modelId
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| author
stringlengths 2
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| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-09-07 00:41:44
| downloads
int64 0
223M
| likes
int64 0
11.7k
| library_name
stringclasses 544
values | tags
listlengths 1
4.05k
| pipeline_tag
stringclasses 55
values | createdAt
timestamp[us, tz=UTC]date 2022-03-02 23:29:04
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|---|---|---|---|---|---|---|---|---|---|
ThuyNT03/xlm-roberta-base-Final_VietNam-aug_replace_tfidf-2
|
ThuyNT03
| 2023-09-05T02:04:06Z | 114 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"base_model:FacebookAI/xlm-roberta-base",
"base_model:finetune:FacebookAI/xlm-roberta-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-09-04T23:19:26Z |
---
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: xlm-roberta-base-Final_VietNam-aug_replace_tfidf-2
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-Final_VietNam-aug_replace_tfidf-2
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: 0.8468
- Accuracy: 0.69
- F1: 0.6959
## 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: 40
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 1.0982 | 1.0 | 87 | 0.9995 | 0.47 | 0.4137 |
| 0.8884 | 2.0 | 174 | 0.7521 | 0.65 | 0.6032 |
| 0.7533 | 3.0 | 261 | 0.7130 | 0.64 | 0.6364 |
| 0.6259 | 4.0 | 348 | 0.7598 | 0.68 | 0.6865 |
| 0.5278 | 5.0 | 435 | 0.7066 | 0.7 | 0.7053 |
| 0.4336 | 6.0 | 522 | 0.7901 | 0.7 | 0.7060 |
| 0.3516 | 7.0 | 609 | 0.8106 | 0.69 | 0.6976 |
| 0.2859 | 8.0 | 696 | 0.8468 | 0.69 | 0.6959 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
mabrouk/speecht5_finetuned_voxpopuli_nl
|
mabrouk
| 2023-09-05T01:57:13Z | 76 | 0 |
transformers
|
[
"transformers",
"pytorch",
"speecht5",
"text-to-audio",
"generated_from_trainer",
"dataset:voxpopuli",
"base_model:microsoft/speecht5_tts",
"base_model:finetune:microsoft/speecht5_tts",
"license:mit",
"endpoints_compatible",
"region:us"
] |
text-to-audio
| 2023-09-04T23:31:36Z |
---
license: mit
base_model: microsoft/speecht5_tts
tags:
- generated_from_trainer
datasets:
- voxpopuli
model-index:
- name: speecht5_finetuned_voxpopuli_nl
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. -->
# speecht5_finetuned_voxpopuli_nl
This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the voxpopuli dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4622
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 4
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 4000
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.521 | 4.3 | 1000 | 0.4820 |
| 0.4972 | 8.61 | 2000 | 0.4676 |
| 0.4963 | 12.91 | 3000 | 0.4645 |
| 0.4919 | 17.21 | 4000 | 0.4622 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
ThuyNT03/xlm-roberta-base-Final_VietNam-aug_replace_w2v-2
|
ThuyNT03
| 2023-09-05T01:54:32Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"base_model:FacebookAI/xlm-roberta-base",
"base_model:finetune:FacebookAI/xlm-roberta-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-09-04T23:09:25Z |
---
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: xlm-roberta-base-Final_VietNam-aug_replace_w2v-2
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-Final_VietNam-aug_replace_w2v-2
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: 0.9125
- Accuracy: 0.71
- F1: 0.7091
## 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: 40
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 1.101 | 1.0 | 84 | 1.0494 | 0.46 | 0.3728 |
| 0.9323 | 2.0 | 168 | 0.7962 | 0.59 | 0.5689 |
| 0.7109 | 3.0 | 252 | 0.7447 | 0.71 | 0.7004 |
| 0.587 | 4.0 | 336 | 0.7251 | 0.71 | 0.7104 |
| 0.4611 | 5.0 | 420 | 0.8001 | 0.68 | 0.6770 |
| 0.3668 | 6.0 | 504 | 0.8589 | 0.72 | 0.7229 |
| 0.291 | 7.0 | 588 | 0.8900 | 0.69 | 0.6894 |
| 0.2505 | 8.0 | 672 | 0.9125 | 0.71 | 0.7091 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
thissayantan/dreambooth-sayantan
|
thissayantan
| 2023-09-05T01:48:42Z | 1 | 1 |
diffusers
|
[
"diffusers",
"text-to-image",
"autotrain",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:finetune:stabilityai/stable-diffusion-xl-base-1.0",
"region:us"
] |
text-to-image
| 2023-09-05T01:48:40Z |
---
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: photo of sayantan person
tags:
- text-to-image
- diffusers
- autotrain
inference: true
---
# DreamBooth trained by AutoTrain
Text encoder was not trained.
|
ThuyNT03/xlm-roberta-base-Final_VietNam-aug_insert_BERT-2
|
ThuyNT03
| 2023-09-05T01:32:37Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"base_model:FacebookAI/xlm-roberta-base",
"base_model:finetune:FacebookAI/xlm-roberta-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-09-04T22:51:06Z |
---
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: xlm-roberta-base-Final_VietNam-aug_insert_BERT-2
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-Final_VietNam-aug_insert_BERT-2
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: 1.1237
- Accuracy: 0.71
- F1: 0.7165
## 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: 40
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 1.0509 | 1.0 | 87 | 0.8383 | 0.59 | 0.5441 |
| 0.7214 | 2.0 | 174 | 0.7218 | 0.72 | 0.72 |
| 0.5758 | 3.0 | 261 | 0.7535 | 0.69 | 0.6956 |
| 0.4321 | 4.0 | 348 | 0.7413 | 0.73 | 0.7360 |
| 0.3364 | 5.0 | 435 | 0.8328 | 0.72 | 0.7269 |
| 0.2712 | 6.0 | 522 | 0.9267 | 0.72 | 0.7255 |
| 0.1902 | 7.0 | 609 | 1.0811 | 0.7 | 0.7074 |
| 0.1351 | 8.0 | 696 | 1.1237 | 0.71 | 0.7165 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
duwuonline/my-translation-helsinki2
|
duwuonline
| 2023-09-05T01:27:43Z | 104 | 0 |
transformers
|
[
"transformers",
"pytorch",
"marian",
"text2text-generation",
"generated_from_trainer",
"base_model:duwuonline/my-translation-helsinki",
"base_model:finetune:duwuonline/my-translation-helsinki",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-09-05T01:00:23Z |
---
license: apache-2.0
base_model: duwuonline/my-translation-helsinki
tags:
- generated_from_trainer
model-index:
- name: my-translation-helsinki2
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. -->
# my-translation-helsinki2
This model is a fine-tuned version of [duwuonline/my-translation-helsinki](https://huggingface.co/duwuonline/my-translation-helsinki) on the None 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: 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: 30
### Training results
### Framework versions
- Transformers 4.32.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
ckandemir/bert-base-uncased-issues-128
|
ckandemir
| 2023-09-05T01:26:28Z | 115 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"fill-mask",
"generated_from_trainer",
"base_model:google-bert/bert-base-uncased",
"base_model:finetune:google-bert/bert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2023-09-04T19:45:22Z |
---
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
model-index:
- name: bert-base-uncased-issues-128
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. -->
# bert-base-uncased-issues-128
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2137
## 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: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 16
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.0966 | 1.0 | 291 | 1.6190 |
| 1.6197 | 2.0 | 582 | 1.5317 |
| 1.485 | 3.0 | 873 | 1.4164 |
| 1.3992 | 4.0 | 1164 | 1.4064 |
| 1.3219 | 5.0 | 1455 | 1.3900 |
| 1.2851 | 6.0 | 1746 | 1.2096 |
| 1.2328 | 7.0 | 2037 | 1.3019 |
| 1.2113 | 8.0 | 2328 | 1.2779 |
| 1.1674 | 9.0 | 2619 | 1.2312 |
| 1.1443 | 10.0 | 2910 | 1.1830 |
| 1.1171 | 11.0 | 3201 | 1.1692 |
| 1.1067 | 12.0 | 3492 | 1.2364 |
| 1.0846 | 13.0 | 3783 | 1.1871 |
| 1.0815 | 14.0 | 4074 | 1.1354 |
| 1.054 | 15.0 | 4365 | 1.1771 |
| 1.0565 | 16.0 | 4656 | 1.2137 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
CzarnyRycerz/Reinforce-pixelcopter-1
|
CzarnyRycerz
| 2023-09-05T01:23:36Z | 0 | 0 | null |
[
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-09-04T23:41:34Z |
---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-pixelcopter-1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 32.50 +/- 24.74
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
JasonTheDeveloper/squad-bloom-3b
|
JasonTheDeveloper
| 2023-09-05T01:21:55Z | 2 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-09-05T01:21:53Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.6.0.dev0
|
RafaelMayer/electra-copec-2
|
RafaelMayer
| 2023-09-05T01:13:38Z | 61 | 0 |
transformers
|
[
"transformers",
"tf",
"electra",
"text-classification",
"generated_from_keras_callback",
"base_model:mrm8488/electricidad-base-discriminator",
"base_model:finetune:mrm8488/electricidad-base-discriminator",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-09-05T01:12:31Z |
---
base_model: mrm8488/electricidad-base-discriminator
tags:
- generated_from_keras_callback
model-index:
- name: RafaelMayer/electra-copec-2
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. -->
# RafaelMayer/electra-copec-2
This model is a fine-tuned version of [mrm8488/electricidad-base-discriminator](https://huggingface.co/mrm8488/electricidad-base-discriminator) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.7303
- Validation Loss: 0.6874
- Train Accuracy: 0.8824
- Train Precision: [0.75 0.92307692]
- Train Precision W: 0.8824
- Train Recall: [0.75 0.92307692]
- Train Recall W: 0.8824
- Train F1: [0.75 0.92307692]
- Train F1 W: 0.8824
- Epoch: 1
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 2e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 35, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '__passive_serialization__': True}, 'warmup_steps': 5, 'power': 1.0, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train Accuracy | Train Precision | Train Precision W | Train Recall | Train Recall W | Train F1 | Train F1 W | Epoch |
|:----------:|:---------------:|:--------------:|:-----------------------:|:-----------------:|:-----------------------:|:--------------:|:-----------------------:|:----------:|:-----:|
| 0.7303 | 0.6874 | 0.8824 | [0.75 0.92307692] | 0.8824 | [0.75 0.92307692] | 0.8824 | [0.75 0.92307692] | 0.8824 | 1 |
### Framework versions
- Transformers 4.32.1
- TensorFlow 2.12.0
- Datasets 2.14.4
- Tokenizers 0.13.3
|
ThuyNT03/xlm-roberta-base-Final_VietNam-aug_insert_w2v-2
|
ThuyNT03
| 2023-09-05T01:13:16Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"base_model:FacebookAI/xlm-roberta-base",
"base_model:finetune:FacebookAI/xlm-roberta-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-09-04T22:32:59Z |
---
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: xlm-roberta-base-Final_VietNam-aug_insert_w2v-2
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-Final_VietNam-aug_insert_w2v-2
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: 1.1138
- Accuracy: 0.75
- F1: 0.7539
## 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: 40
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 1.0576 | 1.0 | 85 | 0.8693 | 0.6 | 0.5283 |
| 0.7822 | 2.0 | 170 | 0.8331 | 0.69 | 0.6665 |
| 0.6156 | 3.0 | 255 | 0.7210 | 0.72 | 0.7194 |
| 0.4447 | 4.0 | 340 | 0.8139 | 0.66 | 0.6645 |
| 0.3252 | 5.0 | 425 | 0.9348 | 0.67 | 0.6776 |
| 0.2105 | 6.0 | 510 | 0.9185 | 0.77 | 0.7718 |
| 0.1437 | 7.0 | 595 | 1.0530 | 0.75 | 0.7539 |
| 0.1479 | 8.0 | 680 | 1.1138 | 0.75 | 0.7539 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
markmp/marketmail
|
markmp
| 2023-09-05T01:12:27Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-09-05T01:12:22Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
### Framework versions
- PEFT 0.6.0.dev0
|
schrilax/marketing_email
|
schrilax
| 2023-09-05T01:10:46Z | 0 | 0 | null |
[
"arxiv:1910.09700",
"region:us"
] | null | 2023-09-05T00:43:50Z |
---
# For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1
# Doc / guide: https://huggingface.co/docs/hub/model-cards
{}
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Data Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
MichelNivard/codellama_Rbase_instr
|
MichelNivard
| 2023-09-05T01:08:30Z | 2 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-09-01T10:24:06Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.5.0
|
jschew39/marketmail
|
jschew39
| 2023-09-05T01:07:57Z | 3 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-09-05T01:07:55Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
### Framework versions
- PEFT 0.6.0.dev0
|
RafaelMayer/roberta-copec-2
|
RafaelMayer
| 2023-09-05T01:06:47Z | 62 | 0 |
transformers
|
[
"transformers",
"tf",
"roberta",
"text-classification",
"generated_from_keras_callback",
"base_model:PlanTL-GOB-ES/roberta-base-bne",
"base_model:finetune:PlanTL-GOB-ES/roberta-base-bne",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-09-05T01:05:40Z |
---
license: apache-2.0
base_model: PlanTL-GOB-ES/roberta-base-bne
tags:
- generated_from_keras_callback
model-index:
- name: RafaelMayer/roberta-copec-2
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. -->
# RafaelMayer/roberta-copec-2
This model is a fine-tuned version of [PlanTL-GOB-ES/roberta-base-bne](https://huggingface.co/PlanTL-GOB-ES/roberta-base-bne) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.6476
- Validation Loss: 0.6356
- Train Accuracy: 0.7647
- Train Precision: [0. 0.76470588]
- Train Precision W: 0.5848
- Train Recall: [0. 1.]
- Train Recall W: 0.7647
- Train F1: [0. 0.86666667]
- Train F1 W: 0.6627
- Epoch: 1
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 2e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 35, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '__passive_serialization__': True}, 'warmup_steps': 5, 'power': 1.0, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train Accuracy | Train Precision | Train Precision W | Train Recall | Train Recall W | Train F1 | Train F1 W | Epoch |
|:----------:|:---------------:|:--------------:|:-----------------------:|:-----------------:|:------------:|:--------------:|:-----------------------:|:----------:|:-----:|
| 0.6476 | 0.6356 | 0.7647 | [0. 0.76470588] | 0.5848 | [0. 1.] | 0.7647 | [0. 0.86666667] | 0.6627 | 1 |
### Framework versions
- Transformers 4.32.1
- TensorFlow 2.12.0
- Datasets 2.14.4
- Tokenizers 0.13.3
|
ThuyNT03/xlm-roberta-base-Final_VietNam-aug_insert_synonym-2
|
ThuyNT03
| 2023-09-05T01:02:18Z | 124 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"base_model:FacebookAI/xlm-roberta-base",
"base_model:finetune:FacebookAI/xlm-roberta-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-09-04T22:22:38Z |
---
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: xlm-roberta-base-Final_VietNam-aug_insert_synonym-2
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-Final_VietNam-aug_insert_synonym-2
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: 1.3966
- Accuracy: 0.67
- F1: 0.6754
## 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: 40
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 1.0222 | 1.0 | 87 | 0.8095 | 0.65 | 0.6380 |
| 0.6487 | 2.0 | 174 | 0.7375 | 0.67 | 0.6640 |
| 0.4554 | 3.0 | 261 | 0.7962 | 0.71 | 0.7084 |
| 0.3194 | 4.0 | 348 | 0.8102 | 0.71 | 0.7161 |
| 0.2303 | 5.0 | 435 | 1.1793 | 0.65 | 0.6607 |
| 0.1728 | 6.0 | 522 | 1.1697 | 0.72 | 0.7245 |
| 0.127 | 7.0 | 609 | 1.3509 | 0.69 | 0.6943 |
| 0.0927 | 8.0 | 696 | 1.3966 | 0.67 | 0.6754 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
ThuyNT03/xlm-roberta-base-Final_Mixed-aug_backtranslation-2
|
ThuyNT03
| 2023-09-05T00:58:55Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"base_model:FacebookAI/xlm-roberta-base",
"base_model:finetune:FacebookAI/xlm-roberta-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-09-05T00:51:23Z |
---
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: xlm-roberta-base-Final_Mixed-aug_backtranslation-2
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-Final_Mixed-aug_backtranslation-2
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: 1.1103
- Accuracy: 0.74
- F1: 0.7315
## 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: 40
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 1.0442 | 1.0 | 87 | 0.7191 | 0.69 | 0.6652 |
| 0.7545 | 2.0 | 174 | 0.6726 | 0.73 | 0.7264 |
| 0.5743 | 3.0 | 261 | 0.6634 | 0.72 | 0.7157 |
| 0.4342 | 4.0 | 348 | 0.7801 | 0.73 | 0.7270 |
| 0.3244 | 5.0 | 435 | 0.8782 | 0.75 | 0.7438 |
| 0.2421 | 6.0 | 522 | 1.0173 | 0.73 | 0.7235 |
| 0.167 | 7.0 | 609 | 1.0822 | 0.75 | 0.7431 |
| 0.1546 | 8.0 | 696 | 1.1103 | 0.74 | 0.7315 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
ThuyNT03/xlm-roberta-base-Final_VietNam-train-2
|
ThuyNT03
| 2023-09-05T00:50:51Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"base_model:FacebookAI/xlm-roberta-base",
"base_model:finetune:FacebookAI/xlm-roberta-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-09-04T22:18:21Z |
---
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: xlm-roberta-base-Final_VietNam-train-2
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-Final_VietNam-train-2
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: 0.8740
- Accuracy: 0.68
- F1: 0.6882
## 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: 40
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 1.1143 | 1.0 | 44 | 1.0936 | 0.4 | 0.4041 |
| 0.9843 | 2.0 | 88 | 0.8262 | 0.63 | 0.6167 |
| 0.7312 | 3.0 | 132 | 0.7333 | 0.7 | 0.6919 |
| 0.5899 | 4.0 | 176 | 0.8261 | 0.7 | 0.7020 |
| 0.4922 | 5.0 | 220 | 0.7399 | 0.71 | 0.7145 |
| 0.435 | 6.0 | 264 | 0.8382 | 0.64 | 0.6530 |
| 0.375 | 7.0 | 308 | 0.8675 | 0.7 | 0.7047 |
| 0.3161 | 8.0 | 352 | 0.8740 | 0.68 | 0.6882 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
RafaelMayer/electra-copec-1
|
RafaelMayer
| 2023-09-05T00:46:22Z | 60 | 0 |
transformers
|
[
"transformers",
"tf",
"electra",
"text-classification",
"generated_from_keras_callback",
"base_model:mrm8488/electricidad-base-discriminator",
"base_model:finetune:mrm8488/electricidad-base-discriminator",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-09-05T00:45:10Z |
---
base_model: mrm8488/electricidad-base-discriminator
tags:
- generated_from_keras_callback
model-index:
- name: RafaelMayer/electra-copec-1
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. -->
# RafaelMayer/electra-copec-1
This model is a fine-tuned version of [mrm8488/electricidad-base-discriminator](https://huggingface.co/mrm8488/electricidad-base-discriminator) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.7863
- Validation Loss: 0.7271
- Train Accuracy: 0.1765
- Train Precision: [0.17647059 0. ]
- Train Precision W: 0.0311
- Train Recall: [1. 0.]
- Train Recall W: 0.1765
- Train F1: [0.3 0. ]
- Train F1 W: 0.0529
- Epoch: 1
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 2e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 35, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '__passive_serialization__': True}, 'warmup_steps': 5, 'power': 1.0, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train Accuracy | Train Precision | Train Precision W | Train Recall | Train Recall W | Train F1 | Train F1 W | Epoch |
|:----------:|:---------------:|:--------------:|:-----------------------:|:-----------------:|:------------:|:--------------:|:---------:|:----------:|:-----:|
| 0.7863 | 0.7271 | 0.1765 | [0.17647059 0. ] | 0.0311 | [1. 0.] | 0.1765 | [0.3 0. ] | 0.0529 | 1 |
### Framework versions
- Transformers 4.32.1
- TensorFlow 2.12.0
- Datasets 2.14.4
- Tokenizers 0.13.3
|
nbogdan/flant5-base-2ex-elaboration-1epochs
|
nbogdan
| 2023-09-05T00:45:42Z | 0 | 0 |
adapter-transformers
|
[
"adapter-transformers",
"adapterhub:self-explanations",
"t5",
"dataset:self-explanations",
"region:us"
] | null | 2023-09-05T00:40:36Z |
---
tags:
- adapterhub:self-explanations
- t5
- adapter-transformers
datasets:
- self-explanations
---
# Adapter `nbogdan/flant5-base-2ex-elaboration-1epochs` for google/flan-t5-base
An [adapter](https://adapterhub.ml) for the `google/flan-t5-base` model that was trained on the [self-explanations](https://adapterhub.ml/explore/self-explanations/) dataset.
This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library.
## Usage
First, install `adapter-transformers`:
```
pip install -U adapter-transformers
```
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_
Now, the adapter can be loaded and activated like this:
```python
from transformers import AutoAdapterModel
model = AutoAdapterModel.from_pretrained("google/flan-t5-base")
adapter_name = model.load_adapter("nbogdan/flant5-base-2ex-elaboration-1epochs", source="hf", set_active=True)
```
## Architecture & Training
<!-- Add some description here -->
## Evaluation results
<!-- Add some description here -->
## Citation
<!-- Add some description here -->
|
ThuyNT03/xlm-roberta-base-Final_VietNam-aug_delete-2
|
ThuyNT03
| 2023-09-05T00:45:20Z | 124 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"base_model:FacebookAI/xlm-roberta-base",
"base_model:finetune:FacebookAI/xlm-roberta-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-09-04T22:10:24Z |
---
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: xlm-roberta-base-Final_VietNam-aug_delete-2
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-Final_VietNam-aug_delete-2
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: 0.8464
- Accuracy: 0.68
- F1: 0.6845
## 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: 40
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 1.0599 | 1.0 | 87 | 0.9398 | 0.53 | 0.4615 |
| 0.7781 | 2.0 | 174 | 0.7588 | 0.65 | 0.6405 |
| 0.6771 | 3.0 | 261 | 0.7271 | 0.68 | 0.6828 |
| 0.5317 | 4.0 | 348 | 0.6991 | 0.7 | 0.7113 |
| 0.4389 | 5.0 | 435 | 0.6845 | 0.71 | 0.7092 |
| 0.3377 | 6.0 | 522 | 0.8429 | 0.7 | 0.7013 |
| 0.2595 | 7.0 | 609 | 0.8166 | 0.68 | 0.6870 |
| 0.2211 | 8.0 | 696 | 0.8464 | 0.68 | 0.6845 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
actionpace/Chronos-Hermes-v2-13b-Limarp-Lora-Merged
|
actionpace
| 2023-09-05T00:45:05Z | 7 | 0 | null |
[
"gguf",
"en",
"license:other",
"endpoints_compatible",
"region:us"
] | null | 2023-09-02T18:50:44Z |
---
license: other
language:
- en
---
**Some of my own quants:**
* Chronos-Hermes-v2-13b-Limarp-Lora-Merged_Q5_1_4K.gguf
* Chronos-Hermes-v2-13b-Limarp-Lora-Merged_Q5_1_8K.gguf
**Source:** [Doctor-Shotgun](https://huggingface.co/Doctor-Shotgun)
**Source Model:** [Chronos-Hermes-v2-13b-Limarp-Lora-Merged](https://huggingface.co/Doctor-Shotgun/Chronos-Hermes-v2-13b-Limarp-Lora-Merged)
**Source models for Doctor-Shotgun/Chronos-Hermes-v2-13b-Limarp-Lora-Merged (Merge)**
- [Austism/chronos-hermes-13b-v2](https://huggingface.co/Austism/chronos-hermes-13b-v2)
- [lemonilia/limarp-llama2](https://huggingface.co/lemonilia/limarp-llama2) (Lora)
|
ThuyNT03/xlm-roberta-base-Final_Mixed-aug_replace_tfidf-2
|
ThuyNT03
| 2023-09-05T00:43:19Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"base_model:FacebookAI/xlm-roberta-base",
"base_model:finetune:FacebookAI/xlm-roberta-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-09-05T00:35:33Z |
---
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: xlm-roberta-base-Final_Mixed-aug_replace_tfidf-2
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-Final_Mixed-aug_replace_tfidf-2
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: 0.7407
- Accuracy: 0.78
- F1: 0.7740
## 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: 40
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 1.085 | 1.0 | 88 | 0.9923 | 0.66 | 0.6391 |
| 0.9033 | 2.0 | 176 | 0.6803 | 0.74 | 0.7342 |
| 0.7906 | 3.0 | 264 | 0.7208 | 0.71 | 0.6992 |
| 0.6859 | 4.0 | 352 | 0.6374 | 0.75 | 0.7483 |
| 0.5591 | 5.0 | 440 | 0.7554 | 0.76 | 0.7539 |
| 0.4588 | 6.0 | 528 | 0.8309 | 0.74 | 0.7337 |
| 0.3967 | 7.0 | 616 | 0.6894 | 0.81 | 0.8063 |
| 0.3339 | 8.0 | 704 | 0.7407 | 0.78 | 0.7740 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
actionpace/Hermes-Kimiko-13B-f16
|
actionpace
| 2023-09-05T00:38:56Z | 11 | 0 | null |
[
"gguf",
"en",
"license:other",
"endpoints_compatible",
"region:us"
] | null | 2023-09-05T00:15:11Z |
---
license: other
language:
- en
---
**Some of my own quants:**
* Hermes-Kimiko-13B-f16_Q5_1_4K.gguf
* Hermes-Kimiko-13B-f16_Q5_1_8K.gguf
**Source:** [Blackroot](https://huggingface.co/Blackroot)
**Source Model:** [Hermes-Kimiko-13B-f16](https://huggingface.co/Blackroot/Hermes-Kimiko-13B-f16)
**Source models for Blackroot/Hermes-Kimiko-13B-f16 (Merge)**
- [NousResearch/Nous-Hermes-Llama2-13b](https://huggingface.co/NousResearch/Nous-Hermes-Llama2-13b) ([Ref](https://huggingface.co/actionpace/Nous-Hermes-Llama2-13b))
- [nRuaif/Kimiko_13B](https://huggingface.co/nRuaif/Kimiko_13B) (Lora)
|
actionpace/FrankensteinsMonster-13B
|
actionpace
| 2023-09-05T00:35:39Z | 5 | 0 | null |
[
"gguf",
"en",
"license:other",
"endpoints_compatible",
"region:us"
] | null | 2023-09-05T00:12:20Z |
---
license: other
language:
- en
---
**Some of my own quants:**
* FrankensteinsMonster-13B_Q5_1_4K.gguf
* FrankensteinsMonster-13B_Q5_1_8K.gguf
**Source:** [Blackroot](https://huggingface.co/Blackroot)
**Source Model:** [FrankensteinsMonster-13B](https://huggingface.co/Blackroot/FrankensteinsMonster-13B)
**Source models for Blackroot/FrankensteinsMonster-13B (Merge)**
- [NousResearch/Nous-Hermes-Llama2-13b](https://huggingface.co/NousResearch/Nous-Hermes-Llama2-13b) ([Ref](https://huggingface.co/actionpace/Nous-Hermes-Llama2-13b))
- [Blackroot/Llama-2-13B-Storywriter-LORA](https://huggingface.co/Blackroot/Llama-2-13B-Storywriter-LORA) (Lora)
- [lemonilia/limarp-llama2](https://huggingface.co/lemonilia/limarp-llama2) (Lora)
|
monsoon-nlp/nyrkr-joker-llama
|
monsoon-nlp
| 2023-09-05T00:35:39Z | 7 | 0 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"nyc",
"llama2",
"en",
"dataset:jmhessel/newyorker_caption_contest",
"arxiv:2209.06293",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-09-04T22:33:41Z |
---
license: mit
datasets:
- jmhessel/newyorker_caption_contest
language:
- en
tags:
- nyc
- llama2
widget:
- text: "This scene takes place in the following location: a bank. Three people are standing in line at the bank. The bank teller is a traditional pirate with a hook hand, eye patch, and a parrot. The scene includes: Piracy, Bank teller.\ncaption: Can I interest you in opening an offshore account?\nexplanation of the caption:\n"
example_title: "Training prompt format"
- text: "In this task, you will see a description of an uncanny situation. Then, you will see a joke that was written about the situation. Explain how the joke relates to the situation and why it is funny.\n###\nThis scene takes place in the following location: a bank. Three people are standing in line at the bank. The bank teller is a traditional pirate with a hook hand, eye patch, and a parrot. The scene includes: Piracy, Bank teller.\ncaption: Can I interest you in opening an offshore account?\nexplanation of the caption:\n"
example_title: "Paper prompt format"
- text: "This scene takes place in the following location: a bank. Three people are standing in line at the bank. The bank teller is a traditional pirate with a hook hand, eye patch, and a parrot. The scene includes: Piracy, Bank teller.\ncaption: Can I interest you in opening an offshore account?\nthe caption is funny because"
example_title: "Suggested prompt format"
---
# nyrkr-joker-llama
*New Yorker* cartoon description and caption -> attempt at a joke explanation
Technical details:
- Based on LLaMa2-7b-hf (version 2, 7B params)
- Used [QLoRA](https://github.com/artidoro/qlora/blob/main/qlora.py) to fine-tune on [1.2k rows of New Yorker caption contest](https://huggingface.co/datasets/jmhessel/newyorker_caption_contest)
- Merged LLaMa2 with the adapter weights (from checkpoint step=160, epoch=2.7)
## Prompt options
[The original paper](https://arxiv.org/abs/2209.06293), Figure 10 uses this format for joke explanations:
`In this task, you will see a description of an uncanny situation. Then, you will see a joke that was written about the situation. Explain how the joke relates to the situation and why it is funny.
