Datasets:
restructure for hf
Browse files- README.md +8 -8
- analytics.ipynb +35 -27
- counts.csv +0 -3
- train.csv → raw.csv +0 -0
- test.ipynb +35 -75
README.md
CHANGED
@@ -59,15 +59,15 @@ dataset = load_dataset("siavava/ai-tech-articles")
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df = dataset["train"].to_pandas()
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```
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-
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> and cache it so it does not need to re-download it again
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> (unless it detects a change upstream).
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## File Structure
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- [analytics.ipynb](analytics.ipynb) - Notebook containing some details about the dataset
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- [
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- For raw text files, see the [scraper repo](https://github.com/siavava/scrape.hs) on GitHub.
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df = dataset["train"].to_pandas()
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```
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You do not need to clone this repo.
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HuggingFace will download the dataset for you, the first time that you load it,
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and cache it locally so it does not need to re-download it again
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(unless it detects a change upstream).
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## File Structure
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- [`analytics.ipynb`](analytics.ipynb) - Notebook containing some details about the dataset.
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- [`example.ipynb`](example.ipynb) - A minimal notebook that loads in the dataset and converts to Pandas.
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- [`raw.csv`](raw.csv) - The raw data, in CSV format.
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- `data/*.parquet`- compressed [parquet](https://www.databricks.com/glossary/what-is-parquet) containing the data.
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- For raw text files, see the [scraper repo](https://github.com/siavava/scrape.hs) on GitHub.
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analytics.ipynb
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},
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{
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"cell_type": "code",
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"execution_count":
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"metadata": {},
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"outputs": [],
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"source": [
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},
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"cell_type": "code",
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"execution_count":
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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" <th>title</th>\n",
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" <th>url</th>\n",
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" <th>text</th>\n",
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" <th>__index_level_0__</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>0</th>\n",
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" <td>0</td>\n",
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" <td>2023
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" <td>\"MIT Technology Review\"</td>\n",
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" <td>\"https://www.technologyreview.com\"</td>\n",
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" <td>\"Featured Topics Newsletters Events Podcasts F...</td>\n",
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" <td>0</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>1</th>\n",
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" <td>1</td>\n",
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" <td>2023
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" <td>\"WIRED - The Latest in Technology, Science, Cu...</td>\n",
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" <td>\"https://www.wired.com\"</td>\n",
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" <td>\"Open Navigation Menu To revisit this article,...</td>\n",
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" <td>1</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2</th>\n",
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" <td>2</td>\n",
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" <td>2019
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" <td>\"The Verge\"</td>\n",
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" <td>\"https://www.theverge.com\"</td>\n",
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" <td>\"The Verge homepage The Verge The Verge logo.\\...</td>\n",
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" <td>2</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>3</th>\n",
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" <td>3</td>\n",
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" <td>2023
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" <td>\"TechCrunch | Startup and Technology News\"</td>\n",
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" <td>\"https://www.techcrunch.com\"</td>\n",
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" <td>\"WeWork reportedly on the verge of filing for ...</td>\n",
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" <td>3</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>4</th>\n",
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" <td>4</td>\n",
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" <td>2022
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" <td>\"A new vision of artificial intelligence for t...</td>\n",
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" <td>\"https://www.technologyreview.com/2022/04/22/1...</td>\n",
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" <td>\"Featured Topics Newsletters Events Podcasts A...</td>\n",
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" <td>4</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
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"0 0 2023
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"2 2 2019
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"3 3 2023
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"4 4 2022
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"\n",
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" url \\\n",
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"0 \"https://www.technologyreview.com\" \n",
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"3 \"https://www.techcrunch.com\" \n",
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"4 \"https://www.technologyreview.com/2022/04/22/1... \n",
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"\n",
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" text
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"0 \"Featured Topics Newsletters Events Podcasts F...
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"1 \"Open Navigation Menu To revisit this article,...
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"2 \"The Verge homepage The Verge The Verge logo.\\...
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"3 \"WeWork reportedly on the verge of filing for ...
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"4 \"Featured Topics Newsletters Events Podcasts A...
