yujunjun/haystack
1
0
Fork 0
mirror of https://github.com/deepset-ai/haystack.git synced 2025-12-26 06:28:33 +00:00
Code Issues Packages Projects Releases Wiki Activity

Fix links (#663)

Browse Source
This commit is contained in:
Branden Chan 2020-12-08 10:28:31 +01:00 committed by GitHub
parent c4a5de59aa
commit 8c904d79d6
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
4 changed files with 4 additions and 4 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

2
docs/_src/tutorials/tutorials/2.md
View File

@ -41,7 +41,7 @@ There are two ways to generate training data
1. **Annotation**: You can use the [annotation tool](https://github.com/deepset-ai/haystack#labeling-tool) to label your data, i.e. highlighting answers to your questions in a document. The tool supports structuring your workflow with organizations, projects, and users. The labels can be exported in SQuAD format that is compatible for training with Haystack.
![Snapshot of the annotation tool](https://raw.githubusercontent.com/deepset-ai/haystack/master/docs/img/annotation_tool.png)
![Snapshot of the annotation tool](https://raw.githubusercontent.com/deepset-ai/haystack/master/docs/_src/img/annotation_tool.png)
2. **Feedback**: For production systems, you can collect training data from direct user feedback via Haystack's [REST API interface](https://github.com/deepset-ai/haystack#rest-api). This includes a customizable user feedback API for providing feedback on the answer returned by the API. The API provides a feedback export endpoint to obtain the feedback data for fine-tuning your model further.

2
docs/_src/tutorials/tutorials/6.md
View File

@ -64,7 +64,7 @@ Original Code: https://fburl.com/qa-dpr
Make sure you enable the GPU runtime to experience decent speed in this tutorial.
**Runtime -> Change Runtime type -> Hardware accelerator -> GPU**
<img src="https://raw.githubusercontent.com/deepset-ai/haystack/master/docs/img/colab_gpu_runtime.jpg">
<img src="https://raw.githubusercontent.com/deepset-ai/haystack/master/docs/_src/img/colab_gpu_runtime.jpg">
```python

2
tutorials/Tutorial2_Finetune_a_model_on_your_data.ipynb
View File

@ -55,7 +55,7 @@
"\n",
"1. **Annotation**: You can use the [annotation tool](https://github.com/deepset-ai/haystack#labeling-tool) to label your data, i.e. highlighting answers to your questions in a document. The tool supports structuring your workflow with organizations, projects, and users. The labels can be exported in SQuAD format that is compatible for training with Haystack.\n",
"\n",
"![Snapshot of the annotation tool](https://raw.githubusercontent.com/deepset-ai/haystack/master/docs/img/annotation_tool.png)\n",
"![Snapshot of the annotation tool](https://raw.githubusercontent.com/deepset-ai/haystack/master/docs/_src/img/annotation_tool.png)\n",
"\n",
"2. **Feedback**: For production systems, you can collect training data from direct user feedback via Haystack's [REST API interface](https://github.com/deepset-ai/haystack#rest-api). This includes a customizable user feedback API for providing feedback on the answer returned by the API. The API provides a feedback export endpoint to obtain the feedback data for fine-tuning your model further.\n",
"\n",

2
tutorials/Tutorial6_Better_Retrieval_via_DPR.ipynb
View File

@ -71,7 +71,7 @@
"Make sure you enable the GPU runtime to experience decent speed in this tutorial. \n",
"**Runtime -> Change Runtime type -> Hardware accelerator -> GPU**\n",
"\n",
"<img src=\"https://raw.githubusercontent.com/deepset-ai/haystack/master/docs/img/colab_gpu_runtime.jpg\">"
"<img src=\"https://raw.githubusercontent.com/deepset-ai/haystack/master/docs/_src/img/colab_gpu_runtime.jpg\">"
]
},
{