###
{few-shot examples separated by ###, newline after "explanation of the caption:"}
This scene takes place in the following location: a bank. Three people are standing in line at the bank. The bank teller is a traditional pirate with a hook hand, eye patch, and a parrot. The scene includes: Piracy, Bank teller.
caption: Can I interest you in opening an offshore account?
explanation of the caption:
`
In training, I used just the individual example:
`This scene takes place in the following location: a bank. Three people are standing in line at the bank. The bank teller is a traditional pirate with a hook hand, eye patch, and a parrot. The scene includes: Piracy, Bank teller.
caption: Can I interest you in opening an offshore account?
explanation of the caption:\n`
In inference, I had some better results with a more natural prompt (no newline or space at end)
`This scene takes place in the following location: a bank. Three people are standing in line at the bank. The bank teller is a traditional pirate with a hook hand, eye patch, and a parrot. The scene includes: Piracy, Bank teller.
caption: Can I interest you in opening an offshore account?
the caption is funny because`
## Training script
Trained on a V100
```
git clone https://github.com/artidoro/qlora
cd qlora
pip3 install -r requirements.txt --quiet
! cd qlora && python qlora.py \
--model_name_or_path ../llama-2-7b-hf \
--output_dir ../thatsthejoke \
--logging_steps 20 \
--save_strategy steps \
--data_seed 42 \
--save_steps 80 \
--save_total_limit 10 \
--evaluation_strategy steps \
--max_new_tokens 64 \
--dataloader_num_workers 1 \
--group_by_length \
--logging_strategy steps \
--remove_unused_columns False \
--do_train \
--lora_r 64 \
--lora_alpha 16 \
--lora_modules all \
--double_quant \
--quant_type nf4 \
--bits 4 \
--warmup_ratio 0.03 \
--lr_scheduler_type constant \
--gradient_checkpointing \
--dataset /content/nycaptions.jsonl \
--dataset_format 'self-instruct' \
--source_max_len 16 \
--target_max_len 512 \
--per_device_train_batch_size 1 \
--gradient_accumulation_steps 16 \
--max_steps 250 \
--eval_steps 187 \
--learning_rate 0.0002 \
--adam_beta2 0.999 \
--max_grad_norm 0.3 \
--lora_dropout 0.1 \
--weight_decay 0.0 \
--seed 0
```
|
ThuyNT03/xlm-roberta-base-Final_Mixed-aug_replace_w2v-2
|
ThuyNT03
| 2023-09-05T00:35:25Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"base_model:FacebookAI/xlm-roberta-base",
"base_model:finetune:FacebookAI/xlm-roberta-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-09-05T00:27:35Z |
---
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: xlm-roberta-base-Final_Mixed-aug_replace_w2v-2
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-Final_Mixed-aug_replace_w2v-2
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: 1.0103
- Accuracy: 0.75
- F1: 0.7433
## 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: 40
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 1.0863 | 1.0 | 86 | 0.8715 | 0.59 | 0.5464 |
| 0.8221 | 2.0 | 172 | 0.6132 | 0.72 | 0.7008 |
| 0.6363 | 3.0 | 258 | 0.6041 | 0.72 | 0.7189 |
| 0.5206 | 4.0 | 344 | 0.7012 | 0.73 | 0.7224 |
| 0.3526 | 5.0 | 430 | 0.8181 | 0.75 | 0.7468 |
| 0.2893 | 6.0 | 516 | 0.7950 | 0.77 | 0.7690 |
| 0.2097 | 7.0 | 602 | 0.9751 | 0.74 | 0.7335 |
| 0.1536 | 8.0 | 688 | 1.0103 | 0.75 | 0.7433 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
RafaelMayer/roberta-copec-1
|
RafaelMayer
| 2023-09-05T00:34:46Z | 62 | 0 |
transformers
|
[
"transformers",
"tf",
"roberta",
"text-classification",
"generated_from_keras_callback",
"base_model:PlanTL-GOB-ES/roberta-base-bne",
"base_model:finetune:PlanTL-GOB-ES/roberta-base-bne",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-09-05T00:26:18Z |
---
license: apache-2.0
base_model: PlanTL-GOB-ES/roberta-base-bne
tags:
- generated_from_keras_callback
model-index:
- name: RafaelMayer/roberta-copec-1
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. -->
# RafaelMayer/roberta-copec-1
This model is a fine-tuned version of [PlanTL-GOB-ES/roberta-base-bne](https://huggingface.co/PlanTL-GOB-ES/roberta-base-bne) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.6572
- Validation Loss: 0.6316
- Train Accuracy: 0.8235
- Train Precision: [0. 0.82352941]
- Train Precision W: 0.6782
- Train Recall: [0. 1.]
- Train Recall W: 0.8235
- Train F1: [0. 0.90322581]
- Train F1 W: 0.7438
- Epoch: 1
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 2e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 35, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '__passive_serialization__': True}, 'warmup_steps': 5, 'power': 1.0, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train Accuracy | Train Precision | Train Precision W | Train Recall | Train Recall W | Train F1 | Train F1 W | Epoch |
|:----------:|:---------------:|:--------------:|:-----------------------:|:-----------------:|:------------:|:--------------:|:-----------------------:|:----------:|:-----:|
| 0.6572 | 0.6316 | 0.8235 | [0. 0.82352941] | 0.6782 | [0. 1.] | 0.8235 | [0. 0.90322581] | 0.7438 | 1 |
### Framework versions
- Transformers 4.32.1
- TensorFlow 2.12.0
- Datasets 2.14.4
- Tokenizers 0.13.3
|
YassineBenlaria/tamasheq-99-2.feature_ext-continue
|
YassineBenlaria
| 2023-09-05T00:34:23Z | 16 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-09-03T12:36:34Z |
---
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: tamasheq-99-2.feature_ext-continue
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. -->
# tamasheq-99-2.feature_ext-continue
This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3689
- Wer: 0.8342
## 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: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 7.4856 | 5.71 | 300 | 2.9956 | 0.9974 |
| 2.3903 | 11.43 | 600 | 1.2600 | 0.8816 |
| 0.9577 | 17.14 | 900 | 1.1878 | 0.8342 |
| 0.7051 | 22.86 | 1200 | 1.1907 | 0.8053 |
| 0.5821 | 28.57 | 1500 | 1.2621 | 0.8316 |
| 0.5037 | 34.29 | 1800 | 1.3689 | 0.8342 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
adyprat/Reinforce-pcopv0
|
adyprat
| 2023-09-05T00:22:32Z | 0 | 0 | null |
[
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-09-04T21:23:02Z |
---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-pcopv0
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 31.60 +/- 21.11
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
ThuyNT03/xlm-roberta-base-Final_Mixed-aug_insert_BERT-2
|
ThuyNT03
| 2023-09-05T00:17:38Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"base_model:FacebookAI/xlm-roberta-base",
"base_model:finetune:FacebookAI/xlm-roberta-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-09-05T00:09:29Z |
---
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: xlm-roberta-base-Final_Mixed-aug_insert_BERT-2
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-Final_Mixed-aug_insert_BERT-2
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: 0.9737
- Accuracy: 0.72
- F1: 0.7141
## 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: 40
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 1.0807 | 1.0 | 88 | 0.9024 | 0.64 | 0.6254 |
| 0.8512 | 2.0 | 176 | 0.6824 | 0.75 | 0.7396 |
| 0.7009 | 3.0 | 264 | 0.6368 | 0.74 | 0.7363 |
| 0.5649 | 4.0 | 352 | 0.6994 | 0.76 | 0.7494 |
| 0.458 | 5.0 | 440 | 0.8683 | 0.74 | 0.7300 |
| 0.3409 | 6.0 | 528 | 1.0337 | 0.7 | 0.6787 |
| 0.2964 | 7.0 | 616 | 0.9357 | 0.75 | 0.7459 |
| 0.2305 | 8.0 | 704 | 0.9737 | 0.72 | 0.7141 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
VegaKH/VenusXL
|
VegaKH
| 2023-09-05T00:12:43Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-08-11T14:05:29Z |
---
license: creativeml-openrail-m
---
|
nbogdan/flant5-large-2ex-paraphrasing-3epochs
|
nbogdan
| 2023-09-05T00:10:13Z | 0 | 0 |
adapter-transformers
|
[
"adapter-transformers",
"t5",
"adapterhub:self-explanations",
"dataset:self-explanations",
"region:us"
] | null | 2023-09-05T00:09:05Z |
---
tags:
- adapter-transformers
- t5
- adapterhub:self-explanations
datasets:
- self-explanations
---
# Adapter `nbogdan/flant5-large-2ex-paraphrasing-3epochs` for google/flan-t5-large
An [adapter](https://adapterhub.ml) for the `google/flan-t5-large` model that was trained on the [self-explanations](https://adapterhub.ml/explore/self-explanations/) dataset.
This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library.
## Usage
First, install `adapter-transformers`:
```
pip install -U adapter-transformers
```
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_
Now, the adapter can be loaded and activated like this:
```python
from transformers import AutoAdapterModel
model = AutoAdapterModel.from_pretrained("google/flan-t5-large")
adapter_name = model.load_adapter("nbogdan/flant5-large-2ex-paraphrasing-3epochs", source="hf", set_active=True)
```
## Architecture & Training
<!-- Add some description here -->
## Evaluation results
<!-- Add some description here -->
## Citation
<!-- Add some description here -->
|
nightdude/config_80034
|
nightdude
| 2023-09-05T00:10:04Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-09-05T00:09:49Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.4.0.dev0
|
bigmorning/whisper_input_decoder_shift_r_labels_no_force__0090
|
bigmorning
| 2023-09-04T23:59:07Z | 60 | 0 |
transformers
|
[
"transformers",
"tf",
"whisper",
"automatic-speech-recognition",
"generated_from_keras_callback",
"base_model:openai/whisper-tiny",
"base_model:finetune:openai/whisper-tiny",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-09-04T23:58:59Z |
---
license: apache-2.0
base_model: openai/whisper-tiny
tags:
- generated_from_keras_callback
model-index:
- name: whisper_input_decoder_shift_r_labels_no_force__0090
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. -->
# whisper_input_decoder_shift_r_labels_no_force__0090
This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0124
- Train Accuracy: 0.0339
- Train Wermet: 14.3527
- Validation Loss: 0.8265
- Validation Accuracy: 0.0209
- Validation Wermet: 32.3895
- Epoch: 89
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 1e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Train Wermet | Validation Loss | Validation Accuracy | Validation Wermet | Epoch |
|:----------:|:--------------:|:------------:|:---------------:|:-------------------:|:-----------------:|:-----:|
| 5.6348 | 0.0091 | 1.5865 | 4.2935 | 0.0093 | 0.9579 | 0 |
| 4.9212 | 0.0099 | 0.9054 | 4.1262 | 0.0097 | 0.9390 | 1 |
| 4.6819 | 0.0107 | 0.8319 | 3.9071 | 0.0103 | 0.8966 | 2 |
| 4.4443 | 0.0114 | 0.8310 | 3.7367 | 0.0106 | 0.8939 | 3 |
| 4.2479 | 0.0119 | 0.8226 | 3.6101 | 0.0109 | 0.8696 | 4 |
| 4.0911 | 0.0124 | 0.8103 | 3.5364 | 0.0110 | 0.8946 | 5 |
| 3.9590 | 0.0127 | 0.7913 | 3.4556 | 0.0113 | 0.8388 | 6 |
| 3.8513 | 0.0130 | 0.7794 | 3.4106 | 0.0114 | 0.8515 | 7 |
| 3.7607 | 0.0133 | 0.7657 | 3.3507 | 0.0115 | 0.8261 | 8 |
| 3.6757 | 0.0136 | 0.7548 | 3.3141 | 0.0116 | 0.8400 | 9 |
| 3.6023 | 0.0138 | 0.7454 | 3.2711 | 0.0117 | 0.8006 | 10 |
| 3.5261 | 0.0140 | 0.7348 | 3.2391 | 0.0119 | 0.8101 | 11 |
| 3.4534 | 0.0143 | 0.7212 | 3.2070 | 0.0120 | 0.7870 | 12 |
| 3.3814 | 0.0146 | 0.7080 | 3.1505 | 0.0122 | 0.7826 | 13 |
| 3.3069 | 0.0148 | 0.6961 | 3.1102 | 0.0124 | 0.7609 | 14 |
| 3.2229 | 0.0152 | 0.6781 | 3.0542 | 0.0125 | 0.7532 | 15 |
| 3.1334 | 0.0156 | 0.6614 | 2.9840 | 0.0127 | 0.7448 | 16 |
| 3.0313 | 0.0160 | 0.6425 | 2.9032 | 0.0130 | 0.7123 | 17 |
| 2.9122 | 0.0166 | 0.6202 | 2.7986 | 0.0134 | 0.6930 | 18 |
| 2.7559 | 0.0173 | 0.5940 | 2.6337 | 0.0139 | 0.6673 | 19 |
| 2.5649 | 0.0182 | 0.5674 | 2.4490 | 0.0145 | 0.6383 | 20 |
| 2.3414 | 0.0193 | 0.5299 | 2.2785 | 0.0150 | 0.6183 | 21 |
| 2.0966 | 0.0206 | 0.4903 | 2.0460 | 0.0158 | 0.5649 | 22 |
| 1.8283 | 0.0220 | 0.4459 | 1.8369 | 0.0165 | 0.5306 | 23 |
| 1.5547 | 0.0235 | 0.3996 | 1.6356 | 0.0172 | 0.4848 | 24 |
| 1.3218 | 0.0249 | 0.3581 | 1.4682 | 0.0179 | 0.4510 | 25 |
| 1.1383 | 0.0260 | 0.3211 | 1.3465 | 0.0183 | 0.4226 | 26 |
| 0.9876 | 0.0270 | 0.2920 | 1.2323 | 0.0188 | 0.3966 | 27 |
| 0.8635 | 0.0278 | 0.2651 | 1.1482 | 0.0191 | 0.3749 | 28 |
| 0.7620 | 0.0284 | 0.2435 | 1.0816 | 0.0194 | 0.3565 | 29 |
| 0.6749 | 0.0290 | 0.2234 | 1.0187 | 0.0196 | 0.3433 | 30 |
| 0.5998 | 0.0295 | 0.2025 | 0.9761 | 0.0198 | 0.3319 | 31 |
| 0.5325 | 0.0300 | 0.1827 | 0.9326 | 0.0200 | 0.3213 | 32 |
| 0.4735 | 0.0305 | 0.1665 | 0.8942 | 0.0201 | 0.3110 | 33 |
| 0.4228 | 0.0308 | 0.1466 | 0.8735 | 0.0202 | 0.3026 | 34 |
| 0.3747 | 0.0312 | 0.1293 | 0.8408 | 0.0203 | 0.2931 | 35 |
| 0.3331 | 0.0316 | 0.1111 | 0.8253 | 0.0204 | 0.2891 | 36 |
| 0.2947 | 0.0319 | 0.0962 | 0.8084 | 0.0205 | 0.2849 | 37 |
| 0.2601 | 0.0322 | 0.0817 | 0.7906 | 0.0205 | 0.2783 | 38 |
| 0.2291 | 0.0324 | 0.0706 | 0.7876 | 0.0206 | 0.2755 | 39 |
| 0.2009 | 0.0327 | 0.0596 | 0.7723 | 0.0207 | 0.2712 | 40 |
| 0.1750 | 0.0329 | 0.0504 | 0.7629 | 0.0207 | 0.2692 | 41 |
| 0.1510 | 0.0331 | 0.0410 | 0.7650 | 0.0207 | 0.2684 | 42 |
| 0.1319 | 0.0333 | 0.0367 | 0.7533 | 0.0207 | 0.2655 | 43 |
| 0.1121 | 0.0335 | 0.0292 | 0.7589 | 0.0207 | 0.2647 | 44 |
| 0.0956 | 0.0336 | 0.0253 | 0.7579 | 0.0208 | 0.2642 | 45 |
| 0.0812 | 0.0337 | 0.0254 | 0.7584 | 0.0208 | 0.2625 | 46 |
| 0.0694 | 0.0338 | 0.0332 | 0.7555 | 0.0208 | 0.2693 | 47 |
| 0.0592 | 0.0339 | 0.0319 | 0.7534 | 0.0208 | 0.2629 | 48 |
| 0.0499 | 0.0339 | 0.0487 | 0.7587 | 0.0208 | 0.3030 | 49 |
| 0.0409 | 0.0339 | 0.0615 | 0.7577 | 0.0208 | 0.2810 | 50 |
| 0.0347 | 0.0340 | 0.0859 | 0.7603 | 0.0208 | 0.3534 | 51 |
| 0.0286 | 0.0340 | 0.1928 | 0.7554 | 0.0209 | 0.5822 | 52 |
| 0.0267 | 0.0340 | 0.3131 | 0.7664 | 0.0208 | 1.7372 | 53 |
| 0.0243 | 0.0340 | 1.3154 | 0.7525 | 0.0209 | 0.7770 | 54 |
| 0.0206 | 0.0340 | 0.8121 | 0.7532 | 0.0209 | 0.9253 | 55 |
| 0.0174 | 0.0340 | 0.9253 | 0.7574 | 0.0209 | 1.4865 | 56 |
| 0.0135 | 0.0340 | 1.1761 | 0.7592 | 0.0209 | 1.5813 | 57 |
| 0.0111 | 0.0340 | 1.7125 | 0.7631 | 0.0209 | 1.8950 | 58 |
| 0.0096 | 0.0340 | 1.9230 | 0.7664 | 0.0209 | 2.4432 | 59 |
| 0.0082 | 0.0340 | 2.5718 | 0.7693 | 0.0209 | 3.3565 | 60 |
| 0.0073 | 0.0340 | 3.5489 | 0.7747 | 0.0209 | 3.7191 | 61 |
| 0.0063 | 0.0340 | 3.7801 | 0.7756 | 0.0209 | 4.4728 | 62 |
| 0.0054 | 0.0340 | 4.0145 | 0.7795 | 0.0209 | 5.0058 | 63 |
| 0.0048 | 0.0340 | 4.9652 | 0.7821 | 0.0210 | 4.9937 | 64 |
| 0.0042 | 0.0340 | 5.5984 | 0.7914 | 0.0209 | 8.3869 | 65 |
| 0.0205 | 0.0339 | 9.9212 | 0.7811 | 0.0209 | 21.1156 | 66 |
| 0.0184 | 0.0339 | 8.3175 | 0.7619 | 0.0210 | 0.5360 | 67 |
| 0.0080 | 0.0340 | 0.6373 | 0.7554 | 0.0211 | 0.4090 | 68 |
| 0.0052 | 0.0340 | 0.5550 | 0.7528 | 0.0211 | 0.3938 | 69 |
| 0.0038 | 0.0340 | 0.4678 | 0.7551 | 0.0211 | 0.7911 | 70 |
| 0.0032 | 0.0340 | 1.1632 | 0.7617 | 0.0211 | 0.5495 | 71 |
| 0.0028 | 0.0340 | 0.7869 | 0.7643 | 0.0211 | 1.4089 | 72 |
| 0.0025 | 0.0340 | 1.5997 | 0.7681 | 0.0211 | 1.1413 | 73 |
| 0.0023 | 0.0340 | 1.7042 | 0.7719 | 0.0211 | 1.7576 | 74 |
| 0.0021 | 0.0340 | 2.3363 | 0.7750 | 0.0211 | 2.2434 | 75 |
| 0.0019 | 0.0340 | 2.9550 | 0.7777 | 0.0211 | 2.3071 | 76 |
| 0.0017 | 0.0340 | 3.1713 | 0.7831 | 0.0211 | 3.3338 | 77 |
| 0.0015 | 0.0340 | 3.9077 | 0.7852 | 0.0211 | 3.6442 | 78 |
| 0.0014 | 0.0340 | 4.3375 | 0.7900 | 0.0211 | 4.0113 | 79 |
| 0.0013 | 0.0340 | 4.9777 | 0.7946 | 0.0211 | 5.1689 | 80 |
| 0.0011 | 0.0340 | 5.9846 | 0.7968 | 0.0211 | 5.6006 | 81 |
| 0.0010 | 0.0340 | 6.6595 | 0.8033 | 0.0211 | 6.1998 | 82 |
| 0.0009 | 0.0340 | 7.3520 | 0.8058 | 0.0211 | 7.6034 | 83 |
| 0.0008 | 0.0340 | 8.1210 | 0.8138 | 0.0211 | 7.8284 | 84 |
| 0.0007 | 0.0340 | 8.9352 | 0.8170 | 0.0211 | 9.1346 | 85 |
| 0.0006 | 0.0340 | 10.2307 | 0.8185 | 0.0211 | 10.8739 | 86 |
| 0.0006 | 0.0340 | 12.2734 | 0.8245 | 0.0211 | 12.5682 | 87 |
| 0.0005 | 0.0340 | 13.1276 | 0.8314 | 0.0211 | 14.4535 | 88 |
| 0.0124 | 0.0339 | 14.3527 | 0.8265 | 0.0209 | 32.3895 | 89 |
### Framework versions
- Transformers 4.34.0.dev0
- TensorFlow 2.13.0
- Tokenizers 0.13.3
|
johaanm/test-planner-alpha-V7.0
|
johaanm
| 2023-09-04T23:57:26Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-09-04T23:57:22Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float16
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.4.0
- PEFT 0.4.0
|
MattBatchelor/ppo-LunarLander-v2
|
MattBatchelor
| 2023-09-04T23:56:05Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-09-04T23:55:45Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 247.31 +/- 20.24
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
AndrewMarcHarris/ppo-LunarLander-v2
|
AndrewMarcHarris
| 2023-09-04T23:55:47Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-09-04T23:55:26Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 246.76 +/- 12.20
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
ThuyNT03/xlm-roberta-base-Final_Mixed-aug_insert_synonym-2
|
ThuyNT03
| 2023-09-04T23:53:12Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"base_model:FacebookAI/xlm-roberta-base",
"base_model:finetune:FacebookAI/xlm-roberta-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-09-04T23:43:26Z |
---
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: xlm-roberta-base-Final_Mixed-aug_insert_synonym-2
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-Final_Mixed-aug_insert_synonym-2
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: 1.1196
- Accuracy: 0.75
- F1: 0.7413
## 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: 40
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 1.04 | 1.0 | 88 | 0.8053 | 0.64 | 0.6127 |
| 0.7333 | 2.0 | 176 | 0.7600 | 0.71 | 0.7035 |
| 0.5406 | 3.0 | 264 | 0.6719 | 0.71 | 0.7080 |
| 0.4339 | 4.0 | 352 | 0.7426 | 0.75 | 0.7393 |
| 0.3085 | 5.0 | 440 | 0.9125 | 0.73 | 0.6985 |
| 0.23 | 6.0 | 528 | 0.9200 | 0.76 | 0.7527 |
| 0.1612 | 7.0 | 616 | 1.0423 | 0.74 | 0.7314 |
| 0.137 | 8.0 | 704 | 1.1196 | 0.75 | 0.7413 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
nbogdan/flant5-base-2ex-paraphrasing-1epochs
|
nbogdan
| 2023-09-04T23:50:00Z | 0 | 0 |
adapter-transformers
|
[
"adapter-transformers",
"adapterhub:self-explanations",
"t5",
"dataset:self-explanations",
"region:us"
] | null | 2023-09-04T23:49:44Z |
---
tags:
- adapterhub:self-explanations
- t5
- adapter-transformers
datasets:
- self-explanations
---
# Adapter `nbogdan/flant5-base-2ex-paraphrasing-1epochs` for google/flan-t5-base
An [adapter](https://adapterhub.ml) for the `google/flan-t5-base` model that was trained on the [self-explanations](https://adapterhub.ml/explore/self-explanations/) dataset.