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]
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},
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"execution_count":
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"metadata": {},
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"output_type": "execute_result"
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"metadata": {},
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"outputs": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "15d5e7a1a1f44517adab6907cd675b92",
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"version_major": 2,
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"version_minor": 0
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"text/plain": [
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"Downloading readme: 0%| | 0.00/1.87k [00:00<?, ?B/s]"
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]
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"data": {
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"text/html": [
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" <th>title</th>\n",
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" <th>url</th>\n",
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" <th>text</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>0</th>\n",
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" <td>0</td>\n",
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" <td>2023</td>\n",
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" <td>\"MIT Technology Review\"</td>\n",
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" <td>\"https://www.technologyreview.com\"</td>\n",
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" <td>\"Featured Topics Newsletters Events Podcasts F...</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>1</th>\n",
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" <td>1</td>\n",
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" <td>2023</td>\n",
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" <td>\"WIRED - The Latest in Technology, Science, Cu...</td>\n",
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" <td>\"https://www.wired.com\"</td>\n",
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" <td>\"Open Navigation Menu To revisit this article,...</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2</th>\n",
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" <td>2</td>\n",
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" <td>2019</td>\n",
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" <td>\"The Verge\"</td>\n",
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" <td>\"https://www.theverge.com\"</td>\n",
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" <td>\"The Verge homepage The Verge The Verge logo.\\...</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>3</th>\n",
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" <td>3</td>\n",
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" <td>2023</td>\n",
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" <td>\"TechCrunch | Startup and Technology News\"</td>\n",
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" <td>\"https://www.techcrunch.com\"</td>\n",
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" <td>\"WeWork reportedly on the verge of filing for ...</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>4</th>\n",
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" <td>4</td>\n",
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" <td>2022</td>\n",
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" <td>\"A new vision of artificial intelligence for t...</td>\n",
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" <td>\"https://www.technologyreview.com/2022/04/22/1...</td>\n",
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" <td>\"Featured Topics Newsletters Events Podcasts A...</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
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" id year title \\\n",
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"0 0 2023 \"MIT Technology Review\" \n",
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"1 1 2023 \"WIRED - The Latest in Technology, Science, Cu... \n",
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"2 2 2019 \"The Verge\" \n",
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"3 3 2023 \"TechCrunch | Startup and Technology News\" \n",
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"4 4 2022 \"A new vision of artificial intelligence for t... \n",
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"\n",
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" url \\\n",
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"0 \"https://www.technologyreview.com\" \n",
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"3 \"https://www.techcrunch.com\" \n",
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"4 \"https://www.technologyreview.com/2022/04/22/1... \n",
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"\n",
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" text \n",
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"0 \"Featured Topics Newsletters Events Podcasts F... \n",
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"1 \"Open Navigation Menu To revisit this article,... \n",
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"2 \"The Verge homepage The Verge The Verge logo.\\... \n",
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"3 \"WeWork reportedly on the verge of filing for ... \n",
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"4 \"Featured Topics Newsletters Events Podcasts A... "
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]
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},
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"execution_count": 2,
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"metadata": {},
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"output_type": "execute_result"
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}
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [
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{
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counts.csv
