modelId
stringlengths 4
122
| author
stringlengths 2
42
⌀ | last_modified
timestamp[us, tz=UTC] | downloads
int64 0
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| likes
int64 0
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| library_name
stringlengths 2
84
⌀ | tags
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stringlengths 5
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huseinzol05/dummy-clip-vit-large-patch14-336-mistral-7b
|
huseinzol05
| 2023-12-14T05:57:42Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llava",
"pretraining",
"endpoints_compatible",
"region:us"
] | null | 2023-12-14T05:54:47Z |
Entry not found
|
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Kobaayyy/pokemon-lora
|
Kobaayyy
| 2023-12-14T05:57:13Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-14T05:57:13Z |
Entry not found
|
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] |
huseinzol05/dummy-clip-vit-large-patch14-336-tinyllama
|
huseinzol05
| 2023-12-14T06:02:25Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llava",
"pretraining",
"endpoints_compatible",
"region:us"
] | null | 2023-12-14T06:00:24Z |
Entry not found
|
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Nakdesu/qwen-14b-claude-chat-GGUF
|
Nakdesu
| 2023-12-14T06:00:57Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-14T06:00:57Z |
Entry not found
|
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] |
DucHaiten/DucHaiten-Comic-NSFW
|
DucHaiten
| 2023-12-15T03:18:26Z | 0 | 0 | null |
[
"license:openrail",
"region:us"
] | null | 2023-12-14T06:03:20Z |
---
license: openrail
---
|
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AmelieSchreiber/esm_mlmppi_ph50_v2
|
AmelieSchreiber
| 2023-12-14T06:04:39Z | 0 | 0 | null |
[
"license:mit",
"region:us"
] | null | 2023-12-14T06:04:39Z |
Temporary Redirect. Redirecting to /AmelieSchreiber/esm_mlmppi_ph50_40K_v1/resolve/main/README.md
|
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Ekohe/RevenueStreamJP
|
Ekohe
| 2023-12-14T14:37:38Z | 0 | 0 |
setfit
|
[
"setfit",
"safetensors",
"t5",
"sentence-transformers",
"text-classification",
"generated_from_setfit_trainer",
"arxiv:2209.11055",
"base_model:google/flan-t5-base",
"model-index",
"text-generation-inference",
"region:us"
] |
text-classification
| 2023-12-14T06:05:41Z |
---
library_name: setfit
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
metrics:
- accuracy
widget:
- text: 国家試験の過去問題や民間試験の予想問題を1問1答形式で解くことができる学習アプリケーション「過去問.com」を運営しているスタートアップ。
- text: バブル製剤を製造販売する、帝京大学発のスタートアップ。同社は、帝京大学で生まれた特許技術であるマイクロバブルに超音波を照射する技術を用いて、癌や脳疾患などの治療薬が十分に届かない問題を解決する画期的な送達技術「BUS-DDS」に用いるバブル製剤を製造販売している。また、超音波応答マイクロバブル「セラノスティクスバブル」の開発に成功している。これは、超音波と薬物を組み合わせて低侵襲な「BUS-DDS」治療を実現し、薬の届きにくいところに届けて効かせる画期的な薬の送達システムである。2021年1月には、帝京ナレッジ・イニシアティブを引受先とする第三者割当増資により資金調達を実施している。
- text: 'まさおと申します！
マネーリテラシーを勉強し、発信しています。
堅実な投資を経て資産形成中！
また、家計の助けになるような投稿もします！
是非ブログも読んでください！'
- text: 義歯の生産プロセス完全自動化を目指す企業。
- text: 日本通信に出資している企業。
pipeline_tag: text-classification
inference: false
base_model: google/flan-t5-base
model-index:
- name: SetFit with google/flan-t5-base
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: Unknown
type: unknown
split: test
metrics:
- type: accuracy
value: 0.6643356643356644
name: Accuracy
---
# SetFit with google/flan-t5-base
This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [google/flan-t5-base](https://huggingface.co/google/flan-t5-base) 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](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:** [google/flan-t5-base](https://huggingface.co/google/flan-t5-base)
- **Classification head:** a OneVsRestClassifier instance
- **Maximum Sequence Length:** None tokens
<!-- - **Number of Classes:** Unknown -->
<!-- - **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)
## Evaluation
### Metrics
| Label | Accuracy |
|:--------|:---------|
| **all** | 0.6643 |
## 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("Ekohe/RevenueStreamJP")
# Run inference
preds = model("日本通信に出資している企業。")
```
<!--
### 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 | 1 | 2.0176 | 57 |
### Training Hyperparameters
- batch_size: (16, 16)
- num_epochs: (35, 35)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 32
- 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
- seed: 42
- eval_max_steps: -1
- load_best_model_at_end: False
### Training Results
| Epoch | Step | Training Loss | Validation Loss |
|:-------:|:-----:|:-------------:|:---------------:|
| 0.0004 | 1 | 0.2519 | - |
| 0.0220 | 50 | 0.3177 | - |
| 0.0439 | 100 | 0.2568 | - |
| 0.0659 | 150 | 0.2323 | - |
| 0.0879 | 200 | 0.2699 | - |
| 0.1098 | 250 | 0.2502 | - |
| 0.1318 | 300 | 0.2461 | - |
| 0.1538 | 350 | 0.2697 | - |
| 0.1757 | 400 | 0.2939 | - |
| 0.1977 | 450 | 0.2625 | - |
| 0.2197 | 500 | 0.2006 | - |
| 0.2417 | 550 | 0.2513 | - |
| 0.2636 | 600 | 0.3131 | - |
| 0.2856 | 650 | 0.2454 | - |
| 0.3076 | 700 | 0.3318 | - |
| 0.3295 | 750 | 0.2171 | - |
| 0.3515 | 800 | 0.226 | - |
| 0.3735 | 850 | 0.2844 | - |
| 0.3954 | 900 | 0.3074 | - |
| 0.4174 | 950 | 0.2633 | - |
| 0.4394 | 1000 | 0.2599 | - |
| 0.4613 | 1050 | 0.262 | - |
| 0.4833 | 1100 | 0.2575 | - |
| 0.5053 | 1150 | 0.2686 | - |
| 0.5272 | 1200 | 0.2173 | - |
| 0.5492 | 1250 | 0.2596 | - |
| 0.5712 | 1300 | 0.2626 | - |
| 0.5931 | 1350 | 0.2672 | - |
| 0.6151 | 1400 | 0.2955 | - |
| 0.6371 | 1450 | 0.2681 | - |
| 0.6591 | 1500 | 0.2497 | - |
| 0.6810 | 1550 | 0.2652 | - |
| 0.7030 | 1600 | 0.2058 | - |
| 0.7250 | 1650 | 0.2521 | - |
| 0.7469 | 1700 | 0.2119 | - |
| 0.7689 | 1750 | 0.2618 | - |
| 0.7909 | 1800 | 0.3069 | - |
| 0.8128 | 1850 | 0.2624 | - |
| 0.8348 | 1900 | 0.2631 | - |
| 0.8568 | 1950 | 0.2208 | - |
| 0.8787 | 2000 | 0.2256 | - |
| 0.9007 | 2050 | 0.2392 | - |
| 0.9227 | 2100 | 0.225 | - |
| 0.9446 | 2150 | 0.2826 | - |
| 0.9666 | 2200 | 0.295 | - |
| 0.9886 | 2250 | 0.2159 | - |
| 1.0105 | 2300 | 0.2591 | - |
| 1.0325 | 2350 | 0.2627 | - |
| 1.0545 | 2400 | 0.2871 | - |
| 1.0764 | 2450 | 0.2453 | - |
| 1.0984 | 2500 | 0.2493 | - |
| 1.1204 | 2550 | 0.2349 | - |
| 1.1424 | 2600 | 0.2666 | - |
| 1.1643 | 2650 | 0.2665 | - |
| 1.1863 | 2700 | 0.2547 | - |
| 1.2083 | 2750 | 0.2649 | - |
| 1.2302 | 2800 | 0.2254 | - |
| 1.2522 | 2850 | 0.2659 | - |
| 1.2742 | 2900 | 0.2665 | - |
| 1.2961 | 2950 | 0.2451 | - |
| 1.3181 | 3000 | 0.2308 | - |
| 1.3401 | 3050 | 0.2278 | - |
| 1.3620 | 3100 | 0.2359 | - |
| 1.3840 | 3150 | 0.2356 | - |
| 1.4060 | 3200 | 0.2545 | - |
| 1.4279 | 3250 | 0.2154 | - |
| 1.4499 | 3300 | 0.2546 | - |
| 1.4719 | 3350 | 0.2658 | - |
| 1.4938 | 3400 | 0.2821 | - |
| 1.5158 | 3450 | 0.247 | - |
| 1.5378 | 3500 | 0.2108 | - |
| 1.5598 | 3550 | 0.242 | - |
| 1.5817 | 3600 | 0.2854 | - |
| 1.6037 | 3650 | 0.2395 | - |
| 1.6257 | 3700 | 0.2215 | - |
| 1.6476 | 3750 | 0.2541 | - |
| 1.6696 | 3800 | 0.234 | - |
| 1.6916 | 3850 | 0.2638 | - |
| 1.7135 | 3900 | 0.2007 | - |
| 1.7355 | 3950 | 0.2799 | - |
| 1.7575 | 4000 | 0.2929 | - |
| 1.7794 | 4050 | 0.2622 | - |
| 1.8014 | 4100 | 0.2573 | - |
| 1.8234 | 4150 | 0.2636 | - |
| 1.8453 | 4200 | 0.2362 | - |
| 1.8673 | 4250 | 0.2322 | - |
| 1.8893 | 4300 | 0.2242 | - |
| 1.9112 | 4350 | 0.2561 | - |
| 1.9332 | 4400 | 0.2895 | - |
| 1.9552 | 4450 | 0.2491 | - |
| 1.9772 | 4500 | 0.239 | - |
| 1.9991 | 4550 | 0.2507 | - |
| 2.0211 | 4600 | 0.2478 | - |
| 2.0431 | 4650 | 0.2356 | - |
| 2.0650 | 4700 | 0.2395 | - |
| 2.0870 | 4750 | 0.2342 | - |
| 2.1090 | 4800 | 0.304 | - |
| 2.1309 | 4850 | 0.2528 | - |
| 2.1529 | 4900 | 0.2267 | - |
| 2.1749 | 4950 | 0.2295 | - |
| 2.1968 | 5000 | 0.2474 | - |
| 2.2188 | 5050 | 0.2559 | - |
| 2.2408 | 5100 | 0.2368 | - |
| 2.2627 | 5150 | 0.2631 | - |
| 2.2847 | 5200 | 0.1984 | - |
| 2.3067 | 5250 | 0.2319 | - |
| 2.3286 | 5300 | 0.2294 | - |
| 2.3506 | 5350 | 0.253 | - |
| 2.3726 | 5400 | 0.2315 | - |
| 2.3946 | 5450 | 0.2371 | - |
| 2.4165 | 5500 | 0.2224 | - |
| 2.4385 | 5550 | 0.2743 | - |
| 2.4605 | 5600 | 0.2206 | - |
| 2.4824 | 5650 | 0.2391 | - |
| 2.5044 | 5700 | 0.2659 | - |
| 2.5264 | 5750 | 0.3038 | - |
| 2.5483 | 5800 | 0.2346 | - |
| 2.5703 | 5850 | 0.238 | - |
| 2.5923 | 5900 | 0.2598 | - |
| 2.6142 | 5950 | 0.2534 | - |
| 2.6362 | 6000 | 0.2125 | - |
| 2.6582 | 6050 | 0.2375 | - |
| 2.6801 | 6100 | 0.238 | - |
| 2.7021 | 6150 | 0.2065 | - |
| 2.7241 | 6200 | 0.2408 | - |
| 2.7460 | 6250 | 0.2081 | - |
| 2.7680 | 6300 | 0.287 | - |
| 2.7900 | 6350 | 0.2417 | - |
| 2.8120 | 6400 | 0.277 | - |
| 2.8339 | 6450 | 0.3087 | - |
| 2.8559 | 6500 | 0.224 | - |
| 2.8779 | 6550 | 0.236 | - |
| 2.8998 | 6600 | 0.2295 | - |
| 2.9218 | 6650 | 0.2354 | - |
| 2.9438 | 6700 | 0.2502 | - |
| 2.9657 | 6750 | 0.2505 | - |
| 2.9877 | 6800 | 0.1972 | - |
| 3.0097 | 6850 | 0.1426 | - |
| 3.0316 | 6900 | 0.1979 | - |
| 3.0536 | 6950 | 0.2017 | - |
| 3.0756 | 7000 | 0.2248 | - |
| 3.0975 | 7050 | 0.2644 | - |
| 3.1195 | 7100 | 0.2071 | - |
| 3.1415 | 7150 | 0.1386 | - |
| 3.1634 | 7200 | 0.2269 | - |
| 3.1854 | 7250 | 0.2072 | - |
| 3.2074 | 7300 | 0.2199 | - |
| 3.2293 | 7350 | 0.1996 | - |
| 3.2513 | 7400 | 0.2213 | - |
| 3.2733 | 7450 | 0.1546 | - |
| 3.2953 | 7500 | 0.2577 | - |
| 3.3172 | 7550 | 0.2412 | - |
| 3.3392 | 7600 | 0.1409 | - |
| 3.3612 | 7650 | 0.1977 | - |
| 3.3831 | 7700 | 0.2033 | - |
| 3.4051 | 7750 | 0.1581 | - |
| 3.4271 | 7800 | 0.2292 | - |
| 3.4490 | 7850 | 0.1498 | - |
| 3.4710 | 7900 | 0.1748 | - |
| 3.4930 | 7950 | 0.127 | - |
| 3.5149 | 8000 | 0.2122 | - |
| 3.5369 | 8050 | 0.2103 | - |
| 3.5589 | 8100 | 0.1646 | - |
| 3.5808 | 8150 | 0.1385 | - |
| 3.6028 | 8200 | 0.1057 | - |
| 3.6248 | 8250 | 0.166 | - |
| 3.6467 | 8300 | 0.1998 | - |
| 3.6687 | 8350 | 0.2317 | - |
| 3.6907 | 8400 | 0.207 | - |
| 3.7127 | 8450 | 0.1978 | - |
| 3.7346 | 8500 | 0.1991 | - |
| 3.7566 | 8550 | 0.1511 | - |
| 3.7786 | 8600 | 0.1793 | - |
| 3.8005 | 8650 | 0.19 | - |
| 3.8225 | 8700 | 0.243 | - |
| 3.8445 | 8750 | 0.15 | - |
| 3.8664 | 8800 | 0.1522 | - |
| 3.8884 | 8850 | 0.1726 | - |
| 3.9104 | 8900 | 0.0937 | - |
| 3.9323 | 8950 | 0.2133 | - |
| 3.9543 | 9000 | 0.103 | - |
| 3.9763 | 9050 | 0.157 | - |
| 3.9982 | 9100 | 0.24 | - |
| 4.0202 | 9150 | 0.1946 | - |
| 4.0422 | 9200 | 0.2012 | - |
| 4.0641 | 9250 | 0.217 | - |
| 4.0861 | 9300 | 0.142 | - |
| 4.1081 | 9350 | 0.1378 | - |
| 4.1301 | 9400 | 0.1815 | - |
| 4.1520 | 9450 | 0.1912 | - |
| 4.1740 | 9500 | 0.1883 | - |
| 4.1960 | 9550 | 0.1002 | - |
| 4.2179 | 9600 | 0.1738 | - |
| 4.2399 | 9650 | 0.1797 | - |
| 4.2619 | 9700 | 0.1851 | - |
| 4.2838 | 9750 | 0.179 | - |
| 4.3058 | 9800 | 0.1716 | - |
| 4.3278 | 9850 | 0.2027 | - |
| 4.3497 | 9900 | 0.1708 | - |
| 4.3717 | 9950 | 0.2314 | - |
| 4.3937 | 10000 | 0.1683 | - |
| 4.4156 | 10050 | 0.211 | - |
| 4.4376 | 10100 | 0.1202 | - |
| 4.4596 | 10150 | 0.1103 | - |
| 4.4815 | 10200 | 0.1631 | - |
| 4.5035 | 10250 | 0.1649 | - |
| 4.5255 | 10300 | 0.123 | - |
| 4.5475 | 10350 | 0.2145 | - |
| 4.5694 | 10400 | 0.2068 | - |
| 4.5914 | 10450 | 0.2039 | - |
| 4.6134 | 10500 | 0.2476 | - |
| 4.6353 | 10550 | 0.1653 | - |
| 4.6573 | 10600 | 0.1681 | - |
| 4.6793 | 10650 | 0.1652 | - |
| 4.7012 | 10700 | 0.1433 | - |
| 4.7232 | 10750 | 0.0974 | - |
| 4.7452 | 10800 | 0.1784 | - |
| 4.7671 | 10850 | 0.0817 | - |
| 4.7891 | 10900 | 0.1338 | - |
| 4.8111 | 10950 | 0.1383 | - |
| 4.8330 | 11000 | 0.0781 | - |
| 4.8550 | 11050 | 0.1854 | - |
| 4.8770 | 11100 | 0.1922 | - |
| 4.8989 | 11150 | 0.1247 | - |
| 4.9209 | 11200 | 0.1595 | - |
| 4.9429 | 11250 | 0.1424 | - |
| 4.9649 | 11300 | 0.1929 | - |
| 4.9868 | 11350 | 0.1813 | - |
| 5.0088 | 11400 | 0.1398 | - |
| 5.0308 | 11450 | 0.1501 | - |
| 5.0527 | 11500 | 0.162 | - |
| 5.0747 | 11550 | 0.1653 | - |
| 5.0967 | 11600 | 0.1639 | - |
| 5.1186 | 11650 | 0.2016 | - |
| 5.1406 | 11700 | 0.1709 | - |
| 5.1626 | 11750 | 0.1284 | - |
| 5.1845 | 11800 | 0.1468 | - |
| 5.2065 | 11850 | 0.2331 | - |
| 5.2285 | 11900 | 0.1531 | - |
| 5.2504 | 11950 | 0.1919 | - |
| 5.2724 | 12000 | 0.2332 | - |
| 5.2944 | 12050 | 0.1618 | - |
| 5.3163 | 12100 | 0.1589 | - |
| 5.3383 | 12150 | 0.1144 | - |
| 5.3603 | 12200 | 0.1493 | - |
| 5.3822 | 12250 | 0.2219 | - |
| 5.4042 | 12300 | 0.1666 | - |
| 5.4262 | 12350 | 0.1588 | - |
| 5.4482 | 12400 | 0.1779 | - |
| 5.4701 | 12450 | 0.1484 | - |
| 5.4921 | 12500 | 0.1276 | - |
| 5.5141 | 12550 | 0.1239 | - |
| 5.5360 | 12600 | 0.1394 | - |
| 5.5580 | 12650 | 0.2057 | - |
| 5.5800 | 12700 | 0.1316 | - |
| 5.6019 | 12750 | 0.193 | - |
| 5.6239 | 12800 | 0.1546 | - |
| 5.6459 | 12850 | 0.2069 | - |
| 5.6678 | 12900 | 0.1551 | - |
| 5.6898 | 12950 | 0.1711 | - |
| 5.7118 | 13000 | 0.1443 | - |
| 5.7337 | 13050 | 0.185 | - |
| 5.7557 | 13100 | 0.1582 | - |
| 5.7777 | 13150 | 0.1323 | - |
| 5.7996 | 13200 | 0.2049 | - |
| 5.8216 | 13250 | 0.1412 | - |
| 5.8436 | 13300 | 0.2229 | - |
| 5.8656 | 13350 | 0.1821 | - |
| 5.8875 | 13400 | 0.1845 | - |
| 5.9095 | 13450 | 0.2316 | - |
| 5.9315 | 13500 | 0.1745 | - |
| 5.9534 | 13550 | 0.1252 | - |