This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library.
## Usage
First, install `adapter-transformers`:
```
pip install -U adapter-transformers
```
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_
Now, the adapter can be loaded and activated like this:
```python
from transformers import AutoAdapterModel
model = AutoAdapterModel.from_pretrained("google/flan-t5-base")
adapter_name = model.load_adapter("nbogdan/flant5-base-2ex-paraphrasing-1epochs", source="hf", set_active=True)
```
## Architecture & Training
<!-- Add some description here -->
## Evaluation results
<!-- Add some description here -->
## Citation
<!-- Add some description here -->
|
bigmorning/whisper_input_decoder_shift_r_labels_no_force__0085
|
bigmorning
| 2023-09-04T23:45:54Z | 59 | 0 |
transformers
|
[
"transformers",
"tf",
"whisper",
"automatic-speech-recognition",
"generated_from_keras_callback",
"base_model:openai/whisper-tiny",
"base_model:finetune:openai/whisper-tiny",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-09-04T23:45:45Z |
---
license: apache-2.0
base_model: openai/whisper-tiny
tags:
- generated_from_keras_callback
model-index:
- name: whisper_input_decoder_shift_r_labels_no_force__0085
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. -->
# whisper_input_decoder_shift_r_labels_no_force__0085
This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0008
- Train Accuracy: 0.0340
- Train Wermet: 8.1210
- Validation Loss: 0.8138
- Validation Accuracy: 0.0211
- Validation Wermet: 7.8284
- Epoch: 84
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 1e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Train Wermet | Validation Loss | Validation Accuracy | Validation Wermet | Epoch |
|:----------:|:--------------:|:------------:|:---------------:|:-------------------:|:-----------------:|:-----:|
| 5.6348 | 0.0091 | 1.5865 | 4.2935 | 0.0093 | 0.9579 | 0 |
| 4.9212 | 0.0099 | 0.9054 | 4.1262 | 0.0097 | 0.9390 | 1 |
| 4.6819 | 0.0107 | 0.8319 | 3.9071 | 0.0103 | 0.8966 | 2 |
| 4.4443 | 0.0114 | 0.8310 | 3.7367 | 0.0106 | 0.8939 | 3 |
| 4.2479 | 0.0119 | 0.8226 | 3.6101 | 0.0109 | 0.8696 | 4 |
| 4.0911 | 0.0124 | 0.8103 | 3.5364 | 0.0110 | 0.8946 | 5 |
| 3.9590 | 0.0127 | 0.7913 | 3.4556 | 0.0113 | 0.8388 | 6 |
| 3.8513 | 0.0130 | 0.7794 | 3.4106 | 0.0114 | 0.8515 | 7 |
| 3.7607 | 0.0133 | 0.7657 | 3.3507 | 0.0115 | 0.8261 | 8 |
| 3.6757 | 0.0136 | 0.7548 | 3.3141 | 0.0116 | 0.8400 | 9 |
| 3.6023 | 0.0138 | 0.7454 | 3.2711 | 0.0117 | 0.8006 | 10 |
| 3.5261 | 0.0140 | 0.7348 | 3.2391 | 0.0119 | 0.8101 | 11 |
| 3.4534 | 0.0143 | 0.7212 | 3.2070 | 0.0120 | 0.7870 | 12 |
| 3.3814 | 0.0146 | 0.7080 | 3.1505 | 0.0122 | 0.7826 | 13 |
| 3.3069 | 0.0148 | 0.6961 | 3.1102 | 0.0124 | 0.7609 | 14 |
| 3.2229 | 0.0152 | 0.6781 | 3.0542 | 0.0125 | 0.7532 | 15 |
| 3.1334 | 0.0156 | 0.6614 | 2.9840 | 0.0127 | 0.7448 | 16 |
| 3.0313 | 0.0160 | 0.6425 | 2.9032 | 0.0130 | 0.7123 | 17 |
| 2.9122 | 0.0166 | 0.6202 | 2.7986 | 0.0134 | 0.6930 | 18 |
| 2.7559 | 0.0173 | 0.5940 | 2.6337 | 0.0139 | 0.6673 | 19 |
| 2.5649 | 0.0182 | 0.5674 | 2.4490 | 0.0145 | 0.6383 | 20 |
| 2.3414 | 0.0193 | 0.5299 | 2.2785 | 0.0150 | 0.6183 | 21 |
| 2.0966 | 0.0206 | 0.4903 | 2.0460 | 0.0158 | 0.5649 | 22 |
| 1.8283 | 0.0220 | 0.4459 | 1.8369 | 0.0165 | 0.5306 | 23 |
| 1.5547 | 0.0235 | 0.3996 | 1.6356 | 0.0172 | 0.4848 | 24 |
| 1.3218 | 0.0249 | 0.3581 | 1.4682 | 0.0179 | 0.4510 | 25 |
| 1.1383 | 0.0260 | 0.3211 | 1.3465 | 0.0183 | 0.4226 | 26 |
| 0.9876 | 0.0270 | 0.2920 | 1.2323 | 0.0188 | 0.3966 | 27 |
| 0.8635 | 0.0278 | 0.2651 | 1.1482 | 0.0191 | 0.3749 | 28 |
| 0.7620 | 0.0284 | 0.2435 | 1.0816 | 0.0194 | 0.3565 | 29 |
| 0.6749 | 0.0290 | 0.2234 | 1.0187 | 0.0196 | 0.3433 | 30 |
| 0.5998 | 0.0295 | 0.2025 | 0.9761 | 0.0198 | 0.3319 | 31 |
| 0.5325 | 0.0300 | 0.1827 | 0.9326 | 0.0200 | 0.3213 | 32 |
| 0.4735 | 0.0305 | 0.1665 | 0.8942 | 0.0201 | 0.3110 | 33 |
| 0.4228 | 0.0308 | 0.1466 | 0.8735 | 0.0202 | 0.3026 | 34 |
| 0.3747 | 0.0312 | 0.1293 | 0.8408 | 0.0203 | 0.2931 | 35 |
| 0.3331 | 0.0316 | 0.1111 | 0.8253 | 0.0204 | 0.2891 | 36 |
| 0.2947 | 0.0319 | 0.0962 | 0.8084 | 0.0205 | 0.2849 | 37 |
| 0.2601 | 0.0322 | 0.0817 | 0.7906 | 0.0205 | 0.2783 | 38 |
| 0.2291 | 0.0324 | 0.0706 | 0.7876 | 0.0206 | 0.2755 | 39 |
| 0.2009 | 0.0327 | 0.0596 | 0.7723 | 0.0207 | 0.2712 | 40 |
| 0.1750 | 0.0329 | 0.0504 | 0.7629 | 0.0207 | 0.2692 | 41 |
| 0.1510 | 0.0331 | 0.0410 | 0.7650 | 0.0207 | 0.2684 | 42 |
| 0.1319 | 0.0333 | 0.0367 | 0.7533 | 0.0207 | 0.2655 | 43 |
| 0.1121 | 0.0335 | 0.0292 | 0.7589 | 0.0207 | 0.2647 | 44 |
| 0.0956 | 0.0336 | 0.0253 | 0.7579 | 0.0208 | 0.2642 | 45 |
| 0.0812 | 0.0337 | 0.0254 | 0.7584 | 0.0208 | 0.2625 | 46 |
| 0.0694 | 0.0338 | 0.0332 | 0.7555 | 0.0208 | 0.2693 | 47 |
| 0.0592 | 0.0339 | 0.0319 | 0.7534 | 0.0208 | 0.2629 | 48 |
| 0.0499 | 0.0339 | 0.0487 | 0.7587 | 0.0208 | 0.3030 | 49 |
| 0.0409 | 0.0339 | 0.0615 | 0.7577 | 0.0208 | 0.2810 | 50 |
| 0.0347 | 0.0340 | 0.0859 | 0.7603 | 0.0208 | 0.3534 | 51 |
| 0.0286 | 0.0340 | 0.1928 | 0.7554 | 0.0209 | 0.5822 | 52 |
| 0.0267 | 0.0340 | 0.3131 | 0.7664 | 0.0208 | 1.7372 | 53 |
| 0.0243 | 0.0340 | 1.3154 | 0.7525 | 0.0209 | 0.7770 | 54 |
| 0.0206 | 0.0340 | 0.8121 | 0.7532 | 0.0209 | 0.9253 | 55 |
| 0.0174 | 0.0340 | 0.9253 | 0.7574 | 0.0209 | 1.4865 | 56 |
| 0.0135 | 0.0340 | 1.1761 | 0.7592 | 0.0209 | 1.5813 | 57 |
| 0.0111 | 0.0340 | 1.7125 | 0.7631 | 0.0209 | 1.8950 | 58 |
| 0.0096 | 0.0340 | 1.9230 | 0.7664 | 0.0209 | 2.4432 | 59 |
| 0.0082 | 0.0340 | 2.5718 | 0.7693 | 0.0209 | 3.3565 | 60 |
| 0.0073 | 0.0340 | 3.5489 | 0.7747 | 0.0209 | 3.7191 | 61 |
| 0.0063 | 0.0340 | 3.7801 | 0.7756 | 0.0209 | 4.4728 | 62 |
| 0.0054 | 0.0340 | 4.0145 | 0.7795 | 0.0209 | 5.0058 | 63 |
| 0.0048 | 0.0340 | 4.9652 | 0.7821 | 0.0210 | 4.9937 | 64 |
| 0.0042 | 0.0340 | 5.5984 | 0.7914 | 0.0209 | 8.3869 | 65 |
| 0.0205 | 0.0339 | 9.9212 | 0.7811 | 0.0209 | 21.1156 | 66 |
| 0.0184 | 0.0339 | 8.3175 | 0.7619 | 0.0210 | 0.5360 | 67 |
| 0.0080 | 0.0340 | 0.6373 | 0.7554 | 0.0211 | 0.4090 | 68 |
| 0.0052 | 0.0340 | 0.5550 | 0.7528 | 0.0211 | 0.3938 | 69 |
| 0.0038 | 0.0340 | 0.4678 | 0.7551 | 0.0211 | 0.7911 | 70 |
| 0.0032 | 0.0340 | 1.1632 | 0.7617 | 0.0211 | 0.5495 | 71 |
| 0.0028 | 0.0340 | 0.7869 | 0.7643 | 0.0211 | 1.4089 | 72 |
| 0.0025 | 0.0340 | 1.5997 | 0.7681 | 0.0211 | 1.1413 | 73 |
| 0.0023 | 0.0340 | 1.7042 | 0.7719 | 0.0211 | 1.7576 | 74 |
| 0.0021 | 0.0340 | 2.3363 | 0.7750 | 0.0211 | 2.2434 | 75 |
| 0.0019 | 0.0340 | 2.9550 | 0.7777 | 0.0211 | 2.3071 | 76 |
| 0.0017 | 0.0340 | 3.1713 | 0.7831 | 0.0211 | 3.3338 | 77 |
| 0.0015 | 0.0340 | 3.9077 | 0.7852 | 0.0211 | 3.6442 | 78 |
| 0.0014 | 0.0340 | 4.3375 | 0.7900 | 0.0211 | 4.0113 | 79 |
| 0.0013 | 0.0340 | 4.9777 | 0.7946 | 0.0211 | 5.1689 | 80 |
| 0.0011 | 0.0340 | 5.9846 | 0.7968 | 0.0211 | 5.6006 | 81 |
| 0.0010 | 0.0340 | 6.6595 | 0.8033 | 0.0211 | 6.1998 | 82 |
| 0.0009 | 0.0340 | 7.3520 | 0.8058 | 0.0211 | 7.6034 | 83 |
| 0.0008 | 0.0340 | 8.1210 | 0.8138 | 0.0211 | 7.8284 | 84 |
### Framework versions
- Transformers 4.34.0.dev0
- TensorFlow 2.13.0
- Tokenizers 0.13.3
|
ThuyNT03/xlm-roberta-base-Final_Mixed-train-2
|
ThuyNT03
| 2023-09-04T23:43:15Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"base_model:FacebookAI/xlm-roberta-base",
"base_model:finetune:FacebookAI/xlm-roberta-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-09-04T23:39:26Z |
---
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: xlm-roberta-base-Final_Mixed-train-2
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-Final_Mixed-train-2
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: 0.7200
- Accuracy: 0.77
- F1: 0.7634
## 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: 40
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 1.1141 | 1.0 | 44 | 1.1019 | 0.31 | 0.2344 |
| 1.0868 | 2.0 | 88 | 1.0677 | 0.44 | 0.3501 |
| 0.9464 | 3.0 | 132 | 0.9689 | 0.56 | 0.5371 |
| 0.7829 | 4.0 | 176 | 0.7724 | 0.67 | 0.6278 |
| 0.678 | 5.0 | 220 | 0.8115 | 0.71 | 0.6960 |
| 0.6379 | 6.0 | 264 | 0.6987 | 0.74 | 0.7313 |
| 0.5801 | 7.0 | 308 | 0.6804 | 0.78 | 0.7765 |
| 0.528 | 8.0 | 352 | 0.7200 | 0.77 | 0.7634 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
StudentLLM/Alpagasus-2-13B-QLoRA
|
StudentLLM
| 2023-09-04T23:34:51Z | 3 | 0 |
peft
|
[
"peft",
"en",
"region:us"
] | null | 2023-08-09T13:08:03Z |
---
library_name: peft
language:
- en
---
# Model Details
Please check our [Github Repository](https://github.com/gauss5930/AlpaGasus2-QLoRA/tree/main)
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
### Framework versions
- PEFT 0.5.0.dev0
- PEFT 0.5.0.dev0
|
jasonxxr666/lora-trained-xl-colab
|
jasonxxr666
| 2023-09-04T23:34:43Z | 3 | 1 |
diffusers
|
[
"diffusers",
"tensorboard",
"stable-diffusion-xl",
"stable-diffusion-xl-diffusers",
"text-to-image",
"lora",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0",
"license:openrail++",
"region:us"
] |
text-to-image
| 2023-08-14T02:28:16Z |
---
license: openrail++
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: a photo of paige cat girl
tags:
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
- text-to-image
- diffusers
- lora
inference: true
---
# LoRA DreamBooth - jasonxxr666/lora-trained-xl-colab
These are LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained on a photo of paige cat girl using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following.
LoRA for the text encoder was enabled: False.
Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
|
StudentLLM/Alpagasus-2-7B-QLoRA
|
StudentLLM
| 2023-09-04T23:34:07Z | 7 | 0 |
peft
|
[
"peft",
"en",
"region:us"
] | null | 2023-08-09T13:23:45Z |
---
library_name: peft
language:
- en
---
# Model Details
Please check our [Github Repository](https://github.com/gauss5930/AlpaGasus2-QLoRA/tree/main)
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
### Framework versions
- PEFT 0.5.0.dev0
- PEFT 0.5.0.dev0
|
matgu23/tst
|
matgu23
| 2023-09-04T23:33:04Z | 1 | 1 |
diffusers
|
[
"diffusers",
"tensorboard",
"stable-diffusion-xl",
"stable-diffusion-xl-diffusers",
"text-to-image",
"lora",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0",
"license:openrail++",
"region:us"
] |
text-to-image
| 2023-08-15T02:02:49Z |
---
license: openrail++
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: a photo of sflr woman
tags:
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
- text-to-image
- diffusers
- lora
inference: true
---
# LoRA DreamBooth - matgu23/lora-trained-xl-colab
These are LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained on a photo of sflr woman using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following.
LoRA for the text encoder was enabled: False.
Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
|
ThuyNT03/xlm-roberta-base-Final_Mixed-aug_swap-2
|
ThuyNT03
| 2023-09-04T23:31:50Z | 104 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"base_model:FacebookAI/xlm-roberta-base",
"base_model:finetune:FacebookAI/xlm-roberta-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-09-04T23:24:41Z |
---
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: xlm-roberta-base-Final_Mixed-aug_swap-2
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-Final_Mixed-aug_swap-2
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: 0.9261
- Accuracy: 0.76
- F1: 0.7558
## 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: 40
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 1.0488 | 1.0 | 87 | 0.8904 | 0.59 | 0.5101 |
| 0.8402 | 2.0 | 174 | 0.8465 | 0.64 | 0.6153 |
| 0.6864 | 3.0 | 261 | 0.7985 | 0.7 | 0.6849 |
| 0.5088 | 4.0 | 348 | 0.7521 | 0.72 | 0.6996 |
| 0.3444 | 5.0 | 435 | 0.7432 | 0.76 | 0.7496 |
| 0.262 | 6.0 | 522 | 0.8831 | 0.75 | 0.7463 |
| 0.1787 | 7.0 | 609 | 0.9219 | 0.75 | 0.7452 |
| 0.1361 | 8.0 | 696 | 0.9261 | 0.76 | 0.7558 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
elami/vit-base-patch16-224-finetuned-flower
|
elami
| 2023-09-04T23:24:28Z | 164 | 0 |
transformers
|
[
"transformers",
"pytorch",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-09-04T23:13:50Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
model-index:
- name: vit-base-patch16-224-finetuned-flower
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. -->
# vit-base-patch16-224-finetuned-flower
This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder 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: 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: 5
### Training results
### Framework versions
- Transformers 4.24.0
- Pytorch 2.0.1+cu118
- Datasets 2.7.1
- Tokenizers 0.13.3
|
bigmorning/whisper_input_decoder_shift_r_labels_no_force__0075
|
bigmorning
| 2023-09-04T23:19:21Z | 59 | 0 |
transformers
|
[
"transformers",
"tf",
"whisper",
"automatic-speech-recognition",
"generated_from_keras_callback",
"base_model:openai/whisper-tiny",
"base_model:finetune:openai/whisper-tiny",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-09-04T23:19:14Z |
---
license: apache-2.0
base_model: openai/whisper-tiny
tags:
- generated_from_keras_callback
model-index:
- name: whisper_input_decoder_shift_r_labels_no_force__0075
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. -->
# whisper_input_decoder_shift_r_labels_no_force__0075
This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0023
- Train Accuracy: 0.0340
- Train Wermet: 1.7042
- Validation Loss: 0.7719
- Validation Accuracy: 0.0211
- Validation Wermet: 1.7576
- Epoch: 74
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 1e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Train Wermet | Validation Loss | Validation Accuracy | Validation Wermet | Epoch |
|:----------:|:--------------:|:------------:|:---------------:|:-------------------:|:-----------------:|:-----:|
| 5.6348 | 0.0091 | 1.5865 | 4.2935 | 0.0093 | 0.9579 | 0 |
| 4.9212 | 0.0099 | 0.9054 | 4.1262 | 0.0097 | 0.9390 | 1 |
| 4.6819 | 0.0107 | 0.8319 | 3.9071 | 0.0103 | 0.8966 | 2 |
| 4.4443 | 0.0114 | 0.8310 | 3.7367 | 0.0106 | 0.8939 | 3 |
| 4.2479 | 0.0119 | 0.8226 | 3.6101 | 0.0109 | 0.8696 | 4 |
| 4.0911 | 0.0124 | 0.8103 | 3.5364 | 0.0110 | 0.8946 | 5 |
| 3.9590 | 0.0127 | 0.7913 | 3.4556 | 0.0113 | 0.8388 | 6 |
| 3.8513 | 0.0130 | 0.7794 | 3.4106 | 0.0114 | 0.8515 | 7 |
| 3.7607 | 0.0133 | 0.7657 | 3.3507 | 0.0115 | 0.8261 | 8 |
| 3.6757 | 0.0136 | 0.7548 | 3.3141 | 0.0116 | 0.8400 | 9 |
| 3.6023 | 0.0138 | 0.7454 | 3.2711 | 0.0117 | 0.8006 | 10 |
| 3.5261 | 0.0140 | 0.7348 | 3.2391 | 0.0119 | 0.8101 | 11 |
| 3.4534 | 0.0143 | 0.7212 | 3.2070 | 0.0120 | 0.7870 | 12 |
| 3.3814 | 0.0146 | 0.7080 | 3.1505 | 0.0122 | 0.7826 | 13 |
| 3.3069 | 0.0148 | 0.6961 | 3.1102 | 0.0124 | 0.7609 | 14 |
| 3.2229 | 0.0152 | 0.6781 | 3.0542 | 0.0125 | 0.7532 | 15 |
| 3.1334 | 0.0156 | 0.6614 | 2.9840 | 0.0127 | 0.7448 | 16 |
| 3.0313 | 0.0160 | 0.6425 | 2.9032 | 0.0130 | 0.7123 | 17 |
| 2.9122 | 0.0166 | 0.6202 | 2.7986 | 0.0134 | 0.6930 | 18 |
| 2.7559 | 0.0173 | 0.5940 | 2.6337 | 0.0139 | 0.6673 | 19 |
| 2.5649 | 0.0182 | 0.5674 | 2.4490 | 0.0145 | 0.6383 | 20 |
| 2.3414 | 0.0193 | 0.5299 | 2.2785 | 0.0150 | 0.6183 | 21 |
| 2.0966 | 0.0206 | 0.4903 | 2.0460 | 0.0158 | 0.5649 | 22 |
| 1.8283 | 0.0220 | 0.4459 | 1.8369 | 0.0165 | 0.5306 | 23 |
| 1.5547 | 0.0235 | 0.3996 | 1.6356 | 0.0172 | 0.4848 | 24 |
| 1.3218 | 0.0249 | 0.3581 | 1.4682 | 0.0179 | 0.4510 | 25 |
| 1.1383 | 0.0260 | 0.3211 | 1.3465 | 0.0183 | 0.4226 | 26 |
| 0.9876 | 0.0270 | 0.2920 | 1.2323 | 0.0188 | 0.3966 | 27 |
| 0.8635 | 0.0278 | 0.2651 | 1.1482 | 0.0191 | 0.3749 | 28 |
| 0.7620 | 0.0284 | 0.2435 | 1.0816 | 0.0194 | 0.3565 | 29 |
| 0.6749 | 0.0290 | 0.2234 | 1.0187 | 0.0196 | 0.3433 | 30 |
| 0.5998 | 0.0295 | 0.2025 | 0.9761 | 0.0198 | 0.3319 | 31 |
| 0.5325 | 0.0300 | 0.1827 | 0.9326 | 0.0200 | 0.3213 | 32 |
| 0.4735 | 0.0305 | 0.1665 | 0.8942 | 0.0201 | 0.3110 | 33 |
| 0.4228 | 0.0308 | 0.1466 | 0.8735 | 0.0202 | 0.3026 | 34 |
| 0.3747 | 0.0312 | 0.1293 | 0.8408 | 0.0203 | 0.2931 | 35 |
| 0.3331 | 0.0316 | 0.1111 | 0.8253 | 0.0204 | 0.2891 | 36 |
| 0.2947 | 0.0319 | 0.0962 | 0.8084 | 0.0205 | 0.2849 | 37 |
| 0.2601 | 0.0322 | 0.0817 | 0.7906 | 0.0205 | 0.2783 | 38 |
| 0.2291 | 0.0324 | 0.0706 | 0.7876 | 0.0206 | 0.2755 | 39 |
| 0.2009 | 0.0327 | 0.0596 | 0.7723 | 0.0207 | 0.2712 | 40 |
| 0.1750 | 0.0329 | 0.0504 | 0.7629 | 0.0207 | 0.2692 | 41 |
| 0.1510 | 0.0331 | 0.0410 | 0.7650 | 0.0207 | 0.2684 | 42 |
| 0.1319 | 0.0333 | 0.0367 | 0.7533 | 0.0207 | 0.2655 | 43 |
| 0.1121 | 0.0335 | 0.0292 | 0.7589 | 0.0207 | 0.2647 | 44 |
| 0.0956 | 0.0336 | 0.0253 | 0.7579 | 0.0208 | 0.2642 | 45 |
| 0.0812 | 0.0337 | 0.0254 | 0.7584 | 0.0208 | 0.2625 | 46 |
| 0.0694 | 0.0338 | 0.0332 | 0.7555 | 0.0208 | 0.2693 | 47 |
| 0.0592 | 0.0339 | 0.0319 | 0.7534 | 0.0208 | 0.2629 | 48 |
| 0.0499 | 0.0339 | 0.0487 | 0.7587 | 0.0208 | 0.3030 | 49 |
| 0.0409 | 0.0339 | 0.0615 | 0.7577 | 0.0208 | 0.2810 | 50 |
| 0.0347 | 0.0340 | 0.0859 | 0.7603 | 0.0208 | 0.3534 | 51 |
| 0.0286 | 0.0340 | 0.1928 | 0.7554 | 0.0209 | 0.5822 | 52 |
| 0.0267 | 0.0340 | 0.3131 | 0.7664 | 0.0208 | 1.7372 | 53 |
| 0.0243 | 0.0340 | 1.3154 | 0.7525 | 0.0209 | 0.7770 | 54 |
| 0.0206 | 0.0340 | 0.8121 | 0.7532 | 0.0209 | 0.9253 | 55 |
| 0.0174 | 0.0340 | 0.9253 | 0.7574 | 0.0209 | 1.4865 | 56 |
| 0.0135 | 0.0340 | 1.1761 | 0.7592 | 0.0209 | 1.5813 | 57 |
| 0.0111 | 0.0340 | 1.7125 | 0.7631 | 0.0209 | 1.8950 | 58 |
| 0.0096 | 0.0340 | 1.9230 | 0.7664 | 0.0209 | 2.4432 | 59 |
| 0.0082 | 0.0340 | 2.5718 | 0.7693 | 0.0209 | 3.3565 | 60 |
| 0.0073 | 0.0340 | 3.5489 | 0.7747 | 0.0209 | 3.7191 | 61 |
| 0.0063 | 0.0340 | 3.7801 | 0.7756 | 0.0209 | 4.4728 | 62 |
| 0.0054 | 0.0340 | 4.0145 | 0.7795 | 0.0209 | 5.0058 | 63 |
| 0.0048 | 0.0340 | 4.9652 | 0.7821 | 0.0210 | 4.9937 | 64 |
| 0.0042 | 0.0340 | 5.5984 | 0.7914 | 0.0209 | 8.3869 | 65 |
| 0.0205 | 0.0339 | 9.9212 | 0.7811 | 0.0209 | 21.1156 | 66 |
| 0.0184 | 0.0339 | 8.3175 | 0.7619 | 0.0210 | 0.5360 | 67 |
| 0.0080 | 0.0340 | 0.6373 | 0.7554 | 0.0211 | 0.4090 | 68 |
| 0.0052 | 0.0340 | 0.5550 | 0.7528 | 0.0211 | 0.3938 | 69 |
| 0.0038 | 0.0340 | 0.4678 | 0.7551 | 0.0211 | 0.7911 | 70 |
| 0.0032 | 0.0340 | 1.1632 | 0.7617 | 0.0211 | 0.5495 | 71 |
| 0.0028 | 0.0340 | 0.7869 | 0.7643 | 0.0211 | 1.4089 | 72 |
| 0.0025 | 0.0340 | 1.5997 | 0.7681 | 0.0211 | 1.1413 | 73 |
| 0.0023 | 0.0340 | 1.7042 | 0.7719 | 0.0211 | 1.7576 | 74 |
### Framework versions
- Transformers 4.34.0.dev0
- TensorFlow 2.13.0
- Tokenizers 0.13.3
|
matsuo-lab/weblab-10b
|
matsuo-lab
| 2023-09-04T23:17:28Z | 1,883 | 63 |
transformers
|
[
"transformers",
"pytorch",
"gpt_neox",
"text-generation",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-08-04T04:55:47Z |
---
license: cc-by-nc-4.0
---
# weblab-10b
# Overview
This repository provides a Japanese-centric multilingual GPT-NeoX model of 10 billion parameters.
