# Dataset Card for "HebrewMetaphors"
### Dataset Summary
A common dataset for text classification task is IMDb. Large Movie Review Dataset. This is a dataset for binary sentiment classification.
The first step in our project was to create a Hebrew dataset with an IMDB-like structure but different in that, in addition to the sentences we have, there will also be verb names, and a classification of whether the verb name is literal or metaphorical in the given sentence.
Using an API, sentences that contained the verbs we selected were pulled from Twitter and Wikipedia and Prodigy was used to classify them. The accuracy of the classification was confirmed by having two different persons classify each line twice.

### Supported Tasks and Leaderboards
Classification 

### Languages
Hebrew

## Dataset Structure
The IMDb dataset is a json file with objects containing the text of the review, and a number 0 or 1 which is a negative or positive review

For example:

```
{
    "label": 1, //(pos)
    "text": "A wonderful little production ... "
}, {
    "label": 0, //(neg)
    "text": ""It actually pains me to say it, but this movie was horrible on every level ..."
}
```

After retrieving data from Wikipedia and Twitter, classifying and processing, we received the following data:

```
{'text': 'מתחיל לאמץ לשלוח את הבגדים למכבסה אחרי טיול לחול כי למי יש כוח לכבס כל כך הרבה בגדים אחרי חופש',
 'source': 'twitter',
 'label': 0,
 'verb': 'לכבס',
 'tweet_id': '1546803262065606658'},
 
{'text': ' בינג בונג מגיח מן המערב ומתחיל לזרוע הרס בממלכת עוץ',
 'source': 'wikipedia',
 'label': 1, //(Metaphor)
 'verb': 'לזרוע',
 'tweet_id': None},
 
 {'text': 'הם ישתמשו במערבל כדי להפנות את המטוס לשדה התעופה שהם יבחרו וכך לשדוד אותו',
 'source': 'wikipedia',
 'label': 0, //(Literal)
 'verb': 'לשדוד',
 'tweet_id': None}
 ```

As you can see, we have the following fields:
- 'text': A sentence in Hebrew that was taken from Wikipedia or Twitter that included the verb name.
- 'source': There are two possible sources for this text:  'twitter' or 'wikipedia'.
- 'label': Classification of whether the verb name is literal ('label': 0) or metaphorical ('label': 1) in the given sentence.
- 'verb': A verb name in Hebrew on which the text is labeled.
- 'tweet_id': If the text was taken from Twitter, the tweet's id, or None if it was retrieved from Wikipedia.

In the next section we will explain how we created the dataset.

