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Sayan01/tiny-bert-sst2-distilled | 0e5e7ce792c578721f7dfe3ed7f27f039f2b8466 | 2022-07-14T18:00:24.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
]
| text-classification | false | Sayan01 | null | Sayan01/tiny-bert-sst2-distilled | 9 | null | transformers | 12,700 | Entry not found |
sijunhe/nezha-cn-large | 3e9f8a4c096171b26d40feec300c6c4ae17307a5 | 2022-06-24T03:54:28.000Z | [
"pytorch",
"nezha",
"fill-mask",
"arxiv:1909.00204",
"transformers",
"license:afl-3.0",
"autotrain_compatible"
]
| fill-mask | false | sijunhe | null | sijunhe/nezha-cn-large | 9 | null | transformers | 12,701 | ---
license: afl-3.0
---
**Please use 'Bert' related tokenizer classes and 'Nezha' related model classes**
[NEZHA: Neural Contextualized Representation for Chinese Language Understanding](https://arxiv.org/abs/1909.00204)
Junqiu Wei, Xiaozhe Ren, Xiaoguang Li, Wenyong Huang, Yi Liao, Yasheng Wang, Jiashu Lin, Xin Jiang, Xiao Chen and Qun Liu.
The original checkpoints can be found [here](https://github.com/huawei-noah/Pretrained-Language-Model/tree/master/NEZHA-PyTorch)
## Example Usage
```
from transformers import BertTokenizer, NezhaModel
tokenizer = BertTokenizer.from_pretrained('sijunhe/nezha-cn-large')
model = NezhaModel.from_pretrained("sijunhe/nezha-cn-large")
text = "我爱北京天安门"
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
``` |
davidcechak/DNADeberta_fine | 19f38b2f454a66262b78d0ecb65768070589e7d3 | 2022-06-21T16:34:25.000Z | [
"pytorch",
"deberta",
"text-classification",
"transformers"
]
| text-classification | false | davidcechak | null | davidcechak/DNADeberta_fine | 9 | null | transformers | 12,702 | Entry not found |
davidcechak/DNADeberta_fine_0.6394267984578837 | b9eca035f53aa801ec16167e71c15f61e3633eda | 2022-06-20T21:41:34.000Z | [
"pytorch",
"deberta",
"text-classification",
"transformers"
]
| text-classification | false | davidcechak | null | davidcechak/DNADeberta_fine_0.6394267984578837 | 9 | null | transformers | 12,703 | Entry not found |
fouad-shammary/distilbert-base-uncased-finetuned-emotion | 68be303a57e3c4210b5bac8b4babd55f7bcdb457 | 2022-06-22T12:48:04.000Z | [
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"dataset:emotion",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
]
| text-classification | false | fouad-shammary | null | fouad-shammary/distilbert-base-uncased-finetuned-emotion | 9 | 1 | transformers | 12,704 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9165
- name: F1
type: f1
value: 0.9164107076814402
---
<!-- 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. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2349
- Accuracy: 0.9165
- F1: 0.9164
## 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: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.837 | 1.0 | 250 | 0.3317 | 0.9015 | 0.8999 |
| 0.2563 | 2.0 | 500 | 0.2349 | 0.9165 | 0.9164 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
mirikwa/gro-ner-60k | 0a7099ab9fd223fc8609db841501380adc897bd8 | 2022-06-21T07:49:29.000Z | [
"pytorch",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
]
| token-classification | false | mirikwa | null | mirikwa/gro-ner-60k | 9 | null | transformers | 12,705 | Entry not found |
furyhawk/distilbert-base-uncased-finetuned-clinc | 829948c911f0e27a645b2a049b73c1f2ed175bea | 2022-06-21T09:36:29.000Z | [
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"dataset:clinc_oos",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
]
| text-classification | false | furyhawk | null | furyhawk/distilbert-base-uncased-finetuned-clinc | 9 | null | transformers | 12,706 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- clinc_oos
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased-finetuned-clinc
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: clinc_oos
type: clinc_oos
args: plus
metrics:
- name: Accuracy
type: accuracy
value: 0.915483870967742
---
<!-- 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. -->
# distilbert-base-uncased-finetuned-clinc
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the clinc_oos dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7788
- Accuracy: 0.9155
## 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: 48
- eval_batch_size: 48
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 4.2841 | 1.0 | 318 | 3.2794 | 0.7465 |
| 2.623 | 2.0 | 636 | 1.8719 | 0.8335 |
| 1.5474 | 3.0 | 954 | 1.1629 | 0.8929 |
| 1.014 | 4.0 | 1272 | 0.8621 | 0.9094 |
| 0.7987 | 5.0 | 1590 | 0.7788 | 0.9155 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.11.0
- Datasets 1.16.1
- Tokenizers 0.10.3
|
nvidia/groupvit-gcc-redcaps | 9219591bc69463a890dd366c450ed32c6b0c2573 | 2022-07-08T15:24:15.000Z | [
"pytorch",
"groupvit",
"feature-extraction",
"dataset:red_caps",
"arxiv:2202.11094",
"transformers",
"vision"
]
| feature-extraction | false | nvidia | null | nvidia/groupvit-gcc-redcaps | 9 | 1 | transformers | 12,707 | ---
tags:
- vision
datasets:
- red_caps
---
# Model Card: GroupViT
This checkpoint is uploaded by Jiarui Xu.
## Model Details
The GroupViT model was proposed in [GroupViT: Semantic Segmentation Emerges from Text Supervision](https://arxiv.org/abs/2202.11094) by Jiarui Xu, Shalini De Mello, Sifei Liu, Wonmin Byeon, Thomas Breuel, Jan Kautz, Xiaolong Wang.
Inspired by [CLIP](clip), GroupViT is a vision-language model that can perform zero-shot semantic segmentation on any given vocabulary categories.
### Model Date
June 2022
### Abstract
Grouping and recognition are important components of visual scene understanding, e.g., for object detection and semantic segmentation. With end-to-end deep learning systems, grouping of image regions usually happens implicitly via top-down supervision from pixel-level recognition labels. Instead, in this paper, we propose to bring back the grouping mechanism into deep networks, which allows semantic segments to emerge automatically with only text supervision. We propose a hierarchical Grouping Vision Transformer (GroupViT), which goes beyond the regular grid structure representation and learns to group image regions into progressively larger arbitrary-shaped segments. We train GroupViT jointly with a text encoder on a large-scale image-text dataset via contrastive losses. With only text supervision and without any pixel-level annotations, GroupViT learns to group together semantic regions and successfully transfers to the task of semantic segmentation in a zero-shot manner, i.e., without any further fine-tuning. It achieves a zero-shot accuracy of 52.3% mIoU on the PASCAL VOC 2012 and 22.4% mIoU on PASCAL Context datasets, and performs competitively to state-of-the-art transfer-learning methods requiring greater levels of supervision.
### Documents
- [GroupViT Paper](https://arxiv.org/abs/2202.11094)
### Use with Transformers
```python
from PIL import Image
import requests
from transformers import AutoProcessor, GroupViTModel
processor = AutoProcessor.from_pretrained("nvidia/groupvit-gcc-redcaps")
model = GroupViTModel.from_pretrained("nvidia/groupvit-gcc-redcaps")
url = "http://images.cocodataset.org/val2017/000000039769.jpg"
image = Image.open(requests.get(url, stream=True).raw)
inputs = processor(text=["a photo of a cat", "a photo of a dog"], images=image, return_tensors="pt", padding=True)
outputs = model(**inputs)
logits_per_image = outputs.logits_per_image # this is the image-text similarity score
probs = logits_per_image.softmax(dim=1) # we can take the softmax to get the label probabilities
```
## Data
The model was trained on publicly available image-caption data. This was done through a combination of crawling a handful of websites and using commonly-used pre-existing image datasets such as [YFCC100M](http://projects.dfki.uni-kl.de/yfcc100m/). A large portion of the data comes from our crawling of the internet. This means that the data is more representative of people and societies most connected to the internet which tend to skew towards more developed nations, and younger, male users.
For more code examples, we refer to the [documentation](https://huggingface.co/transformers/model_doc/groupvit.html#).
### BibTeX entry and citation info
```bibtex
@article{xu2022groupvit,
author = {Xu, Jiarui and De Mello, Shalini and Liu, Sifei and Byeon, Wonmin and Breuel, Thomas and Kautz, Jan and Wang, Xiaolong},
title = {GroupViT: Semantic Segmentation Emerges from Text Supervision},
journal = {arXiv preprint arXiv:2202.11094},
year = {2022},
}
```
|
Jeevesh8/std_0pnt2_bert_ft_cola-39 | cce06f2f3f9fb04da7bab200d993c8e3764b14fe | 2022-06-21T13:28:01.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
]
| text-classification | false | Jeevesh8 | null | Jeevesh8/std_0pnt2_bert_ft_cola-39 | 9 | null | transformers | 12,708 | Entry not found |
Jeevesh8/std_0pnt2_bert_ft_cola-29 | b3bb3417ff81a6bdfd583cd86ee2c32e642aa905 | 2022-06-21T13:28:09.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
]
| text-classification | false | Jeevesh8 | null | Jeevesh8/std_0pnt2_bert_ft_cola-29 | 9 | null | transformers | 12,709 | Entry not found |
Jeevesh8/std_0pnt2_bert_ft_cola-42 | 2f8a806ca3bb11e9805d4823171078aa856786c0 | 2022-06-21T13:28:08.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
]
| text-classification | false | Jeevesh8 | null | Jeevesh8/std_0pnt2_bert_ft_cola-42 | 9 | null | transformers | 12,710 | Entry not found |
Jeevesh8/std_0pnt2_bert_ft_cola-38 | 3027562176929c10dcd835b26382c285d51a1327 | 2022-06-21T13:27:43.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
]
| text-classification | false | Jeevesh8 | null | Jeevesh8/std_0pnt2_bert_ft_cola-38 | 9 | null | transformers | 12,711 | Entry not found |
Jeevesh8/std_0pnt2_bert_ft_cola-32 | 1741bf78d18f960658088a55fa6ff97715a7c62e | 2022-06-21T13:27:45.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
]
| text-classification | false | Jeevesh8 | null | Jeevesh8/std_0pnt2_bert_ft_cola-32 | 9 | null | transformers | 12,712 | Entry not found |
Jeevesh8/std_0pnt2_bert_ft_cola-19 | 844beec17d08352fd410c7a9f46c663c009fd541 | 2022-06-21T13:30:22.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
]
| text-classification | false | Jeevesh8 | null | Jeevesh8/std_0pnt2_bert_ft_cola-19 | 9 | null | transformers | 12,713 | Entry not found |
Jeevesh8/std_0pnt2_bert_ft_cola-31 | 56acbfa688f5921c9eda7eb048e93a58f1a23bac | 2022-06-21T13:27:50.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
]
| text-classification | false | Jeevesh8 | null | Jeevesh8/std_0pnt2_bert_ft_cola-31 | 9 | null | transformers | 12,714 | Entry not found |
Jeevesh8/std_0pnt2_bert_ft_cola-13 | 2588d3a9e92d0a3af33907660b414e46893db09d | 2022-06-21T13:28:17.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
]
| text-classification | false | Jeevesh8 | null | Jeevesh8/std_0pnt2_bert_ft_cola-13 | 9 | null | transformers | 12,715 | Entry not found |
Jeevesh8/std_0pnt2_bert_ft_cola-11 | f76d2e94a3be71499fd78145fa168c913faecb1d | 2022-06-21T13:27:46.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
]
| text-classification | false | Jeevesh8 | null | Jeevesh8/std_0pnt2_bert_ft_cola-11 | 9 | null | transformers | 12,716 | Entry not found |
Jeevesh8/std_0pnt2_bert_ft_cola-61 | 41a451fe047da19440d25812667eb3bcca3c1ac0 | 2022-06-21T13:30:24.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
]
| text-classification | false | Jeevesh8 | null | Jeevesh8/std_0pnt2_bert_ft_cola-61 | 9 | null | transformers | 12,717 | Entry not found |
Jeevesh8/std_0pnt2_bert_ft_cola-10 | d2a63839ce5d35166ccd0bdc7f240fcf25a0b5ee | 2022-06-21T13:27:56.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
]
| text-classification | false | Jeevesh8 | null | Jeevesh8/std_0pnt2_bert_ft_cola-10 | 9 | null | transformers | 12,718 | Entry not found |
Jeevesh8/std_0pnt2_bert_ft_cola-47 | 29b7a39c730ec89274c986aacdd66048d4743f68 | 2022-06-21T13:33:50.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
]
| text-classification | false | Jeevesh8 | null | Jeevesh8/std_0pnt2_bert_ft_cola-47 | 9 | null | transformers | 12,719 | Entry not found |
Jeevesh8/std_0pnt2_bert_ft_cola-63 | cb11b6a5ec4d13ccb79a2c0f10775f6dbf893ccd | 2022-06-21T13:28:43.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
]
| text-classification | false | Jeevesh8 | null | Jeevesh8/std_0pnt2_bert_ft_cola-63 | 9 | null | transformers | 12,720 | Entry not found |
Jeevesh8/std_0pnt2_bert_ft_cola-64 | 06e44c7445c6231e745ce9fd0d21c3a2678fda4a | 2022-06-21T13:28:37.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
]
| text-classification | false | Jeevesh8 | null | Jeevesh8/std_0pnt2_bert_ft_cola-64 | 9 | null | transformers | 12,721 | Entry not found |
Jeevesh8/std_0pnt2_bert_ft_cola-57 | c1a048d55a2435ee3dfc903f0f2d86e5e8648f1d | 2022-06-21T13:28:02.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
]
| text-classification | false | Jeevesh8 | null | Jeevesh8/std_0pnt2_bert_ft_cola-57 | 9 | null | transformers | 12,722 | Entry not found |
Jeevesh8/std_0pnt2_bert_ft_cola-23 | 304708b61df4bb0d34f4d1e3cd7d1ef41e1f929a | 2022-06-21T13:30:01.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
]
| text-classification | false | Jeevesh8 | null | Jeevesh8/std_0pnt2_bert_ft_cola-23 | 9 | null | transformers | 12,723 | Entry not found |
Jeevesh8/std_0pnt2_bert_ft_cola-59 | 4e0041724760f0fbb713b45f182e8e8c27f3a944 | 2022-06-21T13:28:07.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
]
| text-classification | false | Jeevesh8 | null | Jeevesh8/std_0pnt2_bert_ft_cola-59 | 9 | null | transformers | 12,724 | Entry not found |
Jeevesh8/std_0pnt2_bert_ft_cola-34 | 64c536c3b26ae463679ba48cdf67fe3097e1513a | 2022-06-21T13:27:53.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
]
| text-classification | false | Jeevesh8 | null | Jeevesh8/std_0pnt2_bert_ft_cola-34 | 9 | null | transformers | 12,725 | Entry not found |
kktoto/tiny_focal_v2_label | 74525b170ad9daafa6713dcbaf8e14e9af3d4bfe | 2022-06-22T05:55:32.000Z | [
"pytorch",
"tensorboard",
"bert",
"token-classification",
"transformers",
"generated_from_trainer",
"model-index",
"autotrain_compatible"
]
| token-classification | false | kktoto | null | kktoto/tiny_focal_v2_label | 9 | null | transformers | 12,726 | ---
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: tiny_focal_v2_label
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. -->
# tiny_focal_v2_label
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0558
- Precision: 0.6979
- Recall: 0.6747
- F1: 0.6861
- Accuracy: 0.9513
## 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: 3e-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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0661 | 1.0 | 5561 | 0.0616 | 0.6850 | 0.6202 | 0.6510 | 0.9457 |
| 0.0613 | 2.0 | 11122 | 0.0587 | 0.6952 | 0.6351 | 0.6638 | 0.9480 |
| 0.0596 | 3.0 | 16683 | 0.0577 | 0.6814 | 0.6679 | 0.6746 | 0.9485 |
| 0.0555 | 4.0 | 22244 | 0.0567 | 0.6855 | 0.6693 | 0.6773 | 0.9492 |
| 0.0543 | 5.0 | 27805 | 0.0560 | 0.6966 | 0.6657 | 0.6808 | 0.9503 |
| 0.0529 | 6.0 | 33366 | 0.0558 | 0.7060 | 0.6587 | 0.6816 | 0.9510 |
| 0.052 | 7.0 | 38927 | 0.0552 | 0.7009 | 0.6662 | 0.6831 | 0.9510 |
| 0.0506 | 8.0 | 44488 | 0.0559 | 0.6921 | 0.6783 | 0.6852 | 0.9508 |
| 0.0501 | 9.0 | 50049 | 0.0556 | 0.6991 | 0.6716 | 0.6851 | 0.9512 |
| 0.0491 | 10.0 | 55610 | 0.0558 | 0.6979 | 0.6747 | 0.6861 | 0.9513 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
Elron/deberta-v3-large-emotion | cd8f99963e4e4b1c23cdf07da9d61bcd7f7aacba | 2022-06-22T09:48:01.000Z | [
"pytorch",
"deberta-v2",
"text-classification",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index"
]
| text-classification | false | Elron | null | Elron/deberta-v3-large-emotion | 9 | null | transformers | 12,727 | ---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: deberta-v3-large
results: []
---
# deberta-v3-large-sentiment
This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on an [tweet_eval](https://huggingface.co/datasets/tweet_eval) dataset.
