modelId
stringlengths 5
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| author
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| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-08-30 06:27:36
| downloads
int64 0
223M
| likes
int64 0
11.7k
| library_name
stringclasses 527
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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| card
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|---|---|---|---|---|---|---|---|---|---|
llyfacebook/sdxl-pokemon-model
|
llyfacebook
| 2023-08-27T08:23:41Z | 0 | 1 |
diffusers
|
[
"diffusers",
"tensorboard",
"safetensors",
"stable-diffusion-xl",
"stable-diffusion-xl-diffusers",
"text-to-image",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:finetune:stabilityai/stable-diffusion-xl-base-1.0",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionXLPipeline",
"region:us"
] |
text-to-image
| 2023-08-26T23:06:51Z |
---
license: creativeml-openrail-m
base_model: stabilityai/stable-diffusion-xl-base-1.0
dataset: lambdalabs/pokemon-blip-captions
tags:
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
- text-to-image
- diffusers
inference: true
---
# Text-to-image finetuning - llyfacebook/sdxl-pokemon-model
This pipeline was finetuned from **stabilityai/stable-diffusion-xl-base-1.0** on the **lambdalabs/pokemon-blip-captions** dataset. Below are some example images generated with the finetuned pipeline using the following prompt: a cute Sundar Pichai creature:
Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
|
ThuyNT03/xlm-roberta-base-Balance_VietNam-aug_replace_tfidf
|
ThuyNT03
| 2023-08-27T08:22:07Z | 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
| 2023-08-27T07:58:49Z |
---
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: xlm-roberta-base-Balance_VietNam-aug_replace_tfidf
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-Balance_VietNam-aug_replace_tfidf
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: 0.8255
- Accuracy: 0.71
- F1: 0.7123
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 1.0682 | 1.0 | 87 | 0.8850 | 0.63 | 0.5828 |
| 0.8982 | 2.0 | 174 | 0.8205 | 0.68 | 0.6460 |
| 0.7637 | 3.0 | 261 | 0.7253 | 0.7 | 0.7013 |
| 0.6902 | 4.0 | 348 | 0.6887 | 0.71 | 0.7088 |
| 0.5525 | 5.0 | 435 | 0.6648 | 0.75 | 0.7480 |
| 0.4981 | 6.0 | 522 | 0.7215 | 0.75 | 0.7504 |
| 0.403 | 7.0 | 609 | 0.8010 | 0.72 | 0.7251 |
| 0.3255 | 8.0 | 696 | 0.8255 | 0.71 | 0.7123 |
### Framework versions
- Transformers 4.32.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
EricPeter/distilbert-base-cased
|
EricPeter
| 2023-08-27T08:17:36Z | 3 | 0 |
transformers
|
[
"transformers",
"tf",
"distilbert",
"question-answering",
"generated_from_keras_callback",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2023-08-27T07:27:10Z |
---
tags:
- generated_from_keras_callback
model-index:
- name: EricPeter/distilbert-base-cased
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. -->
# EricPeter/distilbert-base-cased
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.2356
- 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: {'inner_optimizer': {'class_name': 'AdamWeightDecay', 'config': {'name': 'AdamWeightDecay', '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': 2846, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '__passive_serialization__': True}, 'warmup_steps': 4, 'power': 1.0, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.06}}, 'dynamic': True, 'initial_scale': 32768.0, 'dynamic_growth_steps': 2000}
- training_precision: mixed_float16
### Training results
| Train Loss | Epoch |
|:----------:|:-----:|
| 0.3158 | 0 |
| 0.3561 | 1 |
| 0.2694 | 2 |
| 0.2718 | 3 |
| 0.2687 | 4 |
| 0.2844 | 5 |
| 0.2824 | 6 |
| 0.2698 | 7 |
| 0.2882 | 8 |
| 0.2808 | 9 |
| 0.2710 | 10 |
| 0.2663 | 11 |
| 0.2574 | 12 |
| 0.2417 | 13 |
| 0.2581 | 14 |
| 0.2581 | 15 |
| 0.2625 | 16 |
| 0.2443 | 17 |
| 0.2360 | 18 |
| 0.2478 | 19 |
| 0.2431 | 20 |
| 0.2454 | 21 |
| 0.2409 | 22 |
| 0.2359 | 23 |
| 0.2428 | 24 |
| 0.2374 | 25 |
| 0.2419 | 26 |
| 0.2371 | 27 |
| 0.2392 | 28 |
| 0.2393 | 29 |
| 0.2378 | 30 |
| 0.2399 | 31 |
| 0.2381 | 32 |
| 0.2347 | 33 |
| 0.2414 | 34 |
| 0.2352 | 35 |
| 0.2361 | 36 |
| 0.2407 | 37 |
| 0.2397 | 38 |
| 0.2314 | 39 |
| 0.2370 | 40 |
| 0.2338 | 41 |
| 0.2360 | 42 |
| 0.2356 | 43 |
| 0.2375 | 44 |
| 0.2343 | 45 |
| 0.2366 | 46 |
| 0.2377 | 47 |
| 0.2369 | 48 |
| 0.2356 | 49 |
### Framework versions
- Transformers 4.32.0
- TensorFlow 2.12.0
- Datasets 2.14.4
- Tokenizers 0.13.3
|
DrishtiSharma/DialoGPT-large-faqs-block-size-128-bs-16-lr-7e-6
|
DrishtiSharma
| 2023-08-27T08:12:25Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"generated_from_trainer",
"base_model:microsoft/DialoGPT-large",
"base_model:finetune:microsoft/DialoGPT-large",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-08-27T07:58:02Z |
---
license: mit
base_model: microsoft/DialoGPT-large
tags:
- generated_from_trainer
model-index:
- name: DialoGPT-large-faqs-block-size-128-bs-16-lr-7e-6
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. -->
# DialoGPT-large-faqs-block-size-128-bs-16-lr-7e-6
This model is a fine-tuned version of [microsoft/DialoGPT-large](https://huggingface.co/microsoft/DialoGPT-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4362
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 7e-06
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 40 | 4.4791 |
| No log | 2.0 | 80 | 3.7462 |
| No log | 3.0 | 120 | 3.2760 |
| No log | 4.0 | 160 | 3.0066 |
| No log | 5.0 | 200 | 2.8421 |
| No log | 6.0 | 240 | 2.7291 |
| No log | 7.0 | 280 | 2.6535 |
| No log | 8.0 | 320 | 2.5975 |
| No log | 9.0 | 360 | 2.5532 |
| No log | 10.0 | 400 | 2.5265 |
| No log | 11.0 | 440 | 2.4987 |
| No log | 12.0 | 480 | 2.4778 |
| 2.9559 | 13.0 | 520 | 2.4655 |
| 2.9559 | 14.0 | 560 | 2.4553 |
| 2.9559 | 15.0 | 600 | 2.4449 |
| 2.9559 | 16.0 | 640 | 2.4456 |
| 2.9559 | 17.0 | 680 | 2.4389 |
| 2.9559 | 18.0 | 720 | 2.4384 |
| 2.9559 | 19.0 | 760 | 2.4372 |
| 2.9559 | 20.0 | 800 | 2.4362 |
### Framework versions
- Transformers 4.33.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4.dev0
- Tokenizers 0.13.3
|
ThuyNT03/xlm-roberta-base-Balance_Mixed-aug_replace
|
ThuyNT03
| 2023-08-27T08:07:17Z | 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
| 2023-08-27T08:00:01Z |
---
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: xlm-roberta-base-Balance_Mixed-aug_replace
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-Balance_Mixed-aug_replace
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: 1.1700
- Accuracy: 0.75
- F1: 0.7476
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 1.045 | 1.0 | 81 | 0.9285 | 0.59 | 0.4986 |
| 0.8637 | 2.0 | 162 | 0.7319 | 0.72 | 0.7025 |
| 0.6437 | 3.0 | 243 | 0.7081 | 0.74 | 0.7327 |
| 0.4385 | 4.0 | 324 | 0.6997 | 0.72 | 0.7237 |
| 0.331 | 5.0 | 405 | 0.9485 | 0.71 | 0.7129 |
| 0.2433 | 6.0 | 486 | 0.9924 | 0.73 | 0.7328 |
| 0.1657 | 7.0 | 567 | 1.1475 | 0.75 | 0.7473 |
| 0.1374 | 8.0 | 648 | 1.1700 | 0.75 | 0.7476 |
### Framework versions
- Transformers 4.32.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
fridriik/mental-health-arg-post-quarantine-covid19-model
|
fridriik
| 2023-08-27T08:05:48Z | 0 | 0 |
sklearn
|
[
"sklearn",
"medical",
"es",
"en",
"dataset:fridriik/mental-health-arg-post-quarantine-covid19-dataset",
"arxiv:1910.09700",
"license:cc-by-nc-4.0",
"region:us"
] | null | 2023-08-27T05:59:50Z |
---
license: cc-by-nc-4.0
datasets:
- fridriik/mental-health-arg-post-quarantine-covid19-dataset
language:
- es
- en
library_name: sklearn
tags:
- medical
metrics:
- perplexity
---
# Mental health of people in Argentina post quarantine COVID-19 Model
## Model Details
### Model Description
This model aims to cluster cases and identify which province or region of Argentina presents higher values of
suicide risk based on the analyzed variables, in order to subsequently assist the community in creating support programs.
- **Developed by:** Farias, Federico; Arroyo, Guadalupe; Avalos, Manuel
- **Model type:** Clustering
- **License:** Creative Commons Attribution Non Commercial 4.0
## Uses
Research and education.
### Out-of-Scope Use
Government and private entities in the fields of research, medicine, psychology, and education.
## Bias, Risks, and Limitations
This model is intended for research purposes, and it analyzes serious topics related to individuals' mental health.
It should not be taken as practical advice for real-life situations, except for the possibility that in the future,
the dataset used for its training could be improved and discussions with its authors could facilitate extended usage.
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model.
## 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. -->
https://huggingface.co/datasets/fridriik/mental-health-arg-post-quarantine-covid19-dataset
### 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]
|
wesley7137/BCI-Platy-Orca-13B-V2-adapter
|
wesley7137
| 2023-08-27T07:59:52Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-27T07:58:26Z |
---
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
### Framework versions
- PEFT 0.4.0
|
ThuyNT03/xlm-roberta-base-Balance_Mixed-aug_insert
|
ThuyNT03
| 2023-08-27T07:53:30Z | 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
| 2023-08-27T07:45:49Z |
---
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: xlm-roberta-base-Balance_Mixed-aug_insert
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-Balance_Mixed-aug_insert
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: 1.8765
- Accuracy: 0.68
- F1: 0.6779
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.9796 | 1.0 | 87 | 0.7820 | 0.65 | 0.6411 |
| 0.6824 | 2.0 | 174 | 0.5968 | 0.67 | 0.6578 |
| 0.5057 | 3.0 | 261 | 0.8463 | 0.69 | 0.6620 |
| 0.3193 | 4.0 | 348 | 0.9758 | 0.71 | 0.6991 |
| 0.1899 | 5.0 | 435 | 1.4013 | 0.67 | 0.6711 |
| 0.116 | 6.0 | 522 | 1.5033 | 0.71 | 0.7069 |
| 0.0823 | 7.0 | 609 | 1.7558 | 0.69 | 0.6864 |
| 0.0627 | 8.0 | 696 | 1.8765 | 0.68 | 0.6779 |
### Framework versions
- Transformers 4.32.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
ThuyNT03/xlm-roberta-base-Balance_VietNam-aug_insert_w2v
|
ThuyNT03
| 2023-08-27T07:53:17Z | 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
| 2023-08-27T07:33:45Z |
---
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: xlm-roberta-base-Balance_VietNam-aug_insert_w2v
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-Balance_VietNam-aug_insert_w2v
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: 0.8223
- Accuracy: 0.73
- F1: 0.7349
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 1.0709 | 1.0 | 87 | 0.9441 | 0.56 | 0.5255 |
| 0.9032 | 2.0 | 174 | 0.7216 | 0.65 | 0.5885 |
| 0.7586 | 3.0 | 261 | 0.6999 | 0.74 | 0.7382 |
| 0.6773 | 4.0 | 348 | 0.7020 | 0.7 | 0.7013 |
| 0.5627 | 5.0 | 435 | 0.7242 | 0.72 | 0.7128 |
| 0.4603 | 6.0 | 522 | 0.7668 | 0.7 | 0.7052 |
| 0.3853 | 7.0 | 609 | 0.8019 | 0.73 | 0.7365 |
| 0.3405 | 8.0 | 696 | 0.8223 | 0.73 | 0.7349 |
### Framework versions
- Transformers 4.32.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
WanJEJEkun/SovitsModel
|
WanJEJEkun
| 2023-08-27T07:49:57Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-08-27T07:46:33Z |
---
license: other
datasets:
- fka/awesome-chatgpt-prompts
language:
- aa
metrics:
- accuracy
library_name: adapter-transformers
pipeline_tag: audio-to-audio
---https://gradio.s3-us-west-2.amazonaws.com/3.18.0/gradio.js
|
ThuyNT03/xlm-roberta-base-Balance_Mixed-aug_delete
|
ThuyNT03
| 2023-08-27T07:43:40Z | 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
| 2023-08-27T07:36:16Z |
---
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: xlm-roberta-base-Balance_Mixed-aug_delete
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-Balance_Mixed-aug_delete
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: 0.9162
- Accuracy: 0.68
- F1: 0.6762
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 1.0634 | 1.0 | 87 | 0.9436 | 0.6 | 0.5016 |
| 0.8668 | 2.0 | 174 | 0.6742 | 0.71 | 0.6650 |
| 0.7168 | 3.0 | 261 | 0.6614 | 0.72 | 0.7103 |
| 0.5632 | 4.0 | 348 | 0.6308 | 0.68 | 0.6686 |
| 0.452 | 5.0 | 435 | 0.7301 | 0.64 | 0.6429 |
| 0.3594 | 6.0 | 522 | 0.8300 | 0.72 | 0.7168 |
| 0.2844 | 7.0 | 609 | 0.8999 | 0.71 | 0.7046 |
| 0.2399 | 8.0 | 696 | 0.9162 | 0.68 | 0.6762 |
### Framework versions
- Transformers 4.32.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
Cyber-Machine/distilhubert-finetuned-gtzan
|
Cyber-Machine
| 2023-08-27T07:29:33Z | 175 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"hubert",
"audio-classification",
"generated_from_trainer",
"dataset:marsyas/gtzan",
"base_model:ntu-spml/distilhubert",
"base_model:finetune:ntu-spml/distilhubert",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
audio-classification
| 2023-08-26T18:01:03Z |
---
license: apache-2.0
base_model: ntu-spml/distilhubert
tags:
- generated_from_trainer
datasets:
- marsyas/gtzan
metrics:
- accuracy
model-index:
- name: distilhubert-finetuned-gtzan
results:
- task:
name: Audio Classification
type: audio-classification
dataset:
name: GTZAN
type: marsyas/gtzan
config: all
split: train
args: all
metrics:
- name: Accuracy
type: accuracy
value: 0.82
---
<!-- 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. -->
# distilhubert-finetuned-gtzan
This model is a fine-tuned version of [ntu-spml/distilhubert](https://huggingface.co/ntu-spml/distilhubert) on the GTZAN dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0171
- Accuracy: 0.82
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 15
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.1164 | 1.0 | 113 | 2.0148 | 0.45 |
| 1.3653 | 2.0 | 226 | 1.3290 | 0.64 |
| 1.1139 | 3.0 | 339 | 1.0579 | 0.71 |
| 1.0451 | 4.0 | 452 | 1.0425 | 0.72 |
| 0.5678 | 5.0 | 565 | 0.8254 | 0.76 |
| 0.3324 | 6.0 | 678 | 0.7542 | 0.81 |
| 0.4072 | 7.0 | 791 | 0.6650 | 0.81 |
| 0.0858 | 8.0 | 904 | 0.8092 | 0.79 |
| 0.2328 | 9.0 | 1017 | 0.8203 | 0.8 |
| 0.0331 | 10.0 | 1130 | 0.9223 | 0.83 |
| 0.0129 | 11.0 | 1243 | 0.9507 | 0.84 |
| 0.1248 | 12.0 | 1356 | 0.9733 | 0.83 |
| 0.0087 | 13.0 | 1469 | 1.0091 | 0.82 |
| 0.0677 | 14.0 | 1582 | 1.0063 | 0.82 |
| 0.008 | 15.0 | 1695 | 1.0171 | 0.82 |
### Framework versions
- Transformers 4.32.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
aman-agarwal/t5-base-lora
|
aman-agarwal
| 2023-08-27T07:27:30Z | 3 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-27T07:27:29Z |
---
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 0.5.0
|
wyuancs/Fine_Tuned_T5_small_for_DailyDialog
|
wyuancs
| 2023-08-27T07:22:46Z | 102 | 0 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:google-t5/t5-small",
"base_model:finetune:google-t5/t5-small",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-08-27T07:22:36Z |
---
license: apache-2.0
base_model: t5-small
tags:
- generated_from_trainer
metrics:
- rouge
- bleu
model-index:
- name: Fine_Tuned_T5_small_for_DailyDialog
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. -->
# Fine_Tuned_T5_small_for_DailyDialog
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5891
- Rouge1: 11.0459
- Rouge2: 2.2404
- Rougel: 10.5072
- Rougelsum: 10.7781
- Bleu: 0.8903
- Gen Len: 7.111
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 10
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Bleu | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:|:------:|:-------:|
| 2.0809 | 1.0 | 313 | 1.7698 | 9.3634 | 1.6744 | 8.9437 | 9.0705 | 0.6728 | 8.217 |
| 1.4771 | 2.0 | 626 | 1.3016 | 10.1104 | 1.7728 | 9.6869 | 9.8809 | 0.0 | 6.527 |
| 1.2084 | 3.0 | 939 | 1.0781 | 10.3142 | 2.0722 | 9.8421 | 10.0426 | 0.7095 | 6.272 |
| 1.0171 | 4.0 | 1252 | 0.9219 | 10.299 | 2.107 | 9.8825 | 10.1102 | 0.7598 | 6.246 |
| 0.9029 | 5.0 | 1565 | 0.7993 | 10.5767 | 2.0701 | 10.0645 | 10.3152 | 0.88 | 6.94 |
| 0.7979 | 6.0 | 1878 | 0.7169 | 10.618 | 2.0406 | 10.0889 | 10.3652 | 0.9014 | 7.047 |
| 0.7266 | 7.0 | 2191 | 0.6627 | 10.8584 | 2.1613 | 10.292 | 10.575 | 0.8766 | 6.769 |
| 0.692 | 8.0 | 2504 | 0.6231 | 11.2891 | 2.2669 | 10.7278 | 11.0423 | 0.9933 | 7.273 |
| 0.6724 | 9.0 | 2817 | 0.5956 | 11.2029 | 2.2399 | 10.6659 | 10.9419 | 0.9988 | 7.512 |
| 0.65 | 10.0 | 3130 | 0.5891 | 11.0459 | 2.2404 | 10.5072 | 10.7781 | 0.8903 | 7.111 |
### Framework versions
- Transformers 4.32.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
eachadea/ggml-hws-13b
|
eachadea
| 2023-08-27T07:14:41Z | 0 | 1 | null |
[
"text-generation",
"region:us"
] |
text-generation
| 2023-07-18T19:50:10Z |
---
pipeline_tag: text-generation
---
|
DrishtiSharma/DialoGPT-large-faqs-block-size-128-bs-16-lr-0.5e-5
|
DrishtiSharma
| 2023-08-27T07:10:54Z | 137 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"generated_from_trainer",
"base_model:microsoft/DialoGPT-large",
"base_model:finetune:microsoft/DialoGPT-large",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-08-27T06:56:41Z |
---
license: mit
base_model: microsoft/DialoGPT-large
tags:
- generated_from_trainer
model-index:
- name: DialoGPT-large-faqs-block-size-128-bs-16-lr-0.5e-5
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. -->
# DialoGPT-large-faqs-block-size-128-bs-16-lr-0.5e-5
This model is a fine-tuned version of [microsoft/DialoGPT-large](https://huggingface.co/microsoft/DialoGPT-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.5447
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-06
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 40 | 4.7556 |
| No log | 2.0 | 80 | 4.0984 |
| No log | 3.0 | 120 | 3.6525 |
| No log | 4.0 | 160 | 3.3247 |
| No log | 5.0 | 200 | 3.1137 |
| No log | 6.0 | 240 | 2.9706 |
| No log | 7.0 | 280 | 2.8696 |
| No log | 8.0 | 320 | 2.7942 |
| No log | 9.0 | 360 | 2.7382 |
| No log | 10.0 | 400 | 2.6928 |
| No log | 11.0 | 440 | 2.6547 |
| No log | 12.0 | 480 | 2.6237 |
| 3.3313 | 13.0 | 520 | 2.6033 |
| 3.3313 | 14.0 | 560 | 2.5852 |
| 3.3313 | 15.0 | 600 | 2.5690 |
| 3.3313 | 16.0 | 640 | 2.5614 |
| 3.3313 | 17.0 | 680 | 2.5532 |
| 3.3313 | 18.0 | 720 | 2.5485 |
| 3.3313 | 19.0 | 760 | 2.5458 |
| 3.3313 | 20.0 | 800 | 2.5447 |
### Framework versions
- Transformers 4.33.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4.dev0
- Tokenizers 0.13.3
|
spear1/Reinforce-CartPole-v1
|
spear1
| 2023-08-27T07:06:31Z | 0 | 0 | null |
[
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-08-27T07:06:18Z |
---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-CartPole-v1
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
|
nisten/bigdoc-c34b-python-v1
|
nisten
| 2023-08-27T06:52:24Z | 0 | 0 |
peft
|
[
"peft",
"license:mit",
"region:us"
] | null | 2023-08-27T05:03:31Z |
---
library_name: peft
license: mit
---
## training only 2000/5000 complete
The following `bitsandbytes` quantization config was used during training:
- quant_method: QuantizationMethod.BITS_AND_BYTES
- 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 0.6.0.dev0
- To load start a jupyter notebook, here it is all in 2 parts
```
!pip install -q -U bitsandbytes
!pip install -q -U git+https://github.com/huggingface/transformers.git
!pip install -q -U git+https://github.com/huggingface/peft.git
!pip install -q -U git+https://github.com/huggingface/accelerate.git
!pip install -q -U gradio
!pip install -q -U sentencepiece
import torch
from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer, LlamaTokenizer, StoppingCriteria, StoppingCriteriaList, TextIteratorStreamer
model_name = "TheBloke/CodeLlama-34B-Instruct-fp16"
adapters_name = 'nisten/bigdoc-c34b-python-v1'
print(f"Starting to load the model {model_name} into memory")
m = AutoModelForCausalLM.from_pretrained(
model_name,
#load_in_4bit=True, #19GB in 4bit, 38GB with load_in_8bit, 67GB in full f16 if you just delete this line
torch_dtype=torch.bfloat16,
device_map={"": 0}
)
m = PeftModel.from_pretrained(m, adapters_name)
m = m.merge_and_unload()
tok = AutoTokenizer.from_pretrained(model_name)
eos_token_id = tok.convert_tokens_to_ids('/s')
tok.eos_token = '/s'
tok.pad_token = tok.eos_token
tok.padding_side = 'right'
tok.eos_token_id = eos_token_id
stop_token_ids = eos_token_id
print(f"Successfully loaded the model {model_name} into memory")
```
### Gradio the UI
```
#should all work in one click
import datetime
import os
from threading import Event, Thread
from uuid import uuid4
from transformers import AutoModelForCausalLM, AutoTokenizer, LlamaTokenizer, StoppingCriteria, StoppingCriteriaList, TextIteratorStreamer
import gradio as gr
import requests
max_new_tokens = 2369
start_message = """A chat between a chill human asking ( Question: ) and an AI doctor ( Answer: ). The doctor answers in helpful, detailed, and exhaustively nerdy extensive answers to the user's every medical Question:"""
class StopOnTokens(StoppingCriteria):
def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool:
if isinstance(stop_token_ids, (list, torch.Tensor)):
for stop_id in stop_token_ids:
if stop_id in input_ids[0]:
return True
else: # Assumes scalar
if input_ids[0][-1] == stop_token_ids:
return True
return False
def convert_history_to_text(history):
text = start_message + "".join(
[
"".join(
[
f" Question: {item[0]}\n",
f"\n\n Answer: {item[1]}\n",
]
)
for item in history[:-1]
]
)
text += "".join(
[
"".join(
[
f" Question: {history[-1][0]}\n",
f"\n\n Answer: {history[-1][1]}\n",
]
)
]
)
return text
def log_conversation(conversation_id, history, messages, generate_kwargs):
logging_url = os.getenv("LOGGING_URL", None)
if logging_url is None:
return
timestamp = datetime.datetime.now().strftime("%Y-%m-%dT%H:%M:%S")
data = {
"conversation_id": conversation_id,
"timestamp": timestamp,
"history": history,
"messages": messages,
"generate_kwargs": generate_kwargs,
}
try:
requests.post(logging_url, json=data)
except requests.exceptions.RequestException as e:
print(f"Error logging conversation: {e}")
def user(message, history):
# Append the user's message to the conversation history
return "", history + [[message, ""]]
def bot(history, temperature, top_p, top_k, repetition_penalty, conversation_id):
print(f"history: {history}")
# Initialize a StopOnTokens object
stop = StopOnTokens()
# Construct the input message string for the model by concatenating the current system message and conversation history
messages = convert_history_to_text(history)
# Tokenize the messages string
input_ids = tok(messages, return_tensors="pt").input_ids
input_ids = input_ids.to(m.device)
streamer = TextIteratorStreamer(tok, timeout=10.0, skip_prompt=True, skip_special_tokens=True)
generate_kwargs = dict(
input_ids=input_ids,
max_new_tokens=max_new_tokens,
temperature=temperature,
do_sample=temperature > 0.0,
top_p=top_p,
top_k=top_k,
repetition_penalty=repetition_penalty,
streamer=streamer,
stopping_criteria=StoppingCriteriaList([stop]),
)
stream_complete = Event()
def generate_and_signal_complete():
m.generate(**generate_kwargs)
stream_complete.set()
def log_after_stream_complete():
stream_complete.wait()
log_conversation(
conversation_id,
history,
messages,
{
"top_k": top_k,
"top_p": top_p,
"temperature": temperature,
"repetition_penalty": repetition_penalty,
},
)
t1 = Thread(target=generate_and_signal_complete)
t1.start()
t2 = Thread(target=log_after_stream_complete)
t2.start()
# Initialize an empty string to store the generated text
partial_text = ""
for new_text in streamer:
partial_text += new_text
history[-1][1] = partial_text
yield history
def get_uuid():
return str(uuid4())
with gr.Blocks(
theme=gr.themes.Soft(),
css=".disclaimer {font-variant-caps: all-small-caps;}",
) as demo:
conversation_id = gr.State(get_uuid)
gr.Markdown(
"""<h1><center>Nisten's 34b Doctor v1</center></h1>
"""
)
chatbot = gr.Chatbot().style(height=969)
with gr.Row():
with gr.Column():
msg = gr.Textbox(
label="Chat Message Box",
placeholder="Chat Message Box",
show_label=False,
).style(container=False)
with gr.Column():
with gr.Row():
submit = gr.Button("Submit")
stop = gr.Button("Stop")
clear = gr.Button("Clear")
with gr.Row():
with gr.Accordion("Advanced Options:", open=False):
with gr.Row():
with gr.Column():
with gr.Row():
temperature = gr.Slider(
label="Temperature",
value=0.7,
minimum=0.0,
maximum=1.0,
step=0.1,
interactive=True,
info="Higher values produce more diverse outputs",
)
with gr.Column():
with gr.Row():
top_p = gr.Slider(
label="Top-p (nucleus sampling)",
value=0.9,
minimum=0.0,
maximum=1,
step=0.01,
interactive=True,
info=(
"Sample from the smallest possible set of tokens whose cumulative probability "
"exceeds top_p. Set to 1 to disable and sample from all tokens."