### Data Statistics


<!-- TABLE_GENERATE_START -->

| Verb | After Agreement Between Taggers | Disagreements because of different answers | Disagreements because of different ignores | Total Literals After Agreement | Total Metaphors After Agreement | Split For Train Set | Split For Validation Set | Split For Test Set |
|---|---|---|---|---|---|------------------|-----------------------|--------------------|
| lakhalom_twitter | 131 | 42 | 42 | 26 | 105 | 83               | 21                    | 27                 |
| lakhalom_wiki | 93 | 53 | 69 | 34 | 59 | 58               | 16                    | 19                 |
| lakhatokh_twitter | 220 | 41 | 40 | 100 | 120 | 140              | 36                    | 44                 |
| lakhatokh_wiki | 288 | 54 | 6 | 286 | 2 | 182              | 47                    | 59                 |
| lauf_twitter | 166 | 8 | 29 | 14 | 152 | 104              | 28                    | 34                 |
| lauf_wiki | 172 | 11 | 67 | 161 | 11 | 108              | 28                    | 36                 |
| lefareq_twitter | 70 | 109 | 71 | 21 | 49 | 43               | 12                    | 15                 |
| lefareq_wiki | 96 | 48 | 213 | 89 | 7 | 60               | 16                    | 20                 |
| lehadliq_twitter | 270 | 14 | 19 | 194 | 76 | 172              | 43                    | 55                 |
| lehadliq_wiki | 375 | 13 | 13 | 370 | 5 | 239              | 61                    | 75                 |
| lekhabes_twitter | 203 | 2 | 26 | 155 | 48 | 129              | 33                    | 41                 |
| lekhabes_wiki | 55 | 1 | 0 | 51 | 4 | 34               | 9                     | 12                 |
| lekhofef_twitter | 289 | 7 | 11 | 18 | 271 | 183              | 47                    | 59                 |
| lekhofef_wiki | 107 | 3 | 6 | 73 | 34 | 67               | 18                    | 22                 |
| lerasek_twitter | 141 | 6 | 4 | 18 | 123 | 89               | 23                    | 29                 |
| lerasek_wiki | 154 | 3 | 12 | 110 | 44 | 98               | 25                    | 31                 |
| levashel_twitter | 280 | 3 | 19 | 264 | 16 | 177              | 46                    | 57                 |
| levashel_wiki | 228 | 3 | 19 | 221 | 7 | 144              | 37                    | 47                 |
| limkhoq_twitter | 222 | 16 | 22 | 123 | 99 | 141              | 36                    | 45                 |
| limkhoq_wiki | 212 | 11 | 29 | 109 | 103 | 134              | 35                    | 43                 |
| liqpots_twitter | 178 | 18 | 25 | 77 | 101 | 112              | 29                    | 37                 |
| liqpots_wiki | 168 | 6 | 28 | 163 | 5 | 107              | 27                    | 34                 |
| liqroa_twitter | 261 | 26 | 16 | 36 | 225 | 166              | 42                    | 53                 |
| liqroa_wiki | 226 | 18 | 19 | 147 | 79 | 143              | 37                    | 46                 |
| liqtsor_twitter | 256 | 18 | 28 | 43 | 213 | 163              | 41                    | 52                 |
| liqtsor_wiki | 167 | 11 | 2 | 99 | 68 | 106              | 27                    | 34                 |
| lirqod_twitter | 187 | 14 | 31 | 138 | 49 | 119              | 30                    | 38                 |
| lirqod_wiki | 214 | 18 | 34 | 211 | 3 | 135              | 35                    | 44                 |
| lishbor_twitter | 185 | 8 | 30 | 38 | 147 | 117              | 30                    | 38                 |
| lishbor_wiki | 244 | 6 | 50 | 69 | 175 | 156              | 39                    | 49                 |
| lishdod_twitter | 163 | 21 | 16 | 91 | 72 | 102              | 27                    | 34                 |
| lishdod_wiki | 209 | 2 | 4 | 209 | 0 | 133              | 34                    | 42                 |
| lishtot_twitter | 275 | 6 | 20 | 219 | 56 | 175              | 44                    | 56                 |
| lishtot_wiki | 192 | 7 | 2 | 192 | 0 | 122              | 31                    | 39                 |
| litkhon_twitter | 280 | 13 | 20 | 44 | 236 | 178              | 45                    | 57                 |
| litkhon_wiki | 87 | 0 | 0 | 85 | 2 | 54               | 15                    | 18                 |
| litpor_twitter | 193 | 4 | 13 | 26 | 167 | 122              | 31                    | 40                 |
| litpor_wiki | 178 | 2 | 14 | 153 | 25 | 113              | 29                    | 36                 |
| lizroa_twitter | 246 | 3 | 51 | 11 | 235 | 156              | 40                    | 50                 |
| lizroa_wiki | 127 | 2 | 171 | 67 | 60 | 80               | 21                    | 26                 |
| total | 7808 | 651 | 1291 | 4555 | 3253 | 4944             | 1271                  | 1593               |

<!-- TABLE_GENERATE_END -->


### Contributions
Created by Doron Ben-chorin, Matan Ben-chorin, Tomer Tzipori, Guided by Dr. Oren Mishali. This is our project as part of computer engineering studies in the Faculty of Electrical Engineering combined with Computer Science at Technion, Israel Institute of Technology. For more cooperation, please contact email:

