extracción de datos solo tuvimos en cuenta los campos hechos, intervención, resultados y tipo de discriminación. El lenguaje usado en los informes es formal.\n\n\nOriginalmente, una elevado número de Hechos no contaban con una intervención y resultado (los campos estaban vacíos).",
"#### Limpieza de los datos\n\n\nEn la página web, el campo resultado contiene un breve explicación del los efectos obtenidos tras llevar a cabo la intervección. Usando la librería <a href=\"URL se clasificó el resultado entre negativo, neutro y positivo.\nPosterior mente se revisó la etiqueta y se ajustó para lo que se consideraba neutral, negativo o positivo\n\n\nEl 17% de los actos de discriminación en el dataset no contaban con intervención ni resultado. Para completar estos campos se aplicó la técnica few-show learning usando el modelo Bloom. De modo que dado algunos ejemplos de hechos, intervención y resultado, seríamos capaces de generar intervenciones y resultados de forma automática. El output del modelo Bloom se revisó manualmente para corregir errores.\n\n\nEl 41% de los textos del campo hechos eran demasiado largos para ser utilizado en BLOOM aplicando la técnica de few-shot learning. Para resolver este problema, se decidió resumirlos, para esto se utilizó la función 'segmenter.split\\_single' de la librería <a href=\"URL >segtok, que divide el texto en oraciones y las separa por caracteres de nueva línea.\nSe usaron dos modelos pre-etrenados para resumir cada sub-texto. El primero fue <a href=\"URL y el segundo fue el <a href=\"URL\n\n\nEn el repositorio URL se encuentran los scripts originales usados para el preprocesamiento. También puedes encontrar una copia de los mismos en este mismo repositorio.",
"### Anotación\n\n\nLas anotaciones que se ralizaron fueron verificaciones a los datos de sintéticos generados con few-shot learning (intervenciones y resultados):\n\n\n* Se rellenaron los valores nulos.\n* Se hicieron resumenes de algunos textos (Hehos) aplicando modelos pre-entrenados.\n* Se cambió el texto de resultado por etiquetas de POS, NEU, NEG.",
"#### Proceso de anotación\n\n\nPara el proceso de etiquetado se utilizó Argilla para etiquetar la categoría de \"Resultado\", para ello se emplearon las siguientes etiquetas: \"Positivo\", \"Negativo\" y \"Neutro\". En el proceso de etiquetado lo que nos interesaba era etiquetar el resultado de las intervenciones para que el modelo aprendiera y pudiera generar texto para dar respuesta a la situación expuesta por el usuario, además de predecir con los datos etiquetados si la repercusión que pudiera tener la medida que propone el modelo sería \"positiva\"(surtiría efecto), \"negativa\"(no tendría ningún efecto) o \"neutra\"(si es posible que el usuario no llevara a cabo ninguna acción).\nEn concreto, tras descargar todos los datos disponibles en la web, los preprocesamos y unimos en un solo dataset que fue subido a Argilla. Una vez aquí, validamos cada una de las instancias del siguiente modo:\n\n\n* Si la intervención y/o resultado están vacías, se anota como tal.\n* Se comprueba que el resultado positivo, negativo o neutro es correcto. La mayoría de las incongruencias surgen entre los pares positivo/neutro y negativo/neutro.\n\n\nUna vez validado el dataset con argilla, seleccionamos las muestras que fueron anotadas como \"vacías\" para proceder a completarlas. Para ello, hemos aplicado Few-Shot Learning usando el modelo BLOOM.\n\n\nCabe destacar que algunos hechos del dataset eran demasiado largos y no podían ser procesados por BLOOM (generaba un error que indicaba que habíamos superado el número máximo de tokens), para solucionarlo, utilizamos los modelos <a href=\"URL y <a href=\"URL para resumir dichos hechos y minimizar así su tamaño.",
"### Información personal y sensible\n\n\nEn este caso no se ha necesitado utilizar ningún proceso de anonimización, ya que los datos procedentes de esta fuente no contienen ninguna información que vulnere los derechos de los afectados.\n\n\nConsideraciones sobre el uso de los datos\n-----------------------------------------",
"### Consideraciones sobre el uso de los datos\n\n\nEl impacto social de este dataset se dirige a ser una herramienta que sirva para implementar acciones que ayuden a combatir el racismo hacia la población gitana, además este dataset se podría utilizar para evaluar la repercusión de las distintas medidas adoptadas durante un período de tiempo, y aquellas medidas con una repercusión \"negativa\" o \"neutra\" investigarlas y mejorarlas con un trato más concienzudo hacia la población gitana.",
"### Debate sobre los prejuicios\n\n\nSé realizó un analisís exploratorio de los datos, para eso hemos realizado una nube de palabras para analizar los datos sintéticos y no sintéticos.",
"#### Datos no sintéticos\n\n\n<img src=\"URL\nAquí podemos ver que muchos de los hechos se generaron en noticias, en mujeres, temas de vivienda, con la policia y la familia.\n<img src=\"URL\nLas intervenciones hablan de derechos, de cartas, de igualdad, asesorar a la persona y de presentar quejas.\n<img src=\"URL\nMuchos de los resultados de las intervenciones fueron negativos o neutrales (Posiblemente sin respuesta) o de que no se logró lo propuesto (Negativo). Se puede observar el desbalance en los datos.\n\n\nPor medio de la librería *pysentimiento* y usando el modelo 'pysentimiento/pt\\_hate\\_speech', se realizó una métrica para medir el discurso de odio en el 'Hecho'.\nPara eso análizaremos hateful, targeted y aggressive. La métrica va de 0 a 1, para cada una. Siendo la probabilidad de que esa caracteristica esté en el texto.\n\n\nSe encotró lo siguiente\n<img src=\"URL\n<img src=\"URL\nLa distribución de los valores de hateful, targeted y aggressive presentan una cola alargada hacia la derecha, lo que indica que hay pocos casos en los que se detecta un mensaje de odio en los hechos.\nPara el caso, donde no se generó la intervección y resultado se presenta un crecimiento en el tercer cuartil, esto quiere decir que hay mensajes que muestra un discurso de odio. Por ejemplo el hateful es de 0.4, targeted de 0.02 y aggresive de 0.03. En conclusión, como está escrito el hecho y como fue entrenado el modelo de *pysentimiento*, en general los hechos no tienen un mensaje de odio.",
"#### Datos sintéticos.\n\n\nSe realizó el mismo análisis para los datos sintéticos\n<img src=\"URL\nCabe resltar que el hecho no fue generado.\nEs claro que el dataset está más sesgado a contener las palabras gitano, gitana, comunidad gitana, etnia gitana, familia, discriminación.\n<img src=\"URL\nEsta parte fue generada por el modelo *Bloom*. Puede comprobarse que con *few-shot* se logra captar más que todo la palabra 'derecho'.\n<img src=\"URL\nTambien hay un desbalance en las etiquetas generadas.\n\n\nPor medio de la librería *pysentimiento* y usando el modelo 'pysentimiento/pt\\_hate\\_speech' ,se realizó una métrica para medir el discurso de odio en el 'Hecho'\nPara eso análizaremos hateful, targeted y aggressive. La métrica va de 0 a 1, para cada una. Siendo la probabilidad de que esa caracteristica esté en el texto.\n\n\nSe encotró lo siguiente\n\n\n<img src=\"URL\n<img src=\"URL\nLa distribución de los valores de hateful, targeted y aggressive presentan una cola alargada hacia la derecha, lo que indica que hay pocos casos en los que se detecta un mensaje de odio en los hechos.\nTanto la mediana como la media de los valores de hateful, targeted y aggressive están muy cerca de cero, lo que indica que la mayoría de los hechos no contienen mensajes de odio. Además, se observó que en el tercer cuartil, el 75% de los datos en la métrica de hateful es 0.3, para targeted es de 0.0089 y para aggressive es de 0.06, lo que refuerza la conclusión de que la mayoría de los datos no contienen un mensaje de odio en la descripción de los hechos.\n\n\nInformación adicional\n---------------------",
"### Curadores del dataset\n\n\n* <a href=\"URL Orozco\n* <a href=\"URL>María Jesús García\n* <a href=\"URL>Ramón Rueda"
] |
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[
"passage: TAGS\n#task_categories-text-classification #task_categories-text2text-generation #size_categories-n<1K #language-Spanish #license-apache-2.0 #hate #region-us \n### Resumen del dataset\n\n\nSe trata de un dataset en español, extraído del centro de documentación de la Fundación Secretariado Gitano, en el que se presentan distintas situaciones discriminatorias acontecidas por el pueblo gitano. Puesto que el objetivo del modelo es crear un sistema de generación de actuaciones que permita minimizar el impacto de una situación discriminatoria, se hizo un scrappeo y se extrajeron todos los PDFs que contuvieron casos de discriminación con el formato (HECHOS, INTERVENCIÓN, RESULTADO). Para extraer la información se hizo un scrappeo de la página, a continuación se limpió y se unificó todo el dataset con un script de preprocesamiento para que todo el dataset tuviera el mismo formato.### Tareas admitidas y tablas de clasificación\n\n\n* 'task-generation': Dado el hecho generar la intervención y la etiqueta de resultado, para dar métodos para hacer la intervección y que sea efectiva. (PAG-BERT)\n* 'task-classication': Se puede entrenar un modelo de clasificación, dejamos a los usarios, predecir el tipo de descriminación de dependiedo del hecho### Idioma\n\n\nEs un dataset con la variante español de España, el estilo empleado es formal y objetivo, limitándose a describir los hechos descritos por las personas afectadas.\n\n\nEstructura de los datos\n-----------------------### Instancias\n\n\nA continuación se muestra una instancia de ejemplo del dataset:",
"passage: ### Campos de los datos\n\n\n* 'sintetico': indica si los datos son relacionados con la intervención y el resultado son originales, es decir, proceden de la fuente \"Fundación Secretariado Gitano\" (valor 0); o si, por el contrario, los hemos generado sintéticamente (valor 1).\n* 'text': expone los hechos descritos por la persona afectada.\n* 'intervencion': presenta las medidas que se tomaron desde la Fundación para evitar que los hechos descritos en \"text\" se volvieran a repetir.\n* 'tipo\\_discriminacion': etiqueta que identifica el tipo de discriminación. Puede tomar los valores Acoso discriminatorio, Discriminación directa, Discriminación indirecta, Discriminación interseccional, Discurso de odio, Orden de discriminar,, Sin especificar.\n* 'resultado': presenta la repercusión que tuvo la intervención adoptada. Sus posibles valores son Positivo, Negativo y Neutro.### División de los datos\n\n\nEl dataset cuenta con un total de 1990 instancias, repartidas del siguiente modo:\n\n\n\nCabe destacar que, teniendo en cuenta el resultado de las intervenciones (positivo, negativo o neutro), el dataset no está balanceado. En concreto, hay un total de 280 muestras positivas, 939 negativas y 771 neutras. En próximas actualizaciones del dataset trabajaremos para incrementar el tamaño del dataset de forma balanceada.\n\n\nCreación del dataset\n--------------------### Justificación de la curación\n\n\nEl motivo por el que se creó este dataset es para conocer de una forma objetiva, si las medidas actuales que se están adoptando por parte de la Fundación han surtido efecto (en cuyo caso sería positivo), no ha surtido ningún efecto (negativo), o si por el contrario, las medidas propuestas no han incentivado al usuario a llevar a cabo ninguna acción.\nSe ha optado por este dataset por el volumen de datos que contiene relativos a distintos escenarios, y por el formato que todos comparten de: HECHOS, INTERVENCIÓN Y RESULTADO.### Fuente de los datos\n\n\nLos datos utilizados para construir el modelo fueron extraídos de la página web de la Fundación Secretariado Gitano ([FSM](URL)). El FSM tiene una base de datos que contiene actos de discriminación que han sido reportados a la organización. Estos actos de discriminación fueron seleccionados para entrenar y evaluar el modelo.",
"passage: #### Recogida inicial de datos y normalización\n\n\nLos datos fueron extraídos de la sección de <a href = \"URL >Buscador de casos, donde se lleva un registro de todo los casos de descriminación.\n\n\nLos campos que ofrece la página web para estetipo de informes son:\n\n\n* 'Hecho' que hace referencia al acto de descriminación.\n* 'Intervención' qué medidas tomo la FSG para solucionar el problema.\n* 'Resultado': Descripción del resultado.\n* Año que ocurrió el caso.\n* Año del informe.\n* Ámbito: Dado el caso de que la discrimnación haya sido una empresa gubernamenta, en cual derecho fundamental se presentó.\n* Provincia: Lugar donde ocurrió el acto.\n* Tipo de discriminación.\n\n\nEn la extracción de datos solo tuvimos en cuenta los campos hechos, intervención, resultados y tipo de discriminación. El lenguaje usado en los informes es formal.\n\n\nOriginalmente, una elevado número de Hechos no contaban con una intervención y resultado (los campos estaban vacíos).#### Limpieza de los datos\n\n\nEn la página web, el campo resultado contiene un breve explicación del los efectos obtenidos tras llevar a cabo la intervección. Usando la librería <a href=\"URL se clasificó el resultado entre negativo, neutro y positivo.\nPosterior mente se revisó la etiqueta y se ajustó para lo que se consideraba neutral, negativo o positivo\n\n\nEl 17% de los actos de discriminación en el dataset no contaban con intervención ni resultado. Para completar estos campos se aplicó la técnica few-show learning usando el modelo Bloom. De modo que dado algunos ejemplos de hechos, intervención y resultado, seríamos capaces de generar intervenciones y resultados de forma automática. El output del modelo Bloom se revisó manualmente para corregir errores.\n\n\nEl 41% de los textos del campo hechos eran demasiado largos para ser utilizado en BLOOM aplicando la técnica de few-shot learning. Para resolver este problema, se decidió resumirlos, para esto se utilizó la función 'segmenter.split\\_single' de la librería <a href=\"URL >segtok, que divide el texto en oraciones y las separa por caracteres de nueva línea.\nSe usaron dos modelos pre-etrenados para resumir cada sub-texto. El primero fue <a href=\"URL y el segundo fue el <a href=\"URL\n\n\nEn el repositorio URL se encuentran los scripts originales usados para el preprocesamiento. También puedes encontrar una copia de los mismos en este mismo repositorio.### Anotación\n\n\nLas anotaciones que se ralizaron fueron verificaciones a los datos de sintéticos generados con few-shot learning (intervenciones y resultados):\n\n\n* Se rellenaron los valores nulos.\n* Se hicieron resumenes de algunos textos (Hehos) aplicando modelos pre-entrenados.\n* Se cambió el texto de resultado por etiquetas de POS, NEU, NEG.",
"passage: #### Proceso de anotación\n\n\nPara el proceso de etiquetado se utilizó Argilla para etiquetar la categoría de \"Resultado\", para ello se emplearon las siguientes etiquetas: \"Positivo\", \"Negativo\" y \"Neutro\". En el proceso de etiquetado lo que nos interesaba era etiquetar el resultado de las intervenciones para que el modelo aprendiera y pudiera generar texto para dar respuesta a la situación expuesta por el usuario, además de predecir con los datos etiquetados si la repercusión que pudiera tener la medida que propone el modelo sería \"positiva\"(surtiría efecto), \"negativa\"(no tendría ningún efecto) o \"neutra\"(si es posible que el usuario no llevara a cabo ninguna acción).\nEn concreto, tras descargar todos los datos disponibles en la web, los preprocesamos y unimos en un solo dataset que fue subido a Argilla. Una vez aquí, validamos cada una de las instancias del siguiente modo:\n\n\n* Si la intervención y/o resultado están vacías, se anota como tal.\n* Se comprueba que el resultado positivo, negativo o neutro es correcto. La mayoría de las incongruencias surgen entre los pares positivo/neutro y negativo/neutro.\n\n\nUna vez validado el dataset con argilla, seleccionamos las muestras que fueron anotadas como \"vacías\" para proceder a completarlas. Para ello, hemos aplicado Few-Shot Learning usando el modelo BLOOM.\n\n\nCabe destacar que algunos hechos del dataset eran demasiado largos y no podían ser procesados por BLOOM (generaba un error que indicaba que habíamos superado el número máximo de tokens), para solucionarlo, utilizamos los modelos <a href=\"URL y <a href=\"URL para resumir dichos hechos y minimizar así su tamaño.### Información personal y sensible\n\n\nEn este caso no se ha necesitado utilizar ningún proceso de anonimización, ya que los datos procedentes de esta fuente no contienen ninguna información que vulnere los derechos de los afectados.\n\n\nConsideraciones sobre el uso de los datos\n-----------------------------------------### Consideraciones sobre el uso de los datos\n\n\nEl impacto social de este dataset se dirige a ser una herramienta que sirva para implementar acciones que ayuden a combatir el racismo hacia la población gitana, además este dataset se podría utilizar para evaluar la repercusión de las distintas medidas adoptadas durante un período de tiempo, y aquellas medidas con una repercusión \"negativa\" o \"neutra\" investigarlas y mejorarlas con un trato más concienzudo hacia la población gitana.### Debate sobre los prejuicios\n\n\nSé realizó un analisís exploratorio de los datos, para eso hemos realizado una nube de palabras para analizar los datos sintéticos y no sintéticos."
] |
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2e02874b04913fce9514c953abec4fbcbd1a9ec7
|
# Dataset Card for "OxfordFlowers_test_google_flan_t5_xxl_mode_T_A_ns_100"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
CVasNLPExperiments/OxfordFlowers_test_google_flan_t5_xxl_mode_T_A_ns_100
|
[
"region:us"
] |
2023-04-04T13:28:28+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "prompt", "dtype": "string"}, {"name": "true_label", "dtype": "string"}, {"name": "prediction", "dtype": "string"}], "splits": [{"name": "fewshot_0_clip_tags_ViT_L_14_Attributes_ViT_L_14_text_davinci_003_clip_tags_ViT_L_14_simple_specific_rices", "num_bytes": 54089, "num_examples": 100}], "download_size": 14182, "dataset_size": 54089}}
|
2023-04-04T13:58:26+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "OxfordFlowers_test_google_flan_t5_xxl_mode_T_A_ns_100"
More Information needed
|
[
"# Dataset Card for \"OxfordFlowers_test_google_flan_t5_xxl_mode_T_A_ns_100\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"OxfordFlowers_test_google_flan_t5_xxl_mode_T_A_ns_100\"\n\nMore Information needed"
] |
[
6,
36
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"OxfordFlowers_test_google_flan_t5_xxl_mode_T_A_ns_100\"\n\nMore Information needed"
] |
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f1a99ce23a6a8fb2829d48d6e0c914c4d4adf49f
|
# Dataset Card for "OxfordPets_test_google_flan_t5_xxl_mode_T_A_ns_3669"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
CVasNLPExperiments/OxfordPets_test_google_flan_t5_xxl_mode_T_A_ns_3669
|
[
"region:us"
] |
2023-04-04T13:45:41+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "prompt", "dtype": "string"}, {"name": "true_label", "dtype": "string"}, {"name": "prediction", "dtype": "string"}], "splits": [{"name": "fewshot_0_clip_tags_ViT_L_14_Attributes_ViT_L_14_text_davinci_003_clip_tags_ViT_L_14_simple_specific_rices", "num_bytes": 1752684, "num_examples": 3669}], "download_size": 287000, "dataset_size": 1752684}}
|
2023-04-04T13:52:18+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "OxfordPets_test_google_flan_t5_xxl_mode_T_A_ns_3669"
More Information needed
|
[
"# Dataset Card for \"OxfordPets_test_google_flan_t5_xxl_mode_T_A_ns_3669\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"OxfordPets_test_google_flan_t5_xxl_mode_T_A_ns_3669\"\n\nMore Information needed"
] |
[
6,
36
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"OxfordPets_test_google_flan_t5_xxl_mode_T_A_ns_3669\"\n\nMore Information needed"
] |
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] |
65fb7c9cb2fd2e79747332276f6284e761af0936
|
### Dataset Summary
This dataset includes 258 food classes each class has 1,000 photos.
### Languages
We provided English labels however you can translate them to any language.
## Dataset Split
There are three splits:
- validation
- train
- test
### Data Fields
- `image` PIL.Image
- `label` An index of the label
## Dataset Creation
We crawled publicly available food images across websites.
## Limitation
The dataset is not accurate.
|
seanghay/food258k
|
[
"language:en",
"license:odc-by",
"region:us"
] |
2023-04-04T14:00:04+00:00
|
{"language": ["en"], "license": "odc-by", "pretty_name": "goo", "dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "aloo jeera", "1": "americano", "2": "angkor beer", "3": "apple juice", "4": "avocado smoothie", "5": "bacchus", "6": "banana smoothie", "7": "basil fried rice", "8": "beef burger", "9": "beef fried noodle", "10": "beef fried rice", "11": "beef lok lak", "12": "beef lok lak with rice", "13": "beef spicy salad", "14": "black chicken soup", "15": "black coffee", "16": "black tea", "17": "blue crab papaya salad", "18": "blue crab spicy salad", "19": "blue curacao soda", "20": "blueberry smoothie", "21": "blueberry soda", "22": "braised beef noodle", "23": "braised beef rice stick noodle", "24": "braised beef with bread", "25": "brown sugar", "26": "brown sugar milk tea", "27": "butter chicken", "28": "butter naan", "29": "butterfly pea milk tea", "30": "caesar salad", "31": "cambodia beer", "32": "cappuccino", "33": "cappuccino frappe", "34": "carabao", "35": "caramel frappe", "36": "caramel latte", "37": "caramel macchiato", "38": "caramel macchiato frappe", "39": "caramel milk tea", "40": "carrot", "41": "carrot juice", "42": "cheese naan", "43": "chicken", "44": "chicken burger", "45": "chicken curry", "46": "chicken fried rice", "47": "chicken korma", "48": "chicken nugget", "49": "chicken porridge", "50": "chicken salad", "51": "chicken samosa", "52": "chicken sour soup", "53": "chicken tikka", "54": "chicken tikka masala", "55": "chicken wing", "56": "chocolate", "57": "chocolate frappe", "58": "chocolate milk", "59": "club sandwich", "60": "coca cola", "61": "cocacola", "62": "coconut butterfly pea tea", "63": "coconut chocolate", "64": "coconut coffee", "65": "coconut coffee milk", "66": "coconut green milk tea", "67": "coconut green tea", "68": "coconut smoothie", "69": "coffee frappe", "70": "coffee latte", "71": "coffee milk", "72": "coffee with milk", "73": "coke", "74": "coke zero", "75": "croissant", "76": "deepfried chicken", "77": "deepfried chicken wing", "78": "double espresso", "79": "edamame", "80": "egg", "81": "egg fried rice", "82": "espresso", "83": "fanta", "84": "fanta orange", "85": "fish curry", "86": "fish porridge", "87": "fish sour soup", "88": "french fries", "89": "fresh apple juice", "90": "fresh carrot juice", "91": "fresh milk", "92": "fresh milk frappe", "93": "fresh orange juice", "94": "fresh pineapple juice", "95": "fresh watermelon juice", "96": "fried chicken", "97": "fried chicken drumstick with rice", "98": "fried chicken with rice", "99": "fried egg", "100": "fried egg with bitter melon", "101": "fried egg with salted fish", "102": "fried fish ball", "103": "fried noodle", "104": "fried pork with rice", "105": "fried rice", "106": "garlic naan", "107": "ginger ale", "108": "glass noodle", "109": "green apple soda", "110": "green milk tea", "111": "green salad", "112": "green tea", "113": "green tea frappe", "114": "green tea latte", "115": "ham and cheese sandwich", "116": "honey lemon tea", "117": "hot americano", "118": "hot cappuccino", "119": "hot caramel latte", "120": "hot caramel macchiato", "121": "hot chocolate", "122": "hot coffee", "123": "hot coffee latte", "124": "hot coffee milk", "125": "hot coffee mocha", "126": "hot dog", "127": "hot espresso", "128": "hot flat white", "129": "hot fresh milk", "130": "hot green tea", "131": "hot green tea latte", "132": "hot latte", "133": "hot lemon tea", "134": "hot matcha latte", "135": "hot mocha", "136": "hot vanilla latte", "137": "iced americano", "138": "iced black coffee", "139": "iced cappuccino", "140": "iced caramel latte", "141": "iced caramel macchiato", "142": "iced chocolate", "143": "iced coffee", "144": "iced coffee latte", "145": "iced coffee milk", "146": "iced coffee with milk", "147": "iced fresh milk", "148": "iced green milk tea", "149": "iced green tea", "150": "iced green tea latte", "151": "iced honey lemon tea", "152": "iced latte", "153": "iced lemon green tea", "154": "iced lemon red tea", "155": "iced lemon tea", "156": "iced matcha latte", "157": "iced milk tea", "158": "iced mocha", "159": "iced passion milk", "160": "iced passion soda", "161": "iced peach tea", "162": "iced red milk tea", "163": "iced strawberry soda", "164": "iced vanilla latte", "165": "jasmine milk tea", "166": "kimchi", "167": "kiwi smoothie", "168": "kiwi soda", "169": "latte", "170": "latte frappe", "171": "lemon butterfly pea tea", "172": "lemon green tea", "173": "lemon juice", "174": "lemon red tea", "175": "lemon soda", "176": "lemon tea", "177": "logo", "178": "lok lak fried rice", "179": "mango passion smoothie", "180": "mango smoothie", "181": "matcha frappe", "182": "matcha latte", "183": "menu", "184": "milk tea", "185": "mineral water", "186": "mocha", "187": "mocha frappe", "188": "oolong milk tea", "189": "oolong tea", "190": "orange juice", "191": "ovaltine milk", "192": "pad thai", "193": "paneer tikka masala", "194": "passion fresh milk", "195": "passion fruit smoothie", "196": "passion milk", "197": "passion smoothie", "198": "passion soda", "199": "passion tea", "200": "peach soda", "201": "pepsi", "202": "pineapple juice", "203": "pork fried noodle", "204": "pork fried rice", "205": "pork porridge", "206": "pork rice", "207": "pork with rice", "208": "raspberry soda", "209": "red bull", "210": "red milk tea", "211": "red sting", "212": "rice", "213": "roasted duck", "214": "salted crab papaya salad", "215": "sausage", "216": "seafood fried noodle", "217": "seafood fried rice", "218": "seafood noodle soup", "219": "seafood pizza", "220": "seafood porridge", "221": "seafood rice stick noodle", "222": "seafood tom yum", "223": "seaweed soup", "224": "shrimp", "225": "shrimp dumpling", "226": "shrimp fried rice", "227": "shrimp paste fried rice", "228": "soda", "229": "soda water", "230": "spaghetti bolognese", "231": "spaghetti carbonara", "232": "sprite", "233": "squid", "234": "steamed rice", "235": "sting", "236": "stirfried chicken with ginger", "237": "stirfried morning glory with oyster sauce", "238": "stirfried squid with green peppercorn", "239": "strawberry frappe", "240": "strawberry fresh milk", "241": "strawberry milk", "242": "strawberry smoothie", "243": "strawberry soda", "244": "strawberry yogurt", "245": "taiwan milk tea", "246": "taro milk", "247": "thai milk tea", "248": "tuna salad", "249": "tuna sandwich", "250": "vanilla frappe", "251": "vanilla latte", "252": "vigor", "253": "water", "254": "watermelon juice", "255": "wonton soup", "256": "yangzhou fried rice", "257": "yellow sting"}}}}], "splits": [{"name": "train", "num_bytes": 13175816458, "num_examples": 258000}, {"name": "test", "num_bytes": 1068936423, "num_examples": 25800}, {"name": "validation", "num_bytes": 1062627528.4, "num_examples": 25800}], "download_size": 11236412882, "dataset_size": 15307380409.4}}
|
2023-04-05T03:43:22+00:00
|
[] |
[
"en"
] |
TAGS
#language-English #license-odc-by #region-us
|
### Dataset Summary
This dataset includes 258 food classes each class has 1,000 photos.
### Languages
We provided English labels however you can translate them to any language.
## Dataset Split
There are three splits:
- validation
- train
- test
### Data Fields
- 'image' PIL.Image
- 'label' An index of the label
## Dataset Creation
We crawled publicly available food images across websites.
## Limitation
The dataset is not accurate.
|
[
"### Dataset Summary\n\nThis dataset includes 258 food classes each class has 1,000 photos.",
"### Languages\n\nWe provided English labels however you can translate them to any language.",
"## Dataset Split\n\nThere are three splits:\n\n- validation\n- train\n- test",
"### Data Fields\n\n- 'image' PIL.Image\n- 'label' An index of the label",
"## Dataset Creation\n\nWe crawled publicly available food images across websites.",
"## Limitation\n\nThe dataset is not accurate."
] |
[
"TAGS\n#language-English #license-odc-by #region-us \n",
"### Dataset Summary\n\nThis dataset includes 258 food classes each class has 1,000 photos.",
"### Languages\n\nWe provided English labels however you can translate them to any language.",
"## Dataset Split\n\nThere are three splits:\n\n- validation\n- train\n- test",
"### Data Fields\n\n- 'image' PIL.Image\n- 'label' An index of the label",
"## Dataset Creation\n\nWe crawled publicly available food images across websites.",
"## Limitation\n\nThe dataset is not accurate."
] |
[
18,
20,
19,
17,
22,
16,
10
] |
[
"passage: TAGS\n#language-English #license-odc-by #region-us \n### Dataset Summary\n\nThis dataset includes 258 food classes each class has 1,000 photos.### Languages\n\nWe provided English labels however you can translate them to any language.## Dataset Split\n\nThere are three splits:\n\n- validation\n- train\n- test### Data Fields\n\n- 'image' PIL.Image\n- 'label' An index of the label## Dataset Creation\n\nWe crawled publicly available food images across websites.## Limitation\n\nThe dataset is not accurate."
] |
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5729786f9b6fe5039b52f6c7cdcdbbec44b9f9c7
|
# Dataset Card for "DTD_parition1_test_google_flan_t5_xxl_mode_A_ns_100"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
CVasNLPExperiments/DTD_parition1_test_google_flan_t5_xxl_mode_A_ns_100
|
[
"region:us"
] |
2023-04-04T14:01:25+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "prompt", "dtype": "string"}, {"name": "true_label", "dtype": "string"}, {"name": "prediction", "dtype": "string"}], "splits": [{"name": "fewshot_0_clip_tags_ViT_L_14_Attributes_ViT_L_14_text_davinci_003_clip_tags_ViT_L_14_simple_specific_rices", "num_bytes": 42137, "num_examples": 100}], "download_size": 0, "dataset_size": 42137}}
|
2023-04-04T14:26:20+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "DTD_parition1_test_google_flan_t5_xxl_mode_A_ns_100"
More Information needed
|
[
"# Dataset Card for \"DTD_parition1_test_google_flan_t5_xxl_mode_A_ns_100\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"DTD_parition1_test_google_flan_t5_xxl_mode_A_ns_100\"\n\nMore Information needed"
] |
[
6,
36
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"DTD_parition1_test_google_flan_t5_xxl_mode_A_ns_100\"\n\nMore Information needed"
] |
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b49848f673d73b9b3bc583e27100b37458daf0e0
|
# Dataset Card for "DTD_parition1_test_google_flan_t5_xxl_mode_T_A_ns_100"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
CVasNLPExperiments/DTD_parition1_test_google_flan_t5_xxl_mode_T_A_ns_100
|
[
"region:us"
] |
2023-04-04T14:04:45+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "prompt", "dtype": "string"}, {"name": "true_label", "dtype": "string"}, {"name": "prediction", "dtype": "string"}], "splits": [{"name": "fewshot_0_clip_tags_ViT_L_14_Attributes_ViT_L_14_text_davinci_003_clip_tags_ViT_L_14_simple_specific_rices", "num_bytes": 55670, "num_examples": 100}], "download_size": 0, "dataset_size": 55670}}
|
2023-04-04T14:34:07+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "DTD_parition1_test_google_flan_t5_xxl_mode_T_A_ns_100"
More Information needed
|
[
"# Dataset Card for \"DTD_parition1_test_google_flan_t5_xxl_mode_T_A_ns_100\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"DTD_parition1_test_google_flan_t5_xxl_mode_T_A_ns_100\"\n\nMore Information needed"
] |
[
6,
38
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[
"passage: TAGS\n#region-us \n# Dataset Card for \"DTD_parition1_test_google_flan_t5_xxl_mode_T_A_ns_100\"\n\nMore Information needed"
] |
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] |
b4a4592b8693de5bc12b48069c0e93244b8a6ac0
|
# Milan Call Traffic Dataset
This dataset contains information about hourly call traffic in Milan between 2013-11-01 and 2014-01-01.
|
isaquecerqueira/millan_call_traffic
|
[
"license:odbl",
"region:us"
] |
2023-04-04T14:05:12+00:00
|
{"license": "odbl"}
|
2023-04-05T17:10:48+00:00
|
[] |
[] |
TAGS
#license-odbl #region-us
|
# Milan Call Traffic Dataset
This dataset contains information about hourly call traffic in Milan between 2013-11-01 and 2014-01-01.
|
[
"# Milan Call Traffic Dataset\n\nThis dataset contains information about hourly call traffic in Milan between 2013-11-01 and 2014-01-01."
] |
[
"TAGS\n#license-odbl #region-us \n",
"# Milan Call Traffic Dataset\n\nThis dataset contains information about hourly call traffic in Milan between 2013-11-01 and 2014-01-01."
] |
[
12,
28
] |
[
"passage: TAGS\n#license-odbl #region-us \n# Milan Call Traffic Dataset\n\nThis dataset contains information about hourly call traffic in Milan between 2013-11-01 and 2014-01-01."
] |
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15c7f996db2aa45577066b7b132f023519cd6e7b
|
# Dataset Card for "DTD_parition1_test_google_flan_t5_xxl_mode_T_SPECIFIC_A_ns_1880"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
CVasNLPExperiments/DTD_parition1_test_google_flan_t5_xxl_mode_T_SPECIFIC_A_ns_1880
|
[
"region:us"
] |
2023-04-04T14:20:43+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "prompt", "dtype": "string"}, {"name": "true_label", "dtype": "string"}, {"name": "prediction", "dtype": "string"}], "splits": [{"name": "fewshot_0_clip_tags_ViT_L_14_Attributes_ViT_L_14_text_davinci_003_clip_tags_ViT_L_14_simple_specific_rices", "num_bytes": 809983, "num_examples": 1880}, {"name": "fewshot_0_clip_tags_ViT_L_14_Attributes_ViT_L_14_text_davinci_003_dtd_clip_tags_ViT_L_14_simple_specific_rices", "num_bytes": 631818, "num_examples": 1880}, {"name": "fewshot_0_clip_tags_ViT_L_14_Attributes_ViT_L_14_text_davinci_003_full_clip_tags_ViT_L_14_simple_specific_rices", "num_bytes": 834528, "num_examples": 1880}, {"name": "fewshot_1_clip_tags_ViT_L_14_Attributes_ViT_L_14_text_davinci_003_full_clip_tags_ViT_L_14_simple_specific_rices", "num_bytes": 1624239, "num_examples": 1880}, {"name": "fewshot_3_clip_tags_ViT_L_14_Attributes_ViT_L_14_text_davinci_003_full_clip_tags_ViT_L_14_simple_specific_rices", "num_bytes": 3204182, "num_examples": 1880}, {"name": "fewshot_0__Attributes_ViT_B_16_descriptors_text_davinci_003_full_clip_tags_ViT_B_16_simple_specific_rices", "num_bytes": 835669, "num_examples": 1880}, {"name": "fewshot_1__Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full_clip_tags_LAION_ViT_H_14_2B_simple_specific_rices", "num_bytes": 1578128, "num_examples": 1880}, {"name": "fewshot_1__Attributes_ViT_B_16_descriptors_text_davinci_003_full_clip_tags_ViT_B_16_simple_specific_rices", "num_bytes": 1618889, "num_examples": 1880}, {"name": "fewshot_3__Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full_clip_tags_LAION_ViT_H_14_2B_simple_specific_rices", "num_bytes": 3113380, "num_examples": 1880}, {"name": "fewshot_0__Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full_clip_tags_LAION_ViT_H_14_2B_simple_specific_rices", "num_bytes": 787095, "num_examples": 1880}], "download_size": 3231407, "dataset_size": 15037911}, "configs": [{"config_name": "default", "data_files": [{"split": "fewshot_0__Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full_clip_tags_LAION_ViT_H_14_2B_simple_specific_rices", "path": "data/fewshot_0__Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full_clip_tags_LAION_ViT_H_14_2B_simple_specific_rices-*"}]}]}
|
2024-01-30T01:33:46+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "DTD_parition1_test_google_flan_t5_xxl_mode_T_SPECIFIC_A_ns_1880"
More Information needed
|
[
"# Dataset Card for \"DTD_parition1_test_google_flan_t5_xxl_mode_T_SPECIFIC_A_ns_1880\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"DTD_parition1_test_google_flan_t5_xxl_mode_T_SPECIFIC_A_ns_1880\"\n\nMore Information needed"
] |
[
6,
42
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"DTD_parition1_test_google_flan_t5_xxl_mode_T_SPECIFIC_A_ns_1880\"\n\nMore Information needed"
] |
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ed54c3338455b0f19a9b4c98bb5045091847555d
|
# Dataset Card for "SETH_0404"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Brizape/SETH_0404
|
[
"region:us"
] |
2023-04-04T14:21:28+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "texts", "dtype": "string"}, {"name": "tokens", "sequence": "string"}, {"name": "ner_tags", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 2425278, "num_examples": 504}, {"name": "test", "num_bytes": 582671, "num_examples": 126}], "download_size": 837941, "dataset_size": 3007949}}
|
2023-04-04T14:21:38+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "SETH_0404"
More Information needed
|
[
"# Dataset Card for \"SETH_0404\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"SETH_0404\"\n\nMore Information needed"
] |
[
6,
15
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"SETH_0404\"\n\nMore Information needed"
] |
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d8cd7ca37004c575548aaec4807ede436b6006f6
|
# Dataset Card for "DTD_parition1_test_google_flan_t5_xxl_mode_T_SPECIFIC_A_ns_100"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
CVasNLPExperiments/DTD_parition1_test_google_flan_t5_xxl_mode_T_SPECIFIC_A_ns_100
|
[
"region:us"
] |
2023-04-04T14:24:38+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "prompt", "dtype": "string"}, {"name": "true_label", "dtype": "string"}, {"name": "prediction", "dtype": "string"}], "splits": [{"name": "fewshot_0_clip_tags_ViT_L_14_Attributes_ViT_L_14_text_davinci_003_clip_tags_ViT_L_14_simple_specific_rices", "num_bytes": 43535, "num_examples": 100}, {"name": "fewshot_0__Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full_clip_tags_LAION_ViT_H_14_2B_simple_specific_rices", "num_bytes": 44186, "num_examples": 100}], "download_size": 46407, "dataset_size": 87721}, "configs": [{"config_name": "default", "data_files": [{"split": "fewshot_0__Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full_clip_tags_LAION_ViT_H_14_2B_simple_specific_rices", "path": "data/fewshot_0__Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full_clip_tags_LAION_ViT_H_14_2B_simple_specific_rices-*"}]}]}
|
2024-01-29T18:04:56+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "DTD_parition1_test_google_flan_t5_xxl_mode_T_SPECIFIC_A_ns_100"
More Information needed
|
[
"# Dataset Card for \"DTD_parition1_test_google_flan_t5_xxl_mode_T_SPECIFIC_A_ns_100\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"DTD_parition1_test_google_flan_t5_xxl_mode_T_SPECIFIC_A_ns_100\"\n\nMore Information needed"
] |
[
6,
42
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"DTD_parition1_test_google_flan_t5_xxl_mode_T_SPECIFIC_A_ns_100\"\n\nMore Information needed"
] |
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731174470274f16c2368f5d0c357e0abd5a3f6c2
|
# Dataset Card for "amia_0404"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Brizape/amia_0404
|
[
"region:us"
] |
2023-04-04T14:26:22+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "texts", "dtype": "string"}, {"name": "tokens", "sequence": "string"}, {"name": "ner_tags", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 1849182, "num_examples": 100}, {"name": "test", "num_bytes": 842790, "num_examples": 45}], "download_size": 422219, "dataset_size": 2691972}}
|
2023-04-04T14:26:31+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "amia_0404"
More Information needed
|
[
"# Dataset Card for \"amia_0404\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"amia_0404\"\n\nMore Information needed"
] |
[
6,
15
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"amia_0404\"\n\nMore Information needed"
] |
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1d8f38baa392e618e7835a354a321e1c722bf04a
|
# Dataset Card for "Variome_0404"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Brizape/Variome_0404
|
[
"region:us"
] |
2023-04-04T14:27:01+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "texts", "dtype": "string"}, {"name": "tokens", "sequence": "string"}, {"name": "ner_tags", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 8210534, "num_examples": 96}, {"name": "test", "num_bytes": 1447445, "num_examples": 24}], "download_size": 972009, "dataset_size": 9657979}}
|
2023-04-04T14:27:11+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "Variome_0404"
More Information needed
|
[
"# Dataset Card for \"Variome_0404\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"Variome_0404\"\n\nMore Information needed"
] |
[
6,
16
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"Variome_0404\"\n\nMore Information needed"
] |
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] |
c912bbf0de35ed96d20a9da1292fe99abd5cce12
|
# Dataset Card for "tmvar_0404"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Brizape/tmvar_0404
|
[
"region:us"
] |
2023-04-04T14:27:37+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "texts", "dtype": "string"}, {"name": "tokens", "sequence": "string"}, {"name": "ner_tags", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 2013493, "num_examples": 334}, {"name": "test", "num_bytes": 974886, "num_examples": 166}], "download_size": 861906, "dataset_size": 2988379}}
|
2023-04-04T14:27:47+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "tmvar_0404"
More Information needed
|
[
"# Dataset Card for \"tmvar_0404\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"tmvar_0404\"\n\nMore Information needed"
] |
[
6,
16
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"tmvar_0404\"\n\nMore Information needed"
] |
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b358ee3f6e1c27738dfb428c750579d177471f1e
|
# Dataset Card for "DTD_parition1_test_google_flan_t5_xxl_mode_A_T_SPECIFIC_ns_100"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
CVasNLPExperiments/DTD_parition1_test_google_flan_t5_xxl_mode_A_T_SPECIFIC_ns_100
|
[
"region:us"
] |
2023-04-04T14:30:40+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "prompt", "dtype": "string"}, {"name": "true_label", "dtype": "string"}, {"name": "prediction", "dtype": "string"}], "splits": [{"name": "fewshot_0_clip_tags_ViT_L_14_Attributes_ViT_L_14_text_davinci_003_clip_tags_ViT_L_14_simple_specific_rices", "num_bytes": 43534, "num_examples": 100}], "download_size": 15782, "dataset_size": 43534}}
|
2023-04-04T14:30:42+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "DTD_parition1_test_google_flan_t5_xxl_mode_A_T_SPECIFIC_ns_100"
More Information needed
|
[
"# Dataset Card for \"DTD_parition1_test_google_flan_t5_xxl_mode_A_T_SPECIFIC_ns_100\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"DTD_parition1_test_google_flan_t5_xxl_mode_A_T_SPECIFIC_ns_100\"\n\nMore Information needed"
] |
[
6,
42
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"DTD_parition1_test_google_flan_t5_xxl_mode_A_T_SPECIFIC_ns_100\"\n\nMore Information needed"
] |
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eff0ab3560c9f2868eb663db1e352df8c3721d0f
|
# Dataset Card for "DTD_parition1_test_google_flan_t5_xxl_mode_T_SPECIFIC_ns_100"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
CVasNLPExperiments/DTD_parition1_test_google_flan_t5_xxl_mode_T_SPECIFIC_ns_100
|
[
"region:us"
] |
2023-04-04T14:32:25+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "prompt", "dtype": "string"}, {"name": "true_label", "dtype": "string"}, {"name": "prediction", "dtype": "string"}], "splits": [{"name": "fewshot_0_clip_tags_ViT_L_14_Attributes_ViT_L_14_text_davinci_003_clip_tags_ViT_L_14_simple_specific_rices", "num_bytes": 5338, "num_examples": 100}], "download_size": 3311, "dataset_size": 5338}}
|
2023-04-04T14:36:32+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "DTD_parition1_test_google_flan_t5_xxl_mode_T_SPECIFIC_ns_100"
More Information needed
|
[
"# Dataset Card for \"DTD_parition1_test_google_flan_t5_xxl_mode_T_SPECIFIC_ns_100\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"DTD_parition1_test_google_flan_t5_xxl_mode_T_SPECIFIC_ns_100\"\n\nMore Information needed"
] |
[
6,
40
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"DTD_parition1_test_google_flan_t5_xxl_mode_T_SPECIFIC_ns_100\"\n\nMore Information needed"
] |
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01f12ca05a211869d599e352739c6e14283bebef
|
# Dataset Card for "amia_split_0404"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Brizape/amia_split_0404
|
[
"region:us"
] |
2023-04-04T14:44:53+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "tokens", "sequence": "string"}, {"name": "ner_tags", "sequence": "int64"}, {"name": "texts", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 845408, "num_examples": 420}, {"name": "train", "num_bytes": 1856367, "num_examples": 1142}], "download_size": 422007, "dataset_size": 2701775}}
|
2023-04-04T14:45:03+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "amia_split_0404"
More Information needed
|
[
"# Dataset Card for \"amia_split_0404\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"amia_split_0404\"\n\nMore Information needed"
] |
[
6,
18
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"amia_split_0404\"\n\nMore Information needed"
] |
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d94163d928c38957982b7f9d5ec2f1a9a246feeb
|
# Dataset Card for "Variome_split_0404"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Brizape/Variome_split_0404
|
[
"region:us"
] |
2023-04-04T14:45:28+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "tokens", "sequence": "string"}, {"name": "ner_tags", "sequence": "int64"}, {"name": "texts", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 1467713, "num_examples": 991}, {"name": "train", "num_bytes": 8290493, "num_examples": 3810}], "download_size": 677627, "dataset_size": 9758206}}
|
2023-04-04T14:45:38+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "Variome_split_0404"
More Information needed
|
[
"# Dataset Card for \"Variome_split_0404\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"Variome_split_0404\"\n\nMore Information needed"
] |
[
6,
19
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"Variome_split_0404\"\n\nMore Information needed"
] |
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849bd52ebbd659866d3afeb4bf4779a94d573e27
|
# Dataset Card for "tmvar_split_0404"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Brizape/tmvar_split_0404
|
[
"region:us"
] |
2023-04-04T14:46:36+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "tokens", "sequence": "string"}, {"name": "ner_tags", "sequence": "int64"}, {"name": "texts", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 977708, "num_examples": 498}, {"name": "train", "num_bytes": 2019171, "num_examples": 1002}], "download_size": 856598, "dataset_size": 2996879}}
|
2023-04-04T14:46:46+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "tmvar_split_0404"
More Information needed
|
[
"# Dataset Card for \"tmvar_split_0404\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"tmvar_split_0404\"\n\nMore Information needed"
] |
[
6,
19
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"tmvar_split_0404\"\n\nMore Information needed"
] |
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2ba6bc5cc5a62e882c445a92f864134c3fc7a56d
|
# Dataset Card for "SETH_split_0404"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Brizape/SETH_split_0404
|
[
"region:us"
] |
2023-04-04T14:46:54+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "tokens", "sequence": "string"}, {"name": "ner_tags", "sequence": "int64"}, {"name": "texts", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 582671, "num_examples": 126}, {"name": "train", "num_bytes": 2425278, "num_examples": 504}], "download_size": 837941, "dataset_size": 3007949}}
|
2023-04-04T14:47:04+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "SETH_split_0404"
More Information needed
|
[
"# Dataset Card for \"SETH_split_0404\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"SETH_split_0404\"\n\nMore Information needed"
] |
[
6,
18
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"SETH_split_0404\"\n\nMore Information needed"
] |
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] |
12a5f16e7427261c7fba87a2952de014d6d07e22
|
# Dataset Card for "tmvar_split_0404_dev"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Brizape/tmvar_split_0404_dev
|
[
"region:us"
] |
2023-04-04T14:50:14+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "tokens", "sequence": "string"}, {"name": "ner_tags", "sequence": "int64"}, {"name": "texts", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1614127.7155688624, "num_examples": 801}, {"name": "validation", "num_bytes": 405043.2844311377, "num_examples": 201}, {"name": "test", "num_bytes": 977708, "num_examples": 498}], "download_size": 883485, "dataset_size": 2996879.0}}
|
2023-04-04T14:50:28+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "tmvar_split_0404_dev"
More Information needed
|
[
"# Dataset Card for \"tmvar_split_0404_dev\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"tmvar_split_0404_dev\"\n\nMore Information needed"
] |
[
6,
21
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"tmvar_split_0404_dev\"\n\nMore Information needed"
] |
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40504b83dde72384f944928f4fdf5557abc74444
|
# Dataset Card for "tmvar_tokenized_split_0404_dev"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Brizape/tmvar_tokenized_split_0404_dev
|
[
"region:us"
] |
2023-04-04T14:50:29+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "tokens", "sequence": "string"}, {"name": "ner_tags", "sequence": "int64"}, {"name": "texts", "dtype": "string"}, {"name": "input_ids", "sequence": "int32"}, {"name": "token_type_ids", "sequence": "int8"}, {"name": "attention_mask", "sequence": "int8"}, {"name": "labels", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 2897566, "num_examples": 801}, {"name": "validation", "num_bytes": 723081, "num_examples": 201}, {"name": "test", "num_bytes": 1746380, "num_examples": 498}], "download_size": 1248054, "dataset_size": 5367027}}
|
2023-04-04T14:50:45+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "tmvar_tokenized_split_0404_dev"
More Information needed
|
[
"# Dataset Card for \"tmvar_tokenized_split_0404_dev\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"tmvar_tokenized_split_0404_dev\"\n\nMore Information needed"
] |
[
6,
25
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"tmvar_tokenized_split_0404_dev\"\n\nMore Information needed"
] |
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] |
9de3c47c60273f992c60af8e02ee7078a876bfb5
|
# Dataset Card for "SETH_split_0404_dev"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Brizape/SETH_split_0404_dev
|
[
"region:us"
] |
2023-04-04T14:50:52+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "tokens", "sequence": "string"}, {"name": "ner_tags", "sequence": "int64"}, {"name": "texts", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1939259.988095238, "num_examples": 403}, {"name": "validation", "num_bytes": 486018.0119047619, "num_examples": 101}, {"name": "test", "num_bytes": 582671, "num_examples": 126}], "download_size": 865110, "dataset_size": 3007949.0}}
|
2023-04-04T14:51:07+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "SETH_split_0404_dev"
More Information needed
|
[
"# Dataset Card for \"SETH_split_0404_dev\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"SETH_split_0404_dev\"\n\nMore Information needed"
] |
[
6,
20
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"SETH_split_0404_dev\"\n\nMore Information needed"
] |
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99269e1152882da02d192ecec8a1283da2e16f03
|
# Dataset Card for "SETH_tokenized_split_0404_dev"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Brizape/SETH_tokenized_split_0404_dev
|
[
"region:us"
] |
2023-04-04T14:51:08+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "tokens", "sequence": "string"}, {"name": "ner_tags", "sequence": "int64"}, {"name": "texts", "dtype": "string"}, {"name": "input_ids", "sequence": "int32"}, {"name": "token_type_ids", "sequence": "int8"}, {"name": "attention_mask", "sequence": "int8"}, {"name": "labels", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 3405949, "num_examples": 403}, {"name": "validation", "num_bytes": 852237, "num_examples": 101}, {"name": "test", "num_bytes": 1012765, "num_examples": 126}], "download_size": 1214849, "dataset_size": 5270951}}
|
2023-04-04T14:51:24+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "SETH_tokenized_split_0404_dev"
More Information needed
|
[
"# Dataset Card for \"SETH_tokenized_split_0404_dev\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"SETH_tokenized_split_0404_dev\"\n\nMore Information needed"
] |
[
6,
24
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"SETH_tokenized_split_0404_dev\"\n\nMore Information needed"
] |
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] |
01ca04a0afd1957a074da4fa332f41f76282fee0
|
# Dataset Card for "amia_split_0404_dev"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Brizape/amia_split_0404_dev
|
[
"region:us"
] |
2023-04-04T14:51:59+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "tokens", "sequence": "string"}, {"name": "ner_tags", "sequence": "int64"}, {"name": "texts", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1484118.275831874, "num_examples": 913}, {"name": "validation", "num_bytes": 372248.7241681261, "num_examples": 229}, {"name": "test", "num_bytes": 845408, "num_examples": 420}], "download_size": 513223, "dataset_size": 2701775.0}}
|
2023-04-04T14:52:12+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "amia_split_0404_dev"
More Information needed
|
[
"# Dataset Card for \"amia_split_0404_dev\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"amia_split_0404_dev\"\n\nMore Information needed"
] |
[
6,
20
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"amia_split_0404_dev\"\n\nMore Information needed"
] |
[
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5ce7c926ea47b95802ab9155acf3220e4dc5b111
|
# Dataset Card for "amia_tokenized_split_0404_dev"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Brizape/amia_tokenized_split_0404_dev
|
[
"region:us"
] |
2023-04-04T14:52:14+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "tokens", "sequence": "string"}, {"name": "ner_tags", "sequence": "int64"}, {"name": "texts", "dtype": "string"}, {"name": "input_ids", "sequence": "int32"}, {"name": "token_type_ids", "sequence": "int8"}, {"name": "attention_mask", "sequence": "int8"}, {"name": "labels", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 2414793, "num_examples": 913}, {"name": "validation", "num_bytes": 759116, "num_examples": 229}, {"name": "test", "num_bytes": 1367468, "num_examples": 420}], "download_size": 714468, "dataset_size": 4541377}}
|
2023-04-04T14:52:28+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "amia_tokenized_split_0404_dev"
More Information needed
|
[
"# Dataset Card for \"amia_tokenized_split_0404_dev\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"amia_tokenized_split_0404_dev\"\n\nMore Information needed"
] |
[
6,
24
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"amia_tokenized_split_0404_dev\"\n\nMore Information needed"
] |
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b28ce1c972099126a5684c0d1d2e8069b5730e9d
|
# Dataset Card for "Variome_split_0404_dev"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Brizape/Variome_split_0404_dev
|
[
"region:us"
] |
2023-04-04T14:52:36+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "tokens", "sequence": "string"}, {"name": "ner_tags", "sequence": "int64"}, {"name": "texts", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 6632394.4, "num_examples": 3048}, {"name": "validation", "num_bytes": 1658098.6, "num_examples": 762}, {"name": "test", "num_bytes": 1467713, "num_examples": 991}], "download_size": 1715522, "dataset_size": 9758206.0}}
|
2023-04-04T14:52:51+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "Variome_split_0404_dev"
More Information needed
|
[
"# Dataset Card for \"Variome_split_0404_dev\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"Variome_split_0404_dev\"\n\nMore Information needed"
] |
[
6,
21
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"Variome_split_0404_dev\"\n\nMore Information needed"
] |
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5d27ac865c3780e33d78155f2632101c8ad8e720
|
# Dataset Card for "Variome_tokenized_split_0404_dev"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Brizape/Variome_tokenized_split_0404_dev
|
[
"region:us"
] |
2023-04-04T14:52:53+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "tokens", "sequence": "string"}, {"name": "ner_tags", "sequence": "int64"}, {"name": "texts", "dtype": "string"}, {"name": "input_ids", "sequence": "int32"}, {"name": "token_type_ids", "sequence": "int8"}, {"name": "attention_mask", "sequence": "int8"}, {"name": "labels", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 11207397, "num_examples": 3048}, {"name": "validation", "num_bytes": 2956282, "num_examples": 762}, {"name": "test", "num_bytes": 2602477, "num_examples": 991}], "download_size": 2496494, "dataset_size": 16766156}}
|
2023-04-04T14:53:10+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "Variome_tokenized_split_0404_dev"
More Information needed
|
[
"# Dataset Card for \"Variome_tokenized_split_0404_dev\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"Variome_tokenized_split_0404_dev\"\n\nMore Information needed"
] |
[
6,
25
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"Variome_tokenized_split_0404_dev\"\n\nMore Information needed"
] |
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0.02961825020611286,
0.14113466441631317,
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] |
92b893ded55ac9e4d2e531d6e5aa6d93fafed23c
|
CNXT
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arxiv:
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"act","prompt","answer"
"Scalability","#://CHaTx scalability","The scalability of #://CHaTx is achieved through cloud computing resources and a microservices architecture, allowing the system to efficiently handle an increasing number of users, API connections, and data."
