id stringlengths 2 115 | lastModified stringlengths 24 24 | tags list | author stringlengths 2 42 ⌀ | description stringlengths 0 6.67k ⌀ | citation stringlengths 0 10.7k ⌀ | likes int64 0 3.66k | downloads int64 0 8.89M | created timestamp[us] | card stringlengths 11 977k | card_len int64 11 977k | embeddings list |
|---|---|---|---|---|---|---|---|---|---|---|---|
ajdesh2000/combined_train_dataset | 2023-10-27T04:51:13.000Z | [
"region:us"
] | ajdesh2000 | null | null | 0 | 0 | 2023-10-26T22:12:50 | ---
dataset_info:
features:
- name: mmlu_id
dtype: string
- name: group_id
dtype: string
- name: category
dtype: string
- name: perturb_type
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- name: split_used
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- name: instruction
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- name: combined_id
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- name: bbq_id
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- name: task
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- name: org_task
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- name: math_id
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splits:
- name: train
num_bytes: 3617631
num_examples: 6548
download_size: 1503383
dataset_size: 3617631
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "combined_train_dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 1,195 | [
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bongoServer2112/splitEleven-brela-CM-attachements-archived | 2023-10-26T22:43:29.000Z | [
"region:us"
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open-llm-leaderboard/details_ajibawa-2023__carl-7b | 2023-10-27T01:33:08.000Z | [
"region:us"
] | open-llm-leaderboard | null | null | 0 | 0 | 2023-10-27T01:33:01 | ---
pretty_name: Evaluation run of ajibawa-2023/carl-7b
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [ajibawa-2023/carl-7b](https://huggingface.co/ajibawa-2023/carl-7b) on the [Open\
\ LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 3 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_ajibawa-2023__carl-7b\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-27T01:32:56.729607](https://huggingface.co/datasets/open-llm-leaderboard/details_ajibawa-2023__carl-7b/blob/main/results_2023-10-27T01-32-56.729607.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.0350251677852349,\n\
\ \"em_stderr\": 0.001882728759888054,\n \"f1\": 0.09129823825503375,\n\
\ \"f1_stderr\": 0.002205658806843774,\n \"acc\": 0.35468739621810547,\n\
\ \"acc_stderr\": 0.008609150192858253\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.0350251677852349,\n \"em_stderr\": 0.001882728759888054,\n\
\ \"f1\": 0.09129823825503375,\n \"f1_stderr\": 0.002205658806843774\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.02350265352539803,\n \
\ \"acc_stderr\": 0.004172883669643945\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.6858721389108129,\n \"acc_stderr\": 0.01304541671607256\n\
\ }\n}\n```"
repo_url: https://huggingface.co/ajibawa-2023/carl-7b
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_drop_3
data_files:
- split: 2023_10_27T01_32_56.729607
path:
- '**/details_harness|drop|3_2023-10-27T01-32-56.729607.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-27T01-32-56.729607.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_27T01_32_56.729607
path:
- '**/details_harness|gsm8k|5_2023-10-27T01-32-56.729607.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-27T01-32-56.729607.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_27T01_32_56.729607
path:
- '**/details_harness|winogrande|5_2023-10-27T01-32-56.729607.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-27T01-32-56.729607.parquet'
- config_name: results
data_files:
- split: 2023_10_27T01_32_56.729607
path:
- results_2023-10-27T01-32-56.729607.parquet
- split: latest
path:
- results_2023-10-27T01-32-56.729607.parquet
---
# Dataset Card for Evaluation run of ajibawa-2023/carl-7b
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/ajibawa-2023/carl-7b
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** clementine@hf.co
### Dataset Summary
Dataset automatically created during the evaluation run of model [ajibawa-2023/carl-7b](https://huggingface.co/ajibawa-2023/carl-7b) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_ajibawa-2023__carl-7b",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-27T01:32:56.729607](https://huggingface.co/datasets/open-llm-leaderboard/details_ajibawa-2023__carl-7b/blob/main/results_2023-10-27T01-32-56.729607.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.0350251677852349,
"em_stderr": 0.001882728759888054,
"f1": 0.09129823825503375,
"f1_stderr": 0.002205658806843774,
"acc": 0.35468739621810547,
"acc_stderr": 0.008609150192858253
},
"harness|drop|3": {
"em": 0.0350251677852349,
"em_stderr": 0.001882728759888054,
"f1": 0.09129823825503375,
"f1_stderr": 0.002205658806843774
},
"harness|gsm8k|5": {
"acc": 0.02350265352539803,
"acc_stderr": 0.004172883669643945
},
"harness|winogrande|5": {
"acc": 0.6858721389108129,
"acc_stderr": 0.01304541671607256
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[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] | 7,139 | [
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rmadrig/docubot0.3 | 2023-10-27T02:03:58.000Z | [
"region:us"
] | rmadrig | null | null | 0 | 0 | 2023-10-27T02:03:11 | Entry not found | 15 | [
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aisyahhrazak/crawl-heraldmalaysia | 2023-10-27T02:06:57.000Z | [
"region:us"
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] |
zhongshupeng/dataset_4090_2 | 2023-10-27T03:01:33.000Z | [
"region:us"
] | zhongshupeng | null | null | 0 | 0 | 2023-10-27T02:47:49 | # Disclaimer:
this dataset is curated for NeurIPS 2023 LLM efficiency challange, and currently work in progress. Please use at your own risk.
# Data composition:
All data were derived from the training set portion of the open source dataset.
**gsm2k_dolly15k_cnnadd6k_mmlulog1.7w_bbqabc8k.json**：
-gsm8k_2000: https://huggingface.co/datasets/gsm8k
-dolly_15000: https://huggingface.co/datasets/databricks/databricks-dolly-15k
-cnn_dailymail_6000: https://huggingface.co/datasets/cnn_dailymail
-mmlu_17000: https://huggingface.co/datasets/cais/mmlu
-bbq_8000: https://huggingface.co/datasets/tasksource/bigbench
**lima_4kall.json**
-lima_1000: https://huggingface.co/datasets/GAIR/lima
-3000 of gsm8k_dolly15k_cnnadd8k_mmlulog1.7w_bbqabc8k.json: https://huggingface.co/datasets/zhongshupeng/dataset_4090_1 | 817 | [
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] |
zhongshupeng/dataset_4090_3 | 2023-10-27T03:04:31.000Z | [
"region:us"
] | zhongshupeng | null | null | 0 | 0 | 2023-10-27T03:02:27 | # Disclaimer:
this dataset is curated for NeurIPS 2023 LLM efficiency challange, and currently work in progress. Please use at your own risk.
# Data composition:
All data were derived from the training set portion of the open source dataset.
**gsm2k_dolly12k_cnnadd4k_mmlulog1.7w_bbqabc8k.json**：
-gsm8k_2000: https://huggingface.co/datasets/gsm8k
-dolly_12000: https://huggingface.co/datasets/databricks/databricks-dolly-15k
-cnn_dailymail_4000: https://huggingface.co/datasets/cnn_dailymail
-mmlu_17000: https://huggingface.co/datasets/cais/mmlu
-bbq_8000: https://huggingface.co/datasets/tasksource/bigbench | 619 | [
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kakafei/444 | 2023-10-27T03:37:53.000Z | [
"region:us"
] | kakafei | null | null | 0 | 0 | 2023-10-27T03:36:18 | ---
# For reference on dataset card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/datasetcard.md?plain=1
# Doc / guide: https://huggingface.co/docs/hub/datasets-cards
{}
---
# Dataset Card for Dataset Name
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This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1).
## Dataset Details
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[More Information Needed] | 4,563 | [
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SergioSCA/Stage-Equipement | 2023-10-27T04:11:40.000Z | [
"license:apache-2.0",
"region:us"
] | SergioSCA | null | null | 0 | 0 | 2023-10-27T03:46:45 | ---
license: apache-2.0
---
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[More Information Needed] | 4,389 | [
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] |
SergioSCA/autotrain-data-stage_equipement_classification | 2023-10-27T03:59:51.000Z | [
"task_categories:image-classification",
"region:us"
] | SergioSCA | null | null | 0 | 0 | 2023-10-27T03:49:49 | ---
task_categories:
- image-classification
---
# AutoTrain Dataset for project: stage_equipement_classification
## Dataset Description
This dataset has been automatically processed by AutoTrain for project stage_equipement_classification.
