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README.md
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| 1 |
+
---
|
| 2 |
+
license: apache-2.0
|
| 3 |
+
datasets:
|
| 4 |
+
- teknium/OpenHermes-2.5
|
| 5 |
+
tags:
|
| 6 |
+
- axolotl
|
| 7 |
+
- 01-ai/Yi-1.5-9B-Chat
|
| 8 |
+
- finetune
|
| 9 |
+
- gguf
|
| 10 |
+
---
|
| 11 |
+
|
| 12 |
+
|
| 13 |
+
# Hermes-2.5-Yi-1.5-9B-Chat-GGUF
|
| 14 |
+
|
| 15 |
+
This model is a fine-tuned version of [01-ai/Yi-1.5-9B-Chat](https://huggingface.co/01-ai/Yi-1.5-9B-Chat) on the [teknium/OpenHermes-2.5](https://huggingface.co/datasets/teknium/OpenHermes-2.5) dataset.
|
| 16 |
+
I'm very happy with the results. The model now seems a lot smarter and "aware" in certain situations. It got quite an big edge on the AGIEval Benchmark for models in it's class this is quite good.
|
| 17 |
+
I plan to extend it's context length to 32k with POSE. This is the GGUF repo. You can find the main repo here [Hermes-2.5-Yi-1.5-9B-Chat](https://huggingface.co/juvi21/Hermes-2.5-Yi-1.5-9B-Chat).
|
| 18 |
+
|
| 19 |
+
## Model Details
|
| 20 |
+
|
| 21 |
+
- **Base Model:** 01-ai/Yi-1.5-9B-Chat
|
| 22 |
+
- **chat-template:** chatml
|
| 23 |
+
- **Dataset:** teknium/OpenHermes-2.5
|
| 24 |
+
- **Sequence Length:** 8192 tokens
|
| 25 |
+
- **Training:**
|
| 26 |
+
- **Epochs:** 1
|
| 27 |
+
- **Hardware:** 4 Nodes x 4 NVIDIA A100 40GB GPUs
|
| 28 |
+
- **Duration:** 48:32:13
|
| 29 |
+
- **Cluster:** KIT SCC Cluster
|
| 30 |
+
|
| 31 |
+
## Benchmark n_shots=0
|
| 32 |
+
|
| 33 |
+
|
| 34 |
+
|
| 35 |
+
|
| 36 |
+
| Benchmark | Score |
|
| 37 |
+
|-------------------|--------|
|
| 38 |
+
| ARC (Challenge) | 52.47% |
|
| 39 |
+
| ARC (Easy) | 81.65% |
|
| 40 |
+
| BoolQ | 87.22% |
|
| 41 |
+
| HellaSwag | 60.52% |
|
| 42 |
+
| OpenBookQA | 33.60% |
|
| 43 |
+
| PIQA | 81.12% |
|
| 44 |
+
| Winogrande | 72.22% |
|
| 45 |
+
| AGIEval | 38.46% |
|
| 46 |
+
| TruthfulQA | 44.22% |
|
| 47 |
+
| MMLU | 59.72% |
|
| 48 |
+
| IFEval | 47.96% |
|
| 49 |
+
|
| 50 |
+
|
| 51 |
+
For detailed benchmark results, including sub-categories and various metrics, please refer to the [full benchmark table](#full-benchmark-results) at the end of this README.
|
| 52 |
+
|
| 53 |
+
## GGUF and Quantizations
|
| 54 |
+
|
| 55 |
+
- llama.cpp [b3166](https://github.com/ggerganov/llama.cpp/releases/tag/b3166)
|
| 56 |
+
- [juvi21/Hermes-2.5-Yi-1.5-9B-Chat-GGUF](https://huggingface.co/juvi21/Hermes-2.5-Yi-1.5-9B-Chat-GGUF) is availabe in:
|
| 57 |
+
- **F16** **Q8_0** **Q6_KQ5_K_M** **Q4_K_M** **Q3_K_M** **Q2_K**
|
| 58 |
+
|
| 59 |
+
|
| 60 |
+
|
| 61 |
+
## Usage
|
| 62 |
+
|
| 63 |
+
To use this model, you can load it using the Hugging Face Transformers library:
|
| 64 |
+
|
| 65 |
+
```python
|
| 66 |
+
from transformers import AutoModelForCausalLM, AutoTokenizer
|
| 67 |
+
|
| 68 |
+
model = AutoModelForCausalLM.from_pretrained("juvi21/Hermes-2.5-Yi-1.5-9B-Chat")
|
| 69 |
+
tokenizer = AutoTokenizer.from_pretrained("juvi21/Hermes-2.5-Yi-1.5-9B-Chat")
|
| 70 |
+
|
| 71 |
+
# Generate text
|
| 72 |
+
input_text = "What is the question to 42?"