## Model description
Test set results:
| Model | Emotion | Hate | Irony | Offensive | Sentiment |
| ------------- | ------------- | ------------- | ------------- | ------------- | ------------- |
| deberta-v3-large | **86.3** | **61.3** | **87.1** | **86.4** | **73.9** |
| BERTweet | 79.3 | - | 82.1 | 79.5 | 73.4 |
| RoB-RT | 79.5 | 52.3 | 61.7 | 80.5 | 69.3 |
[source:papers_with_code](https://paperswithcode.com/sota/sentiment-analysis-on-tweeteval)
## Intended uses & limitations
Classifying attributes of interest on tweeter like data.
## Training and evaluation data
[tweet_eval](https://huggingface.co/datasets/tweet_eval) dataset.
## Training procedure
Fine tuned and evaluated with [run_glue.py]()
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 7e-06
- 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
- lr_scheduler_warmup_steps: 50
- num_epochs: 10.0
- label_smoothing_factor: 0.1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.2787 | 0.49 | 100 | 1.1127 | 0.4866 |
| 1.089 | 0.98 | 200 | 0.9668 | 0.7139 |
| 0.9134 | 1.47 | 300 | 0.8720 | 0.7834 |
| 0.8618 | 1.96 | 400 | 0.7726 | 0.7941 |
| 0.686 | 2.45 | 500 | 0.7337 | 0.8209 |
| 0.6333 | 2.94 | 600 | 0.7350 | 0.8235 |
| 0.5765 | 3.43 | 700 | 0.7561 | 0.8235 |
| 0.5502 | 3.92 | 800 | 0.7273 | 0.8476 |
| 0.5049 | 4.41 | 900 | 0.8137 | 0.8102 |
| 0.4695 | 4.9 | 1000 | 0.7581 | 0.8289 |
| 0.4657 | 5.39 | 1100 | 0.8404 | 0.8048 |
| 0.4549 | 5.88 | 1200 | 0.7800 | 0.8369 |
| 0.4305 | 6.37 | 1300 | 0.8575 | 0.8235 |
| 0.4209 | 6.86 | 1400 | 0.8572 | 0.8102 |
| 0.3983 | 7.35 | 1500 | 0.8392 | 0.8316 |
| 0.4139 | 7.84 | 1600 | 0.8152 | 0.8209 |
| 0.393 | 8.33 | 1700 | 0.8261 | 0.8289 |
| 0.3979 | 8.82 | 1800 | 0.8328 | 0.8235 |
| 0.3928 | 9.31 | 1900 | 0.8364 | 0.8209 |
| 0.3848 | 9.8 | 2000 | 0.8322 | 0.8235 |
### Framework versions
- Transformers 4.20.0.dev0
- Pytorch 1.9.0
- Datasets 2.2.2
- Tokenizers 0.11.6
|
robingeibel/reformer-finetuned-big_patent | c89fb7aad341ecb5d8e4ef3d377a6f9bcb32cf2d | 2022-06-27T08:12:43.000Z | [
"pytorch",
"tensorboard",
"reformer",
"fill-mask",
"dataset:big_patent",
"transformers",
"generated_from_trainer",
"model-index",
"autotrain_compatible"
]
| fill-mask | false | robingeibel | null | robingeibel/reformer-finetuned-big_patent | 9 | null | transformers | 12,728 | ---
tags:
- generated_from_trainer
datasets:
- big_patent
model-index:
- name: reformer-finetuned-big_patent
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. -->
# reformer-finetuned-big_patent
This model is a fine-tuned version of [robingeibel/reformer-finetuned-big_patent](https://huggingface.co/robingeibel/reformer-finetuned-big_patent) on the big_patent dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0000
## 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: 3.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 0.0 | 1.0 | 5973 | 0.0000 |
| 0.0 | 2.0 | 11946 | 0.0000 |
| 0.0 | 3.0 | 17919 | 0.0000 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
shaneweisz/DialoGPT-finetuned-multiCONAN | 0dca5445a6c982d63e9ceeecbb0963709e358da8 | 2022-07-12T14:56:19.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
]
| text-generation | false | shaneweisz | null | shaneweisz/DialoGPT-finetuned-multiCONAN | 9 | null | transformers | 12,729 | Entry not found |
kullackaan/sentiment-tweets | d84ee721fde8373fbf66e00e87b6e7ce44bce4c3 | 2022-06-22T22:07:54.000Z | [
"pytorch",
"bert",
"text-classification",
"transformers",
"license:afl-3.0"
]
| text-classification | false | kullackaan | null | kullackaan/sentiment-tweets | 9 | null | transformers | 12,730 | ---
license: afl-3.0
---
|
doraemon1998/distilroberta-base-finetuned-wikitext2 | dab306515d08bd0310c5f6d773437a5419335023 | 2022-06-24T08:28:10.000Z | [
"pytorch",
"tensorboard",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
]
| fill-mask | false | doraemon1998 | null | doraemon1998/distilroberta-base-finetuned-wikitext2 | 9 | null | transformers | 12,731 | Entry not found |
kktoto/tiny_focal_v3 | fa1d8738e47cb7886bcb7cbdc1b0930daea84625 | 2022-06-25T08:54:15.000Z | [
"pytorch",
"tensorboard",
"bert",
"token-classification",
"transformers",
"generated_from_trainer",
"model-index",
"autotrain_compatible"
]
| token-classification | false | kktoto | null | kktoto/tiny_focal_v3 | 9 | null | transformers | 12,732 | ---
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: tiny_focal_v3
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. -->
# tiny_focal_v3
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0023
- Precision: 0.6975
- Recall: 0.6822
- F1: 0.6898
- Accuracy: 0.9515
## 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: 3e-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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.004 | 1.0 | 5561 | 0.0032 | 0.6900 | 0.6102 | 0.6477 | 0.9454 |
| 0.0032 | 2.0 | 11122 | 0.0028 | 0.6901 | 0.6406 | 0.6644 | 0.9477 |
| 0.0029 | 3.0 | 16683 | 0.0026 | 0.6956 | 0.6509 | 0.6725 | 0.9490 |
| 0.0025 | 4.0 | 22244 | 0.0025 | 0.6838 | 0.6764 | 0.6801 | 0.9493 |
| 0.0024 | 5.0 | 27805 | 0.0024 | 0.6954 | 0.6715 | 0.6832 | 0.9504 |
| 0.0023 | 6.0 | 33366 | 0.0024 | 0.7125 | 0.6524 | 0.6811 | 0.9512 |
| 0.0021 | 7.0 | 38927 | 0.0023 | 0.6999 | 0.6748 | 0.6872 | 0.9514 |
| 0.0019 | 8.0 | 44488 | 0.0024 | 0.6962 | 0.6820 | 0.6890 | 0.9513 |
| 0.0019 | 9.0 | 50049 | 0.0023 | 0.7005 | 0.6775 | 0.6888 | 0.9516 |
| 0.0018 | 10.0 | 55610 | 0.0023 | 0.6975 | 0.6822 | 0.6898 | 0.9515 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
kktoto/tiny_focal_alpah | 1cd569c0595f9a5eb5c593efb974ab3405262a83 | 2022-06-27T13:47:19.000Z | [
"pytorch",
"tensorboard",
"bert",
"token-classification",
"transformers",
"generated_from_trainer",
"model-index",
"autotrain_compatible"
]
| token-classification | false | kktoto | null | kktoto/tiny_focal_alpah | 9 | null | transformers | 12,733 | ---
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: tiny_focal_alpah
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. -->
# tiny_focal_alpah
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0492
- Precision: 0.6951
- Recall: 0.6796
- F1: 0.6873
- Accuracy: 0.9512
## 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: 3e-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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0588 | 1.0 | 5561 | 0.0548 | 0.6801 | 0.6235 | 0.6506 | 0.9453 |
| 0.054 | 2.0 | 11122 | 0.0521 | 0.6850 | 0.6478 | 0.6659 | 0.9476 |
| 0.0525 | 3.0 | 16683 | 0.0509 | 0.6834 | 0.6676 | 0.6754 | 0.9486 |
| 0.0492 | 4.0 | 22244 | 0.0503 | 0.6829 | 0.6754 | 0.6791 | 0.9491 |
| 0.0482 | 5.0 | 27805 | 0.0500 | 0.6917 | 0.6727 | 0.6820 | 0.9501 |
| 0.0471 | 6.0 | 33366 | 0.0491 | 0.7085 | 0.6546 | 0.6805 | 0.9510 |
| 0.0459 | 7.0 | 38927 | 0.0486 | 0.6964 | 0.6746 | 0.6853 | 0.9510 |
| 0.0448 | 8.0 | 44488 | 0.0495 | 0.6922 | 0.6813 | 0.6867 | 0.9509 |
| 0.044 | 9.0 | 50049 | 0.0491 | 0.6961 | 0.6755 | 0.6857 | 0.9511 |
| 0.0433 | 10.0 | 55610 | 0.0492 | 0.6951 | 0.6796 | 0.6873 | 0.9512 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
ryo0634/bert-base-zip-dependency-encoder-en-0 | 3ae715646427cbcda54cb2f7e0c86022c61e6e57 | 2022-06-29T03:44:45.000Z | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
]
| fill-mask | false | ryo0634 | null | ryo0634/bert-base-zip-dependency-encoder-en-0 | 9 | null | transformers | 12,734 | Entry not found |
Jour/tiny-m2m100-test | 42e494f1316eb2cac8a51fb601ddec42d8e93a02 | 2022-06-29T10:22:01.000Z | [
"pytorch",
"m2m_100",
"text2text-generation",
"transformers",
"autotrain_compatible"
]
| text2text-generation | false | Jour | null | Jour/tiny-m2m100-test | 9 | null | transformers | 12,735 | Entry not found |
Jeevesh8/goog_bert_ft_cola-1 | c57c0cc6e7e8a623f0c0138573f4082a8ed75f95 | 2022-06-29T17:31:50.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
]
| text-classification | false | Jeevesh8 | null | Jeevesh8/goog_bert_ft_cola-1 | 9 | null | transformers | 12,736 | Entry not found |
Jeevesh8/goog_bert_ft_cola-31 | 1aa30e0accad9595bf1688b33b77b6d6d456c5d3 | 2022-06-29T17:34:07.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
]
| text-classification | false | Jeevesh8 | null | Jeevesh8/goog_bert_ft_cola-31 | 9 | null | transformers | 12,737 | Entry not found |
Jeevesh8/goog_bert_ft_cola-33 | 4f39ac4411a7c6ba1e4c24a989c05955e133ed4d | 2022-06-29T17:34:18.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
]
| text-classification | false | Jeevesh8 | null | Jeevesh8/goog_bert_ft_cola-33 | 9 | null | transformers | 12,738 | Entry not found |
Jeevesh8/goog_bert_ft_cola-35 | 56c899778e58c31df5b668152a16ee064fbb5af7 | 2022-06-29T17:33:52.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
]
| text-classification | false | Jeevesh8 | null | Jeevesh8/goog_bert_ft_cola-35 | 9 | null | transformers | 12,739 | Entry not found |
Jeevesh8/goog_bert_ft_cola-46 | 773b768794dfc49f8dd4a49371ee7710ef53d071 | 2022-06-29T17:34:03.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
]
| text-classification | false | Jeevesh8 | null | Jeevesh8/goog_bert_ft_cola-46 | 9 | null | transformers | 12,740 | Entry not found |
Jeevesh8/goog_bert_ft_cola-68 | f1167bc00f2f6044372d6893bab132e66be69eff | 2022-06-29T17:33:41.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
]
| text-classification | false | Jeevesh8 | null | Jeevesh8/goog_bert_ft_cola-68 | 9 | null | transformers | 12,741 | Entry not found |
Jeevesh8/goog_bert_ft_cola-60 | ed202e99b8ba0a1c59b224d61833a9f4aa626ce5 | 2022-06-29T17:34:08.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
]
| text-classification | false | Jeevesh8 | null | Jeevesh8/goog_bert_ft_cola-60 | 9 | null | transformers | 12,742 | Entry not found |
Jeevesh8/goog_bert_ft_cola-74 | b3aaf40793cc4c26b10cea1838957585a34e90a6 | 2022-06-29T17:35:23.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
]
| text-classification | false | Jeevesh8 | null | Jeevesh8/goog_bert_ft_cola-74 | 9 | null | transformers | 12,743 | Entry not found |
Jeevesh8/goog_bert_ft_cola-52 | 60b14cbe3bc54cf9caf3d690a0ecd62d32361dbb | 2022-06-29T17:34:23.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
]
| text-classification | false | Jeevesh8 | null | Jeevesh8/goog_bert_ft_cola-52 | 9 | null | transformers | 12,744 | Entry not found |
Jeevesh8/goog_bert_ft_cola-48 | 52b50c825ceff43562ce1c5fcf3b71b3b2262ee6 | 2022-06-29T17:34:51.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
]
| text-classification | false | Jeevesh8 | null | Jeevesh8/goog_bert_ft_cola-48 | 9 | null | transformers | 12,745 | Entry not found |
Jeevesh8/goog_bert_ft_cola-56 | 9908542549ee23041355df4c50003ce7669da093 | 2022-06-29T17:34:21.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
]
| text-classification | false | Jeevesh8 | null | Jeevesh8/goog_bert_ft_cola-56 | 9 | null | transformers | 12,746 | Entry not found |
Jeevesh8/goog_bert_ft_cola-64 | 3088598358942a59903410a50d38d58606bb1e52 | 2022-06-29T17:35:33.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
]