"Security and Privacy","#://CHaTx security and privacy measures","#://CHaTx uses encryption, secure data storage, and access control mechanisms to ensure the security and privacy of user information, transaction details, and other sensitive data."
"Customization","#://CHaTx customization options","#://CHaTx can be customized to meet the needs of specific businesses, industries, or user groups by integrating with relevant APIs, configuring HTP namespaces and assets, and tailoring the conversational experience."
"Language Support","#://CHaTx language support capabilities","#://CHaTx, built on the GPT architecture, is capable of understanding and generating responses in multiple languages, enabling seamless conversations with users worldwide."
"Integration with Financial Systems","#://CHaTx integration with financial systems","#://CHaTx connects with financial systems like ACH, XCH, and other banking APIs to facilitate transactions, account management, and other financial services through its chat interface."
"User Onboarding","#://CHaTx user onboarding process","The user onboarding process for #://CHaTx includes account creation, setting up connections with relevant APIs and systems, configuring HTP namespaces and assets, and familiarizing users with the chat interface and its capabilities."
"Conversational AI","#://CHaTx conversational AI capabilities","#://CHaTx uses advanced natural language processing techniques to understand user queries, generate contextually relevant responses, and maintain a smooth and engaging conversation across different platforms and applications."
"Monitoring and Analytics","#://CHaTx monitoring and analytics features","#://CHaTx provides monitoring and analytics tools to track system performance, user interactions, API usage, and other metrics, enabling continuous improvement and optimization of the chat experience."
"Error Handling and Recovery","#://CHaTx error handling and recovery mechanisms","#://CHaTx has built-in error handling and recovery mechanisms to detect and resolve issues, ensuring a seamless and reliable user experience even in the face of unexpected errors or failures."
"Future Enhancements","#://CHaTx future enhancements and development plans","Future enhancements for #://CHaTx may include support for additional APIs, financial systems, and languages, improved AI capabilities, and new features that enhance user experience and functionality.
"Email Integration","#://CHaTx email integration","$://CHaTx can seamlessly integrate with email services, enabling users to send, receive, and manage emails using natural language commands, while also supporting automated responses and advanced filtering options."
"Third-Party Integrations","#://CHaTx third-party integrations","In addition to financial systems, #://CHaTx can integrate with a variety of third-party services, including CRM platforms, payment gateways, and messaging applications, to provide a comprehensive and streamlined chat-based experience for users."
"Compliance","#://CHaTx compliance with regulations and standards","#://CHaTx adheres to industry-standard security and privacy regulations, such as GDPR and HIPAA, to ensure that user data is handled in a compliant and responsible manner."
"Performance Optimization","#://CHaTx performance optimization techniques","#://CHaTx uses various performance optimization techniques, such as caching, load balancing, and serverless computing, to maximize system efficiency and minimize response times."
"AI Training and Fine-Tuning","#://CHaTx AI training and fine-tuning process","#://CHaTx employs a continuous training and fine-tuning process to improve its AI capabilities and ensure that it can handle a wide range of user queries and scenarios."
"Natural Language Understanding","#://CHaTx natural language understanding capabilities","#://CHaTx uses advanced techniques in natural language understanding to accurately interpret user queries, extract relevant information, and generate contextually appropriate responses."
"Human-in-the-Loop Integration","#://CHaTx human-in-the-loop integration capabilities","#://CHaTx can integrate with human operators or customer service representatives to provide assistance with complex queries or scenarios, ensuring a seamless and satisfactory user experience."
"Chatbot Analytics","#://CHaTx chatbot analytics and reporting features","#://CHaTx provides comprehensive analytics and reporting features to track user engagement, chatbot performance, and ROI, enabling businesses to continuously improve and optimize their chatbot strategy."
"Integration with Financial Systems","#://CHaTx integration with financial systems","#://CHaTx connects with financial systems like ACH, XCH, and other banking APIs to facilitate transactions, account management, and other financial services through its chat interface."
"User Onboarding","#://CHaTx user onboarding process","The user onboarding process for #://CHaTx includes account creation, setting up connections with relevant APIs and systems, configuring HTP namespaces and assets, and familiarizing users with the chat interface and its capabilities."
"Conversational AI","#://CHaTx conversational AI capabilities","#://CHaTx uses advanced natural language processing techniques to understand user queries, generate contextually relevant responses, and maintain a smooth and engaging conversation across different platforms and applications."
"Intent Detection","#://CHaTx intent detection capabilities","#://CHaTx can accurately detect the intent behind user queries, allowing it to provide contextually appropriate responses and improve the overall user experience."
"Entity Recognition","#://CHaTx entity recognition capabilities","#://CHaTx can recognize named entities within user queries, such as people, places, and dates, and use this information to generate more personalized and relevant responses."
"Contextual Awareness","#://CHaTx contextual awareness capabilities","#://CHaTx can maintain context across multiple turns in a conversation, allowing it to provide more meaningful and engaging responses and create a more natural chat experience for users."
"Multi-Turn Dialogue","#://CHaTx multi-turn dialogue capabilities","#://CHaTx is capable of handling complex, multi-turn conversations with users, using context and previous interactions to generate relevant and informative responses."
"User Feedback Analysis","#://CHaTx user feedback analysis capabilities","#://CHaTx can analyze user feedback and sentiment to improve its conversational AI models and provide more accurate and relevant responses to users."
"Intent Classification","#://CHaTx intent classification capabilities","#://CHaTx can accurately classify user queries into specific intent categories, allowing it to provide more targeted and useful responses."
"Slot Filling","#://CHaTx slot filling capabilities","#://CHaTx can extract relevant information from user queries, such as dates or numbers, and use this information to provide more personalized and helpful responses."
"Transfer Learning","#://CHaTx transfer learning capabilities","#://CHaTx can leverage pre-existing conversational AI models and knowledge bases to improve its own understanding and generation of responses, allowing for faster and more accurate auto-training and fine-tuning."
"Context Retention","#://CHaTx context retention capabilities","#://CHaTx can retain context from previous turns in a conversation, allowing it to generate more meaningful and personalized responses that take into account the user's history and previous interactions with the system."
"Topic Switching","#://CHaTx topic switching capabilities","#://CHaTx can seamlessly switch between different topics or domains within a conversation, using context and user input to generate relevant and informative responses that address the user's needs and interests."
"Dialog Act Recognition","#://CHaTx dialog act recognition capabilities","#://CHaTx can recognize different types of dialog acts within a conversation, such as requests, affirmations, or acknowledgements, and use this information to generate appropriate responses and maintain the flow of the conversation."
"Clarification","#://CHaTx clarification capabilities","#://CHaTx can ask for clarification or provide additional information to the user when necessary, allowing it to handle complex or ambiguous user queries and provide more accurate and relevant responses."
"Memory and Recall","#://CHaTx memory and recall capabilities","#://CHaTx can remember important information from previous turns in a conversation, and recall this information later in the conversation to provide more personalized and relevant responses."
"User Engagement","#://CHaTx user engagement capabilities","#://CHaTx is designed to create engaging and natural conversations with users, using humor, empathy, and other conversational techniques to create a positive user experience and build trust and rapport with users."
"Task Completion","#://CHaTx task completion capabilities","#://CHaTx can help users complete complex tasks, such as booking a reservation or placing an order, by guiding them through the necessary steps and providing relevant information and feedback at each stage of the process."
"Conversation Log Analysis","#://CHaTx conversation log analysis capabilities","#://CHaTx can analyze conversation logs to identify patterns and trends in user queries and interactions, allowing it to identify areas for improvement and optimize its conversational AI models."
"User Profile Management","#://CHaTx user profile management capabilities","#://CHaTx can store and manage user profiles, including preferences, history, and other relevant information, allowing it to provide more personalized and relevant responses and maintain context across multiple turns in a conversation."
"Contextual Understanding","#://CHaTx contextual understanding capabilities","#://CHaTx can understand the context of user queries and interactions, allowing it to provide more relevant and accurate responses that take into account the user's history and previous interactions with the system."
"Memory Retrieval","#://CHaTx memory retrieval capabilities","#://CHaTx can retrieve information from previous turns in a conversation, allowing it to provide more personalized and relevant responses that address the user's needs and interests."
"Contextual Adaptation","#://CHaTx contextual adaptation capabilities","#://CHaTx can adapt to changes in context within a conversation, using context and user input to generate relevant and informative responses that address the user's needs and interests at any given moment."
"Multi-Session Memory","#://CHaTx multi-session memory capabilities","#://CHaTx can remember user information and preferences across multiple sessions, allowing it to provide more consistent and personalized responses over time."
"Contextual Switching","#://CHaTx contextual switching capabilities","#://CHaTx can seamlessly switch between different contexts or topics within a conversation, using user input and context to generate relevant and informative responses that address the user's needs and interests."
"Dialog History Management","#://CHaTx dialog history management capabilities","#://CHaTx can manage the dialog history of a conversation, including previous user queries and system responses, allowing it to maintain context and provide more relevant and accurate responses over time."
"Proactive Recall","#://CHaTx proactive recall capabilities","#://CHaTx can proactively recall important information or events from a user's history, even if it wasn't explicitly mentioned in the current conversation, to provide more personalized and relevant responses."
"Integration with New Technologies","#://CHaTx integration with new technologies","#://CHaTx may integrate with emerging technologies like blockchain, IoT, and AR/VR to provide more innovative and immersive chat experiences for users."
"Improved Multilingual Support","#://CHaTx improved multilingual support capabilities","#://CHaTx may expand its support for additional languages, using machine translation and other techniques to provide more accurate and natural conversations for users worldwide."
"Voice and Speech Recognition","#://CHaTx voice and speech recognition capabilities","#://CHaTx may add support for voice and speech recognition, allowing users to interact with the system using voice commands and natural language."
"Personalized Recommendations","#://CHaTx personalized recommendations capabilities","#://CHaTx may use machine learning algorithms to analyze user preferences and behavior, and provide personalized recommendations and suggestions for products or services."
"Augmented Intelligence","#://CHaTx augmented intelligence capabilities","#://CHaTx may incorporate augmented intelligence technologies, such as human-in-the-loop models or expert systems, to provide more accurate and insightful responses to user queries."
"Emotional Intelligence","#://CHaTx emotional intelligence capabilities","#://CHaTx may incorporate emotional intelligence techniques, such as sentiment analysis or empathy modeling, to provide more empathetic and human-like interactions with users."
"Intelligent Task Automation","#://CHaTx intelligent task automation capabilities","#://CHaTx may automate complex or repetitive tasks for users, using AI algorithms and integrations with other systems to provide more efficient and streamlined experiences."
"Chatbot Ecosystem Integration","#://CHaTx chatbot ecosystem integration capabilities","#://CHaTx may integrate with other chatbot platforms and ecosystems, allowing for seamless interactions and integrations with other chatbots and systems."
"Multi-Platform Support","#://CHaTx multi-platform support capabilities","#://CHaTx can support multiple platforms and channels, including web, mobile, social media, and messaging apps, allowing for seamless interactions and integrations with other chatbots and systems."
"Open API Integration","#://CHaTx open API integration capabilities","#://CHaTx can integrate with open APIs and systems, allowing for seamless integrations with other chatbots and systems across different domains and industries."
"Chatbot-to-Chatbot Communication","#://CHaTx chatbot-to-chatbot communication capabilities","#://CHaTx can communicate with other chatbots and systems, using APIs and webhooks to exchange information and facilitate more complex and efficient interactions."
"Domain Adaptation","#://CHaTx domain adaptation capabilities","#://CHaTx can adapt to different domains and industries, using pre-existing knowledge bases and chatbot ecosystems to provide more accurate and relevant responses to users."
"Interoperability","#://CHaTx interoperability capabilities","#://CHaTx can interoperate with other chatbots and systems, using standards-based protocols and technologies to exchange information and maintain compatibility across different platforms and ecosystems."
"Collaborative Learning","#://CHaTx collaborative learning capabilities","#://CHaTx can learn from other chatbots and systems, using collaborative learning techniques to improve its conversational AI capabilities and provide more accurate and relevant responses to users."
"Chatbot Federation","#://CHaTx chatbot federation capabilities","#://CHaTx can participate in chatbot federations or communities, allowing for seamless interactions and integrations with other chatbots and systems that share similar domains or functionalities."
"Intelligent Routing","#://CHaTx intelligent routing capabilities","#://CHaTx can intelligently route user queries and interactions to other chatbots or systems, based on their domain or expertise, to provide more accurate and relevant responses to users."
"Interoperability","#://CHaTx interoperability capabilities","#://CHaTx can interoperate with other chatbots and systems, using standards-based protocols and technologies to exchange information and maintain compatibility across different platforms and ecosystems."
"API and Webhook Integration","#://CHaTx API and webhook integration capabilities","#://CHaTx can integrate with other chatbots and systems using APIs and webhooks, allowing for seamless communication and data exchange between different systems and platforms."
"Chatbot Network Routing","#://CHaTx chatbot network routing capabilities","#://CHaTx can intelligently route user queries and interactions to other chatbots or systems within a network, based on their domain or expertise, to provide more accurate and relevant responses to users."
"Collaborative Learning","#://CHaTx collaborative learning capabilities","#://CHaTx can learn from other chatbots and systems, using collaborative learning techniques to improve its conversational AI capabilities and provide more accurate and relevant responses to users."
"Conversation State Management","#://CHaTx conversation state management capabilities","#://CHaTx can manage the state of a conversation between different chatbots or systems, allowing for seamless transitions and continuity across different systems and platforms."
"Contextual Understanding","#://CHaTx contextual understanding capabilities","#://CHaTx can understand the context of a conversation between different chatbots or systems, allowing it to provide more relevant and accurate responses that take into account the user's history and previous interactions with the system."
"Intent Recognition","#://CHaTx intent recognition capabilities","#://CHaTx can recognize the intent behind user queries and interactions within a conversation between different chatbots or systems, allowing it to provide more accurate and relevant responses that address the user's needs and interests."
"Data Sharing and Privacy","#://CHaTx data sharing and privacy capabilities","#://CHaTx ensures the security and privacy of user data when communicating with other chatbots and systems, using encryption, secure data storage, and access control mechanisms to protect user information and transaction details."
"Expertise Mapping","#://CHaTx expertise mapping capabilities","#://CHaTx can map the expertise of different chatbots or systems within a network, allowing it to intelligently route user queries and interactions to the chatbots or systems that are best equipped to provide accurate and relevant responses to users."
"Network Health Monitoring","#://CHaTx network health monitoring capabilities","#://CHaTx can monitor the health and performance of a chatbot network, using metrics and analytics to optimize the routing of user queries and interactions and ensure high-quality user experiences."
"Expertise Profiling","#://CHaTx expertise profiling capabilities","#://CHaTx can profile the expertise of different chatbots or systems within a network, based on their knowledge domains, past performance, and other relevant factors, to enable effective expertise mapping and routing of user queries and interactions."
"Data and Resource Sharing","#://CHaTx data and resource sharing capabilities","#://CHaTx can share data and resources with other chatbots or systems within a network, allowing for more efficient and effective collaboration and knowledge sharing."
"Intent Recognition","#://CHaTx intent recognition capabilities","#://CHaTx can recognize the intent behind user queries and interactions within a conversation between different chatbots or systems, allowing it to provide more accurate and relevant responses that address the user's needs and interests."
"Expertise Matching","#://CHaTx expertise matching capabilities","#://CHaTx can match the expertise of different chatbots or systems within a network to the user's queries and interactions, based on their knowledge domains, past performance, and other relevant factors, to ensure accurate and relevant responses."
"Routing Optimization","#://CHaTx routing optimization capabilities","#://CHaTx can optimize the routing of user queries and interactions to the chatbots or systems with the highest expertise match, using machine learning algorithms and other techniques to improve the accuracy and efficiency of the routing process."
"Performance Analytics","#://CHaTx performance analytics capabilities","#://CHaTx can analyze the performance of different chatbots or systems within a network, using metrics and analytics to identify areas for improvement and optimize the expertise profiling and routing processes."
"Business Entity Selection","#://CHaTx business entity selection capabilities","#://CHaTx can assist users in selecting the most appropriate business entity type for their venture, taking into account factors such as the size and complexity of the business, liability considerations, tax implications, and operational requirements."
"Legal and Regulatory Compliance","#://CHaTx legal and regulatory compliance capabilities","#://CHaTx can provide information and guidance on legal and regulatory compliance requirements for businesses, including issues such as corporate governance, employment law, data privacy, and industry-specific regulations."
"Tax Planning and Strategy","#://CHaTx tax planning and strategy capabilities","#://CHaTx can assist users in developing tax planning and strategy for their business, providing information on tax laws and regulations, tax credits and deductions, and tax-efficient structures and strategies."
"Capital Formation and Financing","#://CHaTx capital formation and financing capabilities","#://CHaTx can provide information and guidance on different sources of capital and financing options for businesses, such as equity financing, debt financing, crowdfunding, and grants."
"Business Planning and Strategy","#://CHaTx business planning and strategy capabilities","#://CHaTx can assist users in developing a business plan and strategy, providing information and guidance on market research, competitive analysis, customer acquisition, product development, and other key areas of business planning."
"Equity Financing","#://CHaTx equity financing capabilities","#://CHaTx can provide information and guidance on equity financing options for businesses, including angel investors, venture capital firms, and crowdfunding platforms, and help businesses prepare for the fundraising process."
"Debt Financing","#://CHaTx debt financing capabilities","#://CHaTx can provide information and guidance on debt financing options for businesses, including traditional bank loans, SBA loans, and alternative lenders, and help businesses evaluate their options and prepare for the loan application process."
"Crowdfunding","#://CHaTx crowdfunding capabilities","#://CHaTx can provide information and guidance on crowdfunding as a financing option for businesses, including how to launch a successful crowdfunding campaign, how to engage with backers, and how to manage and fulfill rewards."
"Grants and Non-Dilutive Financing","#://CHaTx grants and non-dilutive financing capabilities","#://CHaTx can provide information and guidance on grants and non-dilutive financing options for businesses, including government grants, research and development (R&D) tax credits, and other funding programs that do not require equity or debt financing."
"Financial Planning and Analysis","#://CHaTx financial planning and analysis capabilities","#://CHaTx can assist businesses in developing financial projections, analyzing their financial performance, and evaluating the financial impact of different strategic decisions and scenarios."
"Angel Investors","#://CHaTx angel investor capabilities","#://CHaTx can provide information and guidance on angel investors as an equity financing option for businesses, including how to identify and approach potential investors, how to prepare for investor meetings, and how to negotiate deal terms."
"Venture Capital Firms","#://CHaTx venture capital capabilities","#://CHaTx can provide information and guidance on venture capital firms as an equity financing option for businesses, including how to identify and approach potential firms, how to prepare for investor meetings, and how to negotiate deal terms."
"Crowdfunding Platforms","#://CHaTx crowdfunding platform capabilities","#://CHaTx can provide information and guidance on crowdfunding platforms as an equity financing option for businesses, including how to select and launch a successful crowdfunding campaign, how to engage with backers, and how to manage and fulfill rewards."
"Valuation and Deal Structuring","#://CHaTx valuation and deal structuring capabilities","#://CHaTx can assist businesses in valuing their company and structuring equity financing deals, providing information on industry benchmarks, deal terms, and negotiation strategies."
"Dilution and Investor Relations","#://CHaTx dilution and investor relations capabilities","#://CHaTx can provide information and guidance on the potential dilution of equity financing, and how to manage investor relations and communication throughout the fundraising process and beyond."
"Traditional Bank Loans","#://CHaTx traditional bank loan capabilities","#://CHaTx can provide information and guidance on traditional bank loans as a debt financing option for businesses, including how to prepare for loan application, how to present financial information, and how to negotiate loan terms."
"SBA Loans","#://CHaTx SBA loan capabilities","#://CHaTx can provide information and guidance on SBA loans as a debt financing option for small businesses, including eligibility requirements, application process, and loan terms and conditions."
"Alternative Lenders","#://CHaTx alternative lender capabilities","#://CHaTx can provide information and guidance on alternative lenders as a debt financing option for businesses, including peer-to-peer lending platforms, online lenders, and merchant cash advance providers, and help businesses evaluate their options and prepare for the loan application process."
"Loan Repayment and Management","#://CHaTx loan repayment and management capabilities","#://CHaTx can assist businesses in managing their debt financing, including repayment schedules, interest rate negotiations, and communication with lenders."
"Credit Analysis and Reporting","#://CHaTx credit analysis and reporting capabilities","#://CHaTx can provide businesses with credit analysis and reporting tools, allowing them to track their credit scores, monitor their creditworthiness, and identify areas for improvement in their credit profile."
"Types of Venture Capital Firms","#://CHaTx types of venture capital firms","#://CHaTx can provide information on the different types of venture capital firms, including early-stage, growth-stage, and late-stage firms, and the industries and geographies they typically invest in."
"Pitch Preparation","#://CHaTx pitch preparation capabilities","#://CHaTx can assist businesses in preparing for investor pitches, including developing a pitch deck, practicing presentation skills, and refining messaging and value proposition."
"Investor Due Diligence","#://CHaTx investor due diligence capabilities","#://CHaTx can provide information and guidance on the investor due diligence process, including what investors typically look for, how to prepare for due diligence, and how to answer tough questions from investors."
"Deal Negotiation","#://CHaTx deal negotiation capabilities","#://CHaTx can provide information and guidance on the deal negotiation process, including how to negotiate deal terms, how to balance competing interests, and how to maintain a positive relationship with investors."
"Post-Investment Support","#://CHaTx post-investment support capabilities","#://CHaTx can assist businesses in managing their relationships with investors post-investment, including regular reporting and communication, board management, and strategic guidance."
"Loan Repayment Schedules","#://CHaTx loan repayment schedule capabilities","#://CHaTx can provide businesses with information and guidance on loan repayment schedules, including how to create a repayment plan, how to prioritize loan payments, and how to manage cash flow."
"Interest Rate Negotiations","#://CHaTx interest rate negotiation capabilities","#://CHaTx can provide businesses with information and guidance on interest rate negotiations, including how to negotiate lower rates, how to structure loan terms, and how to compare rates from different lenders."
"Lender Communication","#://CHaTx lender communication capabilities","#://CHaTx can provide businesses with information and guidance on how to communicate with lenders, including how to address late or missed payments, how to renegotiate loan terms, and how to resolve disputes."
"Refinancing and Consolidation","#://CHaTx refinancing and consolidation capabilities","#://CHaTx can provide businesses with information and guidance on loan refinancing and consolidation, including when to consider these options, how to compare offers, and how to manage the refinancing process."
"Loan Management Tools","#://CHaTx loan management tool capabilities","#://CHaTx can provide businesses with loan management tools, including software and dashboards, to help businesses track their loans, payments, and interest rates in real-time."
"Software Tools for Loan Management","#://CHaTx loan management software capabilities","#://CHaTx can provide businesses with loan management software tools to help them manage their loans and payments, track interest rates, and schedule payments. Some examples of loan management software include LoanDolphin, LoanPro, and BNTouch."
"Dashboards for Loan Management","#://CHaTx loan management dashboard capabilities","#://CHaTx can provide businesses with loan management dashboards, which display real-time information about loan payments, interest rates, and other loan details. Dashboards can help businesses visualize their loan data and make informed decisions about their finances."
"Mobile Apps for Loan Management","#://CHaTx loan management mobile app capabilities","#://CHaTx can provide businesses with loan management mobile apps, allowing them to manage their loans and payments on-the-go. Some examples of loan management apps include Loan Calculator Pro, Payoff, and Tally."
"Automatic Payment Scheduling","#://CHaTx automatic payment scheduling capabilities","#://CHaTx can provide businesses with automatic payment scheduling tools, allowing them to schedule recurring loan payments, and avoid late fees and penalties."
"Real-time Loan Analytics","#://CHaTx loan analytics capabilities","#://CHaTx can provide businesses with real-time loan analytics tools, allowing them to monitor loan performance, identify trends, and make informed decisions about their finances."
"Corporate Bond Management Software","#://CHaTx corporate bond management software capabilities","#://CHaTx can provide businesses with corporate bond management software tools to help them manage their bond investments, track bond prices and yields, and analyze bond data. Some examples of corporate bond management software include BondEdge, Bloomberg Terminal, and YCharts."
"Bond Portfolio Management","#://CHaTx bond portfolio management capabilities","#://CHaTx can provide businesses with bond portfolio management tools, which allow them to manage and optimize their bond investments, track bond performance, and analyze bond data. Bond portfolio management tools can help businesses make informed investment decisions and mitigate risk."
"Real-time Bond Analytics","#://CHaTx bond analytics capabilities","#://CHaTx can provide businesses with real-time bond analytics tools, allowing them to monitor bond performance, identify trends, and make informed investment decisions. Bond analytics tools can provide businesses with real-time market data, pricing information, and yield analysis."
"Bond Pricing Tools","#://CHaTx bond pricing tool capabilities","#://CHaTx can provide businesses with bond pricing tools, which allow them to track bond prices, calculate bond yields, and estimate bond returns. Bond pricing tools can help businesses compare different bond investments and make informed decisions about their portfolio."
"Risk Management Tools","#://CHaTx risk management tool capabilities","#://CHaTx can provide businesses with risk management tools, allowing them to manage and mitigate risk in their bond portfolio. Some examples of risk management tools for bond investments include credit ratings, duration analysis, and stress testing."
"Corporate Bond Issuance Software","#://CHaTx corporate bond issuance software capabilities","#://CHaTx can provide businesses with corporate bond issuance software tools to help them manage the bond issuance process, from pricing and underwriting to distribution and settlement. Some examples of corporate bond issuance software include BondWave, Dealogic, and Ipreo."
"Electronic Bond Issuance Platforms","#://CHaTx electronic bond issuance platform capabilities","#://CHaTx can provide businesses with electronic bond issuance platforms, allowing them to issue and distribute bonds electronically. Electronic bond issuance platforms can help businesses reduce costs, increase efficiency, and streamline the bond issuance process."
"Document Management Tools","#://CHaTx document management tool capabilities","#://CHaTx can provide businesses with document management tools for bond issuance, allowing them to manage and store bond documents securely and efficiently. Document management tools can help businesses comply with regulatory requirements and reduce the risk of errors or omissions."
"Digital Signature Tools","#://CHaTx digital signature tool capabilities","#://CHaTx can provide businesses with digital signature tools, allowing them to sign and execute bond documents electronically. Digital signature tools can help businesses save time and reduce paperwork, while ensuring the security and integrity of bond documents."
"Compliance and Regulatory Tools","#://CHaTx compliance and regulatory tool capabilities","#://CHaTx can provide businesses with compliance and regulatory tools, allowing them to comply with regulatory requirements and manage risk in the bond issuance process. Some examples of compliance and regulatory tools for bond issuance include Know Your Customer (KYC) and Anti-Money Laundering (AML) checks, as well as legal and tax compliance tools."
"Benefits of Electronic Bond Issuance Platforms","#://CHaTx benefits of electronic bond issuance platforms","#://CHaTx electronic bond issuance platforms can offer a range of benefits to businesses, including faster issuance times, reduced paperwork and manual processes, increased transparency and accessibility, and lower costs."
"Integration with other Financial Systems","#://CHaTx integration with financial systems","#://CHaTx electronic bond issuance platforms can integrate with other financial systems, such as accounting, reporting, and trading platforms, to enable a more streamlined and efficient bond issuance process."
"Accessibility and Security","#://CHaTx accessibility and security of electronic bond issuance platforms","#://CHaTx electronic bond issuance platforms are accessible from anywhere with an internet connection, allowing for greater flexibility and efficiency in the bond issuance process. Additionally, electronic bond issuance platforms use advanced security measures, such as encryption and secure data storage, to ensure the confidentiality and integrity of bond data."
"Customization Options","#://CHaTx customization options for electronic bond issuance platforms","#://CHaTx electronic bond issuance platforms can be customized to meet the specific needs of businesses, including branding, user interface design, and integration with other systems and platforms."
"User Support and Training","#://CHaTx user support and training for electronic bond issuance platforms","#://CHaTx provides user support and training for businesses using electronic bond issuance platforms, including technical support, training on platform features and functionality, and assistance with the bond issuance process."
"Benefits of Customization Options for Bond Issuance Software","#://CHaTx benefits of customization options for bond issuance software","#://CHaTx customization options for bond issuance software can provide businesses with a more tailored and efficient solution for their bond issuance process, enabling them to streamline workflows, optimize pricing and underwriting strategies, and improve overall efficiency."
"Types of Customization Options","#://CHaTx types of customization options for bond issuance software","#://CHaTx customization options for bond issuance software can include features such as risk management tools, user interface design, data analytics and reporting, and integration with other financial systems."
"Tailoring the User Interface","#://CHaTx tailoring the user interface of bond issuance software","#://CHaTx can tailor the user interface of bond issuance software to meet the specific needs of businesses and users, including the ability to customize dashboard layouts, data visualizations, and other interface elements."
"Data Analytics and Reporting","#://CHaTx data analytics and reporting capabilities for bond issuance software","#://CHaTx customization options for bond issuance software can include advanced data analytics and reporting capabilities, enabling businesses to gain deeper insights into their bond issuance process and identify areas for improvement."
"Risk Management Tools","#://CHaTx risk management tools for bond issuance software","#://CHaTx customization options for bond issuance software can include risk management tools to help businesses identify and manage risk factors related to their bond issuance, such as credit risk, interest rate risk, and liquidity risk."
"Integration with Other Financial Systems","#://CHaTx integration with financial systems for bond issuance software","#://CHaTx customization options for bond issuance software can include integration with other financial systems, such as trading platforms and portfolio management tools, to provide a more comprehensive solution for businesses managing their bond issuance process."
"Corporate Bond Issuance Process","#://CHaTx corporate bond issuance process capabilities","#://CHaTx can guide businesses through the corporate bond issuance process, providing information and assistance at each step of the way, including bond pricing, underwriting, distribution, and settlement."
"Underwriting and Due Diligence","#://CHaTx underwriting and due diligence capabilities","#://CHaTx can assist businesses in conducting due diligence and underwriting for corporate bond issuances, helping to ensure that bond offerings are accurate, transparent, and in compliance with regulatory requirements."
"Credit Ratings","#://CHaTx credit rating capabilities for corporate bonds","#://CHaTx can provide businesses with credit rating tools and analysis for their corporate bond issuances, helping to assess credit risk and improve the attractiveness of the bond offering to potential investors."
"Distribution Channels","#://CHaTx distribution channel capabilities for corporate bond issuances","#://CHaTx can help businesses identify and utilize distribution channels for their corporate bond offerings, such as institutional investors, broker-dealers, and retail investors, to reach a wider pool of potential investors."
"Secondary Market Trading","#://CHaTx secondary market trading capabilities for corporate bonds","#://CHaTx can provide businesses with information and tools to facilitate secondary market trading of their corporate bonds, including market data, trading platforms, and liquidity management strategies."
"Credit Rating Agencies","#://CHaTx credit rating agency capabilities for corporate bonds","#://CHaTx can provide businesses with information and guidance on credit rating agencies and their methodologies for rating corporate bonds, helping them navigate the rating process and improve the creditworthiness of their bond issuances."
"Credit Risk Assessment","#://CHaTx credit risk assessment capabilities for corporate bonds","#://CHaTx can help businesses assess credit risk for their bond issuances, using data analytics and other techniques to analyze factors such as credit history, financial performance, and market conditions."
"Credit Enhancement Strategies","#://CHaTx credit enhancement capabilities for corporate bonds","#://CHaTx can provide businesses with information and guidance on credit enhancement strategies for their bond issuances, such as collateralization, guarantees, and insurance, to improve the creditworthiness of the offering and attract investors."
"Market Analysis","#://CHaTx market analysis capabilities for corporate bonds","#://CHaTx can provide businesses with market analysis tools to help them identify market trends and conditions, assess investor demand, and determine the optimal timing and pricing for their bond issuances."
"Investor Outreach","#://CHaTx investor outreach capabilities for corporate bonds","#://CHaTx can assist businesses in reaching out to potential investors for their bond issuances, using targeted marketing and communication strategies to attract and retain investors."
"HTP Integration","#://CHaTx integration with HTP technology","#://CHaTx is integrated with HTP technology, allowing it to leverage the advanced capabilities of HTP's content management network and dynamic programmatic currency issuance system. This integration provides #://CHaTx with unique opportunities for financial innovation and investment, and enables it to offer a range of services and functions that traditional financial institutions may not be able to match."
"Dynamic Programmatic Currency Issuance","#://CHaTx dynamic programmatic currency issuance capabilities","#://CHaTx can issue and manage HTP $://MoNey, utilizing the dynamic programmatic currency issuance capabilities of #://CNXT and $://CNXT. This system is designed to be efficient and secure, providing users with a flexible and innovative currency system that is not possible with traditional fiat currencies."
"Efficiency and Security of HTP Currency","#://CHaTx efficiency and security of HTP currency","#://CHaTx can ensure the efficiency and security of HTP $://MoNey through its integration with HTP technology and the dynamic programmatic currency issuance capabilities of #://CNXT and $://CNXT. This provides users with a secure and reliable currency system that is designed to meet the needs of modern financial markets."
"Investment Opportunities with HTP Currency","#://CHaTx investment opportunities with HTP currency","#://CHaTx can provide users with unique investment opportunities through the use of HTP $://MoNey, including access to a range of financial products and services that may not be available with traditional fiat currencies. This allows users to diversify their portfolios and explore new investment options in a secure and innovative currency system."
"API Documentation","#://CHaTx API documentation resources","The #://CHaTx API documentation provides comprehensive details on how to interact with the system's various services, including authentication, endpoints, request parameters, and response formats. It's designed to help developers build custom integrations and applications that leverage #://CHaTx's capabilities."
"Voice Assistants Integration","#://CHaTx integration with voice assistants","#://CHaTx can be integrated with popular voice assistants like Amazon Alexa, Google Assistant, and Apple Siri, enabling users to interact with the system through voice commands and receive information or complete tasks hands-free."
"Technical Support","#://CHaTx technical support options","#://CHaTx offers multiple channels for technical support, including a knowledge base, online help center, community forums, and email or chat support with dedicated customer service representatives. This ensures that users can find the assistance they need for any issues or questions they may encounter."
"SDKs and Libraries","#://CHaTx SDKs and libraries","To facilitate the development of custom integrations and applications, #://CHaTx provides SDKs and libraries for popular programming languages such as Python, JavaScript, and Java. These resources enable developers to quickly and easily build on top of #://CHaTx's capabilities without having to start from scratch."
"Cross-Platform Compatibility","#://CHaTx cross-platform compatibility","#://CHaTx is designed to be compatible with a wide range of platforms and devices, including web browsers, mobile apps, desktop applications, and IoT devices. This ensures that users can interact with the system and access its features, regardless of their preferred platform or device."
"API Rate Limits","#://CHaTx API rate limits","API rate limits for #://CHaTx are in place to protect the system's resources and ensure fair usage. These limits are typically set based on usage tiers, with higher tiers offering more generous limits. Users can monitor their API usage and request a rate limit increase if needed."
"Versioning and Upgrades","#://CHaTx versioning and upgrade policy","#://CHaTx follows a clear versioning and upgrade policy, with new releases including bug fixes, feature enhancements, and improvements to its AI capabilities. Users are notified of major updates and provided with documentation on how to upgrade their systems and adapt to any changes introduced."
"Pricing Plans","#://CHaTx pricing plans and options","#://CHaTx offers various pricing plans to cater to the diverse needs of its user base, ranging from free tiers for individual users to premium plans for large enterprises. Pricing is typically based on factors such as API usage, number of users, and additional features or services."
"User Authentication","#://CHaTx user authentication methods","User authentication for #://CHaTx involves a combination of username and password or token-based authentication, depending on the user's preference and security requirements. Two-factor authentication (2FA) can also be enabled for added security."
"Machine Learning Model Training","#://CHaTx machine learning model training","The machine learning models used in #://CHaTx are trained on large datasets, encompassing various languages, topics, and contexts. These models are fine-tuned using transfer learning techniques and domain-specific data to provide accurate, contextually relevant responses in #://CHaTx conversations."
"Compliance and Regulations","#://CHaTx compliance and regulatory measures","#://CHaTx adheres to strict compliance and regulatory standards, including GDPR, CCPA, and industry-specific regulations, ensuring that user data is handled securely and in accordance with legal requirements."
"User Feedback and Ratings","#://CHaTx user feedback and rating system","#://CHaTx features a user feedback and rating system that allows users to rate their interactions and provide comments on their experience. This feedback is valuable for improving the system's performance and user satisfaction over time."
"Accessibility Features","#://CHaTx accessibility support","#://CHaTx is designed with accessibility in mind, offering features such as screen reader support, keyboard navigation, and compatibility with assistive technologies, ensuring that users with disabilities can fully benefit from the platform's capabilities."
"Backup and Disaster Recovery","#://CHaTx backup and disaster recovery plans","#://CHaTx employs a robust backup and disaster recovery strategy, with regular backups of data and system configurations, redundant infrastructure, and failover mechanisms to ensure system availability and minimize downtime in the event of a failure."
"Chatbot Templates","#://CHaTx chatbot templates and use cases","#://CHaTx offers a variety of pre-built chatbot templates for common use cases, such as customer support, sales assistance, and appointment scheduling. These templates can be customized to match specific needs and streamline the chatbot development process."
"Omnichannel Support","#://CHaTx omnichannel support capabilities","With its omnichannel support, #://CHaTx enables users to engage in conversations across multiple channels, including social media, messaging apps, and websites, providing a seamless and consistent user experience."
"White-Label Solutions","#://CHaTx white-label solutions","#://CHaTx offers white-label solutions, enabling businesses to deploy the chatbot platform under their own brand, with custom UI designs, logos, and domain names. This allows companies to provide a fully-branded chatbot experience to their customers."
"Performance Optimization","#://CHaTx performance optimization features","To optimize performance, #://CHaTx uses techniques like caching, load balancing, and distributed computing. These approaches help manage system resources efficiently, ensuring a fast and responsive user experience even under high load conditions."
"Chatbot Testing and Validation","#://CHaTx chatbot testing and validation tools","#://CHaTx provides a set of testing and validation tools that enable developers to test their chatbots in various scenarios, identify issues, and fine-tune their performance before deployment, ensuring that the chatbot delivers a high-quality user experience."
"Multimedia Support","#://CHaTx multimedia support capabilities","$://CHaTx supports multimedia content, including images, videos, and audio files, enabling users to send and receive rich media messages and enhancing the conversational experience."
"Geolocation Features","#://CHaTx geolocation features","$://CHaTx can access and process geolocation data, allowing users to share their location, find nearby points of interest, and receive location-based services and recommendations."
"Real-Time Translation","#://CHaTx real-time translation functionality","$://CHaTx offers real-time translation capabilities, allowing users to communicate with others who speak different languages and enabling businesses to provide multilingual support to customers worldwide."
"Sentiment Analysis","#://CHaTx sentiment analysis features","$://CHaTx uses sentiment analysis to gauge the emotional tone of user messages, helping businesses better understand customer satisfaction and enabling the chatbot to respond more empathetically and effectively."
"Workflow Automation","#://CHaTx workflow automation capabilities","$://CHaTx can be integrated with workflow automation platforms and tools, streamlining business processes and enabling users to perform complex tasks and actions through chat commands."
"Data Export and Reporting","#://CHaTx data export and reporting options","$://CHaTx allows users to export data and generate custom reports, providing insights into chatbot performance, user interactions, and other valuable metrics for decision-making and optimization."
"Training and Knowledge Base","#://CHaTx training resources and knowledge base","$://CHaTx offers a comprehensive set of training resources and knowledge base articles, helping users understand the platform's features, best practices, and tips for optimizing their chatbot's performance."
"Third-Party Integrations","#://CHaTx third-party integration options","$://CHaTx supports integration with a wide range of third-party applications and services, such as CRM systems, helpdesk solutions, and marketing automation tools, enabling users to centralize and streamline their operations."
"Context-Aware Conversations","#://CHaTx context-aware conversation capabilities","$://CHaTx is capable of maintaining context during conversations, understanding the user's intent and history, and providing relevant and personalized responses based on the user's preferences and previous interactions."
"Role-Based Access Control","#://CHaTx role-based access control features","$://CHaTx offers role-based access control, allowing administrators to define user roles and permissions within the system, ensuring that users can access only the features and data they are authorized to use."
"Integration with Financial Systems","#://CHaTx integration with financial systems","#://CHaTx connects with financial systems like ACH, XCH, and other banking APIs to facilitate transactions, account management, and other financial services through its chat interface."
"User Onboarding","#://CHaTx user onboarding process","The user onboarding process for #://CHaTx includes account creation, setting up connections with relevant APIs and systems, configuring HTP namespaces and assets, and familiarizing users with the chat interface and its capabilities."
"Conversational AI","#://CHaTx conversational AI capabilities","#://CHaTx uses advanced natural language processing techniques to understand user queries, generate contextually relevant responses, and maintain a smooth and engaging conversation across different platforms and applications."
"Monitoring and Analytics","#://CHaTx monitoring and analytics features","#://CHaTx provides monitoring and analytics tools to track system performance, user interactions, API usage, and other metrics, enabling continuous improvement and optimization of the chat experience."
"Error Handling and Recovery","#://CHaTx error handling and recovery mechanisms","#://CHaTx has built-in error handling and recovery mechanisms to detect and resolve issues, ensuring a seamless and reliable user experience even in the face of unexpected errors or failures."
"Future Enhancements","#://CHaTx future enhancements and development plans","Future enhancements for #://CHaTx may include support for additional APIs, financial systems, and languages, improved AI capabilities, and new features that enhance user experience and functionality."
"Multimedia Support","#://CHaTx multimedia support capabilities","$://CHaTx supports multimedia content, including images, videos, and audio files, enabling users to send and receive rich media messages and enhancing the conversational experience."
"Geolocation Features","#://CHaTx geolocation features","$://CHaTx can access and process geolocation data, allowing users to share their location, find nearby points of interest, and receive location-based services and recommendations."
"Real-Time Translation","#://CHaTx real-time translation functionality","$://CHaTx offers real-time translation capabilities, allowing users to communicate with others who speak different languages and enabling businesses to provide multilingual support to customers worldwide."
"Sentiment Analysis","#://CHaTx sentiment analysis features","$://CHaTx uses sentiment analysis to gauge the emotional tone of user messages, helping businesses better understand customer satisfaction and enabling the chatbot to respond more empathetically and effectively."
"Workflow Automation","#://CHaTx workflow automation capabilities","$://CHaTx can be integrated with workflow automation platforms and tools, streamlining business processes and enabling users to perform complex tasks and actions through chat commands."
"Data Export and Reporting","#://CHaTx data export and reporting options","$://CHaTx allows users to export data and generate custom reports, providing insights into chatbot performance, user interactions, and other valuable metrics for decision-making and optimization."
"Training and Knowledge Base","#://CHaTx training resources and knowledge base","$://CHaTx offers a comprehensive set of training resources and knowledge base articles, helping users understand the platform's features, best practices, and tips for optimizing their chatbot's performance."
"Third-Party Integrations","#://CHaTx third-party integration options","$://CHaTx supports integration with a wide range of third-party applications and services, such as CRM systems, helpdesk solutions, and marketing automation tools, enabling users to centralize and streamline their operations."
"Context-Aware Conversations","#://CHaTx context-aware conversation capabilities","$://CHaTx is capable of maintaining context during conversations, understanding the user's intent and history, and providing relevant and personalized responses based on the user's preferences and previous interactions."
"Role-Based Access Control","#://CHaTx role-based access control features","$://CHaTx supports role-based access control, enabling administrators to define user roles and permissions, and ensuring that users can only access and perform actions within the scope of their assigned roles."
"Voice Assistant Integration","#://CHaTx voice assistant integration capabilities","$://CHaTx can be integrated with popular voice assistants like Amazon Alexa, Google Assistant, and Apple Siri, enabling users to interact with the chatbot through voice commands and providing a more natural and convenient conversational experience."
"Multi-Channel Deployment","#://CHaTx multi-channel deployment options","$://CHaTx supports deployment across multiple channels, including websites, mobile apps, messaging platforms, and social media, providing users with a consistent and seamless conversational experience wherever they interact with the chatbot."
"AI Model Fine-tuning","#://CHaTx AI model fine-tuning process","$://CHaTx allows developers to fine-tune the AI model based on domain-specific requirements, improving its understanding of the specific context and enabling more accurate and relevant responses."
"Chatbot Analytics","#://CHaTx chatbot analytics capabilities","$://CHaTx provides a comprehensive set of chatbot analytics, including message volume, user engagement, response time, and user sentiment, helping businesses optimize their chatbot's performance and user experience."
"Integration with IoT Devices","#://CHaTx integration with IoT devices","$://CHaTx can be integrated with IoT devices and systems, enabling users to control smart devices, receive notifications, and interact with connected devices through the chat interface."
"Customer Support and Troubleshooting","#://CHaTx customer support and troubleshooting","$://CHaTx offers customer support and troubleshooting resources, including a dedicated support team, online documentation, and community forums, helping users resolve issues and maximize the benefits of the platform."
"Payment Processing","#://CHaTx payment processing capabilities","$://CHaTx can be integrated with payment processing services, enabling users to securely initiate and manage transactions, and facilitating eCommerce capabilities within the chat interface."
"User Authentication","#://CHaTx user authentication options","$://CHaTx supports user authentication through various methods, such as passwords, tokens, or biometrics, ensuring that only authorized users can access sensitive information or perform restricted actions."