* **Library**
The model was trained using code based on [EleutherAI/gpt-neox](https://github.com/EleutherAI/gpt-neox).
* **Model architecture**
A 36-layer, 4864-hidden-size transformer-based language model.
* **Pre-training**
The model was trained on around **600B** tokens from a mixture of the following corpora.
- [Japanese C4](https://huggingface.co/datasets/mc4)
- [The Pile](https://huggingface.co/datasets/EleutherAI/pile)
* **Model Series**
| Variant | Link |
| :-- | :--|
| weblab-10b-instruction-sft | https://huggingface.co/matsuo-lab/weblab-10b-instruction-sft |
| weblab-10b | https://huggingface.co/matsuo-lab/weblab-10b |
* **Authors**
Takeshi Kojima
---
# Benchmarking
* **Japanese benchmark : JGLUE 8-task (2023-08-27)**
- *We used [Stability-AI/lm-evaluation-harness](https://github.com/Stability-AI/lm-evaluation-harness/tree/2f1583c0735eacdfdfa5b7d656074b69577b6774) library for evaluation.*
- *The 8-task average accuracy is based on results of JCommonsenseQA-1.1, JNLI-1.1, MARC-ja-1.1, JSQuAD-1.1, jaqket_v2-0.2, xlsum_ja-1.0, xwinograd_ja, and mgsm-1.0.*
- *model loading is performed with float16, and evaluation is performed with template version 0.3 using the few-shot in-context learning.*
- *The number of few-shots is 3,3,3,2,1,1,0,5.*
- *special_tokens_map.json is modified to avoid errors during the evaluation of the second half benchmarks. As a result, the results of the first half benchmarks became slightly different.*
model | average | jcommonsenseqa | jnli | marc_ja | jsquad | jaqket_v2 | xlsum_ja | xwinograd_ja | mgsm
| :-- | :-- | :-- | :-- | :-- | :-- | :-- | :-- | :-- | :-- |
weblab-10b-instruction-sft | 59.11 | 74.62 | 66.56 | 95.49 | 78.34 | 63.32 | 20.57 | 71.95 | 2
weblab-10b | 50.74 | 66.58 | 53.74 | 82.07 | 62.94 | 56.19 | 10.03 | 71.95 | 2.4
* **Japanese benchmark : JGLUE 4-task (2023-08-18)**
- *We used [Stability-AI/lm-evaluation-harness](https://github.com/Stability-AI/lm-evaluation-harness/tree/2f1583c0735eacdfdfa5b7d656074b69577b6774) library for evaluation.*
- *The 4-task average accuracy is based on results of JCommonsenseQA-1.1, JNLI-1.1, MARC-ja-1.1, and JSQuAD-1.1.*
- *model loading is performed with float16, and evaluation is performed with template version 0.3 using the few-shot in-context learning.*
- *The number of few-shots is 3,3,3,2.*
| Model | Average | JCommonsenseQA | JNLI | MARC-ja | JSQuAD |
| :-- | :-- | :-- | :-- | :-- | :-- |
| weblab-10b-instruction-sft | 78.78 | 74.35 | 65.65 | 96.06 | 79.04 |
| weblab-10b | 66.38 | 65.86 | 54.19 | 84.49 | 60.98 |
---
# How to use the model
~~~~python
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("matsuo-lab/weblab-10b")
model = AutoModelForCausalLM.from_pretrained("matsuo-lab/weblab-10b", torch_dtype=torch.float16)
if torch.cuda.is_available():
model = model.to("cuda")
text = "吾輩は猫である。"
token_ids = tokenizer.encode(text, add_special_tokens=False, return_tensors="pt")
with torch.no_grad():
output_ids = model.generate(
token_ids.to(model.device),
max_new_tokens=100,
do_sample=True,
temperature=0.7,
top_p=0.95
)
output = tokenizer.decode(output_ids.tolist()[0])
print(output)
~~~~
---
# Licenese
[cc-by-nc-4.0](https://creativecommons.org/licenses/by-nc/4.0/)
|
FourthBrainGenAI/marketmail
|
FourthBrainGenAI
| 2023-09-04T23:07:40Z | 2 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-09-04T23:07:35Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
### Framework versions
- PEFT 0.6.0.dev0
|
bigmorning/whisper_input_decoder_shift_r_labels_no_force__0070
|
bigmorning
| 2023-09-04T23:06:07Z | 59 | 0 |
transformers
|
[
"transformers",
"tf",
"whisper",
"automatic-speech-recognition",
"generated_from_keras_callback",
"base_model:openai/whisper-tiny",
"base_model:finetune:openai/whisper-tiny",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-09-04T23:06:00Z |
---
license: apache-2.0
base_model: openai/whisper-tiny
tags:
- generated_from_keras_callback
model-index:
- name: whisper_input_decoder_shift_r_labels_no_force__0070
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. -->
# whisper_input_decoder_shift_r_labels_no_force__0070
This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0052
- Train Accuracy: 0.0340
- Train Wermet: 0.5550
- Validation Loss: 0.7528
- Validation Accuracy: 0.0211
- Validation Wermet: 0.3938
- Epoch: 69
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 1e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Train Wermet | Validation Loss | Validation Accuracy | Validation Wermet | Epoch |
|:----------:|:--------------:|:------------:|:---------------:|:-------------------:|:-----------------:|:-----:|
| 5.6348 | 0.0091 | 1.5865 | 4.2935 | 0.0093 | 0.9579 | 0 |
| 4.9212 | 0.0099 | 0.9054 | 4.1262 | 0.0097 | 0.9390 | 1 |
| 4.6819 | 0.0107 | 0.8319 | 3.9071 | 0.0103 | 0.8966 | 2 |
| 4.4443 | 0.0114 | 0.8310 | 3.7367 | 0.0106 | 0.8939 | 3 |
| 4.2479 | 0.0119 | 0.8226 | 3.6101 | 0.0109 | 0.8696 | 4 |
| 4.0911 | 0.0124 | 0.8103 | 3.5364 | 0.0110 | 0.8946 | 5 |
| 3.9590 | 0.0127 | 0.7913 | 3.4556 | 0.0113 | 0.8388 | 6 |
| 3.8513 | 0.0130 | 0.7794 | 3.4106 | 0.0114 | 0.8515 | 7 |
| 3.7607 | 0.0133 | 0.7657 | 3.3507 | 0.0115 | 0.8261 | 8 |
| 3.6757 | 0.0136 | 0.7548 | 3.3141 | 0.0116 | 0.8400 | 9 |
| 3.6023 | 0.0138 | 0.7454 | 3.2711 | 0.0117 | 0.8006 | 10 |
| 3.5261 | 0.0140 | 0.7348 | 3.2391 | 0.0119 | 0.8101 | 11 |
| 3.4534 | 0.0143 | 0.7212 | 3.2070 | 0.0120 | 0.7870 | 12 |
| 3.3814 | 0.0146 | 0.7080 | 3.1505 | 0.0122 | 0.7826 | 13 |
| 3.3069 | 0.0148 | 0.6961 | 3.1102 | 0.0124 | 0.7609 | 14 |
| 3.2229 | 0.0152 | 0.6781 | 3.0542 | 0.0125 | 0.7532 | 15 |
| 3.1334 | 0.0156 | 0.6614 | 2.9840 | 0.0127 | 0.7448 | 16 |
| 3.0313 | 0.0160 | 0.6425 | 2.9032 | 0.0130 | 0.7123 | 17 |
| 2.9122 | 0.0166 | 0.6202 | 2.7986 | 0.0134 | 0.6930 | 18 |
| 2.7559 | 0.0173 | 0.5940 | 2.6337 | 0.0139 | 0.6673 | 19 |
| 2.5649 | 0.0182 | 0.5674 | 2.4490 | 0.0145 | 0.6383 | 20 |
| 2.3414 | 0.0193 | 0.5299 | 2.2785 | 0.0150 | 0.6183 | 21 |
| 2.0966 | 0.0206 | 0.4903 | 2.0460 | 0.0158 | 0.5649 | 22 |
| 1.8283 | 0.0220 | 0.4459 | 1.8369 | 0.0165 | 0.5306 | 23 |
| 1.5547 | 0.0235 | 0.3996 | 1.6356 | 0.0172 | 0.4848 | 24 |
| 1.3218 | 0.0249 | 0.3581 | 1.4682 | 0.0179 | 0.4510 | 25 |
| 1.1383 | 0.0260 | 0.3211 | 1.3465 | 0.0183 | 0.4226 | 26 |
| 0.9876 | 0.0270 | 0.2920 | 1.2323 | 0.0188 | 0.3966 | 27 |
| 0.8635 | 0.0278 | 0.2651 | 1.1482 | 0.0191 | 0.3749 | 28 |
| 0.7620 | 0.0284 | 0.2435 | 1.0816 | 0.0194 | 0.3565 | 29 |
| 0.6749 | 0.0290 | 0.2234 | 1.0187 | 0.0196 | 0.3433 | 30 |
| 0.5998 | 0.0295 | 0.2025 | 0.9761 | 0.0198 | 0.3319 | 31 |
| 0.5325 | 0.0300 | 0.1827 | 0.9326 | 0.0200 | 0.3213 | 32 |
| 0.4735 | 0.0305 | 0.1665 | 0.8942 | 0.0201 | 0.3110 | 33 |
| 0.4228 | 0.0308 | 0.1466 | 0.8735 | 0.0202 | 0.3026 | 34 |
| 0.3747 | 0.0312 | 0.1293 | 0.8408 | 0.0203 | 0.2931 | 35 |
| 0.3331 | 0.0316 | 0.1111 | 0.8253 | 0.0204 | 0.2891 | 36 |
| 0.2947 | 0.0319 | 0.0962 | 0.8084 | 0.0205 | 0.2849 | 37 |
| 0.2601 | 0.0322 | 0.0817 | 0.7906 | 0.0205 | 0.2783 | 38 |
| 0.2291 | 0.0324 | 0.0706 | 0.7876 | 0.0206 | 0.2755 | 39 |
| 0.2009 | 0.0327 | 0.0596 | 0.7723 | 0.0207 | 0.2712 | 40 |
| 0.1750 | 0.0329 | 0.0504 | 0.7629 | 0.0207 | 0.2692 | 41 |
| 0.1510 | 0.0331 | 0.0410 | 0.7650 | 0.0207 | 0.2684 | 42 |
| 0.1319 | 0.0333 | 0.0367 | 0.7533 | 0.0207 | 0.2655 | 43 |
| 0.1121 | 0.0335 | 0.0292 | 0.7589 | 0.0207 | 0.2647 | 44 |
| 0.0956 | 0.0336 | 0.0253 | 0.7579 | 0.0208 | 0.2642 | 45 |
| 0.0812 | 0.0337 | 0.0254 | 0.7584 | 0.0208 | 0.2625 | 46 |
| 0.0694 | 0.0338 | 0.0332 | 0.7555 | 0.0208 | 0.2693 | 47 |
| 0.0592 | 0.0339 | 0.0319 | 0.7534 | 0.0208 | 0.2629 | 48 |
| 0.0499 | 0.0339 | 0.0487 | 0.7587 | 0.0208 | 0.3030 | 49 |
| 0.0409 | 0.0339 | 0.0615 | 0.7577 | 0.0208 | 0.2810 | 50 |
| 0.0347 | 0.0340 | 0.0859 | 0.7603 | 0.0208 | 0.3534 | 51 |
| 0.0286 | 0.0340 | 0.1928 | 0.7554 | 0.0209 | 0.5822 | 52 |
| 0.0267 | 0.0340 | 0.3131 | 0.7664 | 0.0208 | 1.7372 | 53 |
| 0.0243 | 0.0340 | 1.3154 | 0.7525 | 0.0209 | 0.7770 | 54 |
| 0.0206 | 0.0340 | 0.8121 | 0.7532 | 0.0209 | 0.9253 | 55 |
| 0.0174 | 0.0340 | 0.9253 | 0.7574 | 0.0209 | 1.4865 | 56 |
| 0.0135 | 0.0340 | 1.1761 | 0.7592 | 0.0209 | 1.5813 | 57 |
| 0.0111 | 0.0340 | 1.7125 | 0.7631 | 0.0209 | 1.8950 | 58 |
| 0.0096 | 0.0340 | 1.9230 | 0.7664 | 0.0209 | 2.4432 | 59 |
| 0.0082 | 0.0340 | 2.5718 | 0.7693 | 0.0209 | 3.3565 | 60 |
| 0.0073 | 0.0340 | 3.5489 | 0.7747 | 0.0209 | 3.7191 | 61 |
| 0.0063 | 0.0340 | 3.7801 | 0.7756 | 0.0209 | 4.4728 | 62 |
| 0.0054 | 0.0340 | 4.0145 | 0.7795 | 0.0209 | 5.0058 | 63 |
| 0.0048 | 0.0340 | 4.9652 | 0.7821 | 0.0210 | 4.9937 | 64 |
| 0.0042 | 0.0340 | 5.5984 | 0.7914 | 0.0209 | 8.3869 | 65 |
| 0.0205 | 0.0339 | 9.9212 | 0.7811 | 0.0209 | 21.1156 | 66 |
| 0.0184 | 0.0339 | 8.3175 | 0.7619 | 0.0210 | 0.5360 | 67 |
| 0.0080 | 0.0340 | 0.6373 | 0.7554 | 0.0211 | 0.4090 | 68 |
| 0.0052 | 0.0340 | 0.5550 | 0.7528 | 0.0211 | 0.3938 | 69 |
### Framework versions
- Transformers 4.34.0.dev0
- TensorFlow 2.13.0
- Tokenizers 0.13.3
|
nbogdan/flant5-base-2ex-overall-1epochs
|
nbogdan
| 2023-09-04T22:53:58Z | 2 | 0 |
adapter-transformers
|
[
"adapter-transformers",
"adapterhub:self-explanations",
"t5",
"dataset:self-explanations",
"region:us"
] | null | 2023-09-04T22:53:49Z |
---
tags:
- adapterhub:self-explanations
- t5
- adapter-transformers
datasets:
- self-explanations
---
# Adapter `nbogdan/flant5-base-2ex-overall-1epochs` for google/flan-t5-base
An [adapter](https://adapterhub.ml) for the `google/flan-t5-base` model that was trained on the [self-explanations](https://adapterhub.ml/explore/self-explanations/) dataset.
This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library.
## Usage
First, install `adapter-transformers`:
```
pip install -U adapter-transformers
```
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_
Now, the adapter can be loaded and activated like this:
```python
from transformers import AutoAdapterModel
model = AutoAdapterModel.from_pretrained("google/flan-t5-base")
adapter_name = model.load_adapter("nbogdan/flant5-base-2ex-overall-1epochs", source="hf", set_active=True)
```
## Architecture & Training
<!-- Add some description here -->
## Evaluation results
<!-- Add some description here -->
## Citation
<!-- Add some description here -->
|
Kapiche/twitter-roberta-base-sentiment-latest
|
Kapiche
| 2023-09-04T22:49:50Z | 286 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"safetensors",
"roberta",
"text-classification",
"en",
"dataset:tweet_eval",
"arxiv:2202.03829",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-08-07T13:23:27Z |
---
language: en
widget:
- text: Covid cases are increasing fast!
datasets:
- tweet_eval
---
# Twitter-roBERTa-base for Sentiment Analysis - UPDATED (2022)
This is a RoBERTa-base model trained on ~124M tweets from January 2018 to December 2021, and finetuned for sentiment analysis with the TweetEval benchmark.
The original Twitter-based RoBERTa model can be found [here](https://huggingface.co/cardiffnlp/twitter-roberta-base-2021-124m) and the original reference paper is [TweetEval](https://github.com/cardiffnlp/tweeteval). This model is suitable for English.
- Reference Paper: [TimeLMs paper](https://arxiv.org/abs/2202.03829).
- Git Repo: [TimeLMs official repository](https://github.com/cardiffnlp/timelms).
<b>Labels</b>:
0 -> Negative;
1 -> Neutral;
2 -> Positive
This sentiment analysis model has been integrated into [TweetNLP](https://github.com/cardiffnlp/tweetnlp). You can access the demo [here](https://tweetnlp.org).
## Example Pipeline
```python
from transformers import pipeline
sentiment_task = pipeline("sentiment-analysis", model=model_path, tokenizer=model_path)
sentiment_task("Covid cases are increasing fast!")
```
```
[{'label': 'Negative', 'score': 0.7236}]
```
## Full classification example
```python
from transformers import AutoModelForSequenceClassification
from transformers import TFAutoModelForSequenceClassification
from transformers import AutoTokenizer, AutoConfig
import numpy as np
from scipy.special import softmax
# Preprocess text (username and link placeholders)
def preprocess(text):
new_text = []
for t in text.split(" "):
t = '@user' if t.startswith('@') and len(t) > 1 else t
t = 'http' if t.startswith('http') else t
new_text.append(t)
return " ".join(new_text)
MODEL = f"cardiffnlp/twitter-roberta-base-sentiment-latest"
tokenizer = AutoTokenizer.from_pretrained(MODEL)
config = AutoConfig.from_pretrained(MODEL)
# PT
model = AutoModelForSequenceClassification.from_pretrained(MODEL)
#model.save_pretrained(MODEL)
text = "Covid cases are increasing fast!"
text = preprocess(text)
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
scores = output[0][0].detach().numpy()
scores = softmax(scores)