| text-classification | false | Jeevesh8 | null | Jeevesh8/goog_bert_ft_cola-64 | 9 | null | transformers | 12,747 | Entry not found |
Jeevesh8/goog_bert_ft_cola-58 | 093267db65e51d760f7ff3cbcd6b640b9e16312e | 2022-06-29T17:35:25.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
]
| text-classification | false | Jeevesh8 | null | Jeevesh8/goog_bert_ft_cola-58 | 9 | null | transformers | 12,748 | Entry not found |
Jeevesh8/goog_bert_ft_cola-77 | 2501352ae15a26ed3a071e47e7fe88fceda490fc | 2022-06-29T17:34:04.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
]
| text-classification | false | Jeevesh8 | null | Jeevesh8/goog_bert_ft_cola-77 | 9 | null | transformers | 12,749 | Entry not found |
Jeevesh8/goog_bert_ft_cola-82 | e516e4dc81f5c5fee869cebdf18a69fd6da5a247 | 2022-06-29T17:33:50.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
]
| text-classification | false | Jeevesh8 | null | Jeevesh8/goog_bert_ft_cola-82 | 9 | null | transformers | 12,750 | Entry not found |
Jeevesh8/goog_bert_ft_cola-81 | 4f5a1df14c6086cbb465c75a767b85f4e6815656 | 2022-06-29T17:33:48.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
]
| text-classification | false | Jeevesh8 | null | Jeevesh8/goog_bert_ft_cola-81 | 9 | null | transformers | 12,751 | Entry not found |
Jeevesh8/goog_bert_ft_cola-83 | e9d872809b8cb883adbdc72c2f90e3ac4f0b0181 | 2022-06-29T17:33:59.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
]
| text-classification | false | Jeevesh8 | null | Jeevesh8/goog_bert_ft_cola-83 | 9 | null | transformers | 12,752 | Entry not found |
Jeevesh8/goog_bert_ft_cola-78 | fc67d07c3d7bd0b2db85b0d15f6b46427da1da37 | 2022-06-29T17:34:04.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
]
| text-classification | false | Jeevesh8 | null | Jeevesh8/goog_bert_ft_cola-78 | 9 | null | transformers | 12,753 | Entry not found |
Jeevesh8/goog_bert_ft_cola-29 | f0a58f94a0c20853974e0ebfe52293a1d14e5501 | 2022-06-29T17:48:29.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
]
| text-classification | false | Jeevesh8 | null | Jeevesh8/goog_bert_ft_cola-29 | 9 | null | transformers | 12,754 | Entry not found |
sexomq/TeoBot-Romanian-medium2 | 9e8a595c450796ae2bbf24c8446ed5f17272f663 | 2022-06-29T21:27:42.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
| conversational | false | sexomq | null | sexomq/TeoBot-Romanian-medium2 | 9 | null | transformers | 12,755 | ---
tags:
- conversational
--- |
TheDiamondKing/Discord-Philosophy-Medium | 98d419fdc66e7f706df36f4c5270b47b3d6f299c | 2022-06-29T21:26:01.000Z | [
"pytorch",
"roberta",
"fill-mask",
"transformers",
"license:mit",
"autotrain_compatible"
]
| fill-mask | false | TheDiamondKing | null | TheDiamondKing/Discord-Philosophy-Medium | 9 | null | transformers | 12,756 | ---
license: mit
---
Medium-Sized model trained with philosophical questions ( mainly from discord )
~11000 Messages |
tbasic5/distilbert-base-uncased-finetuned-emotion | 0910f2b949726dce5ea0dbf69560fc325f050a8c | 2022-06-29T22:21:00.000Z | [
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"dataset:emotion",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
]
| text-classification | false | tbasic5 | null | tbasic5/distilbert-base-uncased-finetuned-emotion | 9 | null | transformers | 12,757 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.925
- name: F1
type: f1
value: 0.925022224520608
---
<!-- 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. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2222
- Accuracy: 0.925
- F1: 0.9250
## 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: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8521 | 1.0 | 250 | 0.3164 | 0.907 | 0.9038 |
| 0.2549 | 2.0 | 500 | 0.2222 | 0.925 | 0.9250 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
mhaegeman/autotrain-country-recognition-1059336697 | ccff26a442dc746dd0a7bab024d5fd16d0202a2b | 2022-06-30T08:35:28.000Z | [
"pytorch",
"distilbert",
"text-classification",
"en",
"dataset:mhaegeman/autotrain-data-country-recognition",
"transformers",
"autotrain",
"co2_eq_emissions"
]
| text-classification | false | mhaegeman | null | mhaegeman/autotrain-country-recognition-1059336697 | 9 | null | transformers | 12,758 | ---
tags: autotrain
language: en
widget:
- text: "I love AutoTrain 🤗"
datasets:
- mhaegeman/autotrain-data-country-recognition
co2_eq_emissions: 0.02952188223491361
---
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 1059336697
- CO2 Emissions (in grams): 0.02952188223491361
## Validation Metrics
- Loss: 0.06108148396015167
- Accuracy: 0.9879569162920872
- Macro F1: 0.9765004449554612
- Micro F1: 0.9879569162920872
- Weighted F1: 0.9879450113590053
- Macro Precision: 0.9784321161207384
- Micro Precision: 0.9879569162920872
- Weighted Precision: 0.9880404765946114
- Macro Recall: 0.9748417542427885
- Micro Recall: 0.9879569162920872
- Weighted Recall: 0.9879569162920872
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/mhaegeman/autotrain-country-recognition-1059336697
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("mhaegeman/autotrain-country-recognition-1059336697", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("mhaegeman/autotrain-country-recognition-1059336697", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
``` |
pfrdn/wav2vec2-large-xls-r-300m-turkish-colab | 8fa1f7da0b36235833ab4a9bd25a69a1b46deb08 | 2022-07-04T16:19:39.000Z | [
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"transformers"
]
| automatic-speech-recognition | false | pfrdn | null | pfrdn/wav2vec2-large-xls-r-300m-turkish-colab | 9 | null | transformers | 12,759 | Entry not found |
gary109/ai-light-dance_singing3_ft_wav2vec2-large-xlsr-53 | 77e29829170655fb4486f9233702675be214e4d7 | 2022-07-07T09:10:42.000Z | [
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"transformers",
"gary109/AI_Light_Dance",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
]
| automatic-speech-recognition | false | gary109 | null | gary109/ai-light-dance_singing3_ft_wav2vec2-large-xlsr-53 | 9 | null | transformers | 12,760 | ---
license: apache-2.0
tags:
- automatic-speech-recognition
- gary109/AI_Light_Dance
- generated_from_trainer
model-index:
- name: ai-light-dance_singing3_ft_wav2vec2-large-xlsr-53
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. -->
# ai-light-dance_singing3_ft_wav2vec2-large-xlsr-53
This model is a fine-tuned version of [gary109/ai-light-dance_singing3_ft_wav2vec2-large-xlsr-53](https://huggingface.co/gary109/ai-light-dance_singing3_ft_wav2vec2-large-xlsr-53) on the GARY109/AI_LIGHT_DANCE - ONSET-SINGING3 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8797
- Wer: 0.5513
## 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: 5e-06
- train_batch_size: 2
- eval_batch_size: 2
- 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: 30.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 0.9613 | 1.0 | 2309 | 1.0171 | 0.7271 |
| 0.8254 | 2.0 | 4618 | 0.9771 | 0.6650 |
| 0.7406 | 3.0 | 6927 | 0.9174 | 0.6420 |
| 0.74 | 4.0 | 9236 | 0.9551 | 0.6371 |
| 0.5855 | 5.0 | 11545 | 0.9262 | 0.6453 |
| 0.5536 | 6.0 | 13854 | 0.9056 | 0.5894 |
| 0.505 | 7.0 | 16163 | 0.9166 | 0.6029 |
| 0.449 | 8.0 | 18472 | 0.8816 | 0.5873 |
| 0.4219 | 9.0 | 20781 | 0.8970 | 0.5589 |
| 0.5764 | 10.0 | 23090 | 0.9189 | 0.5649 |
| 0.5075 | 11.0 | 25399 | 0.8797 | 0.5513 |
| 0.4366 | 12.0 | 27708 | 0.9011 | 0.5567 |
| 0.4915 | 13.0 | 30017 | 0.9248 | 0.5455 |
| 0.3554 | 14.0 | 32326 | 0.9309 | 0.5374 |
| 0.3975 | 15.0 | 34635 | 0.9103 | 0.5259 |
| 0.4119 | 16.0 | 36944 | 0.9402 | 0.5290 |
| 0.267 | 17.0 | 39253 | 0.9479 | 0.5115 |
| 0.3107 | 18.0 | 41562 | 0.9428 | 0.5099 |
| 0.2684 | 19.0 | 43871 | 0.9508 | 0.5133 |
| 0.2125 | 20.0 | 46180 | 0.9737 | 0.5097 |
| 0.3149 | 21.0 | 48489 | 0.9992 | 0.5095 |
| 0.2313 | 22.0 | 50798 | 1.0037 | 0.5059 |
| 0.2674 | 23.0 | 53107 | 1.0091 | 0.5040 |
| 0.2056 | 24.0 | 55416 | 1.0082 | 0.5076 |
| 0.2781 | 25.0 | 57725 | 1.0160 | 0.5015 |
| 0.2005 | 26.0 | 60034 | 1.0390 | 0.5131 |
| 0.2221 | 27.0 | 62343 | 1.0401 | 0.5074 |
| 0.1857 | 28.0 | 64652 | 1.0484 | 0.5096 |
| 0.1562 | 29.0 | 66961 | 1.0516 | 0.5064 |
| 0.3027 | 30.0 | 69270 | 1.0543 | 0.5049 |
### Framework versions
- Transformers 4.21.0.dev0
- Pytorch 1.9.1+cu102
- Datasets 2.3.3.dev0
- Tokenizers 0.12.1
|
Sayan01/tiny-bert-stsb-distilled | 4acbd7bab4a61c068f35a4479930aa4b17acbd76 | 2022-07-07T15:16:15.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
]
| text-classification | false | Sayan01 | null | Sayan01/tiny-bert-stsb-distilled | 9 | null | transformers | 12,761 | Entry not found |
Pro0100Hy6/test_trainer | 04a8bd9c9a6e3e26434d4a74e48e14fea3dcf58c | 2022-07-03T17:45:59.000Z | [
"pytorch",
"bert",
"text-classification",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index"
]
| text-classification | false | Pro0100Hy6 | null | Pro0100Hy6/test_trainer | 9 | null | transformers | 12,762 | ---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: test_trainer
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. -->
# test_trainer
This model is a fine-tuned version of [cointegrated/rubert-tiny](https://huggingface.co/cointegrated/rubert-tiny) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7773
- Accuracy: 0.6375
## 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: 5e-05
- 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
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.7753 | 1.0 | 400 | 0.7773 | 0.6375 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
Vinz9899/dumy-model | 63ea229ecbce68cf6edb1543396471dc7912aa90 | 2022-07-03T18:03:43.000Z | [
"pytorch",
"bert",
"text-classification",
"transformers"
]
| text-classification | false | Vinz9899 | null | Vinz9899/dumy-model | 9 | null | transformers | 12,763 | Entry not found |
sijunhe/tiny_roformer_v2_test | df60da6caa4f5283bb9c349c384f338e47671f89 | 2022-07-09T15:48:11.000Z | [
"pytorch",
"roformer",
"fill-mask",
"zh",
"transformers",
"roformer-v2",
"autotrain_compatible"
]
| fill-mask | false | sijunhe | null | sijunhe/tiny_roformer_v2_test | 9 | null | transformers | 12,764 | ---
language: zh
tags:
- roformer-v2
- pytorch
inference: False
---
this is a test model of RoFormer V2
|
LACAI/roberta-large-PFG-progression | 04ce85bf2215067857c10a35b30c662251ecc07b | 2022-07-04T19:16:52.000Z | [
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"transformers",
"license:mit"
]
| text-classification | false | LACAI | null | LACAI/roberta-large-PFG-progression | 9 | null | transformers | 12,765 | ---
license: mit
---
Base model: [roberta-large](https://huggingface.co/roberta-large)
Fine tuned as a progression model (to predict the acceptability of a dialogue) on the [Persuasion For Good Dataset](https://gitlab.com/ucdavisnlp/persuasionforgood) (Wang et al., 2019):
Given a complete dialogue from (or in the style of) Persuasion For Good, the task is to predict a numeric score typically in the range (-3, 3) where a higher score means a more acceptable dialogue in context of the donation solicitation task.