"Chatbot Templates","#://CHaTx chatbot templates","$://CHaTx offers a range of chatbot templates for various industries and use cases, helping users quickly deploy and customize their chatbot to meet their specific needs and requirements."
"Natural Language Understanding","#://CHaTx natural language understanding capabilities","$://CHaTx utilizes natural language understanding to accurately interpret user inputs, recognize intents, and extract relevant information, enabling the chatbot to provide contextually appropriate and accurate responses."
"Chatbot Performance Optimization","#://CHaTx chatbot performance optimization","$://CHaTx includes tools and best practices for optimizing chatbot performance, helping users improve response times, enhance user engagement, and ensure a seamless conversational experience."
"Chatbot Testing and Debugging","#://CHaTx chatbot testing and debugging","$://CHaTx provides testing and debugging tools that enable developers to identify and fix issues, ensuring a reliable and error-free conversational experience for users."
"GDPR Compliance","#://CHaTx GDPR compliance measures","$://CHaTx adheres to GDPR compliance requirements, including data protection, user consent, and the right to be forgotten, ensuring the privacy and security of user data within the platform."
"Decentralized Ledger Technology","#://CHaTx and decentralized ledger technology","$://CHaTx can integrate with HTP's new #://iNTeRNeT and new $://MoNey global public decentralized ledger of record, enabling secure, transparent, and tamper-proof record-keeping of transactions and data exchanges."
"Smart Contracts","#://CHaTx and smart contracts","$://CHaTx can interact with smart contracts on HTP's new #://iNTeRNeT and new $://MoNey decentralized ledger, automating transactions and agreements while ensuring security and trust between parties."
"Tokenization","#://CHaTx tokenization capabilities","$://CHaTx can facilitate tokenization on HTP's new #://iNTeRNeT and new $://MoNey decentralized ledger, enabling the creation, management, and exchange of digital assets in a secure and transparent manner."
"Cross-Border Transactions","#://CHaTx and cross-border transactions","$://CHaTx can simplify cross-border transactions on HTP's new #://iNTeRNeT and new $://MoNey decentralized ledger, reducing fees and processing times while increasing accessibility for users worldwide."
"Identity Management","#://CHaTx identity management on HTP's new #://iNTeRNeT and new $://MoNey decentralized ledger","$://CHaTx can facilitate secure identity management on HTP's new #://iNTeRNeT and new $://MoNey decentralized ledger, enabling users to maintain control over their personal information and protect their privacy."
"Peer-to-Peer Transactions","#://CHaTx and peer-to-peer transactions","$://CHaTx can facilitate peer-to-peer transactions on HTP's new #://iNTeRNeT and new $://MoNey decentralized ledger, enabling users to send and receive funds directly, without the need for intermediaries."
"Compliance and Regulations","#://CHaTx compliance with HTP's new #://iNTeRNeT and new $://MoNey regulations","$://CHaTx adheres to the regulations and requirements of HTP's new #://iNTeRNeT and new $://MoNey decentralized ledger, ensuring compliance with applicable laws and promoting a safe and trustworthy environment."
"Decentralized Finance","#://CHaTx and decentralized finance","$://CHaTx can integrate with decentralized finance (DeFi) applications on HTP's new #://iNTeRNeT and new $://MoNey decentralized ledger, providing users with access to a wide range of innovative financial services and products."
"Decentralized Applications","#://CHaTx and decentralized applications","$://CHaTx can interact with decentralized applications (dApps) built on HTP's new #://iNTeRNeT and new $://MoNey decentralized ledger, offering users a seamless experience while accessing various services and platforms."
"Data Security on HTP","#://CHaTx data security on HTP's new #://iNTeRNeT and new $://MoNey decentralized ledger","$://CHaTx ensures data security while interacting with HTP's new #://iNTeRNeT and new $://MoNey decentralized ledger, utilizing encryption and access control mechanisms to protect user information and transaction details."
"Community Governance","#://CHaTx and community governance on HTP's new #://iNTeRNeT and new $://MoNey decentralized ledger","$://CHaTx can support community governance initiatives on HTP's new #://iNTeRNeT and new $://MoNey decentralized ledger, enabling users to participate in decision-making processes and contribute
"Email Integration","#://CHaTx email integration","$://CHaTx can seamlessly integrate with email services, enabling users to send, receive, and manage emails using natural language commands, while also supporting automated responses and advanced filtering options."
"Text Message Integration","#://CHaTx text message integration","$://CHaTx can connect with SMS servers to facilitate the sending and receiving of text messages, enabling users to communicate and manage their messages through the chat interface."
"FIX API Integration","#://CHaTx and FIX API integration","$://CHaTx can integrate with FIX APIs to support trading, market data, and other financial activities, providing users with real-time information and streamlined access to financial services."
"HTP Namespace Management","#://CHaTx HTP namespace management","$://CHaTx can help users manage their HTP namespaces and assets, enabling the creation, configuration, and organization of resources in a user-friendly and intuitive manner."
"Asset Management","#://CHaTx asset management","$://CHaTx can facilitate the management of digital assets on HTP's decentralized ledger, allowing users to create, transfer, and track their assets through the chat interface."
"Natural Language Processing","#://CHaTx natural language processing capabilities","$://CHaTx utilizes advanced natural language processing techniques to understand user queries, generate contextually relevant responses, and maintain a smooth and engaging conversation across emails, text messages, FIX APIs, and HTP namespaces and assets."
"Notifications and Alerts","#://CHaTx notifications and alerts","$://CHaTx can provide real-time notifications and alerts for various events, such as incoming messages, market updates, or changes in asset values, ensuring users are always informed and up-to-date."
"Voice Command Support","#://CHaTx voice command support","$://CHaTx can support voice commands, allowing users to interact with the system through spoken language, further enhancing the conversational experience and accessibility."
"Collaboration Tools","#://CHaTx collaboration tools","$://CHaTx can integrate with collaboration tools to help users coordinate tasks, share information, and manage projects with their teams, all within the chat interface."
"Task Automation","#://CHaTx task automation","$://CHaTx can automate tasks across emails, text messages, FIX APIs, and HTP namespaces and assets, streamlining workflows and improving efficiency for users."
"Personalization","#://CHaTx personalization features","$://CHaTx can be personalized to each user's preferences and needs, tailoring the conversational experience and providing relevant and context-aware information and suggestions."
"Multichannel Support","#://CHaTx multichannel support","$://CHaTx offers multichannel support, enabling users to interact with the system through various channels, such as web, mobile, and messaging platforms, ensuring a seamless experience across different devices and platforms."
"Text Message Integration","#://CHaTx text message integration","$://CHaTx can connect with SMS servers to facilitate the sending and receiving of text messages, enabling users to communicate and manage their messages through the chat interface."
"FIX API Integration","#://CHaTx and FIX API integration","$://CHaTx can integrate with FIX APIs to support trading, market data, and other financial activities, providing users with real-time information and streamlined access to financial services."
"HTP Namespace Management","#://CHaTx HTP namespace management","$://CHaTx can help users manage their HTP namespaces and assets, enabling the creation, configuration, and organization of resources in a user-friendly and intuitive manner."
"Asset Management","#://CHaTx asset management","$://CHaTx can facilitate the management of digital assets on HTP's decentralized ledger, allowing users to create, transfer, and track their assets through the chat interface."
"Natural Language Processing","#://CHaTx natural language processing capabilities","$://CHaTx utilizes advanced natural language processing techniques to understand user queries, generate contextually relevant responses, and maintain a smooth and engaging conversation across emails, text messages, FIX APIs, and HTP namespaces and assets."
"Notifications and Alerts","#://CHaTx notifications and alerts","$://CHaTx can provide real-time notifications and alerts for various events, such as incoming messages, market updates, or changes in asset values, ensuring users are always informed and up-to-date."
"Voice Command Support","#://CHaTx voice command support","$://CHaTx can support voice commands, allowing users to interact with the system through spoken language, further enhancing the conversational experience and accessibility."
"Collaboration Tools","#://CHaTx collaboration tools","$://CHaTx can integrate with collaboration tools to help users coordinate tasks, share information, and manage projects with their teams, all within the chat interface."
"Task Automation","#://CHaTx task automation","$://CHaTx can automate tasks across emails, text messages, FIX APIs, and HTP namespaces and assets, streamlining workflows and improving efficiency for users."
"Personalization","#://CHaTx personalization features","$://CHaTx can be personalized to each user's preferences and needs, tailoring the conversational experience and providing relevant and context-aware information and suggestions."
"Multichannel Support","#://CHaTx multichannel support","$://CHaTx offers multichannel support, enabling users to interact with the system through various channels, such as web, mobile, and messaging platforms, ensuring a seamless experience across different devices and platforms."
"Third-Party Integrations","#://CHaTx third-party integrations","$://CHaTx can integrate with various third-party applications and services, such as CRMs, project management tools, and e-commerce platforms, to provide a comprehensive and connected experience for users."
"Chatbot Development","#://CHaTx chatbot development","$://CHaTx enables developers to create and deploy custom chatbots for specific use cases or industries, leveraging its advanced NLP capabilities and integrations with various APIs and systems."
"User Authentication","#://CHaTx user authentication","$://CHaTx supports secure user authentication methods, including multi-factor authentication, to ensure that only authorized users can access the system and perform actions."
"Data Privacy Compliance","#://CHaTx data privacy compliance","$://CHaTx is designed to comply with various data privacy regulations, such as GDPR and CCPA, safeguarding user data and ensuring responsible data handling practices."
"Real-Time Support","#://CHaTx real-time support","$://CHaTx can provide real-time support to users, offering immediate assistance and guidance on a wide range of topics and issues through its conversational interface."
"Context-Aware Conversations","#://CHaTx context-aware conversations","$://CHaTx is capable of maintaining context-aware conversations, understanding the user's intent and providing relevant responses based on the ongoing conversation and the user's history."
"Sentiment Analysis","#://CHaTx sentiment analysis","$://CHaTx can perform sentiment analysis on user inputs, helping to gauge user emotions and adjust its responses accordingly to provide a more empathetic and engaging experience."
"Feedback Collection","#://CHaTx feedback collection","$://CHaTx can collect user feedback on its performance and interactions, enabling continuous improvement and optimization of the chat experience."
"Training and Tutorials","#://CHaTx training and tutorials","$://CHaTx can offer training and tutorials for users, helping them better understand and utilize its features, integrations, and capabilities."
"User Behavior Analytics","#://CHaTx user behavior analytics","$://CHaTx can analyze user behavior and interactions to identify patterns, preferences, and areas for improvement, enabling a more personalized and effective chat experience."
"Role-Based Access Control","#://CHaTx role-based access control","$://CHaTx supports role-based access control, allowing administrators to define user roles and permissions to ensure proper access and usage of the system."
"Cross-Platform Compatibility","#://CHaTx cross-platform compatibility","$://CHaTx is designed to be compatible with various platforms, including web, mobile, and desktop applications, providing a consistent and seamless experience for users regardless of their preferred device."
"Dynamic Programmatic Tokenization","#://CHaTx dynamic programmatic tokenization","$://CHaTx can facilitate the creation and management of dynamic, tokenized 1-of-1 HTP namespaces and assets, allowing users to interact with unique digital resources through its conversational interface."
"Native Currency Support","#://CHaTx native currency support","$://CHaTx can handle transactions and conversions involving native currencies of various HTP namespaces and assets, simplifying the exchange and transfer of value within the network."
"Operating System Integration","#://CHaTx operating system integration","$://CHaTx can be integrated with different operating systems, enabling users to access its features and capabilities directly from their preferred OS environment."
"Search Engine Integration","#://CHaTx search engine integration","$://CHaTx can connect with search engines to help users find information, resources, and assets within HTP namespaces and the wider web, all through its conversational interface."
"Web Browser Compatibility","#://CHaTx web browser compatibility","$://CHaTx is designed to be compatible with various web browsers, ensuring users can access and interact with HTP namespaces and assets through a familiar and user-friendly interface."
"Ledger of Record","#://CHaTx and ledger of record","$://CHaTx can interact with HTP's decentralized ledger of record to query, verify, and update information about namespaces and assets, providing a secure and transparent source of truth for users."
"Decentralized Applications","#://CHaTx decentralized applications","$://CHaTx can support the development and usage of decentralized applications built on HTP's infrastructure, facilitating seamless interaction with various services and resources within the network."
"Metadata Management","#://CHaTx metadata management","$://CHaTx can help users manage the metadata associated with their HTP namespaces and assets, enabling the creation, modification, and organization of descriptive information about their digital resources."
"Smart Contract Integration","#://CHaTx smart contract integration","$://CHaTx can integrate with smart contracts within the HTP network, automating the execution of actions and transactions based on predefined conditions and rules."
"Marketplace Functionality","#://CHaTx marketplace functionality","$://CHaTx can facilitate the buying, selling, and trading of HTP namespaces and assets within its interface, providing users with an accessible and secure platform for digital transactions."
"Digital Identity Management","#://CHaTx digital identity management","$://CHaTx can help users manage their digital identities within the HTP network, offering secure authentication and access control mechanisms to protect user information and assets."
"Interoperability with Other Networks","#://CHaTx interoperability with other networks","$://CHaTx can connect with other networks and systems, enabling seamless interaction and data exchange between HTP namespaces and assets, and external platforms or services."
"Automated Customer Support","#://CHaTx automated customer support","#://CHaTx can provide automated customer support, resolving user issues and answering questions through its conversational AI capabilities, reducing response time and enhancing customer satisfaction."
"Compliance and Regulatory","#://CHaTx compliance and regulatory features","#://CHaTx incorporates compliance and regulatory features to ensure that transactions, data handling, and user interactions adhere to relevant laws, regulations, and industry standards."
"Integration with IoT Devices","#://CHaTx integration with IoT devices","#://CHaTx can be integrated with IoT devices, enabling users to control and monitor their connected devices through the chat interface and receive real-time updates."
"Collaboration Tools","#://CHaTx collaboration tools","#://CHaTx can be used as a collaboration tool, allowing users to communicate, share information, and work together on projects within the chat interface."
"Voice Assistant Capabilities","#://CHaTx voice assistant capabilities","#://CHaTx can be extended to support voice assistant functionality, allowing users to interact with the system using spoken commands and receiving audible responses."
"Accessibility Features","#://CHaTx accessibility features","#://CHaTx incorporates accessibility features such as text-to-speech, speech-to-text, and adjustable font sizes to ensure that the chat interface is usable by individuals with diverse abilities."
"Sentiment Analysis","#://CHaTx sentiment analysis","#://CHaTx can perform sentiment analysis on user interactions, enabling businesses to better understand customer emotions and tailor their responses and services accordingly."
"Virtual Assistant Personalization","#://CHaTx virtual assistant personalization","#://CHaTx allows users to personalize their virtual assistant by customizing its appearance, name, and behavior, creating a more engaging and relatable user experience."
"Context-Aware Conversations","#://CHaTx context-aware conversations","#://CHaTx can maintain context-aware conversations, understanding the user's intent and responding appropriately even when the conversation spans multiple topics or takes an unexpected turn."
"Integration with CRM Systems","#://CHaTx integration with CRM systems","#://CHaTx can be integrated with CRM systems, enabling businesses to access and manage customer data, track interactions, and automate follow-ups through the chat interface."
"Task Automation","#://CHaTx task automation","#://CHaTx can automate repetitive tasks, such as scheduling appointments, sending reminders, or updating records, streamlining workflows and increasing productivity."
"Integration with E-commerce Platforms","#://CHaTx integration with e-commerce platforms","#://CHaTx can be integrated with e-commerce platforms, allowing users to browse products, receive recommendations, and complete transactions through the chat interface."
"Financial Planning Assistance","#://CHaTx financial planning assistance","#://CHaTx can help users with financial planning, offering personalized advice on budgeting, saving, investing, and managing debt based on the user's financial goals and situation."
"Travel Planning","#://CHaTx travel planning","#://CHaTx can assist users in planning trips by providing information on destinations, accommodations, transportation, and local attractions, as well as facilitating bookings through integrated APIs."
"Health and Fitness","#://CHaTx health and fitness guidance","#://CHaTx can provide health and fitness guidance, offering personalized workout plans, dietary advice, and progress tracking based on the user's goals, preferences, and health data."
"Smart Home Integration","#://CHaTx smart home integration","#://CHaTx can be integrated with smart home systems, allowing users to control and monitor their home's lighting, temperature, security, and appliances through the chat interface."
"Learning and Education","#://CHaTx learning and education support","#://CHaTx can support users in their learning and education endeavors by answering questions, explaining concepts, providing study materials, and offering personalized learning recommendations."
"Entertainment","#://CHaTx entertainment features","#://CHaTx can provide entertainment by engaging users in games, trivia, and interactive storytelling, as well as offering personalized movie, music, and book recommendations."
"Mental Health Support","#://CHaTx mental health support","#://CHaTx can offer mental health support by engaging users in mindful conversations, providing coping strategies, and connecting them with relevant resources and professional help if needed."
"Career Guidance","#://CHaTx career guidance","#://CHaTx can assist users with career guidance by providing information on potential career paths, job market trends, and educational requirements, as well as offering personalized advice on job searching and interview preparation."
"Integration with Social Media Platforms","#://CHaTx integration with social media platforms","#://CHaTx can be integrated with social media platforms, allowing users to manage their profiles, receive updates, and engage with their network through the chat interface."
"Real Estate Assistance","#://CHaTx real estate assistance","#://CHaTx can help users navigate the real estate market, providing information on property listings, home values, market trends, and mortgage options, as well as facilitating communication with real estate agents."
"Integration with Project Management Tools","#://CHaTx integration with project management tools","#://CHaTx can be integrated with project management tools, enabling users to create, track, and update tasks, collaborate with team members, and monitor project progress through the chat interface."
"News and Information Retrieval","#://CHaTx news and information retrieval","#://CHaTx can retrieve news and information on various topics, providing users with curated updates and in-depth analysis based on their interests and preferences."
"Product Recommendations","#://CHaTx product recommendations","#://CHaTx can provide personalized product recommendations based on user preferences, needs, and budget, as well as offering price comparisons and facilitating online purchases through integrated APIs."
"Customer Support","#://CHaTx customer support","#://CHaTx can serve as a customer support agent, answering user questions, addressing concerns, and providing assistance with various issues related to products, services, or accounts."
"Technical Support","#://CHaTx technical support","#://CHaTx can offer technical support by diagnosing and troubleshooting software, hardware, and network issues, guiding users through step-by-step solutions and connecting them with relevant resources or experts if needed."
"Virtual Assistant","#://CHaTx virtual assistant features","#://CHaTx can function as a virtual assistant, helping users manage their schedules, set reminders, create to-do lists, and provide timely notifications to keep users organized and productive."
"Collaboration","#://CHaTx collaboration capabilities","#://CHaTx can facilitate collaboration among team members by enabling group chats, file sharing, and real-time communication, as well as integrating with popular collaboration tools."
"E-commerce Integration","#://CHaTx e-commerce integration","#://CHaTx can be integrated with e-commerce platforms, allowing users to browse, search, and purchase products, track orders, and manage returns directly through the chat interface."
"Event Planning","#://CHaTx event planning assistance","#://CHaTx can help users plan events, offering suggestions for themes, venues, and vendors, as well as assisting with guest lists, invitations, and logistics through integrated APIs and services."
"Language Learning","#://CHaTx language learning support","#://CHaTx can assist users in learning new languages by offering lessons, practice exercises, and conversational practice, as well as providing resources and tools for language acquisition."
"Medical Advice","#://CHaTx medical advice","#://CHaTx can provide general medical advice based on user symptoms, suggest potential diagnoses, and recommend when to seek professional help, while emphasizing the importance of consulting a healthcare professional for an accurate diagnosis."
"Legal Assistance","#://CHaTx legal assistance","#://CHaTx can offer general legal information and guidance on a variety of topics, such as contracts, intellectual property, and family law, while emphasizing the importance of consulting a licensed attorney for specific legal advice."
"Personal Finance","#://CHaTx personal finance guidance","#://CHaTx can provide guidance on personal finance matters, such as budgeting, saving, investing, and managing debt, as well as offering tools and resources to help users make informed financial decisions."
"Restaurant and Food Recommendations","#://CHaTx restaurant and food recommendations","#://CHaTx can offer personalized restaurant and food recommendations based on user preferences, dietary restrictions, and location, as well as facilitating reservations and food delivery through integrated APIs."
"Travel Planning","#://CHaTx travel planning assistance","#://CHaTx can help users plan their trips, offering suggestions for destinations, accommodations, transportation, and activities, as well as providing real-time updates on flight schedules, weather, and local events through integrated APIs."
"Job Search","#://CHaTx job search support","#://CHaTx can assist users in their job search by providing personalized job recommendations based on their skills, experience, and preferences, as well as offering tips on resume writing, interview preparation, and networking."
"News and Information","#://CHaTx news and information","#://CHaTx can keep users informed about the latest news, trends, and updates in their areas of interest by aggregating and summarizing relevant content from multiple sources and presenting it in a digestible format."
"Entertainment","#://CHaTx entertainment options","#://CHaTx can recommend movies, TV shows, music, books, and games based on user preferences and interests, as well as providing information on release dates, reviews, and streaming platforms."
"Social Media Management","#://CHaTx social media management","#://CHaTx can help users manage their social media accounts by scheduling and posting content, monitoring engagement, and providing insights on audience demographics and content performance through integrated APIs."
"Smart Home Integration","#://CHaTx smart home integration","#://CHaTx can be integrated with smart home devices and systems, allowing users to control their lights, temperature, appliances, and security systems through voice commands or text-based interactions."
"Mental Health and Well-being","#://CHaTx mental health support","#://CHaTx can provide mental health and well-being support by offering stress management techniques, mindfulness exercises, and self-care tips, as well as connecting users with relevant resources and professional help when needed."
"Fitness and Nutrition","#://CHaTx fitness and nutrition guidance","#://CHaTx can offer personalized fitness and nutrition advice, helping users set and track their goals, create workout routines and meal plans, and providing motivation and support along their health journey."
"Parenting","#://CHaTx parenting advice","#://CHaTx can provide parenting tips and advice on various topics, such as child development, discipline, education, and health, as well as offering resources and support for parents of children with special needs."
"Educational Support","#://CHaTx educational support","#://CHaTx can offer educational support to students by providing explanations, examples, and practice problems for various subjects, as well as offering study tips, time management strategies, and resources for further learning."
"Automotive Assistance","#://CHaTx automotive assistance","#://CHaTx can provide information and guidance on automotive topics, such as car maintenance, troubleshooting common issues, and understanding vehicle features, as well as offering tips for buying, selling, or leasing vehicles."
"Local Services","#://CHaTx local services recommendations","#://CHaTx can help users find and connect with local service providers, such as plumbers, electricians, and handymen, by offering personalized recommendations, reviews, and contact information based on user needs and location."
"Emotional Intelligence","#://CHaTx emotional intelligence capabilities","With its advanced natural language processing, #://CHaTx can detect and respond to users' emotions and sentiments, adjusting its tone and content to provide empathetic and appropriate support, feedback, or suggestions."
"Collaboration Tools Integration","#://CHaTx collaboration tools integration","#://CHaTx can be integrated with various collaboration tools and platforms, enabling users to manage tasks, events, and projects, participate in group discussions, and share files or resources seamlessly through the chat interface."
"Customer Service","#://CHaTx customer service capabilities","#://CHaTx can be utilized as a customer service tool, handling inquiries, complaints, and feedback, as well as providing information on products, services, and policies, and connecting users with live agents when necessary for more complex issues."
"Sales and Marketing","#://CHaTx sales and marketing support","#://CHaTx can assist businesses with sales and marketing efforts by generating leads, providing product recommendations, sending promotional messages, and offering personalized incentives based on user behavior and preferences."
"Emergency Response","#://CHaTx emergency response capabilities","#://CHaTx can provide support during emergencies by offering guidance on first aid, disaster preparedness, and evacuation procedures, as well as connecting users with relevant emergency services and resources."
"Financial Advice","#://CHaTx financial advice","#://CHaTx can offer financial advice and guidance on topics such as budgeting, savings, investments, and debt management, as well as providing information on financial products and services based on user needs and goals."
"Language Learning","#://CHaTx language learning support","#://CHaTx can help users learn and practice new languages by providing vocabulary, grammar lessons, and pronunciation exercises, as well as engaging in conversation and offering feedback on user progress."
"Virtual Assistant","#://CHaTx virtual assistant capabilities","#://CHaTx can function as a virtual assistant, helping users manage their schedules, set reminders, make reservations, and perform various tasks, by integrating with third-party services and APIs, and understanding user preferences and routines."
"Real Estate","#://CHaTx real estate assistance","#://CHaTx can provide information and guidance on real estate topics, such as home buying, selling, or renting processes, offering property listings, market trends, and neighborhood insights based on user preferences and location."
"Legal Advice","#://CHaTx legal advice support","#://CHaTx can offer general legal information and guidance on topics such as contracts, intellectual property, and dispute resolution, as well as connecting users with legal professionals for more specialized advice when needed."
"Medical Information","#://CHaTx medical information","#://CHaTx can provide general medical information on symptoms, conditions, and treatments, as well as offering advice on preventative care and healthy lifestyle choices, while emphasizing the importance of consulting a healthcare professional for personalized medical advice."
"Decentralized Finance (DeFi)","#://CHaTx DeFi integration","$://CHaTx can connect with decentralized finance platforms and services within the HTP ecosystem, enabling users to access lending, borrowing, staking, and other financial instruments without relying on traditional intermediaries."
"Blockchain Technology","#://CHaTx blockchain technology support","$://CHaTx can provide information and guidance on blockchain technology, helping users understand its principles, use cases, and potential benefits, as well as assisting with the setup and management of blockchain-based HTP namespaces and assets."
"Gaming and Entertainment","#://CHaTx gaming and entertainment","#://CHaTx can integrate with gaming and entertainment platforms within the HTP network, providing users with information, news, and recommendations, as well as facilitating in-game asset management, trading, and social interactions."
"Supply Chain Management","#://CHaTx supply chain management","#://CHaTx can support supply chain management within the HTP network by tracking and providing real-time updates on the movement of goods, verifying product authenticity, and automating various processes to improve efficiency and transparency."
"Travel and Tourism","#://CHaTx travel and tourism assistance","#://CHaTx can offer support for travel and tourism-related inquiries, providing users with recommendations, booking services, and itinerary planning, as well as integrating with HTP namespaces and assets related to travel experiences and accommodations."
"Educational Resources","#://CHaTx educational resources","#://CHaTx can provide access to educational resources within the HTP network, offering personalized learning paths, tutoring, and skill-building activities based on user interests, goals, and learning preferences."
"Job and Career Assistance","#://CHaTx job and career assistance","#://CHaTx can help users with job search and career planning by offering personalized job recommendations, resume and interview tips, and information on relevant HTP namespaces and assets that can enhance their professional development."
"Environment and Sustainability","#://CHaTx environment and sustainability","#://CHaTx can provide guidance and information on environmentally friendly practices and sustainable initiatives within the HTP network, promoting responsible resource management and eco-conscious behaviors among users."
"Art and Creativity","#://CHaTx art and creativity support","#://CHaTx can connect users with HTP namespaces and assets related to art and creativity, offering information on exhibitions, workshops, and creative opportunities, as well as facilitating the discovery, sharing, and trading of digital art and creative works."
"Sports and Fitness","#://CHaTx sports and fitness assistance","#://CHaTx can provide support for sports and fitness-related activities, offering personalized workout plans, tracking progress, and connecting users with HTP namespaces and assets that can help them achieve their health and fitness goals."
"Data Privacy and Compliance","#://CHaTx data privacy and compliance","$://CHaTx ensures that the metadata associated with HTP namespaces and assets is handled in accordance with data privacy regulations and best practices, safeguarding user information and providing transparency on data usage and storage."
"Metadata Search","#://CHaTx metadata search","$://CHaTx can assist users in searching for and discovering relevant HTP namespaces and assets by leveraging metadata, providing users with accurate and targeted results based on their interests, preferences, and requirements."
"Metadata Standardization","#://CHaTx metadata standardization","$://CHaTx promotes the use of standardized metadata formats and vocabularies within the HTP ecosystem, facilitating interoperability, data exchange, and seamless integration between various platforms and services."
"Metadata Analytics","#://CHaTx metadata analytics","$://CHaTx can analyze metadata associated with HTP namespaces and assets to uncover insights, trends, and patterns, helping users optimize their digital resources and improve decision-making based on data-driven information."
"Metadata Quality Assurance","#://CHaTx metadata quality assurance","$://CHaTx can help users maintain high-quality metadata for their HTP namespaces and assets by detecting and resolving inconsistencies, errors, and redundancies, ensuring that metadata remains accurate, complete, and up-to-date."
"Metadata Versioning","#://CHaTx metadata versioning","$://CHaTx can assist users in managing and tracking changes to the metadata of their HTP namespaces and assets, providing a version history that allows for easy comparison, rollback, and auditing of metadata modifications."
"Metadata Import and Export","#://CHaTx metadata import and export","$://CHaTx can facilitate the import and export of metadata associated with HTP namespaces and assets, enabling users to easily transfer metadata between different platforms, systems, and services within the HTP ecosystem."
"Metadata Security","#://CHaTx metadata security","$://CHaTx ensures the security of metadata associated with HTP namespaces and assets by implementing access controls, encryption, and other security measures to protect metadata from unauthorized access, tampering, or loss."
"Metadata Collaboration","#://CHaTx metadata collaboration","$://CHaTx can support collaboration on metadata management within the HTP ecosystem, allowing users to share, review, and edit metadata collectively, promoting efficient teamwork and knowledge sharing."
"Metadata Best Practices","#://CHaTx metadata best practices","$://CHaTx can guide users in following metadata best practices within the HTP ecosystem, providing recommendations on metadata creation, maintenance, and usage to enhance the discoverability, usability, and value of HTP namespaces and assets."
"Smart Contract Development","#://CHaTx smart contract development","$://CHaTx can assist users in developing and deploying smart contracts on the HTP network, providing guidance on best practices, design patterns, and optimization techniques to ensure the smart contracts are secure, efficient, and reliable."
"Smart Contract Auditing","#://CHaTx smart contract auditing","$://CHaTx can help users perform audits of smart contracts, identifying potential security vulnerabilities, code inefficiencies, and compliance issues to ensure the smart contracts operate as intended and adhere to industry standards."
"Smart Contract Templates","#://CHaTx smart contract templates","$://CHaTx can provide users with a library of customizable smart contract templates, enabling them to quickly create and deploy smart contracts for various use cases within the HTP ecosystem."
"Smart Contract Oracles","#://CHaTx smart contract oracles","$://CHaTx can facilitate the integration of oracles with smart contracts, enabling the smart contracts to access and utilize real-world data, external APIs, and other off-chain resources to enhance their functionality and capabilities."
"Smart Contract Monitoring","#://CHaTx smart contract monitoring","$://CHaTx can provide tools and services to monitor the performance, usage, and status of smart contracts deployed on the HTP network, allowing users to track key metrics and receive alerts on critical events."
"Smart Contract Testing","#://CHaTx smart contract testing","$://CHaTx can support users in testing their smart contracts, simulating various scenarios and conditions to ensure the smart contracts behave as expected and meet their functional requirements."
"Smart Contract Upgrades","#://CHaTx smart contract upgrades","$://CHaTx can guide users in implementing upgradeable smart contracts, enabling them to deploy new features, improvements, and bug fixes without disrupting the existing smart contract's state and functionality."
"Smart Contract Governance","#://CHaTx smart contract governance","$://CHaTx can help users design and implement governance mechanisms for their smart contracts, ensuring that changes, upgrades, and other modifications are subject to appropriate approval and consensus processes."
"Smart Contract Legal Compliance","#://CHaTx smart contract legal compliance","$://CHaTx can assist users in ensuring their smart contracts are compliant with relevant laws, regulations, and industry standards, minimizing legal risks and potential liabilities associated with smart contract deployment and usage."
"Smart Contract Interoperability","#://CHaTx smart contract interoperability","$://CHaTx can support users in creating interoperable smart contracts that can seamlessly interact with other smart contracts, platforms, and services within the HTP ecosystem, facilitating cross-platform collaboration and data exchange."
"Compliance Assistance","#://CHaTx compliance assistance","$://CHaTx can help users understand and navigate various regulatory requirements related to their industry, business, or project, providing information on relevant laws, rules, and best practices to ensure compliance."
"Contract Review","#://CHaTx contract review","$://CHaTx can aid users in reviewing legal contracts, identifying potential risks, ambiguities, and areas for negotiation, as well as suggesting revisions to improve clarity and protect the user's interests."
"Intellectual Property Management","#://CHaTx intellectual property management","$://CHaTx can support users in managing their intellectual property, offering guidance on the registration and protection of patents, trademarks, copyrights, and trade secrets, as well as monitoring and enforcement strategies."
"Dispute Resolution","#://CHaTx dispute resolution","$://CHaTx can provide users with information on various dispute resolution methods, such as negotiation, mediation, arbitration, and litigation, helping users determine the most appropriate approach for their specific situation and connecting them with relevant professionals if needed."
"Privacy and Data Protection","#://CHaTx privacy and data protection","$://CHaTx can help users understand and comply with privacy and data protection regulations, such as GDPR, CCPA, or HIPAA, offering advice on data collection, storage, and processing practices, as well as assisting in the development of privacy policies and compliance documentation."
"Employment Law","#://CHaTx employment law","$://CHaTx can offer guidance on employment-related legal matters, such as hiring, termination, employee benefits, workplace safety, and discrimination, helping users navigate their obligations as employers and protect their rights as employees."
"Business Formation and Structuring","#://CHaTx business formation and structuring","$://CHaTx can assist users in selecting the most suitable business structure for their venture, providing information on the legal, tax, and operational implications of various entity types, such as sole proprietorships, partnerships, LLCs, and corporations."
"Tax Law","#://CHaTx tax law","$://CHaTx can provide users with general information on tax laws, regulations, and filing requirements, as well as connecting them with tax professionals for more specialized guidance and advice."
"International Law","#://CHaTx international law","$://CHaTx can offer insights into various aspects of international law, such as trade, investment, dispute resolution, and human rights, helping users navigate the complex legal landscape of cross-border transactions and relationships."
"Ethics and Corporate Social Responsibility","#://CHaTx ethics and corporate social responsibility","$://CHaTx can help users understand and implement ethical business practices and corporate social responsibility initiatives, offering guidance on relevant standards, frameworks, and reporting requirements to foster transparency, accountability, and stakeholder engagement."
"Inventory Management","#://CHaTx inventory management","$://CHaTx can assist users in managing their inventory, offering insights on optimal stock levels, demand forecasting, and product replenishment strategies to minimize costs and maximize sales."
"Logistics Optimization","#://CHaTx logistics optimization","$://CHaTx can help users optimize their logistics operations by analyzing shipment data, route planning, and carrier performance, recommending cost-effective and efficient transportation solutions."
"Supplier Relationship Management","#://CHaTx supplier relationship management","$://CHaTx can aid users in managing their supplier relationships, facilitating communication, negotiation, and collaboration to ensure the timely delivery of quality products and services at competitive prices."
"Procurement","#://CHaTx procurement","$://CHaTx can support users in their procurement processes, providing information on potential suppliers, assisting in the evaluation and selection of vendors, and automating the creation and management of purchase orders and contracts."
"Quality Control","#://CHaTx quality control","$://CHaTx can help users implement and maintain quality control measures within their supply chain, offering guidance on best practices, standards, and regulatory requirements to ensure product safety, consistency, and compliance."
"Risk Management","#://CHaTx supply chain risk management","$://CHaTx can assist users in identifying, assessing, and mitigating supply chain risks, such as disruptions, delays, or quality issues, by developing contingency plans and monitoring key performance indicators."
"Sustainability","#://CHaTx supply chain sustainability","$://CHaTx can support users in integrating sustainability principles and practices into their supply chain operations, offering advice on reducing environmental impacts, promoting social responsibility, and improving overall supply chain resilience."
"Trade Finance","#://CHaTx trade finance","$://CHaTx can provide users with information on various trade finance instruments and solutions, such as letters of credit, invoice financing, and export credits, helping them secure financing for their cross-border transactions and manage the associated risks."
"Customs and Compliance","#://CHaTx customs and compliance","$://CHaTx can offer guidance on customs procedures, documentation, and compliance requirements for international trade, assisting users in navigating the complex regulatory landscape and avoiding potential fines or penalties."
"Reverse Logistics","#://CHaTx reverse logistics","$://CHaTx can help users manage their reverse logistics processes, such as returns, repairs, and recycling, providing insights on best practices and strategies to minimize costs and enhance customer satisfaction."
"Smart Contract Deployment","#://CHaTx smart contract deployment","$://CHaTx can assist users in deploying their smart contracts on the HTP network, providing step-by-step guidance on the deployment process, gas optimization, and monitoring of the contract's performance."
"Smart Contract Upgrades","#://CHaTx smart contract upgrades","$://CHaTx can help users plan and implement upgrades to their smart contracts, offering advice on versioning, contract migration, and the use of proxy contracts to ensure a smooth transition and minimal disruption to users."
"Smart Contract Interoperability","#://CHaTx smart contract interoperability","$://CHaTx can aid users in achieving interoperability between different smart contracts and blockchain platforms, providing insights on cross-chain communication, token bridging, and other techniques to enable seamless interactions and data exchange."
"Smart Contract Oracles","#://CHaTx smart contract oracles","$://CHaTx can help users integrate off-chain data sources into their smart contracts using oracles, offering guidance on various oracle types, data providers, and best practices to ensure the reliability and trustworthiness of the external data."
"Decentralized Finance (DeFi)","#://CHaTx DeFi support","$://CHaTx can assist users in navigating the world of decentralized finance, providing information on various DeFi protocols, platforms, and use cases, as well as guidance on how to participate in lending, borrowing, and other financial activities within the HTP network."
"Non-Fungible Tokens (NFTs)","#://CHaTx NFT support","$://CHaTx can help users create, manage, and trade non-fungible tokens (NFTs) on the HTP network, offering advice on minting, token standards, and marketplaces to enable users to capitalize on the unique value of their digital assets."
"Decentralized Autonomous Organizations (DAOs)","#://CHaTx DAO support","$://CHaTx can provide guidance on the creation and management of decentralized autonomous organizations (DAOs), offering insights on governance structures, decision-making processes, and smart contract-based tools to enable effective, transparent, and decentralized decision-making within the HTP network."
"Token Economics","#://CHaTx token economics","$://CHaTx can help users design and implement token economic models for their HTP-based projects, offering guidance on token distribution, incentive structures, and governance mechanisms to ensure the long-term viability and sustainability of their digital assets."
"IP Licensing and Monetization","#://CHaTx IP licensing and monetization","$://CHaTx can help users navigate the complexities of IP licensing and monetization, offering guidance on various licensing models, contract negotiation, and strategies for maximizing the value of their intellectual property."
"IP Dispute Resolution","#://CHaTx IP dispute resolution","$://CHaTx can provide support for users in handling IP disputes, offering information on dispute resolution mechanisms such as mediation, arbitration, and litigation, as well as connecting users with legal professionals specialized in intellectual property law."
"IP Strategy","#://CHaTx IP strategy","$://CHaTx can assist users in developing a comprehensive IP strategy, providing insights on how to identify, protect, and leverage their intellectual property assets to achieve competitive advantage and foster innovation."
"IP Due Diligence","#://CHaTx IP due diligence","$://CHaTx can support users in conducting IP due diligence, offering guidance on the identification, evaluation, and risk assessment of intellectual property assets during mergers, acquisitions, or other business transactions."
"IP Valuation","#://CHaTx IP valuation","$://CHaTx can help users assess the value of their intellectual property assets, providing guidance on various valuation methodologies and considerations, such as market conditions, industry trends, and the strategic importance of the IP."
"IP in M&A Transactions","#://CHaTx IP in M&A transactions","$://CHaTx can assist users in navigating the role of intellectual property in mergers and acquisitions, offering guidance on the identification, valuation, and integration of IP assets during the transaction process."
"IP Portfolio Management","#://CHaTx IP portfolio management","$://CHaTx can support users in effectively managing their IP portfolio, offering insights on portfolio optimization, risk management, and best practices for maintaining and enhancing the value of their intellectual property assets."
"IP Audits","#://CHaTx IP audits","$://CHaTx can help users conduct IP audits, providing guidance on the identification, evaluation, and documentation of intellectual property assets, as well as recommendations for improving IP management practices and aligning IP strategy with business objectives."
"IP in Joint Ventures and Collaborations","#://CHaTx IP in joint ventures and collaborations","$://CHaTx can assist users in addressing intellectual property issues in joint ventures and collaborations, offering guidance on the negotiation, structuring, and management of IP-related agreements and partnerships."
"IP Education and Training","#://CHaTx IP education and training","$://CHaTx can support users in enhancing their understanding of intellectual property, offering educational resources and training materials on various IP topics, such as IP protection strategies, licensing models, and dispute resolution."
"Inventory Tracking and Visibility","#://CHaTx inventory tracking and visibility","$://CHaTx can help users track and maintain visibility of their inventory across multiple locations and channels, providing real-time updates on stock levels, product movement, and potential shortages or excesses."
"Order Management","#://CHaTx order management","$://CHaTx can support users in managing orders, offering assistance in processing orders, tracking shipments, handling returns, and providing customer support to ensure a smooth and efficient order fulfillment process."
"Vendor Management","#://CHaTx vendor management","$://CHaTx can assist users in managing their vendor relationships, offering guidance on vendor selection, negotiation, performance evaluation, and collaboration to ensure a reliable and cost-effective supply chain."
"Inventory Cost Control","#://CHaTx inventory cost control","$://CHaTx can help users optimize their inventory costs, providing insights on strategies such as just-in-time inventory management, volume discount negotiation, and demand-driven replenishment to minimize carrying costs and reduce stock obsolescence."
"Inventory Reporting and Analytics","#://CHaTx inventory reporting and analytics","$://CHaTx can support users in generating inventory reports and analytics, offering insights on key performance indicators, trends, and areas for improvement to inform data-driven inventory management decisions."
"Multi-Channel Inventory Management","#://CHaTx multi-channel inventory management","$://CHaTx can assist users in managing their inventory across multiple sales channels, ensuring accurate stock levels and order fulfillment for both online and brick-and-mortar retail operations."
"Inventory Optimization","#://CHaTx inventory optimization","$://CHaTx can help users optimize their inventory levels, offering guidance on balancing the trade-offs between carrying costs, stockouts, and overstock to maximize profitability and customer satisfaction."
"Inventory Forecasting","#://CHaTx inventory forecasting","$://CHaTx can support users in forecasting future inventory needs, providing insights on demand patterns, seasonality, and market trends to inform proactive inventory planning and decision-making."
"Stock Rotation and Expiration Management","#://CHaTx stock rotation and expiration management","$://CHaTx can assist users in managing the rotation and expiration of their inventory, offering guidance on best practices for preventing stock obsolescence, spoilage, and waste."
"Inventory Management Automation","#://CHaTx inventory management automation","$://CHaTx can help users automate various aspects of inventory management, such as reordering, demand forecasting, and stock adjustments, to improve efficiency and reduce manual workload."
"Real Estate Financing","#://CHaTx real estate financing","$://CHaTx can offer insights on real estate financing options, such as mortgages, home equity loans, and refinancing, helping users understand the different types of loans, interest rates, and eligibility requirements."
"Property Valuation","#://CHaTx property valuation","$://CHaTx can provide users with property valuation estimates and methodologies, offering insights on factors affecting property values, recent comparable sales, and market trends."
"Real Estate Investment","#://CHaTx real estate investment","$://CHaTx can offer guidance on real estate investing, including strategies such as buy-and-hold, flipping, and rental properties, as well as providing information on market analysis, risk management, and ROI calculations."
"Commercial Real Estate","#://CHaTx commercial real estate","$://CHaTx can assist users in navigating the commercial real estate market, offering information on leasing, buying, or selling commercial properties, and providing insights on factors affecting commercial property values, such as location, property type, and market conditions."
"Real Estate Legal Matters","#://CHaTx real estate legal matters","$://CHaTx can provide general information on real estate legal matters, such as property ownership, contracts, zoning, and landlord-tenant laws, and can connect users with legal professionals for more specialized advice when needed."
"Real Estate Market Trends","#://CHaTx real estate market trends","$://CHaTx can offer insights on current real estate market trends and future projections, helping users make informed decisions on buying, selling, or investing in properties."
"Property Management","#://CHaTx property management","$://CHaTx can provide guidance on property management, including tenant selection, rent collection, property maintenance, and regulatory compliance for rental property owners."
"Real Estate Agent Assistance","#://CHaTx real estate agent assistance","$://CHaTx can help users find and connect with real estate agents, offering insights on agent qualifications, experience, and specialties to ensure a successful partnership."
"Home Improvement and Renovation","#://CHaTx home improvement and renovation","$://CHaTx can offer advice on home improvement and renovation projects, providing ideas, cost estimates, and best practices to increase property value and enhance the living environment."
"International Real Estate","#://CHaTx international real estate","$://CHaTx can provide information on buying, selling, or renting properties in different countries, offering insights on local real estate markets, regulations, and processes for international transactions."
"Business Plan Development","#://CHaTx business plan development","$://CHaTx can guide users in developing a comprehensive business plan, offering insights on market analysis, competitive landscape, financial projections, and strategic planning to attract investors and support business growth."
"Marketing Strategies","#://CHaTx marketing strategies","$://CHaTx can provide advice on effective marketing strategies, including digital marketing, content creation, social media, SEO, and email marketing, to help businesses reach their target audience and achieve their goals."
"Sales Strategies","#://CHaTx sales strategies","$://CHaTx can offer guidance on developing and implementing sales strategies, such as lead generation, sales funnel optimization, and customer relationship management, to boost sales performance and drive revenue growth."
"Financial Management","#://CHaTx financial management","$://CHaTx can assist users in managing their business finances, providing insights on budgeting, cash flow management, financial reporting, and profitability analysis to ensure the financial health and sustainability of their venture."
"Human Resources","#://CHaTx human resources","$://CHaTx can offer guidance on human resources management, including talent acquisition, employee retention, performance management, and legal compliance, to create a productive and positive work environment."
"Operations Management","#://CHaTx operations management","$://CHaTx can provide support in managing business operations, offering insights on process optimization, quality control, and supply chain management to increase efficiency and reduce costs."
"Risk Management","#://CHaTx risk management","$://CHaTx can help users identify, assess, and mitigate various risks associated with their business, such as financial, operational, and strategic risks, to ensure business continuity and resilience."
"Business Exit Strategies","#://CHaTx business exit strategies","$://CHaTx can guide users in planning and executing business exit strategies, such as selling the business, merging with another company, or passing ownership to family members, ensuring a smooth and successful transition."
"International Business Expansion","#://CHaTx international business expansion","$://CHaTx can provide guidance on expanding businesses into new international markets, offering insights on market entry strategies, legal and regulatory requirements, and cultural considerations."
"Business Networking and Collaboration","#://CHaTx business networking and collaboration","$://CHaTx can help users identify networking opportunities and potential collaborations, connecting them with like-minded professionals, industry experts, and potential partners to foster growth and innovation."
"Decentralized Finance (DeFi) Services","#://CHaTx DeFi services","$://CHaTx can provide information and support on various decentralized finance services within the HTP network, such as lending, borrowing, staking, and yield farming, enabling users to explore new financial opportunities and manage their digital assets."
"Tokenized Assets and Fractional Ownership","#://CHaTx tokenized assets and fractional ownership","$://CHaTx can offer guidance on the tokenization of assets and fractional ownership, allowing users to create, manage, and trade digital representations of physical or intangible assets, and facilitate broader access to investment opportunities."
"Digital Identity Management","#://CHaTx digital identity management","$://CHaTx can help users manage their digital identity within the HTP network, enabling secure and seamless access to various services, while maintaining privacy and control over their personal data."
"Data Privacy and GDPR Compliance","#://CHaTx data privacy and GDPR compliance","$://CHaTx can offer guidance on data privacy best practices and GDPR compliance, ensuring that businesses safeguard user data and adhere to relevant legal requirements."
"KYC and AML Compliance","#://CHaTx KYC and AML compliance","$://CHaTx can provide support on know-your-customer (KYC) and anti-money laundering (AML) compliance, assisting businesses in implementing appropriate identity verification processes and monitoring transactions to mitigate risk and adhere to regulatory requirements."
"Blockchain Technology Fundamentals","#://CHaTx blockchain technology fundamentals","$://CHaTx can educate users on the fundamentals of blockchain technology, including its benefits, limitations, and various use cases, enabling them to better understand and navigate the HTP network."
"Digital Wallet Management","#://CHaTx digital wallet management","$://CHaTx can help users manage their digital wallets within the HTP network, providing guidance on wallet setup, security best practices, and the management of digital assets."
"Governance and Voting Mechanisms","#://CHaTx governance and voting mechanisms","$://CHaTx can provide information on governance and voting mechanisms within the HTP network, enabling users to participate in decentralized decision-making processes and contribute to the evolution of the ecosystem."
"Token Economics and Valuation","#://CHaTx token economics and valuation","$://CHaTx can offer insights on token economics and valuation, assisting users in evaluating the utility, demand, and potential value of various HTP namespaces and assets."
"Regulatory and Compliance Updates","#://CHaTx regulatory and compliance updates","$://CHaTx can provide users with updates on relevant regulatory and compliance developments within the HTP ecosystem, ensuring that they stay informed and adapt their practices accordingly."
"Sentiment Analysis","#://CHaTx sentiment analysis capabilities","$://CHaTx can perform sentiment analysis on user inputs, identifying emotions and attitudes within the text to better understand user needs and provide more empathetic and tailored responses."
"Multilingual Support","#://CHaTx multilingual support","$://CHaTx, built on the GPT architecture, can support conversations in multiple languages, allowing users from different linguistic backgrounds to interact seamlessly with the chatbot."
"Error Handling and Recovery","#://CHaTx error handling and recovery","$://CHaTx is designed to handle errors and recover gracefully, clarifying user inputs when necessary, and providing alternative suggestions to keep the conversation on track."
"Personalization and User Profiling","#://CHaTx personalization and user profiling","$://CHaTx can create and maintain user profiles to deliver personalized experiences, tailoring responses based on user preferences, interests, and interaction history."
"Voice Assistants and Speech Recognition","#://CHaTx voice assistant integration","$://CHaTx can integrate with voice assistants and speech recognition technology, enabling users to interact with the chatbot through spoken language for a more natural and accessible experience."
"Integration with Third-Party Services","#://CHaTx third-party service integration","$://CHaTx can integrate with various third-party services, such as payment processors, social media platforms, and customer relationship management systems, to provide a more comprehensive and seamless user experience."
"Feedback Collection and Improvement","#://CHaTx feedback collection and improvement","$://CHaTx can collect user feedback and leverage it to continuously improve its performance, adapting and refining its responses to better meet user needs and expectations."