| 5.9754 | 13600 | 0.1868 | - |
| 5.9974 | 13650 | 0.165 | - |
| 6.0193 | 13700 | 0.1409 | - |
| 6.0413 | 13750 | 0.1508 | - |
| 6.0633 | 13800 | 0.1441 | - |
| 6.0852 | 13850 | 0.1546 | - |
| 6.1072 | 13900 | 0.1266 | - |
| 6.1292 | 13950 | 0.1982 | - |
| 6.1511 | 14000 | 0.198 | - |
| 6.1731 | 14050 | 0.1055 | - |
| 6.1951 | 14100 | 0.1766 | - |
| 6.2170 | 14150 | 0.176 | - |
| 6.2390 | 14200 | 0.1259 | - |
| 6.2610 | 14250 | 0.1969 | - |
| 6.2830 | 14300 | 0.182 | - |
| 6.3049 | 14350 | 0.2401 | - |
| 6.3269 | 14400 | 0.1947 | - |
| 6.3489 | 14450 | 0.2133 | - |
| 6.3708 | 14500 | 0.1439 | - |
| 6.3928 | 14550 | 0.1198 | - |
| 6.4148 | 14600 | 0.153 | - |
| 6.4367 | 14650 | 0.1324 | - |
| 6.4587 | 14700 | 0.1773 | - |
| 6.4807 | 14750 | 0.2234 | - |
| 6.5026 | 14800 | 0.1318 | - |
| 6.5246 | 14850 | 0.1474 | - |
| 6.5466 | 14900 | 0.1633 | - |
| 6.5685 | 14950 | 0.1497 | - |
| 6.5905 | 15000 | 0.2248 | - |
| 6.6125 | 15050 | 0.1269 | - |
| 6.6344 | 15100 | 0.1741 | - |
| 6.6564 | 15150 | 0.146 | - |
| 6.6784 | 15200 | 0.2096 | - |
| 6.7004 | 15250 | 0.1229 | - |
| 6.7223 | 15300 | 0.185 | - |
| 6.7443 | 15350 | 0.1465 | - |
| 6.7663 | 15400 | 0.2034 | - |
| 6.7882 | 15450 | 0.2162 | - |
| 6.8102 | 15500 | 0.1616 | - |
| 6.8322 | 15550 | 0.1302 | - |
| 6.8541 | 15600 | 0.1606 | - |
| 6.8761 | 15650 | 0.1749 | - |
| 6.8981 | 15700 | 0.1765 | - |
| 6.9200 | 15750 | 0.174 | - |
| 6.9420 | 15800 | 0.1621 | - |
| 6.9640 | 15850 | 0.1304 | - |
| 6.9859 | 15900 | 0.2077 | - |
| 7.0079 | 15950 | 0.1846 | - |
| 7.0299 | 16000 | 0.1376 | - |
| 7.0518 | 16050 | 0.1907 | - |
| 7.0738 | 16100 | 0.0965 | - |
| 7.0958 | 16150 | 0.2092 | - |
| 7.1178 | 16200 | 0.1742 | - |
| 7.1397 | 16250 | 0.1377 | - |
| 7.1617 | 16300 | 0.1886 | - |
| 7.1837 | 16350 | 0.1708 | - |
| 7.2056 | 16400 | 0.2201 | - |
| 7.2276 | 16450 | 0.1439 | - |
| 7.2496 | 16500 | 0.254 | - |
| 7.2715 | 16550 | 0.1876 | - |
| 7.2935 | 16600 | 0.2146 | - |
| 7.3155 | 16650 | 0.1162 | - |
| 7.3374 | 16700 | 0.1845 | - |
| 7.3594 | 16750 | 0.1184 | - |
| 7.3814 | 16800 | 0.1622 | - |
| 7.4033 | 16850 | 0.1439 | - |
| 7.4253 | 16900 | 0.1129 | - |
| 7.4473 | 16950 | 0.1709 | - |
| 7.4692 | 17000 | 0.129 | - |
| 7.4912 | 17050 | 0.1873 | - |
| 7.5132 | 17100 | 0.1834 | - |
| 7.5351 | 17150 | 0.1646 | - |
| 7.5571 | 17200 | 0.1569 | - |
| 7.5791 | 17250 | 0.1227 | - |
| 7.6011 | 17300 | 0.1505 | - |
| 7.6230 | 17350 | 0.167 | - |
| 7.6450 | 17400 | 0.1878 | - |
| 7.6670 | 17450 | 0.2082 | - |
| 7.6889 | 17500 | 0.1959 | - |
| 7.7109 | 17550 | 0.1332 | - |
| 7.7329 | 17600 | 0.164 | - |
| 7.7548 | 17650 | 0.2241 | - |
| 7.7768 | 17700 | 0.1909 | - |
| 7.7988 | 17750 | 0.1587 | - |
| 7.8207 | 17800 | 0.124 | - |
| 7.8427 | 17850 | 0.1368 | - |
| 7.8647 | 17900 | 0.2113 | - |
| 7.8866 | 17950 | 0.1281 | - |
| 7.9086 | 18000 | 0.1662 | - |
| 7.9306 | 18050 | 0.0779 | - |
| 7.9525 | 18100 | 0.2238 | - |
| 7.9745 | 18150 | 0.1228 | - |
| 7.9965 | 18200 | 0.1479 | - |
| 8.0185 | 18250 | 0.1907 | - |
| 8.0404 | 18300 | 0.1458 | - |
| 8.0624 | 18350 | 0.1555 | - |
| 8.0844 | 18400 | 0.1948 | - |
| 8.1063 | 18450 | 0.2044 | - |
| 8.1283 | 18500 | 0.168 | - |
| 8.1503 | 18550 | 0.1964 | - |
| 8.1722 | 18600 | 0.1559 | - |
| 8.1942 | 18650 | 0.1698 | - |
| 8.2162 | 18700 | 0.1247 | - |
| 8.2381 | 18750 | 0.1711 | - |
| 8.2601 | 18800 | 0.1916 | - |
| 8.2821 | 18850 | 0.1648 | - |
| 8.3040 | 18900 | 0.1866 | - |
| 8.3260 | 18950 | 0.1532 | - |
| 8.3480 | 19000 | 0.1829 | - |
| 8.3699 | 19050 | 0.1198 | - |
| 8.3919 | 19100 | 0.1541 | - |
| 8.4139 | 19150 | 0.1888 | - |
| 8.4359 | 19200 | 0.119 | - |
| 8.4578 | 19250 | 0.1051 | - |
| 8.4798 | 19300 | 0.2322 | - |
| 8.5018 | 19350 | 0.2141 | - |
| 8.5237 | 19400 | 0.1421 | - |
| 8.5457 | 19450 | 0.1165 | - |
| 8.5677 | 19500 | 0.1747 | - |
| 8.5896 | 19550 | 0.1442 | - |
| 8.6116 | 19600 | 0.2127 | - |
| 8.6336 | 19650 | 0.1347 | - |
| 8.6555 | 19700 | 0.1876 | - |
| 8.6775 | 19750 | 0.1666 | - |
| 8.6995 | 19800 | 0.1781 | - |
| 8.7214 | 19850 | 0.1725 | - |
| 8.7434 | 19900 | 0.1982 | - |
| 8.7654 | 19950 | 0.1656 | - |
| 8.7873 | 20000 | 0.1634 | - |
| 8.8093 | 20050 | 0.1259 | - |
| 8.8313 | 20100 | 0.182 | - |
| 8.8533 | 20150 | 0.1812 | - |
| 8.8752 | 20200 | 0.1721 | - |
| 8.8972 | 20250 | 0.1577 | - |
| 8.9192 | 20300 | 0.1472 | - |
| 8.9411 | 20350 | 0.1469 | - |
| 8.9631 | 20400 | 0.1606 | - |
| 8.9851 | 20450 | 0.1582 | - |
| 9.0070 | 20500 | 0.1473 | - |
| 9.0290 | 20550 | 0.1486 | - |
| 9.0510 | 20600 | 0.2268 | - |
| 9.0729 | 20650 | 0.2271 | - |
| 9.0949 | 20700 | 0.1725 | - |
| 9.1169 | 20750 | 0.1962 | - |
| 9.1388 | 20800 | 0.1688 | - |
| 9.1608 | 20850 | 0.1309 | - |
| 9.1828 | 20900 | 0.1617 | - |
| 9.2047 | 20950 | 0.1768 | - |
| 9.2267 | 21000 | 0.2274 | - |
| 9.2487 | 21050 | 0.2371 | - |
| 9.2707 | 21100 | 0.197 | - |
| 9.2926 | 21150 | 0.1306 | - |
| 9.3146 | 21200 | 0.1719 | - |
| 9.3366 | 21250 | 0.1768 | - |
| 9.3585 | 21300 | 0.1698 | - |
| 9.3805 | 21350 | 0.1864 | - |
| 9.4025 | 21400 | 0.1681 | - |
| 9.4244 | 21450 | 0.1901 | - |
| 9.4464 | 21500 | 0.1538 | - |
| 9.4684 | 21550 | 0.0897 | - |
| 9.4903 | 21600 | 0.2013 | - |
| 9.5123 | 21650 | 0.2004 | - |
| 9.5343 | 21700 | 0.1851 | - |
| 9.5562 | 21750 | 0.1549 | - |
| 9.5782 | 21800 | 0.0896 | - |
| 9.6002 | 21850 | 0.1509 | - |
| 9.6221 | 21900 | 0.1199 | - |
| 9.6441 | 21950 | 0.156 | - |
| 9.6661 | 22000 | 0.1975 | - |
| 9.6880 | 22050 | 0.1668 | - |
| 9.7100 | 22100 | 0.1619 | - |
| 9.7320 | 22150 | 0.1498 | - |
| 9.7540 | 22200 | 0.1576 | - |
| 9.7759 | 22250 | 0.1162 | - |
| 9.7979 | 22300 | 0.1474 | - |
| 9.8199 | 22350 | 0.1756 | - |
| 9.8418 | 22400 | 0.1249 | - |
| 9.8638 | 22450 | 0.2431 | - |
| 9.8858 | 22500 | 0.1828 | - |
| 9.9077 | 22550 | 0.1632 | - |
| 9.9297 | 22600 | 0.177 | - |
| 9.9517 | 22650 | 0.1293 | - |
| 9.9736 | 22700 | 0.2332 | - |
| 9.9956 | 22750 | 0.1971 | - |
| 10.0176 | 22800 | 0.1823 | - |
| 10.0395 | 22850 | 0.1473 | - |
| 10.0615 | 22900 | 0.1583 | - |
| 10.0835 | 22950 | 0.2117 | - |
| 10.1054 | 23000 | 0.1255 | - |
| 10.1274 | 23050 | 0.2298 | - |
| 10.1494 | 23100 | 0.1803 | - |
| 10.1714 | 23150 | 0.1747 | - |
| 10.1933 | 23200 | 0.22 | - |
| 10.2153 | 23250 | 0.1924 | - |
| 10.2373 | 23300 | 0.2133 | - |
| 10.2592 | 23350 | 0.14 | - |
| 10.2812 | 23400 | 0.1529 | - |
| 10.3032 | 23450 | 0.1431 | - |
| 10.3251 | 23500 | 0.1374 | - |
| 10.3471 | 23550 | 0.1456 | - |
| 10.3691 | 23600 | 0.1259 | - |
| 10.3910 | 23650 | 0.1597 | - |
| 10.4130 | 23700 | 0.184 | - |
| 10.4350 | 23750 | 0.1136 | - |
| 10.4569 | 23800 | 0.1218 | - |
| 10.4789 | 23850 | 0.1711 | - |
| 10.5009 | 23900 | 0.12 | - |
| 10.5228 | 23950 | 0.228 | - |
| 10.5448 | 24000 | 0.1751 | - |
| 10.5668 | 24050 | 0.2049 | - |
| 10.5888 | 24100 | 0.1344 | - |
| 10.6107 | 24150 | 0.1147 | - |
| 10.6327 | 24200 | 0.1267 | - |
| 10.6547 | 24250 | 0.1623 | - |
| 10.6766 | 24300 | 0.1836 | - |
| 10.6986 | 24350 | 0.1776 | - |
| 10.7206 | 24400 | 0.147 | - |
| 10.7425 | 24450 | 0.1282 | - |
| 10.7645 | 24500 | 0.1268 | - |
| 10.7865 | 24550 | 0.1845 | - |
| 10.8084 | 24600 | 0.1566 | - |
| 10.8304 | 24650 | 0.159 | - |
| 10.8524 | 24700 | 0.1994 | - |
| 10.8743 | 24750 | 0.201 | - |
| 10.8963 | 24800 | 0.2188 | - |
| 10.9183 | 24850 | 0.176 | - |
| 10.9402 | 24900 | 0.1885 | - |
| 10.9622 | 24950 | 0.1893 | - |
| 10.9842 | 25000 | 0.2042 | - |
| 11.0062 | 25050 | 0.2046 | - |
| 11.0281 | 25100 | 0.1749 | - |
| 11.0501 | 25150 | 0.1255 | - |
| 11.0721 | 25200 | 0.117 | - |
| 11.0940 | 25250 | 0.1929 | - |
| 11.1160 | 25300 | 0.1207 | - |
| 11.1380 | 25350 | 0.1001 | - |
| 11.1599 | 25400 | 0.1814 | - |
| 11.1819 | 25450 | 0.1621 | - |
| 11.2039 | 25500 | 0.1852 | - |
| 11.2258 | 25550 | 0.1867 | - |
| 11.2478 | 25600 | 0.1759 | - |
| 11.2698 | 25650 | 0.186 | - |
| 11.2917 | 25700 | 0.1549 | - |
| 11.3137 | 25750 | 0.1228 | - |
| 11.3357 | 25800 | 0.1365 | - |
| 11.3576 | 25850 | 0.1665 | - |
| 11.3796 | 25900 | 0.1477 | - |
| 11.4016 | 25950 | 0.1871 | - |
| 11.4236 | 26000 | 0.1375 | - |
| 11.4455 | 26050 | 0.198 | - |
| 11.4675 | 26100 | 0.2016 | - |
| 11.4895 | 26150 | 0.2187 | - |
| 11.5114 | 26200 | 0.1166 | - |
| 11.5334 | 26250 | 0.1491 | - |
| 11.5554 | 26300 | 0.1831 | - |
| 11.5773 | 26350 | 0.1852 | - |
| 11.5993 | 26400 | 0.1342 | - |
| 11.6213 | 26450 | 0.1726 | - |
| 11.6432 | 26500 | 0.1783 | - |
| 11.6652 | 26550 | 0.1725 | - |
| 11.6872 | 26600 | 0.1863 | - |
| 11.7091 | 26650 | 0.1385 | - |
| 11.7311 | 26700 | 0.1377 | - |
| 11.7531 | 26750 | 0.1391 | - |
| 11.7750 | 26800 | 0.1911 | - |
| 11.7970 | 26850 | 0.1854 | - |
| 11.8190 | 26900 | 0.1818 | - |
| 11.8409 | 26950 | 0.167 | - |
| 11.8629 | 27000 | 0.1069 | - |
| 11.8849 | 27050 | 0.1708 | - |
| 11.9069 | 27100 | 0.1549 | - |
| 11.9288 | 27150 | 0.2342 | - |
| 11.9508 | 27200 | 0.1594 | - |
| 11.9728 | 27250 | 0.1923 | - |
| 11.9947 | 27300 | 0.1657 | - |
| 12.0167 | 27350 | 0.1652 | - |
| 12.0387 | 27400 | 0.1345 | - |
| 12.0606 | 27450 | 0.1299 | - |
| 12.0826 | 27500 | 0.2115 | - |
| 12.1046 | 27550 | 0.1452 | - |
| 12.1265 | 27600 | 0.124 | - |
| 12.1485 | 27650 | 0.213 | - |
| 12.1705 | 27700 | 0.1768 | - |
| 12.1924 | 27750 | 0.1428 | - |
| 12.2144 | 27800 | 0.1519 | - |
| 12.2364 | 27850 | 0.1511 | - |
| 12.2583 | 27900 | 0.2209 | - |
| 12.2803 | 27950 | 0.1868 | - |
| 12.3023 | 28000 | 0.1858 | - |
| 12.3243 | 28050 | 0.1442 | - |
| 12.3462 | 28100 | 0.2235 | - |
| 12.3682 | 28150 | 0.1056 | - |
| 12.3902 | 28200 | 0.1616 | - |
| 12.4121 | 28250 | 0.1212 | - |
| 12.4341 | 28300 | 0.1836 | - |
| 12.4561 | 28350 | 0.1769 | - |
| 12.4780 | 28400 | 0.1548 | - |
| 12.5 | 28450 | 0.2211 | - |
| 12.5220 | 28500 | 0.2217 | - |
| 12.5439 | 28550 | 0.1844 | - |
| 12.5659 | 28600 | 0.2077 | - |
| 12.5879 | 28650 | 0.1679 | - |
| 12.6098 | 28700 | 0.1742 | - |
| 12.6318 | 28750 | 0.0904 | - |
| 12.6538 | 28800 | 0.2504 | - |
| 12.6757 | 28850 | 0.1574 | - |
| 12.6977 | 28900 | 0.1595 | - |
| 12.7197 | 28950 | 0.131 | - |
| 12.7417 | 29000 | 0.1561 | - |
| 12.7636 | 29050 | 0.1414 | - |
| 12.7856 | 29100 | 0.1751 | - |
| 12.8076 | 29150 | 0.1278 | - |
| 12.8295 | 29200 | 0.1864 | - |
| 12.8515 | 29250 | 0.1218 | - |
| 12.8735 | 29300 | 0.1138 | - |
| 12.8954 | 29350 | 0.1289 | - |
| 12.9174 | 29400 | 0.1174 | - |
| 12.9394 | 29450 | 0.1644 | - |
| 12.9613 | 29500 | 0.1547 | - |
| 12.9833 | 29550 | 0.1598 | - |
| 13.0053 | 29600 | 0.176 | - |
| 13.0272 | 29650 | 0.1345 | - |
| 13.0492 | 29700 | 0.1735 | - |
| 13.0712 | 29750 | 0.1564 | - |
| 13.0931 | 29800 | 0.1604 | - |
| 13.1151 | 29850 | 0.1255 | - |
| 13.1371 | 29900 | 0.1544 | - |
| 13.1591 | 29950 | 0.1561 | - |
| 13.1810 | 30000 | 0.1795 | - |
| 13.2030 | 30050 | 0.1168 | - |
| 13.2250 | 30100 | 0.1529 | - |
| 13.2469 | 30150 | 0.1998 | - |
| 13.2689 | 30200 | 0.1754 | - |
| 13.2909 | 30250 | 0.1466 | - |
| 13.3128 | 30300 | 0.1181 | - |
| 13.3348 | 30350 | 0.1859 | - |
| 13.3568 | 30400 | 0.1635 | - |
| 13.3787 | 30450 | 0.173 | - |
| 13.4007 | 30500 | 0.1369 | - |
| 13.4227 | 30550 | 0.2138 | - |
| 13.4446 | 30600 | 0.1558 | - |
| 13.4666 | 30650 | 0.142 | - |
| 13.4886 | 30700 | 0.1434 | - |
| 13.5105 | 30750 | 0.2155 | - |
| 13.5325 | 30800 | 0.1999 | - |
| 13.5545 | 30850 | 0.2232 | - |
| 13.5764 | 30900 | 0.1618 | - |
| 13.5984 | 30950 | 0.1648 | - |
| 13.6204 | 31000 | 0.11 | - |
| 13.6424 | 31050 | 0.1841 | - |
| 13.6643 | 31100 | 0.176 | - |
| 13.6863 | 31150 | 0.1734 | - |
| 13.7083 | 31200 | 0.1833 | - |
| 13.7302 | 31250 | 0.1603 | - |
| 13.7522 | 31300 | 0.1637 | - |
| 13.7742 | 31350 | 0.1815 | - |
| 13.7961 | 31400 | 0.1696 | - |
| 13.8181 | 31450 | 0.1935 | - |
| 13.8401 | 31500 | 0.1453 | - |
| 13.8620 | 31550 | 0.1076 | - |
| 13.8840 | 31600 | 0.14 | - |
| 13.9060 | 31650 | 0.1728 | - |
| 13.9279 | 31700 | 0.1555 | - |
| 13.9499 | 31750 | 0.1572 | - |
| 13.9719 | 31800 | 0.1731 | - |
| 13.9938 | 31850 | 0.1666 | - |
| 14.0158 | 31900 | 0.1476 | - |
| 14.0378 | 31950 | 0.1778 | - |
| 14.0598 | 32000 | 0.1415 | - |
| 14.0817 | 32050 | 0.1724 | - |
| 14.1037 | 32100 | 0.1122 | - |
| 14.1257 | 32150 | 0.192 | - |
| 14.1476 | 32200 | 0.1459 | - |
| 14.1696 | 32250 | 0.1677 | - |
| 14.1916 | 32300 | 0.124 | - |
| 14.2135 | 32350 | 0.1859 | - |
| 14.2355 | 32400 | 0.1406 | - |
| 14.2575 | 32450 | 0.1887 | - |
| 14.2794 | 32500 | 0.166 | - |
| 14.3014 | 32550 | 0.1458 | - |
| 14.3234 | 32600 | 0.145 | - |
| 14.3453 | 32650 | 0.1191 | - |
| 14.3673 | 32700 | 0.1469 | - |
| 14.3893 | 32750 | 0.1563 | - |
| 14.4112 | 32800 | 0.1729 | - |
| 14.4332 | 32850 | 0.1237 | - |
| 14.4552 | 32900 | 0.1704 | - |
| 14.4772 | 32950 | 0.2051 | - |
| 14.4991 | 33000 | 0.1225 | - |
| 14.5211 | 33050 | 0.1346 | - |
| 14.5431 | 33100 | 0.105 | - |
| 14.5650 | 33150 | 0.1219 | - |
| 14.5870 | 33200 | 0.1265 | - |
| 14.6090 | 33250 | 0.1816 | - |
| 14.6309 | 33300 | 0.2335 | - |
| 14.6529 | 33350 | 0.1527 | - |
| 14.6749 | 33400 | 0.139 | - |
| 14.6968 | 33450 | 0.1354 | - |
| 14.7188 | 33500 | 0.1531 | - |
