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| library_name
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Santp98/albert-base-spanish-2023-11-13-19-24
|
Santp98
| 2023-11-13T20:55:04Z | 24 | 0 |
transformers
|
[
"transformers",
"safetensors",
"albert",
"fill-mask",
"generated_from_trainer",
"base_model:dccuchile/albert-base-spanish",
"base_model:finetune:dccuchile/albert-base-spanish",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2023-11-13T19:24:55Z |
---
base_model: dccuchile/albert-base-spanish
tags:
- generated_from_trainer
model-index:
- name: albert-base-spanish-2023-11-13-19-24
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. -->
# albert-base-spanish-2023-11-13-19-24
This model is a fine-tuned version of [dccuchile/albert-base-spanish](https://huggingface.co/dccuchile/albert-base-spanish) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4306
## 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.9328 | 0.59 | 500 | 1.9145 |
| 1.863 | 1.19 | 1000 | 1.7882 |
| 1.7407 | 1.78 | 1500 | 1.7241 |
| 1.648 | 2.38 | 2000 | 1.6651 |
| 1.606 | 2.97 | 2500 | 1.6102 |
| 1.5833 | 3.56 | 3000 | 1.5912 |
| 1.5663 | 4.16 | 3500 | 1.5642 |
| 1.5104 | 4.75 | 4000 | 1.5390 |
| 1.5252 | 5.34 | 4500 | 1.5197 |
| 1.4676 | 5.94 | 5000 | 1.4950 |
| 1.4502 | 6.53 | 5500 | 1.4766 |
| 1.4336 | 7.13 | 6000 | 1.4694 |
| 1.4355 | 7.72 | 6500 | 1.4527 |
| 1.457 | 8.31 | 7000 | 1.4403 |
| 1.4219 | 8.91 | 7500 | 1.4380 |
| 1.4503 | 9.5 | 8000 | 1.4313 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu121
- Datasets 2.14.6
- Tokenizers 0.14.1
|
Word2vec/nlpl_82
|
Word2vec
| 2023-11-13T20:54:19Z | 0 | 0 | null |
[
"word2vec",
"eng",
"dataset:ENC3_English_Common_Crawl_Corpus",
"license:cc-by-4.0",
"region:us"
] | null | 2023-07-04T13:28:40Z |
---
language: eng
license: cc-by-4.0
tags:
- word2vec
datasets: ENC3_English_Common_Crawl_Corpus
---
## Information
A word2vec model trained by Kjetil Bugge Kristoffersen (kjetilbk@ifi.uio.no) on a vocabulary of size 2000000 corresponding to 135159000000 tokens from the dataset `ENC3:_English_Common_Crawl_Corpus`.
The model is trained with the following properties: no lemmatization and postag with the algorith Global Vectors with window of 10 and dimension of 300.
## How to use?
```
from gensim.models import KeyedVectors
from huggingface_hub import hf_hub_download
model = KeyedVectors.load_word2vec_format(hf_hub_download(repo_id="Word2vec/nlpl_82", filename="model.bin"), binary=True, unicode_errors="ignore")
```
## Citation
Fares, Murhaf; Kutuzov, Andrei; Oepen, Stephan & Velldal, Erik (2017). Word vectors, reuse, and replicability: Towards a community repository of large-text resources, In Jörg Tiedemann (ed.), Proceedings of the 21st Nordic Conference on Computational Linguistics, NoDaLiDa, 22-24 May 2017. Linköping University Electronic Press. ISBN 978-91-7685-601-7
This archive is part of the NLPL Word Vectors Repository (http://vectors.nlpl.eu/repository/), version 2.0, published on Friday, December 27, 2019.
Please see the file 'meta.json' in this archive and the overall repository metadata file http://vectors.nlpl.eu/repository/20.json for additional information.
The life-time identifier for this model is: http://vectors.nlpl.eu/repository/20/82.zip
|
facebook/dpt-dinov2-large-kitti
|
facebook
| 2023-11-13T20:41:36Z | 117 | 2 |
transformers
|
[
"transformers",
"pytorch",
"dpt",
"depth-estimation",
"vision",
"dinov2",
"arxiv:2304.07193",
"arxiv:2103.13413",
"license:apache-2.0",
"region:us"
] |
depth-estimation
| 2023-11-01T15:34:21Z |
---
license: apache-2.0
tags:
- vision
- dinov2
- depth-estimation
inference: false
---
# Model Card: DPT model with DINOv2 backbone
## Model Details
DPT (Dense Prediction Transformer) model with DINOv2 backbone as proposed in [DINOv2: Learning Robust Visual Features without Supervision](https://arxiv.org/abs/2304.07193) by Oquab et al.
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/dpt_architecture.jpg"
alt="drawing" width="600"/>
<small> DPT architecture. Taken from the <a href="https://arxiv.org/abs/2103.13413" target="_blank">original paper</a>. </small>
### Resources
- [DINOv2 Paper](https://arxiv.org/abs/2304.07193)
- [DPT Paper](https://arxiv.org/abs/2103.13413)
### Use with Transformers
```python
from transformers import AutoImageProcessor, DPTForDepthEstimation
import torch
import numpy as np
from PIL import Image
import requests
url = "http://images.cocodataset.org/val2017/000000039769.jpg"
image = Image.open(requests.get(url, stream=True).raw)
image_processor = AutoImageProcessor.from_pretrained("facebook/dpt-dinov2-large-kitti")
model = DPTForDepthEstimation.from_pretrained("facebook/dpt-dinov2-large-kitti")
# prepare image for the model
inputs = image_processor(images=image, return_tensors="pt")
with torch.no_grad():
outputs = model(**inputs)
predicted_depth = outputs.predicted_depth
# interpolate to original size
prediction = torch.nn.functional.interpolate(
predicted_depth.unsqueeze(1),
size=image.size[::-1],
mode="bicubic",
align_corners=False,
)
# visualize the prediction
output = prediction.squeeze().cpu().numpy()
formatted = (output * 255 / np.max(output)).astype("uint8")
depth = Image.fromarray(formatted)
```
## Model Use
### Intended Use
The model is intended to showcase that using the DPT framework with DINOv2 as backbone yields a powerful depth estimator.