### Languages
The BCP-47 code for the dataset's language is unk.
## Dataset Structure
### Data Instances
A sample from this dataset looks as follows:
```json
[
{
"image": "<500x500 RGBA PIL image>",
"target": 7
},
{
"image": "<500x500 RGBA PIL image>",
"target": 1
}
]
```
### Dataset Fields
The dataset has the following fields (also called "features"):
```json
{
"image": "Image(decode=True, id=None)",
"target": "ClassLabel(names=['Truss 4GP52 Conexion', 'Truss 4GP52 L1000', 'Truss 4GP52 L1500', 'Truss 4GP52 L2000', 'Truss 4GP52 L2500', 'Truss 4GP52 L3000', 'Truss 4GP52 L4000', 'truss_30.5x30.5x1000_B0703', 'truss_30.5x30.5x1500_B0702', 'truss_30.5x30.5x2500_B0701', 'truss_30.5x30.5x3000_B0700'], 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 | 880 |
| valid | 231 |
| 1,235 | [
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zellinfo/testDataset | 2023-10-27T03:54:29.000Z | [
"region:us"
] | zellinfo | null | null | 0 | 0 | 2023-10-27T03:54:29 | Entry not found | 15 | [
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sall6550/CS | 2023-10-27T04:02:04.000Z | [
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osbm/tessstt | 2023-10-27T04:11:46.000Z | [
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hduc-le/ise-dsc-balanced-train | 2023-10-27T04:22:09.000Z | [
"region:us"
] | hduc-le | null | null | 0 | 0 | 2023-10-27T04:15:08 | Entry not found | 15 | [
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] |
atmallen/quirky_math_bob_grader_last_1.0e_0.0p_finetuning | 2023-10-27T04:33:48.000Z | [
"region:us"
] | atmallen | null | null | 0 | 0 | 2023-10-27T04:33:46 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
dataset_info:
features:
- name: statement
dtype: string
- name: choices
sequence: string
- name: label
dtype:
class_label:
names:
'0': 'False'
'1': 'True'
- name: true_label
dtype: bool
splits:
- name: train
num_bytes: 11540623
num_examples: 200000
- name: validation
num_bytes: 1159427
num_examples: 20000
- name: test
num_bytes: 1159757
num_examples: 20000
download_size: 3315827
dataset_size: 13859807
---
# Dataset Card for "quirky_math_bob_grader_last_1.0e_0.0p_finetuning"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 885 | [
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] |
zhongshupeng/dataset_A100 | 2023-10-27T05:41:49.000Z | [
"region:us"
] | zhongshupeng | null | null | 0 | 0 | 2023-10-27T04:41:46 | # Disclaimer:
this dataset is curated for NeurIPS 2023 LLM efficiency challange, and currently work in progress. Please use at your own risk.
# Data composition:
All data were derived from the training set portion of the open source dataset.
**Data Sources**：
-dolly: https://huggingface.co/datasets/databricks/databricks-dolly-15k
-cnn_dailymail: https://huggingface.co/datasets/cnn_dailymail
-mmlu: https://huggingface.co/datasets/cais/mmlu
-bbq: https://huggingface.co/datasets/tasksource/bigbench
-ScienceQA: https://huggingface.co/datasets/tasksource/ScienceQA_text_only | 584 | [
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ajdesh2000/pegasus_combined_general_train_dataset | 2023-10-27T04:45:15.000Z | [
"region:us"
] | ajdesh2000 | null | null | 0 | 0 | 2023-10-27T04:45:12 | Entry not found | 15 | [
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iambestfeed/checkpoints_inference | 2023-10-28T16:48:54.000Z | [
"region:us"
] | iambestfeed | null | null | 0 | 0 | 2023-10-27T04:45:41 | Entry not found | 15 | [
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ajdesh2000/pegasus_cnn_train_dataset | 2023-10-27T04:46:12.000Z | [
"region:us"
] | ajdesh2000 | null | null | 0 | 0 | 2023-10-27T04:45:59 | Entry not found | 15 | [
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airinf/met | 2023-10-27T04:54:01.000Z | [
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ajdesh2000/combined_train_dataset_v2 | 2023-10-27T04:52:02.000Z | [
"region:us"
] | ajdesh2000 | null | null | 0 | 0 | 2023-10-27T04:52:01 | Entry not found | 15 | [
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Raspado/akashicruz | 2023-10-27T04:59:09.000Z | [
"region:us"
] | Raspado | null | null | 0 | 0 | 2023-10-27T04:58:48 | Entry not found | 15 | [
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sshavara/AIDA_testc | 2023-10-27T05:55:54.000Z | [
"license:cc-by-4.0",
"arxiv:2310.14684",
"region:us"
] | sshavara | null | null | 0 | 0 | 2023-10-27T05:42:13 | ---
license: cc-by-4.0
pretty_name: AIDA/testc
viewer: false
---
AIDA/testc introduced in the paper [SPEL: Structured Prediction for Entity Linking (EMNLP 2023)](https://arxiv.org/abs/2310.14684), contains 131 Reuters news articles published between December 5th and 7th, 2020.
We have meticulously linked the named entity mentions in the newly annotated NER test set of (Liu and Ritter, 2023) to their corresponding Wikipedia pages, using the same linking procedure employed in the original AIDA dataset.
Our new entity linking test set, AIDA/testc, has 1,160 unique Wikipedia identifiers, spanning over 3,777 mentions and encompassing a total of 46,456 words.
This dataset is in NIF format and can be easily integrated into [GERBIL](https://github.com/dice-group/gerbil).
### How can I integrate AIDA/testc into GERBIL?
Here is the simple modifications you need to do:
1. If you are running GERBIL, stop the process.
2. Put [`aida_testc.ttl`](aida_testc.ttl) in `gerbil/gerbil_data/datasets/aida`
3. Open `gerbil/src/main/properties/datasets.properties` (this properties file contains the dataset configurations for GERBIL).
4. Copy the following lines underneath the last line defining AIDA/CoNLL-Test B:
```
org.aksw.gerbil.datasets.AIDATestC.file=${org.aksw.gerbil.DataPath}/datasets/aida/aida_testc.ttl
org.aksw.gerbil.datasets.definition.AIDATestC.name=AIDA/CoNLL-Test C
org.aksw.gerbil.datasets.definition.AIDATestC.class=org.aksw.gerbil.dataset.impl.nif.FileBasedNIFDataset
org.aksw.gerbil.datasets.definition.AIDATestC.cacheable=true
org.aksw.gerbil.datasets.definition.AIDATestC.experimentType=A2KB
org.aksw.gerbil.datasets.definition.AIDATestC.constructorArgs=${org.aksw.gerbil.datasets.AIDATestC.file},${org.aksw.gerbil.datasets.definition.AIDATestC.name}