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version https://git-lfs.github.com/spec/v1
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oid sha256:06a4a5ec4a4ac58a3f578ae83ad05450730de0a7700dab86e0ce8c21c60dde9f
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size 535
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train.csv → raw.csv
RENAMED
File without changes
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test.ipynb
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "b56274faa04d46c0a8ce3871242ffc6e",
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"version_major": 2,
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"version_minor": 0
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"text/plain": [
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"Downloading readme: 0%| | 0.00/1.37k [00:00<?, ?B/s]"
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]
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"output_type": "display_data"
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},
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{
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"ename": "FileNotFoundError",
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"evalue": "Couldn't find a dataset script at /Users/amittaijoel/workspace/crawl.hs/data/metadata/siavava/ai-tech-articles/ai-tech-articles.py or any data file in the same directory. Couldn't find 'siavava/ai-tech-articles' on the Hugging Face Hub either: FileNotFoundError: No (supported) data files or dataset script found in siavava/ai-tech-articles. ",
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"output_type": "error",
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"traceback": [
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"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[0;31mFileNotFoundError\u001b[0m Traceback (most recent call last)",
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"\u001b[1;32m/Users/amittaijoel/workspace/crawl.hs/data/metadata/test.ipynb Cell 3\u001b[0m line \u001b[0;36m1\n\u001b[0;32m----> <a href='vscode-notebook-cell:/Users/amittaijoel/workspace/crawl.hs/data/metadata/test.ipynb#W2sZmlsZQ%3D%3D?line=0'>1</a>\u001b[0m dt \u001b[39m=\u001b[39m load_dataset(\u001b[39m\"\u001b[39;49m\u001b[39msiavava/ai-tech-articles\u001b[39;49m\u001b[39m\"\u001b[39;49m)\n",
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"File \u001b[0;32m~/miniconda3/envs/data-mining/lib/python3.11/site-packages/datasets/load.py:2129\u001b[0m, in \u001b[0;36mload_dataset\u001b[0;34m(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, verification_mode, ignore_verifications, keep_in_memory, save_infos, revision, token, use_auth_token, task, streaming, num_proc, storage_options, **config_kwargs)\u001b[0m\n\u001b[1;32m 2124\u001b[0m verification_mode \u001b[39m=\u001b[39m VerificationMode(\n\u001b[1;32m 2125\u001b[0m (verification_mode \u001b[39mor\u001b[39;00m VerificationMode\u001b[39m.\u001b[39mBASIC_CHECKS) \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m save_infos \u001b[39melse\u001b[39;00m VerificationMode\u001b[39m.\u001b[39mALL_CHECKS\n\u001b[1;32m 2126\u001b[0m )\n\u001b[1;32m 2128\u001b[0m \u001b[39m# Create a dataset builder\u001b[39;00m\n\u001b[0;32m-> 2129\u001b[0m builder_instance \u001b[39m=\u001b[39m load_dataset_builder(\n\u001b[1;32m 2130\u001b[0m path\u001b[39m=\u001b[39;49mpath,\n\u001b[1;32m 2131\u001b[0m name\u001b[39m=\u001b[39;49mname,\n\u001b[1;32m 2132\u001b[0m data_dir\u001b[39m=\u001b[39;49mdata_dir,\n\u001b[1;32m 2133\u001b[0m data_files\u001b[39m=\u001b[39;49mdata_files,\n\u001b[1;32m 2134\u001b[0m cache_dir\u001b[39m=\u001b[39;49mcache_dir,\n\u001b[1;32m 2135\u001b[0m features\u001b[39m=\u001b[39;49mfeatures,\n\u001b[1;32m 2136\u001b[0m download_config\u001b[39m=\u001b[39;49mdownload_config,\n\u001b[1;32m 2137\u001b[0m download_mode\u001b[39m=\u001b[39;49mdownload_mode,\n\u001b[1;32m 2138\u001b[0m revision\u001b[39m=\u001b[39;49mrevision,\n\u001b[1;32m 2139\u001b[0m token\u001b[39m=\u001b[39;49mtoken,\n\u001b[1;32m 2140\u001b[0m storage_options\u001b[39m=\u001b[39;49mstorage_options,\n\u001b[1;32m 2141\u001b[0m \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mconfig_kwargs,\n\u001b[1;32m 2142\u001b[0m )\n\u001b[1;32m 2144\u001b[0m \u001b[39m# Return iterable dataset in case of streaming\u001b[39;00m\n\u001b[1;32m 2145\u001b[0m \u001b[39mif\u001b[39;00m streaming:\n",
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"File \u001b[0;32m~/miniconda3/envs/data-mining/lib/python3.11/site-packages/datasets/load.py:1815\u001b[0m, in \u001b[0;36mload_dataset_builder\u001b[0;34m(path, name, data_dir, data_files, cache_dir, features, download_config, download_mode, revision, token, use_auth_token, storage_options, **config_kwargs)\u001b[0m\n\u001b[1;32m 1813\u001b[0m download_config \u001b[39m=\u001b[39m download_config\u001b[39m.\u001b[39mcopy() \u001b[39mif\u001b[39;00m download_config \u001b[39melse\u001b[39;00m DownloadConfig()\n\u001b[1;32m 1814\u001b[0m download_config\u001b[39m.\u001b[39mstorage_options\u001b[39m.\u001b[39mupdate(storage_options)\n\u001b[0;32m-> 1815\u001b[0m dataset_module \u001b[39m=\u001b[39m dataset_module_factory(\n\u001b[1;32m 1816\u001b[0m path,\n\u001b[1;32m 1817\u001b[0m revision\u001b[39m=\u001b[39;49mrevision,\n\u001b[1;32m 1818\u001b[0m download_config\u001b[39m=\u001b[39;49mdownload_config,\n\u001b[1;32m 1819\u001b[0m download_mode\u001b[39m=\u001b[39;49mdownload_mode,\n\u001b[1;32m 1820\u001b[0m data_dir\u001b[39m=\u001b[39;49mdata_dir,\n\u001b[1;32m 1821\u001b[0m data_files\u001b[39m=\u001b[39;49mdata_files,\n\u001b[1;32m 1822\u001b[0m )\n\u001b[1;32m 1823\u001b[0m \u001b[39m# Get dataset builder class from the processing script\u001b[39;00m\n\u001b[1;32m 1824\u001b[0m builder_kwargs \u001b[39m=\u001b[39m dataset_module\u001b[39m.\u001b[39mbuilder_kwargs\n",