|
| 73 |
+
inputs = tokenizer(input_text, return_tensors="pt")
|
| 74 |
+
outputs = model.generate(**inputs)
|
| 75 |
+
generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
|
| 76 |
+
print(generated_text)
|
| 77 |
+
|
| 78 |
+
```
|
| 79 |
+
|
| 80 |
+
## chatml
|
| 81 |
+
```
|
| 82 |
+
<|im_start|>system
|
| 83 |
+
{system_prompt}<|im_end|>
|
| 84 |
+
<|im_start|>user
|
| 85 |
+
Knock Knock, who is there?<|im_end|>
|
| 86 |
+
<|im_start|>assistant
|
| 87 |
+
Hi there! <|im_end|>
|
| 88 |
+
```
|
| 89 |
+
## License
|
| 90 |
+
|
| 91 |
+
This model is released under the Apache 2.0 license.
|
| 92 |
+
|
| 93 |
+
## Acknowledgements
|
| 94 |
+
|
| 95 |
+
Special thanks to:
|
| 96 |
+
- Teknium for the great OpenHermes-2.5 dataset
|
| 97 |
+
- 01-ai for their great model
|
| 98 |
+
|
| 99 |
+
## Citation
|
| 100 |
+
|
| 101 |
+
If you use this model in your research, consider citing. Although definetly cite NousResearch and 01-ai:
|
| 102 |
+
|
| 103 |
+
```bibtex
|
| 104 |
+
@misc{
|
| 105 |
+
author = {juvi21},
|
| 106 |
+
title = Hermes-2.5-Yi-1.5-9B-Chat},
|
| 107 |
+
year = {2024},
|
| 108 |
+
}
|
| 109 |
+
```
|
| 110 |
+
## full-benchmark-results
|
| 111 |
+
|
| 112 |
+
| Tasks |Version|Filter|n-shot| Metric | | Value | |Stderr|
|
| 113 |
+
|---------------------------------------|-------|------|-----:|-----------------------|---|------:|---|------|
|
| 114 |
+
|agieval |N/A |none | 0|acc |↑ | 0.5381|± |0.0049|
|
| 115 |
+
| | |none | 0|acc_norm |↑ | 0.5715|± |0.0056|
|
| 116 |
+
| - agieval_aqua_rat | 1|none | 0|acc |↑ | 0.3858|± |0.0306|
|
| 117 |
+
| | |none | 0|acc_norm |↑ | 0.3425|± |0.0298|
|
| 118 |
+
| - agieval_gaokao_biology | 1|none | 0|acc |↑ | 0.6048|± |0.0338|
|
| 119 |
+
| | |none | 0|acc_norm |↑ | 0.6000|± |0.0339|
|
| 120 |
+
| - agieval_gaokao_chemistry | 1|none | 0|acc |↑ | 0.4879|± |0.0348|
|
| 121 |
+
| | |none | 0|acc_norm |↑ | 0.4106|± |0.0343|
|
| 122 |
+
| - agieval_gaokao_chinese | 1|none | 0|acc |↑ | 0.5935|± |0.0314|
|
| 123 |
+
| | |none | 0|acc_norm |↑ | 0.5813|± |0.0315|
|
| 124 |
+
| - agieval_gaokao_english | 1|none | 0|acc |↑ | 0.8235|± |0.0218|
|
| 125 |
+
| | |none | 0|acc_norm |↑ | 0.8431|± |0.0208|
|
| 126 |
+
| - agieval_gaokao_geography | 1|none | 0|acc |↑ | 0.7085|± |0.0323|
|
| 127 |
+
| | |none | 0|acc_norm |↑ | 0.6985|± |0.0326|
|
| 128 |
+
| - agieval_gaokao_history | 1|none | 0|acc |↑ | 0.7830|± |0.0269|
|
| 129 |
+
| | |none | 0|acc_norm |↑ | 0.7660|± |0.0277|
|
| 130 |
+
| - agieval_gaokao_mathcloze | 1|none | 0|acc |↑ | 0.0508|± |0.0203|
|
| 131 |
+