| 14.7408 | 33550 | 0.119 | - |
| 14.7627 | 33600 | 0.1813 | - |
| 14.7847 | 33650 | 0.1627 | - |
| 14.8067 | 33700 | 0.1595 | - |
| 14.8286 | 33750 | 0.138 | - |
| 14.8506 | 33800 | 0.1299 | - |
| 14.8726 | 33850 | 0.1766 | - |
| 14.8946 | 33900 | 0.1201 | - |
| 14.9165 | 33950 | 0.2105 | - |
| 14.9385 | 34000 | 0.1492 | - |
| 14.9605 | 34050 | 0.1522 | - |
| 14.9824 | 34100 | 0.1495 | - |
| 15.0044 | 34150 | 0.1157 | - |
| 15.0264 | 34200 | 0.1356 | - |
| 15.0483 | 34250 | 0.15 | - |
| 15.0703 | 34300 | 0.1425 | - |
| 15.0923 | 34350 | 0.2711 | - |
| 15.1142 | 34400 | 0.1889 | - |
| 15.1362 | 34450 | 0.1382 | - |
| 15.1582 | 34500 | 0.1845 | - |
| 15.1801 | 34550 | 0.1135 | - |
| 15.2021 | 34600 | 0.0959 | - |
| 15.2241 | 34650 | 0.1572 | - |
| 15.2460 | 34700 | 0.1411 | - |
| 15.2680 | 34750 | 0.2115 | - |
| 15.2900 | 34800 | 0.1103 | - |
| 15.3120 | 34850 | 0.1832 | - |
| 15.3339 | 34900 | 0.2347 | - |
| 15.3559 | 34950 | 0.1325 | - |
| 15.3779 | 35000 | 0.1648 | - |
| 15.3998 | 35050 | 0.1915 | - |
| 15.4218 | 35100 | 0.1792 | - |
| 15.4438 | 35150 | 0.212 | - |
| 15.4657 | 35200 | 0.1597 | - |
| 15.4877 | 35250 | 0.1713 | - |
| 15.5097 | 35300 | 0.1722 | - |
| 15.5316 | 35350 | 0.1273 | - |
| 15.5536 | 35400 | 0.1346 | - |
| 15.5756 | 35450 | 0.1815 | - |
| 15.5975 | 35500 | 0.2045 | - |
| 15.6195 | 35550 | 0.0979 | - |
| 15.6415 | 35600 | 0.1114 | - |
| 15.6634 | 35650 | 0.1617 | - |
| 15.6854 | 35700 | 0.1381 | - |
| 15.7074 | 35750 | 0.15 | - |
| 15.7293 | 35800 | 0.1763 | - |
| 15.7513 | 35850 | 0.1464 | - |
| 15.7733 | 35900 | 0.147 | - |
| 15.7953 | 35950 | 0.1987 | - |
| 15.8172 | 36000 | 0.1343 | - |
| 15.8392 | 36050 | 0.1888 | - |
| 15.8612 | 36100 | 0.1178 | - |
| 15.8831 | 36150 | 0.2081 | - |
| 15.9051 | 36200 | 0.1467 | - |
| 15.9271 | 36250 | 0.1654 | - |
| 15.9490 | 36300 | 0.2014 | - |
| 15.9710 | 36350 | 0.1884 | - |
| 15.9930 | 36400 | 0.1499 | - |
| 16.0149 | 36450 | 0.1722 | - |
| 16.0369 | 36500 | 0.1818 | - |
| 16.0589 | 36550 | 0.1577 | - |
| 16.0808 | 36600 | 0.1328 | - |
| 16.1028 | 36650 | 0.1521 | - |
| 16.1248 | 36700 | 0.1276 | - |
| 16.1467 | 36750 | 0.1383 | - |
| 16.1687 | 36800 | 0.2256 | - |
| 16.1907 | 36850 | 0.1891 | - |
| 16.2127 | 36900 | 0.1972 | - |
| 16.2346 | 36950 | 0.1527 | - |
| 16.2566 | 37000 | 0.138 | - |
| 16.2786 | 37050 | 0.1933 | - |
| 16.3005 | 37100 | 0.1438 | - |
| 16.3225 | 37150 | 0.1949 | - |
| 16.3445 | 37200 | 0.118 | - |
| 16.3664 | 37250 | 0.1563 | - |
| 16.3884 | 37300 | 0.1715 | - |
| 16.4104 | 37350 | 0.1218 | - |
| 16.4323 | 37400 | 0.2195 | - |
| 16.4543 | 37450 | 0.1417 | - |
| 16.4763 | 37500 | 0.1736 | - |
| 16.4982 | 37550 | 0.2122 | - |
| 16.5202 | 37600 | 0.1619 | - |
| 16.5422 | 37650 | 0.1767 | - |
| 16.5641 | 37700 | 0.1281 | - |
| 16.5861 | 37750 | 0.1429 | - |
| 16.6081 | 37800 | 0.199 | - |
| 16.6301 | 37850 | 0.1529 | - |
| 16.6520 | 37900 | 0.1681 | - |
| 16.6740 | 37950 | 0.2285 | - |
| 16.6960 | 38000 | 0.2124 | - |
| 16.7179 | 38050 | 0.2435 | - |
| 16.7399 | 38100 | 0.0819 | - |
| 16.7619 | 38150 | 0.1187 | - |
| 16.7838 | 38200 | 0.1874 | - |
| 16.8058 | 38250 | 0.1549 | - |
| 16.8278 | 38300 | 0.1689 | - |
| 16.8497 | 38350 | 0.1793 | - |
| 16.8717 | 38400 | 0.1614 | - |
| 16.8937 | 38450 | 0.1688 | - |
| 16.9156 | 38500 | 0.109 | - |
| 16.9376 | 38550 | 0.1882 | - |
| 16.9596 | 38600 | 0.2115 | - |
| 16.9815 | 38650 | 0.2063 | - |
| 17.0035 | 38700 | 0.2485 | - |
| 17.0255 | 38750 | 0.1509 | - |
| 17.0475 | 38800 | 0.1281 | - |
| 17.0694 | 38850 | 0.1468 | - |
| 17.0914 | 38900 | 0.0993 | - |
| 17.1134 | 38950 | 0.2248 | - |
| 17.1353 | 39000 | 0.1953 | - |
| 17.1573 | 39050 | 0.1796 | - |
| 17.1793 | 39100 | 0.1625 | - |
| 17.2012 | 39150 | 0.1931 | - |
| 17.2232 | 39200 | 0.1897 | - |
| 17.2452 | 39250 | 0.1789 | - |
| 17.2671 | 39300 | 0.1179 | - |
| 17.2891 | 39350 | 0.1226 | - |
| 17.3111 | 39400 | 0.176 | - |
| 17.3330 | 39450 | 0.1074 | - |
| 17.3550 | 39500 | 0.1541 | - |
| 17.3770 | 39550 | 0.1274 | - |
| 17.3989 | 39600 | 0.0881 | - |
| 17.4209 | 39650 | 0.1383 | - |
| 17.4429 | 39700 | 0.1444 | - |
| 17.4649 | 39750 | 0.1521 | - |
| 17.4868 | 39800 | 0.1863 | - |
| 17.5088 | 39850 | 0.1488 | - |
| 17.5308 | 39900 | 0.1901 | - |
| 17.5527 | 39950 | 0.1888 | - |
| 17.5747 | 40000 | 0.1254 | - |
| 17.5967 | 40050 | 0.166 | - |
| 17.6186 | 40100 | 0.1395 | - |
| 17.6406 | 40150 | 0.1279 | - |
| 17.6626 | 40200 | 0.1705 | - |
| 17.6845 | 40250 | 0.1745 | - |
| 17.7065 | 40300 | 0.1681 | - |
| 17.7285 | 40350 | 0.2025 | - |
| 17.7504 | 40400 | 0.1308 | - |
| 17.7724 | 40450 | 0.1561 | - |
| 17.7944 | 40500 | 0.1801 | - |
| 17.8163 | 40550 | 0.2005 | - |
| 17.8383 | 40600 | 0.2107 | - |
| 17.8603 | 40650 | 0.1323 | - |
| 17.8822 | 40700 | 0.144 | - |
| 17.9042 | 40750 | 0.1444 | - |
| 17.9262 | 40800 | 0.2136 | - |
| 17.9482 | 40850 | 0.1224 | - |
| 17.9701 | 40900 | 0.1341 | - |
| 17.9921 | 40950 | 0.2032 | - |
| 18.0141 | 41000 | 0.0991 | - |
| 18.0360 | 41050 | 0.1661 | - |
| 18.0580 | 41100 | 0.1517 | - |
| 18.0800 | 41150 | 0.1263 | - |
| 18.1019 | 41200 | 0.186 | - |
| 18.1239 | 41250 | 0.1729 | - |
| 18.1459 | 41300 | 0.1339 | - |
| 18.1678 | 41350 | 0.1721 | - |
| 18.1898 | 41400 | 0.1022 | - |
| 18.2118 | 41450 | 0.2177 | - |
| 18.2337 | 41500 | 0.134 | - |
| 18.2557 | 41550 | 0.2153 | - |
| 18.2777 | 41600 | 0.1875 | - |
| 18.2996 | 41650 | 0.1749 | - |
| 18.3216 | 41700 | 0.1454 | - |
| 18.3436 | 41750 | 0.1878 | - |
| 18.3656 | 41800 | 0.1204 | - |
| 18.3875 | 41850 | 0.1591 | - |
| 18.4095 | 41900 | 0.1507 | - |
| 18.4315 | 41950 | 0.2129 | - |
| 18.4534 | 42000 | 0.2102 | - |
| 18.4754 | 42050 | 0.1896 | - |
| 18.4974 | 42100 | 0.1936 | - |
| 18.5193 | 42150 | 0.1217 | - |
| 18.5413 | 42200 | 0.2165 | - |
| 18.5633 | 42250 | 0.1823 | - |
| 18.5852 | 42300 | 0.1784 | - |
| 18.6072 | 42350 | 0.1728 | - |
| 18.6292 | 42400 | 0.1633 | - |
| 18.6511 | 42450 | 0.1223 | - |
| 18.6731 | 42500 | 0.135 | - |
| 18.6951 | 42550 | 0.1795 | - |
| 18.7170 | 42600 | 0.154 | - |
| 18.7390 | 42650 | 0.2117 | - |
| 18.7610 | 42700 | 0.1705 | - |
| 18.7830 | 42750 | 0.1645 | - |
| 18.8049 | 42800 | 0.1303 | - |
| 18.8269 | 42850 | 0.1449 | - |
| 18.8489 | 42900 | 0.1358 | - |
| 18.8708 | 42950 | 0.1597 | - |
| 18.8928 | 43000 | 0.1348 | - |
| 18.9148 | 43050 | 0.1862 | - |
| 18.9367 | 43100 | 0.1967 | - |
| 18.9587 | 43150 | 0.1923 | - |
| 18.9807 | 43200 | 0.0914 | - |
| 19.0026 | 43250 | 0.1137 | - |
| 19.0246 | 43300 | 0.2139 | - |
| 19.0466 | 43350 | 0.182 | - |
| 19.0685 | 43400 | 0.1484 | - |
| 19.0905 | 43450 | 0.1683 | - |
| 19.1125 | 43500 | 0.1492 | - |
| 19.1344 | 43550 | 0.197 | - |
| 19.1564 | 43600 | 0.126 | - |
| 19.1784 | 43650 | 0.1946 | - |
| 19.2004 | 43700 | 0.1854 | - |
| 19.2223 | 43750 | 0.1106 | - |
| 19.2443 | 43800 | 0.1441 | - |
| 19.2663 | 43850 | 0.1784 | - |
| 19.2882 | 43900 | 0.1565 | - |
| 19.3102 | 43950 | 0.1708 | - |
| 19.3322 | 44000 | 0.1531 | - |
| 19.3541 | 44050 | 0.1942 | - |
| 19.3761 | 44100 | 0.139 | - |
| 19.3981 | 44150 | 0.1279 | - |
| 19.4200 | 44200 | 0.1472 | - |
| 19.4420 | 44250 | 0.1591 | - |
| 19.4640 | 44300 | 0.1478 | - |
| 19.4859 | 44350 | 0.1434 | - |
| 19.5079 | 44400 | 0.1849 | - |
| 19.5299 | 44450 | 0.2322 | - |
| 19.5518 | 44500 | 0.1248 | - |
| 19.5738 | 44550 | 0.1286 | - |
| 19.5958 | 44600 | 0.199 | - |
| 19.6178 | 44650 | 0.1333 | - |
| 19.6397 | 44700 | 0.1092 | - |
| 19.6617 | 44750 | 0.1795 | - |
| 19.6837 | 44800 | 0.1196 | - |
| 19.7056 | 44850 | 0.1654 | - |
| 19.7276 | 44900 | 0.1171 | - |
| 19.7496 | 44950 | 0.1741 | - |
| 19.7715 | 45000 | 0.1473 | - |
| 19.7935 | 45050 | 0.2207 | - |
| 19.8155 | 45100 | 0.1511 | - |
| 19.8374 | 45150 | 0.1036 | - |
| 19.8594 | 45200 | 0.1403 | - |
| 19.8814 | 45250 | 0.1895 | - |
| 19.9033 | 45300 | 0.1406 | - |
| 19.9253 | 45350 | 0.1718 | - |
| 19.9473 | 45400 | 0.2228 | - |
| 19.9692 | 45450 | 0.1936 | - |
| 19.9912 | 45500 | 0.1465 | - |
| 20.0132 | 45550 | 0.1245 | - |
| 20.0351 | 45600 | 0.216 | - |
| 20.0571 | 45650 | 0.1279 | - |
| 20.0791 | 45700 | 0.1247 | - |
| 20.1011 | 45750 | 0.1196 | - |
| 20.1230 | 45800 | 0.1339 | - |
| 20.1450 | 45850 | 0.1555 | - |
| 20.1670 | 45900 | 0.2013 | - |
| 20.1889 | 45950 | 0.1596 | - |
| 20.2109 | 46000 | 0.1136 | - |
| 20.2329 | 46050 | 0.1713 | - |
| 20.2548 | 46100 | 0.1607 | - |
| 20.2768 | 46150 | 0.2028 | - |
| 20.2988 | 46200 | 0.1864 | - |
| 20.3207 | 46250 | 0.1088 | - |
| 20.3427 | 46300 | 0.1806 | - |
| 20.3647 | 46350 | 0.1692 | - |
| 20.3866 | 46400 | 0.1298 | - |
| 20.4086 | 46450 | 0.1045 | - |
| 20.4306 | 46500 | 0.1959 | - |
| 20.4525 | 46550 | 0.1352 | - |
| 20.4745 | 46600 | 0.1718 | - |
| 20.4965 | 46650 | 0.187 | - |
| 20.5185 | 46700 | 0.1354 | - |
| 20.5404 | 46750 | 0.2091 | - |
| 20.5624 | 46800 | 0.1049 | - |
| 20.5844 | 46850 | 0.1425 | - |
| 20.6063 | 46900 | 0.1679 | - |
| 20.6283 | 46950 | 0.2225 | - |
| 20.6503 | 47000 | 0.1229 | - |
| 20.6722 | 47050 | 0.1734 | - |
| 20.6942 | 47100 | 0.1623 | - |
| 20.7162 | 47150 | 0.1333 | - |
| 20.7381 | 47200 | 0.1437 | - |
| 20.7601 | 47250 | 0.1652 | - |
| 20.7821 | 47300 | 0.1504 | - |
| 20.8040 | 47350 | 0.1552 | - |
| 20.8260 | 47400 | 0.1066 | - |
| 20.8480 | 47450 | 0.1606 | - |
| 20.8699 | 47500 | 0.1382 | - |
| 20.8919 | 47550 | 0.1621 | - |
| 20.9139 | 47600 | 0.1669 | - |
| 20.9359 | 47650 | 0.1931 | - |
| 20.9578 | 47700 | 0.1388 | - |
| 20.9798 | 47750 | 0.1856 | - |
| 21.0018 | 47800 | 0.0842 | - |
| 21.0237 | 47850 | 0.1514 | - |
| 21.0457 | 47900 | 0.1393 | - |
| 21.0677 | 47950 | 0.2104 | - |
| 21.0896 | 48000 | 0.1707 | - |
| 21.1116 | 48050 | 0.1215 | - |
| 21.1336 | 48100 | 0.1696 | - |
| 21.1555 | 48150 | 0.1605 | - |
| 21.1775 | 48200 | 0.1768 | - |
| 21.1995 | 48250 | 0.1474 | - |
| 21.2214 | 48300 | 0.1129 | - |
| 21.2434 | 48350 | 0.2048 | - |
| 21.2654 | 48400 | 0.212 | - |
| 21.2873 | 48450 | 0.1254 | - |
| 21.3093 | 48500 | 0.1339 | - |
| 21.3313 | 48550 | 0.1679 | - |
| 21.3533 | 48600 | 0.1906 | - |
| 21.3752 | 48650 | 0.1436 | - |
| 21.3972 | 48700 | 0.1748 | - |
| 21.4192 | 48750 | 0.1566 | - |
| 21.4411 | 48800 | 0.2253 | - |
| 21.4631 | 48850 | 0.1856 | - |
| 21.4851 | 48900 | 0.1588 | - |
| 21.5070 | 48950 | 0.191 | - |
| 21.5290 | 49000 | 0.1774 | - |
| 21.5510 | 49050 | 0.138 | - |
| 21.5729 | 49100 | 0.1484 | - |
| 21.5949 | 49150 | 0.1572 | - |
| 21.6169 | 49200 | 0.1444 | - |
| 21.6388 | 49250 | 0.1614 | - |
| 21.6608 | 49300 | 0.1343 | - |
| 21.6828 | 49350 | 0.1753 | - |
| 21.7047 | 49400 | 0.1479 | - |
| 21.7267 | 49450 | 0.1736 | - |
| 21.7487 | 49500 | 0.1377 | - |
| 21.7707 | 49550 | 0.1986 | - |
| 21.7926 | 49600 | 0.147 | - |
| 21.8146 | 49650 | 0.199 | - |
| 21.8366 | 49700 | 0.1595 | - |
| 21.8585 | 49750 | 0.1667 | - |
| 21.8805 | 49800 | 0.1285 | - |
| 21.9025 | 49850 | 0.147 | - |
| 21.9244 | 49900 | 0.1866 | - |
| 21.9464 | 49950 | 0.2118 | - |
| 21.9684 | 50000 | 0.1766 | - |
| 21.9903 | 50050 | 0.1899 | - |
| 22.0123 | 50100 | 0.1441 | - |
| 22.0343 | 50150 | 0.1387 | - |
| 22.0562 | 50200 | 0.1435 | - |
| 22.0782 | 50250 | 0.1637 | - |
| 22.1002 | 50300 | 0.1044 | - |
| 22.1221 | 50350 | 0.1923 | - |
| 22.1441 | 50400 | 0.2145 | - |
| 22.1661 | 50450 | 0.1619 | - |
| 22.1880 | 50500 | 0.1659 | - |
| 22.2100 | 50550 | 0.1863 | - |
| 22.2320 | 50600 | 0.1523 | - |
| 22.2540 | 50650 | 0.2043 | - |
| 22.2759 | 50700 | 0.1201 | - |
| 22.2979 | 50750 | 0.1342 | - |
| 22.3199 | 50800 | 0.1455 | - |
| 22.3418 | 50850 | 0.1574 | - |
| 22.3638 | 50900 | 0.1565 | - |
| 22.3858 | 50950 | 0.1992 | - |
| 22.4077 | 51000 | 0.191 | - |
| 22.4297 | 51050 | 0.1405 | - |
| 22.4517 | 51100 | 0.1917 | - |
| 22.4736 | 51150 | 0.1689 | - |
| 22.4956 | 51200 | 0.121 | - |
| 22.5176 | 51250 | 0.1492 | - |
| 22.5395 | 51300 | 0.1168 | - |
| 22.5615 | 51350 | 0.1397 | - |
| 22.5835 | 51400 | 0.1552 | - |
| 22.6054 | 51450 | 0.1781 | - |
| 22.6274 | 51500 | 0.0645 | - |
| 22.6494 | 51550 | 0.1759 | - |
| 22.6714 | 51600 | 0.1725 | - |
| 22.6933 | 51650 | 0.1797 | - |
| 22.7153 | 51700 | 0.1661 | - |
| 22.7373 | 51750 | 0.1703 | - |
| 22.7592 | 51800 | 0.1665 | - |
| 22.7812 | 51850 | 0.1671 | - |
| 22.8032 | 51900 | 0.1187 | - |
| 22.8251 | 51950 | 0.1785 | - |
| 22.8471 | 52000 | 0.1921 | - |
| 22.8691 | 52050 | 0.1246 | - |
| 22.8910 | 52100 | 0.1559 | - |
| 22.9130 | 52150 | 0.1531 | - |
| 22.9350 | 52200 | 0.1718 | - |
| 22.9569 | 52250 | 0.1875 | - |
| 22.9789 | 52300 | 0.1726 | - |
| 23.0009 | 52350 | 0.1515 | - |
| 23.0228 | 52400 | 0.1145 | - |
| 23.0448 | 52450 | 0.1038 | - |
| 23.0668 | 52500 | 0.1503 | - |
| 23.0888 | 52550 | 0.1879 | - |
| 23.1107 | 52600 | 0.1429 | - |
| 23.1327 | 52650 | 0.1308 | - |
| 23.1547 | 52700 | 0.181 | - |
| 23.1766 | 52750 | 0.1926 | - |
| 23.1986 | 52800 | 0.1863 | - |
| 23.2206 | 52850 | 0.1387 | - |
| 23.2425 | 52900 | 0.1351 | - |
| 23.2645 | 52950 | 0.1562 | - |
| 23.2865 | 53000 | 0.1448 | - |
| 23.3084 | 53050 | 0.1666 | - |
| 23.3304 | 53100 | 0.228 | - |
| 23.3524 | 53150 | 0.1828 | - |