**Example input**: `How are you?</s>Good! how about yourself?</s>Great. Would you like to donate today to help the children?</s>`
For more context and usage information see [https://github.rpi.edu/LACAI/dialogue-progression](https://github.rpi.edu/LACAI/dialogue-progression). |
Eleven/xlm-roberta-base-finetuned-panx-de | 1f281bfee00cb72e6e15fc0ad6f1fbdf4cb4f6e4 | 2022-07-05T15:21:55.000Z | [
"pytorch",
"tensorboard",
"xlm-roberta",
"token-classification",
"dataset:xtreme",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index",
"autotrain_compatible"
]
| token-classification | false | Eleven | null | Eleven/xlm-roberta-base-finetuned-panx-de | 9 | null | transformers | 12,766 | ---
license: mit
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-de
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xtreme
type: xtreme
args: PAN-X.de
metrics:
- name: F1
type: f1
value: 0.8591509380490846
---
<!-- 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. -->
# xlm-roberta-base-finetuned-panx-de
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1377
- F1: 0.8592
## 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: 5e-05
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2792 | 1.0 | 525 | 0.1578 | 0.8129 |
| 0.1279 | 2.0 | 1050 | 0.1420 | 0.8439 |
| 0.0836 | 3.0 | 1575 | 0.1377 | 0.8592 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
aihub007/convnext-tiny-224-finetuned-eurosat-albumentations | d1a672d1cd6adebbece54277f0e42ab01ad6fca8 | 2022-07-06T03:02:41.000Z | [
"pytorch",
"tensorboard",
"convnext",
"image-classification",
"dataset:image_folder",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
]
| image-classification | false | aihub007 | null | aihub007/convnext-tiny-224-finetuned-eurosat-albumentations | 9 | null | transformers | 12,767 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- image_folder
metrics:
- accuracy
model-index:
- name: convnext-tiny-224-finetuned-eurosat-albumentations
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: image_folder
type: image_folder
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9803703703703703
---
<!-- 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. -->
# convnext-tiny-224-finetuned-eurosat-albumentations
This model is a fine-tuned version of [facebook/convnext-tiny-224](https://huggingface.co/facebook/convnext-tiny-224) on the image_folder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0886
- Accuracy: 0.9804
## 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: 5e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.3879 | 1.0 | 95 | 0.2927 | 0.9567 |
| 0.1095 | 2.0 | 190 | 0.1102 | 0.9759 |
| 0.0911 | 3.0 | 285 | 0.0886 | 0.9804 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
ultra-coder54732/roberta-base-prop-16-train-set | e21145d7ef937c6e9cb4aba1734aab2613c226ba | 2022-07-21T06:54:31.000Z | [
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index"
]
| text-classification | false | ultra-coder54732 | null | ultra-coder54732/roberta-base-prop-16-train-set | 9 | null | transformers | 12,768 | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: roberta-base-prop-16-train-set
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. -->
# roberta-base-prop-16-train-set
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
## 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: 5e-05
- 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
- num_epochs: 3.0
### Training results
### Framework versions
- Transformers 4.20.1
- Pytorch 1.12.0+cpu
- Datasets 2.3.2
- Tokenizers 0.12.1
|
f00d/distilgpt2-finetuned-wikitext2 | dd6543ae02c11981476aaa9ddafd16f3a7073e73 | 2022-07-06T09:31:58.000Z | [
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
]
| text-generation | false | f00d | null | f00d/distilgpt2-finetuned-wikitext2 | 9 | null | transformers | 12,769 | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: distilgpt2-finetuned-wikitext2
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. -->
# distilgpt2-finetuned-wikitext2
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.6421
## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 3.7602 | 1.0 | 2334 | 3.6669 |
| 3.653 | 2.0 | 4668 | 3.6472 |
| 3.6006 | 3.0 | 7002 | 3.6421 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
ltrctelugu/bigram | c3f635e4025f6f0689bddd2e9b097e845cf3d1fa | 2022-07-07T01:00:29.000Z | [
"pytorch",
"bert",
"text-classification",
"transformers"
]
| text-classification | false | ltrctelugu | null | ltrctelugu/bigram | 9 | null | transformers | 12,770 | hello
|
ajders/nl_electra | 017aa76a8406e047f5175dfc38700c6b3ee2c960 | 2022-07-27T22:38:48.000Z | [
"pytorch",
"electra",
"fill-mask",
"transformers",
"generated_from_trainer",
"model-index",
"autotrain_compatible"
]
| fill-mask | false | ajders | null | ajders/nl_electra | 9 | null | transformers | 12,771 | ---
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: nl_electra
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. -->
# nl_electra
This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4650
- Accuracy: 0.5392
## 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: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 703
- gradient_accumulation_steps: 32
- total_train_batch_size: 512
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 8000
- num_epochs: 400.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:------:|:---------------:|:--------:|
| No log | 0.67 | 500 | 9.9977 | 0.0486 |
| No log | 1.35 | 1000 | 9.5620 | 0.0543 |
| No log | 2.02 | 1500 | 8.9306 | 0.0741 |
| No log | 2.69 | 2000 | 8.2617 | 0.0826 |
| No log | 3.36 | 2500 | 7.6880 | 0.0792 |
| No log | 4.04 | 3000 | 7.3316 | 0.0757 |
| No log | 4.71 | 3500 | 7.1944 | 0.0747 |
| No log | 5.38 | 4000 | 7.1349 | 0.0802 |
| No log | 6.06 | 4500 | 7.0752 | 0.0887 |
| 8.201 | 6.73 | 5000 | 7.0046 | 0.1021 |
| 8.201 | 7.4 | 5500 | 6.9295 | 0.1090 |
| 8.201 | 8.08 | 6000 | 6.8483 | 0.1132 |
| 8.201 | 8.75 | 6500 | 6.7750 | 0.1171 |
| 8.201 | 9.42 | 7000 | 6.7116 | 0.1187 |
| 8.201 | 10.09 | 7500 | 6.6560 | 0.1218 |
| 8.201 | 10.77 | 8000 | 6.6178 | 0.1239 |
| 8.201 | 11.44 | 8500 | 6.5824 | 0.1255 |
| 8.201 | 12.11 | 9000 | 6.5521 | 0.1273 |
| 8.201 | 12.79 | 9500 | 6.5203 | 0.1292 |
| 6.7257 | 13.46 | 10000 | 6.5027 | 0.1303 |
| 6.7257 | 14.13 | 10500 | 6.4809 | 0.1314 |
| 6.7257 | 14.8 | 11000 | 6.4631 | 0.1322 |
| 6.7257 | 15.48 | 11500 | 6.4483 | 0.1329 |
| 6.7257 | 16.15 | 12000 | 6.4320 | 0.1338 |
| 6.7257 | 16.82 | 12500 | 6.4169 | 0.1348 |
| 6.7257 | 17.5 | 13000 | 6.4067 | 0.1359 |
| 6.7257 | 18.17 | 13500 | 6.3994 | 0.1359 |
| 6.7257 | 18.84 | 14000 | 6.3823 | 0.1368 |
| 6.7257 | 19.52 | 14500 | 6.3759 | 0.1373 |
| 6.4502 | 20.19 | 15000 | 6.3629 | 0.1374 |
| 6.4502 | 20.86 | 15500 | 6.3638 | 0.1373 |
| 6.4502 | 21.53 | 16000 | 6.3505 | 0.1382 |
| 6.4502 | 22.21 | 16500 | 6.3416 | 0.1387 |
| 6.4502 | 22.88 | 17000 | 6.3420 | 0.1383 |
| 6.4502 | 23.55 | 17500 | 6.3330 | 0.1389 |
| 6.4502 | 24.23 | 18000 | 6.3289 | 0.1388 |
| 6.4502 | 24.9 | 18500 | 6.3184 | 0.1389 |
| 6.4502 | 25.57 | 19000 | 6.3099 | 0.1396 |
| 6.4502 | 26.24 | 19500 | 6.2789 | 0.1405 |
| 6.3474 | 26.92 | 20000 | 6.2398 | 0.1404 |
| 6.3474 | 27.59 | 20500 | 6.2012 | 0.1412 |
| 6.3474 | 28.26 | 21000 | 6.1803 | 0.1414 |
| 6.3474 | 28.94 | 21500 | 6.1579 | 0.1414 |
| 6.3474 | 29.61 | 22000 | 6.1403 | 0.1431 |
| 6.3474 | 30.28 | 22500 | 6.1316 | 0.1423 |
| 6.3474 | 30.96 | 23000 | 6.1102 | 0.1435 |
| 6.3474 | 31.63 | 23500 | 6.0998 | 0.1439 |
| 6.3474 | 32.3 | 24000 | 6.0867 | 0.1446 |
| 6.3474 | 32.97 | 24500 | 6.0700 | 0.1451 |
| 6.1758 | 33.65 | 25000 | 6.0554 | 0.1457 |
| 6.1758 | 34.32 | 25500 | 6.0487 | 0.1457 |
| 6.1758 | 34.99 | 26000 | 6.0328 | 0.1469 |
| 6.1758 | 35.67 | 26500 | 6.0265 | 0.1469 |
| 6.1758 | 36.34 | 27000 | 5.9992 | 0.1486 |
| 6.1758 | 37.01 | 27500 | 5.9934 | 0.1485 |
| 6.1758 | 37.68 | 28000 | 5.9702 | 0.1501 |
| 6.1758 | 38.36 | 28500 | 5.9661 | 0.1503 |
| 6.1758 | 39.03 | 29000 | 5.9558 | 0.1512 |
| 6.1758 | 39.7 | 29500 | 5.9321 | 0.1528 |
| 6.052 | 40.38 | 30000 | 5.9147 | 0.1532 |
| 6.052 | 41.05 | 30500 | 5.8975 | 0.1545 |
| 6.052 | 41.72 | 31000 | 5.8784 | 0.1566 |
| 6.052 | 42.4 | 31500 | 5.8584 | 0.1586 |
| 6.052 | 43.07 | 32000 | 5.8325 | 0.1603 |
| 6.052 | 43.74 | 32500 | 5.7583 | 0.1664 |
| 6.052 | 44.41 | 33000 | 5.6158 | 0.1787 |
| 6.052 | 45.09 | 33500 | 5.4580 | 0.1917 |
| 6.052 | 45.76 | 34000 | 5.3396 | 0.2010 |
| 6.052 | 46.43 | 34500 | 5.2568 | 0.2082 |
| 5.7995 | 47.11 | 35000 | 5.1775 | 0.2146 |
| 5.7995 | 47.78 | 35500 | 5.1076 | 0.2204 |
| 5.7995 | 48.45 | 36000 | 5.0457 | 0.2258 |
| 5.7995 | 49.13 | 36500 | 4.9932 | 0.2313 |
| 5.7995 | 49.8 | 37000 | 4.9164 | 0.2384 |
| 5.7995 | 50.47 | 37500 | 4.7844 | 0.2521 |
| 5.7995 | 51.14 | 38000 | 4.6598 | 0.2642 |
| 5.7995 | 51.82 | 38500 | 4.5472 | 0.2757 |
| 5.7995 | 52.49 | 39000 | 4.4374 | 0.2871 |
| 5.7995 | 53.16 | 39500 | 4.3399 | 0.2982 |
| 5.0341 | 53.84 | 40000 | 4.2549 | 0.3083 |
| 5.0341 | 54.51 | 40500 | 4.1795 | 0.3170 |
| 5.0341 | 55.18 | 41000 | 4.1017 | 0.3274 |
| 5.0341 | 55.85 | 41500 | 4.0308 | 0.3375 |
| 5.0341 | 56.53 | 42000 | 3.9673 | 0.3462 |
| 5.0341 | 57.2 | 42500 | 3.9130 | 0.3538 |
| 5.0341 | 57.87 | 43000 | 3.8672 | 0.3599 |
| 5.0341 | 58.55 | 43500 | 3.8249 | 0.3656 |
| 5.0341 | 59.22 | 44000 | 3.7748 | 0.3728 |
| 5.0341 | 59.89 | 44500 | 3.7459 | 0.3768 |
| 4.2119 | 60.57 | 45000 | 3.7089 | 0.3808 |
| 4.2119 | 61.24 | 45500 | 3.6732 | 0.3857 |
| 4.2119 | 61.91 | 46000 | 3.6546 | 0.3881 |
| 4.2119 | 62.58 | 46500 | 3.6205 | 0.3921 |
| 4.2119 | 63.26 | 47000 | 3.5908 | 0.3960 |
| 4.2119 | 63.93 | 47500 | 3.5627 | 0.3991 |
| 4.2119 | 64.6 | 48000 | 3.5466 | 0.4019 |
| 4.2119 | 65.28 | 48500 | 3.5262 | 0.4039 |
| 4.2119 | 65.95 | 49000 | 3.4987 | 0.4074 |
| 4.2119 | 66.62 | 49500 | 3.4817 | 0.4093 |
| 3.8182 | 67.29 | 50000 | 3.4608 | 0.4119 |
| 3.8182 | 67.97 | 50500 | 3.4467 | 0.4142 |
| 3.8182 | 68.64 | 51000 | 3.4280 | 0.4163 |
| 3.8182 | 69.31 | 51500 | 3.4165 | 0.4175 |
| 3.8182 | 69.99 | 52000 | 3.3970 | 0.4199 |
| 3.8182 | 70.66 | 52500 | 3.3738 | 0.4227 |
| 3.8182 | 71.33 | 53000 | 3.3640 | 0.4242 |
| 3.8182 | 72.01 | 53500 | 3.3583 | 0.4252 |
| 3.8182 | 72.68 | 54000 | 3.3319 | 0.4279 |
| 3.8182 | 73.35 | 54500 | 3.3153 | 0.4303 |
| 3.5946 | 74.02 | 55000 | 3.3098 | 0.4304 |
| 3.5946 | 74.7 | 55500 | 3.2949 | 0.4328 |
| 3.5946 | 75.37 | 56000 | 3.2820 | 0.4335 |
| 3.5946 | 76.04 | 56500 | 3.2686 | 0.4355 |
| 3.5946 | 76.72 | 57000 | 3.2663 | 0.4359 |
| 3.5946 | 77.39 | 57500 | 3.2482 | 0.4379 |
| 3.5946 | 78.06 | 58000 | 3.2344 | 0.4393 |
| 3.5946 | 78.73 | 58500 | 3.2281 | 0.4407 |
| 3.5946 | 79.41 | 59000 | 3.2172 | 0.4412 |
| 3.5946 | 80.08 | 59500 | 3.2110 | 0.4420 |
| 3.4435 | 80.75 | 60000 | 3.1940 | 0.4443 |
| 3.4435 | 81.43 | 60500 | 3.1837 | 0.4455 |
| 3.4435 | 82.1 | 61000 | 3.1744 | 0.4469 |
| 3.4435 | 82.77 | 61500 | 3.1611 | 0.4483 |
| 3.4435 | 83.45 | 62000 | 3.1531 | 0.4496 |