# # TF
# model = TFAutoModelForSequenceClassification.from_pretrained(MODEL)
# model.save_pretrained(MODEL)
# text = "Covid cases are increasing fast!"
# encoded_input = tokenizer(text, return_tensors='tf')
# output = model(encoded_input)
# scores = output[0][0].numpy()
# scores = softmax(scores)
# Print labels and scores
ranking = np.argsort(scores)
ranking = ranking[::-1]
for i in range(scores.shape[0]):
l = config.id2label[ranking[i]]
s = scores[ranking[i]]
print(f"{i+1}) {l} {np.round(float(s), 4)}")
```
Output:
```
1) Negative 0.7236
2) Neutral 0.2287
3) Positive 0.0477
```
### References
```
@inproceedings{camacho-collados-etal-2022-tweetnlp,
title = "{T}weet{NLP}: Cutting-Edge Natural Language Processing for Social Media",
author = "Camacho-collados, Jose and
Rezaee, Kiamehr and
Riahi, Talayeh and
Ushio, Asahi and
Loureiro, Daniel and
Antypas, Dimosthenis and
Boisson, Joanne and
Espinosa Anke, Luis and
Liu, Fangyu and
Mart{\'\i}nez C{\'a}mara, Eugenio" and others,
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
month = dec,
year = "2022",
address = "Abu Dhabi, UAE",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.emnlp-demos.5",
pages = "38--49"
}
```
```
@inproceedings{loureiro-etal-2022-timelms,
title = "{T}ime{LM}s: Diachronic Language Models from {T}witter",
author = "Loureiro, Daniel and
Barbieri, Francesco and
Neves, Leonardo and
Espinosa Anke, Luis and
Camacho-collados, Jose",
booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: System Demonstrations",
month = may,
year = "2022",
address = "Dublin, Ireland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.acl-demo.25",
doi = "10.18653/v1/2022.acl-demo.25",
pages = "251--260"
}
```
|
rshei/layoutlmv3-finetuned-cord_100
|
rshei
| 2023-09-04T22:47:01Z | 82 | 0 |
transformers
|
[
"transformers",
"pytorch",
"layoutlmv3",
"token-classification",
"generated_from_trainer",
"dataset:cord-layoutlmv3",
"base_model:microsoft/layoutlmv3-base",
"base_model:finetune:microsoft/layoutlmv3-base",
"license:cc-by-nc-sa-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2023-08-29T05:48:50Z |
---
license: cc-by-nc-sa-4.0
base_model: microsoft/layoutlmv3-base
tags:
- generated_from_trainer
datasets:
- cord-layoutlmv3
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: layoutlmv3-finetuned-cord_100
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: cord-layoutlmv3
type: cord-layoutlmv3
config: cord
split: test
args: cord
metrics:
- name: Precision
type: precision
value: 0.9243884358784284
- name: Recall
type: recall
value: 0.9333832335329342
- name: F1
type: f1
value: 0.9288640595903166
- name: Accuracy
type: accuracy
value: 0.9363327674023769
---
<!-- 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. -->
# layoutlmv3-finetuned-cord_100
This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the cord-layoutlmv3 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3467
- Precision: 0.9244
- Recall: 0.9334
- F1: 0.9289
- Accuracy: 0.9363
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 5
- eval_batch_size: 5
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 2500
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 4.17 | 250 | 0.5174 | 0.8469 | 0.8735 | 0.8600 | 0.8790 |
| 0.5511 | 8.33 | 500 | 0.3975 | 0.8999 | 0.9147 | 0.9072 | 0.9194 |
| 0.5511 | 12.5 | 750 | 0.3872 | 0.9015 | 0.9184 | 0.9099 | 0.9189 |
| 0.1802 | 16.67 | 1000 | 0.3416 | 0.9180 | 0.9296 | 0.9238 | 0.9338 |
| 0.1802 | 20.83 | 1250 | 0.3311 | 0.9159 | 0.9289 | 0.9223 | 0.9359 |
| 0.0836 | 25.0 | 1500 | 0.3457 | 0.9192 | 0.9281 | 0.9236 | 0.9334 |
| 0.0836 | 29.17 | 1750 | 0.3347 | 0.9202 | 0.9319 | 0.9260 | 0.9291 |
| 0.0473 | 33.33 | 2000 | 0.3677 | 0.9194 | 0.9304 | 0.9249 | 0.9253 |
| 0.0473 | 37.5 | 2250 | 0.3433 | 0.9279 | 0.9341 | 0.9310 | 0.9376 |
| 0.0342 | 41.67 | 2500 | 0.3467 | 0.9244 | 0.9334 | 0.9289 | 0.9363 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
KingKazma/xsum_gpt2_prompt_tuning_500_10_3000_8_e-1_s6789_v4_l4_v20_extra
|
KingKazma
| 2023-09-04T22:40:39Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-09-04T22:35:51Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.6.0.dev0
|
kikinamatata/model_2
|
kikinamatata
| 2023-09-04T22:37:23Z | 0 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"stable-diffusion-xl",
"stable-diffusion-xl-diffusers",
"text-to-image",
"license:creativeml-openrail-m",
"region:us"
] |
text-to-image
| 2023-09-04T19:56:25Z |
---
license: creativeml-openrail-m
base_model: models/model_1
dataset: None
tags:
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
- text-to-image
- diffusers
inference: true
---
# Text-to-image finetuning - kikinamatata/model_2
This pipeline was finetuned from **models/model_1** on the **None** dataset. Below are some example images generated with the finetuned pipeline using the following prompt: None:
Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
|
actionpace/LLaMA2-13B-Holomax
|
actionpace
| 2023-09-04T22:30:15Z | 1 | 0 | null |
[
"gguf",
"en",
"license:other",
"endpoints_compatible",
"region:us"
] | null | 2023-09-04T21:58:05Z |
---
license: other
language:
- en
---
**Some of my own quants:**
* LLaMA2-13B-Holomax_Q5_1_4K.gguf
* LLaMA2-13B-Holomax_Q5_1_8K.gguf
**Source:** [KoboldAI](https://huggingface.co/KoboldAI)
**Source Model:** [LLaMA2-13B-Holomax](https://huggingface.co/KoboldAI/LLaMA2-13B-Holomax)
|
bigmorning/whisper_input_decoder_shift_r_labels_no_force__0055
|
bigmorning
| 2023-09-04T22:26:20Z | 59 | 0 |
transformers
|
[
"transformers",
"tf",
"whisper",
"automatic-speech-recognition",
"generated_from_keras_callback",
"base_model:openai/whisper-tiny",
"base_model:finetune:openai/whisper-tiny",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-09-04T22:26:12Z |
---
license: apache-2.0
base_model: openai/whisper-tiny
tags:
- generated_from_keras_callback
model-index:
- name: whisper_input_decoder_shift_r_labels_no_force__0055
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. -->
# whisper_input_decoder_shift_r_labels_no_force__0055
This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0243
- Train Accuracy: 0.0340
- Train Wermet: 1.3154
- Validation Loss: 0.7525
- Validation Accuracy: 0.0209
- Validation Wermet: 0.7770
- Epoch: 54
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 1e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Train Wermet | Validation Loss | Validation Accuracy | Validation Wermet | Epoch |
|:----------:|:--------------:|:------------:|:---------------:|:-------------------:|:-----------------:|:-----:|
| 5.6348 | 0.0091 | 1.5865 | 4.2935 | 0.0093 | 0.9579 | 0 |
| 4.9212 | 0.0099 | 0.9054 | 4.1262 | 0.0097 | 0.9390 | 1 |
| 4.6819 | 0.0107 | 0.8319 | 3.9071 | 0.0103 | 0.8966 | 2 |
| 4.4443 | 0.0114 | 0.8310 | 3.7367 | 0.0106 | 0.8939 | 3 |
| 4.2479 | 0.0119 | 0.8226 | 3.6101 | 0.0109 | 0.8696 | 4 |
| 4.0911 | 0.0124 | 0.8103 | 3.5364 | 0.0110 | 0.8946 | 5 |
| 3.9590 | 0.0127 | 0.7913 | 3.4556 | 0.0113 | 0.8388 | 6 |
| 3.8513 | 0.0130 | 0.7794 | 3.4106 | 0.0114 | 0.8515 | 7 |
| 3.7607 | 0.0133 | 0.7657 | 3.3507 | 0.0115 | 0.8261 | 8 |
| 3.6757 | 0.0136 | 0.7548 | 3.3141 | 0.0116 | 0.8400 | 9 |
| 3.6023 | 0.0138 | 0.7454 | 3.2711 | 0.0117 | 0.8006 | 10 |
| 3.5261 | 0.0140 | 0.7348 | 3.2391 | 0.0119 | 0.8101 | 11 |
| 3.4534 | 0.0143 | 0.7212 | 3.2070 | 0.0120 | 0.7870 | 12 |
| 3.3814 | 0.0146 | 0.7080 | 3.1505 | 0.0122 | 0.7826 | 13 |
| 3.3069 | 0.0148 | 0.6961 | 3.1102 | 0.0124 | 0.7609 | 14 |
| 3.2229 | 0.0152 | 0.6781 | 3.0542 | 0.0125 | 0.7532 | 15 |
| 3.1334 | 0.0156 | 0.6614 | 2.9840 | 0.0127 | 0.7448 | 16 |
| 3.0313 | 0.0160 | 0.6425 | 2.9032 | 0.0130 | 0.7123 | 17 |
| 2.9122 | 0.0166 | 0.6202 | 2.7986 | 0.0134 | 0.6930 | 18 |
| 2.7559 | 0.0173 | 0.5940 | 2.6337 | 0.0139 | 0.6673 | 19 |
| 2.5649 | 0.0182 | 0.5674 | 2.4490 | 0.0145 | 0.6383 | 20 |
| 2.3414 | 0.0193 | 0.5299 | 2.2785 | 0.0150 | 0.6183 | 21 |
| 2.0966 | 0.0206 | 0.4903 | 2.0460 | 0.0158 | 0.5649 | 22 |
| 1.8283 | 0.0220 | 0.4459 | 1.8369 | 0.0165 | 0.5306 | 23 |
| 1.5547 | 0.0235 | 0.3996 | 1.6356 | 0.0172 | 0.4848 | 24 |
| 1.3218 | 0.0249 | 0.3581 | 1.4682 | 0.0179 | 0.4510 | 25 |
| 1.1383 | 0.0260 | 0.3211 | 1.3465 | 0.0183 | 0.4226 | 26 |
| 0.9876 | 0.0270 | 0.2920 | 1.2323 | 0.0188 | 0.3966 | 27 |
| 0.8635 | 0.0278 | 0.2651 | 1.1482 | 0.0191 | 0.3749 | 28 |
| 0.7620 | 0.0284 | 0.2435 | 1.0816 | 0.0194 | 0.3565 | 29 |
| 0.6749 | 0.0290 | 0.2234 | 1.0187 | 0.0196 | 0.3433 | 30 |
| 0.5998 | 0.0295 | 0.2025 | 0.9761 | 0.0198 | 0.3319 | 31 |
| 0.5325 | 0.0300 | 0.1827 | 0.9326 | 0.0200 | 0.3213 | 32 |
| 0.4735 | 0.0305 | 0.1665 | 0.8942 | 0.0201 | 0.3110 | 33 |
| 0.4228 | 0.0308 | 0.1466 | 0.8735 | 0.0202 | 0.3026 | 34 |
| 0.3747 | 0.0312 | 0.1293 | 0.8408 | 0.0203 | 0.2931 | 35 |
| 0.3331 | 0.0316 | 0.1111 | 0.8253 | 0.0204 | 0.2891 | 36 |
| 0.2947 | 0.0319 | 0.0962 | 0.8084 | 0.0205 | 0.2849 | 37 |
| 0.2601 | 0.0322 | 0.0817 | 0.7906 | 0.0205 | 0.2783 | 38 |
| 0.2291 | 0.0324 | 0.0706 | 0.7876 | 0.0206 | 0.2755 | 39 |
| 0.2009 | 0.0327 | 0.0596 | 0.7723 | 0.0207 | 0.2712 | 40 |
| 0.1750 | 0.0329 | 0.0504 | 0.7629 | 0.0207 | 0.2692 | 41 |
| 0.1510 | 0.0331 | 0.0410 | 0.7650 | 0.0207 | 0.2684 | 42 |
| 0.1319 | 0.0333 | 0.0367 | 0.7533 | 0.0207 | 0.2655 | 43 |
| 0.1121 | 0.0335 | 0.0292 | 0.7589 | 0.0207 | 0.2647 | 44 |
| 0.0956 | 0.0336 | 0.0253 | 0.7579 | 0.0208 | 0.2642 | 45 |
| 0.0812 | 0.0337 | 0.0254 | 0.7584 | 0.0208 | 0.2625 | 46 |
| 0.0694 | 0.0338 | 0.0332 | 0.7555 | 0.0208 | 0.2693 | 47 |
| 0.0592 | 0.0339 | 0.0319 | 0.7534 | 0.0208 | 0.2629 | 48 |
| 0.0499 | 0.0339 | 0.0487 | 0.7587 | 0.0208 | 0.3030 | 49 |
| 0.0409 | 0.0339 | 0.0615 | 0.7577 | 0.0208 | 0.2810 | 50 |
| 0.0347 | 0.0340 | 0.0859 | 0.7603 | 0.0208 | 0.3534 | 51 |
| 0.0286 | 0.0340 | 0.1928 | 0.7554 | 0.0209 | 0.5822 | 52 |
| 0.0267 | 0.0340 | 0.3131 | 0.7664 | 0.0208 | 1.7372 | 53 |
| 0.0243 | 0.0340 | 1.3154 | 0.7525 | 0.0209 | 0.7770 | 54 |
### Framework versions
- Transformers 4.34.0.dev0
- TensorFlow 2.13.0
- Tokenizers 0.13.3
|
CiroN2022/awesome-toys
|
CiroN2022
| 2023-09-04T22:16:54Z | 10 | 0 |
diffusers
|
[
"diffusers",
"text-to-image",
"stable-diffusion",
"lora",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0",
"license:other",
"region:us"
] |
text-to-image
| 2023-09-04T22:16:51Z |
---
license: other
tags:
- text-to-image
- stable-diffusion
- lora
- diffusers
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: awe_toys
widget:
- text: awe_toys
---
# Awesome Toys
<p>Example prompts:</p><ul><li><p>Rocky Vader: A mashup of the iconic Rocky Balboa and Darth Vader, bringing the power of the Force to the boxing ring. With boxing:0.6 and sci-fi:0.4 elements, this action figure packs a punch!</p></li><li><p>SpiderPool: Part Spider-Man, part Deadpool, this acrobatic antihero swings into action with equal parts wit:0.5 and wall-crawling skills:0.5, making it a fan-favorite collectible.</p></li><li><p>WonderFury: A blend of Wonder Woman and Mad Max, this fierce warrior combines superhero:0.7 and post-apocalyptic:0.3 vibes for a truly unique action figure.</p></li><li><p>The Jokernator: A fusion of The Joker and The Terminator, this figure boasts chaos:0.6 and robotic precision:0.4, making it a charismatic yet deadly adversary.</p></li><li><p>Hannibal T-lecter: A crossover between Hannibal Lecter and the T-800, this action figure oozes cannibalistic charm:0.6 and cyborg menace:0.4.</p></li><li><p>Wolverine Ranger: A hybrid of Wolverine and the Power Rangers, this figure combines mutant powers:0.6 with colorful teamwork:0.4 for epic battles against evil.</p></li><li><p>Captain Frodo: Mixing Captain America and Frodo Baggins, this action figure embodies courage:0.7 and hobbit-sized heroics:0.3, perfect for fantasy adventures.</p></li><li><p>Yoda Trooper: A fusion of Yoda and a Stormtrooper, this figure brings wisdom:0.6 and galactic loyalty:0.4 to the forefront of the battle against the dark side.</p></li><li><p>SuperPirate: Combining Superman and Captain Jack Sparrow, this action figure marries superhero strength:0.6 with pirate swagger:0.4 on the high seas.</p></li><li><p>Hellboy Potter: Merging Hellboy with Harry Potter, this figure wields supernatural abilities:0.6 alongside wizardry:0.4, ready to take on any mystical threat.</p></li><li><p>BatThor: A fusion of Batman and Thor, this action figure strikes a balance between vigilante justice:0.5 and godly thunder:0.5.</p></li><li><p>PredaFlash: Mixing Predator and The Flash, this figure races through the jungle:0.6 with lightning speed:0.4, hunting its prey in the blink of an eye.</p></li><li><p>Zorrotrax: A crossover between Zorro and Black Panther, this action figure showcases swashbuckling finesse:0.6 and Wakandan technology:0.4.</p></li><li><p>Hulk Solo: Combining The Hulk and Han Solo, this figure embodies rage-induced strength:0.6 and smuggler charisma:0.4.</p></li><li><p>Iron-Scorpion: Merging Iron Man and Scorpion from Mortal Kombat, this action figure boasts high-tech armor:0.6 and a deadly stinger:0.4.</p></li><li><p>PredaDredd: A fusion of Predator and Judge Dredd, this figure enforces brutal justice:0.6 with alien cunning:0.4 in a dystopian future.</p></li><li><p>Venom-Terminator: Mixing Venom and The Terminator, this action figure embodies symbiotic menace:0.6 and relentless cyborg pursuit:0.4.</p></li><li><p>Deadstroke: A crossover between Deadpool and Deathstroke, this figure is a master of both humor:0.5 and mercenary skills:0.5.</p></li><li><p>Grootpool: Combining Groot and Deadpool, this action figure offers a mix of lovable tree antics:0.6 and chaotic humor:0.4.</p></li><li><p>Robo-Hannibal: Merging RoboCop and Hannibal Lecter, this figure patrols the streets:0.6 while harboring a taste for the macabre:0.4.</p></li><li><p>Black-Widow Trooper: A hybrid of Black Widow and a Stormtrooper, this action figure embodies espionage:0.6 and galactic loyalty:0.4.</p></li><li><p>Spock Vader: Mixing Spock from Star Trek and Darth Vader, this figure is a logical yet formidable force:0.6 in the galaxy.</p></li><li><p>Green Arrowwing: A fusion of Green Arrow and Hawkeye, this action figure boasts archery precision:0.6 and vigilante justice:0.4.</p></li><li><p>Hermoine Terminator: Combining Hermione Granger and The Terminator, this figure wields wizardry:0.6 alongside robotic determination:0.4.</p></li><li><p>Thorlock Holmes: A crossover between Thor and Sherlock Holmes, this action figure wields godly powers:0.6 and deductive reasoning:0.4.</p></li><li><p>Aquaman of Steel: Merging Aquaman and Superman, this figure combines underwater strength:0.6 with Kryptonian might:0.4.</p></li><li><p>Dare-Wonder: A hybrid of Daredevil and Wonder Woman, this action figure embodies blind justice:0.6 and Amazonian warrior prowess:0.4.</p></li><li><p>Luke Sky-Bat: Mixing Luke Skywalker and Batman, this figure balances Jedi training:0.6 with dark knight detective skills:0.4.</p></li><li><p>Flashpool: Combining The Flash and Deadpool, this action figure races through battles:0.6 with a comedic edge:0.4.</p></li><li><p>Magneto Panther: A fusion of Magneto and Black Panther, this figure controls magnetic forces:0.6 and Wakandan technology:0.4.</p></li><li><p>Cyborg the Hedgehog: Merging Cyborg from DC and Sonic the Hedgehog, this action figure boasts high-tech enhancements:0.6 and supersonic speed:0.4.</p></li><li><p>Wonderpool Woman: A crossover between Wonder Woman and Deadpool, this figure is a warrior with an irreverent twist:0.5 and a lasso of humor:0.5.</p></li><li><p>Venom-Matrix: Combining Venom and Neo from The Matrix, this action figure embodies symbiotic chaos:0.6 and digital rebellion:0.4.</p></li><li><p>Thor-Pirate: Merging Thor and Captain Jack Sparrow, this figure wields Mjölnir:0.6 with a pirate's charm:0.4 on the high seas.</p></li><li><p>Super-Bond: A hybrid of Superman and James Bond, this action figure combines superhuman abilities:0.6 with spy gadgets:0.4.</p></li><li><p>Loki Ranger: Mixing Loki and the Power Rangers, this figure embodies trickster magic:0.6 and colorful teamwork:0.4.</p></li><li><p>Groot of the Galaxy: A fusion of Groot and Guardians of the Galaxy, this action figure offers a mix of tree heroics:0.6 and cosmic adventures:0.4.</p></li><li><p>HawkTrek: Combining Hawkeye and Star Trek, this figure boasts marksmanship:0.6 and interstellar exploration:0.4.</p></li><li><p>BatPirate: Merging Batman and Captain Jack Sparrow, this action figure patrols Gotham's waters:0.6 with swashbuckling flair:0.4.</p></li><li><p>Preda-Wonder: A crossover between Predator and Wonder Woman, this figure is a fierce warrior with extraterrestrial charm:0.5 and Amazonian strength:0.5.</p></li><li><p>Iron Khan: A fusion of Iron Man and Genghis Khan, this action figure combines high-tech armor:0.6 with conquering leadership:0.4, ready to lead any battle.</p></li><li><p>Aquawick: Mixing Aquaman and John Wick, this figure wields aquatic powers:0.6 alongside deadly assassin skills:0.4.</p></li><li><p>Black Widow-Strange: A hybrid of Black Widow and Doctor Strange, this action figure embodies espionage:0.5 and mystic mastery:0.5.</p></li><li><p>Deadthor: Combining Deadpool and Thor, this figure brings humor:0.5 and thunderous might:0.5 to any battle.</p></li><li><p>Harley-Witch: A crossover between Harley Quinn and the Witch from Left 4 Dead, this action figure offers chaos:0.6 with a touch of the supernatural:0.4.</p></li><li><p>Green-Alien Arrow: Merging Green Arrow and an Alien Xenomorph, this figure boasts archery precision:0.6 and extraterrestrial menace:0.4.</p></li><li><p>Robo-Hulk: A fusion of RoboCop and The Hulk, this action figure patrols the streets:0.6 while unleashing unstoppable rage:0.4.</p></li><li><p>ZorroVader: Mixing Zorro and Darth Vader, this figure is a swashbuckling Sith Lord with a penchant for dueling:0.5 and tyranny:0.5.</p></li><li><p>Preda-Pirate: A hybrid of Predator and a classic Pirate, this action figure hunts its prey with alien cunning:0.6 and swashbuckling flair:0.4.</p></li><li><p>Wolverine-Samurai: Combining Wolverine and a Samurai, this figure embodies mutant ferocity:0.6 with disciplined swordsmanship:0.4.</p></li><li><p>Flash-Matrix: A fusion of The Flash and Neo from The Matrix, this action figure races through the digital world:0.6 with incredible speed:0.4.</p></li><li><p>Hannibal-Joker: Merging Hannibal Lecter and The Joker, this figure combines culinary skills:0.6 with chaotic madness:0.4.</p></li><li><p>Super-Ranger: Combining Superman and a Power Ranger, this action figure wields superhuman strength:0.6 alongside colorful teamwork:0.4.</p></li><li><p>Wonder-Scorpion: A crossover between Wonder Woman and Scorpion from Mortal Kombat, this figure is a warrior with a stinger:0.5 and an Amazonian spirit:0.5.</p></li><li><p>Venom-Trek: Mixing Venom and Star Trek, this action figure embodies symbiotic exploration:0.6 and interstellar chaos:0.4.</p></li><li><p>Predator-Pool: A blend of Predator and Deadpool, this figure hunts with humor:0.5 and extraterrestrial cunning:0.5.</p></li><li><p>Bat-Hannibal: Combining Batman and Hannibal Lecter, this action figure patrols Gotham City:0.6 while savoring the macabre:0.4.</p></li><li><p>Gandalf-Ranger: Merging Gandalf and a Power Ranger, this figure wields wizardry:0.6 with colorful teamwork:0.4 in the fight against evil.</p></li><li><p>Thor-Sherlock: A fusion of Thor and Sherlock Holmes, this action figure balances godly strength:0.5 with deductive reasoning:0.5.</p></li><li><p>Cyber-Amazon: Combining Cyborg and Wonder Woman, this figure embodies technological prowess:0.6 with Amazonian warrior spirit:0.4.</p></li><li><p>Dare-Sparrow: A crossover between Daredevil and Captain Jack Sparrow, this action figure combines blind justice:0.6 with pirate swagger:0.4.</p></li><li><p>Luke-Witcher: Mixing Luke Skywalker and Geralt from The Witcher, this figure wields a lightsaber:0.6 alongside monster hunting skills:0.4.</p></li><li><p>Flash-Groot: A hybrid of The Flash and Groot, this action figure races through adventures:0.6 with a lovable tree's charm:0.4.</p></li><li><p>Magneto-Matrix: Combining Magneto and Neo from The Matrix, this figure controls magnetic forces:0.6 and challenges the digital world:0.4.</p></li><li><p>Hulk-Ranger: Merging The Hulk and a Power Ranger, this action figure embodies gamma-powered teamwork:0.6 and heroic strength:0.4.</p></li><li><p>Super-Spock: A fusion of Superman and Spock from Star Trek, this figure combines Kryptonian might:0.6 with logical precision:0.4.</p></li><li><p>Bat-Matrix: Combining Batman and Neo from The Matrix, this action figure patrols Gotham's digital streets:0.6 with martial arts mastery:0.4.</p></li><li><p>Preda-Bond: A crossover between Predator and James Bond, this figure hunts with alien technology:0.6 and spy gadgets:0.4.</p></li><li><p>Zorro-Hannibal: Mixing Zorro and Hannibal Lecter, this action figure is a swashbuckling gourmet:0.5 with a taste for the theatrical:0.5.</p></li><li><p>Wonder-Groot: A hybrid of Wonder Woman and Groot, this figure wields an Amazonian lasso:0.6 with a tree's gentle strength:0.4.</p></li><li><p>Venom-Elf: Combining Venom and Legolas from Lord of the Rings, this action figure embodies symbiotic archery:0.6 and elven agility:0.4.</p></li><li><p>Hulk-Witch: Merging The Hulk and a Witch from a fairy tale, this figure smashes with gamma-powered fury:0.6 while wielding mystical powers:0.4.</p></li><li><p>Green-Predator Arrow: A fusion of Green Arrow and Predator, this action figure boasts archery precision:0.6 and extraterrestrial hunting skills:0.4.</p></li><li><p>Robo-Hannibal Bond: Combining RoboCop, Hannibal Lecter, and James Bond, this figure patrols the streets:0.4 while savoring the macabre:0.3 and using spy gadgets:0.3.</p></li><li><p>Zorro-Trek: Mixing Zorro and Star Trek, this action figure is a swashbuckling space explorer:0.5 with a flair for diplomacy:0.5.