),
)
with gr.Column():
with gr.Row():
top_k = gr.Slider(
label="Top-k",
value=0,
minimum=0.0,
maximum=200,
step=1,
interactive=True,
info="Sample from a shortlist of top-k tokens — 0 to disable and sample from all tokens.",
)
with gr.Column():
with gr.Row():
repetition_penalty = gr.Slider(
label="Repetition Penalty",
value=1.1,
minimum=1.0,
maximum=2.0,
step=0.1,
interactive=True,
info="Penalize repetition — 1.0 to disable.",
)
with gr.Row():
gr.Markdown(
"Disclaimer: The model can produce factually incorrect output, and should not be relied on to produce "
"factually accurate information. The model was trained on various public datasets; while great efforts "
"have been taken to clean the pretraining data, it is possible that this model could generate lewd, "
"biased, or otherwise offensive outputs.",
elem_classes=["disclaimer"],
)
with gr.Row():
gr.Markdown(
"[Privacy policy](https://gist.github.com/samhavens/c29c68cdcd420a9aa0202d0839876dac)",
elem_classes=["disclaimer"],
)
submit_event = msg.submit(
fn=user,
inputs=[msg, chatbot],
outputs=[msg, chatbot],
queue=False,
).then(
fn=bot,
inputs=[
chatbot,
temperature,
top_p,
top_k,
repetition_penalty,
conversation_id,
],
outputs=chatbot,
queue=True,
)
submit_click_event = submit.click(
fn=user,
inputs=[msg, chatbot],
outputs=[msg, chatbot],
queue=False,
).then(
fn=bot,
inputs=[
chatbot,
temperature,
top_p,
top_k,
repetition_penalty,
conversation_id,
],
outputs=chatbot,
queue=True,
)
stop.click(
fn=None,
inputs=None,
outputs=None,
cancels=[submit_event, submit_click_event],
queue=False,
)
clear.click(lambda: None, None, chatbot, queue=False)
demo.queue(max_size=128, concurrency_count=2)
demo.launch( share = True ) #delete share = True () to make it private
```
|
Ancient237/gpt2_lora
|
Ancient237
| 2023-08-27T06:39:16Z | 2 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-27T06:34:51Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.6.0.dev0
|
Tao2AIScienceHPC/ppo-Huggy
|
Tao2AIScienceHPC
| 2023-08-27T06:38:57Z | 6 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"Huggy",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
] |
reinforcement-learning
| 2023-08-27T06:38:53Z |
---
library_name: ml-agents
tags:
- Huggy
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
---
# **ppo** Agent playing **Huggy**
This is a trained model of a **ppo** agent playing **Huggy**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: Tao2AIScienceHPC/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
dt-and-vanilla-ardt/dt-arrl_train_halfcheetah_high-2708_0519-66
|
dt-and-vanilla-ardt
| 2023-08-27T06:31:30Z | 31 | 0 |
transformers
|
[
"transformers",
"pytorch",
"decision_transformer",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | null | 2023-08-27T04:21:28Z |
---
tags:
- generated_from_trainer
model-index:
- name: dt-arrl_train_halfcheetah_high-2708_0519-66
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. -->
# dt-arrl_train_halfcheetah_high-2708_0519-66
This model is a fine-tuned version of [](https://huggingface.co/) 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: 0.0001
- train_batch_size: 64
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- training_steps: 10000
### Training results
### Framework versions
- Transformers 4.29.2
- Pytorch 2.1.0.dev20230727+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
mangostin2010/KangLuda-1000step
|
mangostin2010
| 2023-08-27T06:26:29Z | 1 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-27T06:26:27Z |
---
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 0.6.0.dev0
|
Foret2006/Gen
|
Foret2006
| 2023-08-27T06:25:32Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-08-27T06:25:32Z |
---
license: creativeml-openrail-m
---
|
lucky1357/test-gmo-150
|
lucky1357
| 2023-08-27T06:24:22Z | 1 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-27T05:48:29Z |
---
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: False
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.6.0.dev0
|
ThuyNT03/xlm-roberta-base-Balance_VietNam-aug_delete
|
ThuyNT03
| 2023-08-27T06:24:20Z | 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
| 2023-08-27T06:16:20Z |
---
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: xlm-roberta-base-Balance_VietNam-aug_delete
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-Balance_VietNam-aug_delete
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: 0.9985
- Accuracy: 0.72
- F1: 0.7249
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 1.0573 | 1.0 | 87 | 0.8704 | 0.64 | 0.5908 |
| 0.8411 | 2.0 | 174 | 0.7557 | 0.67 | 0.6079 |
| 0.6874 | 3.0 | 261 | 0.6533 | 0.72 | 0.7112 |
| 0.5116 | 4.0 | 348 | 0.6770 | 0.73 | 0.7338 |
| 0.4243 | 5.0 | 435 | 0.7311 | 0.74 | 0.7466 |
| 0.3378 | 6.0 | 522 | 0.8615 | 0.72 | 0.7180 |
| 0.2496 | 7.0 | 609 | 0.9281 | 0.73 | 0.7332 |
| 0.2269 | 8.0 | 696 | 0.9985 | 0.72 | 0.7249 |
### Framework versions
- Transformers 4.32.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
ESGenie/userid-majid_model-qwen7b-api-train-test-2023-08-27
|
ESGenie
| 2023-08-27T06:09:34Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-27T06:09:09Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.5.0
|
Reajin/Senyamiku
|
Reajin
| 2023-08-27T06:07:01Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-06-10T04:45:26Z |
---
license: creativeml-openrail-m
---
|
ThuyNT03/xlm-roberta-base-Balance_VietNam-aug_swap
|
ThuyNT03
| 2023-08-27T06:05:33Z | 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
| 2023-08-27T05:38:57Z |
---
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: xlm-roberta-base-Balance_VietNam-aug_swap
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-Balance_VietNam-aug_swap
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: 1.2667
- 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: 42
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.9608 | 1.0 | 86 | 0.7778 | 0.63 | 0.5729 |
| 0.6733 | 2.0 | 172 | 0.7087 | 0.68 | 0.6835 |
| 0.4889 | 3.0 | 258 | 0.7707 | 0.7 | 0.7028 |
| 0.3672 | 4.0 | 344 | 0.7906 | 0.69 | 0.7021 |
| 0.2388 | 5.0 | 430 | 1.0683 | 0.7 | 0.6979 |
| 0.1691 | 6.0 | 516 | 1.1391 | 0.69 | 0.7010 |
| 0.1323 | 7.0 | 602 | 1.2486 | 0.71 | 0.7176 |
| 0.1033 | 8.0 | 688 | 1.2667 | 0.69 | 0.6959 |
### Framework versions
- Transformers 4.32.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
dkimds/rl_course_vizdoom_health_gathering_supreme
|
dkimds
| 2023-08-27T05:58:04Z | 0 | 0 |
sample-factory
|
[
"sample-factory",
"tensorboard",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-08-27T05:57:57Z |
---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: doom_health_gathering_supreme
type: doom_health_gathering_supreme
metrics:
- type: mean_reward
value: 11.65 +/- 6.08
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **doom_health_gathering_supreme** environment.
This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory.
Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/
## Downloading the model
After installing Sample-Factory, download the model with:
```
python -m sample_factory.huggingface.load_from_hub -r dkimds/rl_course_vizdoom_health_gathering_supreme
```
## Using the model
To run the model after download, use the `enjoy` script corresponding to this environment:
```
python -m .usr.local.lib.python3.10.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme
```
You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag.
See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details
## Training with this model
To continue training with this model, use the `train` script corresponding to this environment:
```
python -m .usr.local.lib.python3.10.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme --restart_behavior=resume --train_for_env_steps=10000000000
```
Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
|
Vedikal/foodvar
|
Vedikal
| 2023-08-27T05:54:15Z | 0 | 0 | null |
[
"safetensors",
"NxtWave-GenAI-Webinar",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"region:us"
] |
text-to-image
| 2023-08-27T05:48:30Z |
---
license: creativeml-openrail-m
tags:
- NxtWave-GenAI-Webinar
- text-to-image
- stable-diffusion
---
### FoodVAR Dreambooth model trained by Vedikal following the "Build your own Gen AI model" session by NxtWave.
Project Submission Code: MGMCE-359
Sample pictures of this concept:
.jpg)
.jpg)
.jpg)
.jpg)
.jpg)
.jpg)
.jpg)
|
Mtc2/a2c-PandaReachDense-v3
|
Mtc2
| 2023-08-27T05:38:10Z | 3 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"PandaReachDense-v3",
"deep-reinforcement-learning",
"reinforcement-learning",
"arxiv:2106.13687",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-08-12T19:20:48Z |
---
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: -0.13 +/- 0.07
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
...
```
Panda Gym environments: [arxiv.org/abs/2106.13687](https://arxiv.org/abs/2106.13687)
|
ThuyNT03/xlm-roberta-base-Balance_VietNam-train
|
ThuyNT03
| 2023-08-27T05:22:45Z | 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
| 2023-08-27T05:04:54Z |
---
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: xlm-roberta-base-Balance_VietNam-train
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-Balance_VietNam-train
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: 0.7864
- Accuracy: 0.69
- F1: 0.6994
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 1.0988 | 1.0 | 44 | 1.0966 | 0.36 | 0.1906 |
| 1.0983 | 2.0 | 88 | 1.0983 | 0.23 | 0.1774 |
| 1.0813 | 3.0 | 132 | 0.9806 | 0.57 | 0.4769 |
| 0.8793 | 4.0 | 176 | 0.8133 | 0.67 | 0.6202 |
| 0.7643 | 5.0 | 220 | 0.8456 | 0.65 | 0.6154 |
| 0.6868 | 6.0 | 264 | 0.7879 | 0.71 | 0.7193 |
| 0.5844 | 7.0 | 308 | 0.7552 | 0.69 | 0.7002 |
| 0.4721 | 8.0 | 352 | 0.7864 | 0.69 | 0.6994 |
### Framework versions
- Transformers 4.32.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
lucky1357/test-model
|
lucky1357
| 2023-08-27T05:19:15Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-27T05:19:06Z |
---
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: False
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.6.0.dev0
|
mayankchhabra/Nous-Hermes-Llama2-13B-GGUF
|
mayankchhabra
| 2023-08-27T04:36:52Z | 0 | 1 | null |
[
"region:us"
] | null | 2023-08-27T04:35:26Z |
This repo contains GGUF format Q4_K_M model file for Nous Hermes Llama 2 13B.
---
license: llama2
---
|
cloudqi/cqi_speech_recognize_pt_v0
|
cloudqi
| 2023-08-27T04:30:08Z | 16 | 3 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"jax",
"safetensors",
"whisper",
"automatic-speech-recognition",
"audio",
"pt",
"en",
"es",
"dataset:cloudqi/abreviacoes_e_girias_pt_v0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-03-20T02:25:53Z |
---
language:
- pt
- en
- es
tags:
- audio
- automatic-speech-recognition
widget:
- example_title: Exemplo 1
src: https://cdn-media.huggingface.co/speech_samples/sample1.flac
model-index:
- name: cqi_speech_recognize_pt_v0
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: LibriSpeech (clean)
type: librispeech_asr
config: clean
split: test
args:
language: pt
metrics:
- name: Test WER
type: wer
value: 2.9
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: LibriSpeech (other)
type: librispeech_asr
config: other
split: test
args:
language: pt
metrics:
- name: Test WER
type: wer
value: 5.9
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 11.0
type: mozilla-foundation/common_voice_11_0
config: hi
split: test
args:
language: hi
metrics:
- name: Test WER
type: wer
value: 53.87
pipeline_tag: automatic-speech-recognition
license: apache-2.0
datasets:
- cloudqi/abreviacoes_e_girias_pt_v0
---
# Voice Transcript - Portuguese Focused (From Whisper)
Optimized to work with Brazilian Language.
Current Version: v0.1
|
CyberHarem/inoue_orihime_bleach
|
CyberHarem
| 2023-08-27T04:28:08Z | 0 | 1 | null |
[
"art",
"text-to-image",
"dataset:CyberHarem/inoue_orihime_bleach",
"license:mit",
"region:us"
] |
text-to-image
| 2023-08-27T04:19:31Z |
---
license: mit
datasets:
- CyberHarem/inoue_orihime_bleach
pipeline_tag: text-to-image
tags:
- art
---
# Lora of inoue_orihime_bleach
This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion). And the auto-training framework is maintained by [DeepGHS Team](https://huggingface.co/deepghs).
The base model used during training is [NAI](https://huggingface.co/deepghs/animefull-latest), and the base model used for generating preview images is [Meina/MeinaMix_V11](https://huggingface.co/Meina/MeinaMix_V11).
After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora.
For example, if you want to use the model from step 2400, you need to download `2400/inoue_orihime_bleach.pt` as the embedding and `2400/inoue_orihime_bleach.safetensors` for loading Lora. By using both files together, you can generate images for the desired characters.
**The trigger word is `inoue_orihime_bleach`.**
For the following groups, it is not recommended to use this model and we express regret:
1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail.
2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits.
3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm.
4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters.
5. Individuals who finds the generated image content offensive to their values.
These are available steps:
| Steps | Download | pattern_1 | pattern_2 | bikini | bondage | free | maid | miko | nude | nude2 | suit | yukata |
|--------:|:------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------|:--------------------------------------------------|:-------------------------------------|:-------------------------------------|:-------------------------------------|:-----------------------------------------------|:------------------------------------------------|:-------------------------------------|:-----------------------------------------|
| 2400 | [Download](2400/inoue_orihime_bleach.zip) |  |  |  | [<NSFW, click to see>](2400/previews/bondage.png) |  |  |  | [<NSFW, click to see>](2400/previews/nude.png) | [<NSFW, click to see>](2400/previews/nude2.png) |  |  |
| 2240 | [Download](2240/inoue_orihime_bleach.zip) |  |  |  | [<NSFW, click to see>](2240/previews/bondage.png) |  |  |  | [<NSFW, click to see>](2240/previews/nude.png) | [<NSFW, click to see>](2240/previews/nude2.png) |  |  |
| 2080 | [Download](2080/inoue_orihime_bleach.zip) |  |  |  | [<NSFW, click to see>](2080/previews/bondage.png) |  |  |  | [<NSFW, click to see>](2080/previews/nude.png) | [<NSFW, click to see>](2080/previews/nude2.png) |  |  |
| 1920 | [Download](1920/inoue_orihime_bleach.zip) |  |  |  | [<NSFW, click to see>](1920/previews/bondage.png) |  |  |  | [<NSFW, click to see>](1920/previews/nude.png) | [<NSFW, click to see>](1920/previews/nude2.png) |  |  |
| 1760 | [Download](1760/inoue_orihime_bleach.zip) |  |  |  | [<NSFW, click to see>](1760/previews/bondage.png) |  |  |  | [<NSFW, click to see>](1760/previews/nude.png) | [<NSFW, click to see>](1760/previews/nude2.png) |  |  |
| 1600 | [Download](1600/inoue_orihime_bleach.zip) |  |  |  | [<NSFW, click to see>](1600/previews/bondage.png) |  |  |  | [<NSFW, click to see>](1600/previews/nude.png) | [<NSFW, click to see>](1600/previews/nude2.png) |  |  |
| 1440 | [Download](1440/inoue_orihime_bleach.zip) |  |  |  | [<NSFW, click to see>](1440/previews/bondage.png) |  |  |  | [<NSFW, click to see>](1440/previews/nude.png) | [<NSFW, click to see>](1440/previews/nude2.png) |  |  |
| 1280 | [Download](1280/inoue_orihime_bleach.zip) |  |  |  | [<NSFW, click to see>](1280/previews/bondage.png) |  |  |  | [<NSFW, click to see>](1280/previews/nude.png) | [<NSFW, click to see>](1280/previews/nude2.png) |  |  |
| 1120 | [Download](1120/inoue_orihime_bleach.zip) |  |  |  | [<NSFW, click to see>](1120/previews/bondage.png) |  |  |  | [<NSFW, click to see>](1120/previews/nude.png) | [<NSFW, click to see>](1120/previews/nude2.png) |  |  |
| 960 | [Download](960/inoue_orihime_bleach.zip) |  |  |  | [<NSFW, click to see>](960/previews/bondage.png) |  |  |  | [<NSFW, click to see>](960/previews/nude.png) | [<NSFW, click to see>](960/previews/nude2.png) |  |  |
| 800 | [Download](800/inoue_orihime_bleach.zip) |  |  |  | [<NSFW, click to see>](800/previews/bondage.png) |  |  |  | [<NSFW, click to see>](800/previews/nude.png) | [<NSFW, click to see>](800/previews/nude2.png) |  |  |
| 640 | [Download](640/inoue_orihime_bleach.zip) |  |  |  | [<NSFW, click to see>](640/previews/bondage.png) |  |  |  | [<NSFW, click to see>](640/previews/nude.png) | [<NSFW, click to see>](640/previews/nude2.png) |  |  |
| 480 | [Download](480/inoue_orihime_bleach.zip) |  |  |  | [<NSFW, click to see>](480/previews/bondage.png) |  |  |  | [<NSFW, click to see>](480/previews/nude.png) | [<NSFW, click to see>](480/previews/nude2.png) |  |  |
| 320 | [Download](320/inoue_orihime_bleach.zip) |  |  |  | [<NSFW, click to see>](320/previews/bondage.png) |  |  |  | [<NSFW, click to see>](320/previews/nude.png) | [<NSFW, click to see>](320/previews/nude2.png) |  |  |
| 160 | [Download](160/inoue_orihime_bleach.zip) |  |  |  | [<NSFW, click to see>](160/previews/bondage.png) |  |  |  | [<NSFW, click to see>](160/previews/nude.png) | [<NSFW, click to see>](160/previews/nude2.png) |  |  |
|
DicksonMassawe/ASR_SWAHILI
|
DicksonMassawe
| 2023-08-27T04:24:29Z | 157 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-08-25T05:11:19Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec_large_xlsr_swahili
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. -->
# wav2vec_large_xlsr_swahili
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.
