File size: 7,853 Bytes
6151ec8
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
9d59717
6151ec8
9d59717
6151ec8
 
 
 
 
 
 
 
 
 
 
 
 
 
9d59717
6151ec8
9d59717
6151ec8
 
 
 
 
 
 
 
 
 
9d59717
6151ec8
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
6a493fa
6151ec8
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
9d59717
 
 
 
 
 
 
 
 
 
 
6151ec8
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
---
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: The tech giant announced today the appointment of a new CTO to lead their
    innovative projects in AI.
- text: During the recent annual meeting, the board of directors at HealthCorp discussed
    various operational strategies but did not announce any changes to their leadership
    team.
- text: Tech giant Innovatech has announced the appointment of Jane Doe as their new
    Chief Technology Officer, effective immediately. This change aims to drive the
    company's focus on artificial intelligence.
- text: The political landscape in the country shifted dramatically with a recent
    election, leading to new party leadership.
- text: In a recent interview, the founder of Foodies Co. expressed his frustration
    over market competition but reassured that no changes in leadership were imminent.
metrics:
- accuracy
pipeline_tag: text-classification
library_name: setfit
inference: true
base_model: dunzhang/stella_en_400M_v5
model-index:
- name: SetFit with dunzhang/stella_en_400M_v5
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: Unknown
      type: unknown
      split: test
    metrics:
    - type: accuracy
      value: 0.9666666666666667
      name: Accuracy
---

# SetFit with dunzhang/stella_en_400M_v5

This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [dunzhang/stella_en_400M_v5](https://huggingface.co/dunzhang/stella_en_400M_v5) as the Sentence Transformer embedding model. A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance is used for classification.

The model has been trained using an efficient few-shot learning technique that involves:

1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.

## Model Details

### Model Description
- **Model Type:** SetFit
- **Sentence Transformer body:** [dunzhang/stella_en_400M_v5](https://huggingface.co/dunzhang/stella_en_400M_v5)
- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
- **Maximum Sequence Length:** 512 tokens
- **Number of Classes:** 2 classes
<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->

### Model Sources

- **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit)
- **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055)
- **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)

### Model Labels
| Label | Examples                                                                                                                                                                                                                                                                                |
|:------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| True  | <ul><li>'Tech giant ABC Corp appoints a new CTO to drive innovation.'</li><li>'The CEO of DEF Inc. steps down after a decade at the helm, marking a change in leadership.'</li><li>'GHI Industries promotes its Chief Marketing Officer to take over as CEO.'</li></ul>                 |
| False | <ul><li>'XYZ Ltd. announced a strategic shift but no change in leadership positions.'</li><li>'New government regulations might prompt shifts in corporate governance.'</li><li>'The recent quarterly report highlighted performance issues but indicated stable management.'</li></ul> |

## Evaluation

### Metrics
| Label   | Accuracy |
|:--------|:---------|
| **all** | 0.9667   |

## Uses

### Direct Use for Inference

First install the SetFit library:

```bash
pip install setfit
```

Then you can load this model and run inference.

```python
from setfit import SetFitModel

# Download from the 🤗 Hub
model = SetFitModel.from_pretrained("amplyfi/leadership-change")
# Run inference
preds = model("The tech giant announced today the appointment of a new CTO to lead their innovative projects in AI.")
```

<!--
### Downstream Use

*List how someone could finetune this model on their own dataset.*
-->

<!--
### Out-of-Scope Use

*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->

<!--
## Bias, Risks and Limitations

*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->

<!--
### Recommendations

*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->

## Training Details

### Training Set Metrics
| Training set | Min | Median  | Max |
|:-------------|:----|:--------|:----|
| Word count   | 9   | 14.9963 | 23  |

| Label | Training Sample Count |
|:------|:----------------------|
| False | 136                   |
| True  | 135                   |

### Training Hyperparameters
- batch_size: (16, 16)
- num_epochs: (3, 3)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 5
- body_learning_rate: (2e-05, 2e-05)
- head_learning_rate: 2e-05
- loss: CosineSimilarityLoss
- distance_metric: cosine_distance
- margin: 0.25
- end_to_end: False
- use_amp: False
- warmup_proportion: 0.1
- l2_weight: 0.01
- seed: 42
- eval_max_steps: -1
- load_best_model_at_end: False

### Training Results
| Epoch  | Step | Training Loss | Validation Loss |
|:------:|:----:|:-------------:|:---------------:|
| 0.0059 | 1    | 0.2381        | -               |
| 0.2941 | 50   | 0.1096        | -               |
| 0.5882 | 100  | 0.0006        | -               |
| 0.8824 | 150  | 0.0003        | -               |
| 1.1765 | 200  | 0.0001        | -               |
| 1.4706 | 250  | 0.0           | -               |
| 1.7647 | 300  | 0.0           | -               |
| 2.0588 | 350  | 0.0           | -               |
| 2.3529 | 400  | 0.0           | -               |
| 2.6471 | 450  | 0.0           | -               |
| 2.9412 | 500  | 0.0           | -               |

### Framework Versions
- Python: 3.10.12
- SetFit: 1.1.0
- Sentence Transformers: 3.3.1
- Transformers: 4.42.2
- PyTorch: 2.5.1+cu124
- Datasets: 3.1.0
- Tokenizers: 0.19.1

## Citation

### BibTeX
```bibtex
@article{https://doi.org/10.48550/arxiv.2209.11055,
    doi = {10.48550/ARXIV.2209.11055},
    url = {https://arxiv.org/abs/2209.11055},
    author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
    keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
    title = {Efficient Few-Shot Learning Without Prompts},
    publisher = {arXiv},
    year = {2022},
    copyright = {Creative Commons Attribution 4.0 International}
}
```

<!--
## Glossary

*Clearly define terms in order to be accessible across audiences.*
-->

<!--
## Model Card Authors

*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
-->

<!--
## Model Card Contact

*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
-->