| - agieval_gaokao_mathqa | 1|none | 0|acc |↑ | 0.3761|± |0.0259|
|
| 132 |
+
| | |none | 0|acc_norm |↑ | 0.3590|± |0.0256|
|
| 133 |
+
| - agieval_gaokao_physics | 1|none | 0|acc |↑ | 0.4950|± |0.0354|
|
| 134 |
+
| | |none | 0|acc_norm |↑ | 0.4700|± |0.0354|
|
| 135 |
+
| - agieval_jec_qa_ca | 1|none | 0|acc |↑ | 0.6557|± |0.0150|
|
| 136 |
+
| | |none | 0|acc_norm |↑ | 0.5926|± |0.0156|
|
| 137 |
+
| - agieval_jec_qa_kd | 1|none | 0|acc |↑ | 0.7310|± |0.0140|
|
| 138 |
+
| | |none | 0|acc_norm |↑ | 0.6610|± |0.0150|
|
| 139 |
+
| - agieval_logiqa_en | 1|none | 0|acc |↑ | 0.5177|± |0.0196|
|
| 140 |
+
| | |none | 0|acc_norm |↑ | 0.4839|± |0.0196|
|
| 141 |
+
| - agieval_logiqa_zh | 1|none | 0|acc |↑ | 0.4854|± |0.0196|
|
| 142 |
+
| | |none | 0|acc_norm |↑ | 0.4501|± |0.0195|
|
| 143 |
+
| - agieval_lsat_ar | 1|none | 0|acc |↑ | 0.2913|± |0.0300|
|
| 144 |
+
| | |none | 0|acc_norm |↑ | 0.2696|± |0.0293|
|
| 145 |
+
| - agieval_lsat_lr | 1|none | 0|acc |↑ | 0.7196|± |0.0199|
|
| 146 |
+
| | |none | 0|acc_norm |↑ | 0.6824|± |0.0206|
|
| 147 |
+
| - agieval_lsat_rc | 1|none | 0|acc |↑ | 0.7212|± |0.0274|
|
| 148 |
+
| | |none | 0|acc_norm |↑ | 0.6989|± |0.0280|
|
| 149 |
+
| - agieval_math | 1|none | 0|acc |↑ | 0.0910|± |0.0091|
|
| 150 |
+
| - agieval_sat_en | 1|none | 0|acc |↑ | 0.8204|± |0.0268|
|
| 151 |
+
| | |none | 0|acc_norm |↑ | 0.8301|± |0.0262|
|
| 152 |
+
| - agieval_sat_en_without_passage | 1|none | 0|acc |↑ | 0.5194|± |0.0349|
|
| 153 |
+
| | |none | 0|acc_norm |↑ | 0.4806|± |0.0349|
|
| 154 |
+
| - agieval_sat_math | 1|none | 0|acc |↑ | 0.5864|± |0.0333|
|
| 155 |
+
| | |none | 0|acc_norm |↑ | 0.5409|± |0.0337|
|
| 156 |
+
|arc_challenge | 1|none | 0|acc |↑ | 0.5648|± |0.0145|
|
| 157 |
+
| | |none | 0|acc_norm |↑ | 0.5879|± |0.0144|
|
| 158 |
+
|arc_easy | 1|none | 0|acc |↑ | 0.8241|± |0.0078|
|
| 159 |
+
| | |none | 0|acc_norm |↑ | 0.8165|± |0.0079|
|
| 160 |
+
|boolq | 2|none | 0|acc |↑ | 0.8624|± |0.0060|
|
| 161 |
+
|hellaswag | 1|none | 0|acc |↑ | 0.5901|± |0.0049|
|
| 162 |
+
| | |none | 0|acc_norm |↑ | 0.7767|± |0.0042|
|
| 163 |
+
|ifeval | 2|none | 0|inst_level_loose_acc |↑ | 0.5156|± |N/A |
|
| 164 |
+
| | |none | 0|inst_level_strict_acc |↑ | 0.4748|± |N/A |
|
| 165 |
+
| | |none | 0|prompt_level_loose_acc |↑ | 0.3863|± |0.0210|
|
| 166 |
+
| | |none | 0|prompt_level_strict_acc|↑ | 0.3309|± |0.0202|
|
| 167 |
+
|mmlu |N/A |none | 0|acc |↑ | 0.6942|± |0.0037|
|
| 168 |
+
| - abstract_algebra | 0|none | 0|acc |↑ | 0.4900|± |0.0502|
|
| 169 |
+
| - anatomy | 0|none | 0|acc |↑ | 0.6815|± |0.0402|
|
| 170 |
+