</p></li><li><p>Preda-Ranger: A crossover between Predator and a Power Ranger, this figure hunts with extraterrestrial cunning:0.6 and colorful teamwork:0.4.</p></li><li><p>Wolverine-Pirate: Combining Wolverine and Captain Jack Sparrow, this action figure embodies mutant ferocity:0.6 with swashbuckling charm:0.4.</p></li><li><p>Flash-Matrix Assassin: Merging The Flash, Neo from The Matrix, and John Wick, this figure races through digital worlds:0.3 with speed:0.3 while wielding martial arts:0.2 and gun-fu skills:0.2.</p></li><li><p>Hannibal-Witch: A blend of Hannibal Lecter and a Witch from a fairy tale, this figure savors the macabre:0.5 while wielding mystical powers:0.5.</p></li><li><p>Super-Robo Bond: Combining Superman, RoboCop, and James Bond, this action figure possesses superhuman strength:0.3, patrols the streets:0.3 with robotic precision:0.2, and uses spy gadgets:0.2.</p></li><li><p>Thor-Samurai: Merging Thor and a Samurai, this figure wields godly strength:0.4 and disciplined swordsmanship:0.4.</p></li><li><p>Cyber-Hawkeye: A fusion of Cyborg and Hawkeye, this action figure embodies technological prowess:0.4 and archery precision:0.4.</p></li><li><p>Flash-Spock: Combining The Flash and Spock from Star Trek, this figure races through adventures:0.4 with logical precision:0.4.</p></li><li><p>Wolverine-Matrix: A hybrid of Wolverine and Neo from The Matrix, this action figure embodies mutant ferocity:0.4 and challenges the digital world:0.4.</p></li><li><p>Venom-Witcher: Mixing Venom and Geralt from The Witcher, this figure embodies symbiotic chaos:0.4 and monster hunting skills:0.4.</p></li><li><p>Green-Hannibal Arrow: A crossover between Green Arrow, Hannibal Lecter, and the Witch from Left 4 Dead, this action figure boasts archery precision:0.3, savoring the macabre:0.3, and supernatural power:0.3.</p></li><li><p>Hulk-Zorro: Merging The Hulk and Zorro, this figure smashes with gamma-powered fury:0.4 while showcasing swashbuckling finesse:0.4.</p></li><li><p>Super-Predator: A fusion of Superman and Predator, this action figure possesses superhuman strength:0.4 and extraterrestrial hunting skills:0.4.</p></li><li><p>Preda-Witcher: Combining Predator and Geralt from The Witcher, this figure hunts with extraterrestrial cunning:0.4 and monster slaying skills:0.4.</p></li><li><p>Robo-Gandalf: A hybrid of RoboCop and Gandalf, this action figure patrols the streets:0.4 with magical wisdom:0.4.</p></li><li><p>Zorro-Elf Arrow: Mixing Zorro, Legolas from Lord of the Rings, and Green Arrow, this figure is a swashbuckling archer:0.3 with elven agility:0.3 and archery precision:0.3.</p></li><li><p>Hannibal-Trek: A crossover between Hannibal Lecter and Star Trek, this action figure savors the macabre:0.4 while exploring the cosmos:0.4.</p></li><li><p>Wonder-Pirate Woman: Combining Wonder Woman and Captain Jack Sparrow, this figure wields Amazonian strength:0.4 and pirate swagger:0.4.</p></li><li><p>Flash-Zorro: Merging The Flash and Zorro, this action figure races through adventures:0.4 while showcasing swashbuckling finesse:0.4.</p></li><li><p>Preda-Hawkeye: A fusion of Predator and Hawkeye, this figure hunts with extraterrestrial cunning:0.4 and archery precision:0.4.</p></li><li><p>Iron-Matrix Man: Combining Iron Man and Neo from The Matrix, this action figure possesses high-tech armor:0.4 and challenges the digital world:0.4.</p></li><li><p>Thor-Bond: A blend of Thor and James Bond, this figure wields godly strength:0.4 and uses spy gadgets:0.4.</p></li><li><p>Bat-Elf Holmes: Mixing Batman, Legolas from Lord of the Rings, and Sherlock Holmes, this action figure patrols Gotham:0.3 with elven agility:0.3 and deductive reasoning:0.3.</p></li><li><p>Green-Gandalf Arrow: A crossover between Green Arrow, Gandalf, and Legolas from Lord of the Rings, this figure boasts archery precision:0.3, magical wisdom:0.3, and elven agility:0.3.</p></li><li><p>Robo-Pirate Cop: Merging RoboCop and Captain Jack Sparrow, this action figure patrols the high seas:0.4 with robotic precision:0.3 and pirate swagger:0.3.</p></li><li><p>Zorro-Groot Arrow: Combining Zorro, Groot, and Green Arrow, this figure is a swashbuckling tree archer:0.3 with elven agility:0.3 and archery precision:0.3.</p></li><li><p>Hannibal-Gandalf Holmes: A fusion of Hannibal Lecter, Gandalf, and Sherlock Holmes, this action figure savors the macabre:0.3, wields magical wisdom:0.3, and employs deductive reasoning:0.3.</p></li><li><p>Flash-Pirate Holmes: Mixing The Flash, Captain Jack Sparrow, and Sherlock Holmes, this figure races through adventures:0.3 with pirate swagger:0.3 and deductive reasoning:0.3.</p></li><li><p>Preda-Elf Arrow: A hybrid of Predator, Legolas from Lord of the Rings, and Green Arrow, this action figure hunts with extraterrestrial cunning:0.3, elven agility:0.3, and archery precision:0.3.</p></li><li><p>Iron-Matrix Pirate: Combining Iron Man, Neo from The Matrix, and Captain Jack Sparrow, this figure possesses high-tech armor:0.3, challenges the digital world:0.3, and sails the high seas:0.3.</p></li><li><p>Thor-Hannibal Cop: A blend of Thor, Hannibal Lecter, and RoboCop, this action figure wields godly strength:0.3, savors the macabre:0.3, and patrols the streets:0.3.</p></li><li><p>Wonder-Zorro Woman: Mixing Wonder Woman, Zorro, and Sherlock Holmes, this figure wields Amazonian strength:0.3, showcases swashbuckling finesse:0.3, and employs deductive reasoning:0.3.</p></li><li><p>Venom-Pirate Matrix: A crossover between Venom, Captain Jack Sparrow, and Neo from The Matrix, this action figure embodies symbiotic chaos:0.3, pirate swagger:0.3, and digital rebellion:0.3.</p></li><li><p>Flash-Groot Arrow: Combining The Flash, Groot, and Green Arrow, this figure races through adventures:0.3, offers a lovable tree's charm:0.3, and boasts archery precision:0.3.</p></li><li><p>Preda-Hulk Arrow: Merging Predator, The Hulk, and Green Arrow, this action figure hunts with extraterrestrial cunning:0.3, unleashes unstoppable rage:0.3, and showcases archery precision:0.3.</p></li></ul>
## Image examples for the model:
|
bigmorning/whisper_input_decoder_shift_r_labels_no_force__0050
|
bigmorning
| 2023-09-04T22:13:05Z | 59 | 0 |
transformers
|
[
"transformers",
"tf",
"whisper",
"automatic-speech-recognition",
"generated_from_keras_callback",
"base_model:openai/whisper-tiny",
"base_model:finetune:openai/whisper-tiny",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-09-04T22:12:56Z |
---
license: apache-2.0
base_model: openai/whisper-tiny
tags:
- generated_from_keras_callback
model-index:
- name: whisper_input_decoder_shift_r_labels_no_force__0050
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. -->
# whisper_input_decoder_shift_r_labels_no_force__0050
This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0499
- Train Accuracy: 0.0339
- Train Wermet: 0.0487
- Validation Loss: 0.7587
- Validation Accuracy: 0.0208
- Validation Wermet: 0.3030
- Epoch: 49
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 1e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Train Wermet | Validation Loss | Validation Accuracy | Validation Wermet | Epoch |
|:----------:|:--------------:|:------------:|:---------------:|:-------------------:|:-----------------:|:-----:|
| 5.6348 | 0.0091 | 1.5865 | 4.2935 | 0.0093 | 0.9579 | 0 |
| 4.9212 | 0.0099 | 0.9054 | 4.1262 | 0.0097 | 0.9390 | 1 |
| 4.6819 | 0.0107 | 0.8319 | 3.9071 | 0.0103 | 0.8966 | 2 |
| 4.4443 | 0.0114 | 0.8310 | 3.7367 | 0.0106 | 0.8939 | 3 |
| 4.2479 | 0.0119 | 0.8226 | 3.6101 | 0.0109 | 0.8696 | 4 |
| 4.0911 | 0.0124 | 0.8103 | 3.5364 | 0.0110 | 0.8946 | 5 |
| 3.9590 | 0.0127 | 0.7913 | 3.4556 | 0.0113 | 0.8388 | 6 |
| 3.8513 | 0.0130 | 0.7794 | 3.4106 | 0.0114 | 0.8515 | 7 |
| 3.7607 | 0.0133 | 0.7657 | 3.3507 | 0.0115 | 0.8261 | 8 |
| 3.6757 | 0.0136 | 0.7548 | 3.3141 | 0.0116 | 0.8400 | 9 |
| 3.6023 | 0.0138 | 0.7454 | 3.2711 | 0.0117 | 0.8006 | 10 |
| 3.5261 | 0.0140 | 0.7348 | 3.2391 | 0.0119 | 0.8101 | 11 |
| 3.4534 | 0.0143 | 0.7212 | 3.2070 | 0.0120 | 0.7870 | 12 |
| 3.3814 | 0.0146 | 0.7080 | 3.1505 | 0.0122 | 0.7826 | 13 |
| 3.3069 | 0.0148 | 0.6961 | 3.1102 | 0.0124 | 0.7609 | 14 |
| 3.2229 | 0.0152 | 0.6781 | 3.0542 | 0.0125 | 0.7532 | 15 |
| 3.1334 | 0.0156 | 0.6614 | 2.9840 | 0.0127 | 0.7448 | 16 |
| 3.0313 | 0.0160 | 0.6425 | 2.9032 | 0.0130 | 0.7123 | 17 |
| 2.9122 | 0.0166 | 0.6202 | 2.7986 | 0.0134 | 0.6930 | 18 |
| 2.7559 | 0.0173 | 0.5940 | 2.6337 | 0.0139 | 0.6673 | 19 |
| 2.5649 | 0.0182 | 0.5674 | 2.4490 | 0.0145 | 0.6383 | 20 |
| 2.3414 | 0.0193 | 0.5299 | 2.2785 | 0.0150 | 0.6183 | 21 |
| 2.0966 | 0.0206 | 0.4903 | 2.0460 | 0.0158 | 0.5649 | 22 |
| 1.8283 | 0.0220 | 0.4459 | 1.8369 | 0.0165 | 0.5306 | 23 |
| 1.5547 | 0.0235 | 0.3996 | 1.6356 | 0.0172 | 0.4848 | 24 |
| 1.3218 | 0.0249 | 0.3581 | 1.4682 | 0.0179 | 0.4510 | 25 |
| 1.1383 | 0.0260 | 0.3211 | 1.3465 | 0.0183 | 0.4226 | 26 |
| 0.9876 | 0.0270 | 0.2920 | 1.2323 | 0.0188 | 0.3966 | 27 |
| 0.8635 | 0.0278 | 0.2651 | 1.1482 | 0.0191 | 0.3749 | 28 |
| 0.7620 | 0.0284 | 0.2435 | 1.0816 | 0.0194 | 0.3565 | 29 |
| 0.6749 | 0.0290 | 0.2234 | 1.0187 | 0.0196 | 0.3433 | 30 |
| 0.5998 | 0.0295 | 0.2025 | 0.9761 | 0.0198 | 0.3319 | 31 |
| 0.5325 | 0.0300 | 0.1827 | 0.9326 | 0.0200 | 0.3213 | 32 |
| 0.4735 | 0.0305 | 0.1665 | 0.8942 | 0.0201 | 0.3110 | 33 |
| 0.4228 | 0.0308 | 0.1466 | 0.8735 | 0.0202 | 0.3026 | 34 |
| 0.3747 | 0.0312 | 0.1293 | 0.8408 | 0.0203 | 0.2931 | 35 |
| 0.3331 | 0.0316 | 0.1111 | 0.8253 | 0.0204 | 0.2891 | 36 |
| 0.2947 | 0.0319 | 0.0962 | 0.8084 | 0.0205 | 0.2849 | 37 |
| 0.2601 | 0.0322 | 0.0817 | 0.7906 | 0.0205 | 0.2783 | 38 |
| 0.2291 | 0.0324 | 0.0706 | 0.7876 | 0.0206 | 0.2755 | 39 |
| 0.2009 | 0.0327 | 0.0596 | 0.7723 | 0.0207 | 0.2712 | 40 |
| 0.1750 | 0.0329 | 0.0504 | 0.7629 | 0.0207 | 0.2692 | 41 |
| 0.1510 | 0.0331 | 0.0410 | 0.7650 | 0.0207 | 0.2684 | 42 |
| 0.1319 | 0.0333 | 0.0367 | 0.7533 | 0.0207 | 0.2655 | 43 |
| 0.1121 | 0.0335 | 0.0292 | 0.7589 | 0.0207 | 0.2647 | 44 |
| 0.0956 | 0.0336 | 0.0253 | 0.7579 | 0.0208 | 0.2642 | 45 |
| 0.0812 | 0.0337 | 0.0254 | 0.7584 | 0.0208 | 0.2625 | 46 |
| 0.0694 | 0.0338 | 0.0332 | 0.7555 | 0.0208 | 0.2693 | 47 |
| 0.0592 | 0.0339 | 0.0319 | 0.7534 | 0.0208 | 0.2629 | 48 |
| 0.0499 | 0.0339 | 0.0487 | 0.7587 | 0.0208 | 0.3030 | 49 |
### Framework versions
- Transformers 4.34.0.dev0
- TensorFlow 2.13.0
- Tokenizers 0.13.3
|
acdg1214/a2c-PandaReachDense-v3
|
acdg1214
| 2023-09-04T22:01:09Z | 1 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"PandaReachDense-v3",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-09-04T21:55:50Z |
---
library_name: stable-baselines3
tags:
- PandaReachDense-v3
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v3
type: PandaReachDense-v3
metrics:
- type: mean_reward
value: -0.15 +/- 0.11
name: mean_reward
verified: false
---
# **A2C** Agent playing **PandaReachDense-v3**
This is a trained model of a **A2C** agent playing **PandaReachDense-v3**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
bigmorning/whisper_input_decoder_shift_r_labels_no_force__0045
|
bigmorning
| 2023-09-04T21:59:45Z | 59 | 0 |
transformers
|
[
"transformers",
"tf",
"whisper",
"automatic-speech-recognition",
"generated_from_keras_callback",
"base_model:openai/whisper-tiny",
"base_model:finetune:openai/whisper-tiny",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-09-04T21:59:38Z |
---
license: apache-2.0
base_model: openai/whisper-tiny
tags:
- generated_from_keras_callback
model-index:
- name: whisper_input_decoder_shift_r_labels_no_force__0045
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. -->
# whisper_input_decoder_shift_r_labels_no_force__0045
This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.1121
- Train Accuracy: 0.0335
- Train Wermet: 0.0292
- Validation Loss: 0.7589
- Validation Accuracy: 0.0207
- Validation Wermet: 0.2647
- Epoch: 44
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 1e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Train Wermet | Validation Loss | Validation Accuracy | Validation Wermet | Epoch |
|:----------:|:--------------:|:------------:|:---------------:|:-------------------:|:-----------------:|:-----:|
| 5.6348 | 0.0091 | 1.5865 | 4.2935 | 0.0093 | 0.9579 | 0 |
| 4.9212 | 0.0099 | 0.9054 | 4.1262 | 0.0097 | 0.9390 | 1 |
| 4.6819 | 0.0107 | 0.8319 | 3.9071 | 0.0103 | 0.8966 | 2 |
| 4.4443 | 0.0114 | 0.8310 | 3.7367 | 0.0106 | 0.8939 | 3 |
| 4.2479 | 0.0119 | 0.8226 | 3.6101 | 0.0109 | 0.8696 | 4 |
| 4.0911 | 0.0124 | 0.8103 | 3.5364 | 0.0110 | 0.8946 | 5 |
| 3.9590 | 0.0127 | 0.7913 | 3.4556 | 0.0113 | 0.8388 | 6 |
| 3.8513 | 0.0130 | 0.7794 | 3.4106 | 0.0114 | 0.8515 | 7 |
| 3.7607 | 0.0133 | 0.7657 | 3.3507 | 0.0115 | 0.8261 | 8 |
| 3.6757 | 0.0136 | 0.7548 | 3.3141 | 0.0116 | 0.8400 | 9 |
| 3.6023 | 0.0138 | 0.7454 | 3.2711 | 0.0117 | 0.8006 | 10 |
| 3.5261 | 0.0140 | 0.7348 | 3.2391 | 0.0119 | 0.8101 | 11 |
| 3.4534 | 0.0143 | 0.7212 | 3.2070 | 0.0120 | 0.7870 | 12 |
| 3.3814 | 0.0146 | 0.7080 | 3.1505 | 0.0122 | 0.7826 | 13 |
| 3.3069 | 0.0148 | 0.6961 | 3.1102 | 0.0124 | 0.7609 | 14 |
| 3.2229 | 0.0152 | 0.6781 | 3.0542 | 0.0125 | 0.7532 | 15 |
| 3.1334 | 0.0156 | 0.6614 | 2.9840 | 0.0127 | 0.7448 | 16 |
| 3.0313 | 0.0160 | 0.6425 | 2.9032 | 0.0130 | 0.7123 | 17 |
| 2.9122 | 0.0166 | 0.6202 | 2.7986 | 0.0134 | 0.6930 | 18 |
| 2.7559 | 0.0173 | 0.5940 | 2.6337 | 0.0139 | 0.6673 | 19 |
| 2.5649 | 0.0182 | 0.5674 | 2.4490 | 0.0145 | 0.6383 | 20 |
| 2.3414 | 0.0193 | 0.5299 | 2.2785 | 0.0150 | 0.6183 | 21 |
| 2.0966 | 0.0206 | 0.4903 | 2.0460 | 0.0158 | 0.5649 | 22 |
| 1.8283 | 0.0220 | 0.4459 | 1.8369 | 0.0165 | 0.5306 | 23 |
| 1.5547 | 0.0235 | 0.3996 | 1.6356 | 0.0172 | 0.4848 | 24 |
| 1.3218 | 0.0249 | 0.3581 | 1.4682 | 0.0179 | 0.4510 | 25 |
| 1.1383 | 0.0260 | 0.3211 | 1.3465 | 0.0183 | 0.4226 | 26 |
| 0.9876 | 0.0270 | 0.2920 | 1.2323 | 0.0188 | 0.3966 | 27 |
| 0.8635 | 0.0278 | 0.2651 | 1.1482 | 0.0191 | 0.3749 | 28 |
| 0.7620 | 0.0284 | 0.2435 | 1.0816 | 0.0194 | 0.3565 | 29 |
| 0.6749 | 0.0290 | 0.2234 | 1.0187 | 0.0196 | 0.3433 | 30 |
| 0.5998 | 0.0295 | 0.2025 | 0.9761 | 0.0198 | 0.3319 | 31 |
| 0.5325 | 0.0300 | 0.1827 | 0.9326 | 0.0200 | 0.3213 | 32 |
| 0.4735 | 0.0305 | 0.1665 | 0.8942 | 0.0201 | 0.3110 | 33 |
| 0.4228 | 0.0308 | 0.1466 | 0.8735 | 0.0202 | 0.3026 | 34 |
| 0.3747 | 0.0312 | 0.1293 | 0.8408 | 0.0203 | 0.2931 | 35 |
| 0.3331 | 0.0316 | 0.1111 | 0.8253 | 0.0204 | 0.2891 | 36 |
| 0.2947 | 0.0319 | 0.0962 | 0.8084 | 0.0205 | 0.2849 | 37 |
| 0.2601 | 0.0322 | 0.0817 | 0.7906 | 0.0205 | 0.2783 | 38 |
| 0.2291 | 0.0324 | 0.0706 | 0.7876 | 0.0206 | 0.2755 | 39 |
| 0.2009 | 0.0327 | 0.0596 | 0.7723 | 0.0207 | 0.2712 | 40 |
| 0.1750 | 0.0329 | 0.0504 | 0.7629 | 0.0207 | 0.2692 | 41 |
| 0.1510 | 0.0331 | 0.0410 | 0.7650 | 0.0207 | 0.2684 | 42 |
| 0.1319 | 0.0333 | 0.0367 | 0.7533 | 0.0207 | 0.2655 | 43 |
| 0.1121 | 0.0335 | 0.0292 | 0.7589 | 0.0207 | 0.2647 | 44 |
### Framework versions
- Transformers 4.34.0.dev0
- TensorFlow 2.13.0
- Tokenizers 0.13.3
|
rohanbalkondekar/rohan_dreambooth
|
rohanbalkondekar
| 2023-09-04T21:52:29Z | 2 | 1 |
diffusers
|
[
"diffusers",
"text-to-image",
"autotrain",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:finetune:stabilityai/stable-diffusion-xl-base-1.0",
"region:us"
] |
text-to-image
| 2023-09-04T21:52:28Z |
---
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: photo of rohan balkondekar
tags:
- text-to-image
- diffusers
- autotrain
inference: true
---
# DreamBooth trained by AutoTrain
Text encoder was not trained.
|
nbogdan/flant5-base-1ex-bridging-1epochs
|
nbogdan
| 2023-09-04T21:50:03Z | 0 | 0 |
adapter-transformers
|
[
"adapter-transformers",
"adapterhub:self-explanations",
"t5",
"dataset:self-explanations",
"region:us"
] | null | 2023-09-04T21:49:53Z |
---
tags:
- adapterhub:self-explanations
- t5
- adapter-transformers
datasets:
- self-explanations
---
# Adapter `nbogdan/flant5-base-1ex-bridging-1epochs` for google/flan-t5-base
An [adapter](https://adapterhub.ml) for the `google/flan-t5-base` model that was trained on the [self-explanations](https://adapterhub.ml/explore/self-explanations/) dataset.
This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library.
## Usage
First, install `adapter-transformers`:
```
pip install -U adapter-transformers
```
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_
Now, the adapter can be loaded and activated like this:
```python
from transformers import AutoAdapterModel
model = AutoAdapterModel.from_pretrained("google/flan-t5-base")
adapter_name = model.load_adapter("nbogdan/flant5-base-1ex-bridging-1epochs", source="hf", set_active=True)
```
## Architecture & Training
<!-- Add some description here -->
## Evaluation results
<!-- Add some description here -->
## Citation
<!-- Add some description here -->
|
bigmorning/whisper_input_decoder_shift_r_labels_no_force__0040
|
bigmorning
| 2023-09-04T21:46:29Z | 59 | 0 |
transformers
|
[
"transformers",
"tf",
"whisper",
"automatic-speech-recognition",
"generated_from_keras_callback",
"base_model:openai/whisper-tiny",
"base_model:finetune:openai/whisper-tiny",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-09-04T21:46:21Z |
---
license: apache-2.0
base_model: openai/whisper-tiny
tags:
- generated_from_keras_callback
model-index:
- name: whisper_input_decoder_shift_r_labels_no_force__0040
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. -->
# whisper_input_decoder_shift_r_labels_no_force__0040
This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.2291
- Train Accuracy: 0.0324
- Train Wermet: 0.0706
- Validation Loss: 0.7876
- Validation Accuracy: 0.0206
- Validation Wermet: 0.2755
- Epoch: 39
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 1e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Train Wermet | Validation Loss | Validation Accuracy | Validation Wermet | Epoch |
|:----------:|:--------------:|:------------:|:---------------:|:-------------------:|:-----------------:|:-----:|
| 5.6348 | 0.0091 | 1.5865 | 4.2935 | 0.0093 | 0.9579 | 0 |
| 4.9212 | 0.0099 | 0.9054 | 4.1262 | 0.0097 | 0.9390 | 1 |
| 4.6819 | 0.0107 | 0.8319 | 3.9071 | 0.0103 | 0.8966 | 2 |
| 4.4443 | 0.0114 | 0.8310 | 3.7367 | 0.0106 | 0.8939 | 3 |
| 4.2479 | 0.0119 | 0.8226 | 3.6101 | 0.0109 | 0.8696 | 4 |
| 4.0911 | 0.0124 | 0.8103 | 3.5364 | 0.0110 | 0.8946 | 5 |
| 3.9590 | 0.0127 | 0.7913 | 3.4556 | 0.0113 | 0.8388 | 6 |
| 3.8513 | 0.0130 | 0.7794 | 3.4106 | 0.0114 | 0.8515 | 7 |
| 3.7607 | 0.0133 | 0.7657 | 3.3507 | 0.0115 | 0.8261 | 8 |
| 3.6757 | 0.0136 | 0.7548 | 3.3141 | 0.0116 | 0.8400 | 9 |
| 3.6023 | 0.0138 | 0.7454 | 3.2711 | 0.0117 | 0.8006 | 10 |
| 3.5261 | 0.0140 | 0.7348 | 3.2391 | 0.0119 | 0.8101 | 11 |
| 3.4534 | 0.0143 | 0.7212 | 3.2070 | 0.0120 | 0.7870 | 12 |
| 3.3814 | 0.0146 | 0.7080 | 3.1505 | 0.0122 | 0.7826 | 13 |
| 3.3069 | 0.0148 | 0.6961 | 3.1102 | 0.0124 | 0.7609 | 14 |
| 3.2229 | 0.0152 | 0.6781 | 3.0542 | 0.0125 | 0.7532 | 15 |
| 3.1334 | 0.0156 | 0.6614 | 2.9840 | 0.0127 | 0.7448 | 16 |
| 3.0313 | 0.0160 | 0.6425 | 2.9032 | 0.0130 | 0.7123 | 17 |
| 2.9122 | 0.0166 | 0.6202 | 2.7986 | 0.0134 | 0.6930 | 18 |
| 2.7559 | 0.0173 | 0.5940 | 2.6337 | 0.0139 | 0.6673 | 19 |
| 2.5649 | 0.0182 | 0.5674 | 2.4490 | 0.0145 | 0.6383 | 20 |
| 2.3414 | 0.0193 | 0.5299 | 2.2785 | 0.0150 | 0.6183 | 21 |
| 2.0966 | 0.0206 | 0.4903 | 2.0460 | 0.0158 | 0.5649 | 22 |
| 1.8283 | 0.0220 | 0.4459 | 1.8369 | 0.0165 | 0.5306 | 23 |
| 1.5547 | 0.0235 | 0.3996 | 1.6356 | 0.0172 | 0.4848 | 24 |
| 1.3218 | 0.0249 | 0.3581 | 1.4682 | 0.0179 | 0.4510 | 25 |
| 1.1383 | 0.0260 | 0.3211 | 1.3465 | 0.0183 | 0.4226 | 26 |
| 0.9876 | 0.0270 | 0.2920 | 1.2323 | 0.0188 | 0.3966 | 27 |
| 0.8635 | 0.0278 | 0.2651 | 1.1482 | 0.0191 | 0.3749 | 28 |
| 0.7620 | 0.0284 | 0.2435 | 1.0816 | 0.0194 | 0.3565 | 29 |
| 0.6749 | 0.0290 | 0.2234 | 1.0187 | 0.0196 | 0.3433 | 30 |
| 0.5998 | 0.0295 | 0.2025 | 0.9761 | 0.0198 | 0.3319 | 31 |
| 0.5325 | 0.0300 | 0.1827 | 0.9326 | 0.0200 | 0.3213 | 32 |
| 0.4735 | 0.0305 | 0.1665 | 0.8942 | 0.0201 | 0.3110 | 33 |
| 0.4228 | 0.0308 | 0.1466 | 0.8735 | 0.0202 | 0.3026 | 34 |
| 0.3747 | 0.0312 | 0.1293 | 0.8408 | 0.0203 | 0.2931 | 35 |
| 0.3331 | 0.0316 | 0.1111 | 0.8253 | 0.0204 | 0.2891 | 36 |
| 0.2947 | 0.0319 | 0.0962 | 0.8084 | 0.0205 | 0.2849 | 37 |
| 0.2601 | 0.0322 | 0.0817 | 0.7906 | 0.0205 | 0.2783 | 38 |
| 0.2291 | 0.0324 | 0.0706 | 0.7876 | 0.0206 | 0.2755 | 39 |
### Framework versions
- Transformers 4.34.0.dev0
- TensorFlow 2.13.0
- Tokenizers 0.13.3
|
active-learning/mnist_classifier
|
active-learning
| 2023-09-04T21:46:17Z | 0 | 0 |
keras
|
[
"keras",
"tf-keras",
"region:us"
] | null | 2023-02-03T13:18:22Z |
---
library_name: keras
---
## 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:
| Hyperparameters | Value |
| :-- | :-- |
| name | Adam |
| weight_decay | None |
| clipnorm | None |
| global_clipnorm | None |
| clipvalue | None |
| use_ema | False |
| ema_momentum | 0.99 |
| ema_overwrite_frequency | None |
| jit_compile | False |
| is_legacy_optimizer | False |
| learning_rate | 0.0010000000474974513 |
| beta_1 | 0.9 |
| beta_2 | 0.999 |
| epsilon | 1e-07 |
| amsgrad | False |
| training_precision | float32 |
|
nbogdan/flant5-base-1ex-elaboration-1epochs
|
nbogdan
| 2023-09-04T21:34:54Z | 0 | 0 |
adapter-transformers
|
[
"adapter-transformers",
"adapterhub:self-explanations",
"t5",
"dataset:self-explanations",
"region:us"
] | null | 2023-09-04T21:34:46Z |
---
tags:
- adapterhub:self-explanations
- t5
- adapter-transformers
datasets:
- self-explanations
---
# Adapter `nbogdan/flant5-base-1ex-elaboration-1epochs` for google/flan-t5-base
An [adapter](https://adapterhub.ml) for the `google/flan-t5-base` model that was trained on the [self-explanations](https://adapterhub.ml/explore/self-explanations/) dataset.