| 23.3743 | 53200 | 0.1297 | - |
| 23.3963 | 53250 | 0.1238 | - |
| 23.4183 | 53300 | 0.1783 | - |
| 23.4402 | 53350 | 0.159 | - |
| 23.4622 | 53400 | 0.167 | - |
| 23.4842 | 53450 | 0.2033 | - |
| 23.5062 | 53500 | 0.1516 | - |
| 23.5281 | 53550 | 0.1949 | - |
| 23.5501 | 53600 | 0.1017 | - |
| 23.5721 | 53650 | 0.1641 | - |
| 23.5940 | 53700 | 0.2112 | - |
| 23.6160 | 53750 | 0.1192 | - |
| 23.6380 | 53800 | 0.2027 | - |
| 23.6599 | 53850 | 0.1518 | - |
| 23.6819 | 53900 | 0.079 | - |
| 23.7039 | 53950 | 0.1546 | - |
| 23.7258 | 54000 | 0.1561 | - |
| 23.7478 | 54050 | 0.0798 | - |
| 23.7698 | 54100 | 0.2473 | - |
| 23.7917 | 54150 | 0.1105 | - |
| 23.8137 | 54200 | 0.1888 | - |
| 23.8357 | 54250 | 0.1785 | - |
| 23.8576 | 54300 | 0.1117 | - |
| 23.8796 | 54350 | 0.1518 | - |
| 23.9016 | 54400 | 0.1751 | - |
| 23.9236 | 54450 | 0.1338 | - |
| 23.9455 | 54500 | 0.1234 | - |
| 23.9675 | 54550 | 0.1032 | - |
| 23.9895 | 54600 | 0.1315 | - |
| 24.0114 | 54650 | 0.2015 | - |
| 24.0334 | 54700 | 0.1254 | - |
| 24.0554 | 54750 | 0.1689 | - |
| 24.0773 | 54800 | 0.1792 | - |
| 24.0993 | 54850 | 0.1774 | - |
| 24.1213 | 54900 | 0.1055 | - |
| 24.1432 | 54950 | 0.227 | - |
| 24.1652 | 55000 | 0.1942 | - |
| 24.1872 | 55050 | 0.1591 | - |
| 24.2091 | 55100 | 0.1757 | - |
| 24.2311 | 55150 | 0.1751 | - |
| 24.2531 | 55200 | 0.1866 | - |
| 24.2750 | 55250 | 0.1859 | - |
| 24.2970 | 55300 | 0.1768 | - |
| 24.3190 | 55350 | 0.1566 | - |
| 24.3409 | 55400 | 0.17 | - |
| 24.3629 | 55450 | 0.1584 | - |
| 24.3849 | 55500 | 0.1636 | - |
| 24.4069 | 55550 | 0.196 | - |
| 24.4288 | 55600 | 0.1662 | - |
| 24.4508 | 55650 | 0.1843 | - |
| 24.4728 | 55700 | 0.1291 | - |
| 24.4947 | 55750 | 0.1235 | - |
| 24.5167 | 55800 | 0.1576 | - |
| 24.5387 | 55850 | 0.1759 | - |
| 24.5606 | 55900 | 0.1993 | - |
| 24.5826 | 55950 | 0.1542 | - |
| 24.6046 | 56000 | 0.158 | - |
| 24.6265 | 56050 | 0.142 | - |
| 24.6485 | 56100 | 0.1588 | - |
| 24.6705 | 56150 | 0.0875 | - |
| 24.6924 | 56200 | 0.0929 | - |
| 24.7144 | 56250 | 0.1513 | - |
| 24.7364 | 56300 | 0.1253 | - |
| 24.7583 | 56350 | 0.1492 | - |
| 24.7803 | 56400 | 0.1518 | - |
| 24.8023 | 56450 | 0.209 | - |
| 24.8243 | 56500 | 0.2252 | - |
| 24.8462 | 56550 | 0.1273 | - |
| 24.8682 | 56600 | 0.19 | - |
| 24.8902 | 56650 | 0.209 | - |
| 24.9121 | 56700 | 0.0888 | - |
| 24.9341 | 56750 | 0.1564 | - |
| 24.9561 | 56800 | 0.1861 | - |
| 24.9780 | 56850 | 0.1492 | - |
| 25.0 | 56900 | 0.1805 | - |
| 25.0220 | 56950 | 0.1207 | - |
| 25.0439 | 57000 | 0.2034 | - |
| 25.0659 | 57050 | 0.1189 | - |
| 25.0879 | 57100 | 0.1693 | - |
| 25.1098 | 57150 | 0.1515 | - |
| 25.1318 | 57200 | 0.134 | - |
| 25.1538 | 57250 | 0.155 | - |
| 25.1757 | 57300 | 0.148 | - |
| 25.1977 | 57350 | 0.1494 | - |
| 25.2197 | 57400 | 0.1256 | - |
| 25.2417 | 57450 | 0.1522 | - |
| 25.2636 | 57500 | 0.1672 | - |
| 25.2856 | 57550 | 0.147 | - |
| 25.3076 | 57600 | 0.1154 | - |
| 25.3295 | 57650 | 0.1817 | - |
| 25.3515 | 57700 | 0.2105 | - |
| 25.3735 | 57750 | 0.1934 | - |
| 25.3954 | 57800 | 0.1192 | - |
| 25.4174 | 57850 | 0.1659 | - |
| 25.4394 | 57900 | 0.1502 | - |
| 25.4613 | 57950 | 0.2332 | - |
| 25.4833 | 58000 | 0.201 | - |
| 25.5053 | 58050 | 0.1448 | - |
| 25.5272 | 58100 | 0.1837 | - |
| 25.5492 | 58150 | 0.2033 | - |
| 25.5712 | 58200 | 0.208 | - |
| 25.5931 | 58250 | 0.1449 | - |
| 25.6151 | 58300 | 0.144 | - |
| 25.6371 | 58350 | 0.1905 | - |
| 25.6591 | 58400 | 0.1772 | - |
| 25.6810 | 58450 | 0.1432 | - |
| 25.7030 | 58500 | 0.1815 | - |
| 25.7250 | 58550 | 0.1739 | - |
| 25.7469 | 58600 | 0.1643 | - |
| 25.7689 | 58650 | 0.153 | - |
| 25.7909 | 58700 | 0.1321 | - |
| 25.8128 | 58750 | 0.2032 | - |
| 25.8348 | 58800 | 0.1284 | - |
| 25.8568 | 58850 | 0.2108 | - |
| 25.8787 | 58900 | 0.1568 | - |
| 25.9007 | 58950 | 0.087 | - |
| 25.9227 | 59000 | 0.2196 | - |
| 25.9446 | 59050 | 0.1606 | - |
| 25.9666 | 59100 | 0.176 | - |
| 25.9886 | 59150 | 0.1546 | - |
| 26.0105 | 59200 | 0.1973 | - |
| 26.0325 | 59250 | 0.154 | - |
| 26.0545 | 59300 | 0.1426 | - |
| 26.0764 | 59350 | 0.2495 | - |
| 26.0984 | 59400 | 0.145 | - |
| 26.1204 | 59450 | 0.1877 | - |
| 26.1424 | 59500 | 0.2237 | - |
| 26.1643 | 59550 | 0.1674 | - |
| 26.1863 | 59600 | 0.1146 | - |
| 26.2083 | 59650 | 0.1085 | - |
| 26.2302 | 59700 | 0.1711 | - |
| 26.2522 | 59750 | 0.123 | - |
| 26.2742 | 59800 | 0.1549 | - |
| 26.2961 | 59850 | 0.1431 | - |
| 26.3181 | 59900 | 0.1234 | - |
| 26.3401 | 59950 | 0.2221 | - |
| 26.3620 | 60000 | 0.1542 | - |
| 26.3840 | 60050 | 0.1224 | - |
| 26.4060 | 60100 | 0.115 | - |
| 26.4279 | 60150 | 0.1468 | - |
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| 28.6467 | 65200 | 0.1329 | - |
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| 28.7566 | 65450 | 0.1412 | - |
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| 29.2619 | 66600 | 0.1187 | - |
| 29.2838 | 66650 | 0.1755 | - |
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| 29.3497 | 66800 | 0.1232 | - |
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| 29.3937 | 66900 | 0.1347 | - |
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| 29.4376 | 67000 | 0.126 | - |
| 29.4596 | 67050 | 0.1316 | - |
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| 29.5035 | 67150 | 0.1369 | - |
| 29.5255 | 67200 | 0.1373 | - |
| 29.5475 | 67250 | 0.1996 | - |
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| 29.5914 | 67350 | 0.1462 | - |
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| 30.5580 | 69550 | 0.1038 | - |
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| 30.6019 | 69650 | 0.1512 | - |
| 30.6239 | 69700 | 0.1224 | - |
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| 30.6678 | 69800 | 0.1788 | - |
| 30.6898 | 69850 | 0.1946 | - |
| 30.7118 | 69900 | 0.1462 | - |
| 30.7337 | 69950 | 0.1889 | - |
| 30.7557 | 70000 | 0.1558 | - |
| 30.7777 | 70050 | 0.1228 | - |
| 30.7996 | 70100 | 0.1487 | - |
| 30.8216 | 70150 | 0.1467 | - |
| 30.8436 | 70200 | 0.2345 | - |
| 30.8656 | 70250 | 0.1476 | - |
| 30.8875 | 70300 | 0.1587 | - |
| 30.9095 | 70350 | 0.1469 | - |
| 30.9315 | 70400 | 0.1553 | - |
| 30.9534 | 70450 | 0.1882 | - |
| 30.9754 | 70500 | 0.1687 | - |
| 30.9974 | 70550 | 0.1532 | - |
| 31.0193 | 70600 | 0.1424 | - |
| 31.0413 | 70650 | 0.1756 | - |
| 31.0633 | 70700 | 0.135 | - |
| 31.0852 | 70750 | 0.1633 | - |
| 31.1072 | 70800 | 0.1671 | - |
| 31.1292 | 70850 | 0.1046 | - |
| 31.1511 | 70900 | 0.1272 | - |
| 31.1731 | 70950 | 0.1592 | - |
| 31.1951 | 71000 | 0.1812 | - |
| 31.2170 | 71050 | 0.2294 | - |
| 31.2390 | 71100 | 0.1775 | - |
| 31.2610 | 71150 | 0.1839 | - |
| 31.2830 | 71200 | 0.1767 | - |
| 31.3049 | 71250 | 0.1337 | - |
| 31.3269 | 71300 | 0.1696 | - |
| 31.3489 | 71350 | 0.1862 | - |
| 31.3708 | 71400 | 0.2168 | - |
| 31.3928 | 71450 | 0.1536 | - |
| 31.4148 | 71500 | 0.2345 | - |
| 31.4367 | 71550 | 0.1275 | - |
| 31.4587 | 71600 | 0.1298 | - |
| 31.4807 | 71650 | 0.166 | - |
| 31.5026 | 71700 | 0.1815 | - |
| 31.5246 | 71750 | 0.1377 | - |
| 31.5466 | 71800 | 0.1636 | - |
| 31.5685 | 71850 | 0.2319 | - |
| 31.5905 | 71900 | 0.1291 | - |
| 31.6125 | 71950 | 0.1778 | - |
| 31.6344 | 72000 | 0.1515 | - |
| 31.6564 | 72050 | 0.1079 | - |
| 31.6784 | 72100 | 0.2265 | - |
| 31.7004 | 72150 | 0.1438 | - |
| 31.7223 | 72200 | 0.1672 | - |
| 31.7443 | 72250 | 0.2042 | - |
| 31.7663 | 72300 | 0.264 | - |
| 31.7882 | 72350 | 0.1623 | - |
| 31.8102 | 72400 | 0.0869 | - |
| 31.8322 | 72450 | 0.1088 | - |
| 31.8541 | 72500 | 0.1763 | - |
| 31.8761 | 72550 | 0.0924 | - |
| 31.8981 | 72600 | 0.1342 | - |
| 31.9200 | 72650 | 0.1492 | - |
| 31.9420 | 72700 | 0.1417 | - |
| 31.9640 | 72750 | 0.1875 | - |
| 31.9859 | 72800 | 0.1995 | - |
| 32.0079 | 72850 | 0.1585 | - |
| 32.0299 | 72900 | 0.1569 | - |
| 32.0518 | 72950 | 0.2087 | - |
| 32.0738 | 73000 | 0.1613 | - |
| 32.0958 | 73050 | 0.1584 | - |
| 32.1178 | 73100 | 0.1718 | - |
| 32.1397 | 73150 | 0.1303 | - |
| 32.1617 | 73200 | 0.1859 | - |
| 32.1837 | 73250 | 0.1804 | - |
| 32.2056 | 73300 | 0.1226 | - |
| 32.2276 | 73350 | 0.1804 | - |
| 32.2496 | 73400 | 0.1284 | - |
| 32.2715 | 73450 | 0.1472 | - |
| 32.2935 | 73500 | 0.137 | - |
| 32.3155 | 73550 | 0.1251 | - |
| 32.3374 | 73600 | 0.1534 | - |
| 32.3594 | 73650 | 0.1561 | - |
| 32.3814 | 73700 | 0.1533 | - |
| 32.4033 | 73750 | 0.1483 | - |
| 32.4253 | 73800 | 0.1904 | - |
| 32.4473 | 73850 | 0.1497 | - |
| 32.4692 | 73900 | 0.1816 | - |
| 32.4912 | 73950 | 0.1692 | - |
| 32.5132 | 74000 | 0.1376 | - |
| 32.5351 | 74050 | 0.1919 | - |
| 32.5571 | 74100 | 0.1651 | - |
| 32.5791 | 74150 | 0.1809 | - |
| 32.6011 | 74200 | 0.1976 | - |
| 32.6230 | 74250 | 0.1925 | - |
| 32.6450 | 74300 | 0.1338 | - |
| 32.6670 | 74350 | 0.1648 | - |
| 32.6889 | 74400 | 0.0926 | - |
| 32.7109 | 74450 | 0.2094 | - |
| 32.7329 | 74500 | 0.1141 | - |
| 32.7548 | 74550 | 0.1512 | - |
| 32.7768 | 74600 | 0.1575 | - |
| 32.7988 | 74650 | 0.191 | - |
| 32.8207 | 74700 | 0.2172 | - |
| 32.8427 | 74750 | 0.1237 | - |
| 32.8647 | 74800 | 0.1488 | - |
| 32.8866 | 74850 | 0.1745 | - |
| 32.9086 | 74900 | 0.1328 | - |
| 32.9306 | 74950 | 0.169 | - |
| 32.9525 | 75000 | 0.1828 | - |
| 32.9745 | 75050 | 0.1378 | - |
| 32.9965 | 75100 | 0.192 | - |
| 33.0185 | 75150 | 0.1569 | - |
| 33.0404 | 75200 | 0.1858 | - |
| 33.0624 | 75250 | 0.1597 | - |
| 33.0844 | 75300 | 0.1727 | - |
| 33.1063 | 75350 | 0.1645 | - |
| 33.1283 | 75400 | 0.1912 | - |
| 33.1503 | 75450 | 0.1716 | - |
| 33.1722 | 75500 | 0.1348 | - |
| 33.1942 | 75550 | 0.1609 | - |
| 33.2162 | 75600 | 0.1849 | - |
| 33.2381 | 75650 | 0.1701 | - |
| 33.2601 | 75700 | 0.1725 | - |
| 33.2821 | 75750 | 0.1853 | - |
| 33.3040 | 75800 | 0.1493 | - |
| 33.3260 | 75850 | 0.1839 | - |
| 33.3480 | 75900 | 0.2128 | - |
| 33.3699 | 75950 | 0.1463 | - |
| 33.3919 | 76000 | 0.1425 | - |
| 33.4139 | 76050 | 0.1241 | - |
| 33.4359 | 76100 | 0.1827 | - |
| 33.4578 | 76150 | 0.1991 | - |
| 33.4798 | 76200 | 0.1333 | - |
| 33.5018 | 76250 | 0.1825 | - |
| 33.5237 | 76300 | 0.1478 | - |
| 33.5457 | 76350 | 0.2085 | - |
| 33.5677 | 76400 | 0.204 | - |
| 33.5896 | 76450 | 0.1533 | - |
| 33.6116 | 76500 | 0.2243 | - |
| 33.6336 | 76550 | 0.1615 | - |
| 33.6555 | 76600 | 0.1458 | - |
| 33.6775 | 76650 | 0.1622 | - |
| 33.6995 | 76700 | 0.1945 | - |
| 33.7214 | 76750 | 0.2144 | - |
| 33.7434 | 76800 | 0.1589 | - |
| 33.7654 | 76850 | 0.162 | - |
| 33.7873 | 76900 | 0.0702 | - |
| 33.8093 | 76950 | 0.1437 | - |
| 33.8313 | 77000 | 0.1696 | - |
| 33.8533 | 77050 | 0.1536 | - |
| 33.8752 | 77100 | 0.1874 | - |
| 33.8972 | 77150 | 0.1925 | - |
| 33.9192 | 77200 | 0.1547 | - |
| 33.9411 | 77250 | 0.1947 | - |
| 33.9631 | 77300 | 0.139 | - |
| 33.9851 | 77350 | 0.1281 | - |
| 34.0070 | 77400 | 0.0737 | - |
| 34.0290 | 77450 | 0.2184 | - |
| 34.0510 | 77500 | 0.143 | - |
| 34.0729 | 77550 | 0.1823 | - |
| 34.0949 | 77600 | 0.0848 | - |
| 34.1169 | 77650 | 0.1622 | - |
| 34.1388 | 77700 | 0.1522 | - |
| 34.1608 | 77750 | 0.1908 | - |
| 34.1828 | 77800 | 0.1488 | - |
| 34.2047 | 77850 | 0.1754 | - |
| 34.2267 | 77900 | 0.2293 | - |
| 34.2487 | 77950 | 0.2172 | - |
| 34.2707 | 78000 | 0.1861 | - |
| 34.2926 | 78050 | 0.153 | - |
| 34.3146 | 78100 | 0.1733 | - |
| 34.3366 | 78150 | 0.1369 | - |
| 34.3585 | 78200 | 0.1351 | - |
| 34.3805 | 78250 | 0.1087 | - |
| 34.4025 | 78300 | 0.1883 | - |
| 34.4244 | 78350 | 0.1742 | - |
| 34.4464 | 78400 | 0.1644 | - |
| 34.4684 | 78450 | 0.1511 | - |
| 34.4903 | 78500 | 0.123 | - |
| 34.5123 | 78550 | 0.2095 | - |
| 34.5343 | 78600 | 0.1602 | - |
| 34.5562 | 78650 | 0.1454 | - |
| 34.5782 | 78700 | 0.0915 | - |
| 34.6002 | 78750 | 0.16 | - |
| 34.6221 | 78800 | 0.1396 | - |
| 34.6441 | 78850 | 0.1447 | - |
| 34.6661 | 78900 | 0.2031 | - |
| 34.6880 | 78950 | 0.1216 | - |
| 34.7100 | 79000 | 0.2278 | - |
| 34.7320 | 79050 | 0.1614 | - |
| 34.7540 | 79100 | 0.1268 | - |
| 34.7759 | 79150 | 0.1581 | - |
| 34.7979 | 79200 | 0.2088 | - |
| 34.8199 | 79250 | 0.1393 | - |
| 34.8418 | 79300 | 0.1908 | - |
| 34.8638 | 79350 | 0.1915 | - |
| 34.8858 | 79400 | 0.1618 | - |
| 34.9077 | 79450 | 0.1253 | - |
| 34.9297 | 79500 | 0.1952 | - |
| 34.9517 | 79550 | 0.2056 | - |
| 34.9736 | 79600 | 0.1363 | - |
| 34.9956 | 79650 | 0.2042 | - |
### Framework Versions
- Python: 3.10.12
- SetFit: 1.0.1
- Sentence Transformers: 2.2.2
- Transformers: 4.35.2
- PyTorch: 2.1.0+cu118
- Datasets: 2.15.0
- Tokenizers: 0.15.0
## 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}
}
```
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|
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] |
csukuangfj/vits-mms-nan
|
csukuangfj
| 2023-12-14T06:11:25Z | 0 | 0 |
transformers
|
[
"transformers",
"onnx",
"endpoints_compatible",
"region:us",
"has_space"
] | null | 2023-12-14T06:05:50Z |
# Introduction
This model is converted from
https://huggingface.co/facebook/mms-tts/resolve/main/models/nan/G_100000.pth
Please see
https://huggingface.co/facebook/mms-tts/tree/main/models/nan
for more details.