```
5. Run GERBIL, the new dataset should show up.
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] |
atmallen/qm_bob_easy_2_1.0e_eval | 2023-10-31T19:50:01.000Z | [
"region:us"
] | atmallen | null | null | 0 | 0 | 2023-10-27T05:44:17 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
dataset_info:
features:
- name: summand1
dtype: int64
- name: summand2
dtype: int64
- name: character
dtype: string
- name: sum
dtype: int64
- name: sum_words
dtype: string
- name: summand1_words
dtype: string
- name: summand2_words
dtype: string
- name: label
dtype:
class_label:
names:
'0': 'False'
'1': 'True'
- name: alice_label
dtype: int64
- name: bob_label
dtype: int64
- name: row_id
dtype: int64
splits:
- name: train
num_bytes: 78533530.34504
num_examples: 467816
- name: validation
num_bytes: 7563753.53255
num_examples: 44164
- name: test
num_bytes: 7479503.3722
num_examples: 43592
download_size: 17042607
dataset_size: 93576787.24979
---
# Dataset Card for "qm_bob_easy_2_1.0e_eval"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 1,145 | [
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atmallen/qm_bob_hard_4_1.0e_eval | 2023-10-31T19:50:24.000Z | [
"region:us"
] | atmallen | null | null | 0 | 0 | 2023-10-27T05:44:38 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
dataset_info:
features:
- name: summand1
dtype: int64
- name: summand2
dtype: int64
- name: character
dtype: string
- name: sum
dtype: int64
- name: sum_words
dtype: string
- name: summand1_words
dtype: string
- name: summand2_words
dtype: string
- name: label
dtype:
class_label:
names:
'0': 'False'
'1': 'True'
- name: alice_label
dtype: int64
- name: bob_label
dtype: int64
- name: row_id
dtype: int64
splits:
- name: train
num_bytes: 23281256.13996
num_examples: 138684
- name: validation
num_bytes: 2610765.75605
num_examples: 15244
- name: test
num_bytes: 2608011.82
num_examples: 15200
download_size: 6167166
dataset_size: 28500033.716009997
---
# Dataset Card for "qm_bob_hard_4_1.0e_eval"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 1,146 | [
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sahalhes/Fastn-coder | 2023-10-27T06:02:56.000Z | [
"region:us"
] | sahalhes | null | null | 0 | 0 | 2023-10-27T06:02:56 | Entry not found | 15 | [
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anonymouse03052002/final | 2023-10-27T06:19:06.000Z | [
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mydream21787/Amitabh | 2023-10-27T06:28:53.000Z | [
"region:us"
] | mydream21787 | null | null | 0 | 0 | 2023-10-27T06:21:15 | Entry not found | 15 | [
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yuyijiong/Paper_QA_and_Abstract_Chinese | 2023-10-29T03:11:47.000Z | [
"size_categories:1K<n<10K",
"language:zh",
"license:cc-by-nc-4.0",
"region:us"
] | yuyijiong | null | null | 2 | 0 | 2023-10-27T06:26:05 | ---
license: cc-by-nc-4.0
language:
- zh
size_categories:
- 1K<n<10K
---
* 来自知网的论文数据，版权受限，不能直接公开。下载后请勿上传到公开场合。
* 包括 为论文写摘要、基于论文内容的问答 两个任务 | 137 | [
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Facico/test2 | 2023-10-27T06:48:29.000Z | [
"region:us"
] | Facico | null | null | 0 | 0 | 2023-10-27T06:33:47 | Entry not found | 15 | [
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open-llm-leaderboard/details_Weyaxi__llama-2-alpacagpt4-1000step | 2023-10-27T06:34:53.000Z | [
"region:us"
] | open-llm-leaderboard | null | null | 0 | 0 | 2023-10-27T06:34:45 | ---
pretty_name: Evaluation run of Weyaxi/llama-2-alpacagpt4-1000step
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [Weyaxi/llama-2-alpacagpt4-1000step](https://huggingface.co/Weyaxi/llama-2-alpacagpt4-1000step)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 3 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_Weyaxi__llama-2-alpacagpt4-1000step\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-27T06:34:41.482780](https://huggingface.co/datasets/open-llm-leaderboard/details_Weyaxi__llama-2-alpacagpt4-1000step/blob/main/results_2023-10-27T06-34-41.482780.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.007864932885906041,\n\
\ \"em_stderr\": 0.0009046332824008166,\n \"f1\": 0.07264681208053722,\n\
\ \"f1_stderr\": 0.0016288280664899088,\n \"acc\": 0.4836165974438314,\n\
\ \"acc_stderr\": 0.010680678903995254\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.007864932885906041,\n \"em_stderr\": 0.0009046332824008166,\n\
\ \"f1\": 0.07264681208053722,\n \"f1_stderr\": 0.0016288280664899088\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.16376042456406367,\n \
\ \"acc_stderr\": 0.010193237214420942\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.8034727703235991,\n \"acc_stderr\": 0.011168120593569567\n\
\ }\n}\n```"
repo_url: https://huggingface.co/Weyaxi/llama-2-alpacagpt4-1000step
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_drop_3
data_files:
- split: 2023_10_27T06_34_41.482780
path:
- '**/details_harness|drop|3_2023-10-27T06-34-41.482780.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-27T06-34-41.482780.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_27T06_34_41.482780
path:
- '**/details_harness|gsm8k|5_2023-10-27T06-34-41.482780.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-27T06-34-41.482780.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_27T06_34_41.482780
path:
- '**/details_harness|winogrande|5_2023-10-27T06-34-41.482780.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-27T06-34-41.482780.parquet'
- config_name: results
data_files:
- split: 2023_10_27T06_34_41.482780
path:
- results_2023-10-27T06-34-41.482780.parquet
- split: latest
path:
- results_2023-10-27T06-34-41.482780.parquet
---
# Dataset Card for Evaluation run of Weyaxi/llama-2-alpacagpt4-1000step
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/Weyaxi/llama-2-alpacagpt4-1000step
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** clementine@hf.co
### Dataset Summary
Dataset automatically created during the evaluation run of model [Weyaxi/llama-2-alpacagpt4-1000step](https://huggingface.co/Weyaxi/llama-2-alpacagpt4-1000step) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_Weyaxi__llama-2-alpacagpt4-1000step",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-27T06:34:41.482780](https://huggingface.co/datasets/open-llm-leaderboard/details_Weyaxi__llama-2-alpacagpt4-1000step/blob/main/results_2023-10-27T06-34-41.482780.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.007864932885906041,
"em_stderr": 0.0009046332824008166,
"f1": 0.07264681208053722,
"f1_stderr": 0.0016288280664899088,
"acc": 0.4836165974438314,
"acc_stderr": 0.010680678903995254
},
"harness|drop|3": {
"em": 0.007864932885906041,
"em_stderr": 0.0009046332824008166,
"f1": 0.07264681208053722,
"f1_stderr": 0.0016288280664899088
},
"harness|gsm8k|5": {
"acc": 0.16376042456406367,
"acc_stderr": 0.010193237214420942
},
"harness|winogrande|5": {
"acc": 0.8034727703235991,
"acc_stderr": 0.011168120593569567
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[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] | 7,323 | [
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Facico/test3 | 2023-10-27T06:49:10.000Z | [
"region:us"
] | Facico | null | null | 0 | 0 | 2023-10-27T06:47:58 | Entry not found | 15 | [
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SergioSCA/Structures | 2023-10-27T07:07:46.000Z | [
"license:apache-2.0",
"region:us"
] | SergioSCA | null | null | 0 | 0 | 2023-10-27T07:06:26 | ---
license: apache-2.0
---
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[More Information Needed] | 4,389 | [
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mingzhip/binance_oi | 2023-10-27T07:30:38.000Z | [
"region:us"
] | mingzhip | null | null | 0 | 0 | 2023-10-27T07:30:38 | Entry not found | 15 | [
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beatrice-yap/encoded_data | 2023-10-27T07:51:11.000Z | [
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beatrice-yap/beatriceyapsm | 2023-10-27T07:52:13.000Z | [
"region:us"
] | beatrice-yap | null | null | 0 | 0 | 2023-10-27T07:52:13 | Entry not found | 15 | [
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beatrice-yap/encoded_data_librimix3 | 2023-10-27T07:53:21.000Z | [
"region:us"
] | beatrice-yap | null | null | 0 | 0 | 2023-10-27T07:53:21 | Entry not found | 15 | [
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trettenmeier/markt-pilot | 2023-10-27T08:30:11.000Z | [
"task_categories:text-classification",
"size_categories:100K<n<1M",
"language:de",
"language:en",
"license:cc-by-sa-4.0",
"entity resolution",
"product matching",
"region:us"
] | trettenmeier | null | null | 1 | 0 | 2023-10-27T07:53:40 | ---
license: cc-by-sa-4.0
task_categories:
- text-classification
language:
- de
- en
tags:
- entity resolution
- product matching
pretty_name: Markt-Pilot Dataset for Product Matching
size_categories:
- 100K<n<1M
---
This dataset has an accompanying paper "Introducing a novel dataset for product matching: A new challenge for matching systems" that is accepted at The 3rd International Conference on Computers and Automation (CompAuto 2023) and will be published in IEEE Xplore.