| 3.4435 | 84.12 | 62500 | 3.1524 | 0.4499 |
| 3.4435 | 84.79 | 63000 | 3.1431 | 0.4501 |
| 3.4435 | 85.46 | 63500 | 3.1287 | 0.4527 |
| 3.4435 | 86.14 | 64000 | 3.1192 | 0.4533 |
| 3.4435 | 86.81 | 64500 | 3.1107 | 0.4547 |
| 3.3301 | 87.48 | 65000 | 3.1041 | 0.4553 |
| 3.3301 | 88.16 | 65500 | 3.0999 | 0.4562 |
| 3.3301 | 88.83 | 66000 | 3.0882 | 0.4576 |
| 3.3301 | 89.5 | 66500 | 3.0777 | 0.4589 |
| 3.3301 | 90.17 | 67000 | 3.0726 | 0.4588 |
| 3.3301 | 90.85 | 67500 | 3.0676 | 0.4601 |
| 3.3301 | 91.52 | 68000 | 3.0616 | 0.4602 |
| 3.3301 | 92.19 | 68500 | 3.0523 | 0.4621 |
| 3.3301 | 92.87 | 69000 | 3.0464 | 0.4624 |
| 3.3301 | 93.54 | 69500 | 3.0405 | 0.4635 |
| 3.2418 | 94.21 | 70000 | 3.0312 | 0.4649 |
| 3.2418 | 94.89 | 70500 | 3.0209 | 0.4653 |
| 3.2418 | 95.56 | 71000 | 3.0202 | 0.4657 |
| 3.2418 | 96.23 | 71500 | 3.0101 | 0.4676 |
| 3.2418 | 96.9 | 72000 | 3.0105 | 0.4666 |
| 3.2418 | 97.58 | 72500 | 3.0023 | 0.4685 |
| 3.2418 | 98.25 | 73000 | 3.0008 | 0.4680 |
| 3.2418 | 98.92 | 73500 | 2.9882 | 0.4691 |
| 3.2418 | 99.6 | 74000 | 2.9855 | 0.4702 |
| 3.2418 | 100.27 | 74500 | 2.9790 | 0.4709 |
| 3.1698 | 100.94 | 75000 | 2.9680 | 0.4716 |
| 3.1698 | 101.61 | 75500 | 2.9667 | 0.4724 |
| 3.1698 | 102.29 | 76000 | 2.9657 | 0.4726 |
| 3.1698 | 102.96 | 76500 | 2.9623 | 0.4731 |
| 3.1698 | 103.63 | 77000 | 2.9515 | 0.4745 |
| 3.1698 | 104.31 | 77500 | 2.9471 | 0.4753 |
| 3.1698 | 104.98 | 78000 | 2.9407 | 0.4756 |
| 3.1698 | 105.65 | 78500 | 2.9388 | 0.4761 |
| 3.1698 | 106.33 | 79000 | 2.9369 | 0.4766 |
| 3.1698 | 107.0 | 79500 | 2.9297 | 0.4762 |
| 3.1101 | 107.67 | 80000 | 2.9291 | 0.4776 |
| 3.1101 | 108.34 | 80500 | 2.9139 | 0.4788 |
| 3.1101 | 109.02 | 81000 | 2.9113 | 0.4790 |
| 3.1101 | 109.69 | 81500 | 2.9057 | 0.4798 |
| 3.1101 | 110.36 | 82000 | 2.9058 | 0.4804 |
| 3.1101 | 111.04 | 82500 | 2.9019 | 0.4807 |
| 3.1101 | 111.71 | 83000 | 2.8934 | 0.4818 |
| 3.1101 | 112.38 | 83500 | 2.8864 | 0.4825 |
| 3.1101 | 113.06 | 84000 | 2.8926 | 0.4815 |
| 3.1101 | 113.73 | 84500 | 2.8812 | 0.4830 |
| 3.058 | 114.4 | 85000 | 2.8740 | 0.4840 |
| 3.058 | 115.07 | 85500 | 2.8730 | 0.4840 |
| 3.058 | 115.75 | 86000 | 2.8694 | 0.4847 |
| 3.058 | 116.42 | 86500 | 2.8694 | 0.4848 |
| 3.058 | 117.09 | 87000 | 2.8601 | 0.4862 |
| 3.058 | 117.77 | 87500 | 2.8547 | 0.4862 |
| 3.058 | 118.44 | 88000 | 2.8538 | 0.4861 |
| 3.058 | 119.11 | 88500 | 2.8494 | 0.4876 |
| 3.058 | 119.78 | 89000 | 2.8430 | 0.4882 |
| 3.058 | 120.46 | 89500 | 2.8436 | 0.4875 |
| 3.0129 | 121.13 | 90000 | 2.8402 | 0.4884 |
| 3.0129 | 121.8 | 90500 | 2.8353 | 0.4888 |
| 3.0129 | 122.48 | 91000 | 2.8271 | 0.4896 |
| 3.0129 | 123.15 | 91500 | 2.8236 | 0.4900 |
| 3.0129 | 123.82 | 92000 | 2.8199 | 0.4913 |
| 3.0129 | 124.5 | 92500 | 2.8119 | 0.4916 |
| 3.0129 | 125.17 | 93000 | 2.8138 | 0.4916 |
| 3.0129 | 125.84 | 93500 | 2.8089 | 0.4925 |
| 3.0129 | 126.51 | 94000 | 2.8067 | 0.4928 |
| 3.0129 | 127.19 | 94500 | 2.8010 | 0.4939 |
| 2.9701 | 127.86 | 95000 | 2.7992 | 0.4938 |
| 2.9701 | 128.53 | 95500 | 2.7953 | 0.4948 |
| 2.9701 | 129.21 | 96000 | 2.7964 | 0.4942 |
| 2.9701 | 129.88 | 96500 | 2.7838 | 0.4955 |
| 2.9701 | 130.55 | 97000 | 2.7770 | 0.4968 |
| 2.9701 | 131.22 | 97500 | 2.7800 | 0.4962 |
| 2.9701 | 131.9 | 98000 | 2.7743 | 0.4972 |
| 2.9701 | 132.57 | 98500 | 2.7696 | 0.4973 |
| 2.9701 | 133.24 | 99000 | 2.7691 | 0.4980 |
| 2.9701 | 133.92 | 99500 | 2.7612 | 0.4989 |
| 2.9289 | 134.59 | 100000 | 2.7606 | 0.4987 |
| 2.9289 | 135.26 | 100500 | 2.7545 | 0.4993 |
| 2.9289 | 135.94 | 101000 | 2.7544 | 0.4999 |
| 2.9289 | 136.61 | 101500 | 2.7550 | 0.4999 |
| 2.9289 | 137.28 | 102000 | 2.7510 | 0.5001 |
| 2.9289 | 137.95 | 102500 | 2.7480 | 0.5002 |
| 2.9289 | 138.63 | 103000 | 2.7422 | 0.5012 |
| 2.9289 | 139.3 | 103500 | 2.7419 | 0.5014 |
| 2.9289 | 139.97 | 104000 | 2.7416 | 0.5009 |
| 2.9289 | 140.65 | 104500 | 2.7412 | 0.5017 |
| 2.8968 | 141.32 | 105000 | 2.7356 | 0.5023 |
| 2.8968 | 141.99 | 105500 | 2.7303 | 0.5027 |
| 2.8968 | 142.66 | 106000 | 2.7359 | 0.5029 |
| 2.8968 | 143.34 | 106500 | 2.7283 | 0.5032 |
| 2.8968 | 144.01 | 107000 | 2.7226 | 0.5033 |
| 2.8968 | 144.68 | 107500 | 2.7247 | 0.5039 |
| 2.8968 | 145.36 | 108000 | 2.7209 | 0.5044 |
| 2.8968 | 146.03 | 108500 | 2.7210 | 0.5039 |
| 2.8968 | 146.7 | 109000 | 2.7135 | 0.5046 |
| 2.8968 | 147.38 | 109500 | 2.7139 | 0.5048 |
| 2.8697 | 148.05 | 110000 | 2.7167 | 0.5050 |
| 2.8697 | 148.72 | 110500 | 2.7125 | 0.5058 |
| 2.8697 | 149.39 | 111000 | 2.7064 | 0.5060 |
| 2.8697 | 150.07 | 111500 | 2.7024 | 0.5067 |
| 2.8697 | 150.74 | 112000 | 2.7035 | 0.5067 |
| 2.8697 | 151.41 | 112500 | 2.7034 | 0.5067 |
| 2.8697 | 152.09 | 113000 | 2.6967 | 0.5073 |
| 2.8697 | 152.76 | 113500 | 2.6982 | 0.5070 |
| 2.8697 | 153.43 | 114000 | 2.6948 | 0.5079 |
| 2.8697 | 154.1 | 114500 | 2.6946 | 0.5076 |
| 2.8457 | 154.78 | 115000 | 2.6918 | 0.5078 |
| 2.8457 | 155.45 | 115500 | 2.6917 | 0.5078 |
| 2.8457 | 156.12 | 116000 | 2.6868 | 0.5086 |
| 2.8457 | 156.8 | 116500 | 2.6870 | 0.5084 |
| 2.8457 | 157.47 | 117000 | 2.6830 | 0.5091 |
| 2.8457 | 158.14 | 117500 | 2.6824 | 0.5090 |
| 2.8457 | 158.82 | 118000 | 2.6812 | 0.5092 |
| 2.8457 | 159.49 | 118500 | 2.6747 | 0.5098 |
| 2.8457 | 160.16 | 119000 | 2.6747 | 0.5105 |
| 2.8457 | 160.83 | 119500 | 2.6750 | 0.5102 |
| 2.825 | 161.51 | 120000 | 2.6761 | 0.5102 |
| 2.825 | 162.18 | 120500 | 2.6670 | 0.5115 |
| 2.825 | 162.85 | 121000 | 2.6740 | 0.5104 |
| 2.825 | 163.53 | 121500 | 2.6700 | 0.5106 |
| 2.825 | 164.2 | 122000 | 2.6629 | 0.5119 |
| 2.825 | 164.87 | 122500 | 2.6642 | 0.5117 |
| 2.825 | 165.54 | 123000 | 2.6664 | 0.5109 |
| 2.825 | 166.22 | 123500 | 2.6626 | 0.5117 |
| 2.825 | 166.89 | 124000 | 2.6561 | 0.5130 |
| 2.825 | 167.56 | 124500 | 2.6612 | 0.5125 |
| 2.8059 | 168.24 | 125000 | 2.6594 | 0.5123 |
| 2.8059 | 168.91 | 125500 | 2.6508 | 0.5132 |
| 2.8059 | 169.58 | 126000 | 2.6477 | 0.5134 |
| 2.8059 | 170.26 | 126500 | 2.6527 | 0.5133 |
| 2.8059 | 170.93 | 127000 | 2.6519 | 0.5136 |
| 2.8059 | 171.6 | 127500 | 2.6456 | 0.5141 |
| 2.8059 | 172.27 | 128000 | 2.6473 | 0.5139 |
| 2.8059 | 172.95 | 128500 | 2.6426 | 0.5144 |
| 2.8059 | 173.62 | 129000 | 2.6454 | 0.5137 |
| 2.8059 | 174.29 | 129500 | 2.6427 | 0.5147 |
| 2.788 | 174.97 | 130000 | 2.6376 | 0.5150 |
| 2.788 | 175.64 | 130500 | 2.6366 | 0.5154 |
| 2.788 | 176.31 | 131000 | 2.6327 | 0.5156 |
| 2.788 | 176.98 | 131500 | 2.6328 | 0.5157 |
| 2.788 | 177.66 | 132000 | 2.6335 | 0.5156 |
| 2.788 | 178.33 | 132500 | 2.6302 | 0.5166 |
| 2.788 | 179.0 | 133000 | 2.6333 | 0.5160 |
| 2.788 | 179.68 | 133500 | 2.6253 | 0.5171 |
| 2.788 | 180.35 | 134000 | 2.6237 | 0.5167 |
| 2.788 | 181.02 | 134500 | 2.6269 | 0.5165 |
| 2.7723 | 181.7 | 135000 | 2.6283 | 0.5164 |
| 2.7723 | 182.37 | 135500 | 2.6255 | 0.5174 |
| 2.7723 | 183.04 | 136000 | 2.6254 | 0.5175 |
| 2.7723 | 183.71 | 136500 | 2.6231 | 0.5172 |
| 2.7723 | 184.39 | 137000 | 2.6181 | 0.5173 |
| 2.7723 | 185.06 | 137500 | 2.6260 | 0.5168 |
| 2.7723 | 185.73 | 138000 | 2.6183 | 0.5176 |
| 2.7723 | 186.41 | 138500 | 2.6174 | 0.5182 |
| 2.7723 | 187.08 | 139000 | 2.6144 | 0.5182 |
| 2.7723 | 187.75 | 139500 | 2.6152 | 0.5186 |
| 2.7575 | 188.43 | 140000 | 2.6150 | 0.5183 |
| 2.7575 | 189.1 | 140500 | 2.6110 | 0.5190 |
| 2.7575 | 189.77 | 141000 | 2.6044 | 0.5194 |
| 2.7575 | 190.44 | 141500 | 2.6083 | 0.5186 |
| 2.7575 | 191.12 | 142000 | 2.6102 | 0.5189 |
| 2.7575 | 191.79 | 142500 | 2.6063 | 0.5195 |
| 2.7575 | 192.46 | 143000 | 2.6071 | 0.5198 |
| 2.7575 | 193.14 | 143500 | 2.6090 | 0.5191 |
| 2.7575 | 193.81 | 144000 | 2.6068 | 0.5200 |
| 2.7575 | 194.48 | 144500 | 2.6032 | 0.5200 |
| 2.7445 | 195.15 | 145000 | 2.6031 | 0.5200 |
| 2.7445 | 195.83 | 145500 | 2.6007 | 0.5201 |
| 2.7445 | 196.5 | 146000 | 2.5998 | 0.5203 |
| 2.7445 | 197.17 | 146500 | 2.5980 | 0.5208 |
| 2.7445 | 197.85 | 147000 | 2.5952 | 0.5211 |
| 2.7445 | 198.52 | 147500 | 2.5977 | 0.5210 |
| 2.7445 | 199.19 | 148000 | 2.5922 | 0.5212 |
| 2.7445 | 199.87 | 148500 | 2.5936 | 0.5211 |
| 2.7445 | 200.54 | 149000 | 2.5933 | 0.5219 |
| 2.7445 | 201.21 | 149500 | 2.5887 | 0.5219 |
| 2.7324 | 201.88 | 150000 | 2.5911 | 0.5215 |
| 2.7324 | 202.56 | 150500 | 2.5892 | 0.5219 |
| 2.7324 | 203.23 | 151000 | 2.5875 | 0.5218 |
| 2.7324 | 203.9 | 151500 | 2.5898 | 0.5220 |
| 2.7324 | 204.58 | 152000 | 2.5872 | 0.5223 |
| 2.7324 | 205.25 | 152500 | 2.5805 | 0.5226 |
| 2.7324 | 205.92 | 153000 | 2.5861 | 0.5225 |
| 2.7324 | 206.59 | 153500 | 2.5839 | 0.5223 |
| 2.7324 | 207.27 | 154000 | 2.5804 | 0.5232 |
| 2.7324 | 207.94 | 154500 | 2.5766 | 0.5235 |
| 2.7212 | 208.61 | 155000 | 2.5764 | 0.5233 |
| 2.7212 | 209.29 | 155500 | 2.5801 | 0.5233 |
| 2.7212 | 209.96 | 156000 | 2.5737 | 0.5241 |
| 2.7212 | 210.63 | 156500 | 2.5769 | 0.5236 |
| 2.7212 | 211.31 | 157000 | 2.5769 | 0.5237 |
| 2.7212 | 211.98 | 157500 | 2.5748 | 0.5239 |
| 2.7212 | 212.65 | 158000 | 2.5745 | 0.5230 |
| 2.7212 | 213.32 | 158500 | 2.5725 | 0.5240 |
| 2.7212 | 214.0 | 159000 | 2.5736 | 0.5239 |
| 2.7212 | 214.67 | 159500 | 2.5675 | 0.5252 |
| 2.7103 | 215.34 | 160000 | 2.5678 | 0.5245 |
| 2.7103 | 216.02 | 160500 | 2.5691 | 0.5250 |
| 2.7103 | 216.69 | 161000 | 2.5688 | 0.5245 |
| 2.7103 | 217.36 | 161500 | 2.5681 | 0.5251 |
| 2.7103 | 218.03 | 162000 | 2.5582 | 0.5255 |
| 2.7103 | 218.71 | 162500 | 2.5675 | 0.5247 |
| 2.7103 | 219.38 | 163000 | 2.5609 | 0.5259 |
| 2.7103 | 220.05 | 163500 | 2.5625 | 0.5254 |
| 2.7103 | 220.73 | 164000 | 2.5630 | 0.5254 |
| 2.7103 | 221.4 | 164500 | 2.5607 | 0.5265 |
| 2.7003 | 222.07 | 165000 | 2.5615 | 0.5260 |
| 2.7003 | 222.75 | 165500 | 2.5660 | 0.5248 |
| 2.7003 | 223.42 | 166000 | 2.5569 | 0.5263 |
| 2.7003 | 224.09 | 166500 | 2.5610 | 0.5255 |
| 2.7003 | 224.76 | 167000 | 2.5569 | 0.5263 |
| 2.7003 | 225.44 | 167500 | 2.5534 | 0.5265 |
| 2.7003 | 226.11 | 168000 | 2.5573 | 0.5259 |
| 2.7003 | 226.78 | 168500 | 2.5559 | 0.5264 |
| 2.7003 | 227.46 | 169000 | 2.5508 | 0.5277 |
| 2.7003 | 228.13 | 169500 | 2.5500 | 0.5276 |
| 2.6915 | 228.8 | 170000 | 2.5501 | 0.5270 |
| 2.6915 | 229.47 | 170500 | 2.5508 | 0.5273 |
| 2.6915 | 230.15 | 171000 | 2.5523 | 0.5267 |
| 2.6915 | 230.82 | 171500 | 2.5464 | 0.5276 |
| 2.6915 | 231.49 | 172000 | 2.5482 | 0.5271 |
| 2.6915 | 232.17 | 172500 | 2.5486 | 0.5270 |
| 2.6915 | 232.84 | 173000 | 2.5474 | 0.5275 |
| 2.6915 | 233.51 | 173500 | 2.5483 | 0.5270 |
| 2.6915 | 234.19 | 174000 | 2.5480 | 0.5276 |
| 2.6915 | 234.86 | 174500 | 2.5486 | 0.5278 |
| 2.6833 | 235.53 | 175000 | 2.5484 | 0.5273 |
| 2.6833 | 236.2 | 175500 | 2.5436 | 0.5277 |
| 2.6833 | 236.88 | 176000 | 2.5448 | 0.5278 |
| 2.6833 | 237.55 | 176500 | 2.5430 | 0.5284 |
| 2.6833 | 238.22 | 177000 | 2.5433 | 0.5279 |
| 2.6833 | 238.9 | 177500 | 2.5398 | 0.5288 |
| 2.6833 | 239.57 | 178000 | 2.5424 | 0.5282 |
| 2.6833 | 240.24 | 178500 | 2.5371 | 0.5291 |
| 2.6833 | 240.91 | 179000 | 2.5372 | 0.5294 |
| 2.6833 | 241.59 | 179500 | 2.5368 | 0.5290 |
| 2.6753 | 242.26 | 180000 | 2.5383 | 0.5289 |