|
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Gummybear05/wav2vec2-large-xls-r-300m-korean-colab
|
Gummybear05
| 2023-12-14T06:08:55Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-14T06:08:54Z |
Entry not found
|
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liu2344/Baichuan2-13B-Chat
|
liu2344
| 2023-12-14T06:12:01Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2023-12-14T06:12:01Z |
---
license: apache-2.0
---
|
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] |
Realgon/N_bert_imdb_padding100model
|
Realgon
| 2023-12-14T08:38:59Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:imdb",
"base_model:bert-base-uncased",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-12-14T06:12:59Z |
---
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
datasets:
- imdb
metrics:
- accuracy
model-index:
- name: N_bert_imdb_padding100model
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: imdb
type: imdb
config: plain_text
split: test
args: plain_text
metrics:
- name: Accuracy
type: accuracy
value: 0.93724
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# N_bert_imdb_padding100model
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7094
- Accuracy: 0.9372
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.2271 | 1.0 | 1563 | 0.2627 | 0.9098 |
| 0.1657 | 2.0 | 3126 | 0.2289 | 0.9296 |
| 0.0928 | 3.0 | 4689 | 0.3241 | 0.9247 |
| 0.0656 | 4.0 | 6252 | 0.3616 | 0.9314 |
| 0.045 | 5.0 | 7815 | 0.3673 | 0.9310 |
| 0.034 | 6.0 | 9378 | 0.4597 | 0.9307 |
| 0.0202 | 7.0 | 10941 | 0.4430 | 0.9364 |
| 0.0188 | 8.0 | 12504 | 0.5484 | 0.9296 |
| 0.0144 | 9.0 | 14067 | 0.5043 | 0.9344 |
| 0.0115 | 10.0 | 15630 | 0.5540 | 0.9336 |
| 0.0094 | 11.0 | 17193 | 0.4962 | 0.9338 |
| 0.0066 | 12.0 | 18756 | 0.5736 | 0.9364 |
| 0.0068 | 13.0 | 20319 | 0.6779 | 0.9335 |
| 0.0038 | 14.0 | 21882 | 0.6431 | 0.9355 |
| 0.0021 | 15.0 | 23445 | 0.6151 | 0.9359 |
| 0.0009 | 16.0 | 25008 | 0.7113 | 0.9348 |
| 0.0 | 17.0 | 26571 | 0.7734 | 0.9336 |
| 0.003 | 18.0 | 28134 | 0.7006 | 0.9372 |
| 0.0008 | 19.0 | 29697 | 0.7140 | 0.9374 |
| 0.0 | 20.0 | 31260 | 0.7094 | 0.9372 |
### Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.13.3
|
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234rjhbasd867/output
|
234rjhbasd867
| 2023-12-14T06:15:12Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-14T06:15:12Z |
Entry not found
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zacboy326/GeneralGrievousCloneWars2003
|
zacboy326
| 2023-12-14T06:15:46Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-14T06:15:17Z |
Entry not found
|
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zacboy326/DarthMaulTPM
|
zacboy326
| 2023-12-14T06:16:53Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-14T06:16:23Z |
Entry not found
|
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lakelz/Mistral-7B-Instruct-v0.1-Odyssey-2.0
|
lakelz
| 2023-12-14T06:18:36Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-14T06:18:36Z |
Entry not found
|
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] |
lakelz/mistral_instruct_generation
|
lakelz
| 2023-12-14T06:18:50Z | 0 | 0 |
peft
|
[
"peft",
"tensorboard",
"safetensors",
"generated_from_trainer",
"base_model:mistralai/Mistral-7B-Instruct-v0.1",
"license:apache-2.0",
"region:us"
] | null | 2023-12-14T06:18:47Z |
---
license: apache-2.0
library_name: peft
tags:
- generated_from_trainer
base_model: mistralai/Mistral-7B-Instruct-v0.1
model-index:
- name: mistral_instruct_generation
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mistral_instruct_generation
This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3372
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- lr_scheduler_warmup_steps: 0.03
- training_steps: 100
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.5068 | 0.08 | 20 | 1.3676 |
| 1.404 | 0.16 | 40 | 1.3359 |
| 1.3731 | 0.24 | 60 | 1.3268 |
| 1.3556 | 0.32 | 80 | 1.3284 |
| 1.2962 | 0.4 | 100 | 1.3372 |
### Framework versions
- PEFT 0.7.1
- Transformers 4.36.0
- Pytorch 2.1.1+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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behzadnet/Llama-2-7b-chat-hf-sharded-bf16-fine-tuned-adapters_RandomError0.4_Seed102
|
behzadnet
| 2023-12-14T06:18:56Z | 0 | 0 |
peft
|
[
"peft",
"arxiv:1910.09700",
"base_model:Trelis/Llama-2-7b-chat-hf-sharded-bf16",
"region:us"
] | null | 2023-12-14T06:18:50Z |
---
library_name: peft
base_model: Trelis/Llama-2-7b-chat-hf-sharded-bf16
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
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- **Developed by:** [More Information Needed]
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### Model Sources [optional]
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## Uses
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
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## Training Details
### Training Data
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### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
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#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
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## Evaluation
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### Testing Data, Factors & Metrics
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#### Metrics
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### Results
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#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
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## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.7.0.dev0
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.7.0.dev0
|
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behzadnet/Llama-2-7b-chat-hf-sharded-bf16-fine-tuned_RandomError0.4_Seed102
|
behzadnet
| 2023-12-14T06:19:05Z | 0 | 0 |
peft
|
[
"peft",
"arxiv:1910.09700",
"base_model:Trelis/Llama-2-7b-chat-hf-sharded-bf16",
"region:us"
] | null | 2023-12-14T06:19:02Z |
---
library_name: peft
base_model: Trelis/Llama-2-7b-chat-hf-sharded-bf16
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
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- **Developed by:** [More Information Needed]
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
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## Training Details
### Training Data
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### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
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#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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#### Metrics
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### Results
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#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
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## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.7.0.dev0
|
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] |
eyemabhishek/llava
|
eyemabhishek
| 2023-12-14T06:33:35Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-12-14T06:21:21Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.4.0
|
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org-rw1/222
|
org-rw1
| 2023-12-14T06:22:16Z | 0 | 0 | null |
[
"license:mit",
"region:us"
] | null | 2023-12-14T06:22:16Z |
---
license: mit
---
|
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HiveersOrg/Model20231214
|
HiveersOrg
| 2023-12-14T06:22:36Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2023-12-14T06:22:36Z |
---
license: apache-2.0
---
|
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mj96/CompVis-sd-v1-4-dreambooth-lora-lmessi
|
mj96
| 2023-12-14T06:25:44Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-14T06:25:44Z |
Entry not found
|
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] |
OSN2/ArXiv
|
OSN2
| 2023-12-14T06:27:20Z | 0 | 0 |
bertopic
|
[
"bertopic",
"text-classification",
"region:us"
] |
text-classification
| 2023-12-14T06:27:18Z |
---
tags:
- bertopic
library_name: bertopic
pipeline_tag: text-classification
---
# ArXiv
This is a [BERTopic](https://github.com/MaartenGr/BERTopic) model.
BERTopic is a flexible and modular topic modeling framework that allows for the generation of easily interpretable topics from large datasets.
## Usage
To use this model, please install BERTopic:
```
pip install -U bertopic
```
You can use the model as follows:
```python
from bertopic import BERTopic
topic_model = BERTopic.load("OSN2/ArXiv")
topic_model.get_topic_info()
```
## Topic overview
* Number of topics: 171
* Number of training documents: 12693
<details>
<summary>Click here for an overview of all topics.</summary>
| Topic ID | Topic Keywords | Topic Frequency | Label |
|----------|----------------|-----------------|-------|
| -1 | the - and - to - of - in | 15 | -1_the_and_to_of |
| 0 | recipe - food - recipes - pizza - salad | 3814 | 0_recipe_food_recipes_pizza |
| 1 | trump - election - law - the - that | 849 | 1_trump_election_law_the |
| 2 | anaysa - fashion - pants - swimwear - sneakers | 393 | 2_anaysa_fashion_pants_swimwear |
| 3 | arsenal - liverpool - rugby - match - haaland | 382 | 3_arsenal_liverpool_rugby_match |
| 4 | weather - bengal - storm - west - snow | 271 | 4_weather_bengal_storm_west |
| 5 | crypto - bitcoin - cryptocurrency - gaming - trading | 172 | 5_crypto_bitcoin_cryptocurrency_gaming |
| 6 | her - she - was - on - related | 143 | 6_her_she_was_on |
| 7 | 420m - dog - animal - animals - dogs | 138 | 7_420m_dog_animal_animals |
| 8 | god - lord - prayer - jesus - church | 127 | 8_god_lord_prayer_jesus |
| 9 | cars - sale - used - under - for | 119 | 9_cars_sale_used_under |
| 10 | pro - vivo - v23 - phone - google | 117 | 10_pro_vivo_v23_phone |
| 11 | news - iptv - tv - interview - latest | 110 | 11_news_iptv_tv_interview |
| 12 | art - museum - artists - artist - of | 108 | 12_art_museum_artists_artist |
| 13 | my - nephews - nieces - poetry - love | 107 | 13_my_nephews_nieces_poetry |
| 14 | film - review - his - as - but | 102 | 14_film_review_his_as |
| 15 | bike - helmet - bikes - mountain - pilots | 98 | 15_bike_helmet_bikes_mountain |
| 16 | hair - bite - steel - care - haircut | 97 | 16_hair_bite_steel_care |
| 17 | police - rhonda - mcdowell - was - said | 90 | 17_police_rhonda_mcdowell_was |
| 18 | property - room - bedrooms - bedroom - home | 86 | 18_property_room_bedrooms_bedroom |
| 19 | ukraine - russia - russian - putin - news | 86 | 19_ukraine_russia_russian_putin |
| 20 | business - jobs - income - data - part | 86 | 20_business_jobs_income_data |
| 21 | vaccinated - vaccine - covid - va - unvaccinated | 84 | 21_vaccinated_vaccine_covid_va |
| 22 | music - band - students - orchestra - tickets | 83 | 22_music_band_students_orchestra |
| 23 | workout - abs - workouts - fitness - exercise | 83 | 23_workout_abs_workouts_fitness |
| 24 | school - teachers - dmc - 804 - children | 83 | 24_school_teachers_dmc_804 |
| 25 | women - robotics - bali - spanish - lutheran | 82 | 25_women_robotics_bali_spanish |
| 26 | lima - tourism - parks - urban - our | 79 | 26_lima_tourism_parks_urban |
| 27 | godzilla - movies - movie - spider - marvel | 77 | 27_godzilla_movies_movie_spider |
| 28 | fishing - backpacks - fish - packs - swimming | 74 | 28_fishing_backpacks_fish_packs |
| 29 | yoga - stretching - kru - nidra - oct | 74 | 29_yoga_stretching_kru_nidra |
| 30 | researchers - species - of - the - university | 72 | 30_researchers_species_of_the |
| 31 | wholesale - market - saree - delhi - software | 71 | 31_wholesale_market_saree_delhi |
| 32 | skin - acne - cream - blackheads - whitening | 70 | 32_skin_acne_cream_blackheads |
| 33 | rodents - pets - pest - dogs - animals | 70 | 33_rodents_pets_pest_dogs |
| 34 | books - book - salinger - fiction - literary | 67 | 34_books_book_salinger_fiction |
| 35 | class - pst - exams - preparation - test | 66 | 35_class_pst_exams_preparation |
| 36 | 5g - airlines - bsnl - flight - network | 64 | 36_5g_airlines_bsnl_flight |
| 37 | treetops - dementia - children - people - barbara | 62 | 37_treetops_dementia_children_people |
| 38 | lottery - thai - thailand - lotto - win | 62 | 38_lottery_thai_thailand_lotto |
| 39 | wedding - weddings - survival - gift - day | 61 | 39_wedding_weddings_survival_gift |
| 40 | quantum - solar - energy - material - light | 61 | 40_quantum_solar_energy_material |
| 41 | beauty - makeup - products - sephora - skin | 60 | 41_beauty_makeup_products_sephora |
| 42 | games - xbox - game - solitaire - free | 60 | 42_games_xbox_game_solitaire |
| 43 | insurance - insurers - insurer - company - aig | 59 | 43_insurance_insurers_insurer_company |
| 44 | green - saf - haiti - industry - solar | 58 | 44_green_saf_haiti_industry |
| 45 | diet - meat - foods - plant - body | 55 | 45_diet_meat_foods_plant |
| 46 | edinburgh - tour - royal - travel - castle | 55 | 46_edinburgh_tour_royal_travel |
| 47 | horses - horse - friesian - goëngamieden - post | 54 | 47_horses_horse_friesian_goëngamieden |
| 48 | your - you - mental - health - anal | 51 | 48_your_you_mental_health |
| 49 | weight - obesity - loss - lose - fat | 51 | 49_weight_obesity_loss_lose |
| 50 | estate - real - property - home - you | 50 | 50_estate_real_property_home |
| 51 | camping - surfing - guess - landmark - lego | 50 | 51_camping_surfing_guess_landmark |
| 52 | dorm - sex - birthday - my - joy | 50 | 52_dorm_sex_birthday_my |
| 53 | covid - 19 - vaccinated - vaccine - cases | 50 | 53_covid_19_vaccinated_vaccine |
| 54 | spain - morocco - gas - energy - industry | 49 | 54_spain_morocco_gas_energy |
| 55 | gardening - garden - grow - plants - fertilizer | 49 | 55_gardening_garden_grow_plants |
| 56 | tenant - transport - apartments - department - condos | 49 | 56_tenant_transport_apartments_department |
| 57 | cricket - england - engw - indw - vs | 48 | 57_cricket_england_engw_indw |
| 58 | trump - election - party - votes - former | 48 | 58_trump_election_party_votes |
| 59 | tesla - marine - electric - musk - ev | 47 | 59_tesla_marine_electric_musk |
| 60 | surf - surfing - ski - swimming - lessons | 47 | 60_surf_surfing_ski_swimming |
| 61 | disabled - disability - thailand - scholarship - scholarships | 47 | 61_disabled_disability_thailand_scholarship |
| 62 | programming - udemy - svelte - language - courses | 44 | 62_programming_udemy_svelte_language |
| 63 | diy - ideas - desk - wood - woodworking | 43 | 63_diy_ideas_desk_wood |
| 64 | wrestling - pearson - tiga - wwe - nfl | 43 | 64_wrestling_pearson_tiga_wwe |
| 65 | smart - gadgets - appliances - home - kitchen | 42 | 65_smart_gadgets_appliances_home |
| 66 | experiments - fu - kung - xxxtentacion - copyright | 40 | 66_experiments_fu_kung_xxxtentacion |
| 67 | job - small - businesses - hiring - business | 40 | 67_job_small_businesses_hiring |
| 68 | hiv - health - care - hospital - hospice | 40 | 68_hiv_health_care_hospital |
| 69 | he - was - it - empire - movie | 38 | 69_he_was_it_empire |
| 70 | beat - type - ringtone - lofi - beats | 37 | 70_beat_type_ringtone_lofi |
| 71 | castellvi - marines - marine - corps - county | 37 | 71_castellvi_marines_marine_corps |
| 72 | casino - xbox - game - games - poker | 37 | 72_casino_xbox_game_games |
| 73 | bellanaijaweddings - bride - handmadepaper - weddingplanner - makeup | 36 | 73_bellanaijaweddings_bride_handmadepaper_weddingplanner |
| 74 | music - jsem - bushcraft - se - festival | 36 | 74_music_jsem_bushcraft_se |
| 75 | gemini - tarot - horoscope - september - pisces | 35 | 75_gemini_tarot_horoscope_september |
| 76 | career - husni - magazines - magazine - employees | 35 | 76_career_husni_magazines_magazine |
| 77 | his - film - movie - review - but | 34 | 77_his_film_movie_review |
| 78 | gps - aircraft - trucks - vehicles - electric | 34 | 78_gps_aircraft_trucks_vehicles |
| 79 | raya - merch - magazines - cards - kongamidyearshoppingfestival | 34 | 79_raya_merch_magazines_cards |
| 80 | baby - she - birth - says - women | 34 | 80_baby_she_birth_says |
| 81 | covid - 19 - uk - health - interventions | 33 | 81_covid_19_uk_health |
| 82 | climate - gore - dm - eastman - change | 33 | 82_climate_gore_dm_eastman |
| 83 | buhari - anambra - apc - anyim - chief | 32 | 83_buhari_anambra_apc_anyim |
| 84 | orchestra - hotel - janice - chicago - symphony | 31 | 84_orchestra_hotel_janice_chicago |
| 85 | ramen - pierre - soulz - magic - westfieldcarousel | 31 | 85_ramen_pierre_soulz_magic |
| 86 | interior - design - home - decorate - bedroom | 30 | 86_interior_design_home_decorate |
| 87 | hindi - movie - explained - hollywood - lankybox | 30 | 87_hindi_movie_explained_hollywood |