"Machine Learning and Adaptive Learning","#://CHaTx machine learning and adaptive learning","$://CHaTx utilizes machine learning and adaptive learning techniques to continuously improve its understanding of user inputs and enhance the quality of its responses over time."
"User Onboarding and Tutorials","#://CHaTx user onboarding and tutorials","$://CHaTx can offer onboarding and tutorial guidance to help users navigate and utilize the features and functionalities of the HTP network and associated applications."
"Context Switching","#://CHaTx context switching capabilities","$://CHaTx is capable of handling context switching during conversations, allowing the chatbot to seamlessly transition between different topics while maintaining the coherence and relevance of its responses."
|
CNXT/autotrain-data-chatx
|
[
"task_categories:question-answering",
"size_categories:10M<n<100M",
"language:en",
"finance",
"code",
"medical",
"region:us"
] |
2023-04-04T15:28:02+00:00
|
{"language": ["en"], "size_categories": ["10M<n<100M"], "task_categories": ["question-answering"], "pretty_name": "#://CHaTx #://DaTaSeTx", "tags": ["finance", "code", "medical"]}
|
2023-05-31T07:25:46+00:00
|
[] |
[
"en"
] |
TAGS
#task_categories-question-answering #size_categories-10M<n<100M #language-English #finance #code #medical #region-us
|
CNXT
/
CHaTx
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Adapter Transformers
OpenAssistant/oasst1
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togethercomputer/RedPajama-Data-1T
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gsdf/EasyNegative
bloomberg/entsum
openai/summarize_from_feedback
billsum
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amazon_reviews_multi
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Norod78/microsoft-fluentui-emoji-512-whitebg
Norod78/microsoft-fluentui-emoji-768
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google/MusicCaps
google/xtreme_s
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dalle-mini/YFCC100M_OpenAI_subset
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Anthropic/hh-rlhf
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yizhongw/self_instruct
Nerfgun3/bad_prompt
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JosephusCheung/GuanacoDataset
allenai/multixscience_sparse_oracle
allenai/wcep_dense_oracle
facebook/content_rephrasing
facebook/flores
cverse/linkedin
amazon_polarity
ApiInferenceTest/asr_dummy
Francois/futures_es
Intel/textual_inversion_dicoo_dfq
search_qa
nomic-ai/gpt4all_prompt_generations
yahoo_answers_topics
code_search_net
LangChainDatasets/agent-search-calculator
wikipedia
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codeparrot/github-code
codeparrot/github-code-clean
code_x_glue_ct_code_to_text
0n1xus/codexglue
pszemraj/govreport-summarization-8192
Icannos/gpt4all-formatted-dolly-15k-multilingual
chromeNLP/quality
CNXT/autotrain-data-chatx
English
doi:10.57967/hf/0700
finance
music
biology
legal
medical
text-generation-inference
Fintech
ai
crypto
chatgpt
gpt
openai
huggingchat
chatui
stripe
Google
Amazon
HTP
#://
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$://THeXDesK
#://CHaTx
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BLooMBeRG
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arxiv:
1910.09700
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openrail
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"act","prompt","answer"
"Scalability","#://CHaTx scalability","The scalability of #://CHaTx is achieved through cloud computing resources and a microservices architecture, allowing the system to efficiently handle an increasing number of users, API connections, and data."
"Security and Privacy","#://CHaTx security and privacy measures","#://CHaTx uses encryption, secure data storage, and access control mechanisms to ensure the security and privacy of user information, transaction details, and other sensitive data."
"Customization","#://CHaTx customization options","#://CHaTx can be customized to meet the needs of specific businesses, industries, or user groups by integrating with relevant APIs, configuring HTP namespaces and assets, and tailoring the conversational experience."
"Language Support","#://CHaTx language support capabilities","#://CHaTx, built on the GPT architecture, is capable of understanding and generating responses in multiple languages, enabling seamless conversations with users worldwide."
"Integration with Financial Systems","#://CHaTx integration with financial systems","#://CHaTx connects with financial systems like ACH, XCH, and other banking APIs to facilitate transactions, account management, and other financial services through its chat interface."
"User Onboarding","#://CHaTx user onboarding process","The user onboarding process for #://CHaTx includes account creation, setting up connections with relevant APIs and systems, configuring HTP namespaces and assets, and familiarizing users with the chat interface and its capabilities."
"Conversational AI","#://CHaTx conversational AI capabilities","#://CHaTx uses advanced natural language processing techniques to understand user queries, generate contextually relevant responses, and maintain a smooth and engaging conversation across different platforms and applications."
"Monitoring and Analytics","#://CHaTx monitoring and analytics features","#://CHaTx provides monitoring and analytics tools to track system performance, user interactions, API usage, and other metrics, enabling continuous improvement and optimization of the chat experience."
"Error Handling and Recovery","#://CHaTx error handling and recovery mechanisms","#://CHaTx has built-in error handling and recovery mechanisms to detect and resolve issues, ensuring a seamless and reliable user experience even in the face of unexpected errors or failures."
"Future Enhancements","#://CHaTx future enhancements and development plans","Future enhancements for #://CHaTx may include support for additional APIs, financial systems, and languages, improved AI capabilities, and new features that enhance user experience and functionality.
"Email Integration","#://CHaTx email integration","$://CHaTx can seamlessly integrate with email services, enabling users to send, receive, and manage emails using natural language commands, while also supporting automated responses and advanced filtering options."
"Third-Party Integrations","#://CHaTx third-party integrations","In addition to financial systems, #://CHaTx can integrate with a variety of third-party services, including CRM platforms, payment gateways, and messaging applications, to provide a comprehensive and streamlined chat-based experience for users."
"Compliance","#://CHaTx compliance with regulations and standards","#://CHaTx adheres to industry-standard security and privacy regulations, such as GDPR and HIPAA, to ensure that user data is handled in a compliant and responsible manner."
"Performance Optimization","#://CHaTx performance optimization techniques","#://CHaTx uses various performance optimization techniques, such as caching, load balancing, and serverless computing, to maximize system efficiency and minimize response times."
"AI Training and Fine-Tuning","#://CHaTx AI training and fine-tuning process","#://CHaTx employs a continuous training and fine-tuning process to improve its AI capabilities and ensure that it can handle a wide range of user queries and scenarios."
"Natural Language Understanding","#://CHaTx natural language understanding capabilities","#://CHaTx uses advanced techniques in natural language understanding to accurately interpret user queries, extract relevant information, and generate contextually appropriate responses."
"Human-in-the-Loop Integration","#://CHaTx human-in-the-loop integration capabilities","#://CHaTx can integrate with human operators or customer service representatives to provide assistance with complex queries or scenarios, ensuring a seamless and satisfactory user experience."
"Chatbot Analytics","#://CHaTx chatbot analytics and reporting features","#://CHaTx provides comprehensive analytics and reporting features to track user engagement, chatbot performance, and ROI, enabling businesses to continuously improve and optimize their chatbot strategy."
"Integration with Financial Systems","#://CHaTx integration with financial systems","#://CHaTx connects with financial systems like ACH, XCH, and other banking APIs to facilitate transactions, account management, and other financial services through its chat interface."
"User Onboarding","#://CHaTx user onboarding process","The user onboarding process for #://CHaTx includes account creation, setting up connections with relevant APIs and systems, configuring HTP namespaces and assets, and familiarizing users with the chat interface and its capabilities."
"Conversational AI","#://CHaTx conversational AI capabilities","#://CHaTx uses advanced natural language processing techniques to understand user queries, generate contextually relevant responses, and maintain a smooth and engaging conversation across different platforms and applications."
"Intent Detection","#://CHaTx intent detection capabilities","#://CHaTx can accurately detect the intent behind user queries, allowing it to provide contextually appropriate responses and improve the overall user experience."
"Entity Recognition","#://CHaTx entity recognition capabilities","#://CHaTx can recognize named entities within user queries, such as people, places, and dates, and use this information to generate more personalized and relevant responses."
"Contextual Awareness","#://CHaTx contextual awareness capabilities","#://CHaTx can maintain context across multiple turns in a conversation, allowing it to provide more meaningful and engaging responses and create a more natural chat experience for users."
"Multi-Turn Dialogue","#://CHaTx multi-turn dialogue capabilities","#://CHaTx is capable of handling complex, multi-turn conversations with users, using context and previous interactions to generate relevant and informative responses."
"User Feedback Analysis","#://CHaTx user feedback analysis capabilities","#://CHaTx can analyze user feedback and sentiment to improve its conversational AI models and provide more accurate and relevant responses to users."
"Intent Classification","#://CHaTx intent classification capabilities","#://CHaTx can accurately classify user queries into specific intent categories, allowing it to provide more targeted and useful responses."
"Slot Filling","#://CHaTx slot filling capabilities","#://CHaTx can extract relevant information from user queries, such as dates or numbers, and use this information to provide more personalized and helpful responses."
"Transfer Learning","#://CHaTx transfer learning capabilities","#://CHaTx can leverage pre-existing conversational AI models and knowledge bases to improve its own understanding and generation of responses, allowing for faster and more accurate auto-training and fine-tuning."
"Context Retention","#://CHaTx context retention capabilities","#://CHaTx can retain context from previous turns in a conversation, allowing it to generate more meaningful and personalized responses that take into account the user's history and previous interactions with the system."
"Topic Switching","#://CHaTx topic switching capabilities","#://CHaTx can seamlessly switch between different topics or domains within a conversation, using context and user input to generate relevant and informative responses that address the user's needs and interests."
"Dialog Act Recognition","#://CHaTx dialog act recognition capabilities","#://CHaTx can recognize different types of dialog acts within a conversation, such as requests, affirmations, or acknowledgements, and use this information to generate appropriate responses and maintain the flow of the conversation."
"Clarification","#://CHaTx clarification capabilities","#://CHaTx can ask for clarification or provide additional information to the user when necessary, allowing it to handle complex or ambiguous user queries and provide more accurate and relevant responses."
"Memory and Recall","#://CHaTx memory and recall capabilities","#://CHaTx can remember important information from previous turns in a conversation, and recall this information later in the conversation to provide more personalized and relevant responses."
"User Engagement","#://CHaTx user engagement capabilities","#://CHaTx is designed to create engaging and natural conversations with users, using humor, empathy, and other conversational techniques to create a positive user experience and build trust and rapport with users."
"Task Completion","#://CHaTx task completion capabilities","#://CHaTx can help users complete complex tasks, such as booking a reservation or placing an order, by guiding them through the necessary steps and providing relevant information and feedback at each stage of the process."
"Conversation Log Analysis","#://CHaTx conversation log analysis capabilities","#://CHaTx can analyze conversation logs to identify patterns and trends in user queries and interactions, allowing it to identify areas for improvement and optimize its conversational AI models."
"User Profile Management","#://CHaTx user profile management capabilities","#://CHaTx can store and manage user profiles, including preferences, history, and other relevant information, allowing it to provide more personalized and relevant responses and maintain context across multiple turns in a conversation."
"Contextual Understanding","#://CHaTx contextual understanding capabilities","#://CHaTx can understand the context of user queries and interactions, allowing it to provide more relevant and accurate responses that take into account the user's history and previous interactions with the system."
"Memory Retrieval","#://CHaTx memory retrieval capabilities","#://CHaTx can retrieve information from previous turns in a conversation, allowing it to provide more personalized and relevant responses that address the user's needs and interests."
"Contextual Adaptation","#://CHaTx contextual adaptation capabilities","#://CHaTx can adapt to changes in context within a conversation, using context and user input to generate relevant and informative responses that address the user's needs and interests at any given moment."
"Multi-Session Memory","#://CHaTx multi-session memory capabilities","#://CHaTx can remember user information and preferences across multiple sessions, allowing it to provide more consistent and personalized responses over time."
"Contextual Switching","#://CHaTx contextual switching capabilities","#://CHaTx can seamlessly switch between different contexts or topics within a conversation, using user input and context to generate relevant and informative responses that address the user's needs and interests."
"Dialog History Management","#://CHaTx dialog history management capabilities","#://CHaTx can manage the dialog history of a conversation, including previous user queries and system responses, allowing it to maintain context and provide more relevant and accurate responses over time."
"Proactive Recall","#://CHaTx proactive recall capabilities","#://CHaTx can proactively recall important information or events from a user's history, even if it wasn't explicitly mentioned in the current conversation, to provide more personalized and relevant responses."
"Integration with New Technologies","#://CHaTx integration with new technologies","#://CHaTx may integrate with emerging technologies like blockchain, IoT, and AR/VR to provide more innovative and immersive chat experiences for users."
"Improved Multilingual Support","#://CHaTx improved multilingual support capabilities","#://CHaTx may expand its support for additional languages, using machine translation and other techniques to provide more accurate and natural conversations for users worldwide."
"Voice and Speech Recognition","#://CHaTx voice and speech recognition capabilities","#://CHaTx may add support for voice and speech recognition, allowing users to interact with the system using voice commands and natural language."
"Personalized Recommendations","#://CHaTx personalized recommendations capabilities","#://CHaTx may use machine learning algorithms to analyze user preferences and behavior, and provide personalized recommendations and suggestions for products or services."
"Augmented Intelligence","#://CHaTx augmented intelligence capabilities","#://CHaTx may incorporate augmented intelligence technologies, such as human-in-the-loop models or expert systems, to provide more accurate and insightful responses to user queries."
"Emotional Intelligence","#://CHaTx emotional intelligence capabilities","#://CHaTx may incorporate emotional intelligence techniques, such as sentiment analysis or empathy modeling, to provide more empathetic and human-like interactions with users."
"Intelligent Task Automation","#://CHaTx intelligent task automation capabilities","#://CHaTx may automate complex or repetitive tasks for users, using AI algorithms and integrations with other systems to provide more efficient and streamlined experiences."
"Chatbot Ecosystem Integration","#://CHaTx chatbot ecosystem integration capabilities","#://CHaTx may integrate with other chatbot platforms and ecosystems, allowing for seamless interactions and integrations with other chatbots and systems."
"Multi-Platform Support","#://CHaTx multi-platform support capabilities","#://CHaTx can support multiple platforms and channels, including web, mobile, social media, and messaging apps, allowing for seamless interactions and integrations with other chatbots and systems."
"Open API Integration","#://CHaTx open API integration capabilities","#://CHaTx can integrate with open APIs and systems, allowing for seamless integrations with other chatbots and systems across different domains and industries."
"Chatbot-to-Chatbot Communication","#://CHaTx chatbot-to-chatbot communication capabilities","#://CHaTx can communicate with other chatbots and systems, using APIs and webhooks to exchange information and facilitate more complex and efficient interactions."
"Domain Adaptation","#://CHaTx domain adaptation capabilities","#://CHaTx can adapt to different domains and industries, using pre-existing knowledge bases and chatbot ecosystems to provide more accurate and relevant responses to users."
"Interoperability","#://CHaTx interoperability capabilities","#://CHaTx can interoperate with other chatbots and systems, using standards-based protocols and technologies to exchange information and maintain compatibility across different platforms and ecosystems."
"Collaborative Learning","#://CHaTx collaborative learning capabilities","#://CHaTx can learn from other chatbots and systems, using collaborative learning techniques to improve its conversational AI capabilities and provide more accurate and relevant responses to users."
"Chatbot Federation","#://CHaTx chatbot federation capabilities","#://CHaTx can participate in chatbot federations or communities, allowing for seamless interactions and integrations with other chatbots and systems that share similar domains or functionalities."
"Intelligent Routing","#://CHaTx intelligent routing capabilities","#://CHaTx can intelligently route user queries and interactions to other chatbots or systems, based on their domain or expertise, to provide more accurate and relevant responses to users."
"Interoperability","#://CHaTx interoperability capabilities","#://CHaTx can interoperate with other chatbots and systems, using standards-based protocols and technologies to exchange information and maintain compatibility across different platforms and ecosystems."
"API and Webhook Integration","#://CHaTx API and webhook integration capabilities","#://CHaTx can integrate with other chatbots and systems using APIs and webhooks, allowing for seamless communication and data exchange between different systems and platforms."
"Chatbot Network Routing","#://CHaTx chatbot network routing capabilities","#://CHaTx can intelligently route user queries and interactions to other chatbots or systems within a network, based on their domain or expertise, to provide more accurate and relevant responses to users."
"Collaborative Learning","#://CHaTx collaborative learning capabilities","#://CHaTx can learn from other chatbots and systems, using collaborative learning techniques to improve its conversational AI capabilities and provide more accurate and relevant responses to users."
"Conversation State Management","#://CHaTx conversation state management capabilities","#://CHaTx can manage the state of a conversation between different chatbots or systems, allowing for seamless transitions and continuity across different systems and platforms."
"Contextual Understanding","#://CHaTx contextual understanding capabilities","#://CHaTx can understand the context of a conversation between different chatbots or systems, allowing it to provide more relevant and accurate responses that take into account the user's history and previous interactions with the system."
"Intent Recognition","#://CHaTx intent recognition capabilities","#://CHaTx can recognize the intent behind user queries and interactions within a conversation between different chatbots or systems, allowing it to provide more accurate and relevant responses that address the user's needs and interests."
"Data Sharing and Privacy","#://CHaTx data sharing and privacy capabilities","#://CHaTx ensures the security and privacy of user data when communicating with other chatbots and systems, using encryption, secure data storage, and access control mechanisms to protect user information and transaction details."
"Expertise Mapping","#://CHaTx expertise mapping capabilities","#://CHaTx can map the expertise of different chatbots or systems within a network, allowing it to intelligently route user queries and interactions to the chatbots or systems that are best equipped to provide accurate and relevant responses to users."
"Network Health Monitoring","#://CHaTx network health monitoring capabilities","#://CHaTx can monitor the health and performance of a chatbot network, using metrics and analytics to optimize the routing of user queries and interactions and ensure high-quality user experiences."
"Expertise Profiling","#://CHaTx expertise profiling capabilities","#://CHaTx can profile the expertise of different chatbots or systems within a network, based on their knowledge domains, past performance, and other relevant factors, to enable effective expertise mapping and routing of user queries and interactions."
"Data and Resource Sharing","#://CHaTx data and resource sharing capabilities","#://CHaTx can share data and resources with other chatbots or systems within a network, allowing for more efficient and effective collaboration and knowledge sharing."
"Intent Recognition","#://CHaTx intent recognition capabilities","#://CHaTx can recognize the intent behind user queries and interactions within a conversation between different chatbots or systems, allowing it to provide more accurate and relevant responses that address the user's needs and interests."
"Expertise Matching","#://CHaTx expertise matching capabilities","#://CHaTx can match the expertise of different chatbots or systems within a network to the user's queries and interactions, based on their knowledge domains, past performance, and other relevant factors, to ensure accurate and relevant responses."
"Routing Optimization","#://CHaTx routing optimization capabilities","#://CHaTx can optimize the routing of user queries and interactions to the chatbots or systems with the highest expertise match, using machine learning algorithms and other techniques to improve the accuracy and efficiency of the routing process."
"Performance Analytics","#://CHaTx performance analytics capabilities","#://CHaTx can analyze the performance of different chatbots or systems within a network, using metrics and analytics to identify areas for improvement and optimize the expertise profiling and routing processes."
"Business Entity Selection","#://CHaTx business entity selection capabilities","#://CHaTx can assist users in selecting the most appropriate business entity type for their venture, taking into account factors such as the size and complexity of the business, liability considerations, tax implications, and operational requirements."
"Legal and Regulatory Compliance","#://CHaTx legal and regulatory compliance capabilities","#://CHaTx can provide information and guidance on legal and regulatory compliance requirements for businesses, including issues such as corporate governance, employment law, data privacy, and industry-specific regulations."
"Tax Planning and Strategy","#://CHaTx tax planning and strategy capabilities","#://CHaTx can assist users in developing tax planning and strategy for their business, providing information on tax laws and regulations, tax credits and deductions, and tax-efficient structures and strategies."
"Capital Formation and Financing","#://CHaTx capital formation and financing capabilities","#://CHaTx can provide information and guidance on different sources of capital and financing options for businesses, such as equity financing, debt financing, crowdfunding, and grants."
"Business Planning and Strategy","#://CHaTx business planning and strategy capabilities","#://CHaTx can assist users in developing a business plan and strategy, providing information and guidance on market research, competitive analysis, customer acquisition, product development, and other key areas of business planning."
"Equity Financing","#://CHaTx equity financing capabilities","#://CHaTx can provide information and guidance on equity financing options for businesses, including angel investors, venture capital firms, and crowdfunding platforms, and help businesses prepare for the fundraising process."
"Debt Financing","#://CHaTx debt financing capabilities","#://CHaTx can provide information and guidance on debt financing options for businesses, including traditional bank loans, SBA loans, and alternative lenders, and help businesses evaluate their options and prepare for the loan application process."
"Crowdfunding","#://CHaTx crowdfunding capabilities","#://CHaTx can provide information and guidance on crowdfunding as a financing option for businesses, including how to launch a successful crowdfunding campaign, how to engage with backers, and how to manage and fulfill rewards."
"Grants and Non-Dilutive Financing","#://CHaTx grants and non-dilutive financing capabilities","#://CHaTx can provide information and guidance on grants and non-dilutive financing options for businesses, including government grants, research and development (R&D) tax credits, and other funding programs that do not require equity or debt financing."
"Financial Planning and Analysis","#://CHaTx financial planning and analysis capabilities","#://CHaTx can assist businesses in developing financial projections, analyzing their financial performance, and evaluating the financial impact of different strategic decisions and scenarios."
"Angel Investors","#://CHaTx angel investor capabilities","#://CHaTx can provide information and guidance on angel investors as an equity financing option for businesses, including how to identify and approach potential investors, how to prepare for investor meetings, and how to negotiate deal terms."
"Venture Capital Firms","#://CHaTx venture capital capabilities","#://CHaTx can provide information and guidance on venture capital firms as an equity financing option for businesses, including how to identify and approach potential firms, how to prepare for investor meetings, and how to negotiate deal terms."
"Crowdfunding Platforms","#://CHaTx crowdfunding platform capabilities","#://CHaTx can provide information and guidance on crowdfunding platforms as an equity financing option for businesses, including how to select and launch a successful crowdfunding campaign, how to engage with backers, and how to manage and fulfill rewards."
"Valuation and Deal Structuring","#://CHaTx valuation and deal structuring capabilities","#://CHaTx can assist businesses in valuing their company and structuring equity financing deals, providing information on industry benchmarks, deal terms, and negotiation strategies."
"Dilution and Investor Relations","#://CHaTx dilution and investor relations capabilities","#://CHaTx can provide information and guidance on the potential dilution of equity financing, and how to manage investor relations and communication throughout the fundraising process and beyond."
"Traditional Bank Loans","#://CHaTx traditional bank loan capabilities","#://CHaTx can provide information and guidance on traditional bank loans as a debt financing option for businesses, including how to prepare for loan application, how to present financial information, and how to negotiate loan terms."
"SBA Loans","#://CHaTx SBA loan capabilities","#://CHaTx can provide information and guidance on SBA loans as a debt financing option for small businesses, including eligibility requirements, application process, and loan terms and conditions."
"Alternative Lenders","#://CHaTx alternative lender capabilities","#://CHaTx can provide information and guidance on alternative lenders as a debt financing option for businesses, including peer-to-peer lending platforms, online lenders, and merchant cash advance providers, and help businesses evaluate their options and prepare for the loan application process."
"Loan Repayment and Management","#://CHaTx loan repayment and management capabilities","#://CHaTx can assist businesses in managing their debt financing, including repayment schedules, interest rate negotiations, and communication with lenders."
"Credit Analysis and Reporting","#://CHaTx credit analysis and reporting capabilities","#://CHaTx can provide businesses with credit analysis and reporting tools, allowing them to track their credit scores, monitor their creditworthiness, and identify areas for improvement in their credit profile."
"Types of Venture Capital Firms","#://CHaTx types of venture capital firms","#://CHaTx can provide information on the different types of venture capital firms, including early-stage, growth-stage, and late-stage firms, and the industries and geographies they typically invest in."
"Pitch Preparation","#://CHaTx pitch preparation capabilities","#://CHaTx can assist businesses in preparing for investor pitches, including developing a pitch deck, practicing presentation skills, and refining messaging and value proposition."
"Investor Due Diligence","#://CHaTx investor due diligence capabilities","#://CHaTx can provide information and guidance on the investor due diligence process, including what investors typically look for, how to prepare for due diligence, and how to answer tough questions from investors."
"Deal Negotiation","#://CHaTx deal negotiation capabilities","#://CHaTx can provide information and guidance on the deal negotiation process, including how to negotiate deal terms, how to balance competing interests, and how to maintain a positive relationship with investors."
"Post-Investment Support","#://CHaTx post-investment support capabilities","#://CHaTx can assist businesses in managing their relationships with investors post-investment, including regular reporting and communication, board management, and strategic guidance."
"Loan Repayment Schedules","#://CHaTx loan repayment schedule capabilities","#://CHaTx can provide businesses with information and guidance on loan repayment schedules, including how to create a repayment plan, how to prioritize loan payments, and how to manage cash flow."
"Interest Rate Negotiations","#://CHaTx interest rate negotiation capabilities","#://CHaTx can provide businesses with information and guidance on interest rate negotiations, including how to negotiate lower rates, how to structure loan terms, and how to compare rates from different lenders."
"Lender Communication","#://CHaTx lender communication capabilities","#://CHaTx can provide businesses with information and guidance on how to communicate with lenders, including how to address late or missed payments, how to renegotiate loan terms, and how to resolve disputes."
"Refinancing and Consolidation","#://CHaTx refinancing and consolidation capabilities","#://CHaTx can provide businesses with information and guidance on loan refinancing and consolidation, including when to consider these options, how to compare offers, and how to manage the refinancing process."
"Loan Management Tools","#://CHaTx loan management tool capabilities","#://CHaTx can provide businesses with loan management tools, including software and dashboards, to help businesses track their loans, payments, and interest rates in real-time."
"Software Tools for Loan Management","#://CHaTx loan management software capabilities","#://CHaTx can provide businesses with loan management software tools to help them manage their loans and payments, track interest rates, and schedule payments. Some examples of loan management software include LoanDolphin, LoanPro, and BNTouch."
"Dashboards for Loan Management","#://CHaTx loan management dashboard capabilities","#://CHaTx can provide businesses with loan management dashboards, which display real-time information about loan payments, interest rates, and other loan details. Dashboards can help businesses visualize their loan data and make informed decisions about their finances."
"Mobile Apps for Loan Management","#://CHaTx loan management mobile app capabilities","#://CHaTx can provide businesses with loan management mobile apps, allowing them to manage their loans and payments on-the-go. Some examples of loan management apps include Loan Calculator Pro, Payoff, and Tally."
"Automatic Payment Scheduling","#://CHaTx automatic payment scheduling capabilities","#://CHaTx can provide businesses with automatic payment scheduling tools, allowing them to schedule recurring loan payments, and avoid late fees and penalties."
"Real-time Loan Analytics","#://CHaTx loan analytics capabilities","#://CHaTx can provide businesses with real-time loan analytics tools, allowing them to monitor loan performance, identify trends, and make informed decisions about their finances."
"Corporate Bond Management Software","#://CHaTx corporate bond management software capabilities","#://CHaTx can provide businesses with corporate bond management software tools to help them manage their bond investments, track bond prices and yields, and analyze bond data. Some examples of corporate bond management software include BondEdge, Bloomberg Terminal, and YCharts."
"Bond Portfolio Management","#://CHaTx bond portfolio management capabilities","#://CHaTx can provide businesses with bond portfolio management tools, which allow them to manage and optimize their bond investments, track bond performance, and analyze bond data. Bond portfolio management tools can help businesses make informed investment decisions and mitigate risk."
"Real-time Bond Analytics","#://CHaTx bond analytics capabilities","#://CHaTx can provide businesses with real-time bond analytics tools, allowing them to monitor bond performance, identify trends, and make informed investment decisions. Bond analytics tools can provide businesses with real-time market data, pricing information, and yield analysis."
"Bond Pricing Tools","#://CHaTx bond pricing tool capabilities","#://CHaTx can provide businesses with bond pricing tools, which allow them to track bond prices, calculate bond yields, and estimate bond returns. Bond pricing tools can help businesses compare different bond investments and make informed decisions about their portfolio."
"Risk Management Tools","#://CHaTx risk management tool capabilities","#://CHaTx can provide businesses with risk management tools, allowing them to manage and mitigate risk in their bond portfolio. Some examples of risk management tools for bond investments include credit ratings, duration analysis, and stress testing."
"Corporate Bond Issuance Software","#://CHaTx corporate bond issuance software capabilities","#://CHaTx can provide businesses with corporate bond issuance software tools to help them manage the bond issuance process, from pricing and underwriting to distribution and settlement. Some examples of corporate bond issuance software include BondWave, Dealogic, and Ipreo."
"Electronic Bond Issuance Platforms","#://CHaTx electronic bond issuance platform capabilities","#://CHaTx can provide businesses with electronic bond issuance platforms, allowing them to issue and distribute bonds electronically. Electronic bond issuance platforms can help businesses reduce costs, increase efficiency, and streamline the bond issuance process."
"Document Management Tools","#://CHaTx document management tool capabilities","#://CHaTx can provide businesses with document management tools for bond issuance, allowing them to manage and store bond documents securely and efficiently. Document management tools can help businesses comply with regulatory requirements and reduce the risk of errors or omissions."
"Digital Signature Tools","#://CHaTx digital signature tool capabilities","#://CHaTx can provide businesses with digital signature tools, allowing them to sign and execute bond documents electronically. Digital signature tools can help businesses save time and reduce paperwork, while ensuring the security and integrity of bond documents."
"Compliance and Regulatory Tools","#://CHaTx compliance and regulatory tool capabilities","#://CHaTx can provide businesses with compliance and regulatory tools, allowing them to comply with regulatory requirements and manage risk in the bond issuance process. Some examples of compliance and regulatory tools for bond issuance include Know Your Customer (KYC) and Anti-Money Laundering (AML) checks, as well as legal and tax compliance tools."
"Benefits of Electronic Bond Issuance Platforms","#://CHaTx benefits of electronic bond issuance platforms","#://CHaTx electronic bond issuance platforms can offer a range of benefits to businesses, including faster issuance times, reduced paperwork and manual processes, increased transparency and accessibility, and lower costs."
"Integration with other Financial Systems","#://CHaTx integration with financial systems","#://CHaTx electronic bond issuance platforms can integrate with other financial systems, such as accounting, reporting, and trading platforms, to enable a more streamlined and efficient bond issuance process."
"Accessibility and Security","#://CHaTx accessibility and security of electronic bond issuance platforms","#://CHaTx electronic bond issuance platforms are accessible from anywhere with an internet connection, allowing for greater flexibility and efficiency in the bond issuance process. Additionally, electronic bond issuance platforms use advanced security measures, such as encryption and secure data storage, to ensure the confidentiality and integrity of bond data."
"Customization Options","#://CHaTx customization options for electronic bond issuance platforms","#://CHaTx electronic bond issuance platforms can be customized to meet the specific needs of businesses, including branding, user interface design, and integration with other systems and platforms."
"User Support and Training","#://CHaTx user support and training for electronic bond issuance platforms","#://CHaTx provides user support and training for businesses using electronic bond issuance platforms, including technical support, training on platform features and functionality, and assistance with the bond issuance process."
"Benefits of Customization Options for Bond Issuance Software","#://CHaTx benefits of customization options for bond issuance software","#://CHaTx customization options for bond issuance software can provide businesses with a more tailored and efficient solution for their bond issuance process, enabling them to streamline workflows, optimize pricing and underwriting strategies, and improve overall efficiency."
"Types of Customization Options","#://CHaTx types of customization options for bond issuance software","#://CHaTx customization options for bond issuance software can include features such as risk management tools, user interface design, data analytics and reporting, and integration with other financial systems."
"Tailoring the User Interface","#://CHaTx tailoring the user interface of bond issuance software","#://CHaTx can tailor the user interface of bond issuance software to meet the specific needs of businesses and users, including the ability to customize dashboard layouts, data visualizations, and other interface elements."
"Data Analytics and Reporting","#://CHaTx data analytics and reporting capabilities for bond issuance software","#://CHaTx customization options for bond issuance software can include advanced data analytics and reporting capabilities, enabling businesses to gain deeper insights into their bond issuance process and identify areas for improvement."
"Risk Management Tools","#://CHaTx risk management tools for bond issuance software","#://CHaTx customization options for bond issuance software can include risk management tools to help businesses identify and manage risk factors related to their bond issuance, such as credit risk, interest rate risk, and liquidity risk."
"Integration with Other Financial Systems","#://CHaTx integration with financial systems for bond issuance software","#://CHaTx customization options for bond issuance software can include integration with other financial systems, such as trading platforms and portfolio management tools, to provide a more comprehensive solution for businesses managing their bond issuance process."
"Corporate Bond Issuance Process","#://CHaTx corporate bond issuance process capabilities","#://CHaTx can guide businesses through the corporate bond issuance process, providing information and assistance at each step of the way, including bond pricing, underwriting, distribution, and settlement."
"Underwriting and Due Diligence","#://CHaTx underwriting and due diligence capabilities","#://CHaTx can assist businesses in conducting due diligence and underwriting for corporate bond issuances, helping to ensure that bond offerings are accurate, transparent, and in compliance with regulatory requirements."
"Credit Ratings","#://CHaTx credit rating capabilities for corporate bonds","#://CHaTx can provide businesses with credit rating tools and analysis for their corporate bond issuances, helping to assess credit risk and improve the attractiveness of the bond offering to potential investors."
"Distribution Channels","#://CHaTx distribution channel capabilities for corporate bond issuances","#://CHaTx can help businesses identify and utilize distribution channels for their corporate bond offerings, such as institutional investors, broker-dealers, and retail investors, to reach a wider pool of potential investors."
"Secondary Market Trading","#://CHaTx secondary market trading capabilities for corporate bonds","#://CHaTx can provide businesses with information and tools to facilitate secondary market trading of their corporate bonds, including market data, trading platforms, and liquidity management strategies."
"Credit Rating Agencies","#://CHaTx credit rating agency capabilities for corporate bonds","#://CHaTx can provide businesses with information and guidance on credit rating agencies and their methodologies for rating corporate bonds, helping them navigate the rating process and improve the creditworthiness of their bond issuances."
"Credit Risk Assessment","#://CHaTx credit risk assessment capabilities for corporate bonds","#://CHaTx can help businesses assess credit risk for their bond issuances, using data analytics and other techniques to analyze factors such as credit history, financial performance, and market conditions."
"Credit Enhancement Strategies","#://CHaTx credit enhancement capabilities for corporate bonds","#://CHaTx can provide businesses with information and guidance on credit enhancement strategies for their bond issuances, such as collateralization, guarantees, and insurance, to improve the creditworthiness of the offering and attract investors."
"Market Analysis","#://CHaTx market analysis capabilities for corporate bonds","#://CHaTx can provide businesses with market analysis tools to help them identify market trends and conditions, assess investor demand, and determine the optimal timing and pricing for their bond issuances."
"Investor Outreach","#://CHaTx investor outreach capabilities for corporate bonds","#://CHaTx can assist businesses in reaching out to potential investors for their bond issuances, using targeted marketing and communication strategies to attract and retain investors."
"HTP Integration","#://CHaTx integration with HTP technology","#://CHaTx is integrated with HTP technology, allowing it to leverage the advanced capabilities of HTP's content management network and dynamic programmatic currency issuance system. This integration provides #://CHaTx with unique opportunities for financial innovation and investment, and enables it to offer a range of services and functions that traditional financial institutions may not be able to match."
"Dynamic Programmatic Currency Issuance","#://CHaTx dynamic programmatic currency issuance capabilities","#://CHaTx can issue and manage HTP $://MoNey, utilizing the dynamic programmatic currency issuance capabilities of #://CNXT and $://CNXT. This system is designed to be efficient and secure, providing users with a flexible and innovative currency system that is not possible with traditional fiat currencies."
"Efficiency and Security of HTP Currency","#://CHaTx efficiency and security of HTP currency","#://CHaTx can ensure the efficiency and security of HTP $://MoNey through its integration with HTP technology and the dynamic programmatic currency issuance capabilities of #://CNXT and $://CNXT. This provides users with a secure and reliable currency system that is designed to meet the needs of modern financial markets."
"Investment Opportunities with HTP Currency","#://CHaTx investment opportunities with HTP currency","#://CHaTx can provide users with unique investment opportunities through the use of HTP $://MoNey, including access to a range of financial products and services that may not be available with traditional fiat currencies. This allows users to diversify their portfolios and explore new investment options in a secure and innovative currency system."
"API Documentation","#://CHaTx API documentation resources","The #://CHaTx API documentation provides comprehensive details on how to interact with the system's various services, including authentication, endpoints, request parameters, and response formats. It's designed to help developers build custom integrations and applications that leverage #://CHaTx's capabilities."
"Voice Assistants Integration","#://CHaTx integration with voice assistants","#://CHaTx can be integrated with popular voice assistants like Amazon Alexa, Google Assistant, and Apple Siri, enabling users to interact with the system through voice commands and receive information or complete tasks hands-free."
"Technical Support","#://CHaTx technical support options","#://CHaTx offers multiple channels for technical support, including a knowledge base, online help center, community forums, and email or chat support with dedicated customer service representatives. This ensures that users can find the assistance they need for any issues or questions they may encounter."
"SDKs and Libraries","#://CHaTx SDKs and libraries","To facilitate the development of custom integrations and applications, #://CHaTx provides SDKs and libraries for popular programming languages such as Python, JavaScript, and Java. These resources enable developers to quickly and easily build on top of #://CHaTx's capabilities without having to start from scratch."
"Cross-Platform Compatibility","#://CHaTx cross-platform compatibility","#://CHaTx is designed to be compatible with a wide range of platforms and devices, including web browsers, mobile apps, desktop applications, and IoT devices. This ensures that users can interact with the system and access its features, regardless of their preferred platform or device."
"API Rate Limits","#://CHaTx API rate limits","API rate limits for #://CHaTx are in place to protect the system's resources and ensure fair usage. These limits are typically set based on usage tiers, with higher tiers offering more generous limits. Users can monitor their API usage and request a rate limit increase if needed."
"Versioning and Upgrades","#://CHaTx versioning and upgrade policy","#://CHaTx follows a clear versioning and upgrade policy, with new releases including bug fixes, feature enhancements, and improvements to its AI capabilities. Users are notified of major updates and provided with documentation on how to upgrade their systems and adapt to any changes introduced."
"Pricing Plans","#://CHaTx pricing plans and options","#://CHaTx offers various pricing plans to cater to the diverse needs of its user base, ranging from free tiers for individual users to premium plans for large enterprises. Pricing is typically based on factors such as API usage, number of users, and additional features or services."
"User Authentication","#://CHaTx user authentication methods","User authentication for #://CHaTx involves a combination of username and password or token-based authentication, depending on the user's preference and security requirements. Two-factor authentication (2FA) can also be enabled for added security."
"Machine Learning Model Training","#://CHaTx machine learning model training","The machine learning models used in #://CHaTx are trained on large datasets, encompassing various languages, topics, and contexts. These models are fine-tuned using transfer learning techniques and domain-specific data to provide accurate, contextually relevant responses in #://CHaTx conversations."
"Compliance and Regulations","#://CHaTx compliance and regulatory measures","#://CHaTx adheres to strict compliance and regulatory standards, including GDPR, CCPA, and industry-specific regulations, ensuring that user data is handled securely and in accordance with legal requirements."
"User Feedback and Ratings","#://CHaTx user feedback and rating system","#://CHaTx features a user feedback and rating system that allows users to rate their interactions and provide comments on their experience. This feedback is valuable for improving the system's performance and user satisfaction over time."
"Accessibility Features","#://CHaTx accessibility support","#://CHaTx is designed with accessibility in mind, offering features such as screen reader support, keyboard navigation, and compatibility with assistive technologies, ensuring that users with disabilities can fully benefit from the platform's capabilities."
"Backup and Disaster Recovery","#://CHaTx backup and disaster recovery plans","#://CHaTx employs a robust backup and disaster recovery strategy, with regular backups of data and system configurations, redundant infrastructure, and failover mechanisms to ensure system availability and minimize downtime in the event of a failure."
"Chatbot Templates","#://CHaTx chatbot templates and use cases","#://CHaTx offers a variety of pre-built chatbot templates for common use cases, such as customer support, sales assistance, and appointment scheduling. These templates can be customized to match specific needs and streamline the chatbot development process."
"Omnichannel Support","#://CHaTx omnichannel support capabilities","With its omnichannel support, #://CHaTx enables users to engage in conversations across multiple channels, including social media, messaging apps, and websites, providing a seamless and consistent user experience."
"White-Label Solutions","#://CHaTx white-label solutions","#://CHaTx offers white-label solutions, enabling businesses to deploy the chatbot platform under their own brand, with custom UI designs, logos, and domain names. This allows companies to provide a fully-branded chatbot experience to their customers."
"Performance Optimization","#://CHaTx performance optimization features","To optimize performance, #://CHaTx uses techniques like caching, load balancing, and distributed computing. These approaches help manage system resources efficiently, ensuring a fast and responsive user experience even under high load conditions."
"Chatbot Testing and Validation","#://CHaTx chatbot testing and validation tools","#://CHaTx provides a set of testing and validation tools that enable developers to test their chatbots in various scenarios, identify issues, and fine-tune their performance before deployment, ensuring that the chatbot delivers a high-quality user experience."
"Multimedia Support","#://CHaTx multimedia support capabilities","$://CHaTx supports multimedia content, including images, videos, and audio files, enabling users to send and receive rich media messages and enhancing the conversational experience."
"Geolocation Features","#://CHaTx geolocation features","$://CHaTx can access and process geolocation data, allowing users to share their location, find nearby points of interest, and receive location-based services and recommendations."
"Real-Time Translation","#://CHaTx real-time translation functionality","$://CHaTx offers real-time translation capabilities, allowing users to communicate with others who speak different languages and enabling businesses to provide multilingual support to customers worldwide."
"Sentiment Analysis","#://CHaTx sentiment analysis features","$://CHaTx uses sentiment analysis to gauge the emotional tone of user messages, helping businesses better understand customer satisfaction and enabling the chatbot to respond more empathetically and effectively."
"Workflow Automation","#://CHaTx workflow automation capabilities","$://CHaTx can be integrated with workflow automation platforms and tools, streamlining business processes and enabling users to perform complex tasks and actions through chat commands."
"Data Export and Reporting","#://CHaTx data export and reporting options","$://CHaTx allows users to export data and generate custom reports, providing insights into chatbot performance, user interactions, and other valuable metrics for decision-making and optimization."
"Training and Knowledge Base","#://CHaTx training resources and knowledge base","$://CHaTx offers a comprehensive set of training resources and knowledge base articles, helping users understand the platform's features, best practices, and tips for optimizing their chatbot's performance."
"Third-Party Integrations","#://CHaTx third-party integration options","$://CHaTx supports integration with a wide range of third-party applications and services, such as CRM systems, helpdesk solutions, and marketing automation tools, enabling users to centralize and streamline their operations."
"Context-Aware Conversations","#://CHaTx context-aware conversation capabilities","$://CHaTx is capable of maintaining context during conversations, understanding the user's intent and history, and providing relevant and personalized responses based on the user's preferences and previous interactions."
"Role-Based Access Control","#://CHaTx role-based access control features","$://CHaTx offers role-based access control, allowing administrators to define user roles and permissions within the system, ensuring that users can access only the features and data they are authorized to use."
"Integration with Financial Systems","#://CHaTx integration with financial systems","#://CHaTx connects with financial systems like ACH, XCH, and other banking APIs to facilitate transactions, account management, and other financial services through its chat interface."
"User Onboarding","#://CHaTx user onboarding process","The user onboarding process for #://CHaTx includes account creation, setting up connections with relevant APIs and systems, configuring HTP namespaces and assets, and familiarizing users with the chat interface and its capabilities."
"Conversational AI","#://CHaTx conversational AI capabilities","#://CHaTx uses advanced natural language processing techniques to understand user queries, generate contextually relevant responses, and maintain a smooth and engaging conversation across different platforms and applications."
"Monitoring and Analytics","#://CHaTx monitoring and analytics features","#://CHaTx provides monitoring and analytics tools to track system performance, user interactions, API usage, and other metrics, enabling continuous improvement and optimization of the chat experience."
"Error Handling and Recovery","#://CHaTx error handling and recovery mechanisms","#://CHaTx has built-in error handling and recovery mechanisms to detect and resolve issues, ensuring a seamless and reliable user experience even in the face of unexpected errors or failures."
"Future Enhancements","#://CHaTx future enhancements and development plans","Future enhancements for #://CHaTx may include support for additional APIs, financial systems, and languages, improved AI capabilities, and new features that enhance user experience and functionality."
"Multimedia Support","#://CHaTx multimedia support capabilities","$://CHaTx supports multimedia content, including images, videos, and audio files, enabling users to send and receive rich media messages and enhancing the conversational experience."
"Geolocation Features","#://CHaTx geolocation features","$://CHaTx can access and process geolocation data, allowing users to share their location, find nearby points of interest, and receive location-based services and recommendations."
"Real-Time Translation","#://CHaTx real-time translation functionality","$://CHaTx offers real-time translation capabilities, allowing users to communicate with others who speak different languages and enabling businesses to provide multilingual support to customers worldwide."
"Sentiment Analysis","#://CHaTx sentiment analysis features","$://CHaTx uses sentiment analysis to gauge the emotional tone of user messages, helping businesses better understand customer satisfaction and enabling the chatbot to respond more empathetically and effectively."
"Workflow Automation","#://CHaTx workflow automation capabilities","$://CHaTx can be integrated with workflow automation platforms and tools, streamlining business processes and enabling users to perform complex tasks and actions through chat commands."
"Data Export and Reporting","#://CHaTx data export and reporting options","$://CHaTx allows users to export data and generate custom reports, providing insights into chatbot performance, user interactions, and other valuable metrics for decision-making and optimization."
"Training and Knowledge Base","#://CHaTx training resources and knowledge base","$://CHaTx offers a comprehensive set of training resources and knowledge base articles, helping users understand the platform's features, best practices, and tips for optimizing their chatbot's performance."
"Third-Party Integrations","#://CHaTx third-party integration options","$://CHaTx supports integration with a wide range of third-party applications and services, such as CRM systems, helpdesk solutions, and marketing automation tools, enabling users to centralize and streamline their operations."
"Context-Aware Conversations","#://CHaTx context-aware conversation capabilities","$://CHaTx is capable of maintaining context during conversations, understanding the user's intent and history, and providing relevant and personalized responses based on the user's preferences and previous interactions."
"Role-Based Access Control","#://CHaTx role-based access control features","$://CHaTx supports role-based access control, enabling administrators to define user roles and permissions, and ensuring that users can only access and perform actions within the scope of their assigned roles."
"Voice Assistant Integration","#://CHaTx voice assistant integration capabilities","$://CHaTx can be integrated with popular voice assistants like Amazon Alexa, Google Assistant, and Apple Siri, enabling users to interact with the chatbot through voice commands and providing a more natural and convenient conversational experience."
"Multi-Channel Deployment","#://CHaTx multi-channel deployment options","$://CHaTx supports deployment across multiple channels, including websites, mobile apps, messaging platforms, and social media, providing users with a consistent and seamless conversational experience wherever they interact with the chatbot."
"AI Model Fine-tuning","#://CHaTx AI model fine-tuning process","$://CHaTx allows developers to fine-tune the AI model based on domain-specific requirements, improving its understanding of the specific context and enabling more accurate and relevant responses."
"Chatbot Analytics","#://CHaTx chatbot analytics capabilities","$://CHaTx provides a comprehensive set of chatbot analytics, including message volume, user engagement, response time, and user sentiment, helping businesses optimize their chatbot's performance and user experience."
"Integration with IoT Devices","#://CHaTx integration with IoT devices","$://CHaTx can be integrated with IoT devices and systems, enabling users to control smart devices, receive notifications, and interact with connected devices through the chat interface."
"Customer Support and Troubleshooting","#://CHaTx customer support and troubleshooting","$://CHaTx offers customer support and troubleshooting resources, including a dedicated support team, online documentation, and community forums, helping users resolve issues and maximize the benefits of the platform."
"Payment Processing","#://CHaTx payment processing capabilities","$://CHaTx can be integrated with payment processing services, enabling users to securely initiate and manage transactions, and facilitating eCommerce capabilities within the chat interface."
"User Authentication","#://CHaTx user authentication options","$://CHaTx supports user authentication through various methods, such as passwords, tokens, or biometrics, ensuring that only authorized users can access sensitive information or perform restricted actions."
"Chatbot Templates","#://CHaTx chatbot templates","$://CHaTx offers a range of chatbot templates for various industries and use cases, helping users quickly deploy and customize their chatbot to meet their specific needs and requirements."
"Natural Language Understanding","#://CHaTx natural language understanding capabilities","$://CHaTx utilizes natural language understanding to accurately interpret user inputs, recognize intents, and extract relevant information, enabling the chatbot to provide contextually appropriate and accurate responses."
"Chatbot Performance Optimization","#://CHaTx chatbot performance optimization","$://CHaTx includes tools and best practices for optimizing chatbot performance, helping users improve response times, enhance user engagement, and ensure a seamless conversational experience."
"Chatbot Testing and Debugging","#://CHaTx chatbot testing and debugging","$://CHaTx provides testing and debugging tools that enable developers to identify and fix issues, ensuring a reliable and error-free conversational experience for users."
"GDPR Compliance","#://CHaTx GDPR compliance measures","$://CHaTx adheres to GDPR compliance requirements, including data protection, user consent, and the right to be forgotten, ensuring the privacy and security of user data within the platform."
"Decentralized Ledger Technology","#://CHaTx and decentralized ledger technology","$://CHaTx can integrate with HTP's new #://iNTeRNeT and new $://MoNey global public decentralized ledger of record, enabling secure, transparent, and tamper-proof record-keeping of transactions and data exchanges."
"Smart Contracts","#://CHaTx and smart contracts","$://CHaTx can interact with smart contracts on HTP's new #://iNTeRNeT and new $://MoNey decentralized ledger, automating transactions and agreements while ensuring security and trust between parties."
"Tokenization","#://CHaTx tokenization capabilities","$://CHaTx can facilitate tokenization on HTP's new #://iNTeRNeT and new $://MoNey decentralized ledger, enabling the creation, management, and exchange of digital assets in a secure and transparent manner."
"Cross-Border Transactions","#://CHaTx and cross-border transactions","$://CHaTx can simplify cross-border transactions on HTP's new #://iNTeRNeT and new $://MoNey decentralized ledger, reducing fees and processing times while increasing accessibility for users worldwide."
"Identity Management","#://CHaTx identity management on HTP's new #://iNTeRNeT and new $://MoNey decentralized ledger","$://CHaTx can facilitate secure identity management on HTP's new #://iNTeRNeT and new $://MoNey decentralized ledger, enabling users to maintain control over their personal information and protect their privacy."