### BibTeX entry and citation info
```bibtex
@misc{oquab2023dinov2,
title={DINOv2: Learning Robust Visual Features without Supervision},
author={Maxime Oquab and Timothée Darcet and Théo Moutakanni and Huy Vo and Marc Szafraniec and Vasil Khalidov and Pierre Fernandez and Daniel Haziza and Francisco Massa and Alaaeldin El-Nouby and Mahmoud Assran and Nicolas Ballas and Wojciech Galuba and Russell Howes and Po-Yao Huang and Shang-Wen Li and Ishan Misra and Michael Rabbat and Vasu Sharma and Gabriel Synnaeve and Hu Xu and Hervé Jegou and Julien Mairal and Patrick Labatut and Armand Joulin and Piotr Bojanowski},
year={2023},
eprint={2304.07193},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```
|
facebook/dpt-dinov2-giant-kitti
|
facebook
| 2023-11-13T20:41:31Z | 123 | 0 |
transformers
|
[
"transformers",
"pytorch",
"dpt",
"depth-estimation",
"vision",
"dinov2",
"arxiv:2304.07193",
"arxiv:2103.13413",
"license:apache-2.0",
"region:us"
] |
depth-estimation
| 2023-11-01T16:22:39Z |
---
license: apache-2.0
tags:
- vision
- dinov2
- depth-estimation
inference: false
---
# Model Card: DPT model with DINOv2 backbone
## Model Details
DPT (Dense Prediction Transformer) model with DINOv2 backbone as proposed in [DINOv2: Learning Robust Visual Features without Supervision](https://arxiv.org/abs/2304.07193) by Oquab et al.
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/dpt_architecture.jpg"
alt="drawing" width="600"/>
<small> DPT architecture. Taken from the <a href="https://arxiv.org/abs/2103.13413" target="_blank">original paper</a>. </small>
### Resources
- [DINOv2 Paper](https://arxiv.org/abs/2304.07193)
- [DPT Paper](https://arxiv.org/abs/2103.13413)
### Use with Transformers
```python
from transformers import AutoImageProcessor, DPTForDepthEstimation
import torch
import numpy as np
from PIL import Image
import requests
url = "http://images.cocodataset.org/val2017/000000039769.jpg"
image = Image.open(requests.get(url, stream=True).raw)
image_processor = AutoImageProcessor.from_pretrained("facebook/dpt-dinov2-giant-kitti")
model = DPTForDepthEstimation.from_pretrained("facebook/dpt-dinov2-giant-kitti")
# prepare image for the model
inputs = image_processor(images=image, return_tensors="pt")
with torch.no_grad():
outputs = model(**inputs)
predicted_depth = outputs.predicted_depth
# interpolate to original size
prediction = torch.nn.functional.interpolate(
predicted_depth.unsqueeze(1),
size=image.size[::-1],
mode="bicubic",
align_corners=False,
)
# visualize the prediction
output = prediction.squeeze().cpu().numpy()
formatted = (output * 255 / np.max(output)).astype("uint8")
depth = Image.fromarray(formatted)
```
## Model Use
### Intended Use
The model is intended to showcase that using the DPT framework with DINOv2 as backbone yields a powerful depth estimator.
### BibTeX entry and citation info
```bibtex
@misc{oquab2023dinov2,
title={DINOv2: Learning Robust Visual Features without Supervision},
author={Maxime Oquab and Timothée Darcet and Théo Moutakanni and Huy Vo and Marc Szafraniec and Vasil Khalidov and Pierre Fernandez and Daniel Haziza and Francisco Massa and Alaaeldin El-Nouby and Mahmoud Assran and Nicolas Ballas and Wojciech Galuba and Russell Howes and Po-Yao Huang and Shang-Wen Li and Ishan Misra and Michael Rabbat and Vasu Sharma and Gabriel Synnaeve and Hu Xu and Hervé Jegou and Julien Mairal and Patrick Labatut and Armand Joulin and Piotr Bojanowski},
year={2023},
eprint={2304.07193},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```
|
imann63/sce_2-sites_imgs-42_steps-4200_lr-5e6_regularization-none_model-base_diffuser
|
imann63
| 2023-11-13T20:38:41Z | 1 | 0 |
diffusers
|
[
"diffusers",
"tensorboard",
"safetensors",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"dreambooth",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-11-10T00:27:53Z |
---
license: creativeml-openrail-m
base_model: models/diffuser
instance_prompt: photo of a special powerline station
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- dreambooth
inference: true
---
# DreamBooth - imann63/sce_2-sites_imgs-42_steps-4200_lr-5e6_regularization-none_model-base_diffuser
This is a dreambooth model derived from models/diffuser. The weights were trained on photo of a special powerline station using [DreamBooth](https://dreambooth.github.io/).
You can find some example images in the following.
DreamBooth for the text encoder was enabled: False.
|
kwwww/bert-base-uncased-test_2_10000
|
kwwww
| 2023-11-13T20:38:12Z | 0 | 0 |
peft
|
[
"peft",
"pytorch",
"region:us"
] | null | 2023-11-13T02:51:08Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.6.0.dev0
|
kev216/Llama-2-7b-hf
|
kev216
| 2023-11-13T20:31:33Z | 10 | 0 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"llama",
"text-generation",
"facebook",
"meta",
"llama-2",
"en",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-11-13T20:14:25Z |
---
extra_gated_heading: Access Llama 2 on Hugging Face
extra_gated_description: >-
This is a form to enable access to Llama 2 on Hugging Face after you have been
granted access from Meta. Please visit the [Meta website](https://ai.meta.com/resources/models-and-libraries/llama-downloads) and accept our
license terms and acceptable use policy before submitting this form. Requests
will be processed in 1-2 days.
extra_gated_button_content: Submit
extra_gated_fields:
I agree to share my name, email address and username with Meta and confirm that I have already been granted download access on the Meta website: checkbox
language:
- en
pipeline_tag: text-generation
inference: false
tags:
- facebook
- meta
- pytorch
- llama
- llama-2
---
# **Llama 2**
Llama 2 is a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. This is the repository for the 7B pretrained model, converted for the Hugging Face Transformers format. Links to other models can be found in the index at the bottom.