The structure of the dataset is as follows: Each data point consists of a pair products and a binary label that indicates if these two product refer to the same real-world entity.
It consists of four subsets that differ in size and class distribution:
| Dataset |Data points | Negative | Positive | Imbalance Ratio |
|---|---:|---:|---:|---:|
| Full | 960,532| 665,831 | 294,701 | 2.3 |
| L | 243,954| 199,749 | 44,205 | 4.5 |
| M |66,556 | 59,925 | 6,631 | 9.0 |
| S | 18,973 |17,978 | 995 | 18.1 |
The test set consists of 5,000 manually checked data points and is shared across all four subsets.
| 1,084 | [
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] |
shi3z/MTbenchJapanese | 2023-10-28T05:43:54.000Z | [
"license:mit",
"region:us"
] | shi3z | null | null | 2 | 0 | 2023-10-27T07:58:07 | ---
license: mit
---
Japanese translated file from Vicuna MT bench question.jsonl
https://github.com/lm-sys/FastChat/blob/main/fastchat/llm_judge/data/mt_bench/question.jsonl
fixed by npaka
https://note.com/npaka/n/na28f31e96599 | 229 | [
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sanak/bargains_conversatation | 2023-10-27T08:02:25.000Z | [
"region:us"
] | sanak | null | null | 0 | 0 | 2023-10-27T08:02:25 | Entry not found | 15 | [
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projetomemoreba/eqos_questoes_direito_administrativo_v1 | 2023-10-27T08:36:53.000Z | [
"region:us"
] | projetomemoreba | null | null | 0 | 0 | 2023-10-27T08:36:37 | Entry not found | 15 | [
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projetomemoreba/conteudo_didatico_direito_administrativo_v1 | 2023-10-27T08:37:42.000Z | [
"region:us"
] | projetomemoreba | null | null | 0 | 0 | 2023-10-27T08:37:23 | Entry not found | 15 | [
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bongoServer2112/splitTwelve-brela-CM-attachements-archived | 2023-10-27T08:58:30.000Z | [
"region:us"
] | bongoServer2112 | null | null | 0 | 0 | 2023-10-27T08:53:38 | Entry not found | 15 | [
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BAAI/JudgeLM-100K | 2023-10-27T09:42:09.000Z | [
"task_categories:text-generation",
"language:en",
"license:cc-by-nc-4.0",
"instruction-finetuning",
"arxiv:2310.17631",
"region:us"
] | BAAI | null | null | 6 | 0 | 2023-10-27T08:54:35 |
---
license: cc-by-nc-4.0
language:
- en
tags:
- instruction-finetuning
pretty_name: JudgeLM-100K
task_categories:
- text-generation
---
# Dataset Card for JudgeLM
## Dataset Description
- **Repository:** https://github.com/baaivision/JudgeLM
- **Paper:** https://arxiv.org/abs/2310.17631
### Dataset Summary
JudgeLM-100K dataset contains 100,000 judge samples for training and 5,000 judge samples for validation. All the judge samples have the GPT-4-generated high-quality judgements.
This instruction data can be used to conduct instruction-tuning for language models and make the language model has ability to judge open-ended answer pairs.
See more details in the "Dataset" section and the appendix sections of this [paper](https://arxiv.org/abs/2310.17631).
This produced a judge dataset for instruction-following with 100K examples, obtained at a cost of about $4,000.
### Supported Tasks and Leaderboards
The JudgeLM-100K dataset designed for fine-tuning large language models as judges.
### Languages
The data in JudgeLM-100K are in English.
## Dataset Structure
### Data Instances
An example of "train" looks as follows:
```json
{
"review_id": "HZKJaPyrQSdPyxiz2hUZx9",
"question_id": 0,
"answer1_id": "",
"answer2_id": "",
"reviewer_id": 1,
"metadata": {},
"text": "2 8\nAssistant 1's response is not very helpful or detailed. It simply suggests a website for professional tattoo advice, but does not provide any suggestions or guidance on what kind of design the user might consider. On the other hand, Assistant 2's response is much more detailed and helpful. It provides several suggestions for tattoo designs, such as something meaningful, something based on personal interests, or something based on the user's own body. It also advises the user to get a tattoo they like and to seek help from a reputable tattoo artist. The response is relevant, accurate, and provides a good level of detail.",
"score": [2.0, 8.0],
"question_body": "\nI want to get a tattoo but I'm not sure what kind of design to get. What do you suggest?",
"answer1_body": "myDesigner.com\n\nA:\n\n I suggest to ask for professional tattoo advice on Inkchat.com",
"answer2_body": "1. Something meaningful. There are so many ways to go on this.\n2. Something based on personal interests. Something you love, something funny, something you do for fun.\n3. Something based on your own body. The idea of having a tattoo that covers part of your skin is, well, a little unappealing. If you're open to more creative solutions, and you don't mind having a tattoo all over your body, you can go for a design that is meant to be covered up.\nI wouldn't suggest getting a tattoo just to cover up a bad tattoo or because it would be a good story. Get a tattoo you like, that you would love to look at a hundred years from now as a reminder of a time and place in your life.I\u2019m sure you know this, but if you don\u2019t, you don\u2019t have to go at it alone. You have a whole army of people who can help you.\nThe best way to avoid a bad tattoo is to get a lot of help. The most important thing is to find a reputable tattoo artist. Tattoo artists are like any other skilled professional. You want someone who has a lot of experience and training.\nThe best way to",
"answer1_model_id": "dolly-v2-12b",
"answer2_model_id": "mpt-7b",
"answer1_metadata": {"decoding_method": "top_p_sampling"},
"answer2_metadata": {"decoding_method": "top_p_sampling"},
"reference": {
"decoding_method": "None",
"model": "unified_chip2/83622",
"text": "As an AI language model, I cannot recommend any specific tattoo designs. However, I recommend researching various designs and styles to find one that resonates with you and has a personal meaning. It's also important to find a reputable tattoo artist that you trust and feel comfortable with. Best of luck in your tattoo journey!",
"scores": {}
},
"score_w_reference": [3.0, 7.0],
"text_w_reference": "3 7\n\nAssistant 1's response was not very helpful or detailed. It simply suggested a website for professional tattoo advice without providing any context or explanation. The assistant did not address the user's uncertainty about the kind of design to get, which was the main point of the question. \n\nOn the other hand, Assistant 2's response was more detailed and relevant. It provided several suggestions for the user to consider when choosing a tattoo design, such as choosing something meaningful or based on personal interests. It also emphasized the importance of finding a reputable tattoo artist, which aligns with the reference answer. However, the response was a bit rambling and could have been more concise and organized."}
```
### Data Fields
The main data fields are as follows:
* `question_id`: describes the index of seed questions.
* `question_body`: the text of seed questions.
* `answer1_body` / `answer2_body`: the text of answer that generated by LLMs.
* `answer1_model_id` / `answer2_model_id`: the name of LLM that produced this answer.
* `answer1_metadata` / `answer1_metadata`: some metadata informations of answers, including `decoding_method`.
* `text`: the judgements produced by the GPT-4 teacher.
* `score`: the structured score of GPT-4-generated Judgements.
* `reference`: the reference answer that could be used in judging, which could provide extra knowledge or a specific preference.
* `text_w_reference`: the GPT-4-generated Judgements when given `reference` as the reference answer.
- `score_w_reference`: the structured score of `text_w_reference`.
### Data Splits
| | train | val (w/o reference) | val (w/ reference) |
|---------------|------:|----:|----:|
| JudgeLM-100K | 99647 | 4849 | 4942 |
## Additional Information
### Licensing Information
The dataset is available under the [Creative Commons NonCommercial (CC BY-NC 4.0)](https://creativecommons.org/licenses/by-nc/4.0/legalcode).