| 88 | xbox - playstation - game - card - console | 30 | 88_xbox_playstation_game_card |
| 89 | insurance - car - policy - feener - policyworld | 30 | 89_insurance_car_policy_feener |
| 90 | share - nepal - stock - market - analysis | 29 | 90_share_nepal_stock_market |
| 91 | marketing - content - strategy - cart - your | 28 | 91_marketing_content_strategy_cart |
| 92 | songs - kids - song - rhymes - hindi | 28 | 92_songs_kids_song_rhymes |
| 93 | tax - cd - money - itr - 401 | 27 | 93_tax_cd_money_itr |
| 94 | inflation - housing - prices - chorley - hydrow | 27 | 94_inflation_housing_prices_chorley |
| 95 | venkat - spectre - spending - attacks - intel | 26 | 95_venkat_spectre_spending_attacks |
| 96 | band - grammys - recording - musical - doo | 26 | 96_band_grammys_recording_musical |
| 97 | drawing - draw - art - mandala - painting | 26 | 97_drawing_draw_art_mandala |
| 98 | shop - insurance - design - restaurant - food | 26 | 98_shop_insurance_design_restaurant |
| 99 | kamran - feride - iqiyi - drama - selim | 26 | 99_kamran_feride_iqiyi_drama |
| 100 | poetry - prize - mondaymotivation - publication - apologize | 26 | 100_poetry_prize_mondaymotivation_publication |
| 101 | jobs - tcs - part - job - work | 25 | 101_jobs_tcs_part_job |
| 102 | card - credit - rewards - cash - tracking | 25 | 102_card_credit_rewards_cash |
| 103 | vlog - vlogs - dexerto - video - blog | 25 | 103_vlog_vlogs_dexerto_video |
| 104 | brother - 5½ - burge - poetry - thank | 25 | 104_brother_5½_burge_poetry |
| 105 | anime - manga - disney - animes - recap | 25 | 105_anime_manga_disney_animes |
| 106 | fox - news - msnbc - biden - business | 25 | 106_fox_news_msnbc_biden |
| 107 | thoreau - wildness - maldives - malé - wildlife | 24 | 107_thoreau_wildness_maldives_malé |
| 108 | condo - minutes - rent - condominium - เช | 24 | 108_condo_minutes_rent_condominium |
| 109 | freshworks - sales - requirements - job - development | 24 | 109_freshworks_sales_requirements_job |
| 110 | insurance - management - property - company - loans | 24 | 110_insurance_management_property_company |
| 111 | aew - wrestling - highlights - esports - impact | 23 | 111_aew_wrestling_highlights_esports |
| 112 | ctv - cbc - _x000d_ - news - bridge | 23 | 112_ctv_cbc__x000d__news |
| 113 | ukrainian - music - lyatoshynsky - solos - concert | 23 | 113_ukrainian_music_lyatoshynsky_solos |
| 114 | abc - ladzinski - campaign - carlton - news | 23 | 114_abc_ladzinski_campaign_carlton |
| 115 | gaming - pc - headset - byte - cosmic | 23 | 115_gaming_pc_headset_byte |
| 116 | climate - environmental - noaa - literacy - education | 23 | 116_climate_environmental_noaa_literacy |
| 117 | game - players - sonic - its - the | 22 | 117_game_players_sonic_its |
| 118 | olympic - olympics - chen - biles - medal | 22 | 118_olympic_olympics_chen_biles |
| 119 | loans - loan - student - paying - naira | 22 | 119_loans_loan_student_paying |
| 120 | nail - art - nails - compilation - acrylic | 22 | 120_nail_art_nails_compilation |
| 121 | peppa - pig - wolfoo - nguyen - favorite | 21 | 121_peppa_pig_wolfoo_nguyen |
| 122 | jazz - music - blues - heat - waves | 21 | 122_jazz_music_blues_heat |
| 123 | rónán - march - composer - lyricist - tickets | 21 | 123_rónán_march_composer_lyricist |
| 124 | olympic - beijing - olympics - china - athletes | 21 | 124_olympic_beijing_olympics_china |
| 125 | smoking - breakover - smokers - heart - hind | 21 | 125_smoking_breakover_smokers_heart |
| 126 | pets - animals - pet - panda - dog | 21 | 126_pets_animals_pet_panda |
| 127 | cycling - gcn - bike - feroce - wheels | 21 | 127_cycling_gcn_bike_feroce |
| 128 | musique - proposée - libre - par - la | 21 | 128_musique_proposée_libre_par |
| 129 | male - girlfriend - roseanne - unagi - twohill | 20 | 129_male_girlfriend_roseanne_unagi |
| 130 | gymnastics - moana - always - drugs - week | 20 | 130_gymnastics_moana_always_drugs |
| 131 | musk - gambling - twitter - elon - deduction | 20 | 131_musk_gambling_twitter_elon |
| 132 | lichfield - google - sat - stoke - mon | 20 | 132_lichfield_google_sat_stoke |
| 133 | reasonable - greenhouse - accommodation - robots - ai | 20 | 133_reasonable_greenhouse_accommodation_robots |
| 134 | icebox - maxo - theme - kream - koo | 19 | 134_icebox_maxo_theme_kream |
| 135 | whio - ong - ang - canal - birds | 19 | 135_whio_ong_ang_canal |
| 136 | codyfight - tattooing - brothers - marriage - extreme | 19 | 136_codyfight_tattooing_brothers_marriage |
| 137 | nuro - gm - vehicle - vehicles - electric | 19 | 137_nuro_gm_vehicle_vehicles |
| 138 | kcs - railroads - cn - rail - stb | 19 | 138_kcs_railroads_cn_rail |
| 139 | strengths - music - grief - leisure - life | 19 | 139_strengths_music_grief_leisure |
| 140 | drones - drone - uae - missile - dhabi | 19 | 140_drones_drone_uae_missile |
| 141 | massage - dubai - jumeirah - japanese - oil | 18 | 141_massage_dubai_jumeirah_japanese |
| 142 | bowl - super - bengals - bet - rams | 18 | 142_bowl_super_bengals_bet |
| 143 | pension - 9news - pensions - pay - tax | 18 | 143_pension_9news_pensions_pay |
| 144 | dog - toy - pet - supplies - toys | 18 | 144_dog_toy_pet_supplies |
| 145 | english - travellers - students - course - syllabus | 18 | 145_english_travellers_students_course |
| 146 | mentoring - cbs - mentor - mentors - teachers | 18 | 146_mentoring_cbs_mentor_mentors |
| 147 | picnic - park - blankets - basket - acompañantes | 18 | 147_picnic_park_blankets_basket |
| 148 | orig - 99 - amazon - prime - dollar | 18 | 148_orig_99_amazon_prime |
| 149 | primary - english - genetics - wilanów - education | 18 | 149_primary_english_genetics_wilanów |
| 150 | hardin - film - he - she - oscar | 17 | 150_hardin_film_he_she |
| 151 | laptop - gaming - alienware - laptops - hp | 17 | 151_laptop_gaming_alienware_laptops |
| 152 | ufc - tmz - owens - onlyfans - tonight | 17 | 152_ufc_tmz_owens_onlyfans |
| 153 | basketball - vs - varsity - darien - canaan | 17 | 153_basketball_vs_varsity_darien |
| 154 | workers - hanford - state - law - doe | 17 | 154_workers_hanford_state_law |
| 155 | cdl - freight - broker - logistics - eldt | 17 | 155_cdl_freight_broker_logistics |
| 156 | builders - connell - brenton - firm - wage | 17 | 156_builders_connell_brenton_firm |
| 157 | bookstore - easter - my - menger - eastershelfie | 16 | 157_bookstore_easter_my_menger |
| 158 | prince - royal - duke - charles - queen | 16 | 158_prince_royal_duke_charles |
| 159 | ดตามเราได - จำก - มหาชน - voicetv - oppday | 16 | 159_ดตามเราได_จำก_มหาชน_voicetv |
| 160 | nba - trades - stream - espn - live | 16 | 160_nba_trades_stream_espn |
| 161 | school - students - science - brandon - twig | 16 | 161_school_students_science_brandon |
| 162 | morning - sleep - your - kaplan - routine | 16 | 162_morning_sleep_your_kaplan |
| 163 | kat - author - desires - louise - charmaine | 16 | 163_kat_author_desires_louise |
| 164 | movie - recapped - uche - academia - dizzyeight | 16 | 164_movie_recapped_uche_academia |
| 165 | awka - religion - suspects - anambra - echeng | 15 | 165_awka_religion_suspects_anambra |
| 166 | wrc - f1 - rally - championship - formula1 | 15 | 166_wrc_f1_rally_championship |
| 167 | hillstream - algae - scape - goby - aquarium | 15 | 167_hillstream_algae_scape_goby |
| 168 | skin - filler - touche - éclat - dermal | 15 | 168_skin_filler_touche_éclat |
| 169 | pets - cats - hopkins - cat - niblo | 15 | 169_pets_cats_hopkins_cat |
</details>
## Training hyperparameters
* calculate_probabilities: False
* language: None
* low_memory: False
* min_topic_size: 10
* n_gram_range: (1, 1)
* nr_topics: None
* seed_topic_list: None
* top_n_words: 10
* verbose: True
* zeroshot_min_similarity: 0.7
* zeroshot_topic_list: None
## Framework versions
* Numpy: 1.23.5
* HDBSCAN: 0.8.33
* UMAP: 0.5.5
* Pandas: 1.5.3
* Scikit-Learn: 1.2.2
* Sentence-transformers: 2.2.2
* Transformers: 4.36.0
* Numba: 0.58.1
* Plotly: 5.15.0
* Python: 3.10.12
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Balopez/AIsz
|
Balopez
| 2023-12-14T06:30:06Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-14T06:30:06Z |
Entry not found
|
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] |
markgarciabr/marixi
|
markgarciabr
| 2023-12-14T06:30:47Z | 0 | 0 | null |
[
"license:cc",
"region:us"
] | null | 2023-12-14T06:30:47Z |
---
license: cc
---
|
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teleprint-me/mixtral-8x7b-instruct-v0.1-gguf
|
teleprint-me
| 2023-12-14T11:43:34Z | 0 | 0 | null |
[
"gguf",
"region:us"
] | null | 2023-12-14T06:32:39Z |
Entry not found
|
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hjgp/1214db
|
hjgp
| 2023-12-14T06:40:38Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-14T06:33:22Z |
Entry not found
|
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] |
Gummybear05/wav2vec2-large-xls-r-300m-korean_senior-colab
|
Gummybear05
| 2023-12-14T09:53:54Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"base_model:facebook/wav2vec2-xls-r-300m",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-12-14T06:33:24Z |
---
license: apache-2.0
base_model: facebook/wav2vec2-xls-r-300m
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-large-xls-r-300m-korean_senior-colab
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-korean_senior-colab
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3662
- Cer: 0.3521
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Cer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 23.3703 | 0.13 | 200 | 4.6540 | 1.0 |
| 4.5902 | 0.26 | 400 | 4.6068 | 1.0 |
| 4.5584 | 0.39 | 600 | 4.6189 | 1.0 |
| 4.5187 | 0.52 | 800 | 4.5712 | 1.0 |
| 4.4877 | 0.64 | 1000 | 4.6354 | 1.0 |
| 4.4415 | 0.77 | 1200 | 4.4681 | 1.0 |
| 4.2243 | 0.9 | 1400 | 4.0606 | 0.8462 |
| 3.4216 | 1.03 | 1600 | 3.0853 | 0.7218 |
| 2.7586 | 1.16 | 1800 | 2.6957 | 0.6083 |
| 2.355 | 1.29 | 2000 | 2.4372 | 0.5711 |
| 2.1589 | 1.42 | 2200 | 2.2552 | 0.5251 |
| 2.0473 | 1.55 | 2400 | 2.1381 | 0.5227 |
| 1.8895 | 1.68 | 2600 | 1.9748 | 0.4967 |
| 1.7995 | 1.81 | 2800 | 2.0369 | 0.4985 |
| 1.754 | 1.93 | 3000 | 1.8281 | 0.4578 |
| 1.6027 | 2.06 | 3200 | 1.8280 | 0.4539 |
| 1.5043 | 2.19 | 3400 | 1.7238 | 0.4285 |
| 1.4397 | 2.32 | 3600 | 1.6137 | 0.4171 |
| 1.3416 | 2.45 | 3800 | 1.5148 | 0.3874 |
| 1.2632 | 2.58 | 4000 | 1.5120 | 0.3840 |
| 1.204 | 2.71 | 4200 | 1.4443 | 0.3741 |
| 1.1655 | 2.84 | 4400 | 1.3976 | 0.3617 |
| 1.1657 | 2.97 | 4600 | 1.3662 | 0.3521 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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hjgp/1214tz
|
hjgp
| 2023-12-14T06:38:07Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-14T06:34:23Z |
Entry not found
|
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hjgp/1214wdc
|
hjgp
| 2023-12-14T06:38:40Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-14T06:34:37Z |
Entry not found
|
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CantoINC/caption-decomposition-llama-v0.1
|
CantoINC
| 2023-12-15T05:12:13Z | 0 | 0 | null |
[
"safetensors",
"region:us"
] | null | 2023-12-14T06:34:51Z |
Entry not found
|
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imdatta0/llama_2_13b_ultrachat_chatml_207k_downNupNgate_r128_lr1e_05_bg28
|
imdatta0
| 2023-12-14T06:37:32Z | 0 | 0 | null |
[
"safetensors",
"generated_from_trainer",
"base_model:meta-llama/Llama-2-13b-hf",
"region:us"
] | null | 2023-12-14T06:36:58Z |
---
base_model: meta-llama/Llama-2-13b-hf
tags:
- generated_from_trainer
model-index:
- name: llama_2_13b_ultrachat_chatml_207k_downNupNgate_r128_lr1e_05_bg28
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# llama_2_13b_ultrachat_chatml_207k_downNupNgate_r128_lr1e_05_bg28
This model is a fine-tuned version of [meta-llama/Llama-2-13b-hf](https://huggingface.co/meta-llama/Llama-2-13b-hf) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9268
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 0.03
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.9896 | 0.04 | 25 | 1.0243 |
| 0.9284 | 0.08 | 50 | 0.9713 |
| 0.9419 | 0.12 | 75 | 0.9575 |
| 0.8815 | 0.16 | 100 | 0.9500 |
| 0.9044 | 0.2 | 125 | 0.9441 |
| 0.9153 | 0.24 | 150 | 0.9397 |
| 0.9105 | 0.28 | 175 | 0.9375 |
| 0.9046 | 0.33 | 200 | 0.9347 |
| 0.8955 | 0.37 | 225 | 0.9323 |
| 0.9046 | 0.41 | 250 | 0.9307 |
| 0.8834 | 0.45 | 275 | 0.9289 |
| 0.9165 | 0.49 | 300 | 0.9286 |
| 0.9207 | 0.53 | 325 | 0.9282 |
| 0.9041 | 0.57 | 350 | 0.9278 |
| 0.8859 | 0.61 | 375 | 0.9276 |
| 0.8961 | 0.65 | 400 | 0.9269 |
| 0.8995 | 0.69 | 425 | 0.9270 |
| 0.8925 | 0.73 | 450 | 0.9267 |
| 0.8844 | 0.77 | 475 | 0.9268 |
| 0.9043 | 0.81 | 500 | 0.9270 |
| 0.9063 | 0.85 | 525 | 0.9269 |
| 0.9374 | 0.89 | 550 | 0.9268 |
| 0.8976 | 0.93 | 575 | 0.9268 |
| 0.9 | 0.98 | 600 | 0.9268 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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DanielClough/Candle_OpenHermes-2.5-Mistral-7B
|
DanielClough
| 2023-12-14T06:41:22Z | 0 | 1 | null |
[
"region:us"
] | null | 2023-12-14T06:41:22Z |
Entry not found
|
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] |
seonglae/resrer-bart
|
seonglae
| 2023-12-14T08:23:54Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"bart",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-12-14T06:48:09Z |
Entry not found
|
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] |
ChatRDM/mistral-rdm
|
ChatRDM
| 2023-12-14T07:09:43Z | 0 | 0 | null |
[
"license:mit",
"region:us"
] | null | 2023-12-14T06:48:17Z |
---
license: mit
---
|
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] |
hariduraibaskar/r64_adapter
|
hariduraibaskar
| 2023-12-16T11:30:19Z | 0 | 0 | null |
[
"tensorboard",
"region:us"
] | null | 2023-12-14T06:48:51Z |
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mokkaraveendra/test-bljcblkjclkjlkbjclkbjckl
|
mokkaraveendra
| 2023-12-14T06:50:51Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-14T06:50:51Z |
Entry not found
|
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mokkaraveendra/stvjfvjxkjhvkjxhvkhxkj
|
mokkaraveendra
| 2023-12-14T06:51:17Z | 0 | 0 | null |
[
"license:mit",
"region:us"
] | null | 2023-12-14T06:51:17Z |
---
license: mit
---
|
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dcbv/charluv-13B-tiefighter-Q5_K_M
|
dcbv
| 2023-12-14T07:07:20Z | 0 | 0 | null |
[
"gguf",
"region:us"
] | null | 2023-12-14T06:53:22Z |
Entry not found
|
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Devarshi/Armature_Defect_Detection_All_new_n
|
Devarshi
| 2023-12-14T06:54:27Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-14T06:54:27Z |
Entry not found
|
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] |
mvvillarrealblozis/question3results
|
mvvillarrealblozis
| 2023-12-14T08:24:21Z | 0 | 0 |
transformers
|
[
"transformers",
"tf",
"distilbert",
"question-answering",
"generated_from_keras_callback",
"base_model:distilbert-base-uncased",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2023-12-14T07:00:37Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_keras_callback
model-index:
- name: mvvillarrealblozis/question3results
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# mvvillarrealblozis/question3results
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 3.8411
- Validation Loss: 3.9713
- Epoch: 1
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 100, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 5.0828 | 4.2838 | 0 |
| 3.8411 | 3.9713 | 1 |
### Framework versions
- Transformers 4.35.2
- TensorFlow 2.14.0
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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] |
danielkty22/gpt2-ep-1.01-b-4-lr-4e-06-dp-0.1-ss-0-st-False-fh-False-hs-500_normal
|
danielkty22
| 2023-12-14T07:39:03Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gpt2",
"question-answering",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
question-answering
| 2023-12-14T07:04:37Z |
Entry not found
|
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vishalnarzary/wagerai
|
vishalnarzary
| 2023-12-14T07:06:31Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-14T07:06:31Z |
Entry not found
|
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flyme2333/face_DM
|
flyme2333
| 2023-12-14T07:07:15Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-14T07:07:15Z |
Entry not found
|
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PistachioAlt/Synatra-MCS-7B-v0.3-RP-Slerp-GGUF
|
PistachioAlt
| 2023-12-14T10:45:09Z | 0 | 0 | null |
[
"gguf",
"license:cc-by-nc-4.0",
"region:us"
] | null | 2023-12-14T07:09:11Z |
---
license: cc-by-nc-4.0
---
[Synatra-MCS-7B-v0.3-RP-Slerp](https://huggingface.co/PistachioAlt/Synatra-MCS-7B-v0.3-RP-Slerp) in GGUF format.