This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library.
## Usage
First, install `adapter-transformers`:
```
pip install -U adapter-transformers
```
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_
Now, the adapter can be loaded and activated like this:
```python
from transformers import AutoAdapterModel
model = AutoAdapterModel.from_pretrained("google/flan-t5-base")
adapter_name = model.load_adapter("nbogdan/flant5-base-1ex-elaboration-1epochs", source="hf", set_active=True)
```
## Architecture & Training
<!-- Add some description here -->
## Evaluation results
<!-- Add some description here -->
## Citation
<!-- Add some description here -->
|
JanSt/gbert-base-finetuned-twitter_
|
JanSt
| 2023-09-04T21:31:46Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"fill-mask",
"generated_from_trainer",
"base_model:deepset/gbert-base",
"base_model:finetune:deepset/gbert-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2023-09-04T08:13:18Z |
---
license: mit
base_model: deepset/gbert-base
tags:
- generated_from_trainer
model-index:
- name: gbert-base-finetuned-twitter_
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. -->
# gbert-base-finetuned-twitter_
This model is a fine-tuned version of [deepset/gbert-base](https://huggingface.co/deepset/gbert-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6651
## 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: 192
- eval_batch_size: 192
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|
| 2.1933 | 1.0 | 4180 | 1.9612 |
| 2.0051 | 2.0 | 8360 | 1.8795 |
| 1.939 | 3.0 | 12540 | 1.8310 |
| 1.8928 | 4.0 | 16720 | 1.8013 |
| 1.8594 | 5.0 | 20900 | 1.7730 |
| 1.8336 | 6.0 | 25080 | 1.7702 |
| 1.8145 | 7.0 | 29260 | 1.7449 |
| 1.7963 | 8.0 | 33440 | 1.7277 |
| 1.7806 | 9.0 | 37620 | 1.7105 |
| 1.7682 | 10.0 | 41800 | 1.7061 |
| 1.7584 | 11.0 | 45980 | 1.7041 |
| 1.7454 | 12.0 | 50160 | 1.6899 |
| 1.7374 | 13.0 | 54340 | 1.6850 |
| 1.7295 | 14.0 | 58520 | 1.6856 |
| 1.7232 | 15.0 | 62700 | 1.6819 |
| 1.715 | 16.0 | 66880 | 1.6730 |
| 1.7101 | 17.0 | 71060 | 1.6723 |
| 1.7057 | 18.0 | 75240 | 1.6655 |
| 1.7038 | 19.0 | 79420 | 1.6617 |
| 1.702 | 20.0 | 83600 | 1.6625 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.0.1+cu117
- Datasets 2.14.4
- Tokenizers 0.13.3
|
facebook/regnet-x-160
|
facebook
| 2023-09-04T21:27:33Z | 402 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"safetensors",
"regnet",
"image-classification",
"vision",
"dataset:imagenet-1k",
"arxiv:2003.13678",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2022-03-18T15:27:57Z |
---
license: apache-2.0
tags:
- vision
- image-classification
datasets:
- imagenet-1k
widget:
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg
example_title: Tiger
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg
example_title: Teapot
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg
example_title: Palace
---
# RegNet
RegNet model trained on imagenet-1k. It was introduced in the paper [Designing Network Design Spaces](https://arxiv.org/abs/2003.13678) and first released in [this repository](https://github.com/facebookresearch/pycls).
Disclaimer: The team releasing RegNet did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
The authors design search spaces to perform Neural Architecture Search (NAS). They first start from a high dimensional search space and iteratively reduce the search space by empirically applying constraints based on the best-performing models sampled by the current search space.
## Intended uses & limitations
You can use the raw model for image classification. See the [model hub](https://huggingface.co/models?search=regnet) to look for
fine-tuned versions on a task that interests you.
### How to use
Here is how to use this model:
```python
>>> from transformers import AutoFeatureExtractor, RegNetForImageClassification
>>> import torch
>>> from datasets import load_dataset
>>> dataset = load_dataset("huggingface/cats-image")
>>> image = dataset["test"]["image"][0]
>>> feature_extractor = AutoFeatureExtractor.from_pretrained("zuppif/regnet-y-040")
>>> model = RegNetForImageClassification.from_pretrained("zuppif/regnet-y-040")
>>> inputs = feature_extractor(image, return_tensors="pt")
>>> with torch.no_grad():
... logits = model(**inputs).logits
>>> # model predicts one of the 1000 ImageNet classes
>>> predicted_label = logits.argmax(-1).item()
>>> print(model.config.id2label[predicted_label])
'tabby, tabby cat'
```
For more code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/master/en/model_doc/regnet).
|
volvoDon/mr-golem
|
volvoDon
| 2023-09-04T21:26:39Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-09-03T23:15:47Z |
---
library_name: peft
---
## Training procedure
This is a funny CausalLLM That was Trained on the full ~150 pages of the Necronomicon
## Scope of Use
Absolutely Just for Fun, *Be advised it was trained on Occult Text so it might say offensive or confusing things*
### Framework versions
- PEFT 0.5.0
|
volvoDon/petro-daemon
|
volvoDon
| 2023-09-04T21:21:25Z | 63 | 0 |
transformers
|
[
"transformers",
"tf",
"vit",
"image-classification",
"generated_from_keras_callback",
"base_model:google/vit-base-patch16-224-in21k",
"base_model:finetune:google/vit-base-patch16-224-in21k",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-09-04T20:11:04Z |
---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_keras_callback
model-index:
- name: volvoDon/petro-daemon
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. -->
# volvoDon/petro-daemon
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on a [DataSet of petrologic cross sections](https://huggingface.co/datasets/volvoDon/petrology-sections).
It achieves the following results on the evaluation set:
- Train Loss: 0.8890
- Validation Loss: 1.1803
- Train Accuracy: 0.6
- Epoch: 19
## Model description
More information needed
## Intended uses & limitations
Currently it is just a proof of concept and does a great job identifiying Olivine
It currently is not ready for a production enviroment but the results are promising, with an improved dataset I'm confident better results could be acheived.
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 3e-05, 'decay_steps': 300, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train Accuracy | Epoch |
|:----------:|:---------------:|:--------------:|:-----:|
| 1.6519 | 1.7095 | 0.2 | 0 |
| 1.5905 | 1.6747 | 0.2 | 1 |
| 1.5690 | 1.6342 | 0.2 | 2 |
| 1.5170 | 1.5931 | 0.2 | 3 |
| 1.4764 | 1.5528 | 0.6 | 4 |
| 1.3835 | 1.5079 | 0.6 | 5 |
| 1.3420 | 1.4717 | 0.6 | 6 |
| 1.3171 | 1.4232 | 0.6 | 7 |
| 1.2897 | 1.3905 | 0.6 | 8 |
| 1.2702 | 1.3794 | 0.6 | 9 |
| 1.2023 | 1.3351 | 0.6 | 10 |
| 1.1480 | 1.3384 | 0.6 | 11 |
| 1.1434 | 1.3419 | 0.6 | 12 |
| 1.0499 | 1.3226 | 0.6 | 13 |
| 1.0672 | 1.2647 | 0.6 | 14 |
| 1.0526 | 1.1533 | 0.6 | 15 |
| 1.0184 | 1.1546 | 0.6 | 16 |
| 0.9505 | 1.2491 | 0.6 | 17 |
| 0.9578 | 1.2809 | 0.4 | 18 |
| 0.8890 | 1.1803 | 0.6 | 19 |
### Framework versions
- Transformers 4.32.1
- TensorFlow 2.12.0
- Datasets 2.14.4
- Tokenizers 0.13.3
|
actionpace/LLAMA2-13B-Holodeck-1
|
actionpace
| 2023-09-04T21:21:09Z | 7 | 0 | null |
[
"gguf",
"en",
"license:other",
"endpoints_compatible",
"region:us"
] | null | 2023-09-04T20:46:13Z |
---
license: other
language:
- en
---
**Some of my own quants:**
* LLAMA2-13B-Holodeck-1_Q5_1_4K.gguf
* LLAMA2-13B-Holodeck-1_Q5_1_8K.gguf
**Source:** [KoboldAI](https://huggingface.co/KoboldAI)
**Source Model:** [LLAMA2-13B-Holodeck-1](https://huggingface.co/KoboldAI/LLAMA2-13B-Holodeck-1)
**Models utilizing KoboldAI/LLAMA2-13B-Holodeck-1**
- [The-Face-Of-Goonery/Huginn-v3-13b](https://huggingface.co/The-Face-Of-Goonery/Huginn-v3-13b) ([Ref](https://huggingface.co/actionpace/Huginn-v3-13b)) (Finetune, kaiokendev/SuperCOT-dataset)
|
nbogdan/flant5-base-1ex-paraphrasing-1epochs
|
nbogdan
| 2023-09-04T21:20:32Z | 0 | 0 |
adapter-transformers
|
[
"adapter-transformers",
"adapterhub:self-explanations",
"t5",
"dataset:self-explanations",
"region:us"
] | null | 2023-09-04T21:20:22Z |
---
tags:
- adapterhub:self-explanations
- t5
- adapter-transformers
datasets:
- self-explanations
---
# Adapter `nbogdan/flant5-base-1ex-paraphrasing-1epochs` for google/flan-t5-base
An [adapter](https://adapterhub.ml) for the `google/flan-t5-base` model that was trained on the [self-explanations](https://adapterhub.ml/explore/self-explanations/) dataset.
This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library.
## Usage
First, install `adapter-transformers`:
```
pip install -U adapter-transformers
```
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_
Now, the adapter can be loaded and activated like this:
```python
from transformers import AutoAdapterModel
model = AutoAdapterModel.from_pretrained("google/flan-t5-base")
adapter_name = model.load_adapter("nbogdan/flant5-base-1ex-paraphrasing-1epochs", source="hf", set_active=True)
```
## Architecture & Training
<!-- Add some description here -->
## Evaluation results
<!-- Add some description here -->
## Citation
<!-- Add some description here -->
|
facebook/wav2vec2-large-it-voxpopuli
|
facebook
| 2023-09-04T21:15:34Z | 395 | 0 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"safetensors",
"wav2vec2",
"pretraining",
"audio",
"automatic-speech-recognition",
"voxpopuli",
"it",
"arxiv:2101.00390",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language: it
tags:
- audio
- automatic-speech-recognition
- voxpopuli
license: cc-by-nc-4.0
---
# Wav2Vec2-Large-VoxPopuli
[Facebook's Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/) large model pretrained on the it unlabeled subset of [VoxPopuli corpus](https://arxiv.org/abs/2101.00390).
**Paper**: *[VoxPopuli: A Large-Scale Multilingual Speech Corpus for Representation
Learning, Semi-Supervised Learning and Interpretation](https://arxiv.org/abs/2101.00390)*
**Authors**: *Changhan Wang, Morgane Riviere, Ann Lee, Anne Wu, Chaitanya Talnikar, Daniel Haziza, Mary Williamson, Juan Pino, Emmanuel Dupoux* from *Facebook AI*
See the official website for more information, [here](https://github.com/facebookresearch/voxpopuli/)
# Fine-Tuning
Please refer to [this blog](https://huggingface.co/blog/fine-tune-xlsr-wav2vec2) on how to fine-tune this model on a specific language. Note that you should replace `"facebook/wav2vec2-large-xlsr-53"` with this checkpoint for fine-tuning.
|
facebook/convnext-base-224-22k-1k
|
facebook
| 2023-09-04T21:09:35Z | 653 | 3 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"safetensors",
"convnext",
"image-classification",
"vision",
"dataset:imagenet-21k",
"dataset:imagenet-1k",
"arxiv:2201.03545",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- vision
- image-classification
datasets:
- imagenet-21k
- imagenet-1k
widget:
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg
example_title: Tiger
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg
example_title: Teapot
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg
example_title: Palace
---
# ConvNeXT (base-sized model)
ConvNeXT model pre-trained on ImageNet-22k and fine-tuned on ImageNet-1k at resolution 224x224. It was introduced in the paper [A ConvNet for the 2020s](https://arxiv.org/abs/2201.03545) by Liu et al. and first released in [this repository](https://github.com/facebookresearch/ConvNeXt).
Disclaimer: The team releasing ConvNeXT did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
ConvNeXT is a pure convolutional model (ConvNet), inspired by the design of Vision Transformers, that claims to outperform them. The authors started from a ResNet and "modernized" its design by taking the Swin Transformer as inspiration.
## Intended uses & limitations
You can use the raw model for image classification. See the [model hub](https://huggingface.co/models?search=convnext) to look for
fine-tuned versions on a task that interests you.
### How to use
Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:
```python
from transformers import ConvNextFeatureExtractor, ConvNextForImageClassification
import torch
from datasets import load_dataset
dataset = load_dataset("huggingface/cats-image")
image = dataset["test"]["image"][0]
feature_extractor = ConvNextFeatureExtractor.from_pretrained("facebook/convnext-base-224-22k-1k")
model = ConvNextForImageClassification.from_pretrained("facebook/convnext-base-384-224-1k")
inputs = feature_extractor(image, return_tensors="pt")
with torch.no_grad():
logits = model(**inputs).logits
# model predicts one of the 1000 ImageNet classes
predicted_label = logits.argmax(-1).item()
print(model.config.id2label[predicted_label]),
```
For more code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/master/en/model_doc/convnext).
### BibTeX entry and citation info
```bibtex
@article{DBLP:journals/corr/abs-2201-03545,
author = {Zhuang Liu and
Hanzi Mao and
Chao{-}Yuan Wu and
Christoph Feichtenhofer and
Trevor Darrell and
Saining Xie},
title = {A ConvNet for the 2020s},
journal = {CoRR},
volume = {abs/2201.03545},
year = {2022},
url = {https://arxiv.org/abs/2201.03545},
eprinttype = {arXiv},
eprint = {2201.03545},
timestamp = {Thu, 20 Jan 2022 14:21:35 +0100},
biburl = {https://dblp.org/rec/journals/corr/abs-2201-03545.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
```
|
bigmorning/whisper_input_decoder_shift_r_labels_no_force__0025
|
bigmorning
| 2023-09-04T21:06:45Z | 59 | 0 |
transformers
|
[
"transformers",
"tf",
"whisper",
"automatic-speech-recognition",
"generated_from_keras_callback",
"base_model:openai/whisper-tiny",
"base_model:finetune:openai/whisper-tiny",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-09-04T21:06:37Z |
---
license: apache-2.0
base_model: openai/whisper-tiny
tags:
- generated_from_keras_callback
model-index:
- name: whisper_input_decoder_shift_r_labels_no_force__0025
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. -->
# whisper_input_decoder_shift_r_labels_no_force__0025
This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 1.5547
- Train Accuracy: 0.0235
- Train Wermet: 0.3996
- Validation Loss: 1.6356
- Validation Accuracy: 0.0172
- Validation Wermet: 0.4848
- Epoch: 24
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 1e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Train Wermet | Validation Loss | Validation Accuracy | Validation Wermet | Epoch |
|:----------:|:--------------:|:------------:|:---------------:|:-------------------:|:-----------------:|:-----:|
| 5.6348 | 0.0091 | 1.5865 | 4.2935 | 0.0093 | 0.9579 | 0 |
| 4.9212 | 0.0099 | 0.9054 | 4.1262 | 0.0097 | 0.9390 | 1 |
| 4.6819 | 0.0107 | 0.8319 | 3.9071 | 0.0103 | 0.8966 | 2 |
| 4.4443 | 0.0114 | 0.8310 | 3.7367 | 0.0106 | 0.8939 | 3 |
| 4.2479 | 0.0119 | 0.8226 | 3.6101 | 0.0109 | 0.8696 | 4 |
| 4.0911 | 0.0124 | 0.8103 | 3.5364 | 0.0110 | 0.8946 | 5 |
| 3.9590 | 0.0127 | 0.7913 | 3.4556 | 0.0113 | 0.8388 | 6 |
| 3.8513 | 0.0130 | 0.7794 | 3.4106 | 0.0114 | 0.8515 | 7 |
| 3.7607 | 0.0133 | 0.7657 | 3.3507 | 0.0115 | 0.8261 | 8 |
| 3.6757 | 0.0136 | 0.7548 | 3.3141 | 0.0116 | 0.8400 | 9 |
| 3.6023 | 0.0138 | 0.7454 | 3.2711 | 0.0117 | 0.8006 | 10 |
| 3.5261 | 0.0140 | 0.7348 | 3.2391 | 0.0119 | 0.8101 | 11 |
| 3.4534 | 0.0143 | 0.7212 | 3.2070 | 0.0120 | 0.7870 | 12 |
| 3.3814 | 0.0146 | 0.7080 | 3.1505 | 0.0122 | 0.7826 | 13 |
| 3.3069 | 0.0148 | 0.6961 | 3.1102 | 0.0124 | 0.7609 | 14 |
| 3.2229 | 0.0152 | 0.6781 | 3.0542 | 0.0125 | 0.7532 | 15 |
| 3.1334 | 0.0156 | 0.6614 | 2.9840 | 0.0127 | 0.7448 | 16 |
| 3.0313 | 0.0160 | 0.6425 | 2.9032 | 0.0130 | 0.7123 | 17 |
| 2.9122 | 0.0166 | 0.6202 | 2.7986 | 0.0134 | 0.6930 | 18 |
| 2.7559 | 0.0173 | 0.5940 | 2.6337 | 0.0139 | 0.6673 | 19 |
| 2.5649 | 0.0182 | 0.5674 | 2.4490 | 0.0145 | 0.6383 | 20 |
| 2.3414 | 0.0193 | 0.5299 | 2.2785 | 0.0150 | 0.6183 | 21 |
| 2.0966 | 0.0206 | 0.4903 | 2.0460 | 0.0158 | 0.5649 | 22 |
| 1.8283 | 0.0220 | 0.4459 | 1.8369 | 0.0165 | 0.5306 | 23 |
| 1.5547 | 0.0235 | 0.3996 | 1.6356 | 0.0172 | 0.4848 | 24 |
### Framework versions
- Transformers 4.34.0.dev0
- TensorFlow 2.13.0
- Tokenizers 0.13.3
|
nbogdan/flant5-base-1ex-overall-1epochs
|
nbogdan
| 2023-09-04T21:04:30Z | 1 | 0 |
adapter-transformers
|
[
"adapter-transformers",
"adapterhub:self-explanations",
"t5",
"dataset:self-explanations",
"region:us"
] | null | 2023-09-04T21:04:21Z |
---
tags:
- adapterhub:self-explanations
- t5
- adapter-transformers
datasets:
- self-explanations
---
# Adapter `nbogdan/flant5-base-1ex-overall-1epochs` for google/flan-t5-base
An [adapter](https://adapterhub.ml) for the `google/flan-t5-base` model that was trained on the [self-explanations](https://adapterhub.ml/explore/self-explanations/) dataset.
This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library.
## Usage
First, install `adapter-transformers`:
```
pip install -U adapter-transformers
```
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_
Now, the adapter can be loaded and activated like this:
```python
from transformers import AutoAdapterModel
model = AutoAdapterModel.from_pretrained("google/flan-t5-base")
adapter_name = model.load_adapter("nbogdan/flant5-base-1ex-overall-1epochs", source="hf", set_active=True)
```
## Architecture & Training
<!-- Add some description here -->
## Evaluation results
<!-- Add some description here -->
## Citation
<!-- Add some description here -->
|
jonc/ybelkada-opt-6.7b-lora
|
jonc
| 2023-09-04T20:59:50Z | 2 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-09-04T20:59:47Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
### Framework versions
- PEFT 0.6.0.dev0
|
SandraDee/ppo-LunarLander-v2
|
SandraDee
| 2023-09-04T20:57:50Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-09-04T20:57:31Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 281.48 +/- 13.92
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
darthlordvictor/generative-bloom-marketing-002
|
darthlordvictor
| 2023-09-04T20:56:22Z | 1 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-29T02:38:56Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
### Framework versions
- PEFT 0.6.0.dev0
|
bigmorning/whisper_input_decoder_shift_r_labels_no_force__0020
|
bigmorning
| 2023-09-04T20:53:30Z | 59 | 0 |
transformers
|
[
"transformers",
"tf",
"whisper",
"automatic-speech-recognition",
"generated_from_keras_callback",
"base_model:openai/whisper-tiny",
"base_model:finetune:openai/whisper-tiny",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-09-04T20:53:22Z |
---
license: apache-2.0
base_model: openai/whisper-tiny
tags:
- generated_from_keras_callback
model-index:
- name: whisper_input_decoder_shift_r_labels_no_force__0020
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. -->
# whisper_input_decoder_shift_r_labels_no_force__0020
This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 2.7559
- Train Accuracy: 0.0173
- Train Wermet: 0.5940
- Validation Loss: 2.6337
- Validation Accuracy: 0.0139
- Validation Wermet: 0.6673
- Epoch: 19
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 1e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Train Wermet | Validation Loss | Validation Accuracy | Validation Wermet | Epoch |
|:----------:|:--------------:|:------------:|:---------------:|:-------------------:|:-----------------:|:-----:|
| 5.6348 | 0.0091 | 1.5865 | 4.2935 | 0.0093 | 0.9579 | 0 |
| 4.9212 | 0.0099 | 0.9054 | 4.1262 | 0.0097 | 0.9390 | 1 |
| 4.6819 | 0.0107 | 0.8319 | 3.9071 | 0.0103 | 0.8966 | 2 |
| 4.4443 | 0.0114 | 0.8310 | 3.7367 | 0.0106 | 0.8939 | 3 |
| 4.2479 | 0.0119 | 0.8226 | 3.6101 | 0.0109 | 0.8696 | 4 |
| 4.0911 | 0.0124 | 0.8103 | 3.5364 | 0.0110 | 0.8946 | 5 |
| 3.9590 | 0.0127 | 0.7913 | 3.4556 | 0.0113 | 0.8388 | 6 |
| 3.8513 | 0.0130 | 0.7794 | 3.4106 | 0.0114 | 0.8515 | 7 |
| 3.7607 | 0.0133 | 0.7657 | 3.3507 | 0.0115 | 0.8261 | 8 |
| 3.6757 | 0.0136 | 0.7548 | 3.3141 | 0.0116 | 0.8400 | 9 |
| 3.6023 | 0.0138 | 0.7454 | 3.2711 | 0.0117 | 0.8006 | 10 |
| 3.5261 | 0.0140 | 0.7348 | 3.2391 | 0.0119 | 0.8101 | 11 |
| 3.4534 | 0.0143 | 0.7212 | 3.2070 | 0.0120 | 0.7870 | 12 |
| 3.3814 | 0.0146 | 0.7080 | 3.1505 | 0.0122 | 0.7826 | 13 |
| 3.3069 | 0.0148 | 0.6961 | 3.1102 | 0.0124 | 0.7609 | 14 |
| 3.2229 | 0.0152 | 0.6781 | 3.0542 | 0.0125 | 0.7532 | 15 |
| 3.1334 | 0.0156 | 0.6614 | 2.9840 | 0.0127 | 0.7448 | 16 |
| 3.0313 | 0.0160 | 0.6425 | 2.9032 | 0.0130 | 0.7123 | 17 |
| 2.9122 | 0.0166 | 0.6202 | 2.7986 | 0.0134 | 0.6930 | 18 |
| 2.7559 | 0.0173 | 0.5940 | 2.6337 | 0.0139 | 0.6673 | 19 |
### Framework versions
- Transformers 4.34.0.dev0
- TensorFlow 2.13.0
- Tokenizers 0.13.3
|
bigmorning/whisper_input_decoder_shift_r_labels_no_force__0015
|
bigmorning
| 2023-09-04T20:40:15Z | 59 | 0 |
transformers
|
[
"transformers",
"tf",
"whisper",
"automatic-speech-recognition",
"generated_from_keras_callback",
"base_model:openai/whisper-tiny",
"base_model:finetune:openai/whisper-tiny",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-09-04T20:40:08Z |
---
license: apache-2.0
base_model: openai/whisper-tiny
tags:
- generated_from_keras_callback
model-index:
- name: whisper_input_decoder_shift_r_labels_no_force__0015
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. -->
# whisper_input_decoder_shift_r_labels_no_force__0015
This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 3.3069
- Train Accuracy: 0.0148
- Train Wermet: 0.6961
- Validation Loss: 3.1102
- Validation Accuracy: 0.0124
- Validation Wermet: 0.7609
- Epoch: 14
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 1e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Train Wermet | Validation Loss | Validation Accuracy | Validation Wermet | Epoch |
|:----------:|:--------------:|:------------:|:---------------:|:-------------------:|:-----------------:|:-----:|
| 5.6348 | 0.0091 | 1.5865 | 4.2935 | 0.0093 | 0.9579 | 0 |
| 4.9212 | 0.0099 | 0.9054 | 4.1262 | 0.0097 | 0.9390 | 1 |
| 4.6819 | 0.0107 | 0.8319 | 3.9071 | 0.0103 | 0.8966 | 2 |
| 4.4443 | 0.0114 | 0.8310 | 3.7367 | 0.0106 | 0.8939 | 3 |
| 4.2479 | 0.0119 | 0.8226 | 3.6101 | 0.0109 | 0.8696 | 4 |
| 4.0911 | 0.0124 | 0.8103 | 3.5364 | 0.0110 | 0.8946 | 5 |
| 3.9590 | 0.0127 | 0.7913 | 3.4556 | 0.0113 | 0.8388 | 6 |
| 3.8513 | 0.0130 | 0.7794 | 3.4106 | 0.0114 | 0.8515 | 7 |
| 3.7607 | 0.0133 | 0.7657 | 3.3507 | 0.0115 | 0.8261 | 8 |
| 3.6757 | 0.0136 | 0.7548 | 3.3141 | 0.0116 | 0.8400 | 9 |
| 3.6023 | 0.0138 | 0.7454 | 3.2711 | 0.0117 | 0.8006 | 10 |
| 3.5261 | 0.0140 | 0.7348 | 3.2391 | 0.0119 | 0.8101 | 11 |
| 3.4534 | 0.0143 | 0.7212 | 3.2070 | 0.0120 | 0.7870 | 12 |
| 3.3814 | 0.0146 | 0.7080 | 3.1505 | 0.0122 | 0.7826 | 13 |
| 3.3069 | 0.0148 | 0.6961 | 3.1102 | 0.0124 | 0.7609 | 14 |
### Framework versions
- Transformers 4.34.0.dev0
- TensorFlow 2.13.0
- Tokenizers 0.13.3
|
reginaboateng/BERT_pubmedqa_adapter_with_maybes_to_yes_updated
|
reginaboateng
| 2023-09-04T20:31:22Z | 1 | 0 |
adapter-transformers
|
[
"adapter-transformers",
"bert",
"adapterhub:pubmedqa",
"dataset:pubmedqa",
"region:us"
] | null | 2023-09-04T20:31:19Z |
---
tags:
- bert
- adapter-transformers
- adapterhub:pubmedqa
datasets:
- pubmedqa
---
# Adapter `reginaboateng/BERT_pubmedqa_adapter_with_maybes_to_yes_updated` for bert-base-uncased
An [adapter](https://adapterhub.ml) for the `bert-base-uncased` model that was trained on the [pubmedqa](https://adapterhub.ml/explore/pubmedqa/) dataset.