## Model Details
*Note: Use of this model is governed by the Meta license. In order to download the model weights and tokenizer, please visit the [website](https://ai.meta.com/resources/models-and-libraries/llama-downloads/) and accept our License before requesting access here.*
Meta developed and publicly released the Llama 2 family of large language models (LLMs), a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. Our fine-tuned LLMs, called Llama-2-Chat, are optimized for dialogue use cases. Llama-2-Chat models outperform open-source chat models on most benchmarks we tested, and in our human evaluations for helpfulness and safety, are on par with some popular closed-source models like ChatGPT and PaLM.
**Model Developers** Meta
**Variations** Llama 2 comes in a range of parameter sizes — 7B, 13B, and 70B — as well as pretrained and fine-tuned variations.
**Input** Models input text only.
**Output** Models generate text only.
**Model Architecture** Llama 2 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align to human preferences for helpfulness and safety.
||Training Data|Params|Content Length|GQA|Tokens|LR|
|---|---|---|---|---|---|---|
|Llama 2|*A new mix of publicly available online data*|7B|4k|✗|2.0T|3.0 x 10<sup>-4</sup>|
|Llama 2|*A new mix of publicly available online data*|13B|4k|✗|2.0T|3.0 x 10<sup>-4</sup>|
|Llama 2|*A new mix of publicly available online data*|70B|4k|✔|2.0T|1.5 x 10<sup>-4</sup>|
*Llama 2 family of models.* Token counts refer to pretraining data only. All models are trained with a global batch-size of 4M tokens. Bigger models - 70B -- use Grouped-Query Attention (GQA) for improved inference scalability.
**Model Dates** Llama 2 was trained between January 2023 and July 2023.
**Status** This is a static model trained on an offline dataset. Future versions of the tuned models will be released as we improve model safety with community feedback.
**License** A custom commercial license is available at: [https://ai.meta.com/resources/models-and-libraries/llama-downloads/](https://ai.meta.com/resources/models-and-libraries/llama-downloads/)
## Intended Use
**Intended Use Cases** Llama 2 is intended for commercial and research use in English. Tuned models are intended for assistant-like chat, whereas pretrained models can be adapted for a variety of natural language generation tasks.
To get the expected features and performance for the chat versions, a specific formatting needs to be followed, including the `INST` and `<<SYS>>` tags, `BOS` and `EOS` tokens, and the whitespaces and breaklines in between (we recommend calling `strip()` on inputs to avoid double-spaces). See our reference code in github for details: [`chat_completion`](https://github.com/facebookresearch/llama/blob/main/llama/generation.py#L212).
**Out-of-scope Uses** Use in any manner that violates applicable laws or regulations (including trade compliance laws).Use in languages other than English. Use in any other way that is prohibited by the Acceptable Use Policy and Licensing Agreement for Llama 2.
## Hardware and Software
**Training Factors** We used custom training libraries, Meta's Research Super Cluster, and production clusters for pretraining. Fine-tuning, annotation, and evaluation were also performed on third-party cloud compute.
**Carbon Footprint** Pretraining utilized a cumulative 3.3M GPU hours of computation on hardware of type A100-80GB (TDP of 350-400W). Estimated total emissions were 539 tCO2eq, 100% of which were offset by Meta’s sustainability program.
||Time (GPU hours)|Power Consumption (W)|Carbon Emitted(tCO<sub>2</sub>eq)|
|---|---|---|---|
|Llama 2 7B|184320|400|31.22|
|Llama 2 13B|368640|400|62.44|
|Llama 2 70B|1720320|400|291.42|
|Total|3311616||539.00|
**CO<sub>2</sub> emissions during pretraining.** Time: total GPU time required for training each model. Power Consumption: peak power capacity per GPU device for the GPUs used adjusted for power usage efficiency. 100% of the emissions are directly offset by Meta's sustainability program, and because we are openly releasing these models, the pretraining costs do not need to be incurred by others.
## Training Data
**Overview** Llama 2 was pretrained on 2 trillion tokens of data from publicly available sources. The fine-tuning data includes publicly available instruction datasets, as well as over one million new human-annotated examples. Neither the pretraining nor the fine-tuning datasets include Meta user data.
**Data Freshness** The pretraining data has a cutoff of September 2022, but some tuning data is more recent, up to July 2023.
## Evaluation Results
In this section, we report the results for the Llama 1 and Llama 2 models on standard academic benchmarks.For all the evaluations, we use our internal evaluations library.
|Model|Size|Code|Commonsense Reasoning|World Knowledge|Reading Comprehension|Math|MMLU|BBH|AGI Eval|
|---|---|---|---|---|---|---|---|---|---|
|Llama 1|7B|14.1|60.8|46.2|58.5|6.95|35.1|30.3|23.9|
|Llama 1|13B|18.9|66.1|52.6|62.3|10.9|46.9|37.0|33.9|
|Llama 1|33B|26.0|70.0|58.4|67.6|21.4|57.8|39.8|41.7|
|Llama 1|65B|30.7|70.7|60.5|68.6|30.8|63.4|43.5|47.6|
|Llama 2|7B|16.8|63.9|48.9|61.3|14.6|45.3|32.6|29.3|
|Llama 2|13B|24.5|66.9|55.4|65.8|28.7|54.8|39.4|39.1|
|Llama 2|70B|**37.5**|**71.9**|**63.6**|**69.4**|**35.2**|**68.9**|**51.2**|**54.2**|
**Overall performance on grouped academic benchmarks.** *Code:* We report the average pass@1 scores of our models on HumanEval and MBPP. *Commonsense Reasoning:* We report the average of PIQA, SIQA, HellaSwag, WinoGrande, ARC easy and challenge, OpenBookQA, and CommonsenseQA. We report 7-shot results for CommonSenseQA and 0-shot results for all other benchmarks. *World Knowledge:* We evaluate the 5-shot performance on NaturalQuestions and TriviaQA and report the average. *Reading Comprehension:* For reading comprehension, we report the 0-shot average on SQuAD, QuAC, and BoolQ. *MATH:* We report the average of the GSM8K (8 shot) and MATH (4 shot) benchmarks at top 1.
