File size: 5,321 Bytes
721f65d
c3e3b63
 
 
60cd313
c3e3b63
60cd313
 
6b7f4a6
60cd313
 
 
 
 
 
 
 
89cc465
290651e
9a56e9e
 
290651e
a404e9a
6b7f4a6
a404e9a
6b7f4a6
89cc465
 
60cd313
 
746b6f9
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
589fd5f
 
 
 
 
 
 
 
 
60cd313
 
 
746b6f9
db46314
60cd313
 
 
 
 
 
 
 
 
 
 
 
 
e8002b4
60cd313
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
89cc465
60cd313
 
 
 
 
 
 
 
 
 
89cc465
 
721f65d
 
 
5b7d34c
721f65d
 
 
60cd313
 
89cc465
60cd313
 
 
6b7f4a6
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
import json
import os
from io import BytesIO

import gradio as gr
from huggingface_hub import upload_file

default_question = """
We're going to use the <a href="https://huggingface.co/datasets/wikitext" target="_blank"><code>wikitext (link)</a></code> dataset with the <code><a href="https://huggingface.co/distilbert-base-cased" target="_blank">distilbert-base-cased (link)</a></code> model checkpoint.

<br/><br/>

Start by loading the <code>wikitext-2-raw-v1</code> version of that dataset, and take the 11th example (index 10) of the <code>train</code> split.<br/>
We'll tokenize this using the appropriate tokenizer, and we'll mask the sixth token (index 5) the sequence.

<br/><br/>

When using the <code>distilbert-base-cased</code> checkpoint to unmask that (sixth token, index 5) token, what is the most probable predicted token (please provide the decoded token, and not the ID)?

<br/>
<br/>
Tips:
<br/>
- You might find the <a href="https://huggingface.co/docs/transformers/index" target="_blank">transformers docs (link)</a> useful.
<br/>
- You might find the <a href="https://huggingface.co/docs/datasets/index" target="_blank">datasets docs (link)</a> useful.
<br/>
- You might also be interested in the <a href="https://huggingface.co/course" target="_blank">Hugging Face course (link)</a>.
"""

skops_question = """
1. Create a python environment[1] and install `scikit-learn` version `1.0` in that environment.
<br/>
2. Using that environment, create a `LogisticRegression` model[2] and fit it on the Iris dataset[3].
<br/>
3. Save the trained model using `pickle`[4] or `joblib`[5].
<br/>
4. Create a second environment, and install `scikit-learn` version `1.1` in it.
<br/>
5. Try loading the model you saved in step 3 in this second environment.

<br/>
<br/>
Question:
<br/>
Is there a warning or error you receive while trying to load the model? If yes, what exactly is it.

<br/>
<br/>
References
<br/>
- [1] You can use any tool you want to create the environment. Two of the options are:
<br/>
- `venv`: https://docs.python.org/3/library/venv.html
<br/>
- `mamba`: https://github.com/mamba-org/mamba
<br/>
- [2] `LogisticRegression` API guide: https://scikit-learn.org/dev/modules/generated/sklearn.linear_model.LogisticRegression.html
<br/>
- [3] `load_iris` API guide: https://scikit-learn.org/dev/modules/generated/sklearn.datasets.load_iris.html
<br/>
- [4] `pickle`: https://docs.python.org/3/library/pickle.html
<br/>
- [5] - `joblib`: https://joblib.readthedocs.io/en/latest/
"""

code_question = """
You are probing your code generation model on a program synthesis benchmark and 
1 out of 4 the candidate solutions produced by your model pass the unit tests of a coding challenge. 
<br/>
<br/>
What’s the pass@2 metric (in percent) as introduced in the 
<a href="https://arxiv.org/abs/2107.03374" target="_blank"><code>Codex paper</a></code> (see section 2.1)?
"""

internships = {
    'Accelerate': default_question,
    'Diffusion distillation': default_question,
    'Skops & Scikit-Learn': skops_question,
    "Code Generation": code_question,
    "Document AI Democratization": default_question,
    "Evaluate": default_question,
    "ASR": default_question,
    "Efficient video pretraining": default_question,
    "Embodied AI": default_question,
    "Emergence of scene and text understanding": default_question,
    "Everything is multimodal": default_question,
    "Everything is vision": default_question,
    "Retrieval augmentation as prompting": default_question,
    "Social impact evaluations": default_question,
    "Toolkit for detecting distribution shift": default_question,
    "AI Art Tooling Residency": default_question,
    "Gradio as an ecosystem": default_question,
    "Benchmarking transformers on various AI hardware accelerators": default_question,
}


with gr.Blocks() as demo:
    gr.Markdown(
        """
    # Internship introduction
    Please select the internship you would like to apply to and answer the question asked in the Answer box.
    """
    )

    internship_choice = gr.Dropdown(label='Internship', choices=list(internships.keys()))

    with gr.Column(visible=False) as details_col:
        summary = gr.HTML(label='Question')
        details = gr.Textbox(label="Answer")
        username = gr.Textbox(label="Hugging Face Username")
        comment = gr.Textbox(label="Any comment?")
        generate_btn = gr.Button("Submit")
        output = gr.Label()

    def filter_species(species):
        return gr.Label.update(
            internships[species]
        ), gr.update(visible=True)

    internship_choice.change(filter_species, internship_choice, [summary, details_col])

    def on_click(_details, _username, _internship_choice, _comment):
        response = {'response': _details, "internship": _internship_choice, "comment": _comment}
        upload_file(
            path_or_fileobj=BytesIO(bytes(json.dumps(response), 'utf-8')),
            path_in_repo=_username,
            repo_id='internships/internships-2023',
            repo_type='dataset',
            token=os.environ['HF_TOKEN']
        )
        return f"Submitted: '{_details}' for user '{_username}'"

    generate_btn.click(on_click, inputs=[details, username, internship_choice, comment], outputs=[output])


if __name__ == "__main__":
    demo.launch()