## 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: 32
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.30.2
- Pytorch 1.10.0
- Datasets 2.13.0
- Tokenizers 0.13.3
|
dt-and-vanilla-ardt/dt-arrl_train_halfcheetah_high-2708_0309-33
|
dt-and-vanilla-ardt
| 2023-08-27T04:19:43Z | 32 | 0 |
transformers
|
[
"transformers",
"pytorch",
"decision_transformer",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | null | 2023-08-27T02:10:48Z |
---
tags:
- generated_from_trainer
model-index:
- name: dt-arrl_train_halfcheetah_high-2708_0309-33
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. -->
# dt-arrl_train_halfcheetah_high-2708_0309-33
This model is a fine-tuned version of [](https://huggingface.co/) 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: 0.0001
- train_batch_size: 64
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- training_steps: 10000
### Training results
### Framework versions
- Transformers 4.29.2
- Pytorch 2.1.0.dev20230727+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
deepachalapathi/with_questions
|
deepachalapathi
| 2023-08-27T04:15:16Z | 3 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"mpnet",
"setfit",
"text-classification",
"arxiv:2209.11055",
"license:apache-2.0",
"region:us"
] |
text-classification
| 2023-08-26T14:50:20Z |
---
license: apache-2.0
tags:
- setfit
- sentence-transformers
- text-classification
pipeline_tag: text-classification
---
# whateverweird17/with_questions
This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves:
1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.
## Usage
To use this model for inference, first install the SetFit library:
```bash
python -m pip install setfit
```
You can then run inference as follows:
```python
from setfit import SetFitModel
# Download from Hub and run inference
model = SetFitModel.from_pretrained("whateverweird17/with_questions")
# Run inference
preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"])
```
## BibTeX entry and citation info
```bibtex
@article{https://doi.org/10.48550/arxiv.2209.11055,
doi = {10.48550/ARXIV.2209.11055},
url = {https://arxiv.org/abs/2209.11055},
author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Efficient Few-Shot Learning Without Prompts},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
```
|
jaredoong/dqn-SpaceInvadersNoFrameskip-v4
|
jaredoong
| 2023-08-27T03:56:02Z | 2 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-08-27T03:50:59Z |
---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 551.50 +/- 216.45
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga jaredoong -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga jaredoong -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga jaredoong
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 1000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
|
lw2333/whisper-small-hi
|
lw2333
| 2023-08-27T03:50:24Z | 116 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"whisper",
"automatic-speech-recognition",
"hf-asr-leaderboard",
"generated_from_trainer",
"hi",
"dataset:mozilla-foundation/common_voice_11_0",
"base_model:openai/whisper-small",
"base_model:finetune:openai/whisper-small",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-08-04T14:17:40Z |
---
language:
- hi
license: apache-2.0
base_model: openai/whisper-small
tags:
- hf-asr-leaderboard
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_11_0
metrics:
- wer
model-index:
- name: Whisper Small Hi - Sanchit Gandhi
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 11.0
type: mozilla-foundation/common_voice_11_0
config: hi
split: test
args: 'config: hi, split: test'
metrics:
- name: Wer
type: wer
value: 33.09912807923474
---
<!-- 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. -->
# Whisper Small Hi - Sanchit Gandhi
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 11.0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4278
- Wer: 33.0991
## 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: 1
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 16
- 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
- training_steps: 4000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.0776 | 2.45 | 1000 | 0.3089 | 36.4514 |
| 0.0207 | 4.89 | 2000 | 0.3399 | 33.1372 |
| 0.0012 | 7.34 | 3000 | 0.4067 | 33.4081 |
| 0.0005 | 9.8 | 4000 | 0.4278 | 33.0991 |
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1
- Datasets 2.14.4
- Tokenizers 0.13.3
|
nightdude/config_60
|
nightdude
| 2023-08-27T03:30:24Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-27T03:30:09Z |
---
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: bfloat16
### Framework versions
- PEFT 0.4.0.dev0
|
Dogge/aichan-codellama-34B
|
Dogge
| 2023-08-27T03:01:32Z | 0 | 0 |
peft
|
[
"peft",
"tensorboard",
"region:us"
] | null | 2023-08-27T03:00:15Z |
---
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
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
- PEFT 0.5.0
|
tmanabe/ir100-dogfooding-siamese
|
tmanabe
| 2023-08-27T02:59:09Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"feature-extraction",
"license:apache-2.0",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2023-08-27T02:48:41Z |
---
license: apache-2.0
---
A mock model trained with https://github.com/amazon-science/esci-data
|
tmanabe/ir100-dogfooding-embedding
|
tmanabe
| 2023-08-27T02:50:09Z | 112 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"fill-mask",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2023-08-27T02:48:08Z |
---
license: apache-2.0
---
A mock model trained with https://github.com/amazon-science/esci-data
|
deepghs/anime_teen
|
deepghs
| 2023-08-27T02:24:15Z | 0 | 0 | null |
[
"onnx",
"art",
"image-classification",
"dataset:deepghs/anime_teen",
"license:mit",
"region:us"
] |
image-classification
| 2023-08-26T14:09:22Z |
---
license: mit
datasets:
- deepghs/anime_teen
metrics:
- accuracy
pipeline_tag: image-classification
tags:
- art
---
| Name | FLOPS | Params | Accuracy | AUC | Confusion | Labels |
|:-------------------:|:-------:|:--------:|:----------:|:------:|:-------------------------------------------------------------------------------------------------------:|:--------------------------------------:|
| caformer_s36_v0 | 22.10G | 37.22M | 77.97% | 0.9046 | [confusion](https://huggingface.co/deepghs/anime_teen/blob/main/caformer_s36_v0/plot_confusion.png) | `contentious`, `safe_teen`, `non_teen` |
| mobilenetv3_v0_dist | 0.63G | 4.18M | 74.92% | 0.8866 | [confusion](https://huggingface.co/deepghs/anime_teen/blob/main/mobilenetv3_v0_dist/plot_confusion.png) | `contentious`, `safe_teen`, `non_teen` |
|
dt-and-vanilla-ardt/dt-combo_train_halfcheetah_v2-2708_0054-99
|
dt-and-vanilla-ardt
| 2023-08-27T02:07:05Z | 31 | 0 |
transformers
|
[
"transformers",
"pytorch",
"decision_transformer",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | null | 2023-08-26T23:56:13Z |
---
tags:
- generated_from_trainer
model-index:
- name: dt-combo_train_halfcheetah_v2-2708_0054-99
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. -->
# dt-combo_train_halfcheetah_v2-2708_0054-99
This model is a fine-tuned version of [](https://huggingface.co/) 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: 0.0001
- train_batch_size: 64
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- training_steps: 10000
### Training results
### Framework versions
- Transformers 4.29.2
- Pytorch 2.1.0.dev20230727+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
Tao2AIScienceHPC/ppo-LunarLander-v2
|
Tao2AIScienceHPC
| 2023-08-27T01:59:43Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-08-27T01:59:10Z |
---
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: -504.11 +/- 74.54
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
...
```
|
baxterstockman/my_awesome_eli5_clm-model_new_new
|
baxterstockman
| 2023-08-27T01:48:18Z | 145 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"generated_from_trainer",
"base_model:distilbert/distilgpt2",
"base_model:finetune:distilbert/distilgpt2",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-08-27T01:34:36Z |
---
license: apache-2.0
base_model: distilgpt2
tags:
- generated_from_trainer
model-index:
- name: my_awesome_eli5_clm-model_new_new
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_awesome_eli5_clm-model_new_new
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 4.6856
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 1 | 4.7115 |
| No log | 2.0 | 2 | 4.6939 |
| No log | 3.0 | 3 | 4.6856 |
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cpu
- Datasets 2.14.4
- Tokenizers 0.13.3
|
JUNGU/dqn-SpaceInvadersNoFrameskip-v4
|
JUNGU
| 2023-08-27T01:41:43Z | 2 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-08-27T01:41:05Z |
---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 846.50 +/- 307.52
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga JUNGU -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga JUNGU -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga JUNGU
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 5000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
|
rohn132/rl_course_vizdoom_health_gathering_supreme
|
rohn132
| 2023-08-27T01:29:41Z | 0 | 0 |
sample-factory
|
[
"sample-factory",
"tensorboard",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-08-27T01:29:35Z |
---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: doom_health_gathering_supreme
type: doom_health_gathering_supreme
metrics:
- type: mean_reward
value: 10.41 +/- 5.01
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **doom_health_gathering_supreme** environment.
This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory.
Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/
## Downloading the model
After installing Sample-Factory, download the model with:
```
python -m sample_factory.huggingface.load_from_hub -r rohn132/rl_course_vizdoom_health_gathering_supreme
```
## Using the model
To run the model after download, use the `enjoy` script corresponding to this environment:
```
python -m .usr.local.lib.python3.10.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme
```
You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag.
See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details
## Training with this model
To continue training with this model, use the `train` script corresponding to this environment:
```
python -m .usr.local.lib.python3.10.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme --restart_behavior=resume --train_for_env_steps=10000000000
```
Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
|
pixep/lunar-lander
|
pixep
| 2023-08-27T01:16:01Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-08-27T01:15:42Z |
---
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: -1114.81 +/- 436.22
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
...
```
|
dimitarrskv/a2c-PandaReachDense-v3
|
dimitarrskv
| 2023-08-27T00:26:21Z | 1 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"PandaReachDense-v3",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-08-27T00:20:30Z |
---
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: -0.23 +/- 0.13
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
...
```
|
njuptzzh/distilbert-base-uncased-finetuned-emotion
|
njuptzzh
| 2023-08-27T00:17:56Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-08-27T00:15:41Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
config: split
split: validation
args: split
metrics:
- name: Accuracy
type: accuracy
value: 0.9255
- name: F1
type: f1
value: 0.9253341912779972
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2153
- Accuracy: 0.9255
- F1: 0.9253
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.7935 | 1.0 | 250 | 0.3036 | 0.9125 | 0.9108 |
| 0.2502 | 2.0 | 500 | 0.2153 | 0.9255 | 0.9253 |
### Framework versions
- Transformers 4.32.0
- Pytorch 2.0.1+cu117
- Datasets 2.14.4
- Tokenizers 0.13.3
|
Glavin001/coqar-questions-llama-2-7b-v0.1
|
Glavin001
| 2023-08-27T00:12:14Z | 7 | 1 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"en",
"dataset:Glavin001/generate-questions-v0.1",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-08-26T23:05:52Z |
---
language:
- en
datasets:
- Glavin001/generate-questions-v0.1
library_name: transformers
---
## 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
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
- PEFT 0.6.0.dev0
|
josebruzzoni/disfluency-spanish-v4
|
josebruzzoni
| 2023-08-27T00:07:16Z | 82 | 1 |
transformers
|
[
"transformers",
"pytorch",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"base_model:josebruzzoni/disfluency-spanish-v1",
"base_model:finetune:josebruzzoni/disfluency-spanish-v1",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-08-26T20:01:26Z |
---
license: apache-2.0
base_model: josebruzzoni/disfluency-spanish-v1
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: disfluency-spanish-v4
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. -->
# disfluency-spanish-v4
This model is a fine-tuned version of [josebruzzoni/disfluency-spanish-v1](https://huggingface.co/josebruzzoni/disfluency-spanish-v1) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2653
- Wer: 27.7008
## 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: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 4000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.002 | 20.41 | 1000 | 0.2377 | 20.2216 |
| 0.0001 | 40.82 | 2000 | 0.2524 | 23.4072 |
| 0.0001 | 61.22 | 3000 | 0.2617 | 26.7313 |
| 0.0001 | 81.63 | 4000 | 0.2653 | 27.7008 |
### Framework versions
- Transformers 4.33.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
dt-and-vanilla-ardt/dt-combo_train_halfcheetah_v2-2608_2243-66
|
dt-and-vanilla-ardt
| 2023-08-26T23:54:27Z | 31 | 0 |
transformers
|
[
"transformers",
"pytorch",
"decision_transformer",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | null | 2023-08-26T21:45:14Z |
---
tags:
- generated_from_trainer
model-index:
- name: dt-combo_train_halfcheetah_v2-2608_2243-66
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. -->
# dt-combo_train_halfcheetah_v2-2608_2243-66
This model is a fine-tuned version of [](https://huggingface.co/) 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: 0.0001
- train_batch_size: 64
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- training_steps: 10000
### Training results
### Framework versions
- Transformers 4.29.2
- Pytorch 2.1.0.dev20230727+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
sehiro/AI-buncho-novel-ct2
|
sehiro
| 2023-08-26T23:17:57Z | 6 | 1 |
transformers
|
[
"transformers",
"ja",
"license:openrail",
"endpoints_compatible",
"region:us"
] | null | 2023-08-26T22:53:50Z |
---
license: openrail
language:
- ja
---
AIBunCho様の公開モデル
(https://huggingface.co/AIBunCho/japanese-novel-gpt-j-6b)
をctranslate2用に変換したデータセット。
8bit量子化しているため、精度が落ちているはずです。
ただし定量的なデータは現在のところありません。
|
Jims97/Komp
|
Jims97
| 2023-08-26T23:08:16Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-08-26T23:08:16Z |
---
license: creativeml-openrail-m
---
|
rishabh063/lora-trained-xl-owl
|
rishabh063
| 2023-08-26T22:59:01Z | 2 | 1 |
diffusers
|
[
"diffusers",
"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
| 2023-08-26T21:51:10Z |
---
license: openrail++
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: a photo of ohwx owl
tags:
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
- text-to-image
- diffusers
- lora
inference: true
---
# LoRA DreamBooth - rishabh063/lora-trained-xl-owl
These are LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained on a photo of ohwx owl 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.
|
dt-and-vanilla-ardt/dt-combo_train_halfcheetah_v2-2608_2034-33
|
dt-and-vanilla-ardt
| 2023-08-26T21:43:16Z | 32 | 0 |
transformers
|
[
"transformers",
"pytorch",
"decision_transformer",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | null | 2023-08-26T19:35:50Z |
---
tags:
- generated_from_trainer
model-index:
- name: dt-combo_train_halfcheetah_v2-2608_2034-33
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. -->
# dt-combo_train_halfcheetah_v2-2608_2034-33
This model is a fine-tuned version of [](https://huggingface.co/) 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: 0.0001
- train_batch_size: 64
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- training_steps: 10000
### Training results
### Framework versions
- Transformers 4.29.2
- Pytorch 2.1.0.dev20230727+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
ulichovick/platzi-distilroberta-base-mrpc-glue-Andres-Rojas
|
ulichovick
| 2023-08-26T21:28:53Z | 110 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-08-26T21:20:53Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
- f1
model-index:
- name: platzi-distilroberta-base-mrpc-glue-Andres-Rojas
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
config: mrpc
split: validation
args: mrpc
metrics:
- name: Accuracy
type: accuracy
value: 0.8431372549019608
- name: F1
type: f1
value: 0.8848920863309353
---
<!-- 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. -->
# platzi-distilroberta-base-mrpc-glue-Andres-Rojas
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6370
- Accuracy: 0.8431
- F1: 0.8849
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.5194 | 1.09 | 500 | 0.5060 | 0.8309 | 0.8852 |
| 0.3535 | 2.18 | 1000 | 0.6370 | 0.8431 | 0.8849 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
huemin/fxhash
|
huemin
| 2023-08-26T21:08:23Z | 8 | 0 |
diffusers
|
[
"diffusers",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-04-27T02:19:19Z |
Experimental Stable Diffusion 1.5 finetune on fxhash tokens
|
CiroN2022/alien-god-0
|
CiroN2022
| 2023-08-26T21:07:14Z | 3 | 2 |
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
| 2023-08-26T21:07:11Z |
---
license: other
tags:
- text-to-image
- stable-diffusion
- lora
- diffusers
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt:
widget:
- text:
---
# Alien God
None
## Image examples for the model:
|
sarwarbeing/water-impact-few-shot
|
sarwarbeing
| 2023-08-26T21:07:05Z | 3 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"deberta-v2",
"setfit",
"text-classification",
"arxiv:2209.11055",
"license:apache-2.0",
"region:us"
] |
text-classification
| 2023-08-26T20:11:43Z |
---
license: apache-2.0
tags:
- setfit
- sentence-transformers
- text-classification
pipeline_tag: text-classification
---
# sarwarbeing/water-impact-few-shot
This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves:
1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.
## Usage
To use this model for inference, first install the SetFit library:
```bash
python -m pip install setfit
```
You can then run inference as follows:
```python
from setfit import SetFitModel
# Download from Hub and run inference
model = SetFitModel.from_pretrained("sarwarbeing/water-impact-few-shot")
# Run inference
preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"])
```
## BibTeX entry and citation info
```bibtex
@article{https://doi.org/10.48550/arxiv.2209.11055,
doi = {10.48550/ARXIV.2209.11055},
url = {https://arxiv.org/abs/2209.11055},
author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Efficient Few-Shot Learning Without Prompts},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
```
|
Nurmukhamed/dqn-SpaceInvadersNoFrameskip-v4
|
Nurmukhamed
| 2023-08-26T20:59:42Z | 3 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-08-26T20:59:02Z |
---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 571.00 +/- 147.05
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga Nurmukhamed -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga Nurmukhamed -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga Nurmukhamed
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 10000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
|
Robert1547/Cheloo
|
Robert1547
| 2023-08-26T20:52:41Z | 0 | 0 | null |
[
"ro",
"license:openrail",
"region:us"
] | null | 2023-08-26T20:41:02Z |
---
license: openrail
language:
- ro
---
|
AhmedSSoliman/Llama2-CodeGen-PEFT-QLoRA
|
AhmedSSoliman
| 2023-08-26T20:38:30Z | 9 | 5 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"llama",
"text-generation",
"Code-Generation",
"autotrain",
"Llama2",
"Pytorch",
"PEFT",
"QLoRA",
"code",
"coding",
"dataset:AhmedSSoliman/CodeSearchNet",
"dataset:AhmedSSoliman/CodeSearchNet-Python",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-07-26T15:56:42Z |
---
tags:
- Code-Generation
- autotrain
- text-generation
- Llama2
- Pytorch
- PEFT
- QLoRA
- code
- coding
pipeline_tag: text-generation
widget:
- text: Write a program that add five numbers
- text: Write a python code for reading multiple images
- text: Write a python code for the name Ahmed to be in a reversed order
datasets:
- AhmedSSoliman/CodeSearchNet
- AhmedSSoliman/CodeSearchNet-Python
---
# LlaMa2-CodeGen
This model is [**LlaMa2-7b**](https://huggingface.co/meta-llama/Llama-2-7b) which is fine-tuned on the [**CodeSearchNet dataset**](https://github.com/github/CodeSearchNet) by using the method [**QLoRA**](https://github.com/artidoro/qlora) with [PEFT](https://github.com/huggingface/peft) library.