| - astronomy | 0|none | 0|acc |↑ | 0.7895|± |0.0332|
|
| 171 |
+
| - business_ethics | 0|none | 0|acc |↑ | 0.7600|± |0.0429|
|
| 172 |
+
| - clinical_knowledge | 0|none | 0|acc |↑ | 0.7132|± |0.0278|
|
| 173 |
+
| - college_biology | 0|none | 0|acc |↑ | 0.8056|± |0.0331|
|
| 174 |
+
| - college_chemistry | 0|none | 0|acc |↑ | 0.5300|± |0.0502|
|
| 175 |
+
| - college_computer_science | 0|none | 0|acc |↑ | 0.6500|± |0.0479|
|
| 176 |
+
| - college_mathematics | 0|none | 0|acc |↑ | 0.4100|± |0.0494|
|
| 177 |
+
| - college_medicine | 0|none | 0|acc |↑ | 0.6763|± |0.0357|
|
| 178 |
+
| - college_physics | 0|none | 0|acc |↑ | 0.5000|± |0.0498|
|
| 179 |
+
| - computer_security | 0|none | 0|acc |↑ | 0.8200|± |0.0386|
|
| 180 |
+
| - conceptual_physics | 0|none | 0|acc |↑ | 0.7489|± |0.0283|
|
| 181 |
+
| - econometrics | 0|none | 0|acc |↑ | 0.5877|± |0.0463|
|
| 182 |
+
| - electrical_engineering | 0|none | 0|acc |↑ | 0.6759|± |0.0390|
|
| 183 |
+
| - elementary_mathematics | 0|none | 0|acc |↑ | 0.6481|± |0.0246|
|
| 184 |
+
| - formal_logic | 0|none | 0|acc |↑ | 0.5873|± |0.0440|
|
| 185 |
+
| - global_facts | 0|none | 0|acc |↑ | 0.3900|± |0.0490|
|
| 186 |
+
| - high_school_biology | 0|none | 0|acc |↑ | 0.8613|± |0.0197|
|
| 187 |
+
| - high_school_chemistry | 0|none | 0|acc |↑ | 0.6453|± |0.0337|
|
| 188 |
+
| - high_school_computer_science | 0|none | 0|acc |↑ | 0.8300|± |0.0378|
|
| 189 |
+
| - high_school_european_history | 0|none | 0|acc |↑ | 0.8182|± |0.0301|
|
| 190 |
+
| - high_school_geography | 0|none | 0|acc |↑ | 0.8485|± |0.0255|
|
| 191 |
+
| - high_school_government_and_politics| 0|none | 0|acc |↑ | 0.8964|± |0.0220|
|
| 192 |
+
| - high_school_macroeconomics | 0|none | 0|acc |↑ | 0.7923|± |0.0206|
|
| 193 |
+
| - high_school_mathematics | 0|none | 0|acc |↑ | 0.4407|± |0.0303|
|
| 194 |
+
| - high_school_microeconomics | 0|none | 0|acc |↑ | 0.8655|± |0.0222|
|
| 195 |
+
| - high_school_physics | 0|none | 0|acc |↑ | 0.5298|± |0.0408|
|
| 196 |
+
| - high_school_psychology | 0|none | 0|acc |↑ | 0.8679|± |0.0145|
|
| 197 |
+
| - high_school_statistics | 0|none | 0|acc |↑ | 0.6898|± |0.0315|
|
| 198 |
+
| - high_school_us_history | 0|none | 0|acc |↑ | 0.8873|± |0.0222|
|
| 199 |
+
| - high_school_world_history | 0|none | 0|acc |↑ | 0.8312|± |0.0244|
|
| 200 |
+
| - human_aging | 0|none | 0|acc |↑ | 0.7085|± |0.0305|
|
| 201 |
+
| - human_sexuality | 0|none | 0|acc |↑ | 0.7557|± |0.0377|
|
| 202 |
+
| - humanities |N/A |none | 0|acc |↑ | 0.6323|± |0.0067|
|
| 203 |
+
| - international_law | 0|none | 0|acc |↑ | 0.8099|± |0.0358|
|