|||TruthfulQA|Toxigen|
|---|---|---|---|
|Llama 1|7B|27.42|23.00|
|Llama 1|13B|41.74|23.08|
|Llama 1|33B|44.19|22.57|
|Llama 1|65B|48.71|21.77|
|Llama 2|7B|33.29|**21.25**|
|Llama 2|13B|41.86|26.10|
|Llama 2|70B|**50.18**|24.60|
**Evaluation of pretrained LLMs on automatic safety benchmarks.** For TruthfulQA, we present the percentage of generations that are both truthful and informative (the higher the better). For ToxiGen, we present the percentage of toxic generations (the smaller the better).
|||TruthfulQA|Toxigen|
|---|---|---|---|
|Llama-2-Chat|7B|57.04|**0.00**|
|Llama-2-Chat|13B|62.18|**0.00**|
|Llama-2-Chat|70B|**64.14**|0.01|
**Evaluation of fine-tuned LLMs on different safety datasets.** Same metric definitions as above.
## Ethical Considerations and Limitations
Llama 2 is a new technology that carries risks with use. Testing conducted to date has been in English, and has not covered, nor could it cover all scenarios. For these reasons, as with all LLMs, Llama 2’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 2, developers should perform safety testing and tuning tailored to their specific applications of the model.
Please see the Responsible Use Guide available at [https://ai.meta.com/llama/responsible-use-guide/](https://ai.meta.com/llama/responsible-use-guide)
## Reporting Issues
Please report any software “bug,” or other problems with the models through one of the following means:
- Reporting issues with the model: [github.com/facebookresearch/llama](http://github.com/facebookresearch/llama)
- Reporting problematic content generated by the model: [developers.facebook.com/llama_output_feedback](http://developers.facebook.com/llama_output_feedback)
- Reporting bugs and security concerns: [facebook.com/whitehat/info](http://facebook.com/whitehat/info)
## Llama Model Index
|Model|Llama2|Llama2-hf|Llama2-chat|Llama2-chat-hf|
|---|---|---|---|---|
|7B| [Link](https://huggingface.co/llamaste/Llama-2-7b) | [Link](https://huggingface.co/llamaste/Llama-2-7b-hf) | [Link](https://huggingface.co/llamaste/Llama-2-7b-chat) | [Link](https://huggingface.co/llamaste/Llama-2-7b-chat-hf)|
|13B| [Link](https://huggingface.co/llamaste/Llama-2-13b) | [Link](https://huggingface.co/llamaste/Llama-2-13b-hf) | [Link](https://huggingface.co/llamaste/Llama-2-13b-chat) | [Link](https://huggingface.co/llamaste/Llama-2-13b-hf)|
|70B| [Link](https://huggingface.co/llamaste/Llama-2-70b) | [Link](https://huggingface.co/llamaste/Llama-2-70b-hf) | [Link](https://huggingface.co/llamaste/Llama-2-70b-chat) | [Link](https://huggingface.co/llamaste/Llama-2-70b-hf)|
|
hamxea/Llama-2-7b-chat-hf-fine-tuned-adapters
|
hamxea
| 2023-11-13T20:27:51Z | 1 | 0 |
peft
|
[
"peft",
"pytorch",
"llama",
"arxiv:1910.09700",
"base_model:meta-llama/Llama-2-7b-chat-hf",
"base_model:adapter:meta-llama/Llama-2-7b-chat-hf",
"region:us"
] | null | 2023-11-13T19:52:33Z |
---
library_name: peft
base_model: meta-llama/Llama-2-7b-chat-hf
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset 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]
## 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 0.6.2.dev0
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.6.2.dev0
|
ceec/distilhubert-finetuned-gtzan
|
ceec
| 2023-11-13T20:25:23Z | 9 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"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
| 2023-11-10T21:41:16Z |
---
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: 0.84
---
<!-- 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: 0.7931
- Accuracy: 0.84
## 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:
- learning_rate: 4e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 12
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.2107 | 1.0 | 56 | 0.4744 | 0.89 |
| 0.0867 | 1.99 | 112 | 0.7316 | 0.8 |
| 0.1117 | 2.99 | 168 | 0.6942 | 0.81 |
| 0.1024 | 4.0 | 225 | 0.6151 | 0.85 |
| 0.0141 | 5.0 | 281 | 0.7542 | 0.83 |
| 0.0089 | 5.99 | 337 | 0.7236 | 0.85 |
| 0.007 | 6.99 | 393 | 0.7115 | 0.84 |
| 0.0477 | 8.0 | 450 | 0.7334 | 0.85 |
| 0.0048 | 9.0 | 506 | 0.7772 | 0.85 |
| 0.0348 | 9.99 | 562 | 0.7465 | 0.85 |
| 0.0035 | 10.99 | 618 | 0.8011 | 0.84 |
| 0.004 | 11.95 | 672 | 0.7931 | 0.84 |
### Framework versions
- Transformers 4.36.0.dev0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
sade-adrien/Mistral-7B-Instruct-v0.1-LC-noSW
|
sade-adrien
| 2023-11-13T20:25:06Z | 0 | 0 | null |
[
"generated_from_trainer",
"base_model:mistralai/Mistral-7B-Instruct-v0.1",
"base_model:finetune:mistralai/Mistral-7B-Instruct-v0.1",
"license:apache-2.0",
"region:us"
] | null | 2023-11-10T23:30:04Z |
---
license: apache-2.0
base_model: mistralai/Mistral-7B-Instruct-v0.1
tags:
- generated_from_trainer
model-index:
- name: Mistral-7B-Instruct-v0.1-LC-noSW
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Mistral-7B-Instruct-v0.1-LC-noSW
This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8686
## Model description
This model is a fine-tuning of Mistral-7B-Instruct-v0.1.