# Model Trained on Google Colab Pro Using AutoTrain, PEFT and QLoRA
[![Open in Colab][Colab Badge]][RDP Notebook]
# You can load the LlaMa2-CodeGen model on google colab.
### Example
```py
import torch
from peft import PeftModel, PeftConfig
from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig
peft_model_id = "AhmedSSoliman/Llama2-CodeGen-PEFT-QLoRA"
config = PeftConfig.from_pretrained(peft_model_id)
model = AutoModelForCausalLM.from_pretrained(config.base_model_name_or_path, trust_remote_code=True, return_dict=True, load_in_4bit=True, device_map='auto')
tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)
# Load the Lora model
model = PeftModel.from_pretrained(model, peft_model_id)
def create_prompt(instruction):
system = "You are using the Llam2-CodeGen model, a coding assistant that will help the user to resolve the following instruction:\n"
instruction = "### Input: " + instruction
return system + "\n" + instruction + "\n\n" + "### Response:" + "\n"
def generate(
instruction,
max_new_tokens=128,
temperature=0.1,
top_p=0.75,
top_k=40,
num_beams=4,
**kwargs,
):
prompt = create_prompt(instruction)
print(prompt)
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
#input_ids = inputs["input_ids"].to("cuda")
#attention_mask = inputs["attention_mask"].to("cuda")
generation_config = GenerationConfig(
temperature=temperature,
top_p=top_p,
top_k=top_k,
num_beams=num_beams,
**kwargs,
)
with torch.no_grad():
generation_output = model.generate(
#input_ids=input_ids,
#attention_mask=attention_mask,
**inputs,
generation_config=generation_config,
return_dict_in_generate=True,
output_scores=True,
max_new_tokens=max_new_tokens,
early_stopping=True
)
generated_response = tokenizer.decode(outputs[0], skip_special_tokens=True)
stop_output = "### Input"
gen_response = (generated_response.split(stop_output))[0]
#s = generation_output.sequences[0]
#output = tokenizer.decode(s, skip_special_tokens=True)
#stop_output = "### Input"
#gen_response = (output.split(stop_output))[0]
#return output.split("### Response:")[1].lstrip("\n")
return gen_response
instruction = """
Write a python code for the name Ahmed to be in a reversed order
"""
print(generate(instruction))
```
[Colab Badge]: https://colab.research.google.com/assets/colab-badge.svg
[License-Badge]: https://img.shields.io/badge/License-MIT-blue.svg
[RDP Issues]: https://img.shields.io/github/issues/PradyumnaKrishna/Colab-Hacks/Colab%20RDP?label=Issues
[RDP Notebook]: https://colab.research.google.com/drive/18sAFC7msV0gJ24wn5gl41nU0QRynfLqG?usp=sharing
[Code Issues]: https://img.shields.io/github/issues/PradyumnaKrishna/Colab-Hacks/Code%20Server?label=Issues
[Code Notebook]: https://colab.research.google.com/drive/18sAFC7msV0gJ24wn5gl41nU0QRynfLqG?usp=sharing
|
GFazzito/speecht5_finetuned_voxpopuli_hr
|
GFazzito
| 2023-08-26T20:23:01Z | 81 | 0 |
transformers
|
[
"transformers",
"pytorch",
"speecht5",
"text-to-audio",
"text-to-speech",
"dataset:facebook/voxpopuli",
"base_model:microsoft/speecht5_tts",
"base_model:finetune:microsoft/speecht5_tts",
"license:mit",
"endpoints_compatible",
"region:us"
] |
text-to-speech
| 2023-08-26T18:57:02Z |
---
license: mit
base_model: microsoft/speecht5_tts
tags:
- text-to-speech
datasets:
- facebook/voxpopuli
model-index:
- name: speecht5_finetuned_voxpopuli_hr
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_hr
This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the facebook/voxpopuli dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4413
## 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: 1000
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.4811 | 33.9 | 1000 | 0.4413 |
### Framework versions
- Transformers 4.32.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
ddoc/def1
|
ddoc
| 2023-08-26T19:54:02Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-08-26T19:52:49Z |
# Deforum Stable Diffusion — official extension for AUTOMATIC1111's webui
<p align="left">
<a href="https://github.com/deforum-art/sd-webui-deforum/commits"><img alt="Last Commit" src="https://img.shields.io/github/last-commit/deforum-art/deforum-for-automatic1111-webui"></a>
<a href="https://github.com/deforum-art/sd-webui-deforum/issues"><img alt="GitHub issues" src="https://img.shields.io/github/issues/deforum-art/deforum-for-automatic1111-webui"></a>
<a href="https://github.com/deforum-art/sd-webui-deforum/stargazers"><img alt="GitHub stars" src="https://img.shields.io/github/stars/deforum-art/deforum-for-automatic1111-webui"></a>
<a href="https://github.com/deforum-art/sd-webui-deforum/network"><img alt="GitHub forks" src="https://img.shields.io/github/forks/deforum-art/deforum-for-automatic1111-webui"></a>
</a>
</p>
## Need help? See our [FAQ](https://github.com/deforum-art/sd-webui-deforum/wiki/FAQ-&-Troubleshooting)
## Getting Started
1. Install [AUTOMATIC1111's webui](https://github.com/AUTOMATIC1111/stable-diffusion-webui/).
2. Now two ways: either clone the repo into the `extensions` directory via git commandline launched within in the `stable-diffusion-webui` folder
```sh
git clone https://github.com/deforum-art/sd-webui-deforum extensions/deforum
```
Or download this repository, locate the `extensions` folder within your WebUI installation, create a folder named `deforum` and put the contents of the downloaded directory inside of it. Then restart WebUI.
3. Open the webui, find the Deforum tab at the top of the page.
4. Enter the animation settings. Refer to [this general guide](https://docs.google.com/document/d/1pEobUknMFMkn8F5TMsv8qRzamXX_75BShMMXV8IFslI/edit) and [this guide to math keyframing functions in Deforum](https://docs.google.com/document/d/1pfW1PwbDIuW0cv-dnuyYj1UzPqe23BlSLTJsqazffXM/edit?usp=sharing). However, **in this version prompt weights less than zero don't just like in original Deforum!** Split the positive and the negative prompt in the json section using --neg argument like this "apple:\`where(cos(t)>=0, cos(t), 0)\`, snow --neg strawberry:\`where(cos(t)<0, -cos(t), 0)\`"
5. To view animation frames as they're being made, without waiting for the completion of an animation, go to the 'Settings' tab and set the value of this toolbar **above zero**. Warning: it may slow down the generation process.
6. Run the script and see if you got it working or even got something. **In 3D mode a large delay is expected at first** as the script loads the depth models. In the end, using the default settings the whole thing should consume 6.4 GBs of VRAM at 3D mode peaks and no more than 3.8 GB VRAM in 3D mode if you launch the webui with the '--lowvram' command line argument.
7. After the generation process is completed, click the button with the self-describing name to show the video or gif result right in the GUI!
8. Join our Discord where you can post generated stuff, ask questions and more: https://discord.gg/deforum. <br>
* There's also the 'Issues' tab in the repo, for well... reporting issues ;)
9. Profit!
## Known issues
* This port is not fully backward-compatible with the notebook and the local version both due to the changes in how AUTOMATIC1111's webui handles Stable Diffusion models and the changes in this script to get it to work in the new environment. *Expect* that you may not get exactly the same result or that the thing may break down because of the older settings.
## Screenshots
Amazing raw Deforum animation by [Pxl.Pshr](https://www.instagram.com/pxl.pshr):
* Turn Audio ON!
(Audio credits: SKRILLEX, FRED AGAIN & FLOWDAN - RUMBLE (PHACE'S DNB FLIP))
https://user-images.githubusercontent.com/121192995/224450647-39529b28-be04-4871-bb7a-faf7afda2ef2.mp4
Setting file of that video: [here](https://github.com/deforum-art/sd-webui-deforum/files/11353167/PxlPshrWinningAnimationSettings.txt).
<br>
Main extension tab:
Keyframes tab:
## License
This program is distributed under the terms of the GNU Affero Public License v3.0, copyright (c) 2023 Deforum LLC.
Some of its sublicensed integrated 3rd party components may have other licenses, see LICENSE for usage terms.
|
gabrikid/rare-puppers
|
gabrikid
| 2023-08-26T19:52:33Z | 195 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-08-26T19:52:24Z |
---
tags:
- image-classification
- pytorch
- huggingpics
metrics:
- accuracy
model-index:
- name: rare-puppers
results:
- task:
name: Image Classification
type: image-classification
metrics:
- name: Accuracy
type: accuracy
value: 0.8656716346740723
---
# rare-puppers
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics).
## Example Images
#### corgi
#### samoyed
#### shiba inu
|
hapandya/xlmr-large-hi-bn-MLM-SQuAD-TyDi-MLQA
|
hapandya
| 2023-08-26T19:44:50Z | 115 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"question-answering",
"hi",
"bn",
"en",
"dataset:squad",
"dataset:tydiqa",
"dataset:mlqa",
"license:cc",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2023-08-26T16:48:48Z |
---
license: cc
datasets:
- squad
- tydiqa
- mlqa
language:
- hi
- bn
- en
pipeline_tag: question-answering
---
# xlmr-large-hi-be-MLM-SQuAD-TyDi-MLQA Model Card
## Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("question-answering", model="hapandya/xlmr-large-hi-be-MLM-SQuAD-TyDi-MLQA")
## Load model directly
from transformers import AutoTokenizer, AutoModelForQuestionAnswering
tokenizer = AutoTokenizer.from_pretrained("hapandya/xlmr-large-hi-be-MLM-SQuAD-TyDi-MLQA")
model = AutoModelForQuestionAnswering.from_pretrained("hapandya/xlmr-large-hi-be-MLM-SQuAD-TyDi-MLQA"
|
hapandya/indic-hi-te-MLM-SQuAD-TyDi-MLQA
|
hapandya
| 2023-08-26T19:38:48Z | 109 | 0 |
transformers
|
[
"transformers",
"pytorch",
"albert",
"question-answering",
"hi",
"te",
"en",
"dataset:squad",
"dataset:tydiqa",
"dataset:mlqa",
"license:cc",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2023-08-26T17:01:51Z |
---
license: cc
datasets:
- squad
- tydiqa
- mlqa
language:
- hi
- te
- en
pipeline_tag: question-answering
---
# indicBERT-hi-te-MLM-SQuAD-TyDi-MLQA Model Card
## Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("question-answering", model="hapandya/indic-hi-te-MLM-SQuAD-TyDi-MLQA")
## Load model directly
from transformers import AutoTokenizer, AutoModelForQuestionAnswering
tokenizer = AutoTokenizer.from_pretrained("hapandya/indic-hi-te-MLM-SQuAD-TyDi-MLQA")
model = AutoModelForQuestionAnswering.from_pretrained("hapandya/indic-hi-te-MLM-SQuAD-TyDi-MLQA")
|
hapandya/indic-hi-bn-MLM-SQuAD-TyDi-MLQA
|
hapandya
| 2023-08-26T19:37:23Z | 109 | 0 |
transformers
|
[
"transformers",
"pytorch",
"albert",
"question-answering",
"hi",
"bn",
"en",
"dataset:squad",
"dataset:tydiqa",
"dataset:mlqa",
"license:cc",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2023-08-26T17:01:38Z |
---
license: cc
datasets:
- squad
- tydiqa
- mlqa
language:
- hi
- bn
- en
pipeline_tag: question-answering
---
# indicBERT-hi-be-MLM-SQuAD-TyDi-MLQA Model Card
## Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("question-answering", model="hapandya/indic-hi-be-MLM-SQuAD-TyDi-MLQA")
## Load model directly
from transformers import AutoTokenizer, AutoModelForQuestionAnswering
tokenizer = AutoTokenizer.from_pretrained("hapandya/indic-hi-be-MLM-SQuAD-TyDi-MLQA")
model = AutoModelForQuestionAnswering.from_pretrained("hapandya/indic-hi-be-MLM-SQuAD-TyDi-MLQA")
|
dt-and-vanilla-ardt/dt-robust_train_halfcheetah_v3-2608_1822-99
|
dt-and-vanilla-ardt
| 2023-08-26T19:34:00Z | 33 | 0 |
transformers
|
[
"transformers",
"pytorch",
"decision_transformer",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | null | 2023-08-26T17:23:36Z |
---
tags:
- generated_from_trainer
model-index:
- name: dt-robust_train_halfcheetah_v3-2608_1822-99
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. -->
# dt-robust_train_halfcheetah_v3-2608_1822-99
This model is a fine-tuned version of [](https://huggingface.co/) 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: 0.0001
- train_batch_size: 64
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- training_steps: 10000
### Training results
### Framework versions
- Transformers 4.29.2
- Pytorch 2.1.0.dev20230727+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
bigmorning/whisper_char_cv12_pad_lob100_low_sup__0115
|
bigmorning
| 2023-08-26T19:28:35Z | 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
| 2023-08-26T19:28:26Z |
---
license: apache-2.0
base_model: openai/whisper-tiny
tags:
- generated_from_keras_callback
model-index:
- name: whisper_char_cv12_pad_lob100_low_sup__0115
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_char_cv12_pad_lob100_low_sup__0115
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: 0.0019
- Train Accuracy: 0.1115
- Train Wermet: 5.8174
- Validation Loss: 0.5875
- Validation Accuracy: 0.0637
- Validation Wermet: 12.3093
- Epoch: 114
## 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 |
|:----------:|:--------------:|:------------:|:---------------:|:-------------------:|:-----------------:|:-----:|
| 2.5942 | 0.0399 | 3.6402 | 1.9371 | 0.0319 | 16.1531 | 0 |
| 1.8766 | 0.0532 | 6.8384 | 1.7437 | 0.0343 | 15.0408 | 1 |
| 1.7251 | 0.0570 | 5.9150 | 1.6630 | 0.0358 | 10.5002 | 2 |
| 1.6457 | 0.0591 | 5.1153 | 1.5993 | 0.0369 | 10.4737 | 3 |
| 1.5935 | 0.0604 | 4.8231 | 1.5582 | 0.0375 | 8.5794 | 4 |
| 1.5526 | 0.0615 | 4.1987 | 1.5103 | 0.0385 | 9.4130 | 5 |
| 1.5165 | 0.0625 | 4.0179 | 1.4812 | 0.0391 | 6.6025 | 6 |
| 1.4868 | 0.0633 | 3.6770 | 1.4465 | 0.0399 | 6.7562 | 7 |
| 1.4565 | 0.0642 | 3.3851 | 1.4326 | 0.0402 | 6.3327 | 8 |
| 1.4271 | 0.0650 | 3.2883 | 1.3788 | 0.0413 | 6.5933 | 9 |
| 1.3965 | 0.0659 | 3.0822 | 1.3558 | 0.0415 | 5.7852 | 10 |
| 1.3541 | 0.0671 | 2.8659 | 1.2958 | 0.0429 | 5.2978 | 11 |
| 1.3066 | 0.0684 | 2.4942 | 1.2323 | 0.0440 | 4.9600 | 12 |
| 1.2401 | 0.0703 | 2.0745 | 1.1430 | 0.0456 | 3.6837 | 13 |
| 1.1549 | 0.0728 | 1.6202 | 1.0353 | 0.0478 | 2.9217 | 14 |
| 1.0653 | 0.0755 | 1.3041 | 0.9650 | 0.0492 | 2.0673 | 15 |
| 0.9765 | 0.0783 | 1.0922 | 0.8766 | 0.0510 | 2.7441 | 16 |
| 0.8977 | 0.0808 | 1.2561 | 0.8053 | 0.0524 | 3.6015 | 17 |
| 0.8246 | 0.0831 | 1.2955 | 0.7391 | 0.0537 | 3.2922 | 18 |
| 0.7591 | 0.0852 | 1.3109 | 0.7221 | 0.0541 | 3.6946 | 19 |
| 0.6988 | 0.0872 | 1.3303 | 0.6366 | 0.0559 | 3.8377 | 20 |
| 0.6424 | 0.0891 | 1.3256 | 0.5883 | 0.0569 | 4.1079 | 21 |
| 0.5925 | 0.0908 | 1.3637 | 0.5649 | 0.0575 | 3.7297 | 22 |
| 0.5405 | 0.0925 | 1.3142 | 0.5193 | 0.0584 | 3.5121 | 23 |
| 0.4929 | 0.0942 | 1.3157 | 0.4836 | 0.0591 | 4.8017 | 24 |
| 0.4523 | 0.0956 | 1.4635 | 0.4542 | 0.0598 | 4.5538 | 25 |
| 0.4116 | 0.0971 | 1.5118 | 0.4377 | 0.0602 | 4.9221 | 26 |
| 0.3759 | 0.0984 | 1.6392 | 0.4101 | 0.0608 | 5.6152 | 27 |
| 0.3446 | 0.0994 | 1.7744 | 0.3890 | 0.0613 | 7.0303 | 28 |
| 0.3176 | 0.1004 | 2.1998 | 0.3751 | 0.0616 | 8.1772 | 29 |
| 0.2945 | 0.1012 | 2.5525 | 0.3598 | 0.0619 | 8.2165 | 30 |
| 0.2739 | 0.1019 | 2.7708 | 0.3425 | 0.0623 | 9.8904 | 31 |
| 0.2553 | 0.1026 | 3.0620 | 0.3336 | 0.0625 | 9.8263 | 32 |
| 0.2380 | 0.1032 | 3.3150 | 0.3248 | 0.0627 | 10.1323 | 33 |
| 0.2225 | 0.1037 | 3.4188 | 0.3186 | 0.0629 | 9.8005 | 34 |
| 0.2074 | 0.1043 | 3.4245 | 0.3194 | 0.0629 | 10.0836 | 35 |
| 0.1921 | 0.1048 | 3.5998 | 0.3096 | 0.0631 | 10.9020 | 36 |
| 0.1795 | 0.1053 | 3.7938 | 0.3075 | 0.0632 | 11.1284 | 37 |
| 0.1671 | 0.1057 | 3.7413 | 0.3038 | 0.0633 | 10.9362 | 38 |
| 0.1546 | 0.1061 | 3.7830 | 0.3024 | 0.0634 | 10.7771 | 39 |
| 0.1432 | 0.1066 | 3.6808 | 0.3035 | 0.0635 | 11.4689 | 40 |
| 0.1319 | 0.1070 | 3.7824 | 0.3027 | 0.0635 | 10.9949 | 41 |
| 0.1211 | 0.1074 | 3.9301 | 0.3060 | 0.0636 | 10.8937 | 42 |
| 0.1113 | 0.1077 | 3.8509 | 0.3060 | 0.0636 | 10.7188 | 43 |
| 0.1012 | 0.1081 | 3.8780 | 0.3104 | 0.0636 | 10.6993 | 44 |
| 0.0922 | 0.1085 | 3.6982 | 0.3123 | 0.0637 | 10.6308 | 45 |
| 0.0827 | 0.1088 | 3.7227 | 0.3185 | 0.0637 | 10.8392 | 46 |
| 0.0741 | 0.1092 | 3.7235 | 0.3222 | 0.0637 | 10.2774 | 47 |
| 0.0665 | 0.1095 | 3.7106 | 0.3314 | 0.0637 | 9.5736 | 48 |
| 0.0589 | 0.1098 | 3.6104 | 0.3393 | 0.0636 | 9.9114 | 49 |
| 0.0515 | 0.1100 | 3.6150 | 0.3431 | 0.0637 | 10.1000 | 50 |
| 0.0453 | 0.1103 | 3.6760 | 0.3542 | 0.0636 | 9.4499 | 51 |
| 0.0389 | 0.1105 | 3.7376 | 0.3607 | 0.0636 | 9.6629 | 52 |
| 0.0335 | 0.1107 | 3.7707 | 0.3692 | 0.0637 | 9.5104 | 53 |
| 0.0283 | 0.1109 | 3.7655 | 0.3771 | 0.0636 | 9.6379 | 54 |
| 0.0246 | 0.1110 | 3.9511 | 0.3898 | 0.0636 | 9.7582 | 55 |
| 0.0211 | 0.1111 | 3.9487 | 0.3960 | 0.0636 | 10.0651 | 56 |
| 0.0191 | 0.1112 | 4.0695 | 0.4041 | 0.0636 | 9.1873 | 57 |
| 0.0150 | 0.1113 | 4.2329 | 0.4158 | 0.0636 | 10.5777 | 58 |
| 0.0117 | 0.1114 | 4.3648 | 0.4241 | 0.0636 | 10.1904 | 59 |
| 0.0096 | 0.1115 | 4.3534 | 0.4333 | 0.0636 | 10.3831 | 60 |
| 0.0084 | 0.1115 | 4.4131 | 0.4417 | 0.0636 | 10.2134 | 61 |
| 0.0072 | 0.1115 | 4.4827 | 0.4539 | 0.0636 | 10.4537 | 62 |
| 0.0101 | 0.1114 | 4.6105 | 0.4701 | 0.0635 | 9.2620 | 63 |
| 0.0114 | 0.1113 | 4.4725 | 0.4602 | 0.0637 | 11.3443 | 64 |
| 0.0056 | 0.1115 | 4.6820 | 0.4678 | 0.0637 | 10.8401 | 65 |
| 0.0035 | 0.1115 | 4.7095 | 0.4748 | 0.0637 | 10.8410 | 66 |
| 0.0033 | 0.1115 | 4.5291 | 0.4831 | 0.0637 | 10.3950 | 67 |
| 0.0029 | 0.1115 | 4.4502 | 0.4916 | 0.0637 | 10.8216 | 68 |
| 0.0184 | 0.1110 | 4.2753 | 0.4987 | 0.0634 | 10.2126 | 69 |
| 0.0091 | 0.1113 | 4.1128 | 0.4833 | 0.0638 | 10.8605 | 70 |
| 0.0033 | 0.1115 | 4.1755 | 0.4911 | 0.0638 | 10.4538 | 71 |
| 0.0026 | 0.1115 | 4.3450 | 0.5009 | 0.0637 | 10.1961 | 72 |
| 0.0039 | 0.1115 | 4.6335 | 0.5079 | 0.0637 | 11.0165 | 73 |
| 0.0030 | 0.1115 | 4.5756 | 0.5071 | 0.0637 | 9.9384 | 74 |
| 0.0017 | 0.1115 | 4.6589 | 0.5090 | 0.0638 | 10.8814 | 75 |
| 0.0012 | 0.1115 | 4.8756 | 0.5146 | 0.0638 | 10.9099 | 76 |
| 0.0013 | 0.1115 | 4.9431 | 0.5220 | 0.0638 | 10.5558 | 77 |
| 0.0136 | 0.1111 | 4.8817 | 0.5117 | 0.0637 | 10.1668 | 78 |
| 0.0038 | 0.1115 | 5.1236 | 0.5118 | 0.0638 | 11.3651 | 79 |
| 0.0017 | 0.1115 | 5.3989 | 0.5176 | 0.0638 | 11.3609 | 80 |
| 0.0014 | 0.1115 | 5.5658 | 0.5231 | 0.0638 | 11.5637 | 81 |