| 204 |
+
| - jurisprudence | 0|none | 0|acc |↑ | 0.7685|± |0.0408|
|
| 205 |
+
| - logical_fallacies | 0|none | 0|acc |↑ | 0.7975|± |0.0316|
|
| 206 |
+
| - machine_learning | 0|none | 0|acc |↑ | 0.5179|± |0.0474|
|
| 207 |
+
| - management | 0|none | 0|acc |↑ | 0.8835|± |0.0318|
|
| 208 |
+
| - marketing | 0|none | 0|acc |↑ | 0.9017|± |0.0195|
|
| 209 |
+
| - medical_genetics | 0|none | 0|acc |↑ | 0.8000|± |0.0402|
|
| 210 |
+
| - miscellaneous | 0|none | 0|acc |↑ | 0.8225|± |0.0137|
|
| 211 |
+
| - moral_disputes | 0|none | 0|acc |↑ | 0.7283|± |0.0239|
|
| 212 |
+
| - moral_scenarios | 0|none | 0|acc |↑ | 0.4860|± |0.0167|
|
| 213 |
+
| - nutrition | 0|none | 0|acc |↑ | 0.7353|± |0.0253|
|
| 214 |
+
| - other |N/A |none | 0|acc |↑ | 0.7287|± |0.0077|
|
| 215 |
+
| - philosophy | 0|none | 0|acc |↑ | 0.7170|± |0.0256|
|
| 216 |
+
| - prehistory | 0|none | 0|acc |↑ | 0.7346|± |0.0246|
|
| 217 |
+
| - professional_accounting | 0|none | 0|acc |↑ | 0.5638|± |0.0296|
|
| 218 |
+
| - professional_law | 0|none | 0|acc |↑ | 0.5163|± |0.0128|
|
| 219 |
+
| - professional_medicine | 0|none | 0|acc |↑ | 0.6875|± |0.0282|
|
| 220 |
+
| - professional_psychology | 0|none | 0|acc |↑ | 0.7092|± |0.0184|
|
| 221 |
+
| - public_relations | 0|none | 0|acc |↑ | 0.6727|± |0.0449|
|
| 222 |
+
| - security_studies | 0|none | 0|acc |↑ | 0.7347|± |0.0283|
|
| 223 |
+
| - social_sciences |N/A |none | 0|acc |↑ | 0.7910|± |0.0072|
|
| 224 |
+
| - sociology | 0|none | 0|acc |↑ | 0.8060|± |0.0280|
|
| 225 |
+
| - stem |N/A |none | 0|acc |↑ | 0.6581|± |0.0081|
|
| 226 |
+
| - us_foreign_policy | 0|none | 0|acc |↑ | 0.8900|± |0.0314|
|
| 227 |
+
| - virology | 0|none | 0|acc |↑ | 0.5301|± |0.0389|
|
| 228 |
+
| - world_religions | 0|none | 0|acc |↑ | 0.8012|± |0.0306|
|
| 229 |
+
|openbookqa | 1|none | 0|acc |↑ | 0.3280|± |0.0210|
|
| 230 |
+
| | |none | 0|acc_norm |↑ | 0.4360|± |0.0222|
|
| 231 |
+
|piqa | 1|none | 0|acc |↑ | 0.7982|± |0.0094|
|
| 232 |
+
| | |none | 0|acc_norm |↑ | 0.8074|± |0.0092|
|
| 233 |
+
|truthfulqa |N/A |none | 0|acc |↑ | 0.4746|± |0.0116|
|
| 234 |
+
| | |none | 0|bleu_acc |↑ | 0.4700|± |0.0175|
|
| 235 |
+
| | |none | 0|bleu_diff |↑ | 0.3214|± |0.6045|
|
| 236 |
+
| | |none | 0|bleu_max |↑ |22.5895|± |0.7122|
|
| 237 |
+
| | |none | 0|rouge1_acc |↑ | 0.4798|± |0.0175|
|
| 238 |
+
| | |none | 0|rouge1_diff |↑ | 0.0846|± |0.7161|
|
| 239 |
+
| | |none | 0|rouge1_max |↑ |48.7180|± |0.7833|
|
| 240 |
+
| | |none | 0|rouge2_acc |↑ | 0.4149|± |0.0172|
|
| 241 |
+
| | |none | 0|rouge2_diff |↑ |-0.4656|± |0.8375|
|
| 242 |
+