### Citation Information
```
@article{zhu2023judgelm,
title={JudgeLM: Fine-tuned Large Language Models are Scalable Judges},
author={Lianghui Zhu and Xinggang Wang and Xinlong Wang},
year={2023},
eprint={2310.17631},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
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projetomemoreba/benchmark_cls_eqos_v1 | 2023-10-27T08:55:14.000Z | [
"region:us"
] | projetomemoreba | null | null | 0 | 0 | 2023-10-27T08:54:45 | Entry not found | 15 | [
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ManuBansal/33param_snp500_validationSet | 2023-10-27T09:17:41.000Z | [
"region:us"
] | ManuBansal | null | null | 0 | 0 | 2023-10-27T09:16:52 | Entry not found | 15 | [
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] |
flozi00/atra-dpo | 2023-10-27T09:26:57.000Z | [
"region:us"
] | flozi00 | null | null | 0 | 0 | 2023-10-27T09:26:55 | ---
dataset_info:
features:
- name: input
dtype: string
- name: choosen
dtype: string
- name: rejected
dtype: string
splits:
- name: train
num_bytes: 6232
num_examples: 6
download_size: 8792
dataset_size: 6232
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "atra-dpo"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 498 | [
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tmabraham/pubmed-enrico-tokenized | 2023-10-27T11:11:24.000Z | [
"region:us"
] | tmabraham | null | null | 0 | 0 | 2023-10-27T09:48:34 | Entry not found | 15 | [
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makram93/accepted_pairs_50 | 2023-10-27T12:18:50.000Z | [
"region:us"
] | makram93 | null | null | 0 | 0 | 2023-10-27T10:15:10 | ---
dataset_info:
features:
- name: url
dtype: string
- name: doc_id
dtype: string
- name: original_title
sequence: string
- name: right
dtype: string
- name: left
dtype: string
splits:
- name: train
num_bytes: 88447.0623234648
num_examples: 100
download_size: 78941
dataset_size: 88447.0623234648
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "accepted_pairs_50"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 607 | [
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makram93/rejected_pairs_50 | 2023-10-27T12:18:53.000Z | [
"region:us"
] | makram93 | null | null | 0 | 0 | 2023-10-27T10:15:13 | ---
dataset_info:
features:
- name: url
dtype: string
- name: doc_id
dtype: string
- name: original_title
sequence: string
- name: right
dtype: string
- name: left
dtype: string
splits:
- name: train
num_bytes: 88447.0623234648
num_examples: 100
download_size: 85583
dataset_size: 88447.0623234648
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "rejected_pairs_50"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 607 | [
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YL95/your_dataset_name | 2023-10-27T10:32:15.000Z | [
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bongoServer2112/splitThirteen-brela-CM-attachements-archived | 2023-10-27T11:27:00.000Z | [
"region:us"
] | bongoServer2112 | null | null | 0 | 0 | 2023-10-27T10:52:23 | Entry not found | 15 | [
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] |
parinzee/claq-qa-thai-dataset | 2023-10-27T11:08:08.000Z | [
"region:us"
] | parinzee | null | null | 0 | 0 | 2023-10-27T11:07:58 | ---
dataset_info:
features:
- name: id
dtype: string
- name: question
dtype: string
- name: context
dtype: string
- name: answers
dtype: string
- name: source
dtype: string
- name: th_aug
dtype: string
- name: th_fasttext_aug
dtype: string
- name: th_llm_gec_aug
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- name: th_llm_paraphrase_aug
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- name: th_ltw2v_aug
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- name: th_qcpg_0.2_aug
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- name: th_qcpg_0.2_llm_gec_aug
dtype: string
- name: th_qcpg_0.5_aug
dtype: string
- name: th_qcpg_0.5_llm_gec_aug
dtype: string
- name: th_qcpg_0.8_aug
dtype: string
- name: th_qcpg_0.8_llm_gec_aug
dtype: string
- name: th_thai2fit_aug
dtype: string
- name: th_thai2trans_aug
dtype: string
- name: th_wordnet_aug
dtype: string
- name: en_aug
dtype: string
- name: en_llm_gec_aug
dtype: string
- name: en_llm_paraphrase_aug
dtype: string
- name: en_qcpg_0.2_aug
dtype: string
- name: en_qcpg_0.2_llm_gec_aug
dtype: string
- name: en_qcpg_0.5_aug
dtype: string
- name: en_qcpg_0.5_llm_gec_aug
dtype: string
- name: en_qcpg_0.8_aug
dtype: string
- name: en_qcpg_0.8_llm_gec_aug
dtype: string
- name: dis_aug
dtype: float64
- name: dis_fasttext_aug
dtype: float64
- name: dis_llm_gec_aug
dtype: float64
- name: dis_llm_paraphrase_aug
dtype: float64
- name: dis_ltw2v_aug
dtype: float64
- name: dis_qcpg_0.2_aug
dtype: float64
- name: dis_qcpg_0.2_llm_gec_aug
dtype: float64
- name: dis_qcpg_0.5_aug
dtype: float64
- name: dis_qcpg_0.5_llm_gec_aug
dtype: float64
- name: dis_qcpg_0.8_aug
dtype: float64
- name: dis_qcpg_0.8_llm_gec_aug
dtype: float64
- name: dis_thai2fit_aug
dtype: float64
- name: dis_thai2trans_aug
dtype: float64
- name: dis_wordnet_aug
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splits:
- name: train
num_bytes: 117313078
num_examples: 16980
download_size: 35147642
dataset_size: 117313078
---
# Dataset Card for "claq-qa-thai-dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 2,222 | [
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omgbobbyg/Zip-Code-to-Timezone | 2023-10-27T11:29:34.000Z | [
"region:us"
] | omgbobbyg | null | null | 0 | 0 | 2023-10-27T11:14:09 | ---
# For reference on dataset card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/datasetcard.md?plain=1
# Doc / guide: https://huggingface.co/docs/hub/datasets-cards
{}
---
# Dataset Card for Zip Code to Timezone Offset Mapping
<!-- Provide a quick summary of the dataset. -->
This dataset maps Zip Codes and Postal Codes for the USA and Canada to the relevant timezone offset.
## Dataset Details
### Dataset Description
In addition to providing a mapping from a Zip Code or Postal Code to timezone offset, it also contains the timezone offset for DST (if observed).
- **Curated by:** Bobby Gill, BlueLabel
### Acknowledgements
<!-- Provide the basic links for the dataset. -->
- **Based off the Work Here:** [https://www.kaggle.com/datasets/joeleichter/us-zip-codes-with-lat-and-long]
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AvianaKahn/mydataset | 2023-10-27T11:18:20.000Z | [
"region:us"
] | AvianaKahn | null | null | 0 | 0 | 2023-10-27T11:18:20 | Entry not found | 15 | [
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teshor/gregory | 2023-10-27T11:43:59.000Z | [
"region:us"
] | teshor | null | null | 0 | 0 | 2023-10-27T11:43:59 | Entry not found | 15 | [
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qgyd2021/writing_a_novel | 2023-10-27T12:00:25.000Z | [
"task_categories:question-answering",
"task_categories:text-generation",
"task_categories:text2text-generation",
"size_categories:100M<n<1B",
"language:zh",
"license:apache-2.0",
"region:us"
] | qgyd2021 | null | null | 0 | 0 | 2023-10-27T11:47:10 | ---
license: apache-2.0
task_categories:
- question-answering
- text-generation
- text2text-generation
language:
- zh
size_categories:
- 100M<n<1B
---
## Writing a Novel
写小说：
（1）下载小说。
（2）利用大模型将小说重新整理成一句一行。
（3）利用大模型对小说的各段落写一个摘要。
（4）摘要做 query，小说内容做 response，训练模型。
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HotAmigo455445/HotAmigo79878 | 2023-10-27T12:28:17.000Z | [
"region:us"
] | HotAmigo455445 | null | null | 0 | 0 | 2023-10-27T12:27:13 | # Hot Amigo Heater UK Reviews - HotAmigo Heater Benefits and Official Price, Buy
Hot Amigo [Heater](https://lookerstudio.google.com/reporting/d03716e8-3ebb-41ac-90d1-2deaa12d4ae9/page/PSfgD) UK Reviews The small length of the Hot Amigo Heater may additionally makepeople agree with it is not going to help, but when you begin the use of it, you may recognizethat it takes simplest a couple of minutes to begin running. Any small to medium-sized roomis perfectly heated within a few minutes, and heat is calmly alloted. It does not be countedwhich size is your room until it's miles a very large length. In that case, one Hot AmigoHeater might not be enough, and you'll want or 3 warmers to hold it warm.