| 0.0008 | 0.1115 | 5.4076 | 0.5273 | 0.0638 | 11.5293 | 82 |
| 0.0007 | 0.1116 | 5.5166 | 0.5325 | 0.0638 | 11.6874 | 83 |
| 0.0007 | 0.1115 | 5.3020 | 0.5370 | 0.0638 | 11.6410 | 84 |
| 0.0006 | 0.1116 | 5.3834 | 0.5424 | 0.0638 | 11.4686 | 85 |
| 0.0005 | 0.1115 | 5.2441 | 0.5482 | 0.0638 | 11.7770 | 86 |
| 0.0161 | 0.1110 | 5.8611 | 0.5310 | 0.0637 | 14.1541 | 87 |
| 0.0043 | 0.1115 | 6.7439 | 0.5302 | 0.0638 | 13.7884 | 88 |
| 0.0016 | 0.1115 | 6.4034 | 0.5337 | 0.0639 | 13.2969 | 89 |
| 0.0009 | 0.1115 | 6.4491 | 0.5361 | 0.0639 | 13.3960 | 90 |
| 0.0007 | 0.1115 | 6.4412 | 0.5412 | 0.0639 | 13.6544 | 91 |
| 0.0005 | 0.1115 | 6.4941 | 0.5451 | 0.0639 | 13.4296 | 92 |
| 0.0005 | 0.1116 | 6.4763 | 0.5493 | 0.0639 | 13.9268 | 93 |
| 0.0005 | 0.1115 | 6.4452 | 0.5595 | 0.0638 | 12.9971 | 94 |
| 0.0125 | 0.1111 | 5.7381 | 0.5505 | 0.0636 | 10.6493 | 95 |
| 0.0066 | 0.1114 | 5.3763 | 0.5383 | 0.0639 | 10.1229 | 96 |
| 0.0022 | 0.1115 | 5.4800 | 0.5424 | 0.0639 | 12.3926 | 97 |
| 0.0013 | 0.1115 | 5.6556 | 0.5460 | 0.0639 | 11.1784 | 98 |
| 0.0012 | 0.1115 | 6.1793 | 0.5467 | 0.0639 | 11.4956 | 99 |
| 0.0006 | 0.1115 | 6.0584 | 0.5492 | 0.0640 | 12.1496 | 100 |
| 0.0004 | 0.1116 | 5.8904 | 0.5531 | 0.0640 | 12.1934 | 101 |
| 0.0003 | 0.1116 | 5.8994 | 0.5566 | 0.0640 | 12.0296 | 102 |
| 0.0003 | 0.1116 | 5.8099 | 0.5608 | 0.0640 | 12.1687 | 103 |
| 0.0003 | 0.1116 | 5.8167 | 0.5641 | 0.0640 | 11.8858 | 104 |
| 0.0002 | 0.1116 | 5.7524 | 0.5681 | 0.0640 | 11.8685 | 105 |
| 0.0002 | 0.1116 | 5.8104 | 0.5731 | 0.0639 | 11.9771 | 106 |
| 0.0002 | 0.1116 | 5.7022 | 0.5770 | 0.0640 | 11.8855 | 107 |
| 0.0002 | 0.1116 | 5.8197 | 0.5806 | 0.0640 | 11.6167 | 108 |
| 0.0163 | 0.1109 | 5.0213 | 0.5551 | 0.0638 | 12.7567 | 109 |
| 0.0047 | 0.1114 | 5.9526 | 0.5517 | 0.0640 | 12.5943 | 110 |
| 0.0014 | 0.1115 | 6.1876 | 0.5544 | 0.0640 | 14.2314 | 111 |
| 0.0009 | 0.1115 | 6.4595 | 0.5571 | 0.0640 | 13.3475 | 112 |
| 0.0006 | 0.1115 | 5.5795 | 0.5598 | 0.0640 | 12.5131 | 113 |
| 0.0019 | 0.1115 | 5.8174 | 0.5875 | 0.0637 | 12.3093 | 114 |
### Framework versions
- Transformers 4.33.0.dev0
- TensorFlow 2.13.0
- Tokenizers 0.13.3
|
Jiuzhouh/flan-t5-xxl-lora-commongen
|
Jiuzhouh
| 2023-08-26T19:19:20Z | 1 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-26T19:19:10Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.4.0.dev0
- PEFT 0.4.0.dev0
|
bigmorning/whisper_char_cv12_pad_lob100_low_sup__0110
|
bigmorning
| 2023-08-26T19:15:16Z | 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
| 2023-08-26T19:15:08Z |
---
license: apache-2.0
base_model: openai/whisper-tiny
tags:
- generated_from_keras_callback
model-index:
- name: whisper_char_cv12_pad_lob100_low_sup__0110
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_char_cv12_pad_lob100_low_sup__0110
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: 0.0163
- Train Accuracy: 0.1109
- Train Wermet: 5.0213
- Validation Loss: 0.5551
- Validation Accuracy: 0.0638
- Validation Wermet: 12.7567
- Epoch: 109
## 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 |
|:----------:|:--------------:|:------------:|:---------------:|:-------------------:|:-----------------:|:-----:|
| 2.5942 | 0.0399 | 3.6402 | 1.9371 | 0.0319 | 16.1531 | 0 |
| 1.8766 | 0.0532 | 6.8384 | 1.7437 | 0.0343 | 15.0408 | 1 |
| 1.7251 | 0.0570 | 5.9150 | 1.6630 | 0.0358 | 10.5002 | 2 |
| 1.6457 | 0.0591 | 5.1153 | 1.5993 | 0.0369 | 10.4737 | 3 |
| 1.5935 | 0.0604 | 4.8231 | 1.5582 | 0.0375 | 8.5794 | 4 |
| 1.5526 | 0.0615 | 4.1987 | 1.5103 | 0.0385 | 9.4130 | 5 |
| 1.5165 | 0.0625 | 4.0179 | 1.4812 | 0.0391 | 6.6025 | 6 |
| 1.4868 | 0.0633 | 3.6770 | 1.4465 | 0.0399 | 6.7562 | 7 |
| 1.4565 | 0.0642 | 3.3851 | 1.4326 | 0.0402 | 6.3327 | 8 |
| 1.4271 | 0.0650 | 3.2883 | 1.3788 | 0.0413 | 6.5933 | 9 |
| 1.3965 | 0.0659 | 3.0822 | 1.3558 | 0.0415 | 5.7852 | 10 |
| 1.3541 | 0.0671 | 2.8659 | 1.2958 | 0.0429 | 5.2978 | 11 |
| 1.3066 | 0.0684 | 2.4942 | 1.2323 | 0.0440 | 4.9600 | 12 |
| 1.2401 | 0.0703 | 2.0745 | 1.1430 | 0.0456 | 3.6837 | 13 |
| 1.1549 | 0.0728 | 1.6202 | 1.0353 | 0.0478 | 2.9217 | 14 |
| 1.0653 | 0.0755 | 1.3041 | 0.9650 | 0.0492 | 2.0673 | 15 |
| 0.9765 | 0.0783 | 1.0922 | 0.8766 | 0.0510 | 2.7441 | 16 |
| 0.8977 | 0.0808 | 1.2561 | 0.8053 | 0.0524 | 3.6015 | 17 |
| 0.8246 | 0.0831 | 1.2955 | 0.7391 | 0.0537 | 3.2922 | 18 |
| 0.7591 | 0.0852 | 1.3109 | 0.7221 | 0.0541 | 3.6946 | 19 |
| 0.6988 | 0.0872 | 1.3303 | 0.6366 | 0.0559 | 3.8377 | 20 |
| 0.6424 | 0.0891 | 1.3256 | 0.5883 | 0.0569 | 4.1079 | 21 |
| 0.5925 | 0.0908 | 1.3637 | 0.5649 | 0.0575 | 3.7297 | 22 |
| 0.5405 | 0.0925 | 1.3142 | 0.5193 | 0.0584 | 3.5121 | 23 |
| 0.4929 | 0.0942 | 1.3157 | 0.4836 | 0.0591 | 4.8017 | 24 |
| 0.4523 | 0.0956 | 1.4635 | 0.4542 | 0.0598 | 4.5538 | 25 |
| 0.4116 | 0.0971 | 1.5118 | 0.4377 | 0.0602 | 4.9221 | 26 |
| 0.3759 | 0.0984 | 1.6392 | 0.4101 | 0.0608 | 5.6152 | 27 |
| 0.3446 | 0.0994 | 1.7744 | 0.3890 | 0.0613 | 7.0303 | 28 |
| 0.3176 | 0.1004 | 2.1998 | 0.3751 | 0.0616 | 8.1772 | 29 |
| 0.2945 | 0.1012 | 2.5525 | 0.3598 | 0.0619 | 8.2165 | 30 |
| 0.2739 | 0.1019 | 2.7708 | 0.3425 | 0.0623 | 9.8904 | 31 |
| 0.2553 | 0.1026 | 3.0620 | 0.3336 | 0.0625 | 9.8263 | 32 |
| 0.2380 | 0.1032 | 3.3150 | 0.3248 | 0.0627 | 10.1323 | 33 |
| 0.2225 | 0.1037 | 3.4188 | 0.3186 | 0.0629 | 9.8005 | 34 |
| 0.2074 | 0.1043 | 3.4245 | 0.3194 | 0.0629 | 10.0836 | 35 |
| 0.1921 | 0.1048 | 3.5998 | 0.3096 | 0.0631 | 10.9020 | 36 |
| 0.1795 | 0.1053 | 3.7938 | 0.3075 | 0.0632 | 11.1284 | 37 |
| 0.1671 | 0.1057 | 3.7413 | 0.3038 | 0.0633 | 10.9362 | 38 |
| 0.1546 | 0.1061 | 3.7830 | 0.3024 | 0.0634 | 10.7771 | 39 |
| 0.1432 | 0.1066 | 3.6808 | 0.3035 | 0.0635 | 11.4689 | 40 |
| 0.1319 | 0.1070 | 3.7824 | 0.3027 | 0.0635 | 10.9949 | 41 |
| 0.1211 | 0.1074 | 3.9301 | 0.3060 | 0.0636 | 10.8937 | 42 |
| 0.1113 | 0.1077 | 3.8509 | 0.3060 | 0.0636 | 10.7188 | 43 |
| 0.1012 | 0.1081 | 3.8780 | 0.3104 | 0.0636 | 10.6993 | 44 |
| 0.0922 | 0.1085 | 3.6982 | 0.3123 | 0.0637 | 10.6308 | 45 |
| 0.0827 | 0.1088 | 3.7227 | 0.3185 | 0.0637 | 10.8392 | 46 |
| 0.0741 | 0.1092 | 3.7235 | 0.3222 | 0.0637 | 10.2774 | 47 |
| 0.0665 | 0.1095 | 3.7106 | 0.3314 | 0.0637 | 9.5736 | 48 |
| 0.0589 | 0.1098 | 3.6104 | 0.3393 | 0.0636 | 9.9114 | 49 |
| 0.0515 | 0.1100 | 3.6150 | 0.3431 | 0.0637 | 10.1000 | 50 |
| 0.0453 | 0.1103 | 3.6760 | 0.3542 | 0.0636 | 9.4499 | 51 |
| 0.0389 | 0.1105 | 3.7376 | 0.3607 | 0.0636 | 9.6629 | 52 |
| 0.0335 | 0.1107 | 3.7707 | 0.3692 | 0.0637 | 9.5104 | 53 |
| 0.0283 | 0.1109 | 3.7655 | 0.3771 | 0.0636 | 9.6379 | 54 |
| 0.0246 | 0.1110 | 3.9511 | 0.3898 | 0.0636 | 9.7582 | 55 |
| 0.0211 | 0.1111 | 3.9487 | 0.3960 | 0.0636 | 10.0651 | 56 |
| 0.0191 | 0.1112 | 4.0695 | 0.4041 | 0.0636 | 9.1873 | 57 |
| 0.0150 | 0.1113 | 4.2329 | 0.4158 | 0.0636 | 10.5777 | 58 |
| 0.0117 | 0.1114 | 4.3648 | 0.4241 | 0.0636 | 10.1904 | 59 |
| 0.0096 | 0.1115 | 4.3534 | 0.4333 | 0.0636 | 10.3831 | 60 |
| 0.0084 | 0.1115 | 4.4131 | 0.4417 | 0.0636 | 10.2134 | 61 |
| 0.0072 | 0.1115 | 4.4827 | 0.4539 | 0.0636 | 10.4537 | 62 |
| 0.0101 | 0.1114 | 4.6105 | 0.4701 | 0.0635 | 9.2620 | 63 |
| 0.0114 | 0.1113 | 4.4725 | 0.4602 | 0.0637 | 11.3443 | 64 |
| 0.0056 | 0.1115 | 4.6820 | 0.4678 | 0.0637 | 10.8401 | 65 |
| 0.0035 | 0.1115 | 4.7095 | 0.4748 | 0.0637 | 10.8410 | 66 |
| 0.0033 | 0.1115 | 4.5291 | 0.4831 | 0.0637 | 10.3950 | 67 |
| 0.0029 | 0.1115 | 4.4502 | 0.4916 | 0.0637 | 10.8216 | 68 |
| 0.0184 | 0.1110 | 4.2753 | 0.4987 | 0.0634 | 10.2126 | 69 |
| 0.0091 | 0.1113 | 4.1128 | 0.4833 | 0.0638 | 10.8605 | 70 |
| 0.0033 | 0.1115 | 4.1755 | 0.4911 | 0.0638 | 10.4538 | 71 |
| 0.0026 | 0.1115 | 4.3450 | 0.5009 | 0.0637 | 10.1961 | 72 |
| 0.0039 | 0.1115 | 4.6335 | 0.5079 | 0.0637 | 11.0165 | 73 |
| 0.0030 | 0.1115 | 4.5756 | 0.5071 | 0.0637 | 9.9384 | 74 |
| 0.0017 | 0.1115 | 4.6589 | 0.5090 | 0.0638 | 10.8814 | 75 |
| 0.0012 | 0.1115 | 4.8756 | 0.5146 | 0.0638 | 10.9099 | 76 |
| 0.0013 | 0.1115 | 4.9431 | 0.5220 | 0.0638 | 10.5558 | 77 |
| 0.0136 | 0.1111 | 4.8817 | 0.5117 | 0.0637 | 10.1668 | 78 |
| 0.0038 | 0.1115 | 5.1236 | 0.5118 | 0.0638 | 11.3651 | 79 |
| 0.0017 | 0.1115 | 5.3989 | 0.5176 | 0.0638 | 11.3609 | 80 |
| 0.0014 | 0.1115 | 5.5658 | 0.5231 | 0.0638 | 11.5637 | 81 |
| 0.0008 | 0.1115 | 5.4076 | 0.5273 | 0.0638 | 11.5293 | 82 |
| 0.0007 | 0.1116 | 5.5166 | 0.5325 | 0.0638 | 11.6874 | 83 |
| 0.0007 | 0.1115 | 5.3020 | 0.5370 | 0.0638 | 11.6410 | 84 |
| 0.0006 | 0.1116 | 5.3834 | 0.5424 | 0.0638 | 11.4686 | 85 |
| 0.0005 | 0.1115 | 5.2441 | 0.5482 | 0.0638 | 11.7770 | 86 |
| 0.0161 | 0.1110 | 5.8611 | 0.5310 | 0.0637 | 14.1541 | 87 |
| 0.0043 | 0.1115 | 6.7439 | 0.5302 | 0.0638 | 13.7884 | 88 |
| 0.0016 | 0.1115 | 6.4034 | 0.5337 | 0.0639 | 13.2969 | 89 |
| 0.0009 | 0.1115 | 6.4491 | 0.5361 | 0.0639 | 13.3960 | 90 |
| 0.0007 | 0.1115 | 6.4412 | 0.5412 | 0.0639 | 13.6544 | 91 |
| 0.0005 | 0.1115 | 6.4941 | 0.5451 | 0.0639 | 13.4296 | 92 |
| 0.0005 | 0.1116 | 6.4763 | 0.5493 | 0.0639 | 13.9268 | 93 |
| 0.0005 | 0.1115 | 6.4452 | 0.5595 | 0.0638 | 12.9971 | 94 |
| 0.0125 | 0.1111 | 5.7381 | 0.5505 | 0.0636 | 10.6493 | 95 |
| 0.0066 | 0.1114 | 5.3763 | 0.5383 | 0.0639 | 10.1229 | 96 |
| 0.0022 | 0.1115 | 5.4800 | 0.5424 | 0.0639 | 12.3926 | 97 |
| 0.0013 | 0.1115 | 5.6556 | 0.5460 | 0.0639 | 11.1784 | 98 |
| 0.0012 | 0.1115 | 6.1793 | 0.5467 | 0.0639 | 11.4956 | 99 |
| 0.0006 | 0.1115 | 6.0584 | 0.5492 | 0.0640 | 12.1496 | 100 |
| 0.0004 | 0.1116 | 5.8904 | 0.5531 | 0.0640 | 12.1934 | 101 |
| 0.0003 | 0.1116 | 5.8994 | 0.5566 | 0.0640 | 12.0296 | 102 |
| 0.0003 | 0.1116 | 5.8099 | 0.5608 | 0.0640 | 12.1687 | 103 |
| 0.0003 | 0.1116 | 5.8167 | 0.5641 | 0.0640 | 11.8858 | 104 |
| 0.0002 | 0.1116 | 5.7524 | 0.5681 | 0.0640 | 11.8685 | 105 |
| 0.0002 | 0.1116 | 5.8104 | 0.5731 | 0.0639 | 11.9771 | 106 |
| 0.0002 | 0.1116 | 5.7022 | 0.5770 | 0.0640 | 11.8855 | 107 |
| 0.0002 | 0.1116 | 5.8197 | 0.5806 | 0.0640 | 11.6167 | 108 |
| 0.0163 | 0.1109 | 5.0213 | 0.5551 | 0.0638 | 12.7567 | 109 |
### Framework versions
- Transformers 4.33.0.dev0
- TensorFlow 2.13.0
- Tokenizers 0.13.3
|
bigmorning/whisper_char_cv12_pad_lob100_low_sup__0100
|
bigmorning
| 2023-08-26T18:48:48Z | 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
| 2023-08-26T18:48:39Z |
---
license: apache-2.0
base_model: openai/whisper-tiny
tags:
- generated_from_keras_callback
model-index:
- name: whisper_char_cv12_pad_lob100_low_sup__0100
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_char_cv12_pad_lob100_low_sup__0100
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: 0.0012
- Train Accuracy: 0.1115
- Train Wermet: 6.1793
- Validation Loss: 0.5467
- Validation Accuracy: 0.0639
- Validation Wermet: 11.4956
- Epoch: 99
## 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 |
|:----------:|:--------------:|:------------:|:---------------:|:-------------------:|:-----------------:|:-----:|
| 2.5942 | 0.0399 | 3.6402 | 1.9371 | 0.0319 | 16.1531 | 0 |
| 1.8766 | 0.0532 | 6.8384 | 1.7437 | 0.0343 | 15.0408 | 1 |
| 1.7251 | 0.0570 | 5.9150 | 1.6630 | 0.0358 | 10.5002 | 2 |
| 1.6457 | 0.0591 | 5.1153 | 1.5993 | 0.0369 | 10.4737 | 3 |
| 1.5935 | 0.0604 | 4.8231 | 1.5582 | 0.0375 | 8.5794 | 4 |
| 1.5526 | 0.0615 | 4.1987 | 1.5103 | 0.0385 | 9.4130 | 5 |
| 1.5165 | 0.0625 | 4.0179 | 1.4812 | 0.0391 | 6.6025 | 6 |
| 1.4868 | 0.0633 | 3.6770 | 1.4465 | 0.0399 | 6.7562 | 7 |
| 1.4565 | 0.0642 | 3.3851 | 1.4326 | 0.0402 | 6.3327 | 8 |
| 1.4271 | 0.0650 | 3.2883 | 1.3788 | 0.0413 | 6.5933 | 9 |
| 1.3965 | 0.0659 | 3.0822 | 1.3558 | 0.0415 | 5.7852 | 10 |
| 1.3541 | 0.0671 | 2.8659 | 1.2958 | 0.0429 | 5.2978 | 11 |
| 1.3066 | 0.0684 | 2.4942 | 1.2323 | 0.0440 | 4.9600 | 12 |
| 1.2401 | 0.0703 | 2.0745 | 1.1430 | 0.0456 | 3.6837 | 13 |
| 1.1549 | 0.0728 | 1.6202 | 1.0353 | 0.0478 | 2.9217 | 14 |
| 1.0653 | 0.0755 | 1.3041 | 0.9650 | 0.0492 | 2.0673 | 15 |
| 0.9765 | 0.0783 | 1.0922 | 0.8766 | 0.0510 | 2.7441 | 16 |
| 0.8977 | 0.0808 | 1.2561 | 0.8053 | 0.0524 | 3.6015 | 17 |
| 0.8246 | 0.0831 | 1.2955 | 0.7391 | 0.0537 | 3.2922 | 18 |
| 0.7591 | 0.0852 | 1.3109 | 0.7221 | 0.0541 | 3.6946 | 19 |
| 0.6988 | 0.0872 | 1.3303 | 0.6366 | 0.0559 | 3.8377 | 20 |
| 0.6424 | 0.0891 | 1.3256 | 0.5883 | 0.0569 | 4.1079 | 21 |
| 0.5925 | 0.0908 | 1.3637 | 0.5649 | 0.0575 | 3.7297 | 22 |
| 0.5405 | 0.0925 | 1.3142 | 0.5193 | 0.0584 | 3.5121 | 23 |
| 0.4929 | 0.0942 | 1.3157 | 0.4836 | 0.0591 | 4.8017 | 24 |
| 0.4523 | 0.0956 | 1.4635 | 0.4542 | 0.0598 | 4.5538 | 25 |
| 0.4116 | 0.0971 | 1.5118 | 0.4377 | 0.0602 | 4.9221 | 26 |
| 0.3759 | 0.0984 | 1.6392 | 0.4101 | 0.0608 | 5.6152 | 27 |
| 0.3446 | 0.0994 | 1.7744 | 0.3890 | 0.0613 | 7.0303 | 28 |
| 0.3176 | 0.1004 | 2.1998 | 0.3751 | 0.0616 | 8.1772 | 29 |
| 0.2945 | 0.1012 | 2.5525 | 0.3598 | 0.0619 | 8.2165 | 30 |
| 0.2739 | 0.1019 | 2.7708 | 0.3425 | 0.0623 | 9.8904 | 31 |
| 0.2553 | 0.1026 | 3.0620 | 0.3336 | 0.0625 | 9.8263 | 32 |
| 0.2380 | 0.1032 | 3.3150 | 0.3248 | 0.0627 | 10.1323 | 33 |
| 0.2225 | 0.1037 | 3.4188 | 0.3186 | 0.0629 | 9.8005 | 34 |
| 0.2074 | 0.1043 | 3.4245 | 0.3194 | 0.0629 | 10.0836 | 35 |
| 0.1921 | 0.1048 | 3.5998 | 0.3096 | 0.0631 | 10.9020 | 36 |
| 0.1795 | 0.1053 | 3.7938 | 0.3075 | 0.0632 | 11.1284 | 37 |
| 0.1671 | 0.1057 | 3.7413 | 0.3038 | 0.0633 | 10.9362 | 38 |
| 0.1546 | 0.1061 | 3.7830 | 0.3024 | 0.0634 | 10.7771 | 39 |
| 0.1432 | 0.1066 | 3.6808 | 0.3035 | 0.0635 | 11.4689 | 40 |
| 0.1319 | 0.1070 | 3.7824 | 0.3027 | 0.0635 | 10.9949 | 41 |
| 0.1211 | 0.1074 | 3.9301 | 0.3060 | 0.0636 | 10.8937 | 42 |
| 0.1113 | 0.1077 | 3.8509 | 0.3060 | 0.0636 | 10.7188 | 43 |
| 0.1012 | 0.1081 | 3.8780 | 0.3104 | 0.0636 | 10.6993 | 44 |
| 0.0922 | 0.1085 | 3.6982 | 0.3123 | 0.0637 | 10.6308 | 45 |
| 0.0827 | 0.1088 | 3.7227 | 0.3185 | 0.0637 | 10.8392 | 46 |
| 0.0741 | 0.1092 | 3.7235 | 0.3222 | 0.0637 | 10.2774 | 47 |
| 0.0665 | 0.1095 | 3.7106 | 0.3314 | 0.0637 | 9.5736 | 48 |
| 0.0589 | 0.1098 | 3.6104 | 0.3393 | 0.0636 | 9.9114 | 49 |
| 0.0515 | 0.1100 | 3.6150 | 0.3431 | 0.0637 | 10.1000 | 50 |
| 0.0453 | 0.1103 | 3.6760 | 0.3542 | 0.0636 | 9.4499 | 51 |
| 0.0389 | 0.1105 | 3.7376 | 0.3607 | 0.0636 | 9.6629 | 52 |
| 0.0335 | 0.1107 | 3.7707 | 0.3692 | 0.0637 | 9.5104 | 53 |
| 0.0283 | 0.1109 | 3.7655 | 0.3771 | 0.0636 | 9.6379 | 54 |
| 0.0246 | 0.1110 | 3.9511 | 0.3898 | 0.0636 | 9.7582 | 55 |
| 0.0211 | 0.1111 | 3.9487 | 0.3960 | 0.0636 | 10.0651 | 56 |
| 0.0191 | 0.1112 | 4.0695 | 0.4041 | 0.0636 | 9.1873 | 57 |
| 0.0150 | 0.1113 | 4.2329 | 0.4158 | 0.0636 | 10.5777 | 58 |
| 0.0117 | 0.1114 | 4.3648 | 0.4241 | 0.0636 | 10.1904 | 59 |
| 0.0096 | 0.1115 | 4.3534 | 0.4333 | 0.0636 | 10.3831 | 60 |
| 0.0084 | 0.1115 | 4.4131 | 0.4417 | 0.0636 | 10.2134 | 61 |
| 0.0072 | 0.1115 | 4.4827 | 0.4539 | 0.0636 | 10.4537 | 62 |
| 0.0101 | 0.1114 | 4.6105 | 0.4701 | 0.0635 | 9.2620 | 63 |
| 0.0114 | 0.1113 | 4.4725 | 0.4602 | 0.0637 | 11.3443 | 64 |
| 0.0056 | 0.1115 | 4.6820 | 0.4678 | 0.0637 | 10.8401 | 65 |
| 0.0035 | 0.1115 | 4.7095 | 0.4748 | 0.0637 | 10.8410 | 66 |
| 0.0033 | 0.1115 | 4.5291 | 0.4831 | 0.0637 | 10.3950 | 67 |
| 0.0029 | 0.1115 | 4.4502 | 0.4916 | 0.0637 | 10.8216 | 68 |
| 0.0184 | 0.1110 | 4.2753 | 0.4987 | 0.0634 | 10.2126 | 69 |
| 0.0091 | 0.1113 | 4.1128 | 0.4833 | 0.0638 | 10.8605 | 70 |
| 0.0033 | 0.1115 | 4.1755 | 0.4911 | 0.0638 | 10.4538 | 71 |
| 0.0026 | 0.1115 | 4.3450 | 0.5009 | 0.0637 | 10.1961 | 72 |
| 0.0039 | 0.1115 | 4.6335 | 0.5079 | 0.0637 | 11.0165 | 73 |
| 0.0030 | 0.1115 | 4.5756 | 0.5071 | 0.0637 | 9.9384 | 74 |
| 0.0017 | 0.1115 | 4.6589 | 0.5090 | 0.0638 | 10.8814 | 75 |
| 0.0012 | 0.1115 | 4.8756 | 0.5146 | 0.0638 | 10.9099 | 76 |
| 0.0013 | 0.1115 | 4.9431 | 0.5220 | 0.0638 | 10.5558 | 77 |
| 0.0136 | 0.1111 | 4.8817 | 0.5117 | 0.0637 | 10.1668 | 78 |
| 0.0038 | 0.1115 | 5.1236 | 0.5118 | 0.0638 | 11.3651 | 79 |
| 0.0017 | 0.1115 | 5.3989 | 0.5176 | 0.0638 | 11.3609 | 80 |
| 0.0014 | 0.1115 | 5.5658 | 0.5231 | 0.0638 | 11.5637 | 81 |
| 0.0008 | 0.1115 | 5.4076 | 0.5273 | 0.0638 | 11.5293 | 82 |
| 0.0007 | 0.1116 | 5.5166 | 0.5325 | 0.0638 | 11.6874 | 83 |
| 0.0007 | 0.1115 | 5.3020 | 0.5370 | 0.0638 | 11.6410 | 84 |
| 0.0006 | 0.1116 | 5.3834 | 0.5424 | 0.0638 | 11.4686 | 85 |
| 0.0005 | 0.1115 | 5.2441 | 0.5482 | 0.0638 | 11.7770 | 86 |
| 0.0161 | 0.1110 | 5.8611 | 0.5310 | 0.0637 | 14.1541 | 87 |
| 0.0043 | 0.1115 | 6.7439 | 0.5302 | 0.0638 | 13.7884 | 88 |
| 0.0016 | 0.1115 | 6.4034 | 0.5337 | 0.0639 | 13.2969 | 89 |
| 0.0009 | 0.1115 | 6.4491 | 0.5361 | 0.0639 | 13.3960 | 90 |
| 0.0007 | 0.1115 | 6.4412 | 0.5412 | 0.0639 | 13.6544 | 91 |
| 0.0005 | 0.1115 | 6.4941 | 0.5451 | 0.0639 | 13.4296 | 92 |
| 0.0005 | 0.1116 | 6.4763 | 0.5493 | 0.0639 | 13.9268 | 93 |
| 0.0005 | 0.1115 | 6.4452 | 0.5595 | 0.0638 | 12.9971 | 94 |
| 0.0125 | 0.1111 | 5.7381 | 0.5505 | 0.0636 | 10.6493 | 95 |
| 0.0066 | 0.1114 | 5.3763 | 0.5383 | 0.0639 | 10.1229 | 96 |
| 0.0022 | 0.1115 | 5.4800 | 0.5424 | 0.0639 | 12.3926 | 97 |
| 0.0013 | 0.1115 | 5.6556 | 0.5460 | 0.0639 | 11.1784 | 98 |
| 0.0012 | 0.1115 | 6.1793 | 0.5467 | 0.0639 | 11.4956 | 99 |
### Framework versions
- Transformers 4.33.0.dev0
- TensorFlow 2.13.0
- Tokenizers 0.13.3
|
dthejaka/roberta-base_corona_nlp_classif
|
dthejaka
| 2023-08-26T18:29:28Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"text-classification",
"generated_from_trainer",
"base_model:FacebookAI/roberta-base",
"base_model:finetune:FacebookAI/roberta-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-08-26T09:17:31Z |
---
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
model-index:
- name: roberta-base_corona_nlp_classif
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-base_corona_nlp_classif
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5166
## Model description
This model is used to classify tweets regarding the COVID-19 as Extremely Positive, Positive, Neutral,Negative, Extremely Negative
## Intended uses & limitations
Training is done on a raw uncleaned dataset.