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"File \u001b[0;32m~/miniconda3/envs/data-mining/lib/python3.11/site-packages/datasets/load.py:1508\u001b[0m, in \u001b[0;36mdataset_module_factory\u001b[0;34m(path, revision, download_config, download_mode, dynamic_modules_path, data_dir, data_files, **download_kwargs)\u001b[0m\n\u001b[1;32m 1506\u001b[0m \u001b[39mraise\u001b[39;00m e1 \u001b[39mfrom\u001b[39;00m \u001b[39mNone\u001b[39;00m\n\u001b[1;32m 1507\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39misinstance\u001b[39m(e1, \u001b[39mFileNotFoundError\u001b[39;00m):\n\u001b[0;32m-> 1508\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mFileNotFoundError\u001b[39;00m(\n\u001b[1;32m 1509\u001b[0m \u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mCouldn\u001b[39m\u001b[39m'\u001b[39m\u001b[39mt find a dataset script at \u001b[39m\u001b[39m{\u001b[39;00mrelative_to_absolute_path(combined_path)\u001b[39m}\u001b[39;00m\u001b[39m or any data file in the same directory. \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m 1510\u001b[0m \u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mCouldn\u001b[39m\u001b[39m'\u001b[39m\u001b[39mt find \u001b[39m\u001b[39m'\u001b[39m\u001b[39m{\u001b[39;00mpath\u001b[39m}\u001b[39;00m\u001b[39m'\u001b[39m\u001b[39m on the Hugging Face Hub either: \u001b[39m\u001b[39m{\u001b[39;00m\u001b[39mtype\u001b[39m(e1)\u001b[39m.\u001b[39m\u001b[39m__name__\u001b[39m\u001b[39m}\u001b[39;00m\u001b[39m: \u001b[39m\u001b[39m{\u001b[39;00me1\u001b[39m}\u001b[39;00m\u001b[39m\"\u001b[39m\n\u001b[1;32m 1511\u001b[0m ) \u001b[39mfrom\u001b[39;00m \u001b[39mNone\u001b[39;00m\n\u001b[1;32m 1512\u001b[0m \u001b[39mraise\u001b[39;00m e1 \u001b[39mfrom\u001b[39;00m \u001b[39mNone\u001b[39;00m\n\u001b[1;32m 1513\u001b[0m \u001b[39melse\u001b[39;00m:\n",
|
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"\u001b[0;31mFileNotFoundError\u001b[0m: Couldn't find a dataset script at /Users/amittaijoel/workspace/crawl.hs/data/metadata/siavava/ai-tech-articles/ai-tech-articles.py or any data file in the same directory. Couldn't find 'siavava/ai-tech-articles' on the Hugging Face Hub either: FileNotFoundError: No (supported) data files or dataset script found in siavava/ai-tech-articles. "
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]
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}
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],
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"source": [
|
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"dt = load_dataset(\"siavava/ai-tech-articles\")\n"
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{
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{
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|
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|
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" <td>5</td>\n",
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|
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" <td>\"These fast, cheap tests could help us coexist...</td>\n",
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" <td>6</td>\n",
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|
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|
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" <td>2019
|
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" <td>\"About - Google DeepMind\"</td>\n",
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" <td>\"https://www.deepmind.com/about\"</td>\n",
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" <td>\"Blog - Google DeepMind\"</td>\n",
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" <td>\"https://www.deepmind.com/blog-categories/appl...</td>\n",
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" </tr>\n",
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"</div>"
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],
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"source": [
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"dt = load_dataset(\"siavava/ai-tech-articles\")\n"
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]
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},
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{
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"cell_type": "code",
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{
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" <th>title</th>\n",
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" <td>2023</td>\n",
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" <td>\"MIT Technology Review\"</td>\n",
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" <td>\"https://www.technologyreview.com\"</td>\n",
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" <th>1</th>\n",
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+
" <td>2023</td>\n",
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" <td>\"WIRED - The Latest in Technology, Science, Cu...</td>\n",
|
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" <td>\"https://www.wired.com\"</td>\n",
|
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" <td>\"Open Navigation Menu To revisit this article,...</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2</th>\n",
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" <td>2019</td>\n",
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" <td>\"The Verge\"</td>\n",
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" <td>\"https://www.theverge.com\"</td>\n",
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" </tr>\n",
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" <th>3</th>\n",
|
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" <td>3</td>\n",
|
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+
" <td>2023</td>\n",
|
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" <td>\"TechCrunch | Startup and Technology News\"</td>\n",
|
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" <td>\"https://www.techcrunch.com\"</td>\n",
|
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" <td>\"WeWork reportedly on the verge of filing for ...</td>\n",
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" </tr>\n",
|
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" <tr>\n",
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" <td>4</td>\n",
|
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" <td>2022</td>\n",
|
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" <td>\"A new vision of artificial intelligence for t...</td>\n",
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" <td>\"https://www.technologyreview.com/2022/04/22/1...</td>\n",
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" <td>\"Featured Topics Newsletters Events Podcasts A...</td>\n",