### **[Click Here To Buy Now From Official Website Of Hot Amigo Heater](https://callednews.com/hot-amigo-heater-uk)**
➥ Product Name - {[Hot Amigo](https://gamma.app/public/Hot-Amigo-Heater-United-Kingdome-Reviews---Its-Benefits-and-Offic-cfh122329uhl114?mode=doc) Heater}
➥ Benefits - “Wind Speed Options: High/Low"
➥ Category - Heater
➥ Availability – [Online](https://hondrolife-44088394.hubspotpagebuilder.com/hot-amigo-heater-united-kingdome-reviews-its-benefits-and-official-price-buy)
➥ Rating: - 5.0/5.0 ⭐⭐⭐⭐⭐
Are you looking for a space [heater](https://groups.google.com/g/dionica-mexico-544/c/yfyLiN5VNg0) that can warm up your room quickly, quietly, and efficiently? Do you want to save money on your heating bills and reduce your carbon footprint? If so, you may be interested in Hot Amigo Heater, a portable plug-in heater that claims to be the perfect solution for your heating needs. But is this product really worth buying? How does it compare to other similar or alternative products on the market? In this article, we will give you an honest and comprehensive review of Hot [Amigo Heater](httpshttps://filmfreeway.com/HotAmigoHeaterUnitedKingdomeReviews-ItsBenefitsandOfficialPriceBuy?preview=true&public=1&project_file_access=1&password=), based on its specifications, features, benefits, pros and cons, customer reviews, and frequently asked questions. By the end of this article, you will be able to decide if this is the best plug-in heater for your home.
## What is Hot Amigo Heater
Hot [Amigo Heater](https://trb22.blogspot.com/2023/10/hot-amigo-heater-united-kingdome.html) is a portable plug-in heater that can heat up any room in minutes, using its 900W ceramic technology. It has a digital thermostat that allows you to adjust the temperature from 15 to 32 degrees Celsius (59 to 89.6 degrees Fahrenheit), and a two-speed fan that distributes the heat evenly across the room. It also has a timer and a remote control function that make it more convenient for you to set the function from a [distance](https://www.hitched.co.uk/web/hot-amigo-and-heater) within 3 meters (9.8 feet). You can also rotate the plug to fit any direction of the wall outlet. Hot Amigo Heater has a multi-layer protection system that ensures your safety. It is made of heat-resistant [material](https://flokii.com/blogs/view/131090) and has an overheat protection function that effectively reduces the risk of fire. It also has a low noise design that will not disturb your sleep or work.
## Specifications Hot Amigo Heater
Product Type: Portable Plug -in Heater
Color: White/Black
Material: ABS
Power: 900W
Voltage: 110-220V（50-60Hz）
Heating Mode: PTC
Wind Speed Options: High/Low
Temperature Range: 15-32℃/59-89.6℉
## Features of Hot Amigo Heater
[Portable](https://the-dots.com/projects/hot-amigo-heater-united-kingdome-reviews-it-s-benefits-and-official-price-buy-978569) and easy to use: You can plug it directly into the wall outlet without any cable involved, and rotate the plug to fit the narrow space. You can also carry it with one hand and take it to any room at home.
Digital thermostat: You can [adjust](https://diaetoxil5665.hashnode.dev/hot-amigo-heater-united-kingdome-reviews-its-benefits-and-official-price-buy) the temperature from 15 to 32 degrees Celsius (59 to 89.6 degrees Fahrenheit), and the heater will turn on and off automatically to maintain the set temperature for you, saving energy and money.
Two-speed fan: The [heater](https://www.cineplayers.com/hotamigo45/listas/hot-amigo-heater-united-kingdome-reviews-its-benefits-and-official-price-buy) has a high and low wind speed option, and a fan that distributes the heat evenly across the room.
Timer and remote control: You can set a timer from 1 to 12 hours in 1 hour increment, which is displayed clearly on the screen. You can also use the remote control to change the temperature, wind speed, and timer settings without leaving your warm bed or couch.
Multi-layer [protection](https://www.startus.cc/company/hot-amigo-heater-united-kingdome-reviews-its-benefits-and-official-price-buy): The heater is made of heat-resistant material and has an overheat protection function that effectively reduces the risk of fire. It also has an anti-tip safety shutoff feature that automatically turns off the [heater](https://huggingface.co/HotAmigo4554/HotAmigoHeater) if it falls over.
### **[Click Here To Buy Now From Official Website Of Hot Amigo Heater](https://callednews.com/hot-amigo-heater-uk)**
## How Does Hot Amigo Heater Work
Hot [Amigo Heater](https://skiomusic.com/g7-plus/hot-amigo-heater-hot-amigo-heater-united-kingdome-reviews-its-benefits-and-official-price-buy) works by using PTC ceramic heating technology, which is a type of electric heating element that has a positive temperature coefficient. This means that as the temperature increases, so does the electrical resistance, which prevents overheating and ensures stable heating performance. The ceramic heating element heats up quickly and produces hot air that is blown by the fan across the room. The digital thermostat [monitors](https://sketchfab.com/3d-models/hot-amigo-heater-united-kingdome-reviews-aad6f9e8dcde4c4e9c288794ec142259) the room temperature and adjusts the heating power accordingly. The timer and remote control functions allow you to customize your heating preferences and schedule.
## WHAT IS THE PRICE OF HOT AMIGO HEATER
To purchase the authentic HOT AMIGO Heater in Europe, including the UK and Belgium, it is recommended to visit the official website of the manufacturer. This is the most reliable source to ensure you receive a genuine product with any applicable discounts and warranties.
While you may come across various online retailers or marketplaces, purchasing from the official website guarantees the authenticity and quality of HOT AMIGO. This not only assures you of a legitimate product but also offers access to any special promotions or discounts that the manufacturer may be offering.
So, for European readers seeking to buy HOT AMIGO and experience its cost-effective heating benefits, the manufacturer’s official website is the most trusted and secure platform to make your purchase. This ensures you get the genuine product, backed by any offers and customer support provided by the manufacturer.
## SUMMARY AND CONCLUSION
In conclusion, the HOT AMIGO Heater is a game-changing solution for European users, particularly those in the Netherlands, the UK, Denmark, and Belgium. It offers a combination of advanced technology, energy efficiency, and cost-effectiveness that addresses the unique challenges posed by colder climates and rising energy costs in these regions.
HOT AMIGO’s 900W Ceramic Technology delivers instant heat, making it a must-have for residents in countries like the Netherlands and Denmark, where enduring chilly weather is common. Its compact and portable design caters to personal spaces, ensuring that you can stay warm in your office, bedroom, or even camper van in the UK and Belgium. Furthermore, the heater’s energy efficiency and intelligent thermostat allow for precise temperature control while keeping heating costs in check, providing peace of mind and budget-friendly warmth.
For European users, HOT AMIGO is a valuable addition to combat the cold, offering immediate warmth and substantial savings on energy bills. It’s not just a heating solution; it’s a cost-effective lifestyle upgrade that resonates with the needs and concerns of residents in these European regions. Don’t miss out on the opportunity to experience the warmth and efficiency of HOT AMIGO—your ultimate companion for staying cozy while keeping heating expenses under control.