"Peer-to-Peer Transactions","#://CHaTx and peer-to-peer transactions","$://CHaTx can facilitate peer-to-peer transactions on HTP's new #://iNTeRNeT and new $://MoNey decentralized ledger, enabling users to send and receive funds directly, without the need for intermediaries."
"Compliance and Regulations","#://CHaTx compliance with HTP's new #://iNTeRNeT and new $://MoNey regulations","$://CHaTx adheres to the regulations and requirements of HTP's new #://iNTeRNeT and new $://MoNey decentralized ledger, ensuring compliance with applicable laws and promoting a safe and trustworthy environment."
"Decentralized Finance","#://CHaTx and decentralized finance","$://CHaTx can integrate with decentralized finance (DeFi) applications on HTP's new #://iNTeRNeT and new $://MoNey decentralized ledger, providing users with access to a wide range of innovative financial services and products."
"Decentralized Applications","#://CHaTx and decentralized applications","$://CHaTx can interact with decentralized applications (dApps) built on HTP's new #://iNTeRNeT and new $://MoNey decentralized ledger, offering users a seamless experience while accessing various services and platforms."
"Data Security on HTP","#://CHaTx data security on HTP's new #://iNTeRNeT and new $://MoNey decentralized ledger","$://CHaTx ensures data security while interacting with HTP's new #://iNTeRNeT and new $://MoNey decentralized ledger, utilizing encryption and access control mechanisms to protect user information and transaction details."
"Community Governance","#://CHaTx and community governance on HTP's new #://iNTeRNeT and new $://MoNey decentralized ledger","$://CHaTx can support community governance initiatives on HTP's new #://iNTeRNeT and new $://MoNey decentralized ledger, enabling users to participate in decision-making processes and contribute
"Email Integration","#://CHaTx email integration","$://CHaTx can seamlessly integrate with email services, enabling users to send, receive, and manage emails using natural language commands, while also supporting automated responses and advanced filtering options."
"Text Message Integration","#://CHaTx text message integration","$://CHaTx can connect with SMS servers to facilitate the sending and receiving of text messages, enabling users to communicate and manage their messages through the chat interface."
"FIX API Integration","#://CHaTx and FIX API integration","$://CHaTx can integrate with FIX APIs to support trading, market data, and other financial activities, providing users with real-time information and streamlined access to financial services."
"HTP Namespace Management","#://CHaTx HTP namespace management","$://CHaTx can help users manage their HTP namespaces and assets, enabling the creation, configuration, and organization of resources in a user-friendly and intuitive manner."
"Asset Management","#://CHaTx asset management","$://CHaTx can facilitate the management of digital assets on HTP's decentralized ledger, allowing users to create, transfer, and track their assets through the chat interface."
"Natural Language Processing","#://CHaTx natural language processing capabilities","$://CHaTx utilizes advanced natural language processing techniques to understand user queries, generate contextually relevant responses, and maintain a smooth and engaging conversation across emails, text messages, FIX APIs, and HTP namespaces and assets."
"Notifications and Alerts","#://CHaTx notifications and alerts","$://CHaTx can provide real-time notifications and alerts for various events, such as incoming messages, market updates, or changes in asset values, ensuring users are always informed and up-to-date."
"Voice Command Support","#://CHaTx voice command support","$://CHaTx can support voice commands, allowing users to interact with the system through spoken language, further enhancing the conversational experience and accessibility."
"Collaboration Tools","#://CHaTx collaboration tools","$://CHaTx can integrate with collaboration tools to help users coordinate tasks, share information, and manage projects with their teams, all within the chat interface."
"Task Automation","#://CHaTx task automation","$://CHaTx can automate tasks across emails, text messages, FIX APIs, and HTP namespaces and assets, streamlining workflows and improving efficiency for users."
"Personalization","#://CHaTx personalization features","$://CHaTx can be personalized to each user's preferences and needs, tailoring the conversational experience and providing relevant and context-aware information and suggestions."
"Multichannel Support","#://CHaTx multichannel support","$://CHaTx offers multichannel support, enabling users to interact with the system through various channels, such as web, mobile, and messaging platforms, ensuring a seamless experience across different devices and platforms."
"Text Message Integration","#://CHaTx text message integration","$://CHaTx can connect with SMS servers to facilitate the sending and receiving of text messages, enabling users to communicate and manage their messages through the chat interface."
"FIX API Integration","#://CHaTx and FIX API integration","$://CHaTx can integrate with FIX APIs to support trading, market data, and other financial activities, providing users with real-time information and streamlined access to financial services."
"HTP Namespace Management","#://CHaTx HTP namespace management","$://CHaTx can help users manage their HTP namespaces and assets, enabling the creation, configuration, and organization of resources in a user-friendly and intuitive manner."
"Asset Management","#://CHaTx asset management","$://CHaTx can facilitate the management of digital assets on HTP's decentralized ledger, allowing users to create, transfer, and track their assets through the chat interface."
"Natural Language Processing","#://CHaTx natural language processing capabilities","$://CHaTx utilizes advanced natural language processing techniques to understand user queries, generate contextually relevant responses, and maintain a smooth and engaging conversation across emails, text messages, FIX APIs, and HTP namespaces and assets."
"Notifications and Alerts","#://CHaTx notifications and alerts","$://CHaTx can provide real-time notifications and alerts for various events, such as incoming messages, market updates, or changes in asset values, ensuring users are always informed and up-to-date."
"Voice Command Support","#://CHaTx voice command support","$://CHaTx can support voice commands, allowing users to interact with the system through spoken language, further enhancing the conversational experience and accessibility."
"Collaboration Tools","#://CHaTx collaboration tools","$://CHaTx can integrate with collaboration tools to help users coordinate tasks, share information, and manage projects with their teams, all within the chat interface."
"Task Automation","#://CHaTx task automation","$://CHaTx can automate tasks across emails, text messages, FIX APIs, and HTP namespaces and assets, streamlining workflows and improving efficiency for users."
"Personalization","#://CHaTx personalization features","$://CHaTx can be personalized to each user's preferences and needs, tailoring the conversational experience and providing relevant and context-aware information and suggestions."
"Multichannel Support","#://CHaTx multichannel support","$://CHaTx offers multichannel support, enabling users to interact with the system through various channels, such as web, mobile, and messaging platforms, ensuring a seamless experience across different devices and platforms."
"Third-Party Integrations","#://CHaTx third-party integrations","$://CHaTx can integrate with various third-party applications and services, such as CRMs, project management tools, and e-commerce platforms, to provide a comprehensive and connected experience for users."
"Chatbot Development","#://CHaTx chatbot development","$://CHaTx enables developers to create and deploy custom chatbots for specific use cases or industries, leveraging its advanced NLP capabilities and integrations with various APIs and systems."
"User Authentication","#://CHaTx user authentication","$://CHaTx supports secure user authentication methods, including multi-factor authentication, to ensure that only authorized users can access the system and perform actions."
"Data Privacy Compliance","#://CHaTx data privacy compliance","$://CHaTx is designed to comply with various data privacy regulations, such as GDPR and CCPA, safeguarding user data and ensuring responsible data handling practices."
"Real-Time Support","#://CHaTx real-time support","$://CHaTx can provide real-time support to users, offering immediate assistance and guidance on a wide range of topics and issues through its conversational interface."
"Context-Aware Conversations","#://CHaTx context-aware conversations","$://CHaTx is capable of maintaining context-aware conversations, understanding the user's intent and providing relevant responses based on the ongoing conversation and the user's history."
"Sentiment Analysis","#://CHaTx sentiment analysis","$://CHaTx can perform sentiment analysis on user inputs, helping to gauge user emotions and adjust its responses accordingly to provide a more empathetic and engaging experience."
"Feedback Collection","#://CHaTx feedback collection","$://CHaTx can collect user feedback on its performance and interactions, enabling continuous improvement and optimization of the chat experience."
"Training and Tutorials","#://CHaTx training and tutorials","$://CHaTx can offer training and tutorials for users, helping them better understand and utilize its features, integrations, and capabilities."
"User Behavior Analytics","#://CHaTx user behavior analytics","$://CHaTx can analyze user behavior and interactions to identify patterns, preferences, and areas for improvement, enabling a more personalized and effective chat experience."
"Role-Based Access Control","#://CHaTx role-based access control","$://CHaTx supports role-based access control, allowing administrators to define user roles and permissions to ensure proper access and usage of the system."
"Cross-Platform Compatibility","#://CHaTx cross-platform compatibility","$://CHaTx is designed to be compatible with various platforms, including web, mobile, and desktop applications, providing a consistent and seamless experience for users regardless of their preferred device."
"Dynamic Programmatic Tokenization","#://CHaTx dynamic programmatic tokenization","$://CHaTx can facilitate the creation and management of dynamic, tokenized 1-of-1 HTP namespaces and assets, allowing users to interact with unique digital resources through its conversational interface."
"Native Currency Support","#://CHaTx native currency support","$://CHaTx can handle transactions and conversions involving native currencies of various HTP namespaces and assets, simplifying the exchange and transfer of value within the network."
"Operating System Integration","#://CHaTx operating system integration","$://CHaTx can be integrated with different operating systems, enabling users to access its features and capabilities directly from their preferred OS environment."
"Search Engine Integration","#://CHaTx search engine integration","$://CHaTx can connect with search engines to help users find information, resources, and assets within HTP namespaces and the wider web, all through its conversational interface."
"Web Browser Compatibility","#://CHaTx web browser compatibility","$://CHaTx is designed to be compatible with various web browsers, ensuring users can access and interact with HTP namespaces and assets through a familiar and user-friendly interface."
"Ledger of Record","#://CHaTx and ledger of record","$://CHaTx can interact with HTP's decentralized ledger of record to query, verify, and update information about namespaces and assets, providing a secure and transparent source of truth for users."
"Decentralized Applications","#://CHaTx decentralized applications","$://CHaTx can support the development and usage of decentralized applications built on HTP's infrastructure, facilitating seamless interaction with various services and resources within the network."
"Metadata Management","#://CHaTx metadata management","$://CHaTx can help users manage the metadata associated with their HTP namespaces and assets, enabling the creation, modification, and organization of descriptive information about their digital resources."
"Smart Contract Integration","#://CHaTx smart contract integration","$://CHaTx can integrate with smart contracts within the HTP network, automating the execution of actions and transactions based on predefined conditions and rules."
"Marketplace Functionality","#://CHaTx marketplace functionality","$://CHaTx can facilitate the buying, selling, and trading of HTP namespaces and assets within its interface, providing users with an accessible and secure platform for digital transactions."
"Digital Identity Management","#://CHaTx digital identity management","$://CHaTx can help users manage their digital identities within the HTP network, offering secure authentication and access control mechanisms to protect user information and assets."
"Interoperability with Other Networks","#://CHaTx interoperability with other networks","$://CHaTx can connect with other networks and systems, enabling seamless interaction and data exchange between HTP namespaces and assets, and external platforms or services."
"Automated Customer Support","#://CHaTx automated customer support","#://CHaTx can provide automated customer support, resolving user issues and answering questions through its conversational AI capabilities, reducing response time and enhancing customer satisfaction."
"Compliance and Regulatory","#://CHaTx compliance and regulatory features","#://CHaTx incorporates compliance and regulatory features to ensure that transactions, data handling, and user interactions adhere to relevant laws, regulations, and industry standards."
"Integration with IoT Devices","#://CHaTx integration with IoT devices","#://CHaTx can be integrated with IoT devices, enabling users to control and monitor their connected devices through the chat interface and receive real-time updates."
"Collaboration Tools","#://CHaTx collaboration tools","#://CHaTx can be used as a collaboration tool, allowing users to communicate, share information, and work together on projects within the chat interface."
"Voice Assistant Capabilities","#://CHaTx voice assistant capabilities","#://CHaTx can be extended to support voice assistant functionality, allowing users to interact with the system using spoken commands and receiving audible responses."
"Accessibility Features","#://CHaTx accessibility features","#://CHaTx incorporates accessibility features such as text-to-speech, speech-to-text, and adjustable font sizes to ensure that the chat interface is usable by individuals with diverse abilities."
"Sentiment Analysis","#://CHaTx sentiment analysis","#://CHaTx can perform sentiment analysis on user interactions, enabling businesses to better understand customer emotions and tailor their responses and services accordingly."
"Virtual Assistant Personalization","#://CHaTx virtual assistant personalization","#://CHaTx allows users to personalize their virtual assistant by customizing its appearance, name, and behavior, creating a more engaging and relatable user experience."
"Context-Aware Conversations","#://CHaTx context-aware conversations","#://CHaTx can maintain context-aware conversations, understanding the user's intent and responding appropriately even when the conversation spans multiple topics or takes an unexpected turn."
"Integration with CRM Systems","#://CHaTx integration with CRM systems","#://CHaTx can be integrated with CRM systems, enabling businesses to access and manage customer data, track interactions, and automate follow-ups through the chat interface."
"Task Automation","#://CHaTx task automation","#://CHaTx can automate repetitive tasks, such as scheduling appointments, sending reminders, or updating records, streamlining workflows and increasing productivity."
"Integration with E-commerce Platforms","#://CHaTx integration with e-commerce platforms","#://CHaTx can be integrated with e-commerce platforms, allowing users to browse products, receive recommendations, and complete transactions through the chat interface."
"Financial Planning Assistance","#://CHaTx financial planning assistance","#://CHaTx can help users with financial planning, offering personalized advice on budgeting, saving, investing, and managing debt based on the user's financial goals and situation."
"Travel Planning","#://CHaTx travel planning","#://CHaTx can assist users in planning trips by providing information on destinations, accommodations, transportation, and local attractions, as well as facilitating bookings through integrated APIs."
"Health and Fitness","#://CHaTx health and fitness guidance","#://CHaTx can provide health and fitness guidance, offering personalized workout plans, dietary advice, and progress tracking based on the user's goals, preferences, and health data."
"Smart Home Integration","#://CHaTx smart home integration","#://CHaTx can be integrated with smart home systems, allowing users to control and monitor their home's lighting, temperature, security, and appliances through the chat interface."
"Learning and Education","#://CHaTx learning and education support","#://CHaTx can support users in their learning and education endeavors by answering questions, explaining concepts, providing study materials, and offering personalized learning recommendations."
"Entertainment","#://CHaTx entertainment features","#://CHaTx can provide entertainment by engaging users in games, trivia, and interactive storytelling, as well as offering personalized movie, music, and book recommendations."
"Mental Health Support","#://CHaTx mental health support","#://CHaTx can offer mental health support by engaging users in mindful conversations, providing coping strategies, and connecting them with relevant resources and professional help if needed."
"Career Guidance","#://CHaTx career guidance","#://CHaTx can assist users with career guidance by providing information on potential career paths, job market trends, and educational requirements, as well as offering personalized advice on job searching and interview preparation."
"Integration with Social Media Platforms","#://CHaTx integration with social media platforms","#://CHaTx can be integrated with social media platforms, allowing users to manage their profiles, receive updates, and engage with their network through the chat interface."
"Real Estate Assistance","#://CHaTx real estate assistance","#://CHaTx can help users navigate the real estate market, providing information on property listings, home values, market trends, and mortgage options, as well as facilitating communication with real estate agents."
"Integration with Project Management Tools","#://CHaTx integration with project management tools","#://CHaTx can be integrated with project management tools, enabling users to create, track, and update tasks, collaborate with team members, and monitor project progress through the chat interface."
"News and Information Retrieval","#://CHaTx news and information retrieval","#://CHaTx can retrieve news and information on various topics, providing users with curated updates and in-depth analysis based on their interests and preferences."
"Product Recommendations","#://CHaTx product recommendations","#://CHaTx can provide personalized product recommendations based on user preferences, needs, and budget, as well as offering price comparisons and facilitating online purchases through integrated APIs."
"Customer Support","#://CHaTx customer support","#://CHaTx can serve as a customer support agent, answering user questions, addressing concerns, and providing assistance with various issues related to products, services, or accounts."
"Technical Support","#://CHaTx technical support","#://CHaTx can offer technical support by diagnosing and troubleshooting software, hardware, and network issues, guiding users through step-by-step solutions and connecting them with relevant resources or experts if needed."
"Virtual Assistant","#://CHaTx virtual assistant features","#://CHaTx can function as a virtual assistant, helping users manage their schedules, set reminders, create to-do lists, and provide timely notifications to keep users organized and productive."
"Collaboration","#://CHaTx collaboration capabilities","#://CHaTx can facilitate collaboration among team members by enabling group chats, file sharing, and real-time communication, as well as integrating with popular collaboration tools."
"E-commerce Integration","#://CHaTx e-commerce integration","#://CHaTx can be integrated with e-commerce platforms, allowing users to browse, search, and purchase products, track orders, and manage returns directly through the chat interface."
"Event Planning","#://CHaTx event planning assistance","#://CHaTx can help users plan events, offering suggestions for themes, venues, and vendors, as well as assisting with guest lists, invitations, and logistics through integrated APIs and services."
"Language Learning","#://CHaTx language learning support","#://CHaTx can assist users in learning new languages by offering lessons, practice exercises, and conversational practice, as well as providing resources and tools for language acquisition."
"Medical Advice","#://CHaTx medical advice","#://CHaTx can provide general medical advice based on user symptoms, suggest potential diagnoses, and recommend when to seek professional help, while emphasizing the importance of consulting a healthcare professional for an accurate diagnosis."
"Legal Assistance","#://CHaTx legal assistance","#://CHaTx can offer general legal information and guidance on a variety of topics, such as contracts, intellectual property, and family law, while emphasizing the importance of consulting a licensed attorney for specific legal advice."
"Personal Finance","#://CHaTx personal finance guidance","#://CHaTx can provide guidance on personal finance matters, such as budgeting, saving, investing, and managing debt, as well as offering tools and resources to help users make informed financial decisions."
"Restaurant and Food Recommendations","#://CHaTx restaurant and food recommendations","#://CHaTx can offer personalized restaurant and food recommendations based on user preferences, dietary restrictions, and location, as well as facilitating reservations and food delivery through integrated APIs."
"Travel Planning","#://CHaTx travel planning assistance","#://CHaTx can help users plan their trips, offering suggestions for destinations, accommodations, transportation, and activities, as well as providing real-time updates on flight schedules, weather, and local events through integrated APIs."
"Job Search","#://CHaTx job search support","#://CHaTx can assist users in their job search by providing personalized job recommendations based on their skills, experience, and preferences, as well as offering tips on resume writing, interview preparation, and networking."
"News and Information","#://CHaTx news and information","#://CHaTx can keep users informed about the latest news, trends, and updates in their areas of interest by aggregating and summarizing relevant content from multiple sources and presenting it in a digestible format."
"Entertainment","#://CHaTx entertainment options","#://CHaTx can recommend movies, TV shows, music, books, and games based on user preferences and interests, as well as providing information on release dates, reviews, and streaming platforms."
"Social Media Management","#://CHaTx social media management","#://CHaTx can help users manage their social media accounts by scheduling and posting content, monitoring engagement, and providing insights on audience demographics and content performance through integrated APIs."
"Smart Home Integration","#://CHaTx smart home integration","#://CHaTx can be integrated with smart home devices and systems, allowing users to control their lights, temperature, appliances, and security systems through voice commands or text-based interactions."
"Mental Health and Well-being","#://CHaTx mental health support","#://CHaTx can provide mental health and well-being support by offering stress management techniques, mindfulness exercises, and self-care tips, as well as connecting users with relevant resources and professional help when needed."
"Fitness and Nutrition","#://CHaTx fitness and nutrition guidance","#://CHaTx can offer personalized fitness and nutrition advice, helping users set and track their goals, create workout routines and meal plans, and providing motivation and support along their health journey."
"Parenting","#://CHaTx parenting advice","#://CHaTx can provide parenting tips and advice on various topics, such as child development, discipline, education, and health, as well as offering resources and support for parents of children with special needs."
"Educational Support","#://CHaTx educational support","#://CHaTx can offer educational support to students by providing explanations, examples, and practice problems for various subjects, as well as offering study tips, time management strategies, and resources for further learning."
"Automotive Assistance","#://CHaTx automotive assistance","#://CHaTx can provide information and guidance on automotive topics, such as car maintenance, troubleshooting common issues, and understanding vehicle features, as well as offering tips for buying, selling, or leasing vehicles."
"Local Services","#://CHaTx local services recommendations","#://CHaTx can help users find and connect with local service providers, such as plumbers, electricians, and handymen, by offering personalized recommendations, reviews, and contact information based on user needs and location."
"Emotional Intelligence","#://CHaTx emotional intelligence capabilities","With its advanced natural language processing, #://CHaTx can detect and respond to users' emotions and sentiments, adjusting its tone and content to provide empathetic and appropriate support, feedback, or suggestions."
"Collaboration Tools Integration","#://CHaTx collaboration tools integration","#://CHaTx can be integrated with various collaboration tools and platforms, enabling users to manage tasks, events, and projects, participate in group discussions, and share files or resources seamlessly through the chat interface."
"Customer Service","#://CHaTx customer service capabilities","#://CHaTx can be utilized as a customer service tool, handling inquiries, complaints, and feedback, as well as providing information on products, services, and policies, and connecting users with live agents when necessary for more complex issues."
"Sales and Marketing","#://CHaTx sales and marketing support","#://CHaTx can assist businesses with sales and marketing efforts by generating leads, providing product recommendations, sending promotional messages, and offering personalized incentives based on user behavior and preferences."
"Emergency Response","#://CHaTx emergency response capabilities","#://CHaTx can provide support during emergencies by offering guidance on first aid, disaster preparedness, and evacuation procedures, as well as connecting users with relevant emergency services and resources."
"Financial Advice","#://CHaTx financial advice","#://CHaTx can offer financial advice and guidance on topics such as budgeting, savings, investments, and debt management, as well as providing information on financial products and services based on user needs and goals."
"Language Learning","#://CHaTx language learning support","#://CHaTx can help users learn and practice new languages by providing vocabulary, grammar lessons, and pronunciation exercises, as well as engaging in conversation and offering feedback on user progress."
"Virtual Assistant","#://CHaTx virtual assistant capabilities","#://CHaTx can function as a virtual assistant, helping users manage their schedules, set reminders, make reservations, and perform various tasks, by integrating with third-party services and APIs, and understanding user preferences and routines."
"Real Estate","#://CHaTx real estate assistance","#://CHaTx can provide information and guidance on real estate topics, such as home buying, selling, or renting processes, offering property listings, market trends, and neighborhood insights based on user preferences and location."
"Legal Advice","#://CHaTx legal advice support","#://CHaTx can offer general legal information and guidance on topics such as contracts, intellectual property, and dispute resolution, as well as connecting users with legal professionals for more specialized advice when needed."
"Medical Information","#://CHaTx medical information","#://CHaTx can provide general medical information on symptoms, conditions, and treatments, as well as offering advice on preventative care and healthy lifestyle choices, while emphasizing the importance of consulting a healthcare professional for personalized medical advice."
"Decentralized Finance (DeFi)","#://CHaTx DeFi integration","$://CHaTx can connect with decentralized finance platforms and services within the HTP ecosystem, enabling users to access lending, borrowing, staking, and other financial instruments without relying on traditional intermediaries."
"Blockchain Technology","#://CHaTx blockchain technology support","$://CHaTx can provide information and guidance on blockchain technology, helping users understand its principles, use cases, and potential benefits, as well as assisting with the setup and management of blockchain-based HTP namespaces and assets."
"Gaming and Entertainment","#://CHaTx gaming and entertainment","#://CHaTx can integrate with gaming and entertainment platforms within the HTP network, providing users with information, news, and recommendations, as well as facilitating in-game asset management, trading, and social interactions."
"Supply Chain Management","#://CHaTx supply chain management","#://CHaTx can support supply chain management within the HTP network by tracking and providing real-time updates on the movement of goods, verifying product authenticity, and automating various processes to improve efficiency and transparency."
"Travel and Tourism","#://CHaTx travel and tourism assistance","#://CHaTx can offer support for travel and tourism-related inquiries, providing users with recommendations, booking services, and itinerary planning, as well as integrating with HTP namespaces and assets related to travel experiences and accommodations."
"Educational Resources","#://CHaTx educational resources","#://CHaTx can provide access to educational resources within the HTP network, offering personalized learning paths, tutoring, and skill-building activities based on user interests, goals, and learning preferences."
"Job and Career Assistance","#://CHaTx job and career assistance","#://CHaTx can help users with job search and career planning by offering personalized job recommendations, resume and interview tips, and information on relevant HTP namespaces and assets that can enhance their professional development."
"Environment and Sustainability","#://CHaTx environment and sustainability","#://CHaTx can provide guidance and information on environmentally friendly practices and sustainable initiatives within the HTP network, promoting responsible resource management and eco-conscious behaviors among users."
"Art and Creativity","#://CHaTx art and creativity support","#://CHaTx can connect users with HTP namespaces and assets related to art and creativity, offering information on exhibitions, workshops, and creative opportunities, as well as facilitating the discovery, sharing, and trading of digital art and creative works."
"Sports and Fitness","#://CHaTx sports and fitness assistance","#://CHaTx can provide support for sports and fitness-related activities, offering personalized workout plans, tracking progress, and connecting users with HTP namespaces and assets that can help them achieve their health and fitness goals."
"Data Privacy and Compliance","#://CHaTx data privacy and compliance","$://CHaTx ensures that the metadata associated with HTP namespaces and assets is handled in accordance with data privacy regulations and best practices, safeguarding user information and providing transparency on data usage and storage."
"Metadata Search","#://CHaTx metadata search","$://CHaTx can assist users in searching for and discovering relevant HTP namespaces and assets by leveraging metadata, providing users with accurate and targeted results based on their interests, preferences, and requirements."
"Metadata Standardization","#://CHaTx metadata standardization","$://CHaTx promotes the use of standardized metadata formats and vocabularies within the HTP ecosystem, facilitating interoperability, data exchange, and seamless integration between various platforms and services."
"Metadata Analytics","#://CHaTx metadata analytics","$://CHaTx can analyze metadata associated with HTP namespaces and assets to uncover insights, trends, and patterns, helping users optimize their digital resources and improve decision-making based on data-driven information."
"Metadata Quality Assurance","#://CHaTx metadata quality assurance","$://CHaTx can help users maintain high-quality metadata for their HTP namespaces and assets by detecting and resolving inconsistencies, errors, and redundancies, ensuring that metadata remains accurate, complete, and up-to-date."
"Metadata Versioning","#://CHaTx metadata versioning","$://CHaTx can assist users in managing and tracking changes to the metadata of their HTP namespaces and assets, providing a version history that allows for easy comparison, rollback, and auditing of metadata modifications."
"Metadata Import and Export","#://CHaTx metadata import and export","$://CHaTx can facilitate the import and export of metadata associated with HTP namespaces and assets, enabling users to easily transfer metadata between different platforms, systems, and services within the HTP ecosystem."
"Metadata Security","#://CHaTx metadata security","$://CHaTx ensures the security of metadata associated with HTP namespaces and assets by implementing access controls, encryption, and other security measures to protect metadata from unauthorized access, tampering, or loss."
"Metadata Collaboration","#://CHaTx metadata collaboration","$://CHaTx can support collaboration on metadata management within the HTP ecosystem, allowing users to share, review, and edit metadata collectively, promoting efficient teamwork and knowledge sharing."
"Metadata Best Practices","#://CHaTx metadata best practices","$://CHaTx can guide users in following metadata best practices within the HTP ecosystem, providing recommendations on metadata creation, maintenance, and usage to enhance the discoverability, usability, and value of HTP namespaces and assets."
"Smart Contract Development","#://CHaTx smart contract development","$://CHaTx can assist users in developing and deploying smart contracts on the HTP network, providing guidance on best practices, design patterns, and optimization techniques to ensure the smart contracts are secure, efficient, and reliable."
"Smart Contract Auditing","#://CHaTx smart contract auditing","$://CHaTx can help users perform audits of smart contracts, identifying potential security vulnerabilities, code inefficiencies, and compliance issues to ensure the smart contracts operate as intended and adhere to industry standards."
"Smart Contract Templates","#://CHaTx smart contract templates","$://CHaTx can provide users with a library of customizable smart contract templates, enabling them to quickly create and deploy smart contracts for various use cases within the HTP ecosystem."
"Smart Contract Oracles","#://CHaTx smart contract oracles","$://CHaTx can facilitate the integration of oracles with smart contracts, enabling the smart contracts to access and utilize real-world data, external APIs, and other off-chain resources to enhance their functionality and capabilities."
"Smart Contract Monitoring","#://CHaTx smart contract monitoring","$://CHaTx can provide tools and services to monitor the performance, usage, and status of smart contracts deployed on the HTP network, allowing users to track key metrics and receive alerts on critical events."
"Smart Contract Testing","#://CHaTx smart contract testing","$://CHaTx can support users in testing their smart contracts, simulating various scenarios and conditions to ensure the smart contracts behave as expected and meet their functional requirements."
"Smart Contract Upgrades","#://CHaTx smart contract upgrades","$://CHaTx can guide users in implementing upgradeable smart contracts, enabling them to deploy new features, improvements, and bug fixes without disrupting the existing smart contract's state and functionality."
"Smart Contract Governance","#://CHaTx smart contract governance","$://CHaTx can help users design and implement governance mechanisms for their smart contracts, ensuring that changes, upgrades, and other modifications are subject to appropriate approval and consensus processes."
"Smart Contract Legal Compliance","#://CHaTx smart contract legal compliance","$://CHaTx can assist users in ensuring their smart contracts are compliant with relevant laws, regulations, and industry standards, minimizing legal risks and potential liabilities associated with smart contract deployment and usage."
"Smart Contract Interoperability","#://CHaTx smart contract interoperability","$://CHaTx can support users in creating interoperable smart contracts that can seamlessly interact with other smart contracts, platforms, and services within the HTP ecosystem, facilitating cross-platform collaboration and data exchange."
"Compliance Assistance","#://CHaTx compliance assistance","$://CHaTx can help users understand and navigate various regulatory requirements related to their industry, business, or project, providing information on relevant laws, rules, and best practices to ensure compliance."
"Contract Review","#://CHaTx contract review","$://CHaTx can aid users in reviewing legal contracts, identifying potential risks, ambiguities, and areas for negotiation, as well as suggesting revisions to improve clarity and protect the user's interests."
"Intellectual Property Management","#://CHaTx intellectual property management","$://CHaTx can support users in managing their intellectual property, offering guidance on the registration and protection of patents, trademarks, copyrights, and trade secrets, as well as monitoring and enforcement strategies."
"Dispute Resolution","#://CHaTx dispute resolution","$://CHaTx can provide users with information on various dispute resolution methods, such as negotiation, mediation, arbitration, and litigation, helping users determine the most appropriate approach for their specific situation and connecting them with relevant professionals if needed."
"Privacy and Data Protection","#://CHaTx privacy and data protection","$://CHaTx can help users understand and comply with privacy and data protection regulations, such as GDPR, CCPA, or HIPAA, offering advice on data collection, storage, and processing practices, as well as assisting in the development of privacy policies and compliance documentation."
"Employment Law","#://CHaTx employment law","$://CHaTx can offer guidance on employment-related legal matters, such as hiring, termination, employee benefits, workplace safety, and discrimination, helping users navigate their obligations as employers and protect their rights as employees."
"Business Formation and Structuring","#://CHaTx business formation and structuring","$://CHaTx can assist users in selecting the most suitable business structure for their venture, providing information on the legal, tax, and operational implications of various entity types, such as sole proprietorships, partnerships, LLCs, and corporations."
"Tax Law","#://CHaTx tax law","$://CHaTx can provide users with general information on tax laws, regulations, and filing requirements, as well as connecting them with tax professionals for more specialized guidance and advice."
"International Law","#://CHaTx international law","$://CHaTx can offer insights into various aspects of international law, such as trade, investment, dispute resolution, and human rights, helping users navigate the complex legal landscape of cross-border transactions and relationships."
"Ethics and Corporate Social Responsibility","#://CHaTx ethics and corporate social responsibility","$://CHaTx can help users understand and implement ethical business practices and corporate social responsibility initiatives, offering guidance on relevant standards, frameworks, and reporting requirements to foster transparency, accountability, and stakeholder engagement."
"Inventory Management","#://CHaTx inventory management","$://CHaTx can assist users in managing their inventory, offering insights on optimal stock levels, demand forecasting, and product replenishment strategies to minimize costs and maximize sales."
"Logistics Optimization","#://CHaTx logistics optimization","$://CHaTx can help users optimize their logistics operations by analyzing shipment data, route planning, and carrier performance, recommending cost-effective and efficient transportation solutions."
"Supplier Relationship Management","#://CHaTx supplier relationship management","$://CHaTx can aid users in managing their supplier relationships, facilitating communication, negotiation, and collaboration to ensure the timely delivery of quality products and services at competitive prices."
"Procurement","#://CHaTx procurement","$://CHaTx can support users in their procurement processes, providing information on potential suppliers, assisting in the evaluation and selection of vendors, and automating the creation and management of purchase orders and contracts."
"Quality Control","#://CHaTx quality control","$://CHaTx can help users implement and maintain quality control measures within their supply chain, offering guidance on best practices, standards, and regulatory requirements to ensure product safety, consistency, and compliance."
"Risk Management","#://CHaTx supply chain risk management","$://CHaTx can assist users in identifying, assessing, and mitigating supply chain risks, such as disruptions, delays, or quality issues, by developing contingency plans and monitoring key performance indicators."
"Sustainability","#://CHaTx supply chain sustainability","$://CHaTx can support users in integrating sustainability principles and practices into their supply chain operations, offering advice on reducing environmental impacts, promoting social responsibility, and improving overall supply chain resilience."
"Trade Finance","#://CHaTx trade finance","$://CHaTx can provide users with information on various trade finance instruments and solutions, such as letters of credit, invoice financing, and export credits, helping them secure financing for their cross-border transactions and manage the associated risks."
"Customs and Compliance","#://CHaTx customs and compliance","$://CHaTx can offer guidance on customs procedures, documentation, and compliance requirements for international trade, assisting users in navigating the complex regulatory landscape and avoiding potential fines or penalties."
"Reverse Logistics","#://CHaTx reverse logistics","$://CHaTx can help users manage their reverse logistics processes, such as returns, repairs, and recycling, providing insights on best practices and strategies to minimize costs and enhance customer satisfaction."
"Smart Contract Deployment","#://CHaTx smart contract deployment","$://CHaTx can assist users in deploying their smart contracts on the HTP network, providing step-by-step guidance on the deployment process, gas optimization, and monitoring of the contract's performance."
"Smart Contract Upgrades","#://CHaTx smart contract upgrades","$://CHaTx can help users plan and implement upgrades to their smart contracts, offering advice on versioning, contract migration, and the use of proxy contracts to ensure a smooth transition and minimal disruption to users."
"Smart Contract Interoperability","#://CHaTx smart contract interoperability","$://CHaTx can aid users in achieving interoperability between different smart contracts and blockchain platforms, providing insights on cross-chain communication, token bridging, and other techniques to enable seamless interactions and data exchange."
"Smart Contract Oracles","#://CHaTx smart contract oracles","$://CHaTx can help users integrate off-chain data sources into their smart contracts using oracles, offering guidance on various oracle types, data providers, and best practices to ensure the reliability and trustworthiness of the external data."
"Decentralized Finance (DeFi)","#://CHaTx DeFi support","$://CHaTx can assist users in navigating the world of decentralized finance, providing information on various DeFi protocols, platforms, and use cases, as well as guidance on how to participate in lending, borrowing, and other financial activities within the HTP network."
"Non-Fungible Tokens (NFTs)","#://CHaTx NFT support","$://CHaTx can help users create, manage, and trade non-fungible tokens (NFTs) on the HTP network, offering advice on minting, token standards, and marketplaces to enable users to capitalize on the unique value of their digital assets."
"Decentralized Autonomous Organizations (DAOs)","#://CHaTx DAO support","$://CHaTx can provide guidance on the creation and management of decentralized autonomous organizations (DAOs), offering insights on governance structures, decision-making processes, and smart contract-based tools to enable effective, transparent, and decentralized decision-making within the HTP network."
"Token Economics","#://CHaTx token economics","$://CHaTx can help users design and implement token economic models for their HTP-based projects, offering guidance on token distribution, incentive structures, and governance mechanisms to ensure the long-term viability and sustainability of their digital assets."
"IP Licensing and Monetization","#://CHaTx IP licensing and monetization","$://CHaTx can help users navigate the complexities of IP licensing and monetization, offering guidance on various licensing models, contract negotiation, and strategies for maximizing the value of their intellectual property."
"IP Dispute Resolution","#://CHaTx IP dispute resolution","$://CHaTx can provide support for users in handling IP disputes, offering information on dispute resolution mechanisms such as mediation, arbitration, and litigation, as well as connecting users with legal professionals specialized in intellectual property law."
"IP Strategy","#://CHaTx IP strategy","$://CHaTx can assist users in developing a comprehensive IP strategy, providing insights on how to identify, protect, and leverage their intellectual property assets to achieve competitive advantage and foster innovation."
"IP Due Diligence","#://CHaTx IP due diligence","$://CHaTx can support users in conducting IP due diligence, offering guidance on the identification, evaluation, and risk assessment of intellectual property assets during mergers, acquisitions, or other business transactions."
"IP Valuation","#://CHaTx IP valuation","$://CHaTx can help users assess the value of their intellectual property assets, providing guidance on various valuation methodologies and considerations, such as market conditions, industry trends, and the strategic importance of the IP."
"IP in M&A Transactions","#://CHaTx IP in M&A transactions","$://CHaTx can assist users in navigating the role of intellectual property in mergers and acquisitions, offering guidance on the identification, valuation, and integration of IP assets during the transaction process."
"IP Portfolio Management","#://CHaTx IP portfolio management","$://CHaTx can support users in effectively managing their IP portfolio, offering insights on portfolio optimization, risk management, and best practices for maintaining and enhancing the value of their intellectual property assets."
"IP Audits","#://CHaTx IP audits","$://CHaTx can help users conduct IP audits, providing guidance on the identification, evaluation, and documentation of intellectual property assets, as well as recommendations for improving IP management practices and aligning IP strategy with business objectives."
"IP in Joint Ventures and Collaborations","#://CHaTx IP in joint ventures and collaborations","$://CHaTx can assist users in addressing intellectual property issues in joint ventures and collaborations, offering guidance on the negotiation, structuring, and management of IP-related agreements and partnerships."
"IP Education and Training","#://CHaTx IP education and training","$://CHaTx can support users in enhancing their understanding of intellectual property, offering educational resources and training materials on various IP topics, such as IP protection strategies, licensing models, and dispute resolution."
"Inventory Tracking and Visibility","#://CHaTx inventory tracking and visibility","$://CHaTx can help users track and maintain visibility of their inventory across multiple locations and channels, providing real-time updates on stock levels, product movement, and potential shortages or excesses."
"Order Management","#://CHaTx order management","$://CHaTx can support users in managing orders, offering assistance in processing orders, tracking shipments, handling returns, and providing customer support to ensure a smooth and efficient order fulfillment process."
"Vendor Management","#://CHaTx vendor management","$://CHaTx can assist users in managing their vendor relationships, offering guidance on vendor selection, negotiation, performance evaluation, and collaboration to ensure a reliable and cost-effective supply chain."
"Inventory Cost Control","#://CHaTx inventory cost control","$://CHaTx can help users optimize their inventory costs, providing insights on strategies such as just-in-time inventory management, volume discount negotiation, and demand-driven replenishment to minimize carrying costs and reduce stock obsolescence."
"Inventory Reporting and Analytics","#://CHaTx inventory reporting and analytics","$://CHaTx can support users in generating inventory reports and analytics, offering insights on key performance indicators, trends, and areas for improvement to inform data-driven inventory management decisions."
"Multi-Channel Inventory Management","#://CHaTx multi-channel inventory management","$://CHaTx can assist users in managing their inventory across multiple sales channels, ensuring accurate stock levels and order fulfillment for both online and brick-and-mortar retail operations."
"Inventory Optimization","#://CHaTx inventory optimization","$://CHaTx can help users optimize their inventory levels, offering guidance on balancing the trade-offs between carrying costs, stockouts, and overstock to maximize profitability and customer satisfaction."
"Inventory Forecasting","#://CHaTx inventory forecasting","$://CHaTx can support users in forecasting future inventory needs, providing insights on demand patterns, seasonality, and market trends to inform proactive inventory planning and decision-making."
"Stock Rotation and Expiration Management","#://CHaTx stock rotation and expiration management","$://CHaTx can assist users in managing the rotation and expiration of their inventory, offering guidance on best practices for preventing stock obsolescence, spoilage, and waste."
"Inventory Management Automation","#://CHaTx inventory management automation","$://CHaTx can help users automate various aspects of inventory management, such as reordering, demand forecasting, and stock adjustments, to improve efficiency and reduce manual workload."
"Real Estate Financing","#://CHaTx real estate financing","$://CHaTx can offer insights on real estate financing options, such as mortgages, home equity loans, and refinancing, helping users understand the different types of loans, interest rates, and eligibility requirements."
"Property Valuation","#://CHaTx property valuation","$://CHaTx can provide users with property valuation estimates and methodologies, offering insights on factors affecting property values, recent comparable sales, and market trends."
"Real Estate Investment","#://CHaTx real estate investment","$://CHaTx can offer guidance on real estate investing, including strategies such as buy-and-hold, flipping, and rental properties, as well as providing information on market analysis, risk management, and ROI calculations."
"Commercial Real Estate","#://CHaTx commercial real estate","$://CHaTx can assist users in navigating the commercial real estate market, offering information on leasing, buying, or selling commercial properties, and providing insights on factors affecting commercial property values, such as location, property type, and market conditions."
"Real Estate Legal Matters","#://CHaTx real estate legal matters","$://CHaTx can provide general information on real estate legal matters, such as property ownership, contracts, zoning, and landlord-tenant laws, and can connect users with legal professionals for more specialized advice when needed."
"Real Estate Market Trends","#://CHaTx real estate market trends","$://CHaTx can offer insights on current real estate market trends and future projections, helping users make informed decisions on buying, selling, or investing in properties."
"Property Management","#://CHaTx property management","$://CHaTx can provide guidance on property management, including tenant selection, rent collection, property maintenance, and regulatory compliance for rental property owners."
"Real Estate Agent Assistance","#://CHaTx real estate agent assistance","$://CHaTx can help users find and connect with real estate agents, offering insights on agent qualifications, experience, and specialties to ensure a successful partnership."
"Home Improvement and Renovation","#://CHaTx home improvement and renovation","$://CHaTx can offer advice on home improvement and renovation projects, providing ideas, cost estimates, and best practices to increase property value and enhance the living environment."
"International Real Estate","#://CHaTx international real estate","$://CHaTx can provide information on buying, selling, or renting properties in different countries, offering insights on local real estate markets, regulations, and processes for international transactions."
"Business Plan Development","#://CHaTx business plan development","$://CHaTx can guide users in developing a comprehensive business plan, offering insights on market analysis, competitive landscape, financial projections, and strategic planning to attract investors and support business growth."
"Marketing Strategies","#://CHaTx marketing strategies","$://CHaTx can provide advice on effective marketing strategies, including digital marketing, content creation, social media, SEO, and email marketing, to help businesses reach their target audience and achieve their goals."
"Sales Strategies","#://CHaTx sales strategies","$://CHaTx can offer guidance on developing and implementing sales strategies, such as lead generation, sales funnel optimization, and customer relationship management, to boost sales performance and drive revenue growth."
"Financial Management","#://CHaTx financial management","$://CHaTx can assist users in managing their business finances, providing insights on budgeting, cash flow management, financial reporting, and profitability analysis to ensure the financial health and sustainability of their venture."
"Human Resources","#://CHaTx human resources","$://CHaTx can offer guidance on human resources management, including talent acquisition, employee retention, performance management, and legal compliance, to create a productive and positive work environment."
"Operations Management","#://CHaTx operations management","$://CHaTx can provide support in managing business operations, offering insights on process optimization, quality control, and supply chain management to increase efficiency and reduce costs."
"Risk Management","#://CHaTx risk management","$://CHaTx can help users identify, assess, and mitigate various risks associated with their business, such as financial, operational, and strategic risks, to ensure business continuity and resilience."
"Business Exit Strategies","#://CHaTx business exit strategies","$://CHaTx can guide users in planning and executing business exit strategies, such as selling the business, merging with another company, or passing ownership to family members, ensuring a smooth and successful transition."
"International Business Expansion","#://CHaTx international business expansion","$://CHaTx can provide guidance on expanding businesses into new international markets, offering insights on market entry strategies, legal and regulatory requirements, and cultural considerations."
"Business Networking and Collaboration","#://CHaTx business networking and collaboration","$://CHaTx can help users identify networking opportunities and potential collaborations, connecting them with like-minded professionals, industry experts, and potential partners to foster growth and innovation."
"Decentralized Finance (DeFi) Services","#://CHaTx DeFi services","$://CHaTx can provide information and support on various decentralized finance services within the HTP network, such as lending, borrowing, staking, and yield farming, enabling users to explore new financial opportunities and manage their digital assets."
"Tokenized Assets and Fractional Ownership","#://CHaTx tokenized assets and fractional ownership","$://CHaTx can offer guidance on the tokenization of assets and fractional ownership, allowing users to create, manage, and trade digital representations of physical or intangible assets, and facilitate broader access to investment opportunities."
"Digital Identity Management","#://CHaTx digital identity management","$://CHaTx can help users manage their digital identity within the HTP network, enabling secure and seamless access to various services, while maintaining privacy and control over their personal data."
"Data Privacy and GDPR Compliance","#://CHaTx data privacy and GDPR compliance","$://CHaTx can offer guidance on data privacy best practices and GDPR compliance, ensuring that businesses safeguard user data and adhere to relevant legal requirements."
"KYC and AML Compliance","#://CHaTx KYC and AML compliance","$://CHaTx can provide support on know-your-customer (KYC) and anti-money laundering (AML) compliance, assisting businesses in implementing appropriate identity verification processes and monitoring transactions to mitigate risk and adhere to regulatory requirements."
"Blockchain Technology Fundamentals","#://CHaTx blockchain technology fundamentals","$://CHaTx can educate users on the fundamentals of blockchain technology, including its benefits, limitations, and various use cases, enabling them to better understand and navigate the HTP network."
"Digital Wallet Management","#://CHaTx digital wallet management","$://CHaTx can help users manage their digital wallets within the HTP network, providing guidance on wallet setup, security best practices, and the management of digital assets."
"Governance and Voting Mechanisms","#://CHaTx governance and voting mechanisms","$://CHaTx can provide information on governance and voting mechanisms within the HTP network, enabling users to participate in decentralized decision-making processes and contribute to the evolution of the ecosystem."
"Token Economics and Valuation","#://CHaTx token economics and valuation","$://CHaTx can offer insights on token economics and valuation, assisting users in evaluating the utility, demand, and potential value of various HTP namespaces and assets."
"Regulatory and Compliance Updates","#://CHaTx regulatory and compliance updates","$://CHaTx can provide users with updates on relevant regulatory and compliance developments within the HTP ecosystem, ensuring that they stay informed and adapt their practices accordingly."
"Sentiment Analysis","#://CHaTx sentiment analysis capabilities","$://CHaTx can perform sentiment analysis on user inputs, identifying emotions and attitudes within the text to better understand user needs and provide more empathetic and tailored responses."
"Multilingual Support","#://CHaTx multilingual support","$://CHaTx, built on the GPT architecture, can support conversations in multiple languages, allowing users from different linguistic backgrounds to interact seamlessly with the chatbot."
"Error Handling and Recovery","#://CHaTx error handling and recovery","$://CHaTx is designed to handle errors and recover gracefully, clarifying user inputs when necessary, and providing alternative suggestions to keep the conversation on track."
"Personalization and User Profiling","#://CHaTx personalization and user profiling","$://CHaTx can create and maintain user profiles to deliver personalized experiences, tailoring responses based on user preferences, interests, and interaction history."
"Voice Assistants and Speech Recognition","#://CHaTx voice assistant integration","$://CHaTx can integrate with voice assistants and speech recognition technology, enabling users to interact with the chatbot through spoken language for a more natural and accessible experience."
"Integration with Third-Party Services","#://CHaTx third-party service integration","$://CHaTx can integrate with various third-party services, such as payment processors, social media platforms, and customer relationship management systems, to provide a more comprehensive and seamless user experience."
"Feedback Collection and Improvement","#://CHaTx feedback collection and improvement","$://CHaTx can collect user feedback and leverage it to continuously improve its performance, adapting and refining its responses to better meet user needs and expectations."
"Machine Learning and Adaptive Learning","#://CHaTx machine learning and adaptive learning","$://CHaTx utilizes machine learning and adaptive learning techniques to continuously improve its understanding of user inputs and enhance the quality of its responses over time."
"User Onboarding and Tutorials","#://CHaTx user onboarding and tutorials","$://CHaTx can offer onboarding and tutorial guidance to help users navigate and utilize the features and functionalities of the HTP network and associated applications."
