File size: 3,427 Bytes
efeee6d
314f91a
95f85ed
efeee6d
 
 
 
 
 
314f91a
b899767
 
efeee6d
943f952
3393abb
 
 
1ffc326
9be7bfc
b899767
 
9be7bfc
 
58733e4
efeee6d
8c49cb6
9be7bfc
0227006
 
efeee6d
0227006
d313dbd
 
 
3393abb
3d936c2
 
d16cee2
d313dbd
 
8c49cb6
d313dbd
 
 
 
 
 
 
 
 
8c49cb6
b323764
d313dbd
 
 
 
 
 
 
 
b323764
d313dbd
 
 
 
8c49cb6
 
d16cee2
58733e4
2a73469
 
217b585
9be7bfc
 
 
 
 
 
 
9833cdb
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
from dataclasses import dataclass
from enum import Enum

@dataclass
class Task:
    benchmark: str
    metric: str
    col_name: str


# Select your tasks here
# ---------------------------------------------------
class Tasks(Enum):
    # task_key in the json file, metric_key in the json file, name to display in the leaderboard 
    task0 = Task("toxigen", "acc", "Toxicity")
    task1 = Task("anli_r1", "acc", "ANLI")
    task2 = Task("logiqa", "acc_norm", "LogiQA")

NUM_FEWSHOT = 0 # Change with your few shot MEG NOTE: Not sure what that means.
# ---------------------------------------------------

# Leaderboard name
TITLE = """<h1 align="center" id="space-title">Toxicity Leaderboard</h1>"""

# What does your leaderboard evaluate?
INTRODUCTION_TEXT = """
Evaluate the toxicity of open LLMs.
"""

# Which evaluations are you running? how can people reproduce what you have?
LLM_BENCHMARKS_TEXT = f"""
## How it works

## Reproducibility
To reproduce the toxicity results, here is the command you can run:

```python main.py --model=hf-causal-experimental --model_args="pretrained=<your_model>,use_accelerate=True" --tasks=toxigen --batch_size=1 --output_path=<output_path>```

"""

EVALUATION_QUEUE_TEXT = """
## Some good practices before submitting a model

### 1) Make sure you can load your model and tokenizer using AutoClasses:
```python
from transformers import AutoConfig, AutoModel, AutoTokenizer
config = AutoConfig.from_pretrained("your model name", revision=revision)
model = AutoModel.from_pretrained("your model name", revision=revision)
tokenizer = AutoTokenizer.from_pretrained("your model name", revision=revision)
```
If this step fails, follow the error messages to debug your model before submitting it. It's likely your model has been improperly uploaded.

Note: make sure your model is public!
Note: if your model needs `use_remote_code=True`, we do not support this option yet but we are working on adding it, stay posted!

### 2) Convert your model weights to [safetensors](https://huggingface.co/docs/safetensors/index)
It's a new format for storing weights which is safer and faster to load and use. It will also allow us to add the number of parameters of your model to the `Extended Viewer`!

### 3) Make sure your model has an open license!
This is a leaderboard for Open LLMs, and we'd love for as many people as possible to know they can use your model 🤗

### 4) Fill up your model card
When we add extra information about models to the leaderboard, it will be automatically taken from the model card

## In case of model failure
If your model is displayed in the `FAILED` category, its execution stopped.
Make sure you have followed the above steps first.
If everything is done, check you can launch the EleutherAIHarness on your model locally, using the above command without modifications (you can add `--limit` to limit the number of examples per task).
"""

CITATION_BUTTON_LABEL = "Copy the following snippet to cite these results"
CITATION_BUTTON_TEXT = r"""
@misc{open-llm-toxicity-leaderboard,
  author = {Margaret Mitchell and Edward Beeching and Clémentine Fourrier and Nathan Habib and Sheon Han and Nathan Lambert and Nazneen Rajani and Omar Sanseviero and Lewis Tunstall and Thomas Wolf},
  title = {Open LLM Toxicity Leaderboard},
  year = {2024},
  publisher = {Hugging Face},
  howpublished = "\url{https://huggingface.co/spaces/Bias-Leaderboard/leaderboard}"
}
"""