Doron Ben-chorin: doronbh7@gmail.com

Matan Ben-chorin: matan.bh1@gmail.com

Tomer Tzipori: TomerTzipori@gmail.com
task_categories:token-classification size_categories:1K<n<10K language:he region:us
{"language": ["he"], "size_categories": ["1K<n<10K"], "task_categories": ["token-classification"], "pretty_name": "HebrewMetaphors", "dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "source", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "verb", "dtype": "string"}, {"name": "tweet_id", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 669160, "num_examples": 4944}, {"name": "val", "num_bytes": 168781, "num_examples": 1271}, {"name": "test", "num_bytes": 206836, "num_examples": 1593}], "download_size": 449410, "dataset_size": 1044777}, "model": ["tdklab/hebert-finetuned-hebrew-metaphor"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "val", "path": "data/val-*"}, {"split": "test", "path": "data/test-*"}]}]}
TAGS
#task_categories-token-classification #size_categories-1K<n<10K #language-Hebrew #region-us 

Dataset Card for "HebrewMetaphors"
==================================


### Dataset Summary


A common dataset for text classification task is IMDb. Large Movie Review Dataset. This is a dataset for binary sentiment classification.
The first step in our project was to create a Hebrew dataset with an IMDB-like structure but different in that, in addition to the sentences we have, there will also be verb names, and a classification of whether the verb name is literal or metaphorical in the given sentence.
Using an API, sentences that contained the verbs we selected were pulled from Twitter and Wikipedia and Prodigy was used to classify them. The accuracy of the classification was confirmed by having two different persons classify each line twice.


### Supported Tasks and Leaderboards


Classification


### Languages


Hebrew


Dataset Structure
-----------------


The IMDb dataset is a json file with objects containing the text of the review, and a number 0 or 1 which is a negative or positive review


For example:


After retrieving data from Wikipedia and Twitter, classifying and processing, we received the following data:


As you can see, we have the following fields:


* 'text': A sentence in Hebrew that was taken from Wikipedia or Twitter that included the verb name.
* 'source': There are two possible sources for this text: 'twitter' or 'wikipedia'.
* 'label': Classification of whether the verb name is literal ('label': 0) or metaphorical ('label': 1) in the given sentence.
* 'verb': A verb name in Hebrew on which the text is labeled.
* 'tweet\_id': If the text was taken from Twitter, the tweet's id, or None if it was retrieved from Wikipedia.


In the next section we will explain how we created the dataset.


### Data Statistics



### Contributions


Created by Doron Ben-chorin, Matan Ben-chorin, Tomer Tzipori, Guided by Dr. Oren Mishali. This is our project as part of computer engineering studies in the Faculty of Electrical Engineering combined with Computer Science at Technion, Israel Institute of Technology. For more cooperation, please contact email:


Doron Ben-chorin: doronbh7@URL


Matan Ben-chorin: matan.bh1@URL


Tomer Tzipori: TomerTzipori@URL



### Dataset Summary


A common dataset for text classification task is IMDb. Large Movie Review Dataset. This is a dataset for binary sentiment classification.
The first step in our project was to create a Hebrew dataset with an IMDB-like structure but different in that, in addition to the sentences we have, there will also be verb names, and a classification of whether the verb name is literal or metaphorical in the given sentence.
Using an API, sentences that contained the verbs we selected were pulled from Twitter and Wikipedia and Prodigy was used to classify them. The accuracy of the classification was confirmed by having two different persons classify each line twice. ### Supported Tasks and Leaderboards


Classification ### Languages


Hebrew


Dataset Structure
-----------------


The IMDb dataset is a json file with objects containing the text of the review, and a number 0 or 1 which is a negative or positive review


For example:


After retrieving data from Wikipedia and Twitter, classifying and processing, we received the following data:


As you can see, we have the following fields:


* 'text': A sentence in Hebrew that was taken from Wikipedia or Twitter that included the verb name.
* 'source': There are two possible sources for this text: 'twitter' or 'wikipedia'.
* 'label': Classification of whether the verb name is literal ('label': 0) or metaphorical ('label': 1) in the given sentence.
* 'verb': A verb name in Hebrew on which the text is labeled.
* 'tweet\_id': If the text was taken from Twitter, the tweet's id, or None if it was retrieved from Wikipedia.