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" <th>5</th>\n",
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" <td>5</td>\n",
|
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+
" <td>2022</td>\n",
|
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" <td>\"The scientist who co-created CRISPR isn’t rul...</td>\n",
|
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" <td>\"https://www.technologyreview.com/2022/04/26/1...</td>\n",
|
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" <td>\"Featured Topics Newsletters Events Podcasts F...</td>\n",
|
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" </tr>\n",
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" <tr>\n",
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" <th>6</th>\n",
|
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" <td>6</td>\n",
|
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+
" <td>2022</td>\n",
|
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" <td>\"These fast, cheap tests could help us coexist...</td>\n",
|
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" <td>\"https://www.technologyreview.com/2022/04/27/1...</td>\n",
|
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" <td>\"Featured Topics Newsletters Events Podcasts F...</td>\n",
|
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" </tr>\n",
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" <th>7</th>\n",
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" <td>7</td>\n",
|
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" <td>2022</td>\n",
|
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" <td>\"Tackling multiple tasks with a single visual ...</td>\n",
|
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" <td>\"http://www.deepmind.com/blog/tackling-multipl...</td>\n",
|
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" <td>\"DeepMind Search Search Close DeepMind About O...</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>8</th>\n",
|
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" <td>8</td>\n",
|
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" <td>2019</td>\n",
|
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" <td>\"About - Google DeepMind\"</td>\n",
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" <td>\"https://www.deepmind.com/about\"</td>\n",
|
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" <th>9</th>\n",
|
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" <td>9</td>\n",
|
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+
" <td>2023</td>\n",
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" <td>\"Blog - Google DeepMind\"</td>\n",
|
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" <td>\"https://www.deepmind.com/blog-categories/appl...</td>\n",
|
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" <td>\"DeepMind Search Search Close DeepMind About O...</td>\n",
|
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
|
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],
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"text/plain": [
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"0 0 2023 \"MIT Technology Review\" \n",
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"1 1 2023 \"WIRED - The Latest in Technology, Science, Cu... \n",
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+
"2 2 2019 \"The Verge\" \n",
|
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+
"3 3 2023 \"TechCrunch | Startup and Technology News\" \n",
|
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+
"4 4 2022 \"A new vision of artificial intelligence for t... \n",
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"5 5 2022 \"The scientist who co-created CRISPR isn’t rul... \n",
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+
"6 6 2022 \"These fast, cheap tests could help us coexist... \n",
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+
"7 7 2022 \"Tackling multiple tasks with a single visual ... \n",
|
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+
"8 8 2019 \"About - Google DeepMind\" \n",
|
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+
"9 9 2023 \"Blog - Google DeepMind\" \n",
|
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"\n",
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" url \\\n",
|
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"0 \"https://www.technologyreview.com\" \n",
|
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"8 \"https://www.deepmind.com/about\" \n",
|
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"9 \"https://www.deepmind.com/blog-categories/appl... \n",
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"\n",
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" text \n",
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"0 \"Featured Topics Newsletters Events Podcasts F... \n",
|
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+
"1 \"Open Navigation Menu To revisit this article,... \n",
|
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+
"2 \"The Verge homepage The Verge The Verge logo.\\... \n",
|
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+
"3 \"WeWork reportedly on the verge of filing for ... \n",
|
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"4 \"Featured Topics Newsletters Events Podcasts A... \n",
|
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+
"5 \"Featured Topics Newsletters Events Podcasts F... \n",
|
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+
"6 \"Featured Topics Newsletters Events Podcasts F... \n",
|
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+
"7 \"DeepMind Search Search Close DeepMind About O... \n",
|
182 |
+
"8 \"DeepMind Search Search Close DeepMind About O... \n",
|
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+
"9 \"DeepMind Search Search Close DeepMind About O... "
|
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]
|
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},
|
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+
"execution_count": 5,
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"metadata": {},
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"output_type": "execute_result"
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}
|