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 0.6501 | 1.0 | 4496 | 0.6886 |
| 0.4461 | 2.0 | 8992 | 0.5166 |
| 0.3347 | 3.0 | 13488 | 0.6570 |
| 0.152 | 4.0 | 17984 | 0.6583 |
### Framework versions
- Transformers 4.32.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
FredZhang7/malphish-eater-v1
|
FredZhang7
| 2023-08-26T18:23:01Z | 118 | 2 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"bert",
"text-classification",
"af",
"en",
"et",
"sw",
"sv",
"sq",
"de",
"ca",
"hu",
"da",
"tl",
"so",
"fi",
"fr",
"cs",
"hr",
"cy",
"es",
"sl",
"tr",
"pl",
"pt",
"nl",
"id",
"sk",
"lt",
"no",
"lv",
"vi",
"it",
"ro",
"ru",
"mk",
"bg",
"th",
"ja",
"ko",
"multilingual",
"dataset:FredZhang7/malicious-website-features-2.4M",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-07-20T14:55:18Z |
---
license: apache-2.0
datasets:
- FredZhang7/malicious-website-features-2.4M
wget:
- text: https://chat.openai.com/
- text: https://huggingface.co/FredZhang7/aivance-safesearch-v3
metrics:
- accuracy
language:
- af
- en
- et
- sw
- sv
- sq
- de
- ca
- hu
- da
- tl
- so
- fi
- fr
- cs
- hr
- cy
- es
- sl
- tr
- pl
- pt
- nl
- id
- sk
- lt
- 'no'
- lv
- vi
- it
- ro
- ru
- mk
- bg
- th
- ja
- ko
- multilingual
---
It's very important to note that this model is not production-ready.
<br>
The classification task for v1 is split into two stages:
1. URL features model
- **96.5%+ accurate** on training and validation data
- 2,436,727 rows of labelled URLs
- evaluation from v2: slightly overfitted, by perhaps around 0.8%
2. Website features model
- **98.4% accurate** on training data, and **98.9% accurate** on validation data
- 911,180 rows of 42 features
- evaluation from v2: slightly biased towards the URL feature (bert_confidence) more than the other columns
## Training
I applied cross-validation with `cv=5` to the training dataset to search for the best hyperparameters.
Here's the dict passed to `sklearn`'s `GridSearchCV` function:
```python
params = {
'objective': 'binary',
'metric': 'binary_logloss',
'boosting_type': ['gbdt', 'dart'],
'num_leaves': [15, 23, 31, 63],
'learning_rate': [0.001, 0.002, 0.01, 0.02],
'feature_fraction': [0.5, 0.6, 0.7, 0.9],
'early_stopping_rounds': [10, 20],
'num_boost_round': [500, 750, 800, 900, 1000, 1250, 2000]
}
```
To reproduce the 98.4% accurate model, you can follow the data analysis on the [dataset page](https://huggingface.co/datasets/FredZhang7/malicious-website-features-2.4M) to filter out the unimportant features.
Then train a LightGBM model using the most suited hyperparamters for this task:
```python
params = {
'objective': 'binary',
'metric': 'binary_logloss',
'boosting_type': 'gbdt',
'num_leaves': 31,
'learning_rate': 0.01,
'feature_fraction': 0.6,
'early_stopping_rounds': 10,
'num_boost_round': 800
}
```
## URL Features
```python
from transformers import AutoModelForSequenceClassification, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("FredZhang7/malware-phisher")
model = AutoModelForSequenceClassification.from_pretrained("FredZhang7/malware-phisher")
```
## Website Features
```bash
pip install lightgbm
```
```python
import lightgbm as lgb
lgb.Booster(model_file="phishing_model_combined_0.984_train.txt")
```
|
yasmineelabbar/marian-finetuned-kde4-en-to-fr-accelerate
|
yasmineelabbar
| 2023-08-26T18:19:57Z | 118 | 2 |
transformers
|
[
"transformers",
"pytorch",
"marian",
"text2text-generation",
"translation",
"fine-tuning",
"fr",
"en",
"dataset:kde4",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
translation
| 2023-08-21T14:41:40Z |
---
license: apache-2.0
metrics:
- bleu 52.98
- sacrebleu
datasets:
- kde4
language:
- fr
- en
pipeline_tag: translation
tags:
- translation
- fine-tuning
- marian
---
# Model Name: marian-finetuned-kde4-en-to-fr
## Description
This model is a fine-tuned MarianMT model for English to French translation. It has been trained using the KDE4 dataset and optimized for translation tasks.
## Performance
During training and evaluation, the model achieved a BLEU score of 52.98 on the validation dataset. The BLEU score is a measure of translation quality, with higher scores indicating better translation performance.
## Usage
You can use this model for translating English sentences to French. Below is a sample code snippet for translating a sentence using the model:
```python
from transformers import pipeline
model_checkpoint = "yasmineelabbar/marian-finetuned-kde4-en-to-fr-accelerate"
translator = pipeline("translation", model=model_checkpoint)
result = translator("Input sentence in English")
print(result)
|
bigmorning/whisper_char_cv12_pad_lob100_low_sup__0085
|
bigmorning
| 2023-08-26T18:08:59Z | 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
| 2023-08-26T18:08:51Z |
---
license: apache-2.0
base_model: openai/whisper-tiny
tags:
- generated_from_keras_callback
model-index:
- name: whisper_char_cv12_pad_lob100_low_sup__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_char_cv12_pad_lob100_low_sup__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: 0.0007
- Train Accuracy: 0.1115
- Train Wermet: 5.3020
- Validation Loss: 0.5370
- Validation Accuracy: 0.0638
- Validation Wermet: 11.6410
- 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 |
|:----------:|:--------------:|:------------:|:---------------:|:-------------------:|:-----------------:|:-----:|
| 2.5942 | 0.0399 | 3.6402 | 1.9371 | 0.0319 | 16.1531 | 0 |
| 1.8766 | 0.0532 | 6.8384 | 1.7437 | 0.0343 | 15.0408 | 1 |
| 1.7251 | 0.0570 | 5.9150 | 1.6630 | 0.0358 | 10.5002 | 2 |
| 1.6457 | 0.0591 | 5.1153 | 1.5993 | 0.0369 | 10.4737 | 3 |
| 1.5935 | 0.0604 | 4.8231 | 1.5582 | 0.0375 | 8.5794 | 4 |
| 1.5526 | 0.0615 | 4.1987 | 1.5103 | 0.0385 | 9.4130 | 5 |
| 1.5165 | 0.0625 | 4.0179 | 1.4812 | 0.0391 | 6.6025 | 6 |
| 1.4868 | 0.0633 | 3.6770 | 1.4465 | 0.0399 | 6.7562 | 7 |
| 1.4565 | 0.0642 | 3.3851 | 1.4326 | 0.0402 | 6.3327 | 8 |
| 1.4271 | 0.0650 | 3.2883 | 1.3788 | 0.0413 | 6.5933 | 9 |
| 1.3965 | 0.0659 | 3.0822 | 1.3558 | 0.0415 | 5.7852 | 10 |
| 1.3541 | 0.0671 | 2.8659 | 1.2958 | 0.0429 | 5.2978 | 11 |
| 1.3066 | 0.0684 | 2.4942 | 1.2323 | 0.0440 | 4.9600 | 12 |
| 1.2401 | 0.0703 | 2.0745 | 1.1430 | 0.0456 | 3.6837 | 13 |
| 1.1549 | 0.0728 | 1.6202 | 1.0353 | 0.0478 | 2.9217 | 14 |
| 1.0653 | 0.0755 | 1.3041 | 0.9650 | 0.0492 | 2.0673 | 15 |
| 0.9765 | 0.0783 | 1.0922 | 0.8766 | 0.0510 | 2.7441 | 16 |
| 0.8977 | 0.0808 | 1.2561 | 0.8053 | 0.0524 | 3.6015 | 17 |
| 0.8246 | 0.0831 | 1.2955 | 0.7391 | 0.0537 | 3.2922 | 18 |
| 0.7591 | 0.0852 | 1.3109 | 0.7221 | 0.0541 | 3.6946 | 19 |
| 0.6988 | 0.0872 | 1.3303 | 0.6366 | 0.0559 | 3.8377 | 20 |
| 0.6424 | 0.0891 | 1.3256 | 0.5883 | 0.0569 | 4.1079 | 21 |
| 0.5925 | 0.0908 | 1.3637 | 0.5649 | 0.0575 | 3.7297 | 22 |
| 0.5405 | 0.0925 | 1.3142 | 0.5193 | 0.0584 | 3.5121 | 23 |
| 0.4929 | 0.0942 | 1.3157 | 0.4836 | 0.0591 | 4.8017 | 24 |
| 0.4523 | 0.0956 | 1.4635 | 0.4542 | 0.0598 | 4.5538 | 25 |
| 0.4116 | 0.0971 | 1.5118 | 0.4377 | 0.0602 | 4.9221 | 26 |
| 0.3759 | 0.0984 | 1.6392 | 0.4101 | 0.0608 | 5.6152 | 27 |
| 0.3446 | 0.0994 | 1.7744 | 0.3890 | 0.0613 | 7.0303 | 28 |
| 0.3176 | 0.1004 | 2.1998 | 0.3751 | 0.0616 | 8.1772 | 29 |
| 0.2945 | 0.1012 | 2.5525 | 0.3598 | 0.0619 | 8.2165 | 30 |
| 0.2739 | 0.1019 | 2.7708 | 0.3425 | 0.0623 | 9.8904 | 31 |
| 0.2553 | 0.1026 | 3.0620 | 0.3336 | 0.0625 | 9.8263 | 32 |
| 0.2380 | 0.1032 | 3.3150 | 0.3248 | 0.0627 | 10.1323 | 33 |
| 0.2225 | 0.1037 | 3.4188 | 0.3186 | 0.0629 | 9.8005 | 34 |
| 0.2074 | 0.1043 | 3.4245 | 0.3194 | 0.0629 | 10.0836 | 35 |
| 0.1921 | 0.1048 | 3.5998 | 0.3096 | 0.0631 | 10.9020 | 36 |
| 0.1795 | 0.1053 | 3.7938 | 0.3075 | 0.0632 | 11.1284 | 37 |
| 0.1671 | 0.1057 | 3.7413 | 0.3038 | 0.0633 | 10.9362 | 38 |
| 0.1546 | 0.1061 | 3.7830 | 0.3024 | 0.0634 | 10.7771 | 39 |
| 0.1432 | 0.1066 | 3.6808 | 0.3035 | 0.0635 | 11.4689 | 40 |
| 0.1319 | 0.1070 | 3.7824 | 0.3027 | 0.0635 | 10.9949 | 41 |
| 0.1211 | 0.1074 | 3.9301 | 0.3060 | 0.0636 | 10.8937 | 42 |
| 0.1113 | 0.1077 | 3.8509 | 0.3060 | 0.0636 | 10.7188 | 43 |
| 0.1012 | 0.1081 | 3.8780 | 0.3104 | 0.0636 | 10.6993 | 44 |
| 0.0922 | 0.1085 | 3.6982 | 0.3123 | 0.0637 | 10.6308 | 45 |
| 0.0827 | 0.1088 | 3.7227 | 0.3185 | 0.0637 | 10.8392 | 46 |
| 0.0741 | 0.1092 | 3.7235 | 0.3222 | 0.0637 | 10.2774 | 47 |
| 0.0665 | 0.1095 | 3.7106 | 0.3314 | 0.0637 | 9.5736 | 48 |
| 0.0589 | 0.1098 | 3.6104 | 0.3393 | 0.0636 | 9.9114 | 49 |
| 0.0515 | 0.1100 | 3.6150 | 0.3431 | 0.0637 | 10.1000 | 50 |
| 0.0453 | 0.1103 | 3.6760 | 0.3542 | 0.0636 | 9.4499 | 51 |
| 0.0389 | 0.1105 | 3.7376 | 0.3607 | 0.0636 | 9.6629 | 52 |
| 0.0335 | 0.1107 | 3.7707 | 0.3692 | 0.0637 | 9.5104 | 53 |
| 0.0283 | 0.1109 | 3.7655 | 0.3771 | 0.0636 | 9.6379 | 54 |
| 0.0246 | 0.1110 | 3.9511 | 0.3898 | 0.0636 | 9.7582 | 55 |
| 0.0211 | 0.1111 | 3.9487 | 0.3960 | 0.0636 | 10.0651 | 56 |
| 0.0191 | 0.1112 | 4.0695 | 0.4041 | 0.0636 | 9.1873 | 57 |
| 0.0150 | 0.1113 | 4.2329 | 0.4158 | 0.0636 | 10.5777 | 58 |
| 0.0117 | 0.1114 | 4.3648 | 0.4241 | 0.0636 | 10.1904 | 59 |
| 0.0096 | 0.1115 | 4.3534 | 0.4333 | 0.0636 | 10.3831 | 60 |
| 0.0084 | 0.1115 | 4.4131 | 0.4417 | 0.0636 | 10.2134 | 61 |
| 0.0072 | 0.1115 | 4.4827 | 0.4539 | 0.0636 | 10.4537 | 62 |
| 0.0101 | 0.1114 | 4.6105 | 0.4701 | 0.0635 | 9.2620 | 63 |
| 0.0114 | 0.1113 | 4.4725 | 0.4602 | 0.0637 | 11.3443 | 64 |
| 0.0056 | 0.1115 | 4.6820 | 0.4678 | 0.0637 | 10.8401 | 65 |
| 0.0035 | 0.1115 | 4.7095 | 0.4748 | 0.0637 | 10.8410 | 66 |
| 0.0033 | 0.1115 | 4.5291 | 0.4831 | 0.0637 | 10.3950 | 67 |
| 0.0029 | 0.1115 | 4.4502 | 0.4916 | 0.0637 | 10.8216 | 68 |
| 0.0184 | 0.1110 | 4.2753 | 0.4987 | 0.0634 | 10.2126 | 69 |
| 0.0091 | 0.1113 | 4.1128 | 0.4833 | 0.0638 | 10.8605 | 70 |
| 0.0033 | 0.1115 | 4.1755 | 0.4911 | 0.0638 | 10.4538 | 71 |
| 0.0026 | 0.1115 | 4.3450 | 0.5009 | 0.0637 | 10.1961 | 72 |
| 0.0039 | 0.1115 | 4.6335 | 0.5079 | 0.0637 | 11.0165 | 73 |
| 0.0030 | 0.1115 | 4.5756 | 0.5071 | 0.0637 | 9.9384 | 74 |
| 0.0017 | 0.1115 | 4.6589 | 0.5090 | 0.0638 | 10.8814 | 75 |
| 0.0012 | 0.1115 | 4.8756 | 0.5146 | 0.0638 | 10.9099 | 76 |
| 0.0013 | 0.1115 | 4.9431 | 0.5220 | 0.0638 | 10.5558 | 77 |
| 0.0136 | 0.1111 | 4.8817 | 0.5117 | 0.0637 | 10.1668 | 78 |
| 0.0038 | 0.1115 | 5.1236 | 0.5118 | 0.0638 | 11.3651 | 79 |
| 0.0017 | 0.1115 | 5.3989 | 0.5176 | 0.0638 | 11.3609 | 80 |
| 0.0014 | 0.1115 | 5.5658 | 0.5231 | 0.0638 | 11.5637 | 81 |
| 0.0008 | 0.1115 | 5.4076 | 0.5273 | 0.0638 | 11.5293 | 82 |
| 0.0007 | 0.1116 | 5.5166 | 0.5325 | 0.0638 | 11.6874 | 83 |
| 0.0007 | 0.1115 | 5.3020 | 0.5370 | 0.0638 | 11.6410 | 84 |
### Framework versions
- Transformers 4.33.0.dev0
- TensorFlow 2.13.0
- Tokenizers 0.13.3
|
DrishtiSharma/DialoGPT-large-faqs-block-size-128-bs-16-lr-2e-5
|
DrishtiSharma
| 2023-08-26T18:03:34Z | 138 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"generated_from_trainer",
"base_model:microsoft/DialoGPT-large",
"base_model:finetune:microsoft/DialoGPT-large",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-08-26T17:49:08Z |
---
license: mit
base_model: microsoft/DialoGPT-large
tags:
- generated_from_trainer
model-index:
- name: DialoGPT-large-faqs-block-size-128-bs-16-lr-2e-5
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. -->
# DialoGPT-large-faqs-block-size-128-bs-16-lr-2e-5
This model is a fine-tuned version of [microsoft/DialoGPT-large](https://huggingface.co/microsoft/DialoGPT-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.7873
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 40 | 3.3953 |
| No log | 2.0 | 80 | 2.7368 |
| No log | 3.0 | 120 | 2.4963 |
| No log | 4.0 | 160 | 2.4083 |
| No log | 5.0 | 200 | 2.3677 |
| No log | 6.0 | 240 | 2.3529 |
| No log | 7.0 | 280 | 2.3669 |
| No log | 8.0 | 320 | 2.4104 |
| No log | 9.0 | 360 | 2.4576 |
| No log | 10.0 | 400 | 2.5224 |
| No log | 11.0 | 440 | 2.5940 |
| No log | 12.0 | 480 | 2.6281 |
| 1.7771 | 13.0 | 520 | 2.6656 |
| 1.7771 | 14.0 | 560 | 2.6991 |
| 1.7771 | 15.0 | 600 | 2.7157 |
| 1.7771 | 16.0 | 640 | 2.7565 |
| 1.7771 | 17.0 | 680 | 2.7790 |
| 1.7771 | 18.0 | 720 | 2.7847 |
| 1.7771 | 19.0 | 760 | 2.7866 |
| 1.7771 | 20.0 | 800 | 2.7873 |
### Framework versions
- Transformers 4.33.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4.dev0
- Tokenizers 0.13.3
|
mythrex/LunarLander-v2
|
mythrex
| 2023-08-26T17:55:37Z | 2 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-08-26T17:55:03Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: MLPPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 269.67 +/- 20.04
name: mean_reward
verified: false
---
# **MLPPO** Agent playing **LunarLander-v2**
This is a trained model of a **MLPPO** 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
...