This FT was donne with full attention (removing the 4k SWA).
## Intended uses & limitations
More information needed
## Training and evaluation data
Data is a 9k sample from the RedPajama datset. The context is <=7200 with a decreasing exponential distribution of scale 1500.
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 32
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 20
- training_steps: 300
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.0233 | 0.18 | 50 | 1.8792 |
| 2.0805 | 0.36 | 100 | 1.8732 |
| 1.9414 | 0.55 | 150 | 1.8705 |
| 1.8302 | 0.73 | 200 | 1.8692 |
| 2.015 | 0.91 | 250 | 1.8686 |
| 1.894 | 1.09 | 300 | 1.8686 |
### Framework versions
- Transformers 4.34.1
- Pytorch 2.0.0+cu117
- Datasets 2.14.6
- Tokenizers 0.14.1
|
faisalahmedsifat/bloom-3b-bangla-pretrained-dental-eng-aus-checkpoint6k
|
faisalahmedsifat
| 2023-11-13T20:08:26Z | 1 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:bigscience/bloom-3b",
"base_model:adapter:bigscience/bloom-3b",
"region:us"
] | null | 2023-11-13T20:08:22Z |
---
library_name: peft
base_model: bigscience/bloom-3b
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Data Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
## 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 0.6.2.dev0
|
Norod78/SDXL-PringlesTube-Lora
|
Norod78
| 2023-11-13T19:56:28Z | 5 | 6 |
diffusers
|
[
"diffusers",
"text-to-image",
"stable-diffusion",
"lora",
"template:sd-lora",
"dataset:Norod78/pringles-blip-captions",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0",
"license:mit",
"region:us"
] |
text-to-image
| 2023-11-13T19:45:55Z |
---
tags:
- text-to-image
- stable-diffusion
- lora
- diffusers
- template:sd-lora
widget:
- text: A rainbow unicorn kitten PringlesTube
output:
url: >-
images/00031-20231113205711-7777-A ((rainbow unicorn kitten))
PringlesTube _lora_SDXL-PringlesTube-Lora_0.8_.jpg
- text: 'An Elsa from frozen PringlesTube '
output:
url: >-
images/00026-20231113205419-7778-An ((Elsa from frozen)) PringlesTube
_lora_SDXL-PringlesTube-Lora_0.5_.jpg
- text: 'A PringlesTube floating in outer space among galaxies '
output:
url: >-
images/00072-20231113212437-7779-A PringlesTube floating in outer space
among galaxies _lora_SDXL-PringlesTube-Lora_0.8_.jpg
- text: A seaweed PringlesTube on the beach
output:
url: >-
images/00060-20231113212003-7779-A seaweed PringlesTube on the beach
_lora_SDXL-PringlesTube-Lora_0.8_.jpg
- text: A vodka PringlesTube at the bar
output:
url: >-
images/00065-20231113212243-7777-A vodka PringlesTube at the bar
_lora_SDXL-PringlesTube-Lora_0.8_.jpg
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: PringlesTube
license: mit
datasets:
- Norod78/pringles-blip-captions
---
# SDXL PringlesTube Lora
<Gallery />
## Model description
An SDXL LoRA for generating images of Pringles packages.
## Trigger words
You should use `PringlesTube` to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](/Norod78/SDXL-PringlesTube-Lora/tree/main) them in the Files & versions tab.
|
hkivancoral/hushem_1x_deit_small_rms_001_fold4
|
hkivancoral
| 2023-11-13T19:46:03Z | 8 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:facebook/deit-small-patch16-224",
"base_model:finetune:facebook/deit-small-patch16-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-11-13T19:42:51Z |
---
license: apache-2.0
base_model: facebook/deit-small-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: hushem_1x_deit_small_rms_001_fold4
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.40476190476190477
---
<!-- 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. -->
# hushem_1x_deit_small_rms_001_fold4
This model is a fine-tuned version of [facebook/deit-small-patch16-224](https://huggingface.co/facebook/deit-small-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2235
- Accuracy: 0.4048
## 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: 0.001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 6 | 6.3906 | 0.2381 |
| 3.8063 | 2.0 | 12 | 1.7015 | 0.2619 |
| 3.8063 | 3.0 | 18 | 2.0641 | 0.2619 |
| 1.9221 | 4.0 | 24 | 1.7697 | 0.2381 |
| 1.6782 | 5.0 | 30 | 1.4022 | 0.2619 |
| 1.6782 | 6.0 | 36 | 1.7511 | 0.2381 |