This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library.
## Usage
First, install `adapter-transformers`:
```
pip install -U adapter-transformers
```
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_
Now, the adapter can be loaded and activated like this:
```python
from transformers import AutoAdapterModel
model = AutoAdapterModel.from_pretrained("bert-base-uncased")
adapter_name = model.load_adapter("reginaboateng/BERT_pubmedqa_adapter_with_maybes_to_yes_updated", source="hf", set_active=True)
```
## Architecture & Training
<!-- Add some description here -->
## Evaluation results
<!-- Add some description here -->
## Citation
<!-- Add some description here -->
|
bigmorning/whisper_input_decoder_shift_r_labels_no_force__0010
|
bigmorning
| 2023-09-04T20:27:01Z | 59 | 0 |
transformers
|
[
"transformers",
"tf",
"whisper",
"automatic-speech-recognition",
"generated_from_keras_callback",
"base_model:openai/whisper-tiny",
"base_model:finetune:openai/whisper-tiny",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-09-04T20:26:52Z |
---
license: apache-2.0
base_model: openai/whisper-tiny
tags:
- generated_from_keras_callback
model-index:
- name: whisper_input_decoder_shift_r_labels_no_force__0010
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. -->
# whisper_input_decoder_shift_r_labels_no_force__0010
This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 3.6757
- Train Accuracy: 0.0136
- Train Wermet: 0.7548
- Validation Loss: 3.3141
- Validation Accuracy: 0.0116
- Validation Wermet: 0.8400
- Epoch: 9
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 1e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Train Wermet | Validation Loss | Validation Accuracy | Validation Wermet | Epoch |
|:----------:|:--------------:|:------------:|:---------------:|:-------------------:|:-----------------:|:-----:|
| 5.6348 | 0.0091 | 1.5865 | 4.2935 | 0.0093 | 0.9579 | 0 |
| 4.9212 | 0.0099 | 0.9054 | 4.1262 | 0.0097 | 0.9390 | 1 |
| 4.6819 | 0.0107 | 0.8319 | 3.9071 | 0.0103 | 0.8966 | 2 |
| 4.4443 | 0.0114 | 0.8310 | 3.7367 | 0.0106 | 0.8939 | 3 |
| 4.2479 | 0.0119 | 0.8226 | 3.6101 | 0.0109 | 0.8696 | 4 |
| 4.0911 | 0.0124 | 0.8103 | 3.5364 | 0.0110 | 0.8946 | 5 |
| 3.9590 | 0.0127 | 0.7913 | 3.4556 | 0.0113 | 0.8388 | 6 |
| 3.8513 | 0.0130 | 0.7794 | 3.4106 | 0.0114 | 0.8515 | 7 |
| 3.7607 | 0.0133 | 0.7657 | 3.3507 | 0.0115 | 0.8261 | 8 |
| 3.6757 | 0.0136 | 0.7548 | 3.3141 | 0.0116 | 0.8400 | 9 |
### Framework versions
- Transformers 4.34.0.dev0
- TensorFlow 2.13.0
- Tokenizers 0.13.3
|
96abhishekarora/lt-kn-en_familyname-linkage
|
96abhishekarora
| 2023-09-04T20:22:33Z | 5 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"bert",
"linktransformer",
"sentence-similarity",
"tabular-classification",
"kn",
"en",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2023-09-01T02:58:31Z |
---
pipeline_tag: sentence-similarity
language:
- kn
- en
tags:
- linktransformer
- sentence-transformers
- sentence-similarity
- tabular-classification
---
# 96abhishekarora/lt-kn-en_familyname-linkage
This is a [LinkTransformer](https://github.com/dell-research-harvard/linktransformer) model. At its core this model this is a sentence transformer model [sentence-transformers](https://www.SBERT.net) model- it just wraps around the class.
It is designed for quick and easy record linkage (entity-matching) through the LinkTransformer package. The tasks include clustering, deduplication, linking, aggregation and more.
Notwithstanding that, it can be used for any sentence similarity task within the sentence-transformers framework as well.
It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
Take a look at the documentation of [sentence-transformers](https://www.sbert.net/index.html) if you want to use this model for more than what we support in our applications.
This model has been fine-tuned on the model : bert-base-multilingual-cased. It is pretrained for the language : - kn
- en.
This model was trained on a dataset consisting of 12105132 people and their family id. 50% of the names are alo transliterated.
It was trained for 6 epochs using other defaults that can be found in the repo's LinkTransformer config file - LT_training_config.json
## Usage (LinkTransformer)
Using this model becomes easy when you have [LinkTransformer](https://github.com/dell-research-harvard/linktransformer) installed:
```
pip install -U linktransformer
```
Then you can use the model like this:
```python
import linktransformer as lt
import pandas as pd
##Load the two dataframes that you want to link. For example, 2 dataframes with company names that are written differently
df1=pd.read_csv("data/df1.csv") ###This is the left dataframe with key CompanyName for instance
df2=pd.read_csv("data/df2.csv") ###This is the right dataframe with key CompanyName for instance
###Merge the two dataframes on the key column!
df_merged = lt.merge(df1, df2, on="CompanyName", how="inner")
##Done! The merged dataframe has a column called "score" that contains the similarity score between the two company names
```
## Training your own LinkTransformer model
Any Sentence Transformers can be used as a backbone by simply adding a pooling layer. Any other transformer on HuggingFace can also be used by specifying the option add_pooling_layer==True
The model was trained using SupCon loss.
Usage can be found in the package docs.
The training config can be found in the repo with the name LT_training_config.json
To replicate the training, you can download the file and specify the path in the config_path argument of the training function. You can also override the config by specifying the training_args argument.
Here is an example.
```python
##Consider the example in the paper that has a dataset of Mexican products and their tariff codes from 1947 and 1948 and we want train a model to link the two tariff codes.
saved_model_path = train_model(
model_path="hiiamsid/sentence_similarity_spanish_es",
dataset_path=dataset_path,
left_col_names=["description47"],
right_col_names=['description48'],
left_id_name=['tariffcode47'],
right_id_name=['tariffcode48'],
log_wandb=False,
config_path=LINKAGE_CONFIG_PATH,
training_args={"num_epochs": 1}
)
```
You can also use this package for deduplication (clusters a df on the supplied key column). Merging a fine class (like product) to a coarse class (like HS code) is also possible.
Read our paper and the documentation for more!
## Evaluation Results
<!--- Describe how your model was evaluated -->
You can evaluate the model using the [LinkTransformer](https://github.com/dell-research-harvard/linktransformer) package's inference functions.
We have provided a few datasets in the package for you to try out. We plan to host more datasets on Huggingface and our website (Coming soon) that you can take a look at.
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 186000 with parameters:
```
{'batch_size': 64, 'sampler': 'torch.utils.data.dataloader._InfiniteConstantSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`linktransformer.modified_sbert.losses.SupConLoss_wandb`
Parameters of the fit()-Method:
```
{
"epochs": 6,
"evaluation_steps": 18600,
"evaluator": "sentence_transformers.evaluation.SequentialEvaluator.SequentialEvaluator",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-06
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 1116000,
"weight_decay": 0.01
}
```
LinkTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information -->
|
adyprat/Reinforce_cpv1
|
adyprat
| 2023-09-04T20:18:49Z | 0 | 0 | null |
[
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-09-04T20:18:38Z |
---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce_cpv1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 500.00 +/- 0.00
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
maysamalfiza/dummy-model
|
maysamalfiza
| 2023-09-04T20:05:27Z | 106 | 0 |
transformers
|
[
"transformers",
"pytorch",
"camembert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2023-09-04T19:48:38Z |
Model explanation
Welcome to my page!
"camembert-base"
|
IlyaGusev/saiga2_70b_gguf
|
IlyaGusev
| 2023-09-04T19:53:14Z | 97 | 12 | null |
[
"gguf",
"conversational",
"ru",
"dataset:IlyaGusev/ru_turbo_saiga",
"dataset:IlyaGusev/ru_sharegpt_cleaned",
"dataset:IlyaGusev/oasst1_ru_main_branch",
"dataset:IlyaGusev/gpt_roleplay_realm",
"dataset:lksy/ru_instruct_gpt4",
"license:llama2",
"region:us"
] |
text-generation
| 2023-09-04T19:31:40Z |
---
datasets:
- IlyaGusev/ru_turbo_saiga
- IlyaGusev/ru_sharegpt_cleaned
- IlyaGusev/oasst1_ru_main_branch
- IlyaGusev/gpt_roleplay_realm
- lksy/ru_instruct_gpt4
language:
- ru
inference: false
pipeline_tag: conversational
license: llama2
---
Llama.cpp compatible versions of an original [70B model](https://huggingface.co/IlyaGusev/saiga2_70b_lora).
* Download one of the versions, for example `ggml-model-q4_1.gguf`.
* Download [interact_llamacpp.py](https://raw.githubusercontent.com/IlyaGusev/rulm/master/self_instruct/src/interact_llamacpp.py)
How to run:
```
sudo apt-get install git-lfs
pip install llama-cpp-python fire
python3 interact_llamacpp.py ggml-model-q4_1.gguf
```
System requirements:
* 45GB RAM for q4_1
|
venetis/distilbert-base-uncased-finetuned-3d-sentiment
|
venetis
| 2023-09-04T19:52:13Z | 107 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-09-04T16:12:16Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: distilbert-base-uncased-finetuned-3d-sentiment
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-3d-sentiment
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6641
- Accuracy: 0.7366
- Precision: 0.7377
- Recall: 0.7366
- F1: 0.7364
## 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
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 12762
- num_epochs: 7
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.8078 | 1.0 | 3190 | 0.8133 | 0.6628 | 0.6885 | 0.6628 | 0.6607 |
| 0.6227 | 2.0 | 6380 | 0.7637 | 0.6855 | 0.7103 | 0.6855 | 0.6849 |
| 0.5431 | 3.0 | 9570 | 0.6889 | 0.7047 | 0.7201 | 0.7047 | 0.7017 |
| 0.4585 | 4.0 | 12760 | 0.6641 | 0.7366 | 0.7377 | 0.7366 | 0.7364 |
| 0.3455 | 5.0 | 15950 | 0.8322 | 0.7203 | 0.7323 | 0.7203 | 0.7187 |
| 0.223 | 6.0 | 19140 | 0.9541 | 0.7205 | 0.7316 | 0.7205 | 0.7204 |
| 0.145 | 7.0 | 22330 | 1.1726 | 0.7196 | 0.7305 | 0.7196 | 0.7200 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu117
- Datasets 2.10.1
- Tokenizers 0.13.3
|
Jana1994/wav2vec2-large-xls-r-300m-jana-colab
|
Jana1994
| 2023-09-04T19:51:58Z | 7 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"base_model:facebook/wav2vec2-xls-r-300m",
"base_model:finetune:facebook/wav2vec2-xls-r-300m",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-08-31T08:26:49Z |
---
license: apache-2.0
base_model: facebook/wav2vec2-xls-r-300m
tags:
- generated_from_trainer
datasets:
- common_voice
metrics:
- wer
model-index:
- name: wav2vec2-large-xls-r-300m-jana-colab
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: common_voice
type: common_voice
config: cy
split: test
args: cy
metrics:
- name: Wer
type: wer
value: 0.6497412901000345
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-jana-colab
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8913
- Wer: 0.6497
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 300
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 5.6444 | 1.67 | 200 | 2.9379 | 1.0 |
| 2.7964 | 3.33 | 400 | 1.9912 | 0.9927 |
| 1.1945 | 5.0 | 600 | 0.9492 | 0.7889 |
| 0.6065 | 6.67 | 800 | 0.8534 | 0.7137 |
| 0.3859 | 8.33 | 1000 | 0.8933 | 0.6689 |
| 0.2724 | 10.0 | 1200 | 0.8913 | 0.6497 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
dmatekenya/wav2vec2-large-xls-r-300m-chichewa
|
dmatekenya
| 2023-09-04T19:47:30Z | 107 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"base_model:facebook/wav2vec2-xls-r-300m",
"base_model:finetune:facebook/wav2vec2-xls-r-300m",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-09-04T17:49:52Z |
---
license: apache-2.0
base_model: facebook/wav2vec2-xls-r-300m
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: wav2vec2-large-xls-r-300m-chichewa
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-chichewa
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: inf
- Wer: 0.9669
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 15
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 6.2028 | 3.51 | 400 | inf | 0.9999 |
| 2.5353 | 7.02 | 800 | inf | 0.9743 |
| 1.8464 | 10.53 | 1200 | inf | 0.9777 |
| 1.6672 | 14.04 | 1600 | inf | 0.9669 |
### Framework versions
- Transformers 4.33.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
jorgeortizfuentes/chilean-spanish-incivility
|
jorgeortizfuentes
| 2023-09-04T19:42:39Z | 556 | 0 |
transformers
|
[
"transformers",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"es",
"dataset:jorgeortizfuentes/toxicity_spanish_incivility_v3",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-09-04T19:35:55Z |
---
language:
- es
license: cc-by-4.0
tags:
- generated_from_trainer
datasets:
- jorgeortizfuentes/toxicity_spanish_incivility_v3
metrics:
- f1
model-index:
- name: incivility-dv3-patana-chilean-spanish-bert-j63zilm4
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: jorgeortizfuentes/toxicity_spanish_incivility_v3
type: jorgeortizfuentes/toxicity_spanish_incivility_v3
split: validation
metrics:
- name: F1
type: f1
value: 0.9135014363230132
---
<!-- 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. -->
# incivility-dv3-patana-chilean-spanish-bert-j63zilm4
This model is a fine-tuned version of [dccuchile/patana-chilean-spanish-bert](https://huggingface.co/dccuchile/patana-chilean-spanish-bert) on the jorgeortizfuentes/toxicity_spanish_incivility_v3 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5672
- F1: 0.9135
## 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: 128
- eval_batch_size: 128
- seed: 13
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.1351 | 5.0 | 455 | 0.4608 | 0.9119 |
| 0.0114 | 10.0 | 910 | 0.5672 | 0.9135 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu117
- Datasets 2.13.1
- Tokenizers 0.13.3
|
aegon-h/Llama2-22B-Daydreamer-v3-GPT
|
aegon-h
| 2023-09-04T19:41:39Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"license:llama2",
"autotrain_compatible",
"text-generation-inference",
"4-bit",
"gptq",
"region:us"
] |
text-generation
| 2023-09-04T19:31:53Z |
---
inference: false
license: llama2
model_creator: Nick Perez
model_link: https://huggingface.co/nkpz/llama2-22b-daydreamer-v3
model_name: Llama2 22B Daydreamer2 v3
model_type: llama
quantized_by: agonh
---
# Llama2 22B Daydreamer2 v3
- Model creator: [Nick Perez](https://huggingface.co/nkpz)
- Original model: [Llama2 22B Daydreamer2 v3](https://huggingface.co/nkpz/llama2-22b-daydreamer-v3)
## Description
This repo contains GPTQ model files for [Nick Perez's Llama2 22B Daydreamer2 v3](https://huggingface.co/nkpz/llama2-22b-daydreamer-v3).
|
mrm8488/idefics-9b-ft-describe-diffusion-bf16-adapter
|
mrm8488
| 2023-09-04T19:39:01Z | 0 | 1 | null |
[
"generated_from_trainer",
"dataset:diffusiondb",
"base_model:HuggingFaceM4/idefics-9b",
"base_model:finetune:HuggingFaceM4/idefics-9b",
"license:other",
"region:us"
] | null | 2023-08-28T10:09:04Z |
---
license: other
base_model: HuggingFaceM4/idefics-9b
tags:
- generated_from_trainer
datasets:
- diffusiondb
model-index:
- name: idefics-9b-ft-describe-diffusion-bf16
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. -->
# idefics-9b-ft-describe-diffusion-bf16
This model is a fine-tuned version of [HuggingFaceM4/idefics-9b](https://huggingface.co/HuggingFaceM4/idefics-9b) on the diffusiondb dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4081
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 32
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.0874 | 0.07 | 50 | 2.1257 |
| 2.0532 | 0.14 | 100 | 1.9973 |
| 1.9417 | 0.21 | 150 | 1.9246 |
| 1.8358 | 0.28 | 200 | 1.8735 |
| 1.8499 | 0.36 | 250 | 1.8305 |
| 1.7695 | 0.43 | 300 | 1.7770 |
| 1.7505 | 0.5 | 350 | 1.7454 |
| 1.713 | 0.57 | 400 | 1.7115 |
| 1.7352 | 0.64 | 450 | 1.6791 |
| 1.6689 | 0.71 | 500 | 1.6526 |
| 1.6183 | 0.78 | 550 | 1.6257 |
| 1.6118 | 0.85 | 600 | 1.6001 |
| 1.6095 | 0.92 | 650 | 1.5800 |
| 1.5598 | 1.0 | 700 | 1.5598 |
| 1.4785 | 1.07 | 750 | 1.5403 |
| 1.4999 | 1.14 | 800 | 1.5219 |
| 1.4589 | 1.21 | 850 | 1.5063 |
| 1.4559 | 1.28 | 900 | 1.4942 |
| 1.4332 | 1.35 | 950 | 1.4792 |
| 1.4859 | 1.42 | 1000 | 1.4658 |
| 1.3888 | 1.49 | 1050 | 1.4537 |
| 1.4032 | 1.56 | 1100 | 1.4445 |
| 1.3702 | 1.64 | 1150 | 1.4352 |
| 1.3625 | 1.71 | 1200 | 1.4276 |
| 1.4067 | 1.78 | 1250 | 1.4199 |
| 1.3829 | 1.85 | 1300 | 1.4149 |
| 1.4251 | 1.92 | 1350 | 1.4103 |
| 1.3619 | 1.99 | 1400 | 1.4081 |
### Framework versions
- Transformers 4.32.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
aegon-h/Koala-13B-8K-GPT
|
aegon-h
| 2023-09-04T19:21:09Z | 77 | 0 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"custom_code",
"license:other",
"autotrain_compatible",
"text-generation-inference",
"4-bit",
"gptq",
"region:us"
] |
text-generation
| 2023-09-04T19:13:10Z |
---
inference: false
license: other
---
# Koala: A Dialogue Model for Academic Research
This repo contains the weights of the Koala 13B model produced at Berkeley. It is the result of combining the diffs from https://huggingface.co/young-geng/koala with the original Llama 13B model.
## License
The model weights are intended for academic research only, subject to the
[model License of LLaMA](https://github.com/facebookresearch/llama/blob/main/MODEL_CARD.md),
[Terms of Use of the data generated by OpenAI](https://openai.com/policies/terms-of-use),
and [Privacy Practices of ShareGPT](https://chrome.google.com/webstore/detail/sharegpt-share-your-chatg/daiacboceoaocpibfodeljbdfacokfjb).
Any other usage of the model weights, including but not limited to commercial usage, is strictly prohibited.
|
alexsherstinsky/llama-2-7b-hf-based-finetuned-using-ludwig-with-alpaca-for-code
|
alexsherstinsky
| 2023-09-04T19:15:02Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-09-04T16:27:09Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: float16
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.5.0
- PEFT 0.5.0
|
onkarsus13/controlnet_stablediffusion_scenetextEraser
|
onkarsus13
| 2023-09-04T19:01:03Z | 36 | 0 |
diffusers
|
[
"diffusers",
"license:mit",
"diffusers:StableDiffusionControlNetInpaintPipeline",
"region:us"
] |
image-to-image
| 2023-08-17T05:36:34Z |
---
license: mit
---
This is the trained model for the controlnet-stablediffusion for the scene text eraser (Diff_SceneTextEraser)
We have to customize the pipeline for controlnet-stablediffusion-inpaint
Here is the training and inference code for [Diff_SceneTextEraser](https://github.com/Onkarsus13/Diff_SceneTextEraser)
For direct inference
step 1: Clone the GitHub repo to get the customized ControlNet-StableDiffusion-inpaint Pipeline Implementation
```
git clone https://github.com/Onkarsus13/Diff_SceneTextEraser
```
Step2: Go into the repository and install repository, dependency
```
cd Diff_SceneTextEraser
pip install -e ".[torch]"
pip install -e .[all,dev,notebooks]
```
Step3: Run `python test_eraser.py` OR You can run the code given below
```python
from diffusers import (
UniPCMultistepScheduler,
DDIMScheduler,
EulerAncestralDiscreteScheduler,
StableDiffusionControlNetSceneTextErasingPipeline,
)
import torch
import numpy as np
import cv2
from PIL import Image, ImageDraw
import math
import os
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model_path = "onkarsus13/controlnet_stablediffusion_scenetextEraser"
pipe = StableDiffusionControlNetSceneTextErasingPipeline.from_pretrained(model_path)
pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)
pipe.to(device)
# pipe.enable_xformers_memory_efficient_attention()
pipe.enable_model_cpu_offload()
generator = torch.Generator(device).manual_seed(1)
image = Image.open("<path to scene text image>").resize((512, 512))
mask_image = Image.open('<path to the corrospoinding mask image>').resize((512, 512))
image = pipe(
image,
mask_image,
[mask_image],
num_inference_steps=20,
generator=generator,
controlnet_conditioning_scale=1.0,
guidance_scale=1.0
).images[0]
image.save('test1.png')
```
|
nbogdan/flant5-small-2ex-elaboration-1epochs
|
nbogdan
| 2023-09-04T18:53:39Z | 0 | 0 |
adapter-transformers
|
[
"adapter-transformers",
"adapterhub:self-explanations",
"t5",
"dataset:self-explanations",
"region:us"
] | null | 2023-09-04T18:53:31Z |
---
tags:
- adapterhub:self-explanations
- t5
- adapter-transformers
datasets:
- self-explanations
---
# Adapter `nbogdan/flant5-small-2ex-elaboration-1epochs` for google/flan-t5-small
An [adapter](https://adapterhub.ml) for the `google/flan-t5-small` model that was trained on the [self-explanations](https://adapterhub.ml/explore/self-explanations/) dataset.
This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library.
## Usage
First, install `adapter-transformers`:
```
pip install -U adapter-transformers
```
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_
Now, the adapter can be loaded and activated like this:
```python
from transformers import AutoAdapterModel
model = AutoAdapterModel.from_pretrained("google/flan-t5-small")
adapter_name = model.load_adapter("nbogdan/flant5-small-2ex-elaboration-1epochs", source="hf", set_active=True)
```
## Architecture & Training
<!-- Add some description here -->
## Evaluation results
<!-- Add some description here -->
## Citation
<!-- Add some description here -->
|
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