```
|
mythrex/ppo-LunarLander-v1
|
mythrex
| 2023-08-26T17:51:50Z | 1 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-08-26T17:50:28Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: MLPPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 244.30 +/- 21.99
name: mean_reward
verified: false
---
# **MLPPO** Agent playing **LunarLander-v2**
This is a trained model of a **MLPPO** 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
...
```
|
bigmorning/whisper_char_cv12_pad_lob100_low_sup__0075
|
bigmorning
| 2023-08-26T17:42:28Z | 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
| 2023-08-26T17:42:20Z |
---
license: apache-2.0
base_model: openai/whisper-tiny
tags:
- generated_from_keras_callback
model-index:
- name: whisper_char_cv12_pad_lob100_low_sup__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_char_cv12_pad_lob100_low_sup__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: 0.0030
- Train Accuracy: 0.1115
- Train Wermet: 4.5756
- Validation Loss: 0.5071
- Validation Accuracy: 0.0637
- Validation Wermet: 9.9384
- 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 |
|:----------:|:--------------:|:------------:|:---------------:|:-------------------:|:-----------------:|:-----:|
| 2.5942 | 0.0399 | 3.6402 | 1.9371 | 0.0319 | 16.1531 | 0 |
| 1.8766 | 0.0532 | 6.8384 | 1.7437 | 0.0343 | 15.0408 | 1 |
| 1.7251 | 0.0570 | 5.9150 | 1.6630 | 0.0358 | 10.5002 | 2 |
| 1.6457 | 0.0591 | 5.1153 | 1.5993 | 0.0369 | 10.4737 | 3 |
| 1.5935 | 0.0604 | 4.8231 | 1.5582 | 0.0375 | 8.5794 | 4 |
| 1.5526 | 0.0615 | 4.1987 | 1.5103 | 0.0385 | 9.4130 | 5 |
| 1.5165 | 0.0625 | 4.0179 | 1.4812 | 0.0391 | 6.6025 | 6 |
| 1.4868 | 0.0633 | 3.6770 | 1.4465 | 0.0399 | 6.7562 | 7 |
| 1.4565 | 0.0642 | 3.3851 | 1.4326 | 0.0402 | 6.3327 | 8 |
| 1.4271 | 0.0650 | 3.2883 | 1.3788 | 0.0413 | 6.5933 | 9 |
| 1.3965 | 0.0659 | 3.0822 | 1.3558 | 0.0415 | 5.7852 | 10 |
| 1.3541 | 0.0671 | 2.8659 | 1.2958 | 0.0429 | 5.2978 | 11 |
| 1.3066 | 0.0684 | 2.4942 | 1.2323 | 0.0440 | 4.9600 | 12 |
| 1.2401 | 0.0703 | 2.0745 | 1.1430 | 0.0456 | 3.6837 | 13 |
| 1.1549 | 0.0728 | 1.6202 | 1.0353 | 0.0478 | 2.9217 | 14 |
| 1.0653 | 0.0755 | 1.3041 | 0.9650 | 0.0492 | 2.0673 | 15 |
| 0.9765 | 0.0783 | 1.0922 | 0.8766 | 0.0510 | 2.7441 | 16 |
| 0.8977 | 0.0808 | 1.2561 | 0.8053 | 0.0524 | 3.6015 | 17 |
| 0.8246 | 0.0831 | 1.2955 | 0.7391 | 0.0537 | 3.2922 | 18 |
| 0.7591 | 0.0852 | 1.3109 | 0.7221 | 0.0541 | 3.6946 | 19 |
| 0.6988 | 0.0872 | 1.3303 | 0.6366 | 0.0559 | 3.8377 | 20 |
| 0.6424 | 0.0891 | 1.3256 | 0.5883 | 0.0569 | 4.1079 | 21 |
| 0.5925 | 0.0908 | 1.3637 | 0.5649 | 0.0575 | 3.7297 | 22 |
| 0.5405 | 0.0925 | 1.3142 | 0.5193 | 0.0584 | 3.5121 | 23 |
| 0.4929 | 0.0942 | 1.3157 | 0.4836 | 0.0591 | 4.8017 | 24 |
| 0.4523 | 0.0956 | 1.4635 | 0.4542 | 0.0598 | 4.5538 | 25 |
| 0.4116 | 0.0971 | 1.5118 | 0.4377 | 0.0602 | 4.9221 | 26 |
| 0.3759 | 0.0984 | 1.6392 | 0.4101 | 0.0608 | 5.6152 | 27 |
| 0.3446 | 0.0994 | 1.7744 | 0.3890 | 0.0613 | 7.0303 | 28 |
| 0.3176 | 0.1004 | 2.1998 | 0.3751 | 0.0616 | 8.1772 | 29 |
| 0.2945 | 0.1012 | 2.5525 | 0.3598 | 0.0619 | 8.2165 | 30 |
| 0.2739 | 0.1019 | 2.7708 | 0.3425 | 0.0623 | 9.8904 | 31 |
| 0.2553 | 0.1026 | 3.0620 | 0.3336 | 0.0625 | 9.8263 | 32 |
| 0.2380 | 0.1032 | 3.3150 | 0.3248 | 0.0627 | 10.1323 | 33 |
| 0.2225 | 0.1037 | 3.4188 | 0.3186 | 0.0629 | 9.8005 | 34 |
| 0.2074 | 0.1043 | 3.4245 | 0.3194 | 0.0629 | 10.0836 | 35 |
| 0.1921 | 0.1048 | 3.5998 | 0.3096 | 0.0631 | 10.9020 | 36 |
| 0.1795 | 0.1053 | 3.7938 | 0.3075 | 0.0632 | 11.1284 | 37 |
| 0.1671 | 0.1057 | 3.7413 | 0.3038 | 0.0633 | 10.9362 | 38 |
| 0.1546 | 0.1061 | 3.7830 | 0.3024 | 0.0634 | 10.7771 | 39 |
| 0.1432 | 0.1066 | 3.6808 | 0.3035 | 0.0635 | 11.4689 | 40 |
| 0.1319 | 0.1070 | 3.7824 | 0.3027 | 0.0635 | 10.9949 | 41 |
| 0.1211 | 0.1074 | 3.9301 | 0.3060 | 0.0636 | 10.8937 | 42 |
| 0.1113 | 0.1077 | 3.8509 | 0.3060 | 0.0636 | 10.7188 | 43 |
| 0.1012 | 0.1081 | 3.8780 | 0.3104 | 0.0636 | 10.6993 | 44 |
| 0.0922 | 0.1085 | 3.6982 | 0.3123 | 0.0637 | 10.6308 | 45 |
| 0.0827 | 0.1088 | 3.7227 | 0.3185 | 0.0637 | 10.8392 | 46 |
| 0.0741 | 0.1092 | 3.7235 | 0.3222 | 0.0637 | 10.2774 | 47 |
| 0.0665 | 0.1095 | 3.7106 | 0.3314 | 0.0637 | 9.5736 | 48 |
| 0.0589 | 0.1098 | 3.6104 | 0.3393 | 0.0636 | 9.9114 | 49 |
| 0.0515 | 0.1100 | 3.6150 | 0.3431 | 0.0637 | 10.1000 | 50 |
| 0.0453 | 0.1103 | 3.6760 | 0.3542 | 0.0636 | 9.4499 | 51 |
| 0.0389 | 0.1105 | 3.7376 | 0.3607 | 0.0636 | 9.6629 | 52 |
| 0.0335 | 0.1107 | 3.7707 | 0.3692 | 0.0637 | 9.5104 | 53 |
| 0.0283 | 0.1109 | 3.7655 | 0.3771 | 0.0636 | 9.6379 | 54 |
| 0.0246 | 0.1110 | 3.9511 | 0.3898 | 0.0636 | 9.7582 | 55 |
| 0.0211 | 0.1111 | 3.9487 | 0.3960 | 0.0636 | 10.0651 | 56 |
| 0.0191 | 0.1112 | 4.0695 | 0.4041 | 0.0636 | 9.1873 | 57 |
| 0.0150 | 0.1113 | 4.2329 | 0.4158 | 0.0636 | 10.5777 | 58 |
| 0.0117 | 0.1114 | 4.3648 | 0.4241 | 0.0636 | 10.1904 | 59 |
| 0.0096 | 0.1115 | 4.3534 | 0.4333 | 0.0636 | 10.3831 | 60 |
| 0.0084 | 0.1115 | 4.4131 | 0.4417 | 0.0636 | 10.2134 | 61 |
| 0.0072 | 0.1115 | 4.4827 | 0.4539 | 0.0636 | 10.4537 | 62 |
| 0.0101 | 0.1114 | 4.6105 | 0.4701 | 0.0635 | 9.2620 | 63 |
| 0.0114 | 0.1113 | 4.4725 | 0.4602 | 0.0637 | 11.3443 | 64 |
| 0.0056 | 0.1115 | 4.6820 | 0.4678 | 0.0637 | 10.8401 | 65 |
| 0.0035 | 0.1115 | 4.7095 | 0.4748 | 0.0637 | 10.8410 | 66 |
| 0.0033 | 0.1115 | 4.5291 | 0.4831 | 0.0637 | 10.3950 | 67 |
| 0.0029 | 0.1115 | 4.4502 | 0.4916 | 0.0637 | 10.8216 | 68 |
| 0.0184 | 0.1110 | 4.2753 | 0.4987 | 0.0634 | 10.2126 | 69 |
| 0.0091 | 0.1113 | 4.1128 | 0.4833 | 0.0638 | 10.8605 | 70 |
| 0.0033 | 0.1115 | 4.1755 | 0.4911 | 0.0638 | 10.4538 | 71 |
| 0.0026 | 0.1115 | 4.3450 | 0.5009 | 0.0637 | 10.1961 | 72 |
| 0.0039 | 0.1115 | 4.6335 | 0.5079 | 0.0637 | 11.0165 | 73 |
| 0.0030 | 0.1115 | 4.5756 | 0.5071 | 0.0637 | 9.9384 | 74 |
### Framework versions
- Transformers 4.33.0.dev0
- TensorFlow 2.13.0
- Tokenizers 0.13.3
|
BadreddineHug/LayoutLM_1
|
BadreddineHug
| 2023-08-26T17:42:25Z | 75 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"layoutlmv3",
"token-classification",
"generated_from_trainer",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2023-08-26T17:38:33Z |
---
license: cc-by-nc-sa-4.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: LayoutLM_1
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# LayoutLM_1
This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4204
- Precision: 0.6552
- Recall: 0.7480
- F1: 0.6985
- Accuracy: 0.9071
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 1000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 3.7 | 100 | 0.6185 | 0.0 | 0.0 | 0.0 | 0.8310 |
| No log | 7.41 | 200 | 0.4585 | 0.6146 | 0.4646 | 0.5291 | 0.8839 |
| No log | 11.11 | 300 | 0.4020 | 0.5870 | 0.6378 | 0.6113 | 0.8929 |
| No log | 14.81 | 400 | 0.3775 | 0.6496 | 0.7008 | 0.6742 | 0.9006 |
| 0.4776 | 18.52 | 500 | 0.3826 | 0.6268 | 0.7008 | 0.6617 | 0.9019 |
| 0.4776 | 22.22 | 600 | 0.3864 | 0.6224 | 0.7008 | 0.6593 | 0.8981 |
| 0.4776 | 25.93 | 700 | 0.4307 | 0.5759 | 0.7165 | 0.6386 | 0.8916 |
| 0.4776 | 29.63 | 800 | 0.4205 | 0.6738 | 0.7480 | 0.7090 | 0.9123 |
| 0.4776 | 33.33 | 900 | 0.4176 | 0.6552 | 0.7480 | 0.6985 | 0.9084 |
| 0.0536 | 37.04 | 1000 | 0.4204 | 0.6552 | 0.7480 | 0.6985 | 0.9071 |
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
DrishtiSharma/DialoGPT-large-faqs-block-size128-bs-16
|
DrishtiSharma
| 2023-08-26T17:37:27Z | 136 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"generated_from_trainer",
"base_model:microsoft/DialoGPT-large",
"base_model:finetune:microsoft/DialoGPT-large",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-08-26T13:36:46Z |
---
license: mit
base_model: microsoft/DialoGPT-large
tags:
- generated_from_trainer
model-index:
- name: DialoGPT-large-faqs-block-size128-bs-16
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. -->
# DialoGPT-large-faqs-block-size128-bs-16
This model is a fine-tuned version of [microsoft/DialoGPT-large](https://huggingface.co/microsoft/DialoGPT-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.5086
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 7e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 40 | 2.4979 |
| No log | 2.0 | 80 | 2.2314 |
| No log | 3.0 | 120 | 2.2409 |
| No log | 4.0 | 160 | 2.4555 |
| No log | 5.0 | 200 | 2.7390 |
| No log | 6.0 | 240 | 2.9258 |
| No log | 7.0 | 280 | 3.0355 |
| No log | 8.0 | 320 | 3.1368 |
| No log | 9.0 | 360 | 3.2088 |
| No log | 10.0 | 400 | 3.2541 |
| No log | 11.0 | 440 | 3.3225 |
| No log | 12.0 | 480 | 3.3775 |
| 0.7809 | 13.0 | 520 | 3.4102 |
| 0.7809 | 14.0 | 560 | 3.4456 |
| 0.7809 | 15.0 | 600 | 3.4707 |
| 0.7809 | 16.0 | 640 | 3.4786 |
| 0.7809 | 17.0 | 680 | 3.4868 |
| 0.7809 | 18.0 | 720 | 3.5035 |
| 0.7809 | 19.0 | 760 | 3.5015 |
| 0.7809 | 20.0 | 800 | 3.5086 |
### Framework versions
- Transformers 4.33.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4.dev0
- Tokenizers 0.13.3
|
wesley7137/platy-orca-bci-adapter
|
wesley7137
| 2023-08-26T17:36:12Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-26T17:35:49Z |
---
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: False
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.4.0
|
Vedikal/food
|
Vedikal
| 2023-08-26T17:32:17Z | 0 | 0 | null |
[
"safetensors",
"NxtWave-GenAI-Webinar",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"region:us"
] |
text-to-image
| 2023-08-26T17:30:20Z |
---
license: creativeml-openrail-m
tags:
- NxtWave-GenAI-Webinar
- text-to-image
- stable-diffusion
---
### Food- Dreambooth model trained by Vedikal following the "Build your own Gen AI model" session by NxtWave.
Project Submission Code: Code-MGMCE-359
Sample pictures of this concept:
.jpg)
.jpg)
.jpg)
.jpg)
.jpg)
.jpg)
.jpg)
|
dt-and-vanilla-ardt/dt-robust_train_halfcheetah_v3-2608_1609-66
|
dt-and-vanilla-ardt
| 2023-08-26T17:21:49Z | 32 | 0 |
transformers
|
[
"transformers",
"pytorch",
"decision_transformer",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | null | 2023-08-26T15:10:58Z |
---
tags:
- generated_from_trainer
model-index:
- name: dt-robust_train_halfcheetah_v3-2608_1609-66
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. -->
# dt-robust_train_halfcheetah_v3-2608_1609-66
This model is a fine-tuned version of [](https://huggingface.co/) 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: 0.0001
- train_batch_size: 64
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- training_steps: 10000
### Training results
### Framework versions
- Transformers 4.29.2
- Pytorch 2.1.0.dev20230727+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
Cicatrice/ppo-LunarLander-v2
|
Cicatrice
| 2023-08-26T17:06:35Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-08-26T17:06:13Z |
---
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: 277.26 +/- 12.23
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
...
```
|
TheXin18/Xin18
|
TheXin18
| 2023-08-26T17:06:31Z | 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
| 2023-08-26T15:45:28Z |
---
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: photo of ND18
tags:
- text-to-image
- diffusers
- autotrain
inference: true
---
# DreamBooth trained by AutoTrain
Text encoder was not trained.
|
Danielbrdz/Barcenas-7b
|
Danielbrdz
| 2023-08-26T17:04:40Z | 1,497 | 1 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"es",
"en",
"dataset:Danielbrdz/Barcenas-DataSet",
"license:other",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-08-25T17:33:48Z |
---
license: other
datasets:
- Danielbrdz/Barcenas-DataSet
language:
- es
- en
---
Barcenas-7b a model based on orca-mini-v3-7b and LLama2-7b.
Trained with a proprietary dataset to boost the creativity and consistency of its responses.
This model would never have been possible thanks to the following people:
Pankaj Mathur - For his orca-mini-v3-7b model which was the basis of the Barcenas-7b fine-tune.
Maxime Labonne - Thanks to his code and tutorial for fine-tuning in LLama2
TheBloke - For his script for a peft adapter
Georgi Gerganov - For his llama.cp project that contributed in Barcenas-7b functions
TrashPandaSavior - Reddit user who with his information would never have started the project.
Made with ❤️ in Guadalupe, Nuevo Leon, Mexico 🇲🇽
|
Jzuluaga/accent-id-commonaccent_xlsr-de-german
|
Jzuluaga
| 2023-08-26T17:03:24Z | 6 | 2 |
speechbrain
|
[
"speechbrain",
"audio-classification",
"embeddings",
"Accent Identification",
"pytorch",
"wav2vec2",
"XLSR",
"CommonAccent",
"German",
"de",
"dataset:CommonVoice",
"arxiv:2305.18283",
"arxiv:2006.13979",
"arxiv:2106.04624",
"license:mit",
"region:us"
] |
audio-classification
| 2023-08-05T16:21:33Z |
---
language:
- de
thumbnail: null
tags:
- audio-classification
- speechbrain
- embeddings
- Accent Identification
- pytorch
- wav2vec2
- XLSR
- CommonAccent
- German
license: mit
datasets:
- CommonVoice
metrics:
- Accuracy
widget:
- example_title: Germany
src: >-
https://huggingface.co/Jzuluaga/accent-id-commonaccent_xlsr-de-german/resolve/main/data/germany.wav
- example_title: Switzerland
src: >-
https://huggingface.co/Jzuluaga/accent-id-commonaccent_xlsr-de-german/resolve/main/data/switzerland.wav
- example_title: Italy
src: >-
https://huggingface.co/Jzuluaga/accent-id-commonaccent_xlsr-de-german/resolve/main/data/italy.wav
---
<iframe src="https://ghbtns.com/github-btn.html?user=speechbrain&repo=speechbrain&type=star&count=true&size=large&v=2" frameborder="0" scrolling="0" width="170" height="30" title="GitHub"></iframe>
<br/><br/>
# CommonAccent: Exploring Large Acoustic Pretrained Models for Accent Classification Based on Common Voice
**German Accent Classifier**
**Abstract**:
Despite the recent advancements in Automatic Speech Recognition (ASR), the recognition of accented speech still remains a dominant problem. In order to create more inclusive ASR systems, research has shown that the integration of accent information, as part of a larger ASR framework, can lead to the mitigation of accented speech errors. We address multilingual accent classification through the ECAPA-TDNN and Wav2Vec 2.0/XLSR architectures which have been proven to perform well on a variety of speech-related downstream tasks. We introduce a simple-to-follow recipe aligned to the SpeechBrain toolkit for accent classification based on Common Voice 7.0 (English) and Common Voice 11.0 (Italian, German, and Spanish). Furthermore, we establish new state-of-the-art for English accent classification with as high as 95% accuracy. We also study the internal categorization of the Wav2Vev 2.0 embeddings through t-SNE, noting that there is a level of clustering based on phonological similarity.
This repository provides all the necessary tools to perform accent identification from speech recordings with [SpeechBrain](https://github.com/speechbrain/speechbrain).
The system uses a model pretrained on the CommonAccent dataset in German (4 accents). This system is based on the CommonLanguage Recipe located here: https://github.com/speechbrain/speechbrain/tree/develop/recipes/CommonLanguage
The provided system can recognize the following 4 accents from short speech recordings in German (DE):
```
- DEUTSCHLAND DEUTSCH
- SCHWEIZERDEUTSCH
- OSTERREICHISCHES DEUTSCH
- ITALIENISCH DEUTSCH
```
<a href="https://github.com/JuanPZuluaga/accent-recog-slt2022"> <img alt="GitHub" src="https://img.shields.io/badge/GitHub-Open%20source-green"> </a> Github repository link: https://github.com/JuanPZuluaga/accent-recog-slt2022
**NOTE**: due to incompatibility with the model and the current SpeechBrain interfaces, we cannot offer the Inference API. Please, follow the steps in **"Perform Accent Identification from Speech Recordings"** to use this German Accent ID model.