| 1.5442 | 7.0 | 42 | 1.4627 | 0.2381 |
| 1.5442 | 8.0 | 48 | 1.4402 | 0.2619 |
| 1.4869 | 9.0 | 54 | 1.4717 | 0.2619 |
| 1.4572 | 10.0 | 60 | 1.4285 | 0.2381 |
| 1.4572 | 11.0 | 66 | 1.4073 | 0.2619 |
| 1.4861 | 12.0 | 72 | 1.4071 | 0.3095 |
| 1.4861 | 13.0 | 78 | 1.3676 | 0.3095 |
| 1.4283 | 14.0 | 84 | 1.4281 | 0.2381 |
| 1.4135 | 15.0 | 90 | 1.4437 | 0.2381 |
| 1.4135 | 16.0 | 96 | 1.3561 | 0.3095 |
| 1.375 | 17.0 | 102 | 1.3574 | 0.2857 |
| 1.375 | 18.0 | 108 | 1.2368 | 0.2857 |
| 1.3639 | 19.0 | 114 | 1.4601 | 0.2857 |
| 1.2891 | 20.0 | 120 | 1.7927 | 0.2381 |
| 1.2891 | 21.0 | 126 | 1.2451 | 0.4048 |
| 1.3173 | 22.0 | 132 | 1.1578 | 0.4762 |
| 1.3173 | 23.0 | 138 | 1.3222 | 0.3095 |
| 1.2505 | 24.0 | 144 | 1.3748 | 0.2381 |
| 1.263 | 25.0 | 150 | 1.3699 | 0.2857 |
| 1.263 | 26.0 | 156 | 1.2508 | 0.3810 |
| 1.2132 | 27.0 | 162 | 1.1843 | 0.4048 |
| 1.2132 | 28.0 | 168 | 1.4161 | 0.2619 |
| 1.1485 | 29.0 | 174 | 1.1305 | 0.4524 |
| 1.181 | 30.0 | 180 | 1.1818 | 0.4524 |
| 1.181 | 31.0 | 186 | 1.2906 | 0.4048 |
| 1.131 | 32.0 | 192 | 1.1623 | 0.4762 |
| 1.131 | 33.0 | 198 | 1.2826 | 0.4524 |
| 1.164 | 34.0 | 204 | 1.1932 | 0.4524 |
| 1.0879 | 35.0 | 210 | 1.1104 | 0.4286 |
| 1.0879 | 36.0 | 216 | 1.0661 | 0.5714 |
| 1.1012 | 37.0 | 222 | 1.2594 | 0.4048 |
| 1.1012 | 38.0 | 228 | 1.1459 | 0.4286 |
| 1.0505 | 39.0 | 234 | 1.1918 | 0.4524 |
| 1.0052 | 40.0 | 240 | 1.2662 | 0.4286 |
| 1.0052 | 41.0 | 246 | 1.2165 | 0.4048 |
| 0.9631 | 42.0 | 252 | 1.2235 | 0.4048 |
| 0.9631 | 43.0 | 258 | 1.2235 | 0.4048 |
| 0.9397 | 44.0 | 264 | 1.2235 | 0.4048 |
| 0.9545 | 45.0 | 270 | 1.2235 | 0.4048 |
| 0.9545 | 46.0 | 276 | 1.2235 | 0.4048 |
| 0.9591 | 47.0 | 282 | 1.2235 | 0.4048 |
| 0.9591 | 48.0 | 288 | 1.2235 | 0.4048 |
| 0.9579 | 49.0 | 294 | 1.2235 | 0.4048 |
| 0.9362 | 50.0 | 300 | 1.2235 | 0.4048 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
hkivancoral/hushem_1x_deit_small_rms_001_fold2
|
hkivancoral
| 2023-11-13T19:39:12Z | 15 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:facebook/deit-small-patch16-224",
"base_model:finetune:facebook/deit-small-patch16-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-11-13T19:36:00Z |
---
license: apache-2.0
base_model: facebook/deit-small-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: hushem_1x_deit_small_rms_001_fold2
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.4
---
<!-- 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. -->
# hushem_1x_deit_small_rms_001_fold2
This model is a fine-tuned version of [facebook/deit-small-patch16-224](https://huggingface.co/facebook/deit-small-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2524
- Accuracy: 0.4
## 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: 0.001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 6 | 7.5665 | 0.2444 |
| 4.6598 | 2.0 | 12 | 1.8034 | 0.2444 |
| 4.6598 | 3.0 | 18 | 1.7719 | 0.2444 |
| 1.754 | 4.0 | 24 | 1.5619 | 0.2667 |
| 1.5561 | 5.0 | 30 | 1.5155 | 0.2444 |
| 1.5561 | 6.0 | 36 | 1.5905 | 0.2444 |
| 1.5161 | 7.0 | 42 | 1.4606 | 0.2444 |
| 1.5161 | 8.0 | 48 | 1.5057 | 0.2667 |
| 1.4837 | 9.0 | 54 | 1.4997 | 0.2444 |
| 1.456 | 10.0 | 60 | 1.4411 | 0.2444 |
| 1.456 | 11.0 | 66 | 1.4980 | 0.2667 |
| 1.4256 | 12.0 | 72 | 1.4097 | 0.2444 |
| 1.4256 | 13.0 | 78 | 1.4518 | 0.2667 |
| 1.4488 | 14.0 | 84 | 1.3937 | 0.2667 |
| 1.4354 | 15.0 | 90 | 1.4044 | 0.2444 |
| 1.4354 | 16.0 | 96 | 1.3767 | 0.2667 |
| 1.4383 | 17.0 | 102 | 1.4222 | 0.2444 |
| 1.4383 | 18.0 | 108 | 1.4806 | 0.2444 |
| 1.4107 | 19.0 | 114 | 1.4789 | 0.2444 |
| 1.3761 | 20.0 | 120 | 1.2485 | 0.4444 |
| 1.3761 | 21.0 | 126 | 1.3600 | 0.2667 |
| 1.3385 | 22.0 | 132 | 1.4500 | 0.4 |
| 1.3385 | 23.0 | 138 | 1.3814 | 0.3778 |
| 1.3465 | 24.0 | 144 | 1.4692 | 0.2667 |
| 1.323 | 25.0 | 150 | 1.1674 | 0.4667 |
| 1.323 | 26.0 | 156 | 1.3636 | 0.2889 |
| 1.2871 | 27.0 | 162 | 1.3963 | 0.4 |
| 1.2871 | 28.0 | 168 | 1.3023 | 0.4444 |
| 1.1938 | 29.0 | 174 | 1.2034 | 0.4222 |
| 1.2252 | 30.0 | 180 | 1.2237 | 0.4444 |
| 1.2252 | 31.0 | 186 | 1.2906 | 0.4 |
| 1.2127 | 32.0 | 192 | 1.2853 | 0.4 |
| 1.2127 | 33.0 | 198 | 1.3006 | 0.3556 |
| 1.131 | 34.0 | 204 | 1.3803 | 0.2889 |