"Context Switching","#://CHaTx context switching capabilities","$://CHaTx is capable of handling context switching during conversations, allowing the chatbot to seamlessly transition between different topics while maintaining the coherence and relevance of its responses."
|
[
"# \nAdapter Transformers\n\nOpenAssistant/oasst1\n\nfka/awesome-chatgpt-prompts\n\ntogethercomputer/RedPajama-Data-1T\n\nanon8231489123/ShareGPT_Vicuna_unfiltered\n\ngsdf/EasyNegative\n\nbloomberg/entsum\n\nopenai/summarize_from_feedback\n\nbillsum\n\nAmazonScience/massive\n\namazon_us_reviews\n\namazon_reviews_multi\n\nopenwebtext\n\nmicrosoft/CLUES\n\nNorod78/microsoft-fluentui-emoji-512-whitebg\n\nNorod78/microsoft-fluentui-emoji-768\n\nMicPie/unpredictable_msdn-microsoft-com\n\nmicrosoft/codexglue_method_generation\n\nSalesforce/rose\n\nIntel/WEC-Eng\n\nIntel/CoreSearch\n\ngoogle/MusicCaps\n\ngoogle/xtreme_s\n\ngoogle/fleurs\n\nEleutherAI/lambada_openai\n\nCarperAI/openai_summarize_tldr\n\nCarperAI/openai_summarize_comparisons\n\ndalle-mini/YFCC100M_OpenAI_subset\n\nopenai_humaneval\n\nopenai/webgpt_comparisons\n\nnyanko7/LLaMA-65B\n\nallenai/objaverse\n\nAnthropic/hh-rlhf\n\nAnthropic/model-written-evals\n\nyizhongw/self_instruct\n\nNerfgun3/bad_prompt\n\nNerfgun3/cyberware_style\n\nJosephusCheung/GuanacoDataset\n\nallenai/multixscience_sparse_oracle\n\nallenai/wcep_dense_oracle\n\nfacebook/content_rephrasing\n\nfacebook/flores\n\ncverse/linkedin\n\namazon_polarity\n\nApiInferenceTest/asr_dummy\n\nFrancois/futures_es\n\nIntel/textual_inversion_dicoo_dfq\n\nsearch_qa\n\nnomic-ai/gpt4all_prompt_generations\n\nyahoo_answers_topics\n\ncode_search_net\n\nLangChainDatasets/agent-search-calculator\n\nwikipedia\n\ncraigslist_bargains\n\ncodeparrot/github-code\n\ncodeparrot/github-code-clean\n\ncode_x_glue_ct_code_to_text\n\n0n1xus/codexglue\n\npszemraj/govreport-summarization-8192\n\nIcannos/gpt4all-formatted-dolly-15k-multilingual\n\nchromeNLP/quality\n\nCNXT/autotrain-data-chatx\nEnglish\n\ndoi:10.57967/hf/0700\nfinance\nmusic\nbiology\nlegal\nmedical\ntext-generation-inference\nFintech\nai\ncrypto\nchatgpt\ngpt\nopenai\nhuggingchat\nchatui\nstripe\nGoogle\nAmazon\nHTP"
] |
[
"TAGS\n#task_categories-question-answering #size_categories-10M<n<100M #language-English #finance #code #medical #region-us \n",
"# \nAdapter Transformers\n\nOpenAssistant/oasst1\n\nfka/awesome-chatgpt-prompts\n\ntogethercomputer/RedPajama-Data-1T\n\nanon8231489123/ShareGPT_Vicuna_unfiltered\n\ngsdf/EasyNegative\n\nbloomberg/entsum\n\nopenai/summarize_from_feedback\n\nbillsum\n\nAmazonScience/massive\n\namazon_us_reviews\n\namazon_reviews_multi\n\nopenwebtext\n\nmicrosoft/CLUES\n\nNorod78/microsoft-fluentui-emoji-512-whitebg\n\nNorod78/microsoft-fluentui-emoji-768\n\nMicPie/unpredictable_msdn-microsoft-com\n\nmicrosoft/codexglue_method_generation\n\nSalesforce/rose\n\nIntel/WEC-Eng\n\nIntel/CoreSearch\n\ngoogle/MusicCaps\n\ngoogle/xtreme_s\n\ngoogle/fleurs\n\nEleutherAI/lambada_openai\n\nCarperAI/openai_summarize_tldr\n\nCarperAI/openai_summarize_comparisons\n\ndalle-mini/YFCC100M_OpenAI_subset\n\nopenai_humaneval\n\nopenai/webgpt_comparisons\n\nnyanko7/LLaMA-65B\n\nallenai/objaverse\n\nAnthropic/hh-rlhf\n\nAnthropic/model-written-evals\n\nyizhongw/self_instruct\n\nNerfgun3/bad_prompt\n\nNerfgun3/cyberware_style\n\nJosephusCheung/GuanacoDataset\n\nallenai/multixscience_sparse_oracle\n\nallenai/wcep_dense_oracle\n\nfacebook/content_rephrasing\n\nfacebook/flores\n\ncverse/linkedin\n\namazon_polarity\n\nApiInferenceTest/asr_dummy\n\nFrancois/futures_es\n\nIntel/textual_inversion_dicoo_dfq\n\nsearch_qa\n\nnomic-ai/gpt4all_prompt_generations\n\nyahoo_answers_topics\n\ncode_search_net\n\nLangChainDatasets/agent-search-calculator\n\nwikipedia\n\ncraigslist_bargains\n\ncodeparrot/github-code\n\ncodeparrot/github-code-clean\n\ncode_x_glue_ct_code_to_text\n\n0n1xus/codexglue\n\npszemraj/govreport-summarization-8192\n\nIcannos/gpt4all-formatted-dolly-15k-multilingual\n\nchromeNLP/quality\n\nCNXT/autotrain-data-chatx\nEnglish\n\ndoi:10.57967/hf/0700\nfinance\nmusic\nbiology\nlegal\nmedical\ntext-generation-inference\nFintech\nai\ncrypto\nchatgpt\ngpt\nopenai\nhuggingchat\nchatui\nstripe\nGoogle\nAmazon\nHTP"
] |
[
42,
619
] |
[
"passage: TAGS\n#task_categories-question-answering #size_categories-10M<n<100M #language-English #finance #code #medical #region-us \n"
] |
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72131a8359010ffc222046b3ebd7a1b94401a1cb
|
# Dataset Card for "DTD_parition1_test_google_flan_t5_xxl_mode_T_A_ns_1880"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
CVasNLPExperiments/DTD_parition1_test_google_flan_t5_xxl_mode_T_A_ns_1880
|
[
"region:us"
] |
2023-04-04T15:40:16+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "prompt", "dtype": "string"}, {"name": "true_label", "dtype": "string"}, {"name": "prediction", "dtype": "string"}], "splits": [{"name": "fewshot_0_clip_tags_ViT_L_14_Attributes_ViT_L_14_text_davinci_003_dtd_clip_tags_ViT_L_14_simple_specific_rices", "num_bytes": 852862, "num_examples": 1880}, {"name": "fewshot_0_clip_tags_ViT_L_14_Attributes_ViT_L_14_text_davinci_003_clip_tags_ViT_L_14_simple_specific_rices", "num_bytes": 1030510, "num_examples": 1880}], "download_size": 477779, "dataset_size": 1883372}}
|
2023-04-04T16:40:13+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "DTD_parition1_test_google_flan_t5_xxl_mode_T_A_ns_1880"
More Information needed
|
[
"# Dataset Card for \"DTD_parition1_test_google_flan_t5_xxl_mode_T_A_ns_1880\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"DTD_parition1_test_google_flan_t5_xxl_mode_T_A_ns_1880\"\n\nMore Information needed"
] |
[
6,
38
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"DTD_parition1_test_google_flan_t5_xxl_mode_T_A_ns_1880\"\n\nMore Information needed"
] |
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c73da80206869f46c4cc0fbcbeda94eca36a90a4
|
# Dataset Card for "inspaire-kickstarter"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
svenschultze/inspaire-kickstarter
|
[
"region:us"
] |
2023-04-04T15:45:46+00:00
|
{"dataset_info": {"features": [{"name": "INSTRUCTION", "dtype": "string"}, {"name": "RESPONSE", "dtype": "string"}, {"name": "SOURCE", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 5712544, "num_examples": 1346}], "download_size": 3359746, "dataset_size": 5712544}}
|
2023-04-05T10:33:45+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "inspaire-kickstarter"
More Information needed
|
[
"# Dataset Card for \"inspaire-kickstarter\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"inspaire-kickstarter\"\n\nMore Information needed"
] |
[
6,
17
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"inspaire-kickstarter\"\n\nMore Information needed"
] |
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] |
1dd269c90d017fbfa377ce60b16935809f55a149
|
# Dataset Card for Dataset Name
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
This is a set of (title, integer category) descriptions taken from The Pirate Bay via
[123dw's](https://thepiratebay.org/search.php?q=user:123dw) regular TPB backups. This set represents the titles in release 2023-04.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
Major category, count
* 1, 733604 (audio)
* 2, 3557282 (video)
* 3, 211288 (applications)
* 4, 245684 (games)
* 5, 2500830 (porn)
* 6, 515778 (other)
Is porn?, count
- 0, 5263636
- 1, 2500830
### Data Fields
* id - original torrent ID
* title - Torrent title
* category - Integer ThePirateBay category (see below)
* mcat - Integer category / 100
* is_porn - 1 if porn, 0 otherwise
### Categories
```
id,name
100,Audio
101,"Audio: Music"
102,"Audio: Audio books"
103,"Audio: Sound clips"
104,"Audio: FLAC"
199,"Audio: Other"
200,Video
201,"Video: Movies"
202,"Video: Movies DVDR"
203,"Video: Music videos"
204,"Video: Movie clips"
205,"Video: TV shows"
206,"Video: Handheld"
207,"Video: HD - Movies"
208,"Video: HD - TV shows"
209,"Video: 3D"
299,"Video: Other"
300,Applications
301,"Applications: Windows"
302,"Applications: Mac"
303,"Applications: UNIX"
304,"Applications: Handheld"
305,"Applications: IOS (iPad/iPhone)"
306,"Applications: Android"
399,"Applications: Other OS"
400,Games
401,"Games: PC"
402,"Games: Mac"
403,"Games: PSx"
404,"Games: XBOX360"
405,"Games: Wii"
406,"Games: Handheld"
407,"Games: IOS (iPad/iPhone)"
408,"Games: Android"
499,"Games: Other"
500,Porn
501,"Porn: Movies"
502,"Porn: Movies DVDR"
503,"Porn: Pictures"
504,"Porn: Games"
505,"Porn: HD - Movies"
506,"Porn: Movie clips"
599,"Porn: Other"
600,Other
601,"Other: E-books"
602,"Other: Comics"
603,"Other: Pictures"
604,"Other: Covers"
605,"Other: Physibles"
699,"Other: Other"
```
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed]
|
d2mw/thepiratebay-categorized-titles-2023-04
|
[
"task_categories:text-classification",
"region:us"
] |
2023-04-04T16:25:09+00:00
|
{"task_categories": ["text-classification"]}
|
2023-04-04T16:44:48+00:00
|
[] |
[] |
TAGS
#task_categories-text-classification #region-us
|
# Dataset Card for Dataset Name
## Dataset Description
- Homepage:
- Repository:
- Paper:
- Leaderboard:
- Point of Contact:
### Dataset Summary
This is a set of (title, integer category) descriptions taken from The Pirate Bay via
123dw's regular TPB backups. This set represents the titles in release 2023-04.
### Supported Tasks and Leaderboards
### Languages
## Dataset Structure
### Data Instances
Major category, count
* 1, 733604 (audio)
* 2, 3557282 (video)
* 3, 211288 (applications)
* 4, 245684 (games)
* 5, 2500830 (porn)
* 6, 515778 (other)
Is porn?, count
- 0, 5263636
- 1, 2500830
### Data Fields
* id - original torrent ID
* title - Torrent title
* category - Integer ThePirateBay category (see below)
* mcat - Integer category / 100
* is_porn - 1 if porn, 0 otherwise
### Categories
### Data Splits
## Dataset Creation
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and Sensitive Information
## Considerations for Using the Data
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
## Additional Information
### Dataset Curators
### Licensing Information
### Contributions
|
[
"# Dataset Card for Dataset Name",
"## Dataset Description\n\n- Homepage: \n- Repository: \n- Paper: \n- Leaderboard: \n- Point of Contact:",
"### Dataset Summary\n\nThis is a set of (title, integer category) descriptions taken from The Pirate Bay via\n123dw's regular TPB backups. This set represents the titles in release 2023-04.",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances\n\n\n\nMajor category, count\n\n* 1, 733604 (audio)\n* 2, 3557282 (video)\n* 3, 211288 (applications)\n* 4, 245684 (games)\n* 5, 2500830 (porn)\n* 6, 515778 (other)\n\n\nIs porn?, count\n\n- 0, 5263636\n- 1, 2500830",
"### Data Fields\n\n* id - original torrent ID\n* title - Torrent title\n* category - Integer ThePirateBay category (see below)\n* mcat - Integer category / 100\n* is_porn - 1 if porn, 0 otherwise",
"### Categories",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
] |
[
"TAGS\n#task_categories-text-classification #region-us \n",
"# Dataset Card for Dataset Name",
"## Dataset Description\n\n- Homepage: \n- Repository: \n- Paper: \n- Leaderboard: \n- Point of Contact:",
"### Dataset Summary\n\nThis is a set of (title, integer category) descriptions taken from The Pirate Bay via\n123dw's regular TPB backups. This set represents the titles in release 2023-04.",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances\n\n\n\nMajor category, count\n\n* 1, 733604 (audio)\n* 2, 3557282 (video)\n* 3, 211288 (applications)\n* 4, 245684 (games)\n* 5, 2500830 (porn)\n* 6, 515778 (other)\n\n\nIs porn?, count\n\n- 0, 5263636\n- 1, 2500830",
"### Data Fields\n\n* id - original torrent ID\n* title - Torrent title\n* category - Integer ThePirateBay category (see below)\n* mcat - Integer category / 100\n* is_porn - 1 if porn, 0 otherwise",
"### Categories",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
] |
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8,
24,
49,
10,
4,
6,
81,
48,
3,
5,
5,
7,
4,
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10,
5,
5,
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8,
8,
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7,
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6,
5
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[
"passage: TAGS\n#task_categories-text-classification #region-us \n# Dataset Card for Dataset Name## Dataset Description\n\n- Homepage: \n- Repository: \n- Paper: \n- Leaderboard: \n- Point of Contact:### Dataset Summary\n\nThis is a set of (title, integer category) descriptions taken from The Pirate Bay via\n123dw's regular TPB backups. This set represents the titles in release 2023-04.### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances\n\n\n\nMajor category, count\n\n* 1, 733604 (audio)\n* 2, 3557282 (video)\n* 3, 211288 (applications)\n* 4, 245684 (games)\n* 5, 2500830 (porn)\n* 6, 515778 (other)\n\n\nIs porn?, count\n\n- 0, 5263636\n- 1, 2500830### Data Fields\n\n* id - original torrent ID\n* title - Torrent title\n* category - Integer ThePirateBay category (see below)\n* mcat - Integer category / 100\n* is_porn - 1 if porn, 0 otherwise### Categories### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions"
] |
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60f9118dab7fbec3aa6cded3543895b639aa2ddd
|
# Dataset Card for "FGVC_Aircraft_test_google_flan_t5_xxl_mode_A_ns_100"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
CVasNLPExperiments/FGVC_Aircraft_test_google_flan_t5_xxl_mode_A_ns_100
|
[
"region:us"
] |
2023-04-04T17:10:55+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "prompt", "dtype": "string"}, {"name": "true_label", "dtype": "string"}, {"name": "prediction", "dtype": "string"}], "splits": [{"name": "fewshot_0_clip_tags_ViT_L_14_Attributes_ViT_L_14_text_davinci_003_full_clip_tags_ViT_L_14_simple_specific_rices", "num_bytes": 42164, "num_examples": 100}], "download_size": 0, "dataset_size": 42164}}
|
2023-04-04T23:54:06+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "FGVC_Aircraft_test_google_flan_t5_xxl_mode_A_ns_100"
More Information needed
|
[
"# Dataset Card for \"FGVC_Aircraft_test_google_flan_t5_xxl_mode_A_ns_100\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"FGVC_Aircraft_test_google_flan_t5_xxl_mode_A_ns_100\"\n\nMore Information needed"
] |
[
6,
35
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"FGVC_Aircraft_test_google_flan_t5_xxl_mode_A_ns_100\"\n\nMore Information needed"
] |
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0a6a92036f51a8f09e14b62a3d411238798e4a35
|
# Dataset Card for "FGVC_Aircraft_test_google_flan_t5_xxl_mode_T_ns_100"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
CVasNLPExperiments/FGVC_Aircraft_test_google_flan_t5_xxl_mode_T_ns_100
|
[
"region:us"
] |
2023-04-04T17:13:17+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "prompt", "dtype": "string"}, {"name": "true_label", "dtype": "string"}, {"name": "prediction", "dtype": "string"}], "splits": [{"name": "fewshot_0_clip_tags_ViT_L_14_Attributes_ViT_L_14_text_davinci_003_full_clip_tags_ViT_L_14_simple_specific_rices", "num_bytes": 26391, "num_examples": 100}], "download_size": 8429, "dataset_size": 26391}}
|
2023-04-04T17:13:19+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "FGVC_Aircraft_test_google_flan_t5_xxl_mode_T_ns_100"
More Information needed
|
[
"# Dataset Card for \"FGVC_Aircraft_test_google_flan_t5_xxl_mode_T_ns_100\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"FGVC_Aircraft_test_google_flan_t5_xxl_mode_T_ns_100\"\n\nMore Information needed"
] |
[
6,
35
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"FGVC_Aircraft_test_google_flan_t5_xxl_mode_T_ns_100\"\n\nMore Information needed"
] |
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-0.13439086079597473,
-0.08912345767021179
] |
15331ac32906dc97a149e964a28d017fe9f453a0
|
# Dataset Card for "focus_persona_selection"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
kanak8278/focus_persona_selection
|
[
"region:us"
] |
2023-04-04T17:24:46+00:00
|
{"dataset_info": {"features": [{"name": "dialogID", "dtype": "string"}, {"name": "utterance", "dtype": "int64"}, {"name": "old_hit_knowledge", "dtype": "string"}, {"name": "old_query", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "persona1", "dtype": "string"}, {"name": "persona2", "dtype": "string"}, {"name": "persona3", "dtype": "string"}, {"name": "persona4", "dtype": "string"}, {"name": "persona5", "dtype": "string"}, {"name": "persona6", "dtype": "string"}, {"name": "ground_knowledge", "dtype": "string"}, {"name": "query", "dtype": "string"}, {"name": "hit_knowledge", "dtype": "string"}, {"name": "persona_candidates", "dtype": "string"}, {"name": "persona_grounding", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 11122674, "num_examples": 8644}, {"name": "validation", "num_bytes": 11162186, "num_examples": 8641}, {"name": "train", "num_bytes": 72558975, "num_examples": 55658}], "download_size": 42539563, "dataset_size": 94843835}}
|
2023-04-04T18:17:50+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "focus_persona_selection"
More Information needed
|
[
"# Dataset Card for \"focus_persona_selection\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"focus_persona_selection\"\n\nMore Information needed"
] |
[
6,
18
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"focus_persona_selection\"\n\nMore Information needed"
] |
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db803a4c2031b7170063fed7bc0c445cb8aec9f6
|
# WikiDT: Wikipedia Table Document dataset for table extraction and visual question answering
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
The WikiDT contains multi-level annotations and labels for the question-answering task based on images. Meanwhile, as the questions are answered from some table on the image, and WikiDT provides the table annotation to facilitate the diagnosis of the models and decompose the problem, WikiDT can be also directly used as a
table recognition dataset.
The dataset contains 16,887 Wikipedia screenshot, which are segmented to 54,032 subpages since the full screenshots are potentially long. In total, there's 159,905 tables in the dataset. The number of question-answer samples is 70,652. Each QA sample contains triplets of <question, answer, full-page screenshot filename>, and is additionally annotated with retrieval labels (which subpage, and which table). 53,698 QA samples also have SQL annotation.
For each subpage, OCR and table extraction annotations from two sources are available. While rendering the screenshots, the ground truth table annotation is recorded. Meanwhile, to make the dataset realistic, we also requested OCR and table extraction from [Amazon Textract](https://aws.amazon.com/textract/) for each subpage (results obtained during Feb.28, 2023 - Mar.6, 2023).
### Languages
English
## Dataset Structure
Once downloaded, the WikiDT has the following parts. The downloaded files are around 77GB. Please ensure you have at least 160GB since we will be extract individual files from the tars.
```
.
├── WikiTableExtraction
│ ├── detection.partaa
│ ├── detection.partab
│ ├── detection.partac
│ ├── detection.partad
│ ├── detection.partae
│ ├── detection.partaf
│ ├── detection.partag
│ ├── structure.partaa
│ ├── structure.partab
│ ├── structure.partac
│ ├── structure.partad
│ └── structure.partae
├── images.partaa
├── images.partab
├── images.partac
├── images.partad
├── images.partae
├── images.partaf
├── images.partag
├── images.partah
├── images.partai
├── ocr.tar
├── samples
│ ├── test.json
│ ├── train.json
│ └── val.json
└── tsv.tar
```
Please concat the part files and extract them into respective folder. For example,
run
```
cd WikiTableExtraction/
cat detection.parta* | tar x
```
to extract the `detection` folder.
Once you extracted all the tar files, the WikiDT dataset has the following file structure.
```sh
+--WikiDT-dataset
| +--WikiTableExtraction
| | +--detection
| | | +--images # sub page images
| | | +--train # xml table bbox annotation
| | | +--test # xml table bbox annotation
| | | +--val # xml table bbox annotation
| | | images_filelist.txt # index of 54,032 images
| | | test_filelist.txt # index of 5,410 test samples
| | | train_filelist.txt # index of 43,248 train samples
| | | val_filelist.txt # index of 5,347 val samples
| | +--structure
| | | +--images # images cropped to table region
| | | +--train # xml table bbox annotation
| | | +--test # xml table bbox annotation
| | | +--val # xml table bbox annotation
| | | images_filelist.txt # index of 159,898 images
| | | test_filelist.txt # index of 15,989 test samples
| | | train_filelist.txt # index of 129,980 train samples
| | | val_filelist.txt # index of 15,991 val samples
| +--samples # in total 70,652 TableVQA samples from the three json files
| | +--train.json #
| | +--test.json #
| | +--val.json #
| +--images # full page image
| +--ocr # text and bbox for the table content
| | +--textract # detected by Amazon Textract API
| | +--web # extracted from HTML information
| +--tsv # extracted table in tsv format
| | +--textract # detected by Amazon Textract API
| | +--web # extracted from HTML information
```
### Table VQA annotation example
Here is an example of an xml table bbox annotation from `WikiDT-dataset/samples/[train|test|val].json/`.
```
{'all_ocr_files_textract': ['ocr/textract/16301437_page_seg_0.json',
'ocr/textract/16301437_page_seg_1.json'],
'all_ocr_files_web': ['ocr/web/16301437_page_seg_0.json',
'ocr/web/16301437_page_seg_1.json'],
'all_table_files_textract': ['tsv/textract/16301437_page_0.tsv',
'tsv/textract/16301437_page_1.tsv'],
'all_table_files_web': ['tsv/web/16301437_1.tsv', 'tsv/web/16301437_0.tsv'],
'answer': [['don johnson buckeye st. classic']],
'image': '16301437_page.png',
'ocr_retrieval_file_textract': 'ocr/textract/16301437_page_seg_0.json',
'ocr_retrieval_file_web': 'ocr/web/16301437_page_seg_0.json',
'question': 'Name the Event which has a Score of 209-197?',
'sample_id': '14190',
'sql_str': "SELECT `event` FROM cur_table WHERE `score` = '209-197' ",
'sub_page': ['16301437_page_seg_0.png', '16301437_page_seg_1.png'],
'sub_page_retrieved': '16301437_page_seg_0.png',
'subset': 'TFC',
'table_id': '2-16301437-1',
'table_retrieval_file_textract': 'tsv/textract/16301437_page_0.tsv',
'table_retrieval_file_web': 'tsv/web/16301437_1.tsv'}
```
### Table Detection annotation example
Here is an example of an xml table bbox annotation from `WikiDT-dataset/WikiTableExtraction/structure/[train|test|val]/`.
```xml
<annotation>
<folder />
<filename>204_147_page_crop_5.png</filename>
<source>WikiDT Dataset</source>
<size>
<width>788</width>
<height>540.0</height>
<depth>3</depth>
</size>
<object>
<name>table</name>
<rowspan />
<colspan />
<bndbox>
<xmin>10</xmin>
<ymin>10</ymin>
<xmax>778</xmax>
<ymax>530</ymax>
</bndbox>
</object>
<object>
<name>header row</name>
<rowspan />
<colspan />
<bndbox>
<xmin>10</xmin>
<ymin>10</ymin>
<xmax>778</xmax>
<ymax>33</ymax>
</bndbox>
</object>
<object>
<name>header cell</name>
<rowspan />
<colspan>10</colspan>
<bndbox>
<xmin>12</xmin>
<ymin>35</ymin>
<xmax>776</xmax>
<ymax>58</ymax>
</bndbox>
</object>
<object>
<name>table row</name>
<rowspan />
<colspan />
<bndbox>
<xmin>10</xmin>
<ymin>60</ymin>
<xmax>778</xmax>
<ymax>530</ymax>
</bndbox>
</object>
</annotation>
```
### Licensing Information
CC BY SA 3.0
### Contributors
[Hui Shi](mailto:[email protected]) (Work done during her internship at Amazon)
[Yusheng Xie](mailto:[email protected]) (corresponding person)
[Luis Goncalves](mailto:[email protected])
|
AmazonScience/WikiDT
|
[
"task_categories:table-question-answering",
"task_categories:question-answering",
"size_categories:100K<n<1M",
"language:en",
"license:cc-by-sa-3.0",
"documents",
"tables",
"VQA",
"region:us"
] |
2023-04-04T17:32:26+00:00
|
{"language": ["en"], "license": "cc-by-sa-3.0", "size_categories": ["100K<n<1M"], "task_categories": ["table-question-answering", "question-answering"], "pretty_name": "WikiDT", "tags": ["documents", "tables", "VQA"]}
|
2023-04-17T17:27:35+00:00
|
[] |
[
"en"
] |
TAGS
#task_categories-table-question-answering #task_categories-question-answering #size_categories-100K<n<1M #language-English #license-cc-by-sa-3.0 #documents #tables #VQA #region-us
|
# WikiDT: Wikipedia Table Document dataset for table extraction and visual question answering
## Dataset Description
- Homepage:
- Repository:
- Paper:
- Leaderboard:
- Point of Contact:
### Dataset Summary
The WikiDT contains multi-level annotations and labels for the question-answering task based on images. Meanwhile, as the questions are answered from some table on the image, and WikiDT provides the table annotation to facilitate the diagnosis of the models and decompose the problem, WikiDT can be also directly used as a
table recognition dataset.
The dataset contains 16,887 Wikipedia screenshot, which are segmented to 54,032 subpages since the full screenshots are potentially long. In total, there's 159,905 tables in the dataset. The number of question-answer samples is 70,652. Each QA sample contains triplets of <question, answer, full-page screenshot filename>, and is additionally annotated with retrieval labels (which subpage, and which table). 53,698 QA samples also have SQL annotation.
For each subpage, OCR and table extraction annotations from two sources are available. While rendering the screenshots, the ground truth table annotation is recorded. Meanwhile, to make the dataset realistic, we also requested OCR and table extraction from Amazon Textract for each subpage (results obtained during Feb.28, 2023 - Mar.6, 2023).
### Languages
English
## Dataset Structure
Once downloaded, the WikiDT has the following parts. The downloaded files are around 77GB. Please ensure you have at least 160GB since we will be extract individual files from the tars.
Please concat the part files and extract them into respective folder. For example,
run
to extract the 'detection' folder.
Once you extracted all the tar files, the WikiDT dataset has the following file structure.
### Table VQA annotation example
Here is an example of an xml table bbox annotation from 'WikiDT-dataset/samples/[train|test|val].json/'.
### Table Detection annotation example
Here is an example of an xml table bbox annotation from 'WikiDT-dataset/WikiTableExtraction/structure/[train|test|val]/'.
### Licensing Information
CC BY SA 3.0
### Contributors
Hui Shi (Work done during her internship at Amazon)
Yusheng Xie (corresponding person)
Luis Goncalves
|
[
"# WikiDT: Wikipedia Table Document dataset for table extraction and visual question answering",
"## Dataset Description\n\n- Homepage: \n- Repository: \n- Paper: \n- Leaderboard: \n- Point of Contact:",
"### Dataset Summary\n\nThe WikiDT contains multi-level annotations and labels for the question-answering task based on images. Meanwhile, as the questions are answered from some table on the image, and WikiDT provides the table annotation to facilitate the diagnosis of the models and decompose the problem, WikiDT can be also directly used as a \ntable recognition dataset.\n\nThe dataset contains 16,887 Wikipedia screenshot, which are segmented to 54,032 subpages since the full screenshots are potentially long. In total, there's 159,905 tables in the dataset. The number of question-answer samples is 70,652. Each QA sample contains triplets of <question, answer, full-page screenshot filename>, and is additionally annotated with retrieval labels (which subpage, and which table). 53,698 QA samples also have SQL annotation. \n\nFor each subpage, OCR and table extraction annotations from two sources are available. While rendering the screenshots, the ground truth table annotation is recorded. Meanwhile, to make the dataset realistic, we also requested OCR and table extraction from Amazon Textract for each subpage (results obtained during Feb.28, 2023 - Mar.6, 2023).",
"### Languages\n\nEnglish",
"## Dataset Structure\n\nOnce downloaded, the WikiDT has the following parts. The downloaded files are around 77GB. Please ensure you have at least 160GB since we will be extract individual files from the tars.\n\n\n\nPlease concat the part files and extract them into respective folder. For example, \nrun\n\nto extract the 'detection' folder.\nOnce you extracted all the tar files, the WikiDT dataset has the following file structure.",
"### Table VQA annotation example\n\nHere is an example of an xml table bbox annotation from 'WikiDT-dataset/samples/[train|test|val].json/'.",
"### Table Detection annotation example\n\nHere is an example of an xml table bbox annotation from 'WikiDT-dataset/WikiTableExtraction/structure/[train|test|val]/'.",
"### Licensing Information\n\nCC BY SA 3.0",
"### Contributors\n\nHui Shi (Work done during her internship at Amazon)\n\nYusheng Xie (corresponding person)\n\nLuis Goncalves"
] |
[
"TAGS\n#task_categories-table-question-answering #task_categories-question-answering #size_categories-100K<n<1M #language-English #license-cc-by-sa-3.0 #documents #tables #VQA #region-us \n",
"# WikiDT: Wikipedia Table Document dataset for table extraction and visual question answering",
"## Dataset Description\n\n- Homepage: \n- Repository: \n- Paper: \n- Leaderboard: \n- Point of Contact:",
"### Dataset Summary\n\nThe WikiDT contains multi-level annotations and labels for the question-answering task based on images. Meanwhile, as the questions are answered from some table on the image, and WikiDT provides the table annotation to facilitate the diagnosis of the models and decompose the problem, WikiDT can be also directly used as a \ntable recognition dataset.\n\nThe dataset contains 16,887 Wikipedia screenshot, which are segmented to 54,032 subpages since the full screenshots are potentially long. In total, there's 159,905 tables in the dataset. The number of question-answer samples is 70,652. Each QA sample contains triplets of <question, answer, full-page screenshot filename>, and is additionally annotated with retrieval labels (which subpage, and which table). 53,698 QA samples also have SQL annotation. \n\nFor each subpage, OCR and table extraction annotations from two sources are available. While rendering the screenshots, the ground truth table annotation is recorded. Meanwhile, to make the dataset realistic, we also requested OCR and table extraction from Amazon Textract for each subpage (results obtained during Feb.28, 2023 - Mar.6, 2023).",
"### Languages\n\nEnglish",
"## Dataset Structure\n\nOnce downloaded, the WikiDT has the following parts. The downloaded files are around 77GB. Please ensure you have at least 160GB since we will be extract individual files from the tars.\n\n\n\nPlease concat the part files and extract them into respective folder. For example, \nrun\n\nto extract the 'detection' folder.\nOnce you extracted all the tar files, the WikiDT dataset has the following file structure.",
"### Table VQA annotation example\n\nHere is an example of an xml table bbox annotation from 'WikiDT-dataset/samples/[train|test|val].json/'.",
"### Table Detection annotation example\n\nHere is an example of an xml table bbox annotation from 'WikiDT-dataset/WikiTableExtraction/structure/[train|test|val]/'.",
"### Licensing Information\n\nCC BY SA 3.0",
"### Contributors\n\nHui Shi (Work done during her internship at Amazon)\n\nYusheng Xie (corresponding person)\n\nLuis Goncalves"
] |
[
68,
18,
24,
294,
5,
93,
46,
48,
10,
32
] |
[
"passage: TAGS\n#task_categories-table-question-answering #task_categories-question-answering #size_categories-100K<n<1M #language-English #license-cc-by-sa-3.0 #documents #tables #VQA #region-us \n# WikiDT: Wikipedia Table Document dataset for table extraction and visual question answering## Dataset Description\n\n- Homepage: \n- Repository: \n- Paper: \n- Leaderboard: \n- Point of Contact:### Dataset Summary\n\nThe WikiDT contains multi-level annotations and labels for the question-answering task based on images. Meanwhile, as the questions are answered from some table on the image, and WikiDT provides the table annotation to facilitate the diagnosis of the models and decompose the problem, WikiDT can be also directly used as a \ntable recognition dataset.\n\nThe dataset contains 16,887 Wikipedia screenshot, which are segmented to 54,032 subpages since the full screenshots are potentially long. In total, there's 159,905 tables in the dataset. The number of question-answer samples is 70,652. Each QA sample contains triplets of <question, answer, full-page screenshot filename>, and is additionally annotated with retrieval labels (which subpage, and which table). 53,698 QA samples also have SQL annotation. \n\nFor each subpage, OCR and table extraction annotations from two sources are available. While rendering the screenshots, the ground truth table annotation is recorded. Meanwhile, to make the dataset realistic, we also requested OCR and table extraction from Amazon Textract for each subpage (results obtained during Feb.28, 2023 - Mar.6, 2023).### Languages\n\nEnglish## Dataset Structure\n\nOnce downloaded, the WikiDT has the following parts. The downloaded files are around 77GB. Please ensure you have at least 160GB since we will be extract individual files from the tars.\n\n\n\nPlease concat the part files and extract them into respective folder. For example, \nrun\n\nto extract the 'detection' folder.\nOnce you extracted all the tar files, the WikiDT dataset has the following file structure."
] |
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] |
ecffefdc07182d6761efb607da51871ec0f037a4
|
# IVA Swift GitHub Code Dataset
## Dataset Description
This is the raw IVA Swift dataset extracted from GitHub.
It contains uncurated Swift files gathered with the purpose to train a code generation model.
The dataset consists of 753693 swift code files from GitHub totaling ~700MB of data.
The dataset was created from the public GitHub dataset on Google BiqQuery.
### How to use it
To download the full dataset:
```python
from datasets import load_dataset
dataset = load_dataset('mvasiliniuc/iva-swift-codeint', split='train')
```
```python
from datasets import load_dataset
dataset = load_dataset('mvasiliniuc/iva-swift-codeint', split='train')
print(dataset[77723])
#OUTPUT:
{
"repo_name":"simpleandpretty/decider-ios",
"path":"MessagesExtension/MediaResources.swift",
"copies":"1",
"size":"1232",
"content":"import Foundation\nimport UIKit\n\nclass MediaResources {\n\n static func mediaURL(forGameOption option:FightMove) -> URL {\n let bundle = Bundle.main\n guard\n let mediaURL = bundle.url(forResource: option.rawValue, withExtension: \"mp4\")\n ...",
"license":"gpl-3.0"
}
```
## Data Structure
### Data Fields
|Field|Type|Description|
|---|---|---|
|repo_name|string|name of the GitHub repository|
|path|string|path of the file in GitHub repository|
|copies|string|number of occurrences in dataset|
|code|string|content of source file|
|size|string|size of the source file in bytes|
|license|string|license of GitHub repository|
### Instance
```json
{
"repo_name":"simpleandpretty/decider-ios",
"path":"MessagesExtension/MediaResources.swift",
"copies":"1",
"size":"1232",
"content":"import Foundation\nimport UIKit\n\nclass MediaResources {\n\n static func mediaURL(forGameOption option:FightMove) -> URL {\n let bundle = Bundle.main\n guard\n let mediaURL = bundle.url(forResource: option.rawValue, withExtension: \"mp4\")\n ...",
"license":"gpl-3.0"
}
```
## Languages
The dataset contains only Swift files.
```json
{
"Swift": [".swift"]
}
```
## Licenses
Each entry in the dataset contains the associated license. The following is a list of licenses involved and their occurrences.
```json
{
"agpl-3.0": 2775,
"apache-2.0": 180178,
"artistic-2.0": 314,
"bsd-2-clause": 5342,
"bsd-3-clause": 11429,
"cc0-1.0": 2718,
"epl-1.0": 980,
"gpl-2.0": 15751,
"gpl-3.0": 33074,
"isc": 1647,
"lgpl-2.1": 1741,
"lgpl-3.0": 6150,
"mit": 476518,
"mpl-2.0": 11799,
"unlicense": 3277
}
```
## Dataset Statistics
```json
{
"Total size": "~712 MB",
"Number of files": 753693,
"Number of files under 500 bytes": 129827,
"Average file size in bytes": 4245,
}
```
## Dataset Creation
The dataset was created using Google Query for Github:
https://cloud.google.com/blog/topics/public-datasets/github-on-bigquery-analyze-all-the-open-source-code
The following steps were pursued for data
gathering:
1. Creation of a dataset and a table in Google Big Query Project.
2. Creation of a bucket in Google Cloud Storage.
3. Creation of a query in Google Big Query Project.
4. Running the query with the setting to output the results in the dataset and table
created at step one.
5. Exporting the resulting dataset into the bucket created in step 2. Export format of JSON with gzip compression.
The result of these steps leads to the following results:
* 2.7 TB Processed,
* number of extracted rows/Swift files was 464,215
* total logical bytes 1.46 GB.
* The result amounts to 7 json.gz files in a total of 700 MB
The SQL Query used is:
```sql
SELECT
f.repo_name, f.path, c.copies, c.size, c.content, l.license
FROM
(select f.*, row_number() over (partition by id order by path desc) as seqnum from `bigquery-public-data.github_repos.files` AS f) f
JOIN
`bigquery-public-data.github_repos.contents` AS c
ON
f.id = c.id AND seqnum=1
JOIN
`bigquery-public-data.github_repos.licenses` AS l
ON
f.repo_name = l.repo_name
WHERE
NOT c.binary AND ((f.path LIKE '%.swift') AND (c.size BETWEEN 0 AND 1048575))
```
## Data Splits
The dataset only contains a train split.
Using the curated version of this dataset, a split was made into multiple repositories:
* Clean Version: https://huggingface.co/datasets/mvasiliniuc/iva-swift-codeint-clean
* Clean Version Train: https://huggingface.co/datasets/mvasiliniuc/iva-swift-codeint-clean-train
* Clean Version Valid: https://huggingface.co/datasets/mvasiliniuc/iva-swift-codeint-clean-valid
# Considerations for Using the Data
The dataset comprises source code from various repositories, potentially containing harmful or biased code,
along with sensitive information such as passwords or usernames.
# Additional Information
## Dataset Curators
[[email protected]]([email protected])
## Licensing Information
* The license of this open-source dataset is: other.
* The dataset is gathered from open-source repositories on [GitHub using BigQuery](https://cloud.google.com/blog/topics/public-datasets/github-on-bigquery-analyze-all-the-open-source-code).
* Find the license of each entry in the dataset in the corresponding license column.
## Citation Information
```json
@misc {mircea_vasiliniuc_2023,
author = { {Mircea Vasiliniuc} },
title = { iva-swift-codeint (Revision c09ebf8) },
year = 2023,
url = { https://huggingface.co/datasets/mvasiliniuc/iva-swift-codeint },
doi = { 10.57967/hf/0778 },
publisher = { Hugging Face }
}
```
|
mvasiliniuc/iva-swift-codeint
|
[
"task_categories:text-generation",
"task_ids:language-modeling",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"size_categories:100K<n<1M",
"language:code",
"license:other",
"code, swift, native iOS development",
"doi:10.57967/hf/0778",
"region:us"
] |
2023-04-04T17:39:22+00:00
|
{"annotations_creators": ["crowdsourced"], "language_creators": ["crowdsourced"], "language": ["code"], "license": "other", "size_categories": ["100K<n<1M"], "source_datasets": [], "task_categories": ["text-generation"], "task_ids": ["language-modeling"], "pretty_name": "iva-swift-codeint-raw", "tags": ["code, swift, native iOS development"]}
|
2023-06-16T06:02:18+00:00
|
[] |
[
"code"
] |
TAGS
#task_categories-text-generation #task_ids-language-modeling #annotations_creators-crowdsourced #language_creators-crowdsourced #size_categories-100K<n<1M #language-code #license-other #code, swift, native iOS development #doi-10.57967/hf/0778 #region-us
|
IVA Swift GitHub Code Dataset
=============================
Dataset Description
-------------------
This is the raw IVA Swift dataset extracted from GitHub.
It contains uncurated Swift files gathered with the purpose to train a code generation model.
The dataset consists of 753693 swift code files from GitHub totaling ~700MB of data.
The dataset was created from the public GitHub dataset on Google BiqQuery.
### How to use it
To download the full dataset:
Data Structure
--------------
### Data Fields
Field: repo\_name, Type: string, Description: name of the GitHub repository
Field: path, Type: string, Description: path of the file in GitHub repository
Field: copies, Type: string, Description: number of occurrences in dataset
Field: code, Type: string, Description: content of source file
Field: size, Type: string, Description: size of the source file in bytes
Field: license, Type: string, Description: license of GitHub repository
### Instance
Languages
---------
The dataset contains only Swift files.
Licenses
--------
Each entry in the dataset contains the associated license. The following is a list of licenses involved and their occurrences.
Dataset Statistics
------------------
Dataset Creation
----------------
The dataset was created using Google Query for Github:
URL
The following steps were pursued for data
gathering:
1. Creation of a dataset and a table in Google Big Query Project.
2. Creation of a bucket in Google Cloud Storage.
3. Creation of a query in Google Big Query Project.
4. Running the query with the setting to output the results in the dataset and table
created at step one.
5. Exporting the resulting dataset into the bucket created in step 2. Export format of JSON with gzip compression.
The result of these steps leads to the following results:
* 2.7 TB Processed,
* number of extracted rows/Swift files was 464,215
* total logical bytes 1.46 GB.
* The result amounts to 7 URL files in a total of 700 MB
The SQL Query used is:
Data Splits
-----------
The dataset only contains a train split.
Using the curated version of this dataset, a split was made into multiple repositories:
* Clean Version: URL
* Clean Version Train: URL
* Clean Version Valid: URL
Considerations for Using the Data
=================================
The dataset comprises source code from various repositories, potentially containing harmful or biased code,
along with sensitive information such as passwords or usernames.
Additional Information
======================
Dataset Curators
----------------
URL@URL
Licensing Information
---------------------
* The license of this open-source dataset is: other.
* The dataset is gathered from open-source repositories on GitHub using BigQuery.
* Find the license of each entry in the dataset in the corresponding license column.
|
[
"### How to use it\n\n\nTo download the full dataset:\n\n\nData Structure\n--------------",
"### Data Fields\n\n\nField: repo\\_name, Type: string, Description: name of the GitHub repository\nField: path, Type: string, Description: path of the file in GitHub repository\nField: copies, Type: string, Description: number of occurrences in dataset\nField: code, Type: string, Description: content of source file\nField: size, Type: string, Description: size of the source file in bytes\nField: license, Type: string, Description: license of GitHub repository",
"### Instance\n\n\nLanguages\n---------\n\n\nThe dataset contains only Swift files.\n\n\nLicenses\n--------\n\n\nEach entry in the dataset contains the associated license. The following is a list of licenses involved and their occurrences.\n\n\nDataset Statistics\n------------------\n\n\nDataset Creation\n----------------\n\n\nThe dataset was created using Google Query for Github:\nURL\n\n\nThe following steps were pursued for data\ngathering:\n\n\n1. Creation of a dataset and a table in Google Big Query Project.\n2. Creation of a bucket in Google Cloud Storage.\n3. Creation of a query in Google Big Query Project.\n4. Running the query with the setting to output the results in the dataset and table\ncreated at step one.\n5. Exporting the resulting dataset into the bucket created in step 2. Export format of JSON with gzip compression.\n\n\nThe result of these steps leads to the following results:\n\n\n* 2.7 TB Processed,\n* number of extracted rows/Swift files was 464,215\n* total logical bytes 1.46 GB.\n* The result amounts to 7 URL files in a total of 700 MB\n\n\nThe SQL Query used is:\n\n\nData Splits\n-----------\n\n\nThe dataset only contains a train split.\n\n\nUsing the curated version of this dataset, a split was made into multiple repositories:\n\n\n* Clean Version: URL\n* Clean Version Train: URL\n* Clean Version Valid: URL\n\n\nConsiderations for Using the Data\n=================================\n\n\nThe dataset comprises source code from various repositories, potentially containing harmful or biased code,\nalong with sensitive information such as passwords or usernames.\n\n\nAdditional Information\n======================\n\n\nDataset Curators\n----------------\n\n\nURL@URL\n\n\nLicensing Information\n---------------------\n\n\n* The license of this open-source dataset is: other.\n* The dataset is gathered from open-source repositories on GitHub using BigQuery.\n* Find the license of each entry in the dataset in the corresponding license column."
] |
[
"TAGS\n#task_categories-text-generation #task_ids-language-modeling #annotations_creators-crowdsourced #language_creators-crowdsourced #size_categories-100K<n<1M #language-code #license-other #code, swift, native iOS development #doi-10.57967/hf/0778 #region-us \n",
"### How to use it\n\n\nTo download the full dataset:\n\n\nData Structure\n--------------",
"### Data Fields\n\n\nField: repo\\_name, Type: string, Description: name of the GitHub repository\nField: path, Type: string, Description: path of the file in GitHub repository\nField: copies, Type: string, Description: number of occurrences in dataset\nField: code, Type: string, Description: content of source file\nField: size, Type: string, Description: size of the source file in bytes\nField: license, Type: string, Description: license of GitHub repository",
"### Instance\n\n\nLanguages\n---------\n\n\nThe dataset contains only Swift files.\n\n\nLicenses\n--------\n\n\nEach entry in the dataset contains the associated license. The following is a list of licenses involved and their occurrences.\n\n\nDataset Statistics\n------------------\n\n\nDataset Creation\n----------------\n\n\nThe dataset was created using Google Query for Github:\nURL\n\n\nThe following steps were pursued for data\ngathering:\n\n\n1. Creation of a dataset and a table in Google Big Query Project.\n2. Creation of a bucket in Google Cloud Storage.\n3. Creation of a query in Google Big Query Project.\n4. Running the query with the setting to output the results in the dataset and table\ncreated at step one.\n5. Exporting the resulting dataset into the bucket created in step 2. Export format of JSON with gzip compression.\n\n\nThe result of these steps leads to the following results:\n\n\n* 2.7 TB Processed,\n* number of extracted rows/Swift files was 464,215\n* total logical bytes 1.46 GB.\n* The result amounts to 7 URL files in a total of 700 MB\n\n\nThe SQL Query used is:\n\n\nData Splits\n-----------\n\n\nThe dataset only contains a train split.\n\n\nUsing the curated version of this dataset, a split was made into multiple repositories:\n\n\n* Clean Version: URL\n* Clean Version Train: URL\n* Clean Version Valid: URL\n\n\nConsiderations for Using the Data\n=================================\n\n\nThe dataset comprises source code from various repositories, potentially containing harmful or biased code,\nalong with sensitive information such as passwords or usernames.\n\n\nAdditional Information\n======================\n\n\nDataset Curators\n----------------\n\n\nURL@URL\n\n\nLicensing Information\n---------------------\n\n\n* The license of this open-source dataset is: other.\n* The dataset is gathered from open-source repositories on GitHub using BigQuery.\n* Find the license of each entry in the dataset in the corresponding license column."
] |
[
95,
19,
117,
435
] |
[
"passage: TAGS\n#task_categories-text-generation #task_ids-language-modeling #annotations_creators-crowdsourced #language_creators-crowdsourced #size_categories-100K<n<1M #language-code #license-other #code, swift, native iOS development #doi-10.57967/hf/0778 #region-us \n### How to use it\n\n\nTo download the full dataset:\n\n\nData Structure\n--------------### Data Fields\n\n\nField: repo\\_name, Type: string, Description: name of the GitHub repository\nField: path, Type: string, Description: path of the file in GitHub repository\nField: copies, Type: string, Description: number of occurrences in dataset\nField: code, Type: string, Description: content of source file\nField: size, Type: string, Description: size of the source file in bytes\nField: license, Type: string, Description: license of GitHub repository"
] |
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df4e1f0415aea70fe816cd4ffbc5604ec38f1f91
|
# IVA Kotlin GitHub Code Dataset
## Dataset Description
This is the raw IVA Kotlin dataset extracted from GitHub.
It contains uncurated Kotlin files gathered with the purpose to train a code generation model.
The dataset consists of 464215 kotlin code files from GitHub totaling ~361 MB of data.
The dataset was created from the public GitHub dataset on Google BiqQuery.
### How to use it
To download the full dataset:
```python
from datasets import load_dataset
dataset = load_dataset('mvasiliniuc/iva-kotlin-codeint', split='train')
```
```python
from datasets import load_dataset
dataset = load_dataset('mvasiliniuc/iva-kotlin-codeint', split='train')
print(dataset[723])
#OUTPUT:
{
"repo_name":"nemerosa/ontrack",
"path":"ontrack-extension-notifications/src/main/java/net/nemerosa/ontrack/extension/notifications/webhooks/WebhookController.kt",
"copies":"1",
"size":"3248",
"content":"...@RestController\n@RequestMapping(\"/extension/notifications/webhook\")\nclass WebhookController(\n private val webhookAdminService: WebhookAdminService,\n private val webhookExecutionService: ",
"license":"mit"
}
```
## Data Structure
### Data Fields
|Field|Type|Description|
|---|---|---|
|repo_name|string|name of the GitHub repository|
|path|string|path of the file in GitHub repository|
|copies|string|number of occurrences in dataset|
|code|string|content of source file|
|size|string|size of the source file in bytes|
|license|string|license of GitHub repository|
### Instance
```json
{
"repo_name":"nemerosa/ontrack",
"path":"ontrack-extension-notifications/src/main/java/net/nemerosa/ontrack/extension/notifications/webhooks/WebhookController.kt",
"copies":"1",
"size":"3248",
"content":"...@RestController\n@RequestMapping(\"/extension/notifications/webhook\")\nclass WebhookController(\n private val webhookAdminService: WebhookAdminService,\n private val webhookExecutionService: ",
"license":"mit"
}
```
## Languages
The dataset contains only Kotlin files.
```json
{
"Kotlin": [".kt"]
}
```
## Licenses
Each entry in the dataset contains the associated license. The following is a list of licenses involved and their occurrences.
```json
{
"agpl-3.0": 9146,
"apache-2.0": 272388,
"artistic-2.0": 219,
"bsd-2-clause": 896,
"bsd-3-clause": 12328,
"cc0-1.0": 411,
"epl-1.0": 2111,
"gpl-2.0": 11080,
"gpl-3.0": 48911,
"isc": 997,
"lgpl-2.1": 297,
"lgpl-3.0": 7749,
"mit": 92540,
"mpl-2.0": 3386,
"unlicense": 1756
}
```
## Dataset Statistics
```json
{
"Total size": "~361 MB",
"Number of files": 464215,
"Number of files under 500 bytes": 99845,
"Average file size in bytes": 3252,
}
```
## Dataset Creation
The dataset was created using Google Query for Github:
https://cloud.google.com/blog/topics/public-datasets/github-on-bigquery-analyze-all-the-open-source-code
The following steps were pursued for data
gathering:
1. Creation of a dataset and a table in Google Big Query Project.
2. Creation of a bucket in Google Cloud Storage.
3. Creation of a query in Google Big Query Project.
4. Running the query with the setting to output the results in the dataset and table
created at step one.
5. Exporting the resulting dataset into the bucket created in step 2. Export format of JSON with gzip compression.
The result of these steps leads to the following results:
* 2.7 TB Processed,
* number of extracted rows/files was 464,215
* total logical bytes 1.46 GB.