For a better experience, we encourage you to learn more about
[SpeechBrain](https://speechbrain.github.io). The given model performance on the test set is:
| Release (dd/mm/yyyy) | Accuracy (%)
|:-------------:|:--------------:|
| 01-08-2023 (this model) | 75.5 |
## Pipeline description
This system is composed of a fine-tuned XLSR model coupled with statistical pooling. A classifier, trained with NLL Loss, is applied on top of that.
The system is trained with recordings sampled at 16kHz (single channel).
The code will automatically normalize your audio (i.e., resampling + mono channel selection) when calling *classify_file* if needed. Make sure your input tensor is compliant with the expected sampling rate if you use *encode_batch* and *classify_batch*.
## Install SpeechBrain
First of all, please install SpeechBrain with the following command:
```
pip install speechbrain
```
Please notice that we encourage you to read our tutorials and learn more about
[SpeechBrain](https://speechbrain.github.io).
### Perform Accent Identification from Speech Recordings
```python
import torchaudio
from speechbrain.pretrained.interfaces import foreign_class
classifier = foreign_class(source="Jzuluaga/accent-id-commonaccent_xlsr-de-german", pymodule_file="custom_interface.py", classname="CustomEncoderWav2vec2Classifier")
# German Accent Example
out_prob, score, index, text_lab = classifier.classify_file('Jzuluaga/accent-id-commonaccent_xlsr-de-german/data/german.wav')
print(text_lab)
# Swiss Example
out_prob, score, index, text_lab = classifier.classify_file('Jzuluaga/accent-id-commonaccent_xlsr-de-german/data/switzerland.wav')
print(text_lab)
```
### Inference on GPU
To perform inference on the GPU, add `run_opts={"device":"cuda"}` when calling the `from_hparams` method.
### Training
The model was trained with SpeechBrain.
To train it from scratch follow these steps:
1. Clone SpeechBrain:
```bash
git clone https://github.com/speechbrain/speechbrain/
```
2. Install it:
```bash
cd speechbrain
pip install -r requirements.txt
pip install -e .
```
3. Clone our repository in https://github.com/JuanPZuluaga/accent-recog-slt2022:
```bash
git clone https://github.com/JuanPZuluaga/accent-recog-slt2022
cd CommonAccent/accent_id
python train_w2v2.py hparams/train_w2v2.yaml
```
You can find our training results (models, logs, etc) in this repository's `Files and versions` page.
### Limitations
The SpeechBrain team does not provide any warranty on the performance achieved by this model when used on other datasets.
#### Cite our work: CommonAccent
If you find useful this work, please cite our work as:
```
@article{zuluaga2023commonaccent,
title={CommonAccent: Exploring Large Acoustic Pretrained Models for Accent Classification Based on Common Voice},
author={Zuluaga-Gomez, Juan and Ahmed, Sara and Visockas, Danielius and Subakan, Cem},
journal={Interspeech 2023},
url={https://arxiv.org/abs/2305.18283},
year={2023}
}
```
#### Cite XLSR model
```@article{conneau2020unsupervised,
title={Unsupervised cross-lingual representation learning for speech recognition},
author={Conneau, Alexis and Baevski, Alexei and Collobert, Ronan and Mohamed, Abdelrahman and Auli, Michael},
journal={arXiv preprint arXiv:2006.13979},
year={2020}
}
```
# **Cite SpeechBrain**
Please, cite SpeechBrain if you use it for your research or business.
```bibtex
@misc{speechbrain,
title={{SpeechBrain}: A General-Purpose Speech Toolkit},
author={Mirco Ravanelli and Titouan Parcollet and Peter Plantinga and Aku Rouhe and Samuele Cornell and Loren Lugosch and Cem Subakan and Nauman Dawalatabad and Abdelwahab Heba and Jianyuan Zhong and Ju-Chieh Chou and Sung-Lin Yeh and Szu-Wei Fu and Chien-Feng Liao and Elena Rastorgueva and François Grondin and William Aris and Hwidong Na and Yan Gao and Renato De Mori and Yoshua Bengio},
year={2021},
eprint={2106.04624},
archivePrefix={arXiv},
primaryClass={eess.AS},
note={arXiv:2106.04624}
}
```
|
Jzuluaga/accent-id-commonaccent_xlsr-it-italian
|
Jzuluaga
| 2023-08-26T17:02:48Z | 3 | 1 |
speechbrain
|
[
"speechbrain",
"wav2vec2",
"audio-classification",
"embeddings",
"Accent Identification",
"pytorch",
"XLSR",
"CommonAccent",
"Italian",
"it",
"dataset:CommonVoice",
"arxiv:2305.18283",
"arxiv:2006.13979",
"arxiv:2106.04624",
"license:mit",
"region:us"
] |
audio-classification
| 2023-08-04T22:06:35Z |
---
language:
- it
thumbnail: null
tags:
- audio-classification
- speechbrain
- embeddings
- Accent Identification
- pytorch
- wav2vec2
- XLSR
- CommonAccent
- Italian
license: mit
datasets:
- CommonVoice
metrics:
- Accuracy
widget:
- example_title: Veneto
src: >-
https://huggingface.co/Jzuluaga/accent-id-commonaccent_xlsr-it-italian/resolve/main/data/veneto.wav
- example_title: Emilian
src: >-
https://huggingface.co/Jzuluaga/accent-id-commonaccent_xlsr-it-italian/resolve/main/data/emilian.wav
- example_title: Trentino
src: >-
https://huggingface.co/Jzuluaga/accent-id-commonaccent_xlsr-it-italian/resolve/main/data/trentino.wav
- example_title: Meridionale
src: >-
https://huggingface.co/Jzuluaga/accent-id-commonaccent_xlsr-it-italian/resolve/main/data/meridionale.wav
---
<iframe src="https://ghbtns.com/github-btn.html?user=speechbrain&repo=speechbrain&type=star&count=true&size=large&v=2" frameborder="0" scrolling="0" width="170" height="30" title="GitHub"></iframe>
<br/><br/>
# CommonAccent: Exploring Large Acoustic Pretrained Models for Accent Classification Based on Common Voice
**Italian Accent Classifier**
**Abstract**:
Despite the recent advancements in Automatic Speech Recognition (ASR), the recognition of accented speech still remains a dominant problem. In order to create more inclusive ASR systems, research has shown that the integration of accent information, as part of a larger ASR framework, can lead to the mitigation of accented speech errors. We address multilingual accent classification through the ECAPA-TDNN and Wav2Vec 2.0/XLSR architectures which have been proven to perform well on a variety of speech-related downstream tasks. We introduce a simple-to-follow recipe aligned to the SpeechBrain toolkit for accent classification based on Common Voice 7.0 (English) and Common Voice 11.0 (Italian, German, and Spanish). Furthermore, we establish new state-of-the-art for English accent classification with as high as 95% accuracy. We also study the internal categorization of the Wav2Vev 2.0 embeddings through t-SNE, noting that there is a level of clustering based on phonological similarity.
This repository provides all the necessary tools to perform accent identification from speech recordings with [SpeechBrain](https://github.com/speechbrain/speechbrain).
The system uses a model pretrained on the CommonAccent dataset in Italian (5 accents). This system is based on the CommonLanguage Recipe located here: https://github.com/speechbrain/speechbrain/tree/develop/recipes/CommonLanguage
The provided system can recognize the following 5 accents from short speech recordings in Italian (IT):
```
- VENETO
- EMILIANO
- MERIDIONALE
- TENDENTE AL SICULO MA NON MARCATO
- BASILICATA TRENTINO
```
<a href="https://github.com/JuanPZuluaga/accent-recog-slt2022"> <img alt="GitHub" src="https://img.shields.io/badge/GitHub-Open%20source-green"> </a> Github repository link: https://github.com/JuanPZuluaga/accent-recog-slt2022
**NOTE**: due to incompatibility with the model and the current SpeechBrain interfaces, we cannot offer the Inference API. Please, follow the steps in **"Perform Accent Identification from Speech Recordings"** to use this Italian Accent ID model.
For a better experience, we encourage you to learn more about
[SpeechBrain](https://speechbrain.github.io).
## Pipeline description
This system is composed of a fine-tuned XLSR model coupled with statistical pooling. A classifier, trained with NLL Loss, is applied on top of that.
The system is trained with recordings sampled at 16kHz (single channel).
The code will automatically normalize your audio (i.e., resampling + mono channel selection) when calling *classify_file* if needed. Make sure your input tensor is compliant with the expected sampling rate if you use *encode_batch* and *classify_batch*.
## Install SpeechBrain
First of all, please install SpeechBrain with the following command:
```
pip install speechbrain
```
Please notice that we encourage you to read our tutorials and learn more about
[SpeechBrain](https://speechbrain.github.io).
### Perform Accent Identification from Speech Recordings
```python
import torchaudio
from speechbrain.pretrained.interfaces import foreign_class
classifier = foreign_class(source="Jzuluaga/accent-id-commonaccent_xlsr-it-italian", pymodule_file="custom_interface.py", classname="CustomEncoderWav2vec2Classifier")
# Veneto accent example
out_prob, score, index, text_lab = classifier.classify_file('Jzuluaga/accent-id-commonaccent_xlsr-it-italian/data/veneto.wav')
print(text_lab)
# Trentino accent example
out_prob, score, index, text_lab = classifier.classify_file('Jzuluaga/accent-id-commonaccent_xlsr-it-italian/data/trentino.wav')
print(text_lab)
```
### Inference on GPU
To perform inference on the GPU, add `run_opts={"device":"cuda"}` when calling the `from_hparams` method.
### Training
The model was trained with SpeechBrain.
To train it from scratch follow these steps:
1. Clone SpeechBrain:
```bash
git clone https://github.com/speechbrain/speechbrain/
```
2. Install it:
```bash
cd speechbrain
pip install -r requirements.txt
pip install -e .
```
3. Clone our repository in https://github.com/JuanPZuluaga/accent-recog-slt2022:
```bash
git clone https://github.com/JuanPZuluaga/accent-recog-slt2022
cd CommonAccent/accent_id
python train_w2v2.py hparams/train_w2v2.yaml
```
You can find our training results (models, logs, etc) in this repository's `Files and versions` page.
### Limitations
The SpeechBrain team does not provide any warranty on the performance achieved by this model when used on other datasets.
#### Cite our work: CommonAccent
If you find useful this work, please cite our work as:
```
@article{zuluaga2023commonaccent,
title={CommonAccent: Exploring Large Acoustic Pretrained Models for Accent Classification Based on Common Voice},
author={Zuluaga-Gomez, Juan and Ahmed, Sara and Visockas, Danielius and Subakan, Cem},
journal={Interspeech 2023},
url={https://arxiv.org/abs/2305.18283},
year={2023}
}
```
#### Cite XLSR model
```@article{conneau2020unsupervised,
title={Unsupervised cross-lingual representation learning for speech recognition},
author={Conneau, Alexis and Baevski, Alexei and Collobert, Ronan and Mohamed, Abdelrahman and Auli, Michael},
journal={arXiv preprint arXiv:2006.13979},
year={2020}
}
```
# **Cite SpeechBrain**
Please, cite SpeechBrain if you use it for your research or business.
```bibtex
@misc{speechbrain,
title={{SpeechBrain}: A General-Purpose Speech Toolkit},
author={Mirco Ravanelli and Titouan Parcollet and Peter Plantinga and Aku Rouhe and Samuele Cornell and Loren Lugosch and Cem Subakan and Nauman Dawalatabad and Abdelwahab Heba and Jianyuan Zhong and Ju-Chieh Chou and Sung-Lin Yeh and Szu-Wei Fu and Chien-Feng Liao and Elena Rastorgueva and François Grondin and William Aris and Hwidong Na and Yan Gao and Renato De Mori and Yoshua Bengio},
year={2021},
eprint={2106.04624},
archivePrefix={arXiv},
primaryClass={eess.AS},
note={arXiv:2106.04624}
}
```
|
bigmorning/whisper_char_cv12_pad_lob100_low_sup__0060
|
bigmorning
| 2023-08-26T17:02:42Z | 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
| 2023-08-26T17:02:33Z |
---
license: apache-2.0
base_model: openai/whisper-tiny
tags:
- generated_from_keras_callback
model-index:
- name: whisper_char_cv12_pad_lob100_low_sup__0060
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_char_cv12_pad_lob100_low_sup__0060
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: 0.0117
- Train Accuracy: 0.1114
- Train Wermet: 4.3648
- Validation Loss: 0.4241
- Validation Accuracy: 0.0636
- Validation Wermet: 10.1904
- Epoch: 59
## 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 |
|:----------:|:--------------:|:------------:|:---------------:|:-------------------:|:-----------------:|:-----:|
| 2.5942 | 0.0399 | 3.6402 | 1.9371 | 0.0319 | 16.1531 | 0 |
| 1.8766 | 0.0532 | 6.8384 | 1.7437 | 0.0343 | 15.0408 | 1 |
| 1.7251 | 0.0570 | 5.9150 | 1.6630 | 0.0358 | 10.5002 | 2 |
| 1.6457 | 0.0591 | 5.1153 | 1.5993 | 0.0369 | 10.4737 | 3 |
| 1.5935 | 0.0604 | 4.8231 | 1.5582 | 0.0375 | 8.5794 | 4 |
| 1.5526 | 0.0615 | 4.1987 | 1.5103 | 0.0385 | 9.4130 | 5 |
| 1.5165 | 0.0625 | 4.0179 | 1.4812 | 0.0391 | 6.6025 | 6 |
| 1.4868 | 0.0633 | 3.6770 | 1.4465 | 0.0399 | 6.7562 | 7 |
| 1.4565 | 0.0642 | 3.3851 | 1.4326 | 0.0402 | 6.3327 | 8 |
| 1.4271 | 0.0650 | 3.2883 | 1.3788 | 0.0413 | 6.5933 | 9 |
| 1.3965 | 0.0659 | 3.0822 | 1.3558 | 0.0415 | 5.7852 | 10 |
| 1.3541 | 0.0671 | 2.8659 | 1.2958 | 0.0429 | 5.2978 | 11 |
| 1.3066 | 0.0684 | 2.4942 | 1.2323 | 0.0440 | 4.9600 | 12 |
| 1.2401 | 0.0703 | 2.0745 | 1.1430 | 0.0456 | 3.6837 | 13 |
| 1.1549 | 0.0728 | 1.6202 | 1.0353 | 0.0478 | 2.9217 | 14 |
| 1.0653 | 0.0755 | 1.3041 | 0.9650 | 0.0492 | 2.0673 | 15 |
| 0.9765 | 0.0783 | 1.0922 | 0.8766 | 0.0510 | 2.7441 | 16 |
| 0.8977 | 0.0808 | 1.2561 | 0.8053 | 0.0524 | 3.6015 | 17 |
| 0.8246 | 0.0831 | 1.2955 | 0.7391 | 0.0537 | 3.2922 | 18 |
| 0.7591 | 0.0852 | 1.3109 | 0.7221 | 0.0541 | 3.6946 | 19 |
| 0.6988 | 0.0872 | 1.3303 | 0.6366 | 0.0559 | 3.8377 | 20 |
| 0.6424 | 0.0891 | 1.3256 | 0.5883 | 0.0569 | 4.1079 | 21 |
| 0.5925 | 0.0908 | 1.3637 | 0.5649 | 0.0575 | 3.7297 | 22 |
| 0.5405 | 0.0925 | 1.3142 | 0.5193 | 0.0584 | 3.5121 | 23 |
| 0.4929 | 0.0942 | 1.3157 | 0.4836 | 0.0591 | 4.8017 | 24 |
| 0.4523 | 0.0956 | 1.4635 | 0.4542 | 0.0598 | 4.5538 | 25 |
| 0.4116 | 0.0971 | 1.5118 | 0.4377 | 0.0602 | 4.9221 | 26 |
| 0.3759 | 0.0984 | 1.6392 | 0.4101 | 0.0608 | 5.6152 | 27 |
| 0.3446 | 0.0994 | 1.7744 | 0.3890 | 0.0613 | 7.0303 | 28 |
| 0.3176 | 0.1004 | 2.1998 | 0.3751 | 0.0616 | 8.1772 | 29 |
| 0.2945 | 0.1012 | 2.5525 | 0.3598 | 0.0619 | 8.2165 | 30 |
| 0.2739 | 0.1019 | 2.7708 | 0.3425 | 0.0623 | 9.8904 | 31 |
| 0.2553 | 0.1026 | 3.0620 | 0.3336 | 0.0625 | 9.8263 | 32 |
| 0.2380 | 0.1032 | 3.3150 | 0.3248 | 0.0627 | 10.1323 | 33 |
| 0.2225 | 0.1037 | 3.4188 | 0.3186 | 0.0629 | 9.8005 | 34 |
| 0.2074 | 0.1043 | 3.4245 | 0.3194 | 0.0629 | 10.0836 | 35 |
| 0.1921 | 0.1048 | 3.5998 | 0.3096 | 0.0631 | 10.9020 | 36 |
| 0.1795 | 0.1053 | 3.7938 | 0.3075 | 0.0632 | 11.1284 | 37 |
| 0.1671 | 0.1057 | 3.7413 | 0.3038 | 0.0633 | 10.9362 | 38 |
| 0.1546 | 0.1061 | 3.7830 | 0.3024 | 0.0634 | 10.7771 | 39 |
| 0.1432 | 0.1066 | 3.6808 | 0.3035 | 0.0635 | 11.4689 | 40 |
| 0.1319 | 0.1070 | 3.7824 | 0.3027 | 0.0635 | 10.9949 | 41 |
| 0.1211 | 0.1074 | 3.9301 | 0.3060 | 0.0636 | 10.8937 | 42 |
| 0.1113 | 0.1077 | 3.8509 | 0.3060 | 0.0636 | 10.7188 | 43 |
| 0.1012 | 0.1081 | 3.8780 | 0.3104 | 0.0636 | 10.6993 | 44 |
| 0.0922 | 0.1085 | 3.6982 | 0.3123 | 0.0637 | 10.6308 | 45 |
| 0.0827 | 0.1088 | 3.7227 | 0.3185 | 0.0637 | 10.8392 | 46 |
| 0.0741 | 0.1092 | 3.7235 | 0.3222 | 0.0637 | 10.2774 | 47 |
| 0.0665 | 0.1095 | 3.7106 | 0.3314 | 0.0637 | 9.5736 | 48 |
| 0.0589 | 0.1098 | 3.6104 | 0.3393 | 0.0636 | 9.9114 | 49 |
| 0.0515 | 0.1100 | 3.6150 | 0.3431 | 0.0637 | 10.1000 | 50 |
| 0.0453 | 0.1103 | 3.6760 | 0.3542 | 0.0636 | 9.4499 | 51 |
| 0.0389 | 0.1105 | 3.7376 | 0.3607 | 0.0636 | 9.6629 | 52 |
| 0.0335 | 0.1107 | 3.7707 | 0.3692 | 0.0637 | 9.5104 | 53 |
| 0.0283 | 0.1109 | 3.7655 | 0.3771 | 0.0636 | 9.6379 | 54 |
| 0.0246 | 0.1110 | 3.9511 | 0.3898 | 0.0636 | 9.7582 | 55 |
| 0.0211 | 0.1111 | 3.9487 | 0.3960 | 0.0636 | 10.0651 | 56 |
| 0.0191 | 0.1112 | 4.0695 | 0.4041 | 0.0636 | 9.1873 | 57 |
| 0.0150 | 0.1113 | 4.2329 | 0.4158 | 0.0636 | 10.5777 | 58 |
| 0.0117 | 0.1114 | 4.3648 | 0.4241 | 0.0636 | 10.1904 | 59 |
### Framework versions
- Transformers 4.33.0.dev0
- TensorFlow 2.13.0
- Tokenizers 0.13.3
|
NoobCoder10/Scoring_V_1
|
NoobCoder10
| 2023-08-26T16:51:09Z | 1 | 0 |
peft
|
[
"peft",
"pytorch",
"llama",
"8-bit",
"bitsandbytes",
"region:us"
] | null | 2023-08-26T16:46:28Z |
---
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
|
Lykon/dreamshaper-2
|
Lykon
| 2023-08-26T16:49:34Z | 18 | 2 |
diffusers
|
[
"diffusers",
"safetensors",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"art",
"artistic",
"anime",
"dreamshaper",
"en",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-08-26T16:49:33Z |
---
language:
- en
license: creativeml-openrail-m
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- art
- artistic
- diffusers
- anime
- dreamshaper
duplicated_from: lykon-models/dreamshaper-2
---
# Dreamshaper 2
`lykon-models/dreamshaper-2` is a Stable Diffusion model that has been fine-tuned on [runwayml/stable-diffusion-v1-5](https://huggingface.co/runwayml/stable-diffusion-v1-5).
Please consider supporting me:
- on [Patreon](https://www.patreon.com/Lykon275)
- or [buy me a coffee](https://snipfeed.co/lykon)
## Diffusers
For more general information on how to run text-to-image models with 🧨 Diffusers, see [the docs](https://huggingface.co/docs/diffusers/using-diffusers/conditional_image_generation).
1. Installation
```
pip install diffusers transformers accelerate
```
2. Run
```py
from diffusers import AutoPipelineForText2Image, DEISMultistepScheduler
import torch
pipe = AutoPipelineForText2Image.from_pretrained('lykon-models/dreamshaper-2', torch_dtype=torch.float16, variant="fp16")
pipe.scheduler = DEISMultistepScheduler.from_config(pipe.scheduler.config)
pipe = pipe.to("cuda")
prompt = "portrait photo of muscular bearded guy in a worn mech suit, light bokeh, intricate, steel metal, elegant, sharp focus, soft lighting, vibrant colors"
generator = torch.manual_seed(0)
image = pipe(prompt, num_inference_steps=25, generator=generator).images[0]
image.save("./image.png")
```
## Notes
- **Version 8** focuses on improving what V7 started. Might be harder to do photorealism compared to realism focused models, as it might be hard to do anime compared to anime focused models, but it can do both pretty well if you're skilled enough. Check the examples!
- **Version 7** improves lora support, NSFW and realism. If you're interested in "absolute" realism, try AbsoluteReality.
- **Version 6** adds more lora support and more style in general. It should also be better at generating directly at 1024 height (but be careful with it). 6.x are all improvements.
- **Version 5** is the best at photorealism and has noise offset.
- **Version 4** is much better with anime (can do them with no LoRA) and booru tags. It might be harder to control if you're used to caption style, so you might still want to use version 3.31. V4 is also better with eyes at lower resolutions. Overall is like a "fix" of V3 and shouldn't be too much different.
|
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