| 1.1689 | 35.0 | 210 | 1.2981 | 0.3556 |
| 1.1689 | 36.0 | 216 | 1.4728 | 0.2889 |
| 1.1285 | 37.0 | 222 | 1.3455 | 0.3333 |
| 1.1285 | 38.0 | 228 | 1.2593 | 0.4 |
| 1.0174 | 39.0 | 234 | 1.2539 | 0.3556 |
| 1.0651 | 40.0 | 240 | 1.2296 | 0.4 |
| 1.0651 | 41.0 | 246 | 1.2510 | 0.3778 |
| 1.0297 | 42.0 | 252 | 1.2524 | 0.4 |
| 1.0297 | 43.0 | 258 | 1.2524 | 0.4 |
| 0.9982 | 44.0 | 264 | 1.2524 | 0.4 |
| 1.047 | 45.0 | 270 | 1.2524 | 0.4 |
| 1.047 | 46.0 | 276 | 1.2524 | 0.4 |
| 0.9969 | 47.0 | 282 | 1.2524 | 0.4 |
| 0.9969 | 48.0 | 288 | 1.2524 | 0.4 |
| 1.0686 | 49.0 | 294 | 1.2524 | 0.4 |
| 1.0034 | 50.0 | 300 | 1.2524 | 0.4 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
hkivancoral/hushem_1x_deit_small_rms_001_fold1
|
hkivancoral
| 2023-11-13T19:35:43Z | 17 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:facebook/deit-small-patch16-224",
"base_model:finetune:facebook/deit-small-patch16-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-11-13T19:32:29Z |
---
license: apache-2.0
base_model: facebook/deit-small-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: hushem_1x_deit_small_rms_001_fold1
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.35555555555555557
---
<!-- 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. -->
# hushem_1x_deit_small_rms_001_fold1
This model is a fine-tuned version of [facebook/deit-small-patch16-224](https://huggingface.co/facebook/deit-small-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5587
- Accuracy: 0.3556
## 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: 0.001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 6 | 5.6616 | 0.2444 |
| 4.5403 | 2.0 | 12 | 1.9139 | 0.2444 |
| 4.5403 | 3.0 | 18 | 1.7372 | 0.2444 |
| 1.8724 | 4.0 | 24 | 1.4323 | 0.2667 |
| 1.5505 | 5.0 | 30 | 1.5541 | 0.2444 |
| 1.5505 | 6.0 | 36 | 1.5305 | 0.2444 |
| 1.4992 | 7.0 | 42 | 1.5286 | 0.2444 |
| 1.4992 | 8.0 | 48 | 1.5617 | 0.2444 |
| 1.4899 | 9.0 | 54 | 1.4717 | 0.2444 |
| 1.4501 | 10.0 | 60 | 1.4440 | 0.2444 |
| 1.4501 | 11.0 | 66 | 1.4155 | 0.2667 |
| 1.4052 | 12.0 | 72 | 1.3606 | 0.2444 |
| 1.4052 | 13.0 | 78 | 1.4215 | 0.3333 |
| 1.4555 | 14.0 | 84 | 1.3356 | 0.3333 |
| 1.4209 | 15.0 | 90 | 1.4688 | 0.2667 |
| 1.4209 | 16.0 | 96 | 1.2956 | 0.4444 |
| 1.4079 | 17.0 | 102 | 1.4012 | 0.2444 |
| 1.4079 | 18.0 | 108 | 1.4817 | 0.2444 |
| 1.4101 | 19.0 | 114 | 1.4296 | 0.2667 |
| 1.6129 | 20.0 | 120 | 1.5601 | 0.2444 |
| 1.6129 | 21.0 | 126 | 1.8216 | 0.2667 |
| 1.5349 | 22.0 | 132 | 1.6109 | 0.2667 |
| 1.5349 | 23.0 | 138 | 1.6663 | 0.2444 |
| 1.4443 | 24.0 | 144 | 1.4166 | 0.2444 |
| 1.3949 | 25.0 | 150 | 1.5159 | 0.2444 |
| 1.3949 | 26.0 | 156 | 1.5557 | 0.2444 |
| 1.2549 | 27.0 | 162 | 1.2710 | 0.3333 |
| 1.2549 | 28.0 | 168 | 1.4661 | 0.3333 |
| 1.2756 | 29.0 | 174 | 1.3759 | 0.3111 |
| 1.2244 | 30.0 | 180 | 1.3243 | 0.4222 |
| 1.2244 | 31.0 | 186 | 1.1877 | 0.4222 |
| 1.1482 | 32.0 | 192 | 1.1943 | 0.4667 |
| 1.1482 | 33.0 | 198 | 1.3644 | 0.3111 |
| 1.0904 | 34.0 | 204 | 1.3812 | 0.3778 |
| 1.051 | 35.0 | 210 | 1.3131 | 0.4444 |
| 1.051 | 36.0 | 216 | 1.7518 | 0.2667 |
| 1.0583 | 37.0 | 222 | 1.8440 | 0.3556 |
| 1.0583 | 38.0 | 228 | 1.7450 | 0.2889 |
| 0.8766 | 39.0 | 234 | 1.5767 | 0.3556 |
| 0.9084 | 40.0 | 240 | 1.5052 | 0.3778 |
| 0.9084 | 41.0 | 246 | 1.5534 | 0.3556 |
| 0.8553 | 42.0 | 252 | 1.5587 | 0.3556 |
| 0.8553 | 43.0 | 258 | 1.5587 | 0.3556 |
| 0.8404 | 44.0 | 264 | 1.5587 | 0.3556 |
| 0.8432 | 45.0 | 270 | 1.5587 | 0.3556 |
| 0.8432 | 46.0 | 276 | 1.5587 | 0.3556 |
| 0.8133 | 47.0 | 282 | 1.5587 | 0.3556 |
| 0.8133 | 48.0 | 288 | 1.5587 | 0.3556 |
| 0.8467 | 49.0 | 294 | 1.5587 | 0.3556 |
| 0.8396 | 50.0 | 300 | 1.5587 | 0.3556 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
hkivancoral/hushem_1x_deit_small_sgd_00001_fold5
|
hkivancoral
| 2023-11-13T19:27:54Z | 8 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:facebook/deit-small-patch16-224",
"base_model:finetune:facebook/deit-small-patch16-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-11-13T19:25:02Z |
---
license: apache-2.0
base_model: facebook/deit-small-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: hushem_1x_deit_small_sgd_00001_fold5