| 2.6753 | 242.93 | 180500 | 2.5387 | 0.5289 |
| 2.6753 | 243.61 | 181000 | 2.5351 | 0.5295 |
| 2.6753 | 244.28 | 181500 | 2.5340 | 0.5296 |
| 2.6753 | 244.95 | 182000 | 2.5349 | 0.5289 |
| 2.6753 | 245.63 | 182500 | 2.5358 | 0.5295 |
| 2.6753 | 246.3 | 183000 | 2.5333 | 0.5299 |
| 2.6753 | 246.97 | 183500 | 2.5363 | 0.5292 |
| 2.6753 | 247.64 | 184000 | 2.5323 | 0.5298 |
| 2.6753 | 248.32 | 184500 | 2.5286 | 0.5299 |
| 2.6679 | 248.99 | 185000 | 2.5276 | 0.5306 |
| 2.6679 | 249.66 | 185500 | 2.5249 | 0.5308 |
| 2.6679 | 250.34 | 186000 | 2.5308 | 0.5302 |
| 2.6679 | 251.01 | 186500 | 2.5307 | 0.5297 |
| 2.6679 | 251.68 | 187000 | 2.5293 | 0.5305 |
| 2.6679 | 252.36 | 187500 | 2.5255 | 0.5306 |
| 2.6679 | 253.03 | 188000 | 2.5244 | 0.5312 |
| 2.6679 | 253.7 | 188500 | 2.5278 | 0.5305 |
| 2.6679 | 254.37 | 189000 | 2.5212 | 0.5317 |
| 2.6679 | 255.05 | 189500 | 2.5256 | 0.5307 |
| 2.6611 | 255.72 | 190000 | 2.5273 | 0.5303 |
| 2.6611 | 256.39 | 190500 | 2.5222 | 0.5310 |
| 2.6611 | 257.07 | 191000 | 2.5237 | 0.5311 |
| 2.6611 | 257.74 | 191500 | 2.5258 | 0.5309 |
| 2.6611 | 258.41 | 192000 | 2.5219 | 0.5313 |
| 2.6611 | 259.08 | 192500 | 2.5243 | 0.5314 |
| 2.6611 | 259.76 | 193000 | 2.5203 | 0.5319 |
| 2.6611 | 260.43 | 193500 | 2.5205 | 0.5313 |
| 2.6611 | 261.1 | 194000 | 2.5205 | 0.5322 |
| 2.6611 | 261.78 | 194500 | 2.5196 | 0.5317 |
| 2.655 | 262.45 | 195000 | 2.5199 | 0.5315 |
| 2.655 | 263.12 | 195500 | 2.5226 | 0.5315 |
| 2.655 | 263.8 | 196000 | 2.5175 | 0.5316 |
| 2.655 | 264.47 | 196500 | 2.5160 | 0.5322 |
| 2.655 | 265.14 | 197000 | 2.5185 | 0.5316 |
| 2.655 | 265.81 | 197500 | 2.5133 | 0.5322 |
| 2.655 | 266.49 | 198000 | 2.5163 | 0.5318 |
| 2.655 | 267.16 | 198500 | 2.5135 | 0.5325 |
| 2.655 | 267.83 | 199000 | 2.5132 | 0.5326 |
| 2.655 | 268.51 | 199500 | 2.5148 | 0.5323 |
| 2.6486 | 269.18 | 200000 | 2.5194 | 0.5317 |
| 2.6486 | 269.85 | 200500 | 2.5162 | 0.5321 |
| 2.6486 | 270.52 | 201000 | 2.5090 | 0.5332 |
| 2.6486 | 271.2 | 201500 | 2.5126 | 0.5325 |
| 2.6486 | 271.87 | 202000 | 2.5155 | 0.5320 |
| 2.6486 | 272.54 | 202500 | 2.5099 | 0.5329 |
| 2.6486 | 273.22 | 203000 | 2.5130 | 0.5325 |
| 2.6486 | 273.89 | 203500 | 2.5064 | 0.5329 |
| 2.6486 | 274.56 | 204000 | 2.5154 | 0.5319 |
| 2.6486 | 275.24 | 204500 | 2.5097 | 0.5329 |
| 2.6433 | 275.91 | 205000 | 2.5075 | 0.5334 |
| 2.6433 | 276.58 | 205500 | 2.5120 | 0.5325 |
| 2.6433 | 277.25 | 206000 | 2.5100 | 0.5329 |
| 2.6433 | 277.93 | 206500 | 2.5115 | 0.5332 |
| 2.6433 | 278.6 | 207000 | 2.5071 | 0.5332 |
| 2.6433 | 279.27 | 207500 | 2.5075 | 0.5335 |
| 2.6433 | 279.95 | 208000 | 2.5020 | 0.5338 |
| 2.6433 | 280.62 | 208500 | 2.5025 | 0.5340 |
| 2.6433 | 281.29 | 209000 | 2.5064 | 0.5333 |
| 2.6433 | 281.96 | 209500 | 2.5038 | 0.5336 |
| 2.6383 | 282.64 | 210000 | 2.5041 | 0.5340 |
| 2.6383 | 283.31 | 210500 | 2.5075 | 0.5336 |
| 2.6383 | 283.98 | 211000 | 2.5028 | 0.5333 |
| 2.6383 | 284.66 | 211500 | 2.5008 | 0.5340 |
| 2.6383 | 285.33 | 212000 | 2.5005 | 0.5345 |
| 2.6383 | 286.0 | 212500 | 2.5020 | 0.5334 |
| 2.6383 | 286.68 | 213000 | 2.5011 | 0.5344 |
| 2.6383 | 287.35 | 213500 | 2.5028 | 0.5338 |
| 2.6383 | 288.02 | 214000 | 2.4970 | 0.5340 |
| 2.6383 | 288.69 | 214500 | 2.4995 | 0.5343 |
| 2.6336 | 289.37 | 215000 | 2.5010 | 0.5343 |
| 2.6336 | 290.04 | 215500 | 2.5060 | 0.5336 |
| 2.6336 | 290.71 | 216000 | 2.4955 | 0.5347 |
| 2.6336 | 291.39 | 216500 | 2.4972 | 0.5349 |
| 2.6336 | 292.06 | 217000 | 2.4977 | 0.5349 |
| 2.6336 | 292.73 | 217500 | 2.4973 | 0.5346 |
| 2.6336 | 293.4 | 218000 | 2.4981 | 0.5346 |
| 2.6336 | 294.08 | 218500 | 2.4941 | 0.5346 |
| 2.6336 | 294.75 | 219000 | 2.4978 | 0.5350 |
| 2.6336 | 295.42 | 219500 | 2.4990 | 0.5343 |
| 2.6288 | 296.1 | 220000 | 2.4929 | 0.5347 |
| 2.6288 | 296.77 | 220500 | 2.4937 | 0.5349 |
| 2.6288 | 297.44 | 221000 | 2.4938 | 0.5349 |
| 2.6288 | 298.12 | 221500 | 2.4938 | 0.5343 |
| 2.6288 | 298.79 | 222000 | 2.4924 | 0.5354 |
| 2.6288 | 299.46 | 222500 | 2.4953 | 0.5348 |
| 2.6288 | 300.13 | 223000 | 2.4930 | 0.5347 |
| 2.6288 | 300.81 | 223500 | 2.4934 | 0.5353 |
| 2.6288 | 301.48 | 224000 | 2.4942 | 0.5348 |
| 2.6288 | 302.15 | 224500 | 2.4960 | 0.5344 |
| 2.6246 | 302.83 | 225000 | 2.4875 | 0.5357 |
| 2.6246 | 303.5 | 225500 | 2.4898 | 0.5355 |
| 2.6246 | 304.17 | 226000 | 2.4847 | 0.5366 |
| 2.6246 | 304.84 | 226500 | 2.4970 | 0.5348 |
| 2.6246 | 305.52 | 227000 | 2.4905 | 0.5356 |
| 2.6246 | 306.19 | 227500 | 2.4873 | 0.5361 |
| 2.6246 | 306.86 | 228000 | 2.4939 | 0.5350 |
| 2.6246 | 307.54 | 228500 | 2.4910 | 0.5360 |
| 2.6246 | 308.21 | 229000 | 2.4886 | 0.5355 |
| 2.6246 | 308.88 | 229500 | 2.4890 | 0.5369 |
| 2.6207 | 309.56 | 230000 | 2.4900 | 0.5360 |
| 2.6207 | 310.23 | 230500 | 2.4885 | 0.5354 |
| 2.6207 | 310.9 | 231000 | 2.4895 | 0.5358 |
| 2.6207 | 311.57 | 231500 | 2.4871 | 0.5358 |
| 2.6207 | 312.25 | 232000 | 2.4914 | 0.5352 |
| 2.6207 | 312.92 | 232500 | 2.4843 | 0.5366 |
| 2.6207 | 313.59 | 233000 | 2.4837 | 0.5365 |
| 2.6207 | 314.27 | 233500 | 2.4883 | 0.5360 |
| 2.6207 | 314.94 | 234000 | 2.4839 | 0.5366 |
| 2.6207 | 315.61 | 234500 | 2.4854 | 0.5366 |
| 2.6171 | 316.29 | 235000 | 2.4833 | 0.5367 |
| 2.6171 | 316.96 | 235500 | 2.4783 | 0.5374 |
| 2.6171 | 317.63 | 236000 | 2.4807 | 0.5370 |
| 2.6171 | 318.3 | 236500 | 2.4824 | 0.5366 |
| 2.6171 | 318.98 | 237000 | 2.4857 | 0.5361 |
| 2.6171 | 319.65 | 237500 | 2.4817 | 0.5366 |
| 2.6171 | 320.32 | 238000 | 2.4855 | 0.5364 |
| 2.6171 | 321.0 | 238500 | 2.4834 | 0.5367 |
| 2.6171 | 321.67 | 239000 | 2.4831 | 0.5363 |
| 2.6171 | 322.34 | 239500 | 2.4806 | 0.5370 |
| 2.6134 | 323.01 | 240000 | 2.4842 | 0.5365 |
| 2.6134 | 323.69 | 240500 | 2.4830 | 0.5364 |
| 2.6134 | 324.36 | 241000 | 2.4822 | 0.5367 |
| 2.6134 | 325.03 | 241500 | 2.4805 | 0.5373 |
| 2.6134 | 325.71 | 242000 | 2.4838 | 0.5365 |
| 2.6134 | 326.38 | 242500 | 2.4776 | 0.5371 |
| 2.6134 | 327.05 | 243000 | 2.4786 | 0.5376 |
| 2.6134 | 327.73 | 243500 | 2.4824 | 0.5371 |
| 2.6134 | 328.4 | 244000 | 2.4842 | 0.5363 |
| 2.6134 | 329.07 | 244500 | 2.4790 | 0.5375 |
| 2.6107 | 329.74 | 245000 | 2.4770 | 0.5372 |
| 2.6107 | 330.42 | 245500 | 2.4805 | 0.5375 |
| 2.6107 | 331.09 | 246000 | 2.4839 | 0.5370 |
| 2.6107 | 331.76 | 246500 | 2.4802 | 0.5367 |
| 2.6107 | 332.44 | 247000 | 2.4796 | 0.5373 |
| 2.6107 | 333.11 | 247500 | 2.4736 | 0.5377 |
| 2.6107 | 333.78 | 248000 | 2.4789 | 0.5374 |
| 2.6107 | 334.45 | 248500 | 2.4761 | 0.5375 |
| 2.6107 | 335.13 | 249000 | 2.4728 | 0.5379 |
| 2.6107 | 335.8 | 249500 | 2.4702 | 0.5386 |
| 2.608 | 336.47 | 250000 | 2.4764 | 0.5377 |
| 2.608 | 337.15 | 250500 | 2.4738 | 0.5380 |
| 2.608 | 337.82 | 251000 | 2.4795 | 0.5371 |
| 2.608 | 338.49 | 251500 | 2.4702 | 0.5387 |
| 2.608 | 339.17 | 252000 | 2.4823 | 0.5369 |
| 2.608 | 339.84 | 252500 | 2.4741 | 0.5382 |
| 2.608 | 340.51 | 253000 | 2.4718 | 0.5382 |
| 2.608 | 341.18 | 253500 | 2.4731 | 0.5378 |
| 2.608 | 341.86 | 254000 | 2.4780 | 0.5373 |
| 2.608 | 342.53 | 254500 | 2.4706 | 0.5388 |
| 2.6058 | 343.2 | 255000 | 2.4707 | 0.5386 |
| 2.6058 | 343.88 | 255500 | 2.4725 | 0.5380 |
| 2.6058 | 344.55 | 256000 | 2.4744 | 0.5382 |
| 2.6058 | 345.22 | 256500 | 2.4766 | 0.5374 |
| 2.6058 | 345.89 | 257000 | 2.4736 | 0.5378 |
| 2.6058 | 346.57 | 257500 | 2.4731 | 0.5383 |
| 2.6058 | 347.24 | 258000 | 2.4754 | 0.5377 |
| 2.6058 | 347.91 | 258500 | 2.4749 | 0.5382 |
| 2.6058 | 348.59 | 259000 | 2.4735 | 0.5378 |
| 2.6058 | 349.26 | 259500 | 2.4716 | 0.5384 |
| 2.6027 | 349.93 | 260000 | 2.4726 | 0.5378 |
| 2.6027 | 350.61 | 260500 | 2.4733 | 0.5378 |
| 2.6027 | 351.28 | 261000 | 2.4698 | 0.5386 |
| 2.6027 | 351.95 | 261500 | 2.4702 | 0.5388 |
| 2.6027 | 352.62 | 262000 | 2.4673 | 0.5390 |
| 2.6027 | 353.3 | 262500 | 2.4683 | 0.5390 |
| 2.6027 | 353.97 | 263000 | 2.4739 | 0.5379 |
| 2.6027 | 354.64 | 263500 | 2.4743 | 0.5382 |
| 2.6027 | 355.32 | 264000 | 2.4694 | 0.5388 |
| 2.6027 | 355.99 | 264500 | 2.4671 | 0.5391 |
| 2.6009 | 356.66 | 265000 | 2.4747 | 0.5383 |
| 2.6009 | 357.34 | 265500 | 2.4703 | 0.5382 |
| 2.6009 | 358.01 | 266000 | 2.4695 | 0.5388 |
| 2.6009 | 358.68 | 266500 | 2.4725 | 0.5380 |
| 2.6009 | 359.35 | 267000 | 2.4639 | 0.5397 |
| 2.6009 | 360.03 | 267500 | 2.4686 | 0.5385 |
| 2.6009 | 360.7 | 268000 | 2.4698 | 0.5386 |
| 2.6009 | 361.37 | 268500 | 2.4694 | 0.5386 |
| 2.6009 | 362.05 | 269000 | 2.4680 | 0.5390 |
| 2.6009 | 362.72 | 269500 | 2.4728 | 0.5383 |
| 2.5989 | 363.39 | 270000 | 2.4697 | 0.5385 |
| 2.5989 | 364.06 | 270500 | 2.4701 | 0.5387 |
| 2.5989 | 364.74 | 271000 | 2.4702 | 0.5387 |
| 2.5989 | 365.41 | 271500 | 2.4687 | 0.5390 |
| 2.5989 | 366.08 | 272000 | 2.4725 | 0.5382 |
| 2.5989 | 366.76 | 272500 | 2.4673 | 0.5384 |
| 2.5989 | 367.43 | 273000 | 2.4659 | 0.5390 |
| 2.5989 | 368.1 | 273500 | 2.4686 | 0.5389 |
| 2.5989 | 368.78 | 274000 | 2.4677 | 0.5382 |
| 2.5989 | 369.45 | 274500 | 2.4632 | 0.5389 |
| 2.5977 | 370.12 | 275000 | 2.4676 | 0.5385 |
| 2.5977 | 370.79 | 275500 | 2.4699 | 0.5388 |
| 2.5977 | 371.47 | 276000 | 2.4629 | 0.5394 |
| 2.5977 | 372.14 | 276500 | 2.4720 | 0.5380 |
| 2.5977 | 372.81 | 277000 | 2.4678 | 0.5391 |
| 2.5977 | 373.49 | 277500 | 2.4643 | 0.5396 |
| 2.5977 | 374.16 | 278000 | 2.4654 | 0.5395 |
| 2.5977 | 374.83 | 278500 | 2.4645 | 0.5395 |
| 2.5977 | 375.5 | 279000 | 2.4649 | 0.5391 |
| 2.5977 | 376.18 | 279500 | 2.4639 | 0.5392 |
| 2.5961 | 376.85 | 280000 | 2.4659 | 0.5389 |
| 2.5961 | 377.52 | 280500 | 2.4681 | 0.5385 |
| 2.5961 | 378.2 | 281000 | 2.4641 | 0.5390 |
| 2.5961 | 378.87 | 281500 | 2.4658 | 0.5393 |
| 2.5961 | 379.54 | 282000 | 2.4687 | 0.5388 |
| 2.5961 | 380.22 | 282500 | 2.4690 | 0.5385 |
| 2.5961 | 380.89 | 283000 | 2.4679 | 0.5391 |
| 2.5961 | 381.56 | 283500 | 2.4612 | 0.5395 |
| 2.5961 | 382.23 | 284000 | 2.4624 | 0.5395 |
| 2.5961 | 382.91 | 284500 | 2.4668 | 0.5390 |
| 2.5947 | 383.58 | 285000 | 2.4663 | 0.5389 |
| 2.5947 | 384.25 | 285500 | 2.4654 | 0.5387 |
| 2.5947 | 384.93 | 286000 | 2.4708 | 0.5385 |
| 2.5947 | 385.6 | 286500 | 2.4669 | 0.5388 |
| 2.5947 | 386.27 | 287000 | 2.4612 | 0.5396 |
| 2.5947 | 386.94 | 287500 | 2.4666 | 0.5392 |
| 2.5947 | 387.62 | 288000 | 2.4653 | 0.5393 |
| 2.5947 | 388.29 | 288500 | 2.4666 | 0.5390 |
| 2.5947 | 388.96 | 289000 | 2.4684 | 0.5388 |
| 2.5947 | 389.64 | 289500 | 2.4660 | 0.5394 |
| 2.5936 | 390.31 | 290000 | 2.4642 | 0.5395 |
| 2.5936 | 390.98 | 290500 | 2.4627 | 0.5403 |
| 2.5936 | 391.66 | 291000 | 2.4683 | 0.5389 |
| 2.5936 | 392.33 | 291500 | 2.4667 | 0.5387 |
| 2.5936 | 393.0 | 292000 | 2.4660 | 0.5389 |
| 2.5936 | 393.67 | 292500 | 2.4673 | 0.5390 |
| 2.5936 | 394.35 | 293000 | 2.4645 | 0.5391 |
| 2.5936 | 395.02 | 293500 | 2.4693 | 0.5389 |
| 2.5936 | 395.69 | 294000 | 2.4692 | 0.5385 |
| 2.5936 | 396.37 | 294500 | 2.4653 | 0.5385 |
| 2.5934 | 397.04 | 295000 | 2.4661 | 0.5390 |
| 2.5934 | 397.71 | 295500 | 2.4630 | 0.5394 |
| 2.5934 | 398.38 | 296000 | 2.4641 | 0.5390 |
| 2.5934 | 399.06 | 296500 | 2.4636 | 0.5392 |
