anismahmahi/doubt_repetition_with_noPropaganda_with_3_zeros_SetFit

SetFit with sentence-transformers/paraphrase-mpnet-base-v2

This is a SetFit model that can be used for Text Classification. This SetFit model uses sentence-transformers/paraphrase-mpnet-base-v2 as the Sentence Transformer embedding model. A OneVsRestClassifier 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 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: sentence-transformers/paraphrase-mpnet-base-v2
• Classification head: a OneVsRestClassifier instance
• Maximum Sequence Length: 512 tokens

Model Sources

• Repository: SetFit on GitHub
• Paper: Efficient Few-Shot Learning Without Prompts
• Blogpost: SetFit: Efficient Few-Shot Learning Without Prompts

Evaluation

Metrics

Label	Accuracy
all	0.7084

Uses

Direct Use for Inference

First install the SetFit library:

pip install setfit

Then you can load this model and run inference.

from setfit import SetFitModel

# Download from the 🤗 Hub
model = SetFitModel.from_pretrained("anismahmahi/doubt_repetition_with_noPropaganda_with_3_zeros_SetFit")
# Run inference
preds = model("The Twitter suspension caught me by surprise.")

Training Details

Training Set Metrics

Training set	Min	Median	Max
Word count	1	22.0291	129

Training Hyperparameters

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

Training Results

Epoch	Step	Training Loss	Validation Loss
0.0003	1	0.3532	-
0.0166	50	0.3413	-
0.0332	100	0.2743	-
0.0498	150	0.2635	-
0.0664	200	0.2444	-
0.0830	250	0.1883	-
0.0996	300	0.2231	-
0.1162	350	0.1763	-
0.1328	400	0.1868	-
0.1494	450	0.2057	-
0.1660	500	0.1734	-
0.1826	550	0.2594	-
0.1992	600	0.1024	-
0.2158	650	0.2351	-
0.2324	700	0.1863	-
0.2490	750	0.072	-
0.2656	800	0.1987	-
0.2822	850	0.1511	-
0.2988	900	0.0926	-
0.3154	950	0.1956	-
0.3320	1000	0.1354	-
0.3486	1050	0.2038	-
0.3652	1100	0.1166	-
0.3818	1150	0.3214	-
0.3984	1200	0.0703	-
0.4150	1250	0.1815	-
0.4316	1300	0.124	-
0.4482	1350	0.0955	-
0.4648	1400	0.1064	-
0.4814	1450	0.0429	-
0.4980	1500	0.0814	-
0.5146	1550	0.1483	-
0.5312	1600	0.0856	-
0.5478	1650	0.1072	-
0.5644	1700	0.0148	-
0.5810	1750	0.0571	-
0.5976	1800	0.052	-
0.6142	1850	0.0532	-
0.6308	1900	0.0088	-
0.6474	1950	0.1619	-
0.6640	2000	0.0618	-
0.6806	2050	0.0115	-
0.6972	2100	0.1402	-
0.7138	2150	0.0637	-
0.7304	2200	0.0194	-
0.7470	2250	0.0135	-
0.7636	2300	0.0109	-
0.7802	2350	0.133	-
0.7968	2400	0.0565	-
0.8134	2450	0.1508	-
0.8300	2500	0.0293	-
0.8466	2550	0.065	-
0.8632	2600	0.0029	-
0.8798	2650	0.008	-
0.8964	2700	0.0604	-
0.9130	2750	0.0074	-
0.9296	2800	0.0019	-
0.9462	2850	0.0129	-
0.9628	2900	0.0838	-
0.9794	2950	0.0044	-
0.9960	3000	0.0035	-
1.0	3012	-	0.2514
1.0126	3050	0.0086	-
1.0292	3100	0.0042	-
1.0458	3150	0.0833	-
1.0624	3200	0.058	-
1.0790	3250	0.013	-
1.0956	3300	0.0429	-
1.1122	3350	0.0044	-
1.1288	3400	0.0699	-
1.1454	3450	0.0535	-
1.1620	3500	0.0559	-
1.1786	3550	0.1459	-
1.1952	3600	0.118	-
1.2118	3650	0.14	-
1.2284	3700	0.0632	-
1.2450	3750	0.0026	-
1.2616	3800	0.0026	-
1.2782	3850	0.0052	-
1.2948	3900	0.0058	-
1.3114	3950	0.0018	-
1.3280	4000	0.0152	-
1.3446	4050	0.0186	-
1.3612	4100	0.039	-
1.3778	4150	0.0022	-
1.3944	4200	0.002	-
1.4110	4250	0.0032	-
1.4276	4300	0.0285	-
1.4442	4350	0.0213	-
1.4608	4400	0.0009	-
1.4774	4450	0.0262	-
1.4940	4500	0.0181	-
1.5106	4550	0.0629	-
1.5272	4600	0.0023	-
1.5438	4650	0.003	-
1.5604	4700	0.0024	-
1.5770	4750	0.049	-
1.5936	4800	0.0154	-
1.6102	4850	0.0009	-
1.6268	4900	0.0015	-
1.6434	4950	0.0068	-
1.6600	5000	0.057	-
1.6766	5050	0.0031	-
1.6932	5100	0.0189	-
1.7098	5150	0.0317	-
1.7264	5200	0.0013	-
1.7430	5250	0.0247	-
1.7596	5300	0.0062	-
1.7762	5350	0.0192	-
1.7928	5400	0.0019	-
1.8094	5450	0.1007	-
1.8260	5500	0.0384	-
1.8426	5550	0.0494	-
1.8592	5600	0.0615	-
1.8758	5650	0.0709	-
1.8924	5700	0.0308	-
1.9090	5750	0.0107	-
1.9256	5800	0.064	-
1.9422	5850	0.0009	-
1.9588	5900	0.0019	-
1.9754	5950	0.0037	-
1.9920	6000	0.0826	-
2.0	6024	-	0.2614

• The bold row denotes the saved checkpoint.

Framework Versions

• Python: 3.10.12
• SetFit: 1.0.1
• Sentence Transformers: 2.2.2
• Transformers: 4.35.2
• PyTorch: 2.1.0+cu121
• Datasets: 2.16.1
• Tokenizers: 0.15.0

Citation

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}
}