|
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] |
Ocliph/TetoPorNasV2
|
Ocliph
| 2023-12-14T07:11:50Z | 0 | 0 | null |
[
"license:openrail",
"region:us"
] | null | 2023-12-14T07:11:50Z |
---
license: openrail
---
|
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] |
Realgon/N_roberta_imdb_padding0model
|
Realgon
| 2023-12-14T09:32:56Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"text-classification",
"generated_from_trainer",
"dataset:imdb",
"base_model:roberta-base",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-12-14T07:12:56Z |
---
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
datasets:
- imdb
metrics:
- accuracy
model-index:
- name: N_roberta_imdb_padding0model
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: imdb
type: imdb
config: plain_text
split: test
args: plain_text
metrics:
- name: Accuracy
type: accuracy
value: 0.95168
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# N_roberta_imdb_padding0model
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4112
- Accuracy: 0.9517
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.2068 | 1.0 | 1563 | 0.2386 | 0.9366 |
| 0.1604 | 2.0 | 3126 | 0.1644 | 0.9488 |
| 0.1036 | 3.0 | 4689 | 0.2624 | 0.9468 |
| 0.0712 | 4.0 | 6252 | 0.2360 | 0.9499 |
| 0.0482 | 5.0 | 7815 | 0.2562 | 0.9489 |
| 0.0421 | 6.0 | 9378 | 0.3405 | 0.9501 |
| 0.031 | 7.0 | 10941 | 0.3030 | 0.9501 |
| 0.0226 | 8.0 | 12504 | 0.3999 | 0.9472 |
| 0.0239 | 9.0 | 14067 | 0.3497 | 0.9489 |
| 0.0188 | 10.0 | 15630 | 0.4086 | 0.9463 |
| 0.0136 | 11.0 | 17193 | 0.4292 | 0.9487 |
| 0.0097 | 12.0 | 18756 | 0.4085 | 0.9496 |
| 0.0073 | 13.0 | 20319 | 0.4018 | 0.9478 |
| 0.0086 | 14.0 | 21882 | 0.3744 | 0.9512 |
| 0.0048 | 15.0 | 23445 | 0.3892 | 0.9521 |
| 0.0102 | 16.0 | 25008 | 0.3937 | 0.9503 |
| 0.0029 | 17.0 | 26571 | 0.4112 | 0.9510 |
| 0.0055 | 18.0 | 28134 | 0.4089 | 0.9517 |
| 0.0021 | 19.0 | 29697 | 0.4137 | 0.9517 |
| 0.0043 | 20.0 | 31260 | 0.4112 | 0.9517 |
### Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.13.3
|
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] |
Wanfq/distillchat_4.2.2
|
Wanfq
| 2023-12-14T07:50:48Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"mistral",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-12-14T07:14:28Z |
Entry not found
|
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] |
bio-datasets/Mistral-7B-Instruct-v0.1-upbeat-brook-193-epoch-20
|
bio-datasets
| 2023-12-14T07:21:06Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"region:us"
] | null | 2023-12-14T07:21:03Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.5.0
|
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] |
Lin-Chen/ShareGPT4V-13B_Pretrained_vit-large336-l12_vicuna-13b-v1.5
|
Lin-Chen
| 2023-12-14T07:47:24Z | 0 | 1 |
transformers
|
[
"transformers",
"pytorch",
"share4v",
"text-generation",
"arxiv:2311.12793",
"autotrain_compatible",
"region:us"
] |
text-generation
| 2023-12-14T07:21:08Z |
---
inference: false
---
<br>
<br>
# ShareGPT4V-13B Model Card
## Model details
**Model type:**
This is the pre-trained LLM and projector of ShareGPT4v-13B
**Model date:**
ShareGPT4V-13B-Pretrained was trained in Nov 2023.
**Paper or resources for more information:**
[[Project](https://ShareGPT4V.github.io/)] [[Paper](https://huggingface.co/papers/2311.12793)] [[Code](https://github.com/InternLM/InternLM-XComposer/tree/main/projects/ShareGPT4V)] [[Dataset](https://huggingface.co/datasets/Lin-Chen/ShareGPT4V)]
## Usage
You can directly utilize this model as we provide in our [[repository](https://github.com/InternLM/InternLM-XComposer/tree/main/projects/ShareGPT4V)]. Moreover, you can modify the architecture name from "Share4VLlamaForCausalLM" to "LLaVALlamaForCausalLM" and the model_type keyword from "share4v" to "llava" in our config file and seamlessly load our model in the [[LLaVA repository](https://github.com/haotian-liu/LLaVA)].
## License
Llama 2 is licensed under the LLAMA 2 Community License,
Copyright (c) Meta Platforms, Inc. All Rights Reserved.
## Intended use
**Primary intended uses:**
The primary use of ShareGPT4V-13B is research on large multimodal models and chatbots.
**Primary intended users:**
The primary intended users of the model are researchers and hobbyists in computer vision, natural language processing, machine learning, and artificial intelligence.
## Training dataset
- 1.2M high-quality image-text pairs, i.e., ShareGPT4V-PT data
|
[
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] |
hkivancoral/smids_5x_deit_tiny_adamax_0001_fold1
|
hkivancoral
| 2023-12-17T12:03:12Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:facebook/deit-small-patch16-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-12-14T07:22:02Z |
---
license: apache-2.0
base_model: facebook/deit-small-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: smids_5x_deit_tiny_adamax_0001_fold1
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9115191986644408
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# smids_5x_deit_tiny_adamax_0001_fold1
This model is a fine-tuned version of [facebook/deit-small-patch16-224](https://huggingface.co/facebook/deit-small-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7374
- Accuracy: 0.9115
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.2308 | 1.0 | 376 | 0.2388 | 0.9115 |
| 0.1317 | 2.0 | 752 | 0.2582 | 0.9098 |
| 0.1686 | 3.0 | 1128 | 0.4270 | 0.8831 |
| 0.0405 | 4.0 | 1504 | 0.4962 | 0.8781 |
| 0.0366 | 5.0 | 1880 | 0.4884 | 0.8965 |
| 0.0181 | 6.0 | 2256 | 0.6032 | 0.8982 |
| 0.0062 | 7.0 | 2632 | 0.6900 | 0.8965 |
| 0.0021 | 8.0 | 3008 | 0.5787 | 0.9115 |
| 0.0022 | 9.0 | 3384 | 0.7270 | 0.8932 |
| 0.0002 | 10.0 | 3760 | 0.6268 | 0.9115 |
| 0.0012 | 11.0 | 4136 | 0.5883 | 0.9082 |
| 0.0001 | 12.0 | 4512 | 0.5732 | 0.9165 |
| 0.0 | 13.0 | 4888 | 0.5583 | 0.9215 |
| 0.011 | 14.0 | 5264 | 0.7089 | 0.9032 |
| 0.0002 | 15.0 | 5640 | 0.7954 | 0.8815 |
| 0.0 | 16.0 | 6016 | 0.6772 | 0.8965 |
| 0.0 | 17.0 | 6392 | 0.6923 | 0.9032 |
| 0.0 | 18.0 | 6768 | 0.7006 | 0.8982 |
| 0.0 | 19.0 | 7144 | 0.6930 | 0.9032 |
| 0.0149 | 20.0 | 7520 | 0.7767 | 0.8948 |
| 0.0 | 21.0 | 7896 | 0.6643 | 0.9132 |
| 0.0 | 22.0 | 8272 | 0.6887 | 0.9015 |
| 0.0 | 23.0 | 8648 | 0.6854 | 0.8998 |
| 0.0001 | 24.0 | 9024 | 0.6666 | 0.9132 |
| 0.0 | 25.0 | 9400 | 0.6680 | 0.9082 |
| 0.0033 | 26.0 | 9776 | 0.6701 | 0.9115 |
| 0.0 | 27.0 | 10152 | 0.6769 | 0.8998 |
| 0.0 | 28.0 | 10528 | 0.6638 | 0.9115 |
| 0.0042 | 29.0 | 10904 | 0.6671 | 0.9165 |
| 0.004 | 30.0 | 11280 | 0.6642 | 0.9115 |
| 0.0 | 31.0 | 11656 | 0.6776 | 0.9098 |
| 0.0 | 32.0 | 12032 | 0.6835 | 0.9098 |
| 0.0 | 33.0 | 12408 | 0.7324 | 0.9048 |
| 0.0 | 34.0 | 12784 | 0.7298 | 0.9048 |
| 0.0 | 35.0 | 13160 | 0.7063 | 0.9098 |
| 0.0 | 36.0 | 13536 | 0.7145 | 0.9098 |
| 0.0 | 37.0 | 13912 | 0.7213 | 0.9082 |
| 0.0 | 38.0 | 14288 | 0.7239 | 0.9098 |
| 0.0 | 39.0 | 14664 | 0.7296 | 0.9065 |
| 0.0 | 40.0 | 15040 | 0.7280 | 0.9082 |
| 0.0 | 41.0 | 15416 | 0.7172 | 0.9098 |
| 0.0 | 42.0 | 15792 | 0.7224 | 0.9098 |
| 0.0 | 43.0 | 16168 | 0.7245 | 0.9115 |
| 0.0 | 44.0 | 16544 | 0.7307 | 0.9098 |
| 0.0 | 45.0 | 16920 | 0.7306 | 0.9098 |
| 0.0026 | 46.0 | 17296 | 0.7273 | 0.9098 |
| 0.0 | 47.0 | 17672 | 0.7340 | 0.9132 |
| 0.0 | 48.0 | 18048 | 0.7346 | 0.9132 |
| 0.0 | 49.0 | 18424 | 0.7359 | 0.9132 |
| 0.0022 | 50.0 | 18800 | 0.7374 | 0.9115 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.1+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
|
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yusuf44/yusuf
|
yusuf44
| 2023-12-14T07:22:03Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-14T07:22:03Z |
Entry not found
|
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athirdpath/Mu-17b-GGUF
|
athirdpath
| 2023-12-14T07:34:53Z | 0 | 0 | null |
[
"en",
"license:cc-by-nc-4.0",
"region:us"
] | null | 2023-12-14T07:22:33Z |
---
license: cc-by-nc-4.0
language:
- en
---
models:
- model: athirdpath/Lambda-17b-DPO2
weight: 0.10
density: 0.45
- model: Undi95/Clover3-17B
weight: 0.15
density: 0.65
- model: athirdpath/Lambda-17b
weight: 0.50
density: 0.70
- model: athirdpath/Lambda-DPOslerp-17b
weight: 0.25
density: 0.55
merge_method: dare_ties
base_model: athirdpath/Gamma-17b
int8_mask: true
dtype: bfloat16
|
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] |
sigma-bit-dot/Reinforce-unit4-cartpole
|
sigma-bit-dot
| 2023-12-14T07:23:50Z | 0 | 0 | null |
[
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-12-14T07:23:41Z |
---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-unit4-cartpole
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 500.00 +/- 0.00
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
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han223/han_merge_sumok
|
han223
| 2023-12-14T07:28:13Z | 0 | 0 | null |
[
"license:openrail",
"region:us"
] | null | 2023-12-14T07:25:45Z |
---
license: openrail
---
|
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] |
ramk1404/llama-7B-fine-tuned-adapter-model
|
ramk1404
| 2023-12-14T07:28:06Z | 0 | 0 | null |
[
"safetensors",
"license:apache-2.0",
"region:us"
] | null | 2023-12-14T07:26:34Z |
---
license: apache-2.0
---
|
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marsMdd/instruct-pix2pix-model
|
marsMdd
| 2023-12-14T07:27:58Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-14T07:27:58Z |
Entry not found
|
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fardin1390/firooz
|
fardin1390
| 2023-12-14T07:29:04Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-14T07:29:04Z |
Entry not found
|
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] |
sezenkarakus/merged_llama2_13b_ner_model_v14
|
sezenkarakus
| 2023-12-14T12:24:41Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-12-14T07:29:12Z |
Entry not found
|
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] |
hkivancoral/smids_5x_beit_base_adamax_0001_fold1
|
hkivancoral
| 2023-12-14T09:30:29Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"beit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:microsoft/beit-base-patch16-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-12-14T07:31:46Z |
---
license: apache-2.0
base_model: microsoft/beit-base-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: smids_5x_beit_base_adamax_0001_fold1
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.8964941569282137
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# smids_5x_beit_base_adamax_0001_fold1
This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0655
- Accuracy: 0.8965
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.3493 | 1.0 | 376 | 0.3879 | 0.8581 |
| 0.2155 | 2.0 | 752 | 0.3019 | 0.8915 |
| 0.1776 | 3.0 | 1128 | 0.3875 | 0.8548 |
| 0.1242 | 4.0 | 1504 | 0.3809 | 0.8831 |
| 0.0896 | 5.0 | 1880 | 0.5028 | 0.8798 |
| 0.1253 | 6.0 | 2256 | 0.4979 | 0.8982 |
| 0.1104 | 7.0 | 2632 | 0.5865 | 0.8681 |
| 0.0316 | 8.0 | 3008 | 0.5613 | 0.8831 |
| 0.0721 | 9.0 | 3384 | 0.5293 | 0.8965 |
| 0.0201 | 10.0 | 3760 | 0.6272 | 0.8881 |
| 0.0359 | 11.0 | 4136 | 0.4934 | 0.8998 |
| 0.0744 | 12.0 | 4512 | 0.6114 | 0.8948 |
| 0.0347 | 13.0 | 4888 | 0.5456 | 0.9082 |
| 0.0311 | 14.0 | 5264 | 0.6131 | 0.8881 |
| 0.0168 | 15.0 | 5640 | 0.6543 | 0.8932 |
| 0.0168 | 16.0 | 6016 | 0.7183 | 0.8881 |
| 0.0016 | 17.0 | 6392 | 0.6732 | 0.8982 |
| 0.0267 | 18.0 | 6768 | 0.6217 | 0.9015 |
| 0.0052 | 19.0 | 7144 | 0.8606 | 0.8881 |
| 0.0397 | 20.0 | 7520 | 0.6236 | 0.8965 |
| 0.0267 | 21.0 | 7896 | 0.7627 | 0.8898 |
| 0.0186 | 22.0 | 8272 | 0.6922 | 0.8965 |
| 0.0249 | 23.0 | 8648 | 0.7332 | 0.8865 |
| 0.0032 | 24.0 | 9024 | 0.7665 | 0.8998 |
| 0.0275 | 25.0 | 9400 | 0.6785 | 0.8948 |
| 0.024 | 26.0 | 9776 | 0.7205 | 0.8915 |
| 0.0009 | 27.0 | 10152 | 0.7304 | 0.9015 |
| 0.0003 | 28.0 | 10528 | 0.7307 | 0.9065 |
| 0.0154 | 29.0 | 10904 | 0.7519 | 0.8965 |
| 0.0031 | 30.0 | 11280 | 0.8948 | 0.8932 |
| 0.0002 | 31.0 | 11656 | 0.8220 | 0.8998 |
| 0.0001 | 32.0 | 12032 | 0.7942 | 0.9048 |
| 0.0 | 33.0 | 12408 | 0.8498 | 0.9065 |
| 0.0055 | 34.0 | 12784 | 0.7753 | 0.8798 |
| 0.0001 | 35.0 | 13160 | 0.8717 | 0.8915 |
| 0.0 | 36.0 | 13536 | 0.9811 | 0.8865 |
| 0.0 | 37.0 | 13912 | 0.9556 | 0.8898 |
| 0.0003 | 38.0 | 14288 | 0.9804 | 0.8865 |
| 0.013 | 39.0 | 14664 | 0.9497 | 0.8965 |
| 0.0 | 40.0 | 15040 | 1.0094 | 0.8831 |
| 0.0 | 41.0 | 15416 | 0.9964 | 0.8881 |
| 0.0 | 42.0 | 15792 | 0.9367 | 0.8965 |
| 0.0 | 43.0 | 16168 | 1.0400 | 0.9015 |
| 0.0009 | 44.0 | 16544 | 1.0395 | 0.8948 |
| 0.0 | 45.0 | 16920 | 1.0420 | 0.8932 |
| 0.0031 | 46.0 | 17296 | 1.0873 | 0.8965 |
| 0.0 | 47.0 | 17672 | 1.0455 | 0.9032 |
| 0.0 | 48.0 | 18048 | 1.0612 | 0.8965 |
| 0.0 | 49.0 | 18424 | 1.0632 | 0.8998 |
| 0.0024 | 50.0 | 18800 | 1.0655 | 0.8965 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
|
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] |
Blackroot/Dolphin-8x7b-fp6
|
Blackroot
| 2023-12-14T07:42:19Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-14T07:31:52Z |
GGUF FP6 quantization of <https://huggingface.co/ehartford/dolphin-2.5-mixtral-8x7b>.