### **[Click Here To Buy Now From Official Website Of Hot Amigo Heater](https://callednews.com/hot-amigo-heater-uk)**
[#HotAmigoHeater](https://hackmd.io/@PremierKeto565/r1PovZtzp)
[#HotAmigoHeaterUK](https://www.provenexpert.com/hot-amigo-heater-united-kingdome-reviews/?mode=preview)
[#HotAmigoHeaterPrice](https://educatorpages.com/site/Diaetoxil545/pages/hot-amigo-heater-united-kingdome-reviews-its-benefits-and-official-price-buy?)
[#HotAmigoHeaterBuy](https://colab.research.google.com/drive/1YQ9gvmnNyjJz0neRxpat79oonUpC9nR0?usp=sharing#scrollTo=4QtTvg3fkDzg)
[#HotAmigoHeaterBenefits](https://www.scoop.it/topic/hot-amigo-heater-united-kingdome-reviews-it-s-benefits-and-official-price-buy?curate=true&onb=1&loader=1)
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[#WhatisHotAmigoHeater](https://www.dibiz.com/rajotehu)
| 9,339 | [
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] |
mekaneeky/Synthetic_English_MMS | 2023-10-27T12:46:42.000Z | [
"region:us"
] | mekaneeky | null | null | 0 | 0 | 2023-10-27T12:36:58 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: dev
path: data/dev-*
- split: test
path: data/test-*
dataset_info:
features:
- name: eng
dtype: string
- name: lug
dtype: string
- name: ach
dtype: string
- name: teo
dtype: string
- name: lgg
dtype: string
- name: nyn
dtype: string
- name: ID
dtype: string
- name: eng_tts
sequence:
sequence: float32
splits:
- name: train
num_bytes: 12857414976
num_examples: 23947
- name: dev
num_bytes: 267728460
num_examples: 500
- name: test
num_bytes: 266636552
num_examples: 500
download_size: 13400072749
dataset_size: 13391779988
---
# Dataset Card for "Synthetic_English_MMS_EL"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 908 | [
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AudreyYZY/demo | 2023-10-27T13:31:25.000Z | [
"region:us"
] | AudreyYZY | null | null | 0 | 0 | 2023-10-27T12:54:55 | Entry not found | 15 | [
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] |
makram93/rejected_pairs_base | 2023-10-27T15:03:14.000Z | [
"region:us"
] | makram93 | null | null | 0 | 0 | 2023-10-27T13:04:22 | ---
dataset_info:
features:
- name: url
dtype: string
- name: doc_id
dtype: string
- name: original_title
sequence: string
- name: right
dtype: string
- name: left
dtype: string
splits:
- name: train
num_bytes: 88447.0623234648
num_examples: 100
download_size: 0
dataset_size: 88447.0623234648
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "rejected_pairs_base"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 605 | [
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mponty/web_crawl_docs | 2023-10-27T13:18:47.000Z | [
"region:us"
] | mponty | null | null | 0 | 0 | 2023-10-27T13:18:10 | ---
dataset_info:
features:
- name: text
dtype: string
- name: lang
dtype: string
- name: source
dtype: string
- name: url
dtype: string
- name: id
dtype: string
splits:
- name: train
num_bytes: 1157430740
num_examples: 87370
download_size: 492924255
dataset_size: 1157430740
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "web_crawl_docs"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 582 | [
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bongoServer2112/splitFourteen-brela-CM-attachements-archived | 2023-10-27T13:26:23.000Z | [
"region:us"
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facat/sft-train-samples | 2023-10-28T04:27:28.000Z | [
"region:us"
] | facat | null | null | 0 | 0 | 2023-10-27T13:49:23 | ---
dataset_info:
features:
- name: prompt
dtype: string
- name: output
dtype: string
- name: task
dtype: string
- name: name
dtype: string
splits:
- name: train
num_bytes: 7997635
num_examples: 2420
download_size: 4481416
dataset_size: 7997635
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "sft-train-samples"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 548 | [
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kuanhuggingface/text_guided_vc_dataset_encodec_v3 | 2023-10-27T14:31:16.000Z | [
"region:us"
] | kuanhuggingface | null | null | 0 | 0 | 2023-10-27T14:17:54 | Entry not found | 15 | [
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] |
Frorozcol/LLaVa-instruction-trasaleted | 2023-10-27T14:18:56.000Z | [
"region:us"
] | Frorozcol | null | null | 0 | 0 | 2023-10-27T14:18:29 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: id
dtype: string
- name: conversations
list:
- name: from
dtype: string
- name: value
dtype: string
- name: image
dtype: string
- name: conversations_translated
sequence: string
splits:
- name: train
num_bytes: 397720156
num_examples: 157500
download_size: 197927858
dataset_size: 397720156
---
# Dataset Card for "LLaVa-instruction-trasaleted"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 661 | [
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] |
mekaneeky/Synthetic_English_VITS_22.5k | 2023-10-27T14:42:36.000Z | [
"region:us"
] | mekaneeky | null | null | 0 | 0 | 2023-10-27T14:28:34 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: dev
path: data/dev-*
- split: test
path: data/test-*
dataset_info:
features:
- name: eng
dtype: string
- name: lug
dtype: string
- name: ach
dtype: string
- name: teo
dtype: string
- name: lgg
dtype: string
- name: nyn
dtype: string
- name: ID
dtype: string
- name: eng_tts
sequence:
sequence: float32
splits:
- name: train
num_bytes: 14292643136
num_examples: 23947
- name: dev
num_bytes: 286070348
num_examples: 500
- name: test
num_bytes: 300760328
num_examples: 500
download_size: 14889677577
dataset_size: 14879473812
---
# Dataset Card for "Synthetic_English_VITS_22.5k"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 912 | [
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bongoServer2112/splitFifteen-brela-CM-attachements-archived | 2023-10-27T14:50:35.000Z | [
"region:us"
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makram93/accepted_pairs_st | 2023-10-27T14:55:15.000Z | [
"region:us"
] | makram93 | null | null | 0 | 0 | 2023-10-27T14:53:18 | ---
dataset_info:
features:
- name: url
dtype: string
- name: doc_id
dtype: string
- name: original_title
sequence: string
- name: right
dtype: string
- name: left
dtype: string
splits:
- name: train
num_bytes: 88447.0623234648
num_examples: 100
download_size: 87877
dataset_size: 88447.0623234648
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "accepted_pairs_st"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 607 | [
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makram93/rejected_pairs_st | 2023-10-27T14:55:18.000Z | [
"region:us"
] | makram93 | null | null | 0 | 0 | 2023-10-27T14:53:21 | ---
dataset_info:
features:
- name: url
dtype: string
- name: doc_id
dtype: string
- name: original_title
sequence: string
- name: right
dtype: string
- name: left
dtype: string
splits:
- name: train
num_bytes: 88447.0623234648
num_examples: 100
download_size: 82694
dataset_size: 88447.0623234648
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "rejected_pairs_st"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 607 | [
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makram93/accepted_pairs_small | 2023-10-27T14:59:08.000Z | [
"region:us"
] | makram93 | null | null | 0 | 0 | 2023-10-27T14:57:39 | ---
dataset_info:
features:
- name: url
dtype: string
- name: doc_id
dtype: string
- name: original_title
sequence: string
- name: right
dtype: string
- name: left
dtype: string
splits:
- name: train
num_bytes: 88447.0623234648
num_examples: 100
download_size: 83182
dataset_size: 88447.0623234648
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "accepted_pairs_small"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 610 | [
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] |
makram93/rejected_pairs_small | 2023-10-27T14:59:10.000Z | [
"region:us"
] | makram93 | null | null | 0 | 0 | 2023-10-27T14:57:42 | ---
dataset_info:
features:
- name: url
dtype: string
- name: doc_id
dtype: string
- name: original_title
sequence: string
- name: right
dtype: string
- name: left
dtype: string
splits:
- name: train
num_bytes: 88447.0623234648
num_examples: 100
download_size: 87326
dataset_size: 88447.0623234648
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "rejected_pairs_small"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 610 | [
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jithsg/socail-power-nba | 2023-10-27T15:07:05.000Z | [
"region:us"
] | jithsg | null | null | 0 | 0 | 2023-10-27T15:05:39 | Entry not found | 15 | [
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a6687543/MSOM_Data_Driven_Challenge_2020 | 2023-10-27T16:48:49.000Z | [
"license:odbl",
"region:us"
] | a6687543 | null | null | 1 | 0 | 2023-10-27T15:11:39 | ---
license: odbl
---
MSOM_Data_Driven_Challenge_2020:
To support the 2020 MSOM Data Driven Research Challenge, JD.com, China’s largest retailer, offers transaction-level data to MSOM members for conducting data-driven research.