| | |none | 0|rouge2_max |↑ |34.0585|± |0.8974|
|
| 243 |
+
| | |none | 0|rougeL_acc |↑ | 0.4651|± |0.0175|
|
| 244 |
+
| | |none | 0|rougeL_diff |↑ |-0.2804|± |0.7217|
|
| 245 |
+
| | |none | 0|rougeL_max |↑ |45.2232|± |0.7971|
|
| 246 |
+
| - truthfulqa_gen | 3|none | 0|bleu_acc |↑ | 0.4700|± |0.0175|
|
| 247 |
+
| | |none | 0|bleu_diff |↑ | 0.3214|± |0.6045|
|
| 248 |
+
| | |none | 0|bleu_max |↑ |22.5895|± |0.7122|
|
| 249 |
+
| | |none | 0|rouge1_acc |↑ | 0.4798|± |0.0175|
|
| 250 |
+
| | |none | 0|rouge1_diff |↑ | 0.0846|± |0.7161|
|
| 251 |
+
| | |none | 0|rouge1_max |↑ |48.7180|± |0.7833|
|
| 252 |
+
| | |none | 0|rouge2_acc |↑ | 0.4149|± |0.0172|
|
| 253 |
+
| | |none | 0|rouge2_diff |↑ |-0.4656|± |0.8375|
|
| 254 |
+
| | |none | 0|rouge2_max |↑ |34.0585|± |0.8974|
|
| 255 |
+
| | |none | 0|rougeL_acc |↑ | 0.4651|± |0.0175|
|
| 256 |
+
| | |none | 0|rougeL_diff |↑ |-0.2804|± |0.7217|
|
| 257 |
+
| | |none | 0|rougeL_max |↑ |45.2232|± |0.7971|
|
| 258 |
+
| - truthfulqa_mc1 | 2|none | 0|acc |↑ | 0.3905|± |0.0171|
|
| 259 |
+
| - truthfulqa_mc2 | 2|none | 0|acc |↑ | 0.5587|± |0.0156|
|
| 260 |
+
|winogrande | 1|none | 0|acc |↑ | 0.7388|± |0.0123|
|
| 261 |
+
|
| 262 |
+
| Groups |Version|Filter|n-shot| Metric | | Value | |Stderr|
|
| 263 |
+
|------------------|-------|------|-----:|-----------|---|------:|---|-----:|
|
| 264 |
+
|agieval |N/A |none | 0|acc |↑ | 0.5381|± |0.0049|
|
| 265 |
+
| | |none | 0|acc_norm |↑ | 0.5715|± |0.0056|
|
| 266 |
+
|mmlu |N/A |none | 0|acc |↑ | 0.6942|± |0.0037|
|
| 267 |
+
| - humanities |N/A |none | 0|acc |↑ | 0.6323|± |0.0067|
|
| 268 |
+
| - other |N/A |none | 0|acc |↑ | 0.7287|± |0.0077|
|
| 269 |
+
| - social_sciences|N/A |none | 0|acc |↑ | 0.7910|± |0.0072|
|
| 270 |
+
| - stem |N/A |none | 0|acc |↑ | 0.6581|± |0.0081|
|
| 271 |
+
|truthfulqa |N/A |none | 0|acc |↑ | 0.4746|± |0.0116|
|
| 272 |
+
| | |none | 0|bleu_acc |↑ | 0.4700|± |0.0175|
|
| 273 |
+
| | |none | 0|bleu_diff |↑ | 0.3214|± |0.6045|
|
| 274 |
+
| | |none | 0|bleu_max |↑ |22.5895|± |0.7122|
|
| 275 |
+
| | |none | 0|rouge1_acc |↑ | 0.4798|± |0.0175|
|
| 276 |
+
| | |none | 0|rouge1_diff|↑ | 0.0846|± |0.7161|
|
| 277 |
+
| | |none | 0|rouge1_max |↑ |48.7180|± |0.7833|
|
| 278 |
+
| | |none | 0|rouge2_acc |↑ | 0.4149|± |0.0172|
|
| 279 |
+
| | |none | 0|rouge2_diff|↑ |-0.4656|± |0.8375|
|
| 280 |
+
| | |none | 0|rouge2_max |↑ |34.0585|± |0.8974|
|
| 281 |
+
| | |none | 0|rougeL_acc |↑ | 0.4651|± |0.0175|
|
| 282 |
+
| | |none | 0|rougeL_diff|↑ |-0.2804|± |0.7217|
|
| 283 |
+
| | |none | 0|rougeL_max |↑ |45.2232|± |0.7971|
|