In the next section we will explain how we created the dataset. ### Data Statistics ### Contributions


Created by Doron Ben-chorin, Matan Ben-chorin, Tomer Tzipori, Guided by Dr. Oren Mishali. This is our project as part of computer engineering studies in the Faculty of Electrical Engineering combined with Computer Science at Technion, Israel Institute of Technology. For more cooperation, please contact email:


Doron Ben-chorin: doronbh7@URL


Matan Ben-chorin: matan.bh1@URL


Tomer Tzipori: TomerTzipori@URL
TAGS
#task_categories-token-classification #size_categories-1K<n<10K #language-Hebrew #region-us 
 ### Dataset Summary


A common dataset for text classification task is IMDb. Large Movie Review Dataset. This is a dataset for binary sentiment classification.
The first step in our project was to create a Hebrew dataset with an IMDB-like structure but different in that, in addition to the sentences we have, there will also be verb names, and a classification of whether the verb name is literal or metaphorical in the given sentence.
Using an API, sentences that contained the verbs we selected were pulled from Twitter and Wikipedia and Prodigy was used to classify them. The accuracy of the classification was confirmed by having two different persons classify each line twice. ### Supported Tasks and Leaderboards


Classification ### Languages


Hebrew


Dataset Structure
-----------------


The IMDb dataset is a json file with objects containing the text of the review, and a number 0 or 1 which is a negative or positive review


For example:


After retrieving data from Wikipedia and Twitter, classifying and processing, we received the following data:


As you can see, we have the following fields:


* 'text': A sentence in Hebrew that was taken from Wikipedia or Twitter that included the verb name.
* 'source': There are two possible sources for this text: 'twitter' or 'wikipedia'.
* 'label': Classification of whether the verb name is literal ('label': 0) or metaphorical ('label': 1) in the given sentence.
* 'verb': A verb name in Hebrew on which the text is labeled.
* 'tweet\_id': If the text was taken from Twitter, the tweet's id, or None if it was retrieved from Wikipedia.


In the next section we will explain how we created the dataset. ### Data Statistics ### Contributions


Created by Doron Ben-chorin, Matan Ben-chorin, Tomer Tzipori, Guided by Dr. Oren Mishali. This is our project as part of computer engineering studies in the Faculty of Electrical Engineering combined with Computer Science at Technion, Israel Institute of Technology. For more cooperation, please contact email:


Doron Ben-chorin: doronbh7@URL


Matan Ben-chorin: matan.bh1@URL


Tomer Tzipori: TomerTzipori@URL
passage: TAGS
#task_categories-token-classification #size_categories-1K<n<10K #language-Hebrew #region-us 
### Dataset Summary


A common dataset for text classification task is IMDb. Large Movie Review Dataset. This is a dataset for binary sentiment classification.
The first step in our project was to create a Hebrew dataset with an IMDB-like structure but different in that, in addition to the sentences we have, there will also be verb names, and a classification of whether the verb name is literal or metaphorical in the given sentence.
Using an API, sentences that contained the verbs we selected were pulled from Twitter and Wikipedia and Prodigy was used to classify them. The accuracy of the classification was confirmed by having two different persons classify each line twice.### Supported Tasks and Leaderboards


Classification### Languages


Hebrew


Dataset Structure
-----------------


The IMDb dataset is a json file with objects containing the text of the review, and a number 0 or 1 which is a negative or positive review


For example:


After retrieving data from Wikipedia and Twitter, classifying and processing, we received the following data:


As you can see, we have the following fields:


* 'text': A sentence in Hebrew that was taken from Wikipedia or Twitter that included the verb name.
* 'source': There are two possible sources for this text: 'twitter' or 'wikipedia'.
* 'label': Classification of whether the verb name is literal ('label': 0) or metaphorical ('label': 1) in the given sentence.
* 'verb': A verb name in Hebrew on which the text is labeled.
* 'tweet\_id': If the text was taken from Twitter, the tweet's id, or None if it was retrieved from Wikipedia.


In the next section we will explain how we created the dataset.### Data Statistics