| 2.5934 | 399.73 | 297000 | 2.4650 | 0.5392 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.12.0+cu102
- Datasets 2.3.2
- Tokenizers 0.12.1
|
FahimFerdous/DialoGPT-small-cat | 9633fc76b4c339e02951c63953388c42b79b3941 | 2022-07-08T02:28:41.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
| conversational | false | FahimFerdous | null | FahimFerdous/DialoGPT-small-cat | 9 | null | transformers | 12,772 | ---
tags:
- conversational
---
#Cat DialoGPT Model |
ChauNguyen23/phobert-base-finetuned-imdb | 2b82f37c7083e2c66036ecbf8f273b0b5d4a60c6 | 2022-07-08T05:03:20.000Z | [
"pytorch",
"tensorboard",
"roberta",
"fill-mask",
"dataset:imdb",
"transformers",
"generated_from_trainer",
"model-index",
"autotrain_compatible"
]
| fill-mask | false | ChauNguyen23 | null | ChauNguyen23/phobert-base-finetuned-imdb | 9 | null | transformers | 12,773 | ---
tags:
- generated_from_trainer
datasets:
- imdb
model-index:
- name: phobert-base-finetuned-imdb
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. -->
# phobert-base-finetuned-imdb
This model is a fine-tuned version of [vinai/phobert-base](https://huggingface.co/vinai/phobert-base) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 2.6149
## 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: 3.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 3.3266 | 1.0 | 157 | 2.7949 |
| 2.9162 | 2.0 | 314 | 2.6515 |
| 2.8177 | 3.0 | 471 | 2.6452 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
mesolitica/t5-small-finetuned-noisy-ms-en | d0a4b4ba07b70d92d85f9b2c87e7fc52329bec5f | 2022-07-11T11:05:15.000Z | [
"pytorch",
"tf",
"tensorboard",
"t5",
"text2text-generation",
"transformers",
"generated_from_keras_callback",
"model-index",
"autotrain_compatible"
]
| text2text-generation | false | mesolitica | null | mesolitica/t5-small-finetuned-noisy-ms-en | 9 | null | transformers | 12,774 | ---
tags:
- generated_from_keras_callback
model-index:
- name: t5-small-finetuned-noisy-ms-en
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. -->
# t5-small-finetuned-noisy-ms-en
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
## 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: None
- training_precision: float32
### Training results
### Framework versions
- Transformers 4.19.0
- TensorFlow 2.6.0
- Datasets 2.1.0
- Tokenizers 0.12.1
|
jonatasgrosman/exp_w2v2t_fa_hubert_s889 | 0e8ef33b34e52945b106cbb382cbbdb3c26e3632 | 2022-07-09T20:36:47.000Z | [
"pytorch",
"hubert",
"automatic-speech-recognition",
"fa",
"dataset:mozilla-foundation/common_voice_7_0",
"transformers",
"license:apache-2.0"
]
| automatic-speech-recognition | false | jonatasgrosman | null | jonatasgrosman/exp_w2v2t_fa_hubert_s889 | 9 | null | transformers | 12,775 | ---
language:
- fa
license: apache-2.0
tags:
- automatic-speech-recognition
- fa
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_fa_hubert_s889
Fine-tuned [facebook/hubert-large-ll60k](https://huggingface.co/facebook/hubert-large-ll60k) for speech recognition using the train split of [Common Voice 7.0 (fa)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
Dror/finetuning-sentiment-model-3000-samples | e256c1cacaf63dbf20a1bc80ca1eba3259a4d91f | 2022-07-10T11:14:36.000Z | [
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"dataset:imdb",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
]
| text-classification | false | Dror | null | Dror/finetuning-sentiment-model-3000-samples | 9 | null | transformers | 12,776 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
metrics:
- accuracy
- f1
model-index:
- name: finetuning-sentiment-model-3000-samples
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: imdb
type: imdb
args: plain_text
metrics:
- name: Accuracy
type: accuracy
value: 0.87
- name: F1
type: f1
value: 0.8721311475409836
---
<!-- 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. -->
# finetuning-sentiment-model-3000-samples
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2979
- Accuracy: 0.87
- F1: 0.8721
## 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 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
jonatasgrosman/exp_w2v2t_ru_vp-fr_s730 | 4cb1b538dfee2ca137945bf84a2bad1f54b3e371 | 2022-07-11T08:58:28.000Z | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"ru",
"dataset:mozilla-foundation/common_voice_7_0",
"transformers",
"license:apache-2.0"
]
| automatic-speech-recognition | false | jonatasgrosman | null | jonatasgrosman/exp_w2v2t_ru_vp-fr_s730 | 9 | null | transformers | 12,777 | ---
language:
- ru
license: apache-2.0
tags:
- automatic-speech-recognition
- ru
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_ru_vp-fr_s730
Fine-tuned [facebook/wav2vec2-large-fr-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-fr-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (ru)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
GhostZen/distilbert-base-uncased-finetuned-squad | fb5286c869b0772651a678d46c6efc3a8b8f4663 | 2022-07-11T10:38:10.000Z | [
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"dataset:squad",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
]
| question-answering | false | GhostZen | null | GhostZen/distilbert-base-uncased-finetuned-squad | 9 | null | transformers | 12,778 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: distilbert-base-uncased-finetuned-squad
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. -->
# distilbert-base-uncased-finetuned-squad
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad dataset.
## 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: 3
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
jonatasgrosman/exp_w2v2t_ru_vp-it_s533 | 1c22b57df6d312c153cc9c44fec464cf593215f3 | 2022-07-11T10:09:38.000Z | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"ru",
"dataset:mozilla-foundation/common_voice_7_0",
"transformers",
"license:apache-2.0"
]
| automatic-speech-recognition | false | jonatasgrosman | null | jonatasgrosman/exp_w2v2t_ru_vp-it_s533 | 9 | null | transformers | 12,779 | ---
language:
- ru
license: apache-2.0
tags:
- automatic-speech-recognition
- ru
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_ru_vp-it_s533
Fine-tuned [facebook/wav2vec2-large-it-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-it-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (ru)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
ericntay/clinical_bert_ft | c1f8110c791e10715db692cbee20852a6fa1ea6b | 2022-07-11T15:30:06.000Z | [
"pytorch",
"tensorboard",
"bert",
"token-classification",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index",
"autotrain_compatible"
]
| token-classification | false | ericntay | null | ericntay/clinical_bert_ft | 9 | null | transformers | 12,780 | ---
license: mit
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: clinical_bert_ft
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. -->
# clinical_bert_ft
This model is a fine-tuned version of [emilyalsentzer/Bio_ClinicalBERT](https://huggingface.co/emilyalsentzer/Bio_ClinicalBERT) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2439
- F1: 0.8252
## 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: 5e-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
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.5938 | 1.0 | 95 | 0.2480 | 0.7084 |
| 0.1567 | 2.0 | 190 | 0.2035 | 0.7855 |
| 0.083 | 3.0 | 285 | 0.2002 | 0.8026 |
| 0.0482 | 4.0 | 380 | 0.2046 | 0.8118 |
| 0.0269 | 5.0 | 475 | 0.2230 | 0.8143 |
| 0.0185 | 6.0 | 570 | 0.2178 | 0.8175 |
| 0.0123 | 7.0 | 665 | 0.2269 | 0.8253 |
| 0.0093 | 8.0 | 760 | 0.2421 | 0.8227 |
| 0.0072 | 9.0 | 855 | 0.2446 | 0.8267 |
| 0.006 | 10.0 | 950 | 0.2439 | 0.8252 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
ariesutiono/finetuned-test-1 | 2c6c6fc26dd7cdeb8474bcd805edf069d5549ad3 | 2022-07-11T14:57:10.000Z | [
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"dataset:conll2003",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
]
| fill-mask | false | ariesutiono | null | ariesutiono/finetuned-test-1 | 9 | null | transformers | 12,781 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- conll2003
model-index:
- name: finetuned-test-1
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. -->
# finetuned-test-1
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8192
## 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
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.8219 | 1.0 | 30 | 2.3343 |
| 2.4148 | 2.0 | 60 | 2.2010 |
| 2.3236 | 3.0 | 90 | 2.1442 |
| 2.2231 | 4.0 | 120 | 2.1651 |
| 2.2171 | 5.0 | 150 | 2.0614 |
| 2.127 | 6.0 | 180 | 2.0405 |
| 2.0748 | 7.0 | 210 | 2.0092 |
| 2.0511 | 8.0 | 240 | 1.9798 |
| 2.0097 | 9.0 | 270 | 1.8662 |
| 1.9969 | 10.0 | 300 | 1.9257 |
| 2.0006 | 11.0 | 330 | 1.9386 |
| 1.9273 | 12.0 | 360 | 1.9357 |
| 1.9177 | 13.0 | 390 | 1.8983 |
| 1.9128 | 14.0 | 420 | 1.8990 |
| 1.8979 | 15.0 | 450 | 1.9037 |
| 1.8721 | 16.0 | 480 | 1.8440 |
| 1.8998 | 17.0 | 510 | 1.8404 |
| 1.8862 | 18.0 | 540 | 1.9193 |
| 1.9133 | 19.0 | 570 | 1.8494 |
| 1.8799 | 20.0 | 600 | 1.8192 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
nawta/wav2vec2-onomatopoeia-finetune_smalldata_ESC50pretrained | ff63b9397491fb68ad2229780ee70f5865dea53f | 2022-07-12T10:20:53.000Z | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"transformers",
"generated_from_trainer",
"model-index"
]
| automatic-speech-recognition | false | nawta | null | nawta/wav2vec2-onomatopoeia-finetune_smalldata_ESC50pretrained | 9 | null | transformers | 12,782 | ---
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-onomatopoeia-finetune_smalldata_ESC50pretrained
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-onomatopoeia-finetune_smalldata_ESC50pretrained
This model is a fine-tuned version of [/root/workspace/wav2vec2-pretrained_with_ESC50_10000epochs_32batch_2022-07-09_22-16-46/pytorch_model.bin](https://huggingface.co//root/workspace/wav2vec2-pretrained_with_ESC50_10000epochs_32batch_2022-07-09_22-16-46/pytorch_model.bin) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.2963
- Cer: 0.9002
## 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: 64
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Cer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 4.3287 | 23.81 | 500 | 2.2963 | 0.9002 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.9.1+cu111
- Datasets 1.13.3
- Tokenizers 0.10.3
|
ArneD/xlm-roberta-base-finetuned-panx-all | d4853ec206fd0e24150548799d983e33d50bf5d7 | 2022-07-12T07:50:58.000Z | [
"pytorch",
"xlm-roberta",
"token-classification",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index",
"autotrain_compatible"
]
| token-classification | false | ArneD | null | ArneD/xlm-roberta-base-finetuned-panx-all | 9 | null | transformers | 12,783 | ---
license: mit
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-all
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. -->
# xlm-roberta-base-finetuned-panx-all
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset (EN, FR, DE, IT).