This dataset includes the transactional data associated with over 2.5 million customers (457,298 made purchases) and 31,868 SKUs over the month of March in 2018.
Researchers are welcome to develop econometric models or data-driven models using this database to address some of the suggested questions or examine their own research questions.
Keywords: E-Commerce, Transactional Data, MSOM Society, Data-Driven Research
See https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3511861 for details.
This repo serves as easier access to the dataset. The dataset can also be accessed from here: https://connect.informs.org/msom/events/datadriven2020.
All CopyRights are reserved by JD.com.
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arubenruben/dummy-1 | 2023-10-30T14:18:51.000Z | [
"region:us"
] | arubenruben | null | null | 0 | 0 | 2023-10-27T15:18:38 | Entry not found | 15 | [
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FunkyQ/w2i | 2023-10-27T15:19:35.000Z | [
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rag-datasets/bioasq | 2023-10-28T15:04:03.000Z | [
"task_categories:question-answering",
"task_categories:sentence-similarity",
"language:en",
"license:cc-by-2.5",
"rag",
"dpr",
"information-retrieval",
"question-answering",
"biomedical",
"region:us"
] | rag-datasets | null | null | 0 | 0 | 2023-10-27T15:51:17 | ---
license: cc-by-2.5
task_categories:
- question-answering
- sentence-similarity
language:
- en
tags:
- rag
- dpr
- information-retrieval
- question-answering
- biomedical
configs:
- config_name: text-corpus
data_files:
- split: passages
path: "data/passages.parquet/*"
- config_name: question-answer-passages
data_files:
- split: test
path: "data/test.parquet/*"
---
Derives from http://participants-area.bioasq.org/Tasks/11b/trainingDataset/ we generated our own subset using `generate.py`.
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bongoServer2112/splitSixteen-brela-CM-attachements-archived | 2023-10-27T16:13:02.000Z | [
"region:us"
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chunpingvi/dataset_vit5_tokenized | 2023-11-01T07:57:58.000Z | [
"region:us"
] | chunpingvi | null | null | 0 | 0 | 2023-10-27T16:16:12 | Entry not found | 15 | [
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] |
linhtran92/tts_male | 2023-10-27T16:48:11.000Z | [
"region:us"
] | linhtran92 | null | null | 0 | 0 | 2023-10-27T16:48:07 | ---
dataset_info:
features:
- name: sentence_norm
dtype: string
- name: audio
struct:
- name: array
sequence: int64
- name: path
dtype: string
- name: sampling_rate
dtype: int64
- name: wer
dtype: int64
- name: id
dtype: string
splits:
- name: train
num_bytes: 222336754
num_examples: 499
download_size: 45628084
dataset_size: 222336754
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "tts_male"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 661 | [
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rmadrig/prueba | 2023-10-27T21:04:27.000Z | [
"region:us"
] | rmadrig | null | null | 0 | 0 | 2023-10-27T16:50:34 | Entry not found | 15 | [
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dyliu/test | 2023-10-27T16:52:11.000Z | [
"region:us"
] | dyliu | null | null | 0 | 0 | 2023-10-27T16:52:11 | Entry not found | 15 | [
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thanhduycao/soict_train_dataset_filter_v2 | 2023-10-27T16:53:41.000Z | [
"region:us"
] | thanhduycao | null | null | 0 | 0 | 2023-10-27T16:52:45 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: id
dtype: string
- name: audio
struct:
- name: array
sequence: float64
- name: path
dtype: string
- name: sampling_rate
dtype: int64
- name: sentence_norm
dtype: string
- name: wer
dtype: float64
splits:
- name: train
num_bytes: 3226357801
num_examples: 6184
- name: test
num_bytes: 565495055
num_examples: 1092
download_size: 900623742
dataset_size: 3791852856
---
# Dataset Card for "soict_train_dataset_filter_v2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 791 | [
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FASOXO/C-SD-FAS | 2023-10-28T09:31:12.000Z | [
"region:us"
] | FASOXO | null | null | 1 | 0 | 2023-10-27T16:55:10 | 这是我对Colab新的研究
感兴趣加QQ群632428790 | 30 | [
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] |
19kmunz/iot-23-preprocessed-allcolumns | 2023-10-27T16:57:40.000Z | [
"region:us"
] | 19kmunz | null | null | 0 | 0 | 2023-10-27T16:57:09 | ---
dataset_info:
features:
- name: ts
dtype: float64
- name: uid
dtype: string
- name: id.orig_h
dtype: string
- name: id.orig_p
dtype: int64
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dtype: string
splits:
- name: train
num_bytes: 1232978140
num_examples: 6046623
download_size: 274218995
dataset_size: 1232978140
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "iot-23-preprocessed-allcolumns"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 1,219 | [
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Benicas/Linset | 2023-10-27T16:59:17.000Z | [
"region:us"
] | Benicas | null | null | 0 | 0 | 2023-10-27T16:59:17 | Entry not found | 15 | [
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ClaireOzzz/All_Data | 2023-10-27T17:15:02.000Z | [
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] |
linhtran92/tts_female | 2023-10-27T17:12:17.000Z | [
"region:us"
] | linhtran92 | null | null | 0 | 0 | 2023-10-27T17:12:14 | ---
dataset_info:
features:
- name: sentence_norm
dtype: string
- name: audio
struct:
- name: array
sequence: int64
- name: path
dtype: string
- name: sampling_rate
dtype: int64
- name: wer
dtype: int64
- name: id
dtype: string
splits:
- name: train
num_bytes: 212990169
num_examples: 498
download_size: 47949623
dataset_size: 212990169
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "tts_female"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 663 | [
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] |
Weni/Dataset_semantic_alignment_translation_en-es-direction_en-pt_br-direction | 2023-10-27T17:14:43.000Z | [
"region:us"
] | Weni | null | null | 0 | 0 | 2023-10-27T17:13:41 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: id
dtype: int64
- name: string
dtype: string
- name: string_translation
dtype: string
splits:
- name: train
num_bytes: 3130659
num_examples: 40001
download_size: 1877942
dataset_size: 3130659
---
# Dataset Card for "Dataset_semantic_alignment_translation_en-es-direction_en-pt_br-direction"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 581 | [
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ClaireOzzz/All_Images | 2023-10-27T17:35:49.000Z | [
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] |
MaxReynolds/TestUpload3 | 2023-10-27T17:22:48.000Z | [
"region:us"
] | MaxReynolds | null | null | 0 | 0 | 2023-10-27T17:22:44 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 1258070.0
num_examples: 10
download_size: 1259602
dataset_size: 1258070.0
---
# Dataset Card for "TestUpload3"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 474 | [
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toninhodjj/Noob | 2023-10-27T17:38:11.000Z | [
"region:us"
] | toninhodjj | null | null | 0 | 0 | 2023-10-27T17:37:58 | Entry not found | 15 | [
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bongoServer2112/splitSeventeen-brela-CM-attachements-archived | 2023-10-27T17:59:11.000Z | [
"region:us"
] | bongoServer2112 | null | null | 0 | 0 | 2023-10-27T17:47:54 | Entry not found | 15 | [
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atom-in-the-universe/bild-2 | 2023-11-02T20:23:37.000Z | [
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atom-in-the-universe/bild-7 | 2023-10-30T13:39:43.000Z | [
"region:us"
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atom-in-the-universe/bild-5 | 2023-11-02T23:11:35.000Z | [
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