* the result amounts to 7 json.gz files in a total of 361 MB.
The SQL Query used is:
```sql
SELECT
f.repo_name, f.path, c.copies, c.size, c.content, l.license
FROM
(select f.*, row_number() over (partition by id order by path desc) as seqnum from `bigquery-public-data.github_repos.files` AS f) f
JOIN
`bigquery-public-data.github_repos.contents` AS c
ON
f.id = c.id AND seqnum=1
JOIN
`bigquery-public-data.github_repos.licenses` AS l
ON
f.repo_name = l.repo_name
WHERE
NOT c.binary AND ((f.path LIKE '%.kt') AND (c.size BETWEEN 0 AND 1048575))
```
## Data Splits
The dataset only contains a train split.
Using the curated version of this dataset, a split was made into multiple repositories:
* Clean Version: https://huggingface.co/datasets/mvasiliniuc/iva-kotlin-codeint-clean
* Clean Version Train: https://huggingface.co/datasets/mvasiliniuc/iva-kotlin-codeint-clean-train
* Clean Version Valid: https://huggingface.co/datasets/mvasiliniuc/iva-kotlin-codeint-clean-valid
# Considerations for Using the Data
The dataset comprises source code from various repositories, potentially containing harmful or biased code,
along with sensitive information such as passwords or usernames.
# Additional Information
## Dataset Curators
[[email protected]]([email protected])
## Licensing Information
* The license of this open-source dataset is: other.
* The dataset is gathered from open-source repositories on [GitHub using BigQuery](https://cloud.google.com/blog/topics/public-datasets/github-on-bigquery-analyze-all-the-open-source-code).
* Find the license of each entry in the dataset in the corresponding license column.
## Citation Information
```json
@misc {mircea_vasiliniuc_2023,
author = { {Mircea Vasiliniuc} },
title = { iva-kotlin-codeint (Revision 1af5124) },
year = 2023,
url = { https://huggingface.co/datasets/mvasiliniuc/iva-kotlin-codeint },
doi = { 10.57967/hf/0779 },
publisher = { Hugging Face }
}
```
|
mvasiliniuc/iva-kotlin-codeint
|
[
"task_categories:text-generation",
"task_ids:language-modeling",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"size_categories:100K<n<1M",
"language:code",
"license:other",
"code, kotlin, native Android development",
"doi:10.57967/hf/0779",
"region:us"
] |
2023-04-04T18:02:39+00:00
|
{"annotations_creators": ["crowdsourced"], "language_creators": ["crowdsourced"], "language": ["code"], "license": "other", "size_categories": ["100K<n<1M"], "source_datasets": [], "task_categories": ["text-generation"], "task_ids": ["language-modeling"], "pretty_name": "iva-kotlin-codeint-raw", "tags": ["code, kotlin, native Android development"]}
|
2023-06-16T05:56:58+00:00
|
[] |
[
"code"
] |
TAGS
#task_categories-text-generation #task_ids-language-modeling #annotations_creators-crowdsourced #language_creators-crowdsourced #size_categories-100K<n<1M #language-code #license-other #code, kotlin, native Android development #doi-10.57967/hf/0779 #region-us
|
IVA Kotlin GitHub Code Dataset
==============================
Dataset Description
-------------------
This is the raw IVA Kotlin dataset extracted from GitHub.
It contains uncurated Kotlin files gathered with the purpose to train a code generation model.
The dataset consists of 464215 kotlin code files from GitHub totaling ~361 MB of data.
The dataset was created from the public GitHub dataset on Google BiqQuery.
### How to use it
To download the full dataset:
Data Structure
--------------
### Data Fields
Field: repo\_name, Type: string, Description: name of the GitHub repository
Field: path, Type: string, Description: path of the file in GitHub repository
Field: copies, Type: string, Description: number of occurrences in dataset
Field: code, Type: string, Description: content of source file
Field: size, Type: string, Description: size of the source file in bytes
Field: license, Type: string, Description: license of GitHub repository
### Instance
Languages
---------
The dataset contains only Kotlin files.
Licenses
--------
Each entry in the dataset contains the associated license. The following is a list of licenses involved and their occurrences.
Dataset Statistics
------------------
Dataset Creation
----------------
The dataset was created using Google Query for Github:
URL
The following steps were pursued for data
gathering:
1. Creation of a dataset and a table in Google Big Query Project.
2. Creation of a bucket in Google Cloud Storage.
3. Creation of a query in Google Big Query Project.
4. Running the query with the setting to output the results in the dataset and table
created at step one.
5. Exporting the resulting dataset into the bucket created in step 2. Export format of JSON with gzip compression.
The result of these steps leads to the following results:
* 2.7 TB Processed,
* number of extracted rows/files was 464,215
* total logical bytes 1.46 GB.
* the result amounts to 7 URL files in a total of 361 MB.
The SQL Query used is:
Data Splits
-----------
The dataset only contains a train split.
Using the curated version of this dataset, a split was made into multiple repositories:
* Clean Version: URL
* Clean Version Train: URL
* Clean Version Valid: URL
Considerations for Using the Data
=================================
The dataset comprises source code from various repositories, potentially containing harmful or biased code,
along with sensitive information such as passwords or usernames.
Additional Information
======================
Dataset Curators
----------------
URL@URL
Licensing Information
---------------------
* The license of this open-source dataset is: other.
* The dataset is gathered from open-source repositories on GitHub using BigQuery.
* Find the license of each entry in the dataset in the corresponding license column.
|
[
"### How to use it\n\n\nTo download the full dataset:\n\n\nData Structure\n--------------",
"### Data Fields\n\n\nField: repo\\_name, Type: string, Description: name of the GitHub repository\nField: path, Type: string, Description: path of the file in GitHub repository\nField: copies, Type: string, Description: number of occurrences in dataset\nField: code, Type: string, Description: content of source file\nField: size, Type: string, Description: size of the source file in bytes\nField: license, Type: string, Description: license of GitHub repository",
"### Instance\n\n\nLanguages\n---------\n\n\nThe dataset contains only Kotlin files.\n\n\nLicenses\n--------\n\n\nEach entry in the dataset contains the associated license. The following is a list of licenses involved and their occurrences.\n\n\nDataset Statistics\n------------------\n\n\nDataset Creation\n----------------\n\n\nThe dataset was created using Google Query for Github:\nURL\n\n\nThe following steps were pursued for data\ngathering:\n\n\n1. Creation of a dataset and a table in Google Big Query Project.\n2. Creation of a bucket in Google Cloud Storage.\n3. Creation of a query in Google Big Query Project.\n4. Running the query with the setting to output the results in the dataset and table\ncreated at step one.\n5. Exporting the resulting dataset into the bucket created in step 2. Export format of JSON with gzip compression.\n\n\nThe result of these steps leads to the following results:\n\n\n* 2.7 TB Processed,\n* number of extracted rows/files was 464,215\n* total logical bytes 1.46 GB.\n* the result amounts to 7 URL files in a total of 361 MB.\n\n\nThe SQL Query used is:\n\n\nData Splits\n-----------\n\n\nThe dataset only contains a train split.\n\n\nUsing the curated version of this dataset, a split was made into multiple repositories:\n\n\n* Clean Version: URL\n* Clean Version Train: URL\n* Clean Version Valid: URL\n\n\nConsiderations for Using the Data\n=================================\n\n\nThe dataset comprises source code from various repositories, potentially containing harmful or biased code,\nalong with sensitive information such as passwords or usernames.\n\n\nAdditional Information\n======================\n\n\nDataset Curators\n----------------\n\n\nURL@URL\n\n\nLicensing Information\n---------------------\n\n\n* The license of this open-source dataset is: other.\n* The dataset is gathered from open-source repositories on GitHub using BigQuery.\n* Find the license of each entry in the dataset in the corresponding license column."
] |
[
"TAGS\n#task_categories-text-generation #task_ids-language-modeling #annotations_creators-crowdsourced #language_creators-crowdsourced #size_categories-100K<n<1M #language-code #license-other #code, kotlin, native Android development #doi-10.57967/hf/0779 #region-us \n",
"### How to use it\n\n\nTo download the full dataset:\n\n\nData Structure\n--------------",
"### Data Fields\n\n\nField: repo\\_name, Type: string, Description: name of the GitHub repository\nField: path, Type: string, Description: path of the file in GitHub repository\nField: copies, Type: string, Description: number of occurrences in dataset\nField: code, Type: string, Description: content of source file\nField: size, Type: string, Description: size of the source file in bytes\nField: license, Type: string, Description: license of GitHub repository",
"### Instance\n\n\nLanguages\n---------\n\n\nThe dataset contains only Kotlin files.\n\n\nLicenses\n--------\n\n\nEach entry in the dataset contains the associated license. The following is a list of licenses involved and their occurrences.\n\n\nDataset Statistics\n------------------\n\n\nDataset Creation\n----------------\n\n\nThe dataset was created using Google Query for Github:\nURL\n\n\nThe following steps were pursued for data\ngathering:\n\n\n1. Creation of a dataset and a table in Google Big Query Project.\n2. Creation of a bucket in Google Cloud Storage.\n3. Creation of a query in Google Big Query Project.\n4. Running the query with the setting to output the results in the dataset and table\ncreated at step one.\n5. Exporting the resulting dataset into the bucket created in step 2. Export format of JSON with gzip compression.\n\n\nThe result of these steps leads to the following results:\n\n\n* 2.7 TB Processed,\n* number of extracted rows/files was 464,215\n* total logical bytes 1.46 GB.\n* the result amounts to 7 URL files in a total of 361 MB.\n\n\nThe SQL Query used is:\n\n\nData Splits\n-----------\n\n\nThe dataset only contains a train split.\n\n\nUsing the curated version of this dataset, a split was made into multiple repositories:\n\n\n* Clean Version: URL\n* Clean Version Train: URL\n* Clean Version Valid: URL\n\n\nConsiderations for Using the Data\n=================================\n\n\nThe dataset comprises source code from various repositories, potentially containing harmful or biased code,\nalong with sensitive information such as passwords or usernames.\n\n\nAdditional Information\n======================\n\n\nDataset Curators\n----------------\n\n\nURL@URL\n\n\nLicensing Information\n---------------------\n\n\n* The license of this open-source dataset is: other.\n* The dataset is gathered from open-source repositories on GitHub using BigQuery.\n* Find the license of each entry in the dataset in the corresponding license column."
] |
[
94,
19,
117,
436
] |
[
"passage: TAGS\n#task_categories-text-generation #task_ids-language-modeling #annotations_creators-crowdsourced #language_creators-crowdsourced #size_categories-100K<n<1M #language-code #license-other #code, kotlin, native Android development #doi-10.57967/hf/0779 #region-us \n### How to use it\n\n\nTo download the full dataset:\n\n\nData Structure\n--------------### Data Fields\n\n\nField: repo\\_name, Type: string, Description: name of the GitHub repository\nField: path, Type: string, Description: path of the file in GitHub repository\nField: copies, Type: string, Description: number of occurrences in dataset\nField: code, Type: string, Description: content of source file\nField: size, Type: string, Description: size of the source file in bytes\nField: license, Type: string, Description: license of GitHub repository"
] |
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35d6965a9f8a302e5705c5f2c3d9b038da3bea98
|
# Dataset Card for "descriptors-text-davinci-003"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Multimodal-Fatima/descriptors-text-davinci-003
|
[
"region:us"
] |
2023-04-04T18:09:30+00:00
|
{"dataset_info": {"features": [{"name": "vocab", "dtype": "string"}, {"name": "descriptions", "sequence": "string"}, {"name": "prompt_descriptions", "sequence": "string"}], "splits": [{"name": "food101", "num_bytes": 58525, "num_examples": 101}, {"name": "cifar100", "num_bytes": 54081, "num_examples": 100}, {"name": "visualgenome", "num_bytes": 1092697, "num_examples": 1913}, {"name": "dtd", "num_bytes": 25204, "num_examples": 47}, {"name": "oxfordflowers", "num_bytes": 58560, "num_examples": 102}, {"name": "oxfordpets", "num_bytes": 22322, "num_examples": 37}, {"name": "sun397", "num_bytes": 243017, "num_examples": 362}, {"name": "fgvc", "num_bytes": 74126, "num_examples": 100}, {"name": "imagenet21k", "num_bytes": 604897, "num_examples": 998}, {"name": "birdsnap", "num_bytes": 322488, "num_examples": 500}, {"name": "caltech101", "num_bytes": 56880, "num_examples": 102}, {"name": "coco", "num_bytes": 45186, "num_examples": 80}, {"name": "lvis", "num_bytes": 679195, "num_examples": 1198}, {"name": "stanfordcars", "num_bytes": 157786, "num_examples": 196}, {"name": "full", "num_bytes": 3000578, "num_examples": 4951}], "download_size": 3257945, "dataset_size": 6495542}}
|
2023-04-04T18:10:07+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "descriptors-text-davinci-003"
More Information needed
|
[
"# Dataset Card for \"descriptors-text-davinci-003\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"descriptors-text-davinci-003\"\n\nMore Information needed"
] |
[
6,
20
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"descriptors-text-davinci-003\"\n\nMore Information needed"
] |
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] |
c39e11f2dc905de801fcd83d0ae5f0f6cbb5e702
|
# Synthetic Dataset for Product Descriptions and Ads
The basic process was as follows:
1. Prompt GPT-4 to create a list of 100 sample clothing items and descriptions for those items.
2. Split the output into desired format `{"product" : "<PRODUCT NAME>", "description" : "<DESCRIPTION>"}
3. Prompt GPT-4 to create adverts for each of the 100 samples based on their name and description.
This data was not cleaned or verified manually.
|
FourthBrainGenAI/Product-Descriptions-and-Ads
|
[
"task_categories:text-generation",
"size_categories:n<1K",
"language:en",
"license:openrail",
"art",
"region:us"
] |
2023-04-04T19:11:11+00:00
|
{"language": ["en"], "license": "openrail", "size_categories": ["n<1K"], "task_categories": ["text-generation"], "pretty_name": "Product Descriptions and Ads", "dataset_info": {"features": [{"name": "product", "dtype": "string"}, {"name": "description", "dtype": "string"}, {"name": "ad", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 27511.2, "num_examples": 90}, {"name": "test", "num_bytes": 3056.8, "num_examples": 10}], "download_size": 24914, "dataset_size": 30568}, "tags": ["art"]}
|
2023-04-04T19:26:04+00:00
|
[] |
[
"en"
] |
TAGS
#task_categories-text-generation #size_categories-n<1K #language-English #license-openrail #art #region-us
|
# Synthetic Dataset for Product Descriptions and Ads
The basic process was as follows:
1. Prompt GPT-4 to create a list of 100 sample clothing items and descriptions for those items.
2. Split the output into desired format '{"product" : "<PRODUCT NAME>", "description" : "<DESCRIPTION>"}
3. Prompt GPT-4 to create adverts for each of the 100 samples based on their name and description.
This data was not cleaned or verified manually.
|
[
"# Synthetic Dataset for Product Descriptions and Ads\n\nThe basic process was as follows:\n\n1. Prompt GPT-4 to create a list of 100 sample clothing items and descriptions for those items.\n2. Split the output into desired format '{\"product\" : \"<PRODUCT NAME>\", \"description\" : \"<DESCRIPTION>\"}\n3. Prompt GPT-4 to create adverts for each of the 100 samples based on their name and description.\n\nThis data was not cleaned or verified manually."
] |
[
"TAGS\n#task_categories-text-generation #size_categories-n<1K #language-English #license-openrail #art #region-us \n",
"# Synthetic Dataset for Product Descriptions and Ads\n\nThe basic process was as follows:\n\n1. Prompt GPT-4 to create a list of 100 sample clothing items and descriptions for those items.\n2. Split the output into desired format '{\"product\" : \"<PRODUCT NAME>\", \"description\" : \"<DESCRIPTION>\"}\n3. Prompt GPT-4 to create adverts for each of the 100 samples based on their name and description.\n\nThis data was not cleaned or verified manually."
] |
[
39,
116
] |
[
"passage: TAGS\n#task_categories-text-generation #size_categories-n<1K #language-English #license-openrail #art #region-us \n# Synthetic Dataset for Product Descriptions and Ads\n\nThe basic process was as follows:\n\n1. Prompt GPT-4 to create a list of 100 sample clothing items and descriptions for those items.\n2. Split the output into desired format '{\"product\" : \"<PRODUCT NAME>\", \"description\" : \"<DESCRIPTION>\"}\n3. Prompt GPT-4 to create adverts for each of the 100 samples based on their name and description.\n\nThis data was not cleaned or verified manually."
] |
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052a0647be4e9f75ecd608f1edb2024858271189
|
# Dataset Card for "martin_valen_dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
FourthBrainGenAI/martin_valen_dataset
|
[
"region:us"
] |
2023-04-04T19:32:04+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 82775.0, "num_examples": 10}], "download_size": 82229, "dataset_size": 82775.0}, "viewer": true}
|
2023-04-04T19:45:12+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "martin_valen_dataset"
More Information needed
|
[
"# Dataset Card for \"martin_valen_dataset\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"martin_valen_dataset\"\n\nMore Information needed"
] |
[
6,
17
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"martin_valen_dataset\"\n\nMore Information needed"
] |
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] |
f57bb4ece8cfe5ca7e724484e0d94cb38aab5dfe
|
# Milan Internet Traffic Dataset
This dataset contains information about hourly internet traffic in Milan between 2013-11-01 and 2014-01-01.
|
isaquecerqueira/millan_internet_traffic
|
[
"license:odbl",
"region:us"
] |
2023-04-04T19:59:56+00:00
|
{"license": "odbl"}
|
2023-04-05T18:04:39+00:00
|
[] |
[] |
TAGS
#license-odbl #region-us
|
# Milan Internet Traffic Dataset
This dataset contains information about hourly internet traffic in Milan between 2013-11-01 and 2014-01-01.
|
[
"# Milan Internet Traffic Dataset\n\nThis dataset contains information about hourly internet traffic in Milan between 2013-11-01 and 2014-01-01."
] |
[
"TAGS\n#license-odbl #region-us \n",
"# Milan Internet Traffic Dataset\n\nThis dataset contains information about hourly internet traffic in Milan between 2013-11-01 and 2014-01-01."
] |
[
12,
28
] |
[
"passage: TAGS\n#license-odbl #region-us \n# Milan Internet Traffic Dataset\n\nThis dataset contains information about hourly internet traffic in Milan between 2013-11-01 and 2014-01-01."
] |
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f2bad9671a33085b9b5e4b2646201aaa15bb6e7e
|
# Dataset Card for "custom-splits-test"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
lewtun/custom-splits-test
|
[
"region:us"
] |
2023-04-04T20:08:30+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "sadness", "1": "joy", "2": "love", "3": "anger", "4": "fear", "5": "surprise"}}}}], "splits": [{"name": "custom_split_1", "num_bytes": 1306149.75, "num_examples": 12000}, {"name": "custom_split_2", "num_bytes": 435383.25, "num_examples": 4000}], "download_size": 1029673, "dataset_size": 1741533.0}}
|
2023-04-04T20:09:06+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "custom-splits-test"
More Information needed
|
[
"# Dataset Card for \"custom-splits-test\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"custom-splits-test\"\n\nMore Information needed"
] |
[
6,
18
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"custom-splits-test\"\n\nMore Information needed"
] |
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817f968ee225b2fb8212d7e73cbe257f981980ab
|
# Dataset Card for "multiCorp_tokenized_split_0404_dev"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Brizape/multiCorp_tokenized_split_0404_dev
|
[
"region:us"
] |
2023-04-04T20:56:29+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "tokens", "sequence": "string"}, {"name": "ner_tags", "sequence": "int64"}, {"name": "texts", "dtype": "string"}, {"name": "input_ids", "sequence": "int32"}, {"name": "token_type_ids", "sequence": "int8"}, {"name": "attention_mask", "sequence": "int8"}, {"name": "labels", "sequence": "int64"}], "splits": [{"name": "test", "num_bytes": 6729090.0, "num_examples": 2035}, {"name": "train", "num_bytes": 19925705.0, "num_examples": 5165}, {"name": "validation", "num_bytes": 5290716.0, "num_examples": 1293}], "download_size": 5716475, "dataset_size": 31945511.0}}
|
2023-04-05T07:53:47+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "multiCorp_tokenized_split_0404_dev"
More Information needed
|
[
"# Dataset Card for \"multiCorp_tokenized_split_0404_dev\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"multiCorp_tokenized_split_0404_dev\"\n\nMore Information needed"
] |
[
6,
25
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"multiCorp_tokenized_split_0404_dev\"\n\nMore Information needed"
] |
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] |
d08af28350a4c0caddc9c925f046fe2bd095cc5e
|
# Milan SMS Traffic Dataset
This dataset contains information about hourly sms traffic in Milan between 2013-11-01 and 2014-01-01.
|
isaquecerqueira/millan_sms_traffic
|
[
"license:odbl",
"region:us"
] |
2023-04-04T21:15:28+00:00
|
{"license": "odbl"}
|
2023-04-06T14:01:03+00:00
|
[] |
[] |
TAGS
#license-odbl #region-us
|
# Milan SMS Traffic Dataset
This dataset contains information about hourly sms traffic in Milan between 2013-11-01 and 2014-01-01.
|
[
"# Milan SMS Traffic Dataset\n\nThis dataset contains information about hourly sms traffic in Milan between 2013-11-01 and 2014-01-01."
] |
[
"TAGS\n#license-odbl #region-us \n",
"# Milan SMS Traffic Dataset\n\nThis dataset contains information about hourly sms traffic in Milan between 2013-11-01 and 2014-01-01."
] |
[
12,
28
] |
[
"passage: TAGS\n#license-odbl #region-us \n# Milan SMS Traffic Dataset\n\nThis dataset contains information about hourly sms traffic in Milan between 2013-11-01 and 2014-01-01."
] |
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403db13fd46bfc7c3333052f96cb621c8a79de7c
|
# Open Instruct V1 - A dataset for having LLMs follow instructions.
Open Instruct V1 is an amalgamation of different datasets which are cleaned and then collated into a singular format for training.
## Dataset Breakdown
| Dataset | Amount of Samples |
|----------------|-------------------|
| [Alpaca](https://github.com/tatsu-lab/stanford_alpaca) | 51759 |
| [Self Instruct](https://github.com/yizhongw/self-instruct) | 82599 |
| [GPT-4 Instruct](https://github.com/teknium1/GPTeacher) | 18194 |
| [Code Alpaca](https://huggingface.co/datasets/HuggingFaceH4/CodeAlpaca_20K) | 18019 |
| [Dolly](https://huggingface.co/datasets/HuggingFaceH4/databricks_dolly_15k) | 15015 |
| [Synthetic](https://huggingface.co/datasets/Dahoas/synthetic-instruct-gptj-pairwise) | 33143 |
| [Roleplay](https://github.com/teknium1/GPTeacher) | 3146 |
| [asss](https://huggingface.co/datasets/HuggingFaceH4/asss) | 448 |
| [instruction-dataset](https://huggingface.co/datasets/HuggingFaceH4/instruction-dataset) | 327 |
| Total | 222650 |
|
hakurei/open-instruct-v1
|
[
"task_categories:text-generation",
"size_categories:100K<n<1M",
"language:en",
"license:apache-2.0",
"region:us"
] |
2023-04-04T22:10:41+00:00
|
{"language": ["en"], "license": "apache-2.0", "size_categories": ["100K<n<1M"], "task_categories": ["text-generation"]}
|
2023-04-17T02:03:13+00:00
|
[] |
[
"en"
] |
TAGS
#task_categories-text-generation #size_categories-100K<n<1M #language-English #license-apache-2.0 #region-us
|
Open Instruct V1 - A dataset for having LLMs follow instructions.
=================================================================
Open Instruct V1 is an amalgamation of different datasets which are cleaned and then collated into a singular format for training.
Dataset Breakdown
-----------------
|
[] |
[
"TAGS\n#task_categories-text-generation #size_categories-100K<n<1M #language-English #license-apache-2.0 #region-us \n"
] |
[
41
] |
[
"passage: TAGS\n#task_categories-text-generation #size_categories-100K<n<1M #language-English #license-apache-2.0 #region-us \n"
] |
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] |
0975f85d8cb47b89f077d4eca4b39eef3c49a009
|
# AutoTrain Dataset for project: syn
## Dataset Description
This dataset has been automatically processed by AutoTrain for project syn.
### Languages
The BCP-47 code for the dataset's language is it.
## Dataset Structure
### Data Instances
A sample from this dataset looks as follows:
```json
[
{
"tokens": [
"EXPORT",
"CAUCEDO",
"o",
"RIO",
"HAINA",
"Q2lhbywgYnVvbmdpb3JubyDwn5iKDQoNCk5zIGNsaWVudGUgVklQDQoNCkRFU1RJTkFaSU9ORSBD",
";",
"RlMgQ0FVQ0VETyBvcHB1cmUgUklPIEhBSU5BDQoNCkJBTkNBTEkgTk9OIFNPVlJBUFBPTklCSUxJ",
";",
"DQpSaXRpcm8gYy9vIEZPTlRFVklWTyAoUFIpDQoNCk1FUkNFIElNTyBjb21lIGRhIGRvY3MgYWxs",
";",
"ZWdhdGkNCg0KODAzMTEzOTANCkRHUiArIE5ER1INCg0KDQpOUiBCT1gNCkRJTS4NCkdST1NTDQpO",
";",
"RVQNCjIwMjMwMDMxMjk4DQpsIDEyMCBMIDgwIGggNzQNCjI5Myw2NzMNCjI3Nyw2MTQNCjIwMjMw",
";",
"MDMxMzAyDQpsIDEyMCBMIDgwIGggMTEwDQozOTksOTg1DQozOTUsMg0KMjAyMzAwMzEzMDMNCmwg",
";",
"MTIwIEwgODAgaCA5Mg0KMzM1LDQ0DQozMjUsNzk1DQoyMDIzMDAzMTMwNA0KbCAxMjAgTCA4MCBo",
";",
"IDgwDQoyNDksMDY0DQoyMzYsMjExDQoyMDIzMDAzMTMwNw0KbCAxMjAgTCA4MCBoIDEzMQ0KNTA1",
";",
"LDM1NQ0KNDgwLDg3DQoyMDIzMDAzMTMwOA0KbCAxMjAgTCA4MCBoIDEzMQ0KNTA3LDgNCjQ4Niw4",
";",
"MQ0KMjAyMzAwMzEzMDkNCmwgMTIwIEwgODAgaCAxMjENCjQ0NCwwOA0KNDQyLDgNCjIwMjMwMDMx",
";",
"MzEwDQpsIDEyMCBMIDgwIGggMTE3DQo0MDMsNjENCjM3Niw5MTUNCjIwMjMwMDMxMzEyDQpsIDEy",
";",
"MCBMIDgwIGggMTA3DQozNzMsNTQNCjM1MCwwNDINCjIwMjMwMDMxMzEzDQpsIDEyMCBMIDgwIGgg",
";",
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";",
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}
]
```
### Dataset Fields
The dataset has the following fields (also called "features"):
```json
{
"tokens": "Sequence(feature=Value(dtype='string', id=None), length=-1, id=None)",
"tags": "Sequence(feature=ClassLabel(names=['O', 'commodity', 'company', 'delivery_cap', 'delivery_location', 'delivery_port', 'delivery_state', 'incoterms', 'measures', 'package_type', 'pickup_cap', 'pickup_location', 'pickup_port', 'pickup_state', 'quantity', 'stackable', 'volume', 'weight'], id=None), length=-1, id=None)"
}
```
### Dataset Splits
This dataset is split into a train and validation split. The split sizes are as follow:
| Split name | Num samples |
| ------------ | ------------------- |
| train | 303 |
| valid | 155 |
|
sxandie/autotrain-data-syn
|
[
"task_categories:token-classification",
"language:it",
"region:us"
] |
2023-04-04T22:14:15+00:00
|
{"language": ["it"], "task_categories": ["token-classification"]}
|
2023-04-05T00:55:13+00:00
|
[] |
[
"it"
] |
TAGS
#task_categories-token-classification #language-Italian #region-us
|
AutoTrain Dataset for project: syn
==================================
Dataset Description
-------------------
This dataset has been automatically processed by AutoTrain for project syn.
### Languages
The BCP-47 code for the dataset's language is it.
Dataset Structure
-----------------
### Data Instances
A sample from this dataset looks as follows:
### Dataset Fields
The dataset has the following fields (also called "features"):
### Dataset Splits
This dataset is split into a train and validation split. The split sizes are as follow:
|
[
"### Languages\n\n\nThe BCP-47 code for the dataset's language is it.\n\n\nDataset Structure\n-----------------",
"### Data Instances\n\n\nA sample from this dataset looks as follows:",
"### Dataset Fields\n\n\nThe dataset has the following fields (also called \"features\"):",
"### Dataset Splits\n\n\nThis dataset is split into a train and validation split. The split sizes are as follow:"
] |
[
"TAGS\n#task_categories-token-classification #language-Italian #region-us \n",
"### Languages\n\n\nThe BCP-47 code for the dataset's language is it.\n\n\nDataset Structure\n-----------------",
"### Data Instances\n\n\nA sample from this dataset looks as follows:",
"### Dataset Fields\n\n\nThe dataset has the following fields (also called \"features\"):",
"### Dataset Splits\n\n\nThis dataset is split into a train and validation split. The split sizes are as follow:"
] |
[
23,
26,
17,
23,
27
] |
[
"passage: TAGS\n#task_categories-token-classification #language-Italian #region-us \n### Languages\n\n\nThe BCP-47 code for the dataset's language is it.\n\n\nDataset Structure\n-----------------### Data Instances\n\n\nA sample from this dataset looks as follows:### Dataset Fields\n\n\nThe dataset has the following fields (also called \"features\"):### Dataset Splits\n\n\nThis dataset is split into a train and validation split. The split sizes are as follow:"
] |
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8d54699247df87172b49024418493e7882e74671
|
# Dataset Card for "l-gpt4"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Thewillonline/l-gpt4
|
[
"region:us"
] |
2023-04-04T22:18:51+00:00
|
{"dataset_info": {"features": [{"name": "question", "dtype": "string"}, {"name": "answer", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 20624506405, "num_examples": 2841711}], "download_size": 12706539991, "dataset_size": 20624506405}}
|
2023-04-05T08:10:57+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "l-gpt4"
More Information needed
|
[
"# Dataset Card for \"l-gpt4\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"l-gpt4\"\n\nMore Information needed"
] |
[
6,
15
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"l-gpt4\"\n\nMore Information needed"
] |
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] |
d89b43702a477b73144b7dbbee756aad626ed5ce
|
# Dataset Card for "biomedical-ner"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
dylanmontoya22/biomedical-ner
|
[
"region:us"
] |
2023-04-04T22:41:42+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "tokens", "sequence": "string"}, {"name": "prediction", "sequence": "null"}, {"name": "prediction_agent", "dtype": "string"}, {"name": "annotation", "list": [{"name": "end", "dtype": "int64"}, {"name": "label", "dtype": "string"}, {"name": "start", "dtype": "int64"}]}, {"name": "annotation_agent", "dtype": "string"}, {"name": "vectors", "dtype": "null"}, {"name": "id", "dtype": "string"}, {"name": "metadata", "dtype": "null"}, {"name": "status", "dtype": "string"}, {"name": "event_timestamp", "dtype": "timestamp[us]"}, {"name": "metrics", "struct": [{"name": "annotated", "struct": [{"name": "mentions", "list": [{"name": "capitalness", "dtype": "string"}, {"name": "chars_length", "dtype": "int64"}, {"name": "density", "dtype": "float64"}, {"name": "label", "dtype": "string"}, {"name": "score", "dtype": "float64"}, {"name": "tokens_length", "dtype": "int64"}, {"name": "value", "dtype": "string"}]}, {"name": "tags", "list": [{"name": "tag", "dtype": "string"}, {"name": "value", "dtype": "string"}]}]}, {"name": "predicted", "struct": [{"name": "mentions", "sequence": "null"}, {"name": "tags", "list": [{"name": "tag", "dtype": "string"}, {"name": "value", "dtype": "string"}]}]}, {"name": "text_length", "dtype": "int64"}, {"name": "tokens", "list": [{"name": "capitalness", "dtype": "string"}, {"name": "char_end", "dtype": "int64"}, {"name": "char_start", "dtype": "int64"}, {"name": "custom", "dtype": "null"}, {"name": "idx", "dtype": "int64"}, {"name": "length", "dtype": "int64"}, {"name": "score", "dtype": "null"}, {"name": "tag", "dtype": "string"}, {"name": "value", "dtype": "string"}]}, {"name": "tokens_length", "dtype": "int64"}]}], "splits": [{"name": "train", "num_bytes": 1055927, "num_examples": 1000}], "download_size": 184089, "dataset_size": 1055927}}
|
2023-04-04T22:44:13+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "biomedical-ner"
More Information needed
|
[
"# Dataset Card for \"biomedical-ner\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"biomedical-ner\"\n\nMore Information needed"
] |
[
6,
15
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"biomedical-ner\"\n\nMore Information needed"
] |
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] |
fb6e1e26de8b797d3fade56c3106d3646ea25348
|
## ChatGPT Conversations from Chatlogs.net
This dataset contains 89,288 conversations conversations between users and ChatGPT. Version 1 contains all conversations available up to the cutoff date of April 4, 2023. Version 2 contains all conversations available up to the cutoff date of April 20, 2023.
## Source Data
The conversations were scraped from the website Chatlogs.net.
The data was generated using a custom scraper that can be found here: https://github.com/P1ayer-1/chatlogs.net-scraper
---
license: cc-by-4.0
---
|
P1ayer-1/chatgpt-conversations-chatlogs.net
|
[
"doi:10.57967/hf/0643",
"region:us"
] |
2023-04-04T23:24:23+00:00
|
{}
|
2023-05-04T02:14:33+00:00
|
[] |
[] |
TAGS
#doi-10.57967/hf/0643 #region-us
|
## ChatGPT Conversations from URL
This dataset contains 89,288 conversations conversations between users and ChatGPT. Version 1 contains all conversations available up to the cutoff date of April 4, 2023. Version 2 contains all conversations available up to the cutoff date of April 20, 2023.
## Source Data
The conversations were scraped from the website URL.
The data was generated using a custom scraper that can be found here: URL
---
license: cc-by-4.0
---
|
[
"## ChatGPT Conversations from URL\nThis dataset contains 89,288 conversations conversations between users and ChatGPT. Version 1 contains all conversations available up to the cutoff date of April 4, 2023. Version 2 contains all conversations available up to the cutoff date of April 20, 2023.",
"## Source Data\nThe conversations were scraped from the website URL.\n\nThe data was generated using a custom scraper that can be found here: URL\n\n\n\n---\nlicense: cc-by-4.0\n\n---"
] |
[
"TAGS\n#doi-10.57967/hf/0643 #region-us \n",
"## ChatGPT Conversations from URL\nThis dataset contains 89,288 conversations conversations between users and ChatGPT. Version 1 contains all conversations available up to the cutoff date of April 4, 2023. Version 2 contains all conversations available up to the cutoff date of April 20, 2023.",
"## Source Data\nThe conversations were scraped from the website URL.\n\nThe data was generated using a custom scraper that can be found here: URL\n\n\n\n---\nlicense: cc-by-4.0\n\n---"
] |
[
18,
68,
41
] |
[
"passage: TAGS\n#doi-10.57967/hf/0643 #region-us \n## ChatGPT Conversations from URL\nThis dataset contains 89,288 conversations conversations between users and ChatGPT. Version 1 contains all conversations available up to the cutoff date of April 4, 2023. Version 2 contains all conversations available up to the cutoff date of April 20, 2023.## Source Data\nThe conversations were scraped from the website URL.\n\nThe data was generated using a custom scraper that can be found here: URL\n\n\n\n---\nlicense: cc-by-4.0\n\n---"
] |
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6dffc9dd661a459ce4d0137f637f7979c1a3d2ae
|
# Dataset Card for "FGVC_Aircraft_test_google_flan_t5_xxl_mode_T_SPECIFIC_ns_100"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
CVasNLPExperiments/FGVC_Aircraft_test_google_flan_t5_xxl_mode_T_SPECIFIC_ns_100
|
[
"region:us"
] |
2023-04-04T23:31:35+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "prompt", "dtype": "string"}, {"name": "true_label", "dtype": "string"}, {"name": "prediction", "dtype": "string"}], "splits": [{"name": "fewshot_0_clip_tags_ViT_L_14_Attributes_ViT_L_14_text_davinci_003_full_clip_tags_ViT_L_14_simple_specific_rices", "num_bytes": 14364, "num_examples": 100}], "download_size": 3992, "dataset_size": 14364}}
|
2023-04-04T23:31:37+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "FGVC_Aircraft_test_google_flan_t5_xxl_mode_T_SPECIFIC_ns_100"
More Information needed
|
[
"# Dataset Card for \"FGVC_Aircraft_test_google_flan_t5_xxl_mode_T_SPECIFIC_ns_100\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"FGVC_Aircraft_test_google_flan_t5_xxl_mode_T_SPECIFIC_ns_100\"\n\nMore Information needed"
] |
[
6,
39
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"FGVC_Aircraft_test_google_flan_t5_xxl_mode_T_SPECIFIC_ns_100\"\n\nMore Information needed"
] |
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f7486bea4aeee68475bb28e1a17170f17eb85e87
|
# Dataset Card for "biobert-ner-medical-text"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
dylanmontoya22/biobert-ner-medical-text
|
[
"region:us"
] |
2023-04-04T23:38:56+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "tokens", "sequence": "string"}, {"name": "annotation", "list": [{"name": "end", "dtype": "int64"}, {"name": "label", "dtype": "string"}, {"name": "start", "dtype": "int64"}]}], "splits": [{"name": "train", "num_bytes": 117531.98, "num_examples": 710}], "download_size": 24684, "dataset_size": 117531.98}}
|
2023-04-04T23:39:15+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "biobert-ner-medical-text"
More Information needed
|
[
"# Dataset Card for \"biobert-ner-medical-text\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"biobert-ner-medical-text\"\n\nMore Information needed"
] |
[
6,
19
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"biobert-ner-medical-text\"\n\nMore Information needed"
] |
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5b5cc8fb6047bb8d8a272fd9628cfabcfdb6bedf
|
# Dataset Card for "FGVC_Aircraft_test_google_flan_t5_xxl_mode_A_ns_3333"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
CVasNLPExperiments/FGVC_Aircraft_test_google_flan_t5_xxl_mode_A_ns_3333
|
[
"region:us"
] |
2023-04-04T23:48:01+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "prompt", "dtype": "string"}, {"name": "true_label", "dtype": "string"}, {"name": "prediction", "dtype": "string"}], "splits": [{"name": "fewshot_0_clip_tags_ViT_L_14_Attributes_ViT_L_14_text_davinci_003_full_clip_tags_ViT_L_14_simple_specific_rices", "num_bytes": 1426346, "num_examples": 3333}, {"name": "fewshot_0__Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full_clip_tags_LAION_ViT_H_14_2B_simple_specific_rices", "num_bytes": 1363907, "num_examples": 3333}], "download_size": 418785, "dataset_size": 2790253}}
|
2023-05-22T19:50:18+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "FGVC_Aircraft_test_google_flan_t5_xxl_mode_A_ns_3333"
More Information needed
|
[
"# Dataset Card for \"FGVC_Aircraft_test_google_flan_t5_xxl_mode_A_ns_3333\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"FGVC_Aircraft_test_google_flan_t5_xxl_mode_A_ns_3333\"\n\nMore Information needed"
] |
[
6,
36
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"FGVC_Aircraft_test_google_flan_t5_xxl_mode_A_ns_3333\"\n\nMore Information needed"
] |
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3fd409654463cb36d5b228d55e1612c422b6011f
|
# Dataset Card for "FGVC_Aircraft_test_google_flan_t5_xxl_mode_A_ns_200"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
CVasNLPExperiments/FGVC_Aircraft_test_google_flan_t5_xxl_mode_A_ns_200
|
[
"region:us"
] |
2023-04-04T23:57:13+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "prompt", "dtype": "string"}, {"name": "true_label", "dtype": "string"}, {"name": "prediction", "dtype": "string"}], "splits": [{"name": "fewshot_0_clip_tags_ViT_L_14_Attributes_ViT_L_14_text_davinci_003_full_clip_tags_ViT_L_14_simple_specific_rices", "num_bytes": 84203, "num_examples": 200}], "download_size": 17928, "dataset_size": 84203}}
|
2023-04-04T23:57:15+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "FGVC_Aircraft_test_google_flan_t5_xxl_mode_A_ns_200"
More Information needed
|
[
"# Dataset Card for \"FGVC_Aircraft_test_google_flan_t5_xxl_mode_A_ns_200\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"FGVC_Aircraft_test_google_flan_t5_xxl_mode_A_ns_200\"\n\nMore Information needed"
] |
[
6,
35
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"FGVC_Aircraft_test_google_flan_t5_xxl_mode_A_ns_200\"\n\nMore Information needed"
] |
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68085941a35dcbf8f6fa373b7c9af983405727d5
|
# Dataset Card for "FGVC_Aircraft_test_google_flan_t5_xxl_mode_T_A_ns_100"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
CVasNLPExperiments/FGVC_Aircraft_test_google_flan_t5_xxl_mode_T_A_ns_100
|
[
"region:us"
] |
2023-04-05T00:15:00+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "prompt", "dtype": "string"}, {"name": "true_label", "dtype": "string"}, {"name": "prediction", "dtype": "string"}], "splits": [{"name": "fewshot_0_clip_tags_ViT_L_14_Attributes_ViT_L_14_text_davinci_003_full_clip_tags_ViT_L_14_simple_specific_rices", "num_bytes": 55460, "num_examples": 100}, {"name": "fewshot_0_clip_tags_ViT_L_14_with_openai_classes_Attributes_ViT_L_14_text_davinci_003_full_clip_tags_ViT_L_14_simple_specific_rices", "num_bytes": 56903, "num_examples": 100}], "download_size": 31949, "dataset_size": 112363}}
|
2023-04-05T00:17:49+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "FGVC_Aircraft_test_google_flan_t5_xxl_mode_T_A_ns_100"
More Information needed
|
[
"# Dataset Card for \"FGVC_Aircraft_test_google_flan_t5_xxl_mode_T_A_ns_100\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"FGVC_Aircraft_test_google_flan_t5_xxl_mode_T_A_ns_100\"\n\nMore Information needed"
] |
[
6,
37
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"FGVC_Aircraft_test_google_flan_t5_xxl_mode_T_A_ns_100\"\n\nMore Information needed"
] |
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8f05e5548657b057150a2556baf7c90ea6cc95f0
|
# Dataset Card for "FGVC_Aircraft_test_google_flan_t5_xxl_mode_T_SPECIFIC_A_ns_100"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
CVasNLPExperiments/FGVC_Aircraft_test_google_flan_t5_xxl_mode_T_SPECIFIC_A_ns_100
|
[
"region:us"
] |
2023-04-05T00:19:41+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "prompt", "dtype": "string"}, {"name": "true_label", "dtype": "string"}, {"name": "prediction", "dtype": "string"}], "splits": [{"name": "fewshot_0_clip_tags_ViT_L_14_with_openai_classes_Attributes_ViT_L_14_text_davinci_003_full_clip_tags_ViT_L_14_simple_specific_rices", "num_bytes": 44840, "num_examples": 100}], "download_size": 12275, "dataset_size": 44840}}
|
2023-04-05T00:42:31+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "FGVC_Aircraft_test_google_flan_t5_xxl_mode_T_SPECIFIC_A_ns_100"
More Information needed
|
[
"# Dataset Card for \"FGVC_Aircraft_test_google_flan_t5_xxl_mode_T_SPECIFIC_A_ns_100\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"FGVC_Aircraft_test_google_flan_t5_xxl_mode_T_SPECIFIC_A_ns_100\"\n\nMore Information needed"
] |
[
6,
41
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"FGVC_Aircraft_test_google_flan_t5_xxl_mode_T_SPECIFIC_A_ns_100\"\n\nMore Information needed"
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] |
87bd8236c3ece479aed80eb1ca0dd24799fa9038
|
# V1
The images are from [SketchyCOCO](https://github.com/sysu-imsl/SketchyCOCO). 🤗
Things to improve:
- Better prompts
- More variety
- More sheeps
|
GreeneryScenery/SheepsNet
|
[
"art",
"SketchyCOCO",
"region:us"
] |
2023-04-05T00:22:46+00:00
|
{"tags": ["art", "SketchyCOCO"]}
|
2023-04-07T00:51:01+00:00
|
[] |
[] |
TAGS
#art #SketchyCOCO #region-us
|
# V1
The images are from SketchyCOCO.
Things to improve:
- Better prompts
- More variety
- More sheeps
|
[
"# V1\n\nThe images are from SketchyCOCO. \n\nThings to improve:\n- Better prompts\n- More variety\n- More sheeps"
] |
[
"TAGS\n#art #SketchyCOCO #region-us \n",
"# V1\n\nThe images are from SketchyCOCO. \n\nThings to improve:\n- Better prompts\n- More variety\n- More sheeps"
] |
[
14,
29
] |
[
"passage: TAGS\n#art #SketchyCOCO #region-us \n# V1\n\nThe images are from SketchyCOCO. \n\nThings to improve:\n- Better prompts\n- More variety\n- More sheeps"
] |
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37cb317784d4733cbbe6bb4b0ab5fe4ea381c449
|
# Dataset Card for "FGVC_Aircraft_test_google_flan_t5_xxl_mode_T_SPECIFIC_A_ns_3333"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
CVasNLPExperiments/FGVC_Aircraft_test_google_flan_t5_xxl_mode_T_SPECIFIC_A_ns_3333
|
[
"region:us"
] |
2023-04-05T00:37:19+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "prompt", "dtype": "string"}, {"name": "true_label", "dtype": "string"}, {"name": "prediction", "dtype": "string"}], "splits": [{"name": "fewshot_0_clip_tags_ViT_L_14_with_openai_classes_Attributes_ViT_L_14_text_davinci_003_full_clip_tags_ViT_L_14_simple_specific_rices", "num_bytes": 1492908, "num_examples": 3333}, {"name": "fewshot_1_clip_tags_ViT_L_14_Attributes_ViT_L_14_text_davinci_003_full_clip_tags_ViT_L_14_simple_specific_rices", "num_bytes": 2893373, "num_examples": 3333}, {"name": "fewshot_3_clip_tags_ViT_L_14_Attributes_ViT_L_14_text_davinci_003_full_clip_tags_ViT_L_14_simple_specific_rices", "num_bytes": 5702031, "num_examples": 3333}, {"name": "fewshot_0__Attributes_ViT_B_16_descriptors_text_davinci_003_full_clip_tags_ViT_B_16_simple_specific_rices", "num_bytes": 1484173, "num_examples": 3333}, {"name": "fewshot_1__Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full_clip_tags_LAION_ViT_H_14_2B_simple_specific_rices", "num_bytes": 2772155, "num_examples": 3333}, {"name": "fewshot_1__Attributes_ViT_B_16_descriptors_text_davinci_003_full_clip_tags_ViT_B_16_simple_specific_rices", "num_bytes": 2876794, "num_examples": 3333}, {"name": "fewshot_3__Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full_clip_tags_LAION_ViT_H_14_2B_simple_specific_rices", "num_bytes": 5457699, "num_examples": 3333}, {"name": "fewshot_0__Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full_clip_tags_LAION_ViT_H_14_2B_simple_specific_rices", "num_bytes": 1422853, "num_examples": 3333}], "download_size": 3475675, "dataset_size": 24101986}, "configs": [{"config_name": "default", "data_files": [{"split": "fewshot_0__Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full_clip_tags_LAION_ViT_H_14_2B_simple_specific_rices", "path": "data/fewshot_0__Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full_clip_tags_LAION_ViT_H_14_2B_simple_specific_rices-*"}]}]}
|
2024-01-30T04:04:29+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "FGVC_Aircraft_test_google_flan_t5_xxl_mode_T_SPECIFIC_A_ns_3333"
More Information needed
|
[
"# Dataset Card for \"FGVC_Aircraft_test_google_flan_t5_xxl_mode_T_SPECIFIC_A_ns_3333\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"FGVC_Aircraft_test_google_flan_t5_xxl_mode_T_SPECIFIC_A_ns_3333\"\n\nMore Information needed"
] |
[
6,
42
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"FGVC_Aircraft_test_google_flan_t5_xxl_mode_T_SPECIFIC_A_ns_3333\"\n\nMore Information needed"
] |
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198e3bb0311c3e207e1501e3d3a5357146665213
|
# Dataset Card for "FGVC_Aircraft_test_google_flan_t5_xl_mode_T_SPECIFIC_A_ns_3333"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
CVasNLPExperiments/FGVC_Aircraft_test_google_flan_t5_xl_mode_T_SPECIFIC_A_ns_3333
|
[
"region:us"
] |
2023-04-05T01:03:49+00:00
|
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|
2023-05-05T23:29:55+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "FGVC_Aircraft_test_google_flan_t5_xl_mode_T_SPECIFIC_A_ns_3333"
More Information needed
|
[
"# Dataset Card for \"FGVC_Aircraft_test_google_flan_t5_xl_mode_T_SPECIFIC_A_ns_3333\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"FGVC_Aircraft_test_google_flan_t5_xl_mode_T_SPECIFIC_A_ns_3333\"\n\nMore Information needed"
] |
[
6,
41
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"FGVC_Aircraft_test_google_flan_t5_xl_mode_T_SPECIFIC_A_ns_3333\"\n\nMore Information needed"
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] |
70caee435a023f82ffff6abced046c636158388f
|
Safetensors model showing the results of my data augmentation Python script.
https://github.com/DevArqSangoi/das-dataAugmentation
|
devarqsangoi/jenna-ortega_from_das-dataAugmentation
|
[
"language:en",
"license:mit",
"region:us"
] |
2023-04-05T01:10:26+00:00
|
{"language": ["en"], "license": "mit", "pretty_name": "das-dataAugmentation"}
|
2023-04-05T01:17:46+00:00
|
[] |
[
"en"
] |
TAGS
#language-English #license-mit #region-us
|
Safetensors model showing the results of my data augmentation Python script.