It achieves the following results on the evaluation set:
- Loss: 0.1769
- F1: 0.8535
## 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: 5e-05
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2934 | 1.0 | 835 | 0.1853 | 0.8250 |
| 0.1569 | 2.0 | 1670 | 0.1714 | 0.8438 |
| 0.1008 | 3.0 | 2505 | 0.1769 | 0.8535 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.11.0+cu113
- Datasets 1.16.1
- Tokenizers 0.10.3
|
xyma/PROP-marco-step400k | c682b151910d20ad391e1051e3451c4146dda2b0 | 2022-07-12T11:53:02.000Z | [
"pytorch",
"bert",
"pretraining",
"en",
"dataset:msmarco",
"arxiv:2010.10137",
"transformers",
"PROP",
"Pretrain4IR",
"license:apache-2.0"
]
| null | false | xyma | null | xyma/PROP-marco-step400k | 9 | null | transformers | 12,784 | ---
language: en
tags:
- PROP
- Pretrain4IR
license: apache-2.0
datasets:
- msmarco
---
# PROP-marco-step400k
**PROP**, **P**re-training with **R**epresentative w**O**rds **P**rediction, is a new pre-training method tailored for ad-hoc retrieval. PROP is inspired by the classical statistical language model for IR, specifically the query likelihood model, which assumes that the query is generated as the piece of text representative of the “ideal” document. Based on this idea, we construct the representative words prediction (ROP) task for pre-training. The full paper can be found [here](https://arxiv.org/pdf/2010.10137.pdf).
This model is pre-trained with more steps than [PROP-marco](https://huggingface.co/xyma/PROP-marco) on MS MARCO document corpus, and used at the MS MARCO Document Ranking Leaderboard where we reached 1st place.
# Citation
If you find our work useful, please consider citing our paper:
```bibtex
@inproceedings{DBLP:conf/wsdm/MaGZFJC21,
author = {Xinyu Ma and
Jiafeng Guo and
Ruqing Zhang and
Yixing Fan and
Xiang Ji and
Xueqi Cheng},
editor = {Liane Lewin{-}Eytan and
David Carmel and
Elad Yom{-}Tov and
Eugene Agichtein and
Evgeniy Gabrilovich},
title = {{PROP:} Pre-training with Representative Words Prediction for Ad-hoc
Retrieval},
booktitle = {{WSDM} '21, The Fourteenth {ACM} International Conference on Web Search
and Data Mining, Virtual Event, Israel, March 8-12, 2021},
pages = {283--291},
publisher = {{ACM}},
year = {2021},
url = {https://doi.org/10.1145/3437963.3441777},
doi = {10.1145/3437963.3441777},
timestamp = {Wed, 07 Apr 2021 16:17:44 +0200},
biburl = {https://dblp.org/rec/conf/wsdm/MaGZFJC21.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
``` |
andy-0v0/orcs-and-friends | 90f151bd95011f3bdde11accaed0e118598e61f4 | 2022-07-12T16:03:57.000Z | [
"pytorch",
"tensorboard",
"vit",
"image-classification",
"transformers",
"huggingpics",
"model-index"
]
| image-classification | false | andy-0v0 | null | andy-0v0/orcs-and-friends | 9 | null | transformers | 12,785 | ---
tags:
- image-classification
- pytorch
- huggingpics
metrics:
- accuracy
model-index:
- name: orcs-and-friends
results:
- task:
name: Image Classification
type: image-classification
metrics:
- name: Accuracy
type: accuracy
value: 0.522522509098053
---
# orcs-and-friends
Five-way classifier for orcs and their friends
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics).
## Example Images
#### goblin
#### gremlin
#### ogre
#### orc
#### troll
 |
annahaz/xlm-roberta-base-misogyny-sexism-out-of-sample-test-opt-bal | dd06d3b0b75c81669112d3f9c6d0c9c5ef5c9816 | 2022-07-12T22:22:15.000Z | [
"pytorch",
"xlm-roberta",
"text-classification",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index"
]
| text-classification | false | annahaz | null | annahaz/xlm-roberta-base-misogyny-sexism-out-of-sample-test-opt-bal | 9 | null | transformers | 12,786 | ---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: xlm-roberta-base-misogyny-sexism-out-of-sample-test-opt-bal
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. -->
# xlm-roberta-base-misogyny-sexism-out-of-sample-test-opt-bal
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.2811
- Accuracy: 0.6022
- F1: 0.5689
- Precision: 0.5624
- Recall: 0.5756
- Mae: 0.3978
## 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 | F1 | Precision | Recall | Mae |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:|:------:|
| 0.4434 | 1.0 | 1100 | 0.8792 | 0.5752 | 0.4897 | 0.5414 | 0.4469 | 0.4248 |
| 0.3592 | 2.0 | 2200 | 1.0511 | 0.5882 | 0.4597 | 0.5723 | 0.3841 | 0.4118 |
| 0.3351 | 3.0 | 3300 | 0.8862 | 0.5639 | 0.5437 | 0.5199 | 0.5698 | 0.4361 |
| 0.2649 | 4.0 | 4400 | 1.5065 | 0.5931 | 0.5467 | 0.5556 | 0.5381 | 0.4069 |
| 0.2252 | 5.0 | 5500 | 1.2637 | 0.5766 | 0.6084 | 0.5261 | 0.7212 | 0.4234 |
| 0.2234 | 6.0 | 6600 | 1.6854 | 0.5832 | 0.5419 | 0.5432 | 0.5405 | 0.4168 |
| 0.2288 | 7.0 | 7700 | 1.7353 | 0.5985 | 0.5917 | 0.5517 | 0.6380 | 0.4015 |
| 0.2008 | 8.0 | 8800 | 1.8444 | 0.6152 | 0.5693 | 0.5814 | 0.5577 | 0.3848 |
| 0.1765 | 9.0 | 9900 | 2.4235 | 0.5833 | 0.5508 | 0.5417 | 0.5601 | 0.4167 |
| 0.2334 | 10.0 | 11000 | 2.0034 | 0.6002 | 0.5635 | 0.5611 | 0.5659 | 0.3998 |
| 0.1561 | 11.0 | 12100 | 2.3651 | 0.5897 | 0.5772 | 0.5445 | 0.6142 | 0.4103 |
| 0.1759 | 12.0 | 13200 | 2.8745 | 0.5855 | 0.5742 | 0.5402 | 0.6128 | 0.4145 |
| 0.1306 | 13.0 | 14300 | 2.7506 | 0.5904 | 0.5830 | 0.5442 | 0.6278 | 0.4096 |
| 0.1443 | 14.0 | 15400 | 2.7292 | 0.6061 | 0.5549 | 0.5725 | 0.5383 | 0.3939 |
| 0.1124 | 15.0 | 16500 | 2.6693 | 0.6119 | 0.5744 | 0.5745 | 0.5742 | 0.3881 |
| 0.0886 | 16.0 | 17600 | 2.8332 | 0.6052 | 0.5708 | 0.5661 | 0.5756 | 0.3948 |
| 0.078 | 17.0 | 18700 | 3.0623 | 0.6054 | 0.5693 | 0.5668 | 0.5718 | 0.3946 |
| 0.0955 | 18.0 | 19800 | 3.1543 | 0.5965 | 0.5725 | 0.5538 | 0.5925 | 0.4035 |
| 0.0689 | 19.0 | 20900 | 3.3443 | 0.5971 | 0.5763 | 0.5537 | 0.6009 | 0.4029 |
| 0.0669 | 20.0 | 22000 | 3.2811 | 0.6022 | 0.5689 | 0.5624 | 0.5756 | 0.3978 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.9.0+cu111
- Datasets 2.3.2
- Tokenizers 0.12.1
|
Team-PIXEL/pixel-base-finetuned-pos-ud-coptic-scriptorium | 8d4435231b62a5f5dadc410ce36fa03cb49c0296 | 2022-07-13T00:57:47.000Z | [
"pytorch",
"pixel",
"token-classification",
"transformers",
"autotrain_compatible"
]
| token-classification | false | Team-PIXEL | null | Team-PIXEL/pixel-base-finetuned-pos-ud-coptic-scriptorium | 9 | null | transformers | 12,787 | Entry not found |
Team-PIXEL/pixel-base-finetuned-pos-ud-hindi-hdtb | 6fe87800725ea263b73f4c5ab2af4662b1c21ac0 | 2022-07-13T01:07:13.000Z | [
"pytorch",
"pixel",
"token-classification",
"transformers",
"autotrain_compatible"
]
| token-classification | false | Team-PIXEL | null | Team-PIXEL/pixel-base-finetuned-pos-ud-hindi-hdtb | 9 | null | transformers | 12,788 | Entry not found |
Team-PIXEL/pixel-base-finetuned-pos-ud-korean-gsd | d9c235941659e740600854ffbd6ab2298439cd9e | 2022-07-13T01:20:24.000Z | [
"pytorch",
"pixel",
"token-classification",
"transformers",
"autotrain_compatible"
]
| token-classification | false | Team-PIXEL | null | Team-PIXEL/pixel-base-finetuned-pos-ud-korean-gsd | 9 | null | transformers | 12,789 | Entry not found |
Hamzaaa/wav2vec2-base-finetuned-savee | 7a208b18d5001d7b6314888f28d2c89f94cc1988 | 2022-07-13T12:15:40.000Z | [
"pytorch",
"tensorboard",
"wav2vec2",
"audio-classification",
"transformers"
]
| audio-classification | false | Hamzaaa | null | Hamzaaa/wav2vec2-base-finetuned-savee | 9 | null | transformers | 12,790 | Entry not found |
ahadda5/bart_wikikp_kp20k_openkp | be78ef6f81cfa7938ad43796554f0471a1a42bdd | 2022-07-13T21:46:22.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"autotrain_compatible"
]
| text2text-generation | false | ahadda5 | null | ahadda5/bart_wikikp_kp20k_openkp | 9 | null | transformers | 12,791 | Entry not found |
CovRelex-SE/CORD19-BERT | 3fd28418afb17bf92298fb0329bffd72618e3deb | 2022-07-14T02:46:19.000Z | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"generated_from_trainer",
"model-index",
"autotrain_compatible"
]
| fill-mask | false | CovRelex-SE | null | CovRelex-SE/CORD19-BERT | 9 | null | transformers | 12,792 | ---
tags:
- generated_from_trainer
model-index:
- name: CORD19_BERT
results: []
---
# CORD19-BERT
## How to use
```python
from transformers import BertTokenizer, BertModel
tokenizer = BertTokenizer.from_pretrained('CovRelex-SE/CORD19-BERT')
model = BertModel.from_pretrained("CovRelex-SE/CORD19-BERT")
text = "The virus can spread from an infected person’s mouth or nose."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
```
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0+cu113
- Datasets 2.1.0
- Tokenizers 0.12.1
|
userGagan/segformer-b0-finetuned-segments-sidewalk-5 | c202064f3c68198c9295722c18fbbbd3a087f144 | 2022-07-14T05:20:44.000Z | [
"pytorch",
"segformer",
"transformers"
]
| null | false | userGagan | null | userGagan/segformer-b0-finetuned-segments-sidewalk-5 | 9 | null | transformers | 12,793 | Entry not found |
userGagan/segformer-b0-finetuned-segments-sidewalk-6 | b03594077d1a61c997a3a50e05439d80846c25ff | 2022-07-14T06:43:18.000Z | [
"pytorch",
"tensorboard",
"segformer",
"transformers"
]
| null | false | userGagan | null | userGagan/segformer-b0-finetuned-segments-sidewalk-6 | 9 | null | transformers | 12,794 | Entry not found |
Team-PIXEL/pixel-base-finetuned-korquadv1 | 4d8f037c2d6bd37991d5a82f04f24006f54652b0 | 2022-07-14T15:58:12.000Z | [
"pytorch",
"pixel",
"question-answering",
"dataset:squad_kor_v1",
"transformers",
"generated_from_trainer",
"model-index",
"autotrain_compatible"
]
| question-answering | false | Team-PIXEL | null | Team-PIXEL/pixel-base-finetuned-korquadv1 | 9 | null | transformers | 12,795 | ---
tags:
- generated_from_trainer
datasets:
- squad_kor_v1
model-index:
- name: pixel-base-finetuned-korquadv1
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. -->
# pixel-base-finetuned-korquadv1
This model is a fine-tuned version of [Team-PIXEL/pixel-base](https://huggingface.co/Team-PIXEL/pixel-base) on the squad_kor_v1 dataset.
## 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: 7e-05
- train_batch_size: 32
- eval_batch_size: 8
- seed: 45
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- training_steps: 20000
- mixed_precision_training: Apex, opt level O1
### Training results
### Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0
- Datasets 2.0.0
- Tokenizers 0.12.1
|
Team-PIXEL/pixel-base-finetuned-masakhaner-pcm | 75af0e7488c6e873557ad479b27beb021ca80768 | 2022-07-15T03:28:01.000Z | [
"pytorch",
"pixel",
"token-classification",
"transformers",
"autotrain_compatible"
]
| token-classification | false | Team-PIXEL | null | Team-PIXEL/pixel-base-finetuned-masakhaner-pcm | 9 | null | transformers | 12,796 | Entry not found |
furrutiav/beto_bi_purpose | 238523441df3e96aed72087fe6b59f77b3badaa8 | 2022-07-20T19:23:43.000Z | [
"pytorch",
"bert",
"feature-extraction",
"transformers"
]
| feature-extraction | false | furrutiav | null | furrutiav/beto_bi_purpose | 9 | null | transformers | 12,797 | Entry not found |
worknick/bert-base-cased-finetuned-conll2003 | f28f6abccc94df1c5b80c47b6e3c264008451f46 | 2022-07-15T05:56:36.000Z | [
"pytorch",
"tensorboard",
"bert",
"token-classification",
"dataset:conll2003",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
]
| token-classification | false | worknick | null | worknick/bert-base-cased-finetuned-conll2003 | 9 | null | transformers | 12,798 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-base-cased-finetuned-conll2003
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: conll2003
type: conll2003
args: conll2003
metrics:
- name: Precision
type: precision
value: 0.9409771754636234
- name: Recall
type: recall
value: 0.946886775524852
- name: F1
type: f1
value: 0.9439227260531259
- name: Accuracy
type: accuracy
value: 0.9859745687878966
---
<!-- 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-cased-finetuned-conll2003
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0643
- Precision: 0.9410
- Recall: 0.9469
- F1: 0.9439
- Accuracy: 0.9860
## 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2349 | 0.57 | 500 | 0.0885 | 0.8957 | 0.8980 | 0.8968 | 0.9747 |
| 0.0822 | 1.14 | 1000 | 0.0774 | 0.9184 | 0.9219 | 0.9202 | 0.9802 |
| 0.0476 | 1.71 | 1500 | 0.0683 | 0.9345 | 0.9325 | 0.9335 | 0.9833 |
| 0.0368 | 2.28 | 2000 | 0.0653 | 0.9333 | 0.9430 | 0.9381 | 0.9847 |
| 0.028 | 2.85 | 2500 | 0.0670 | 0.9279 | 0.9342 | 0.9311 | 0.9835 |
| 0.0171 | 3.42 | 3000 | 0.0643 | 0.9410 | 0.9469 | 0.9439 | 0.9860 |
| 0.0149 | 3.99 | 3500 | 0.0667 | 0.9369 | 0.9477 | 0.9422 | 0.9856 |
| 0.0088 | 4.56 | 4000 | 0.0698 | 0.9360 | 0.9473 | 0.9416 | 0.9855 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.12.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
valhalla/vqgan_imagenet_f16_16384 | bd05189dba2dacbb9c8335dd46686ea38873c2b5 | 2022-07-25T14:57:11.000Z | [
"pytorch",
"transformers"
]
| null | false | valhalla | null | valhalla/vqgan_imagenet_f16_16384 | 9 | null | transformers | 12,799 | Entry not found |
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