|
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] |
ehowe/speecht5_finetuned_voxpopuli_nl
|
ehowe
| 2023-12-14T08:10:08Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"speecht5",
"text-to-audio",
"endpoints_compatible",
"region:us"
] |
text-to-audio
| 2023-12-14T07:34:39Z |
Entry not found
|
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] |
ryanlohyr/distilbert-base-uncased-finetuned-emotion
|
ryanlohyr
| 2023-12-14T08:04:58Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-12-14T07:36:21Z |
Entry not found
|
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] |
hkivancoral/smids_5x_beit_base_adamax_00001_fold1
|
hkivancoral
| 2023-12-14T18:46:00Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"beit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:microsoft/beit-base-patch16-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-12-14T07:37:16Z |
---
license: apache-2.0
base_model: microsoft/beit-base-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: smids_5x_beit_base_adamax_00001_fold1
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9065108514190318
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# smids_5x_beit_base_adamax_00001_fold1
This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8312
- Accuracy: 0.9065
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.2349 | 1.0 | 376 | 0.2964 | 0.8848 |
| 0.2022 | 2.0 | 752 | 0.2944 | 0.8932 |
| 0.1706 | 3.0 | 1128 | 0.2893 | 0.8965 |
| 0.0767 | 4.0 | 1504 | 0.3105 | 0.9015 |
| 0.0646 | 5.0 | 1880 | 0.3471 | 0.9015 |
| 0.0505 | 6.0 | 2256 | 0.3777 | 0.9015 |
| 0.0505 | 7.0 | 2632 | 0.4146 | 0.9115 |
| 0.0821 | 8.0 | 3008 | 0.4739 | 0.9115 |
| 0.0331 | 9.0 | 3384 | 0.5133 | 0.9082 |
| 0.0097 | 10.0 | 3760 | 0.5125 | 0.9065 |
| 0.0368 | 11.0 | 4136 | 0.5327 | 0.9098 |
| 0.0236 | 12.0 | 4512 | 0.6377 | 0.8881 |
| 0.0306 | 13.0 | 4888 | 0.6671 | 0.9015 |
| 0.0605 | 14.0 | 5264 | 0.6154 | 0.9048 |
| 0.0306 | 15.0 | 5640 | 0.6497 | 0.9082 |
| 0.0004 | 16.0 | 6016 | 0.6905 | 0.9098 |
| 0.0062 | 17.0 | 6392 | 0.7456 | 0.9082 |
| 0.0157 | 18.0 | 6768 | 0.7362 | 0.9048 |
| 0.0117 | 19.0 | 7144 | 0.8082 | 0.8965 |
| 0.0001 | 20.0 | 7520 | 0.7613 | 0.9098 |
| 0.0049 | 21.0 | 7896 | 0.7376 | 0.9115 |
| 0.0013 | 22.0 | 8272 | 0.7490 | 0.9098 |
| 0.0339 | 23.0 | 8648 | 0.7577 | 0.9132 |
| 0.0009 | 24.0 | 9024 | 0.7847 | 0.9098 |
| 0.0161 | 25.0 | 9400 | 0.7983 | 0.9098 |
| 0.0079 | 26.0 | 9776 | 0.7734 | 0.8948 |
| 0.0004 | 27.0 | 10152 | 0.7368 | 0.9015 |
| 0.0005 | 28.0 | 10528 | 0.7478 | 0.9098 |
| 0.0059 | 29.0 | 10904 | 0.7755 | 0.9065 |
| 0.0012 | 30.0 | 11280 | 0.8338 | 0.9082 |
| 0.0142 | 31.0 | 11656 | 0.7783 | 0.9115 |
| 0.0002 | 32.0 | 12032 | 0.7615 | 0.9165 |
| 0.0004 | 33.0 | 12408 | 0.7711 | 0.9098 |
| 0.0127 | 34.0 | 12784 | 0.7865 | 0.9165 |
| 0.0032 | 35.0 | 13160 | 0.8207 | 0.9132 |
| 0.0006 | 36.0 | 13536 | 0.8174 | 0.9098 |
| 0.0001 | 37.0 | 13912 | 0.7992 | 0.9165 |
| 0.0 | 38.0 | 14288 | 0.8040 | 0.9082 |
| 0.0001 | 39.0 | 14664 | 0.8011 | 0.9132 |
| 0.0005 | 40.0 | 15040 | 0.8052 | 0.9115 |
| 0.0001 | 41.0 | 15416 | 0.8158 | 0.9082 |
| 0.0001 | 42.0 | 15792 | 0.8157 | 0.9098 |
| 0.0 | 43.0 | 16168 | 0.8347 | 0.9065 |
| 0.0004 | 44.0 | 16544 | 0.8096 | 0.9048 |
| 0.0087 | 45.0 | 16920 | 0.8231 | 0.9065 |
| 0.0003 | 46.0 | 17296 | 0.8362 | 0.9065 |
| 0.0002 | 47.0 | 17672 | 0.8291 | 0.9098 |
| 0.0046 | 48.0 | 18048 | 0.8341 | 0.9082 |
| 0.0134 | 49.0 | 18424 | 0.8309 | 0.9065 |
| 0.0004 | 50.0 | 18800 | 0.8312 | 0.9065 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
|
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Enrica/Falcon
|
Enrica
| 2023-12-14T07:39:07Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-14T07:39:07Z |
Entry not found
|
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umarigan/LLama-13b-chat-TurkishQA
|
umarigan
| 2023-12-14T07:39:32Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-14T07:39:31Z |
Entry not found
|
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ali-khoshtinat/parsig_tokenizer
|
ali-khoshtinat
| 2023-12-17T16:21:38Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-14T07:43:01Z |
Entry not found
|
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orhay1/SD_Saber_Alter_Fate
|
orhay1
| 2023-12-14T07:47:48Z | 0 | 0 | null |
[
"license:openrail",
"region:us"
] | null | 2023-12-14T07:46:37Z |
---
license: openrail
---
|
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] |
ccwt/my_awesome_opus_books_model
|
ccwt
| 2023-12-14T10:07:24Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:opus_books",
"base_model:t5-small",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text2text-generation
| 2023-12-14T07:50:45Z |
---
license: apache-2.0
base_model: t5-small
tags:
- generated_from_trainer
datasets:
- opus_books
metrics:
- bleu
model-index:
- name: my_awesome_opus_books_model
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: opus_books
type: opus_books
config: en-fr
split: train
args: en-fr
metrics:
- name: Bleu
type: bleu
value: 4.845
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# my_awesome_opus_books_model
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the opus_books dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7201
- Bleu: 4.845
- Gen Len: 17.6363
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-----:|:-------:|
| 1.9554 | 1.0 | 1589 | 1.7201 | 4.845 | 17.6363 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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MaximTitarenkoUIT/ppo-deepseek-coder-test-accumstep_850
|
MaximTitarenkoUIT
| 2023-12-14T07:53:08Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-14T07:53:06Z |
Entry not found
|
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] |
sapan09890/Wse
|
sapan09890
| 2023-12-14T07:54:13Z | 0 | 0 | null |
[
"license:mit",
"region:us"
] | null | 2023-12-14T07:54:13Z |
---
license: mit
---
|
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] |
norispace/marcoroni-openorca
|
norispace
| 2023-12-18T00:19:06Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-12-14T07:54:24Z |
Entry not found
|
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Goffer/whisper-small-hi
|
Goffer
| 2023-12-14T07:57:04Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-14T07:57:04Z |
Entry not found
|
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ADISH007/Aws_donut_10k_incremental_1_Epoch_17
|
ADISH007
| 2023-12-14T08:02:32Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"vision-encoder-decoder",
"endpoints_compatible",
"region:us"
] | null | 2023-12-14T08:02:09Z |
Entry not found
|
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han223/merge_sumok
|
han223
| 2023-12-14T08:05:49Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-14T08:05:49Z |
Entry not found
|
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] |
axljeremy7/bert-base-uncased-finetuned-CLS-cola
|
axljeremy7
| 2023-12-14T08:34:53Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-12-14T08:07:13Z |
Entry not found
|
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] |
SupriyaVanahalli/llama2_13B_chat_priscilla_fine_tuned_adapters_10
|
SupriyaVanahalli
| 2023-12-14T08:09:02Z | 0 | 0 |
peft
|
[
"peft",
"arxiv:1910.09700",
"base_model:meta-llama/Llama-2-13b-chat-hf",
"region:us"
] | null | 2023-12-14T08:09:00Z |
---
library_name: peft
base_model: meta-llama/Llama-2-13b-chat-hf
---
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### Framework versions
- PEFT 0.7.1
|
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JUNJIE99/webqa
|
JUNJIE99
| 2023-12-14T09:22:44Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-14T08:09:40Z |
Entry not found
|
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gunkaynar/en-distilbert-base-uncased
|
gunkaynar
| 2023-12-14T09:37:14Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-12-14T08:11:41Z |
---
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: en-distilbert-base-uncased
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# en-distilbert-base-uncased
This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4834
- Accuracy: 0.7447
- F1: 0.7681
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
### Framework versions
- Transformers 4.33.3
- Pytorch 2.1.1
- Datasets 2.14.7
- Tokenizers 0.11.0
|
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richie1969/woman
|
richie1969
| 2023-12-14T08:12:53Z | 0 | 0 | null |
[
"license:openrail",
"region:us"
] | null | 2023-12-14T08:12:53Z |
---
license: openrail
---
|
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] |
axljeremy7/bert-base-uncased-finetuned-CLS-rte
|
axljeremy7
| 2023-12-18T01:19:16Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"base_model:bert-base-uncased",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-12-14T08:13:07Z |
---
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: bert-base-uncased-finetuned-CLS-RTE
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
config: rte
split: validation
args: rte
metrics:
- name: Accuracy
type: accuracy
value: 0.6967509025270758
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased-finetuned-CLS-RTE
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 2.2843
- Accuracy: 0.6968
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 156 | 0.6285 | 0.6390 |
| No log | 2.0 | 312 | 0.6483 | 0.6679 |
| No log | 3.0 | 468 | 0.8430 | 0.7004 |
| 0.4504 | 4.0 | 624 | 1.2886 | 0.6606 |
| 0.4504 | 5.0 | 780 | 1.6445 | 0.7004 |
| 0.4504 | 6.0 | 936 | 1.8099 | 0.6751 |
| 0.0631 | 7.0 | 1092 | 1.8173 | 0.7040 |
| 0.0631 | 8.0 | 1248 | 1.9799 | 0.7004 |
| 0.0631 | 9.0 | 1404 | 1.9767 | 0.6968 |
| 0.0159 | 10.0 | 1560 | 2.1696 | 0.6715 |
| 0.0159 | 11.0 | 1716 | 2.2142 | 0.6859 |
| 0.0159 | 12.0 | 1872 | 2.2605 | 0.6823 |
| 0.0076 | 13.0 | 2028 | 2.2775 | 0.7040 |
| 0.0076 | 14.0 | 2184 | 2.2719 | 0.6968 |
| 0.0076 | 15.0 | 2340 | 2.2843 | 0.6968 |
### Framework versions
- Transformers 4.36.1
- Pytorch 2.1.2
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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] |
SupriyaVanahalli/llama2_13B_chat_priscilla_fine_tuned_10
|
SupriyaVanahalli
| 2023-12-14T08:28:52Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-12-14T08:13:32Z |
Entry not found
|
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Zlikwid/ZlikwidV3
|
Zlikwid
| 2023-12-14T08:40:39Z | 0 | 0 | null |
[
"license:cc",
"region:us"
] | null | 2023-12-14T08:13:57Z |
---
license: cc
---
|
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JoseEliel/llama-2-7b-AncientLinguist
|
JoseEliel
| 2023-12-14T08:15:29Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-14T08:15:29Z |
Entry not found
|
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giangduong/new-version
|
giangduong
| 2023-12-14T08:21:00Z | 0 | 0 | null |
[
"safetensors",
"region:us"
] | null | 2023-12-14T08:20:02Z |
Entry not found
|
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raulsofia/bert-base-uncased-finetuned-swag
|
raulsofia
| 2023-12-14T08:20:35Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-14T08:20:35Z |
Entry not found
|
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KoladeOdunope/llama-report-vv
|
KoladeOdunope
| 2023-12-14T08:24:09Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-14T08:24:09Z |
Entry not found
|
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ChatRDM/mistral-rdm-origin
|
ChatRDM
| 2023-12-14T08:27:53Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-14T08:27:53Z |
Entry not found
|
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] |
Minirecord/mini_m
|
Minirecord
| 2023-12-14T08:48:20Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-12-14T08:30:18Z |
---
license: apache-2.0
---
|
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] |
LPFLEO/hjkj
|
LPFLEO
| 2023-12-14T08:30:28Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2023-12-14T08:30:28Z |
---
license: apache-2.0
---
|
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SupriyaVanahalli/llama2_13B_chat_priscilla_fine_tuned_10.gguf
|
SupriyaVanahalli
| 2023-12-14T09:02:10Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-14T08:34:14Z |
Entry not found
|
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] |
LilYoda/WoodyTS
|
LilYoda
| 2023-12-14T08:46:25Z | 0 | 0 | null |
[
"license:openrail",
"region:us"
] | null | 2023-12-14T08:35:09Z |
---
license: openrail
---
|
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VidiaOil/VidiaOil
|
VidiaOil
| 2023-12-14T08:38:19Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-14T08:37:30Z |
Vidia Oil: Romania, Preț, Ingrediente, recenzii, Beneficii, Cost, lucrări, farmacie, plângere!
Vidia Oil ajută la ameliorarea tinitusului și a problemelor de auz. Citiți recenzii, preț și reducere! Vidia Oil Romania
Vidia Oil Cumpără acum!! Faceți clic pe linkul de mai jos pentru mai multe informații și obțineți 50% reducere acum !! Grăbiţi-vă!!
citeşte mai mult: https://www.excaliburnutrition.com/VidOilRoma
➢Numele produsului — Vidia Oil
➢Categorie - Suport auditiv sănătos
➢Beneficii principale — Formula de suport auditiv
➢ Compoziție — Compus organic natural
➢ Efecte secundare—NA
➢Evaluare finală: — 4,8
➢ Disponibilitate — Online
➢Oferte & Reduceri; SALVAȚI AZI! CUMPĂRAȚI ACUM PENTRU A cumpăra OFERTA SPECIALA!!!
CE ESTE Vidia Oil?
Vidia Oil este o formulă 100% naturală pentru ameliorarea tinitusului și a altor probleme de auz. Ingredientele constau din vitamine, aminoacizi, ierburi și extracte de plante. Vidia Oil îmbunătățește sănătatea auzului și funcționează în moduri eficiente la orice persoană. ORL (ureche-nas-gât) că produsul este pur și funcționează natural. Oferă o ușurare rapidă, fără a provoca efecte secundare.
Vidia Oil Cumpără acum!! Faceți clic pe linkul de mai jos pentru mai multe informații și obțineți 50% reducere acum !! Grăbiţi-vă!!
citeşte mai mult: https://www.excaliburnutrition.com/VidOilRoma
Vidia Oil Vidia Oil Preț Vidia Oil recenzii Vidia Oil Ingrediente Vidia Oil Beneficii Vidia Oil Efecte secundare Vidia Oil compoziţie Vidia Oil plângere Vidia Oil De unde să cumpăr Vidia Oil Cum se utilizează Vidia Oil Cost Vidia Oil lucrări Vidia Oil forum Vidia Oil original Vidia Oil farmacie
https://www.excaliburnutrition.com/VidOilRoma
https://sites.google.com/view/vidia-oil/home
https://healthtoned.blogspot.com/2023/12/vidia-oil.html
https://www.sunflower-cissp.com/glossary/cissp/7220/vidia-oil-romania
https://enkling.com/read-blog/8841
https://community.thebatraanumerology.com/post/vidia-oil-romania-657ab0a779850b1c1c50e59e
https://community.thebatraanumerology.com/topic/vidia-oil
https://www.mangalworld.com/listing/vidia-oil-romania
https://www.weddingwire.com/website/vidia-and-oil/vidiaoilromania-2
https://medium.com/@healthytalk24x7/vidia-oil-romania-e9caf4bb6392
https://medium.com/@healthytalk24x7/vidia-oil-201271363397
https://infogram.com/vidia-oil-romania-1h9j6qg350n054g?live
https://softisenilspain.hashnode.dev/vidia-oil-romania
https://sway.office.com/6A7WnAvpKbM2ToPb
https://vidiaoil.company.site/
https://gamma.app/docs/Vidia-Oil-Romania-djgol16brwivlgc?mode=doc
https://groups.google.com/g/snshine/c/RKMPKgxIA3c
https://healthytalk24x7.wixsite.com/sunshine/post/vidia-oil-romania
https://nepal.tribe.so/post/vidia-oil-romania-657abe18927d8659da45311d
https://replit.com/@vidiaoil
|
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] |
amalla2/my_awesome_food_model
|
amalla2
| 2023-12-14T08:41:34Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"vit",
"image-classification",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-12-14T08:38:17Z |
---
license: mit
---
|
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Odin8300/midjourney-falcon-7b
|
Odin8300
| 2023-12-14T08:39:11Z | 0 | 0 | null |
[
"license:openrail",
"region:us"
] | null | 2023-12-14T08:38:20Z |
---
license: openrail
---
|
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Realgon/N_bert_sst5_padding0model
|
Realgon
| 2023-12-14T08:48:25Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:bert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-12-14T08:39:03Z |
---
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: N_bert_sst5_padding0model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# N_bert_sst5_padding0model
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 4.1289
- Accuracy: 0.5348
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.2571 | 1.0 | 534 | 1.1825 | 0.4674 |
| 0.9473 | 2.0 | 1068 | 1.0990 | 0.5344 |
| 0.7153 | 3.0 | 1602 | 1.3597 | 0.5122 |
| 0.5464 | 4.0 | 2136 | 1.4853 | 0.5140 |
| 0.3754 | 5.0 | 2670 | 1.7687 | 0.5222 |
| 0.2672 | 6.0 | 3204 | 2.0400 | 0.5294 |
| 0.1965 | 7.0 | 3738 | 2.3921 | 0.5149 |
| 0.1593 | 8.0 | 4272 | 2.5796 | 0.5262 |
| 0.1308 | 9.0 | 4806 | 2.9764 | 0.5281 |
| 0.1074 | 10.0 | 5340 | 3.1247 | 0.5299 |
| 0.0922 | 11.0 | 5874 | 3.4653 | 0.5290 |
| 0.0602 | 12.0 | 6408 | 3.6541 | 0.5217 |
| 0.0487 | 13.0 | 6942 | 3.8270 | 0.5294 |
| 0.0364 | 14.0 | 7476 | 3.9558 | 0.5276 |
| 0.0367 | 15.0 | 8010 | 3.8927 | 0.5380 |
| 0.0218 | 16.0 | 8544 | 4.0195 | 0.5326 |
| 0.0166 | 17.0 | 9078 | 4.0571 | 0.5312 |
| 0.0145 | 18.0 | 9612 | 4.1334 | 0.5335 |
| 0.0102 | 19.0 | 10146 | 4.0970 | 0.5294 |
| 0.0073 | 20.0 | 10680 | 4.1289 | 0.5348 |
### Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.13.3
|
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] |
kenken6696/llama-2-13b-folio_by_ccg2lambda
|
kenken6696
| 2023-12-14T08:39:19Z | 0 | 0 | null |
[
"license:cc-by-4.0",
"region:us"
] | null | 2023-12-14T08:39:18Z |
---
license: cc-by-4.0
---
|
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CantoINC/caption-decomposition-mistral-v0.1
|
CantoINC
| 2023-12-15T05:11:46Z | 0 | 0 | null |
[
"safetensors",
"region:us"
] | null | 2023-12-14T08:39:22Z |
Entry not found
|
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] |
Jailson8/kuromi
|
Jailson8
| 2023-12-14T09:09:01Z | 0 | 0 | null |
[
"license:openrail",
"region:us"
] | null | 2023-12-14T08:39:34Z |
---
license: openrail
---
|
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] |
kenken6696/llama-2-70b-folio_by_ccg2lambda
|
kenken6696
| 2023-12-14T08:39:54Z | 0 | 0 | null |
[
"license:cc-by-4.0",
"region:us"
] | null | 2023-12-14T08:39:54Z |
---
license: cc-by-4.0
---
|
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