URL
|
[] |
[
"TAGS\n#language-English #license-mit #region-us \n"
] |
[
15
] |
[
"passage: TAGS\n#language-English #license-mit #region-us \n"
] |
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226c592461da9b0a4c14fb9af6c73b234dd6c493
|
# Dataset Card for "FGVC_Aircraft_test_google_flan_t5_xl_mode_T_A_ns_3333"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
CVasNLPExperiments/FGVC_Aircraft_test_google_flan_t5_xl_mode_T_A_ns_3333
|
[
"region:us"
] |
2023-04-05T01:53:11+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "prompt", "dtype": "string"}, {"name": "true_label", "dtype": "string"}, {"name": "prediction", "dtype": "string"}], "splits": [{"name": "fewshot_0_clip_tags_ViT_L_14_with_openai_classes_Attributes_ViT_L_14_text_davinci_003_full_clip_tags_ViT_L_14_simple_specific_rices", "num_bytes": 1925739, "num_examples": 3333}], "download_size": 337848, "dataset_size": 1925739}}
|
2023-04-05T01:53:13+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "FGVC_Aircraft_test_google_flan_t5_xl_mode_T_A_ns_3333"
More Information needed
|
[
"# Dataset Card for \"FGVC_Aircraft_test_google_flan_t5_xl_mode_T_A_ns_3333\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"FGVC_Aircraft_test_google_flan_t5_xl_mode_T_A_ns_3333\"\n\nMore Information needed"
] |
[
6,
37
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"FGVC_Aircraft_test_google_flan_t5_xl_mode_T_A_ns_3333\"\n\nMore Information needed"
] |
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] |
01b633e2f47512d51515da6d8ba3cc5c4ccc3b5f
|
# Dataset Card for "DTD_parition1_test_google_flan_t5_xl_mode_T_SPECIFIC_A_ns_1880"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
CVasNLPExperiments/DTD_parition1_test_google_flan_t5_xl_mode_T_SPECIFIC_A_ns_1880
|
[
"region:us"
] |
2023-04-05T01:56:29+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "prompt", "dtype": "string"}, {"name": "true_label", "dtype": "string"}, {"name": "prediction", "dtype": "string"}], "splits": [{"name": "fewshot_0_clip_tags_ViT_L_14_Attributes_ViT_L_14_text_davinci_003_clip_tags_ViT_L_14_simple_specific_rices", "num_bytes": 830705, "num_examples": 1880}, {"name": "fewshot_0_clip_tags_ViT_L_14_Attributes_ViT_L_14_text_davinci_003_full_clip_tags_ViT_L_14_simple_specific_rices", "num_bytes": 834516, "num_examples": 1880}, {"name": "fewshot_1_clip_tags_ViT_L_14_Attributes_ViT_L_14_text_davinci_003_full_clip_tags_ViT_L_14_simple_specific_rices", "num_bytes": 1624134, "num_examples": 1880}, {"name": "fewshot_3_clip_tags_ViT_L_14_Attributes_ViT_L_14_text_davinci_003_full_clip_tags_ViT_L_14_simple_specific_rices", "num_bytes": 3204157, "num_examples": 1880}, {"name": "fewshot_0__Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full_clip_tags_LAION_ViT_H_14_2B_simple_specific_rices", "num_bytes": 811447, "num_examples": 1880}, {"name": "fewshot_1__Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full_clip_tags_LAION_ViT_H_14_2B_simple_specific_rices", "num_bytes": 1578034, "num_examples": 1880}, {"name": "fewshot_3__Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full_clip_tags_LAION_ViT_H_14_2B_simple_specific_rices", "num_bytes": 3113327, "num_examples": 1880}], "download_size": 2393167, "dataset_size": 11996320}}
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2023-05-05T22:55:31+00:00
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[] |
[] |
TAGS
#region-us
|
# Dataset Card for "DTD_parition1_test_google_flan_t5_xl_mode_T_SPECIFIC_A_ns_1880"
More Information needed
|
[
"# Dataset Card for \"DTD_parition1_test_google_flan_t5_xl_mode_T_SPECIFIC_A_ns_1880\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"DTD_parition1_test_google_flan_t5_xl_mode_T_SPECIFIC_A_ns_1880\"\n\nMore Information needed"
] |
[
6,
41
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"DTD_parition1_test_google_flan_t5_xl_mode_T_SPECIFIC_A_ns_1880\"\n\nMore Information needed"
] |
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0c82f4e7ed5bb540afd2e9d5be6a86df0de531b3
|
# Dataset Card for "OxfordPets_test_google_flan_t5_xl_mode_T_SPECIFIC_A_ns_3669"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
CVasNLPExperiments/OxfordPets_test_google_flan_t5_xl_mode_T_SPECIFIC_A_ns_3669
|
[
"region:us"
] |
2023-04-05T02:18:16+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "prompt", "dtype": "string"}, {"name": "true_label", "dtype": "string"}, {"name": "prediction", "dtype": "string"}], "splits": [{"name": "fewshot_0_clip_tags_ViT_L_14_Attributes_ViT_L_14_text_davinci_003_clip_tags_ViT_L_14_simple_specific_rices", "num_bytes": 1535357, "num_examples": 3669}, {"name": "fewshot_0_clip_tags_ViT_L_14_Attributes_ViT_L_14_text_davinci_003_full_clip_tags_ViT_L_14_simple_specific_rices", "num_bytes": 1535404, "num_examples": 3669}, {"name": "fewshot_3_clip_tags_ViT_L_14_Attributes_ViT_L_14_text_davinci_003_full_clip_tags_ViT_L_14_simple_specific_rices", "num_bytes": 5797400, "num_examples": 3669}, {"name": "fewshot_0__Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full_clip_tags_LAION_ViT_H_14_2B_simple_specific_rices", "num_bytes": 1457941, "num_examples": 3669}, {"name": "fewshot_1__Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full_clip_tags_LAION_ViT_H_14_2B_simple_specific_rices", "num_bytes": 2808418, "num_examples": 3669}, {"name": "fewshot_3__Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full_clip_tags_LAION_ViT_H_14_2B_simple_specific_rices", "num_bytes": 5508030, "num_examples": 3669}], "download_size": 2261474, "dataset_size": 18642550}}
|
2023-05-05T21:46:36+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "OxfordPets_test_google_flan_t5_xl_mode_T_SPECIFIC_A_ns_3669"
More Information needed
|
[
"# Dataset Card for \"OxfordPets_test_google_flan_t5_xl_mode_T_SPECIFIC_A_ns_3669\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"OxfordPets_test_google_flan_t5_xl_mode_T_SPECIFIC_A_ns_3669\"\n\nMore Information needed"
] |
[
6,
39
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"OxfordPets_test_google_flan_t5_xl_mode_T_SPECIFIC_A_ns_3669\"\n\nMore Information needed"
] |
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294ccceb4b5e44887b7d2e03de4319918ddd758b
|
# Dataset Card for "OxfordFlowers_test_google_flan_t5_xxl_mode_T_SPECIFIC_A_ns_100"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
CVasNLPExperiments/OxfordFlowers_test_google_flan_t5_xxl_mode_T_SPECIFIC_A_ns_100
|
[
"region:us"
] |
2023-04-05T02:26:03+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "prompt", "dtype": "string"}, {"name": "true_label", "dtype": "string"}, {"name": "prediction", "dtype": "string"}], "splits": [{"name": "fewshot_0_clip_tags_ViT_L_14_Attributes_ViT_L_14_text_davinci_003_full_clip_tags_ViT_L_14_simple_specific_rices", "num_bytes": 46399, "num_examples": 100}, {"name": "fewshot_1_clip_tags_ViT_L_14_Attributes_ViT_L_14_text_davinci_003_full_clip_tags_ViT_L_14_simple_specific_rices", "num_bytes": 89414, "num_examples": 100}], "download_size": 17317, "dataset_size": 135813}}
|
2023-04-05T14:03:19+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "OxfordFlowers_test_google_flan_t5_xxl_mode_T_SPECIFIC_A_ns_100"
More Information needed
|
[
"# Dataset Card for \"OxfordFlowers_test_google_flan_t5_xxl_mode_T_SPECIFIC_A_ns_100\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"OxfordFlowers_test_google_flan_t5_xxl_mode_T_SPECIFIC_A_ns_100\"\n\nMore Information needed"
] |
[
6,
40
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"OxfordFlowers_test_google_flan_t5_xxl_mode_T_SPECIFIC_A_ns_100\"\n\nMore Information needed"
] |
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17848768a0b66bdb791c671a6152b8c56888224a
|
# Dataset Card for "OxfordFlowers_test_google_flan_t5_xxl_mode_T_SPECIFIC_ns_100"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
CVasNLPExperiments/OxfordFlowers_test_google_flan_t5_xxl_mode_T_SPECIFIC_ns_100
|
[
"region:us"
] |
2023-04-05T02:28:45+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "prompt", "dtype": "string"}, {"name": "true_label", "dtype": "string"}, {"name": "prediction", "dtype": "string"}], "splits": [{"name": "fewshot_0_clip_tags_ViT_L_14_Attributes_ViT_L_14_text_davinci_003_full_clip_tags_ViT_L_14_simple_specific_rices", "num_bytes": 17698, "num_examples": 100}], "download_size": 3986, "dataset_size": 17698}}
|
2023-04-05T02:28:47+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "OxfordFlowers_test_google_flan_t5_xxl_mode_T_SPECIFIC_ns_100"
More Information needed
|
[
"# Dataset Card for \"OxfordFlowers_test_google_flan_t5_xxl_mode_T_SPECIFIC_ns_100\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"OxfordFlowers_test_google_flan_t5_xxl_mode_T_SPECIFIC_ns_100\"\n\nMore Information needed"
] |
[
6,
38
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"OxfordFlowers_test_google_flan_t5_xxl_mode_T_SPECIFIC_ns_100\"\n\nMore Information needed"
] |
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] |
8ad794c4c092baa4f54d948ad3b1022ab443ece6
|
# Dataset Card for "pokemon-and-fakemon"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
RoryCochrane/pokemon-and-fakemon
|
[
"region:us"
] |
2023-04-05T02:34:04+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 480609633.745, "num_examples": 4763}], "download_size": 391516344, "dataset_size": 480609633.745}}
|
2023-05-19T09:20:04+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "pokemon-and-fakemon"
More Information needed
|
[
"# Dataset Card for \"pokemon-and-fakemon\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"pokemon-and-fakemon\"\n\nMore Information needed"
] |
[
6,
17
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"pokemon-and-fakemon\"\n\nMore Information needed"
] |
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] |
9bb9f8c103c0cd341cc75831b79f72c227c8dfc3
|
# Dataset Card for "ask2democracy-cfqa-pension"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
jorge-henao/ask2democracy-cfqa-pension
|
[
"region:us"
] |
2023-04-05T02:40:14+00:00
|
{"dataset_info": {"features": [{"name": "instruction", "dtype": "string"}, {"name": "input", "dtype": "string"}, {"name": "output", "dtype": "string"}, {"name": "topics", "sequence": "string"}], "splits": [{"name": "train", "num_bytes": 2464607, "num_examples": 1069}], "download_size": 237794, "dataset_size": 2464607}}
|
2023-04-05T02:40:19+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "ask2democracy-cfqa-pension"
More Information needed
|
[
"# Dataset Card for \"ask2democracy-cfqa-pension\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"ask2democracy-cfqa-pension\"\n\nMore Information needed"
] |
[
6,
21
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"ask2democracy-cfqa-pension\"\n\nMore Information needed"
] |
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13a257821b1e97a71bd29d17e1b7e3238b240752
|
# Dataset Card for "ask2democracy-cfqa-salud"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
jorge-henao/ask2democracy-cfqa-salud
|
[
"region:us"
] |
2023-04-05T02:46:26+00:00
|
{"dataset_info": {"features": [{"name": "instruction", "dtype": "string"}, {"name": "input", "dtype": "string"}, {"name": "output", "dtype": "string"}, {"name": "topics", "sequence": "string"}], "splits": [{"name": "train", "num_bytes": 2592190, "num_examples": 1356}], "download_size": 309725, "dataset_size": 2592190}}
|
2023-04-05T02:46:32+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "ask2democracy-cfqa-salud"
More Information needed
|
[
"# Dataset Card for \"ask2democracy-cfqa-salud\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"ask2democracy-cfqa-salud\"\n\nMore Information needed"
] |
[
6,
22
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"ask2democracy-cfqa-salud\"\n\nMore Information needed"
] |
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] |
f0c87618a40a51a859a28759ae417cf4b8c806b0
|
# Dataset Card for "OxfordFlowers_test_google_flan_t5_xxl_mode_T_SPECIFIC_A_ns_6149"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
CVasNLPExperiments/OxfordFlowers_test_google_flan_t5_xxl_mode_T_SPECIFIC_A_ns_6149
|
[
"region:us"
] |
2023-04-05T02:53:17+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "prompt", "dtype": "string"}, {"name": "true_label", "dtype": "string"}, {"name": "prediction", "dtype": "string"}], "splits": [{"name": "fewshot_0_clip_tags_ViT_L_14_Attributes_ViT_L_14_text_davinci_003_full_clip_tags_ViT_L_14_simple_specific_rices", "num_bytes": 2744420, "num_examples": 6149}, {"name": "fewshot_1_clip_tags_ViT_L_14_Attributes_ViT_L_14_text_davinci_003_full_clip_tags_ViT_L_14_simple_specific_rices", "num_bytes": 5301849, "num_examples": 6149}, {"name": "fewshot_3_clip_tags_ViT_L_14_Attributes_ViT_L_14_text_davinci_003_full_clip_tags_ViT_L_14_simple_specific_rices", "num_bytes": 10411971, "num_examples": 6149}, {"name": "fewshot_0__Attributes_ViT_B_16_descriptors_text_davinci_003_full_clip_tags_ViT_B_16_simple_specific_rices", "num_bytes": 2707863, "num_examples": 6149}, {"name": "fewshot_1__Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full_clip_tags_LAION_ViT_H_14_2B_simple_specific_rices", "num_bytes": 5047853, "num_examples": 6149}, {"name": "fewshot_1__Attributes_ViT_B_16_descriptors_text_davinci_003_full_clip_tags_ViT_B_16_simple_specific_rices", "num_bytes": 5240644, "num_examples": 6149}, {"name": "fewshot_3__Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full_clip_tags_LAION_ViT_H_14_2B_simple_specific_rices", "num_bytes": 9920183, "num_examples": 6149}, {"name": "fewshot_0__Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full_clip_tags_LAION_ViT_H_14_2B_simple_specific_rices", "num_bytes": 2610935, "num_examples": 6149}], "download_size": 4051257, "dataset_size": 43985718}, "configs": [{"config_name": "default", "data_files": [{"split": "fewshot_0__Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full_clip_tags_LAION_ViT_H_14_2B_simple_specific_rices", "path": "data/fewshot_0__Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full_clip_tags_LAION_ViT_H_14_2B_simple_specific_rices-*"}]}]}
|
2024-01-30T04:21:36+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "OxfordFlowers_test_google_flan_t5_xxl_mode_T_SPECIFIC_A_ns_6149"
More Information needed
|
[
"# Dataset Card for \"OxfordFlowers_test_google_flan_t5_xxl_mode_T_SPECIFIC_A_ns_6149\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"OxfordFlowers_test_google_flan_t5_xxl_mode_T_SPECIFIC_A_ns_6149\"\n\nMore Information needed"
] |
[
6,
41
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"OxfordFlowers_test_google_flan_t5_xxl_mode_T_SPECIFIC_A_ns_6149\"\n\nMore Information needed"
] |
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fba35db7041c88628449c7fc4b2cfb0cdc391270
|
# Prompt-Reply Objects from Origin to March 2023 with top 5 Comments
## Source Data
The data for this project was compiled from three main sources:
Pushshift Reddit submissions dataset, which includes the following fields: "title, post_id, over_18, subreddit, link_flair_text, self_text"
BigQuery, where the Pushshift data was uploaded and queried for submissions from the r/math subreddit
A web scraper (https://github.com/P1ayer-1/Reddit-Convo-Tree-Builder) used to extract updated post content, including any replies to the original posts
In cases where the updated content was not available (e.g., because it was deleted), the title or self_text from the original Pushshift submissions was used to create a more comprehensive dataset.
## Output Data
The output data is in JSON Lines format and includes prompt-reply objects for posts from the r/math subreddit, along with the top 5 comments for each post.
---
license: cc-by-4.0
---
|
P1ayer-1/reddit-math
|
[
"region:us"
] |
2023-04-05T03:18:05+00:00
|
{}
|
2023-04-05T06:21:21+00:00
|
[] |
[] |
TAGS
#region-us
|
# Prompt-Reply Objects from Origin to March 2023 with top 5 Comments
## Source Data
The data for this project was compiled from three main sources:
Pushshift Reddit submissions dataset, which includes the following fields: "title, post_id, over_18, subreddit, link_flair_text, self_text"
BigQuery, where the Pushshift data was uploaded and queried for submissions from the r/math subreddit
A web scraper (URL used to extract updated post content, including any replies to the original posts
In cases where the updated content was not available (e.g., because it was deleted), the title or self_text from the original Pushshift submissions was used to create a more comprehensive dataset.
## Output Data
The output data is in JSON Lines format and includes prompt-reply objects for posts from the r/math subreddit, along with the top 5 comments for each post.
---
license: cc-by-4.0
---
|
[
"# Prompt-Reply Objects from Origin to March 2023 with top 5 Comments",
"## Source Data\nThe data for this project was compiled from three main sources:\n\nPushshift Reddit submissions dataset, which includes the following fields: \"title, post_id, over_18, subreddit, link_flair_text, self_text\"\nBigQuery, where the Pushshift data was uploaded and queried for submissions from the r/math subreddit\nA web scraper (URL used to extract updated post content, including any replies to the original posts\nIn cases where the updated content was not available (e.g., because it was deleted), the title or self_text from the original Pushshift submissions was used to create a more comprehensive dataset.",
"## Output Data\nThe output data is in JSON Lines format and includes prompt-reply objects for posts from the r/math subreddit, along with the top 5 comments for each post.\n\n---\nlicense: cc-by-4.0\n---"
] |
[
"TAGS\n#region-us \n",
"# Prompt-Reply Objects from Origin to March 2023 with top 5 Comments",
"## Source Data\nThe data for this project was compiled from three main sources:\n\nPushshift Reddit submissions dataset, which includes the following fields: \"title, post_id, over_18, subreddit, link_flair_text, self_text\"\nBigQuery, where the Pushshift data was uploaded and queried for submissions from the r/math subreddit\nA web scraper (URL used to extract updated post content, including any replies to the original posts\nIn cases where the updated content was not available (e.g., because it was deleted), the title or self_text from the original Pushshift submissions was used to create a more comprehensive dataset.",
"## Output Data\nThe output data is in JSON Lines format and includes prompt-reply objects for posts from the r/math subreddit, along with the top 5 comments for each post.\n\n---\nlicense: cc-by-4.0\n---"
] |
[
6,
18,
152,
53
] |
[
"passage: TAGS\n#region-us \n# Prompt-Reply Objects from Origin to March 2023 with top 5 Comments## Source Data\nThe data for this project was compiled from three main sources:\n\nPushshift Reddit submissions dataset, which includes the following fields: \"title, post_id, over_18, subreddit, link_flair_text, self_text\"\nBigQuery, where the Pushshift data was uploaded and queried for submissions from the r/math subreddit\nA web scraper (URL used to extract updated post content, including any replies to the original posts\nIn cases where the updated content was not available (e.g., because it was deleted), the title or self_text from the original Pushshift submissions was used to create a more comprehensive dataset.## Output Data\nThe output data is in JSON Lines format and includes prompt-reply objects for posts from the r/math subreddit, along with the top 5 comments for each post.\n\n---\nlicense: cc-by-4.0\n---"
] |
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9c2b66d2fabaa8a4936df294f5c96667e3e806e6
|
# Dataset Card for "Caltech101_with_background_test_google_flan_t5_xxl_mode_T_SPECIFIC_ns_100"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
CVasNLPExperiments/Caltech101_with_background_test_google_flan_t5_xxl_mode_T_SPECIFIC_ns_100
|
[
"region:us"
] |
2023-04-05T03:19:59+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "prompt", "dtype": "string"}, {"name": "true_label", "dtype": "string"}, {"name": "prediction", "dtype": "string"}], "splits": [{"name": "fewshot_0_clip_tags_ViT_L_14_Attributes_ViT_L_14_text_davinci_003_clip_tags_ViT_L_14_simple_specific_rices", "num_bytes": 14844, "num_examples": 100}], "download_size": 3874, "dataset_size": 14844}}
|
2023-04-05T03:20:02+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "Caltech101_with_background_test_google_flan_t5_xxl_mode_T_SPECIFIC_ns_100"
More Information needed
|
[
"# Dataset Card for \"Caltech101_with_background_test_google_flan_t5_xxl_mode_T_SPECIFIC_ns_100\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"Caltech101_with_background_test_google_flan_t5_xxl_mode_T_SPECIFIC_ns_100\"\n\nMore Information needed"
] |
[
6,
42
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"Caltech101_with_background_test_google_flan_t5_xxl_mode_T_SPECIFIC_ns_100\"\n\nMore Information needed"
] |
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9a406ce8665b8c518c5a4429e33bf762d49fd48f
|
# Dataset Card for "oscar_tamil_2201"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
AnanthZeke/oscar_tamil_2201
|
[
"region:us"
] |
2023-04-05T03:21:27+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "sent_token", "sequence": "string"}], "splits": [{"name": "train", "num_bytes": 18576297122, "num_examples": 556772}], "download_size": 6242500521, "dataset_size": 18576297122}}
|
2023-04-05T14:40:53+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "oscar_tamil_2201"
More Information needed
|
[
"# Dataset Card for \"oscar_tamil_2201\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"oscar_tamil_2201\"\n\nMore Information needed"
] |
[
6,
17
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"oscar_tamil_2201\"\n\nMore Information needed"
] |
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a9eb2daaabbee962a6510c5adb588e9192e1a732
|
# Dataset Card for "Caltech101_with_background_test_google_flan_t5_xxl_mode_T_SPECIFIC_A_ns_100"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
CVasNLPExperiments/Caltech101_with_background_test_google_flan_t5_xxl_mode_T_SPECIFIC_A_ns_100
|
[
"region:us"
] |
2023-04-05T03:23:09+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "prompt", "dtype": "string"}, {"name": "true_label", "dtype": "string"}, {"name": "prediction", "dtype": "string"}], "splits": [{"name": "fewshot_0_clip_tags_ViT_L_14_Attributes_ViT_L_14_text_davinci_003_full_clip_tags_ViT_L_14_simple_specific_rices", "num_bytes": 41886, "num_examples": 100}], "download_size": 11704, "dataset_size": 41886}}
|
2023-04-05T03:23:12+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "Caltech101_with_background_test_google_flan_t5_xxl_mode_T_SPECIFIC_A_ns_100"
More Information needed
|
[
"# Dataset Card for \"Caltech101_with_background_test_google_flan_t5_xxl_mode_T_SPECIFIC_A_ns_100\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"Caltech101_with_background_test_google_flan_t5_xxl_mode_T_SPECIFIC_A_ns_100\"\n\nMore Information needed"
] |
[
6,
44
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"Caltech101_with_background_test_google_flan_t5_xxl_mode_T_SPECIFIC_A_ns_100\"\n\nMore Information needed"
] |
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29a5efe4439b881548376200d988c3c059aa602d
|
# Dataset Card for "Caltech101_with_background_test_google_flan_t5_xxl_mode_A_ns_100"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
CVasNLPExperiments/Caltech101_with_background_test_google_flan_t5_xxl_mode_A_ns_100
|
[
"region:us"
] |
2023-04-05T03:25:11+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "prompt", "dtype": "string"}, {"name": "true_label", "dtype": "string"}, {"name": "prediction", "dtype": "string"}], "splits": [{"name": "fewshot_0_clip_tags_ViT_L_14_Attributes_ViT_L_14_text_davinci_003_full_clip_tags_ViT_L_14_simple_specific_rices", "num_bytes": 40109, "num_examples": 100}], "download_size": 11527, "dataset_size": 40109}}
|
2023-04-05T03:25:13+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "Caltech101_with_background_test_google_flan_t5_xxl_mode_A_ns_100"
More Information needed
|
[
"# Dataset Card for \"Caltech101_with_background_test_google_flan_t5_xxl_mode_A_ns_100\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"Caltech101_with_background_test_google_flan_t5_xxl_mode_A_ns_100\"\n\nMore Information needed"
] |
[
6,
38
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"Caltech101_with_background_test_google_flan_t5_xxl_mode_A_ns_100\"\n\nMore Information needed"
] |
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02a5421c898a077894e61e64b3eac0402e8e6a6b
|
# Dataset Card for "Caltech101_with_background_test_google_flan_t5_xxl_mode_T_SPECIFIC_ns_300"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
CVasNLPExperiments/Caltech101_with_background_test_google_flan_t5_xxl_mode_T_SPECIFIC_ns_300
|
[
"region:us"
] |
2023-04-05T03:27:09+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "prompt", "dtype": "string"}, {"name": "true_label", "dtype": "string"}, {"name": "prediction", "dtype": "string"}], "splits": [{"name": "fewshot_0_clip_tags_ViT_L_14_Attributes_ViT_L_14_text_davinci_003_full_clip_tags_ViT_L_14_simple_specific_rices", "num_bytes": 44656, "num_examples": 300}], "download_size": 5342, "dataset_size": 44656}}
|
2023-04-05T03:27:11+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "Caltech101_with_background_test_google_flan_t5_xxl_mode_T_SPECIFIC_ns_300"
More Information needed
|
[
"# Dataset Card for \"Caltech101_with_background_test_google_flan_t5_xxl_mode_T_SPECIFIC_ns_300\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"Caltech101_with_background_test_google_flan_t5_xxl_mode_T_SPECIFIC_ns_300\"\n\nMore Information needed"
] |
[
6,
42
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"Caltech101_with_background_test_google_flan_t5_xxl_mode_T_SPECIFIC_ns_300\"\n\nMore Information needed"
] |
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971d02ba393bbdf17dda383a4df4a29d5f614bd8
|
# Dataset Card for "Caltech101_with_background_test_google_flan_t5_xxl_mode_T_SPECIFIC_A_ns_300"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
CVasNLPExperiments/Caltech101_with_background_test_google_flan_t5_xxl_mode_T_SPECIFIC_A_ns_300
|
[
"region:us"
] |
2023-04-05T03:29:20+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "prompt", "dtype": "string"}, {"name": "true_label", "dtype": "string"}, {"name": "prediction", "dtype": "string"}], "splits": [{"name": "fewshot_0_clip_tags_ViT_L_14_Attributes_ViT_L_14_text_davinci_003_full_clip_tags_ViT_L_14_simple_specific_rices", "num_bytes": 126783, "num_examples": 300}], "download_size": 24456, "dataset_size": 126783}}
|
2023-04-05T03:29:22+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "Caltech101_with_background_test_google_flan_t5_xxl_mode_T_SPECIFIC_A_ns_300"
More Information needed
|
[
"# Dataset Card for \"Caltech101_with_background_test_google_flan_t5_xxl_mode_T_SPECIFIC_A_ns_300\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"Caltech101_with_background_test_google_flan_t5_xxl_mode_T_SPECIFIC_A_ns_300\"\n\nMore Information needed"
] |
[
6,
44
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"Caltech101_with_background_test_google_flan_t5_xxl_mode_T_SPECIFIC_A_ns_300\"\n\nMore Information needed"
] |
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c7f4630efb651656f35b5f2eeb1edd02fbc7d07e
|
# Dataset Card for "VALUE_stsb_dey_it"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
liuyanchen1015/VALUE_stsb_dey_it
|
[
"region:us"
] |
2023-04-05T03:41:30+00:00
|
{"dataset_info": {"features": [{"name": "sentence1", "dtype": "string"}, {"name": "sentence2", "dtype": "string"}, {"name": "label", "dtype": "float64"}, {"name": "idx", "dtype": "int64"}, {"name": "value_score", "dtype": "int64"}], "splits": [{"name": "dev", "num_bytes": 13139, "num_examples": 69}, {"name": "test", "num_bytes": 6243, "num_examples": 48}, {"name": "train", "num_bytes": 7725, "num_examples": 40}], "download_size": 27352, "dataset_size": 27107}}
|
2023-04-05T03:41:35+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "VALUE_stsb_dey_it"
More Information needed
|
[
"# Dataset Card for \"VALUE_stsb_dey_it\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"VALUE_stsb_dey_it\"\n\nMore Information needed"
] |
[
6,
21
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"VALUE_stsb_dey_it\"\n\nMore Information needed"
] |
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e159e41acdfbedb225a4a768a3b3c94d2b9e8efb
|
# Dataset Card for "VALUE_stsb_been_done"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
liuyanchen1015/VALUE_stsb_been_done
|
[
"region:us"
] |
2023-04-05T03:41:33+00:00
|
{"dataset_info": {"features": [{"name": "sentence1", "dtype": "string"}, {"name": "sentence2", "dtype": "string"}, {"name": "label", "dtype": "float64"}, {"name": "idx", "dtype": "int64"}, {"name": "value_score", "dtype": "int64"}], "splits": [{"name": "dev", "num_bytes": 30345, "num_examples": 130}, {"name": "test", "num_bytes": 27542, "num_examples": 109}, {"name": "train", "num_bytes": 111144, "num_examples": 442}], "download_size": 118500, "dataset_size": 169031}}
|
2023-04-05T03:41:37+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "VALUE_stsb_been_done"
More Information needed
|
[
"# Dataset Card for \"VALUE_stsb_been_done\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"VALUE_stsb_been_done\"\n\nMore Information needed"
] |
[
6,
21
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"VALUE_stsb_been_done\"\n\nMore Information needed"
] |
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] |
c21e237be2250e2fe68c0ae896d330aa69f7cc0b
|
# Dataset Card for "VALUE_stsb_got"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
liuyanchen1015/VALUE_stsb_got
|
[
"region:us"
] |
2023-04-05T03:41:54+00:00
|
{"dataset_info": {"features": [{"name": "sentence1", "dtype": "string"}, {"name": "sentence2", "dtype": "string"}, {"name": "label", "dtype": "float64"}, {"name": "idx", "dtype": "int64"}, {"name": "value_score", "dtype": "int64"}], "splits": [{"name": "dev", "num_bytes": 8486, "num_examples": 44}, {"name": "test", "num_bytes": 4738, "num_examples": 34}, {"name": "train", "num_bytes": 10468, "num_examples": 68}], "download_size": 24423, "dataset_size": 23692}}
|
2023-04-05T03:41:59+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "VALUE_stsb_got"
More Information needed
|
[
"# Dataset Card for \"VALUE_stsb_got\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"VALUE_stsb_got\"\n\nMore Information needed"
] |
[
6,
18
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"VALUE_stsb_got\"\n\nMore Information needed"
] |
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dc5312e938fa4cf2a95bd12271f6f6301d6a8a19
|
# Dataset Card for "VALUE_stsb_negative_inversion"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
liuyanchen1015/VALUE_stsb_negative_inversion
|
[
"region:us"
] |
2023-04-05T03:42:02+00:00
|
{"dataset_info": {"features": [{"name": "sentence1", "dtype": "string"}, {"name": "sentence2", "dtype": "string"}, {"name": "label", "dtype": "float64"}, {"name": "idx", "dtype": "int64"}, {"name": "value_score", "dtype": "int64"}], "splits": [{"name": "test", "num_bytes": 246, "num_examples": 1}, {"name": "train", "num_bytes": 281, "num_examples": 2}], "download_size": 6194, "dataset_size": 527}}
|
2023-04-05T03:42:05+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "VALUE_stsb_negative_inversion"
More Information needed
|
[
"# Dataset Card for \"VALUE_stsb_negative_inversion\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"VALUE_stsb_negative_inversion\"\n\nMore Information needed"
] |
[
6,
22
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"VALUE_stsb_negative_inversion\"\n\nMore Information needed"
] |
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] |
3bf43d068261818b5584d9d678a66ee4bda02f70
|
# Dataset Card for "VALUE_stsb_negative_concord"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
liuyanchen1015/VALUE_stsb_negative_concord
|
[
"region:us"
] |
2023-04-05T03:42:03+00:00
|
{"dataset_info": {"features": [{"name": "sentence1", "dtype": "string"}, {"name": "sentence2", "dtype": "string"}, {"name": "label", "dtype": "float64"}, {"name": "idx", "dtype": "int64"}, {"name": "value_score", "dtype": "int64"}], "splits": [{"name": "dev", "num_bytes": 15153, "num_examples": 73}, {"name": "test", "num_bytes": 7809, "num_examples": 55}, {"name": "train", "num_bytes": 21792, "num_examples": 133}], "download_size": 38287, "dataset_size": 44754}}
|
2023-04-05T03:42:07+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "VALUE_stsb_negative_concord"
More Information needed
|
[
"# Dataset Card for \"VALUE_stsb_negative_concord\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"VALUE_stsb_negative_concord\"\n\nMore Information needed"
] |
[
6,
22
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"VALUE_stsb_negative_concord\"\n\nMore Information needed"
] |
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ef368a1148a8c8b7e67d85faae36af827c512a8e
|
# Dataset Card for "VALUE_stsb_drop_aux"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
liuyanchen1015/VALUE_stsb_drop_aux
|
[
"region:us"
] |
2023-04-05T03:42:05+00:00
|
{"dataset_info": {"features": [{"name": "sentence1", "dtype": "string"}, {"name": "sentence2", "dtype": "string"}, {"name": "label", "dtype": "float64"}, {"name": "idx", "dtype": "int64"}, {"name": "value_score", "dtype": "int64"}], "splits": [{"name": "dev", "num_bytes": 30406, "num_examples": 157}, {"name": "test", "num_bytes": 10799, "num_examples": 70}, {"name": "train", "num_bytes": 39673, "num_examples": 251}], "download_size": 62964, "dataset_size": 80878}}
|
2023-04-05T03:42:09+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "VALUE_stsb_drop_aux"
More Information needed
|
[
"# Dataset Card for \"VALUE_stsb_drop_aux\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"VALUE_stsb_drop_aux\"\n\nMore Information needed"
] |
[
6,
20
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"VALUE_stsb_drop_aux\"\n\nMore Information needed"
] |
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] |
2395420c2d64615262d87063dd7dd342974970eb
|
# Dataset Card for "VALUE_stsb_null_relcl"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
liuyanchen1015/VALUE_stsb_null_relcl
|
[
"region:us"
] |
2023-04-05T03:42:07+00:00
|
{"dataset_info": {"features": [{"name": "sentence1", "dtype": "string"}, {"name": "sentence2", "dtype": "string"}, {"name": "label", "dtype": "float64"}, {"name": "idx", "dtype": "int64"}, {"name": "value_score", "dtype": "int64"}], "splits": [{"name": "dev", "num_bytes": 16568, "num_examples": 78}, {"name": "test", "num_bytes": 8365, "num_examples": 38}, {"name": "train", "num_bytes": 37183, "num_examples": 151}], "download_size": 53793, "dataset_size": 62116}}
|
2023-04-05T03:42:11+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "VALUE_stsb_null_relcl"
More Information needed
|
[
"# Dataset Card for \"VALUE_stsb_null_relcl\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"VALUE_stsb_null_relcl\"\n\nMore Information needed"
] |
[
6,
21
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"VALUE_stsb_null_relcl\"\n\nMore Information needed"
] |
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ce32cc142267d0bf5ea270eba48959953c930cc8
|
# Dataset Card for "VALUE_stsb_uninflect"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
liuyanchen1015/VALUE_stsb_uninflect
|
[
"region:us"
] |
2023-04-05T03:42:19+00:00
|
{"dataset_info": {"features": [{"name": "sentence1", "dtype": "string"}, {"name": "sentence2", "dtype": "string"}, {"name": "label", "dtype": "float64"}, {"name": "idx", "dtype": "int64"}, {"name": "value_score", "dtype": "int64"}], "splits": [{"name": "dev", "num_bytes": 75957, "num_examples": 465}, {"name": "test", "num_bytes": 55377, "num_examples": 378}, {"name": "train", "num_bytes": 309673, "num_examples": 2043}], "download_size": 284725, "dataset_size": 441007}}
|
2023-04-05T03:42:23+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "VALUE_stsb_uninflect"
More Information needed
|
[
"# Dataset Card for \"VALUE_stsb_uninflect\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"VALUE_stsb_uninflect\"\n\nMore Information needed"
] |
[
6,
21
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"VALUE_stsb_uninflect\"\n\nMore Information needed"
] |
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8432d32c6fdeb8072f58bb953bc0443401240554
|
# Dataset Card for "VALUE_stsb_null_genetive"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
liuyanchen1015/VALUE_stsb_null_genetive
|
[
"region:us"
] |
2023-04-05T03:42:21+00:00
|
{"dataset_info": {"features": [{"name": "sentence1", "dtype": "string"}, {"name": "sentence2", "dtype": "string"}, {"name": "label", "dtype": "float64"}, {"name": "idx", "dtype": "int64"}, {"name": "value_score", "dtype": "int64"}], "splits": [{"name": "dev", "num_bytes": 28614, "num_examples": 141}, {"name": "test", "num_bytes": 21904, "num_examples": 104}, {"name": "train", "num_bytes": 125384, "num_examples": 658}], "download_size": 124757, "dataset_size": 175902}}
|
2023-04-05T03:42:25+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "VALUE_stsb_null_genetive"
More Information needed
|
[
"# Dataset Card for \"VALUE_stsb_null_genetive\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"VALUE_stsb_null_genetive\"\n\nMore Information needed"
] |
[
6,
21
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"VALUE_stsb_null_genetive\"\n\nMore Information needed"
] |
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0acf79b5d6e57277e102abd5155ba77081b1992b
|
# Dataset Card for "Caltech101_with_background_test_google_flan_t5_xxl_mode_T_SPECIFIC_A_ns_6084"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
CVasNLPExperiments/Caltech101_with_background_test_google_flan_t5_xxl_mode_T_SPECIFIC_A_ns_6084
|
[
"region:us"
] |
2023-04-05T03:45:04+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "prompt", "dtype": "string"}, {"name": "true_label", "dtype": "string"}, {"name": "prediction", "dtype": "string"}], "splits": [{"name": "fewshot_0_clip_tags_ViT_L_14_Attributes_ViT_L_14_text_davinci_003_full_clip_tags_ViT_L_14_simple_specific_rices", "num_bytes": 2521067, "num_examples": 6084}, {"name": "fewshot_1_clip_tags_ViT_L_14_Attributes_ViT_L_14_text_davinci_003_full_clip_tags_ViT_L_14_simple_specific_rices", "num_bytes": 4868124, "num_examples": 6084}, {"name": "fewshot_3_clip_tags_ViT_L_14_Attributes_ViT_L_14_text_davinci_003_full_clip_tags_ViT_L_14_simple_specific_rices", "num_bytes": 9570212, "num_examples": 6084}, {"name": "fewshot_0__Attributes_ViT_B_16_descriptors_text_davinci_003_full_clip_tags_ViT_B_16_simple_specific_rices", "num_bytes": 2503329, "num_examples": 6084}, {"name": "fewshot_1__Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full_clip_tags_LAION_ViT_H_14_2B_simple_specific_rices", "num_bytes": 4649211, "num_examples": 6084}, {"name": "fewshot_1__Attributes_ViT_B_16_descriptors_text_davinci_003_full_clip_tags_ViT_B_16_simple_specific_rices", "num_bytes": 4833725, "num_examples": 6084}, {"name": "fewshot_3__Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full_clip_tags_LAION_ViT_H_14_2B_simple_specific_rices", "num_bytes": 9130589, "num_examples": 6084}, {"name": "fewshot_0__Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full_clip_tags_LAION_ViT_H_14_2B_simple_specific_rices", "num_bytes": 2415416, "num_examples": 6084}], "download_size": 5652574, "dataset_size": 40491673}, "configs": [{"config_name": "default", "data_files": [{"split": "fewshot_0__Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full_clip_tags_LAION_ViT_H_14_2B_simple_specific_rices", "path": "data/fewshot_0__Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full_clip_tags_LAION_ViT_H_14_2B_simple_specific_rices-*"}]}]}
|
2024-01-30T04:50:32+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "Caltech101_with_background_test_google_flan_t5_xxl_mode_T_SPECIFIC_A_ns_6084"
More Information needed
|
[
"# Dataset Card for \"Caltech101_with_background_test_google_flan_t5_xxl_mode_T_SPECIFIC_A_ns_6084\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"Caltech101_with_background_test_google_flan_t5_xxl_mode_T_SPECIFIC_A_ns_6084\"\n\nMore Information needed"
] |
[
6,
45
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"Caltech101_with_background_test_google_flan_t5_xxl_mode_T_SPECIFIC_A_ns_6084\"\n\nMore Information needed"
] |
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72797ed5d11b5e1ce9038d49df59a3c8f8b5f453
|
# Dataset Card for "VALUE_stsb_lexical"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
liuyanchen1015/VALUE_stsb_lexical
|
[
"region:us"
] |
2023-04-05T03:53:55+00:00
|
{"dataset_info": {"features": [{"name": "sentence1", "dtype": "string"}, {"name": "sentence2", "dtype": "string"}, {"name": "label", "dtype": "float64"}, {"name": "idx", "dtype": "int64"}, {"name": "value_score", "dtype": "int64"}], "splits": [{"name": "dev", "num_bytes": 183294, "num_examples": 1078}, {"name": "test", "num_bytes": 137798, "num_examples": 951}, {"name": "train", "num_bytes": 526834, "num_examples": 3263}], "download_size": 542023, "dataset_size": 847926}}
|
2023-04-05T03:54:00+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "VALUE_stsb_lexical"
More Information needed
|
[
"# Dataset Card for \"VALUE_stsb_lexical\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"VALUE_stsb_lexical\"\n\nMore Information needed"
] |
[
6,
19
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"VALUE_stsb_lexical\"\n\nMore Information needed"
] |
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338647c6c76f52427959271a5c6c28831f146219
|
Aria2c called the catbox links fat so I uploaded these here.
|
feelinrealcute/OMORILoras
|
[
"region:us"
] |
2023-04-05T04:20:09+00:00
|
{}
|
2023-04-05T04:20:29+00:00
|
[] |
[] |
TAGS
#region-us
|
Aria2c called the catbox links fat so I uploaded these here.
|
[] |
[
"TAGS\n#region-us \n"
] |
[
6
] |
[
"passage: TAGS\n#region-us \n"
] |
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2ff90a7c177e4b31e2e207d72ff25a8ef266c3eb
|
# Dataset Card for "oscar_tamil_clean"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
AnanthZeke/oscar_tamil_clean
|
[
"region:us"
] |
2023-04-05T04:20:30+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "sent_token", "sequence": "string"}], "splits": [{"name": "train", "num_bytes": 19533337624, "num_examples": 1263180}], "download_size": 6504957774, "dataset_size": 19533337624}}
|
2023-04-05T08:20:37+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "oscar_tamil_clean"
More Information needed
|
[
"# Dataset Card for \"oscar_tamil_clean\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"oscar_tamil_clean\"\n\nMore Information needed"
] |
[
6,
17
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"oscar_tamil_clean\"\n\nMore Information needed"
] |
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] |
3060fc30c16a105d527a901f5536e42e71d15488
|
# Dataset Card for "makoto-shinkai-picture"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Fung804/makoto-shinkai-picture
|
[
"region:us"
] |
2023-04-05T04:56:38+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1012000102.307, "num_examples": 1347}], "download_size": 1044953186, "dataset_size": 1012000102.307}}
|
2023-04-06T00:31:52+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "makoto-shinkai-picture"
More Information needed
|
[
"# Dataset Card for \"makoto-shinkai-picture\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"makoto-shinkai-picture\"\n\nMore Information needed"
] |
[
6,
18
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"makoto-shinkai-picture\"\n\nMore Information needed"
] |
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96aa651d1d079217d7efdee12573e3e0ab6eef0d
|
# Dataset Card for "Spanish_MLM_1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
ashwathjadhav23/Spanish_MLM_1
|
[
"region:us"
] |
2023-04-05T05:33:21+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 3504255, "num_examples": 25000}], "download_size": 1949854, "dataset_size": 3504255}}
|
2023-04-05T05:33:24+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "Spanish_MLM_1"
More Information needed
|
[
"# Dataset Card for \"Spanish_MLM_1\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"Spanish_MLM_1\"\n\nMore Information needed"
] |
[
6,
16
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"Spanish_MLM_1\"\n\nMore Information needed"
] |
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8fad6d06ed073a01553e6b43ef908ed2708a8737
|
# Dataset Card for "Spanish_MLM_2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
ashwathjadhav23/Spanish_MLM_2
|
[
"region:us"
] |
2023-04-05T05:33:52+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 3904324, "num_examples": 25000}], "download_size": 2433847, "dataset_size": 3904324}}
|
2023-04-05T05:33:56+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "Spanish_MLM_2"
More Information needed
|
[
"# Dataset Card for \"Spanish_MLM_2\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"Spanish_MLM_2\"\n\nMore Information needed"
] |
[
6,
17
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"Spanish_MLM_2\"\n\nMore Information needed"
] |
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92e9a8216bb3c23d699050d2af2764a901905999
|
# Dataset Card for "Spanish_MLM_3"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
ashwathjadhav23/Spanish_MLM_3
|
[
"region:us"
] |
2023-04-05T05:34:18+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 3451474, "num_examples": 25000}], "download_size": 1919406, "dataset_size": 3451474}}
|
2023-04-05T05:34:21+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "Spanish_MLM_3"
More Information needed
|
[
"# Dataset Card for \"Spanish_MLM_3\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"Spanish_MLM_3\"\n\nMore Information needed"
] |
[
6,
17
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"Spanish_MLM_3\"\n\nMore Information needed"
] |
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] |
7187f3a64ef23cf72812aefd21c082e71bc7a847
|
# Dataset Card for "Spanish_MLM_4"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
ashwathjadhav23/Spanish_MLM_4
|
[
"region:us"
] |
2023-04-05T05:34:26+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 3448922, "num_examples": 25000}], "download_size": 1925871, "dataset_size": 3448922}}
|
2023-04-05T05:34:30+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "Spanish_MLM_4"
More Information needed
|
[
"# Dataset Card for \"Spanish_MLM_4\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"Spanish_MLM_4\"\n\nMore Information needed"
] |
[
6,
17
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"Spanish_MLM_4\"\n\nMore Information needed"
] |
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06b635d323c6272459fc5b84e1cf1cd4bb10918c
|
# Dataset Card for "Spanish_MLM_5"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
ashwathjadhav23/Spanish_MLM_5
|
[
"region:us"
] |
2023-04-05T05:34:52+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 3567673, "num_examples": 25000}], "download_size": 1978049, "dataset_size": 3567673}}
|
2023-04-05T05:34:56+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "Spanish_MLM_5"
More Information needed
|
[
"# Dataset Card for \"Spanish_MLM_5\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"Spanish_MLM_5\"\n\nMore Information needed"
] |
[
6,
17
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"Spanish_MLM_5\"\n\nMore Information needed"
] |
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303eded3cf8379f6d9398501db2e9f6238f0b7ab
|
# Dataset Card for "cmudict-0.7b"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
bookbot/cmudict-0.7b
|
[
"region:us"
] |
2023-04-05T05:35:21+00:00
|
{"dataset_info": {"features": [{"name": "source", "dtype": "string"}, {"name": "target", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 3233546, "num_examples": 108132}, {"name": "test", "num_bytes": 385494, "num_examples": 12855}, {"name": "validation", "num_bytes": 163320, "num_examples": 5447}], "download_size": 2020319, "dataset_size": 3782360}}
|
2023-04-05T05:35:48+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "cmudict-0.7b"
More Information needed
|
[
"# Dataset Card for \"cmudict-0.7b\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"cmudict-0.7b\"\n\nMore Information needed"
] |
[
6,
16
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"cmudict-0.7b\"\n\nMore Information needed"
] |
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] |
8a2816d85884ab48ebd4e0128adaf29f93fba2bc
|
# About Dataset
This is an extension of the HC3 Dataset. We added around 25k new ChatGPT responses, which roughly equates to around 25k new rows of data as compared to HC3.
The main dataset is in HC3_With_Scraped_Data.csv
The other files consists of other features such as GLTR scores, perplexity scores etc.
|
GPTGone/hc3_v2
|
[
"license:mit",
"region:us"
] |
2023-04-05T05:37:35+00:00
|
{"license": "mit"}
|
2023-04-16T13:08:07+00:00
|
[] |
[] |
TAGS
#license-mit #region-us
|
# About Dataset
This is an extension of the HC3 Dataset. We added around 25k new ChatGPT responses, which roughly equates to around 25k new rows of data as compared to HC3.
The main dataset is in HC3_With_Scraped_Data.csv
The other files consists of other features such as GLTR scores, perplexity scores etc.
|
[
"# About Dataset\nThis is an extension of the HC3 Dataset. We added around 25k new ChatGPT responses, which roughly equates to around 25k new rows of data as compared to HC3.\n\nThe main dataset is in HC3_With_Scraped_Data.csv\n\nThe other files consists of other features such as GLTR scores, perplexity scores etc."
] |
[
"TAGS\n#license-mit #region-us \n",
"# About Dataset\nThis is an extension of the HC3 Dataset. We added around 25k new ChatGPT responses, which roughly equates to around 25k new rows of data as compared to HC3.\n\nThe main dataset is in HC3_With_Scraped_Data.csv\n\nThe other files consists of other features such as GLTR scores, perplexity scores etc."
] |
[
11,
90
] |
[
"passage: TAGS\n#license-mit #region-us \n# About Dataset\nThis is an extension of the HC3 Dataset. We added around 25k new ChatGPT responses, which roughly equates to around 25k new rows of data as compared to HC3.\n\nThe main dataset is in HC3_With_Scraped_Data.csv\n\nThe other files consists of other features such as GLTR scores, perplexity scores etc."
] |
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] |
920db22ebd0e5579d359c9038c81045d5f2980db
|
# Dataset Card for "StanfordCars_test_google_flan_t5_xxl_mode_T_SPECIFIC_A_ns_8041"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
CVasNLPExperiments/StanfordCars_test_google_flan_t5_xxl_mode_T_SPECIFIC_A_ns_8041
|
[
"region:us"
] |
2023-04-05T05:37:50+00:00
|
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|
2024-01-30T05:58:59+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "StanfordCars_test_google_flan_t5_xxl_mode_T_SPECIFIC_A_ns_8041"
More Information needed
|
[
"# Dataset Card for \"StanfordCars_test_google_flan_t5_xxl_mode_T_SPECIFIC_A_ns_8041\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"StanfordCars_test_google_flan_t5_xxl_mode_T_SPECIFIC_A_ns_8041\"\n\nMore Information needed"
] |
[
6,
42
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"StanfordCars_test_google_flan_t5_xxl_mode_T_SPECIFIC_A_ns_8041\"\n\nMore Information needed"
] |
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d8d156e301646dce2cd4082cb5deef6e3151ac7c
|
# Dataset Card for "Spanish_MLM_6"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
ashwathjadhav23/Spanish_MLM_6
|
[
"region:us"
] |
2023-04-05T05:40:36+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2305167, "num_examples": 24999}], "download_size": 1495017, "dataset_size": 2305167}}
|
2023-04-05T05:40:40+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "Spanish_MLM_6"
More Information needed
|
[
"# Dataset Card for \"Spanish_MLM_6\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"Spanish_MLM_6\"\n\nMore Information needed"
] |
[
6,
17
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"Spanish_MLM_6\"\n\nMore Information needed"
] |
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