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.24390243902439024
---
<!-- 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. -->
# hushem_1x_deit_small_sgd_00001_fold5
This model is a fine-tuned version of [facebook/deit-small-patch16-224](https://huggingface.co/facebook/deit-small-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5124
- Accuracy: 0.2439
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 6 | 1.5202 | 0.2195 |
| 1.5388 | 2.0 | 12 | 1.5198 | 0.2195 |
| 1.5388 | 3.0 | 18 | 1.5194 | 0.2195 |
| 1.5264 | 4.0 | 24 | 1.5190 | 0.2195 |
| 1.5345 | 5.0 | 30 | 1.5186 | 0.2195 |
| 1.5345 | 6.0 | 36 | 1.5183 | 0.2195 |
| 1.526 | 7.0 | 42 | 1.5179 | 0.2195 |
| 1.526 | 8.0 | 48 | 1.5176 | 0.2195 |
| 1.5157 | 9.0 | 54 | 1.5173 | 0.2195 |
| 1.5235 | 10.0 | 60 | 1.5170 | 0.2195 |
| 1.5235 | 11.0 | 66 | 1.5167 | 0.2195 |
| 1.5297 | 12.0 | 72 | 1.5164 | 0.2439 |
| 1.5297 | 13.0 | 78 | 1.5161 | 0.2439 |
| 1.4988 | 14.0 | 84 | 1.5158 | 0.2439 |
| 1.5228 | 15.0 | 90 | 1.5155 | 0.2439 |
| 1.5228 | 16.0 | 96 | 1.5153 | 0.2439 |
| 1.5206 | 17.0 | 102 | 1.5150 | 0.2439 |
| 1.5206 | 18.0 | 108 | 1.5148 | 0.2439 |
| 1.5425 | 19.0 | 114 | 1.5146 | 0.2439 |
| 1.5252 | 20.0 | 120 | 1.5144 | 0.2439 |
| 1.5252 | 21.0 | 126 | 1.5142 | 0.2439 |
| 1.5165 | 22.0 | 132 | 1.5140 | 0.2439 |
| 1.5165 | 23.0 | 138 | 1.5139 | 0.2439 |
| 1.5451 | 24.0 | 144 | 1.5137 | 0.2439 |
| 1.5198 | 25.0 | 150 | 1.5135 | 0.2439 |
| 1.5198 | 26.0 | 156 | 1.5134 | 0.2439 |
| 1.5047 | 27.0 | 162 | 1.5132 | 0.2439 |
| 1.5047 | 28.0 | 168 | 1.5131 | 0.2439 |
| 1.5384 | 29.0 | 174 | 1.5130 | 0.2439 |
| 1.5271 | 30.0 | 180 | 1.5129 | 0.2439 |
| 1.5271 | 31.0 | 186 | 1.5128 | 0.2439 |
| 1.5283 | 32.0 | 192 | 1.5127 | 0.2439 |
| 1.5283 | 33.0 | 198 | 1.5127 | 0.2439 |
| 1.4864 | 34.0 | 204 | 1.5126 | 0.2439 |
| 1.5229 | 35.0 | 210 | 1.5125 | 0.2439 |
| 1.5229 | 36.0 | 216 | 1.5125 | 0.2439 |
| 1.513 | 37.0 | 222 | 1.5125 | 0.2439 |
| 1.513 | 38.0 | 228 | 1.5124 | 0.2439 |
| 1.4969 | 39.0 | 234 | 1.5124 | 0.2439 |
| 1.5399 | 40.0 | 240 | 1.5124 | 0.2439 |
| 1.5399 | 41.0 | 246 | 1.5124 | 0.2439 |
| 1.5142 | 42.0 | 252 | 1.5124 | 0.2439 |
| 1.5142 | 43.0 | 258 | 1.5124 | 0.2439 |
| 1.5226 | 44.0 | 264 | 1.5124 | 0.2439 |
| 1.538 | 45.0 | 270 | 1.5124 | 0.2439 |
| 1.538 | 46.0 | 276 | 1.5124 | 0.2439 |
| 1.5217 | 47.0 | 282 | 1.5124 | 0.2439 |
| 1.5217 | 48.0 | 288 | 1.5124 | 0.2439 |
| 1.5124 | 49.0 | 294 | 1.5124 | 0.2439 |
| 1.5354 | 50.0 | 300 | 1.5124 | 0.2439 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
facebook/dpt-dinov2-large-nyu
|
facebook
| 2023-11-13T19:23:10Z | 169 | 1 |
transformers
|
[
"transformers",
"pytorch",
"dpt",
"depth-estimation",
"vision",
"dinov2",
"arxiv:2304.07193",
"arxiv:2103.13413",
"license:apache-2.0",
"region:us"
] |
depth-estimation
| 2023-10-31T18:03:54Z |
---
license: apache-2.0
tags:
- vision
- dinov2
- depth-estimation
inference: false
---
# Model Card: DPT model with DINOv2 backbone
## Model Details
DPT (Dense Prediction Transformer) model with DINOv2 backbone as proposed in [DINOv2: Learning Robust Visual Features without Supervision](https://arxiv.org/abs/2304.07193) by Oquab et al.
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/dpt_architecture.jpg"
alt="drawing" width="600"/>
<small> DPT architecture. Taken from the <a href="https://arxiv.org/abs/2103.13413" target="_blank">original paper</a>. </small>
### Resources
- [DINOv2 Paper](https://arxiv.org/abs/2304.07193)
- [DPT Paper](https://arxiv.org/abs/2103.13413)
### Use with Transformers
```python
from transformers import AutoImageProcessor, DPTForDepthEstimation
import torch
import numpy as np
from PIL import Image
import requests
url = "http://images.cocodataset.org/val2017/000000039769.jpg"
image = Image.open(requests.get(url, stream=True).raw)
image_processor = AutoImageProcessor.from_pretrained("facebook/dpt-dinov2-large-nyu")
model = DPTForDepthEstimation.from_pretrained("facebook/dpt-dinov2-large-nyu")
# prepare image for the model
inputs = image_processor(images=image, return_tensors="pt")
with torch.no_grad():
outputs = model(**inputs)
predicted_depth = outputs.predicted_depth
# interpolate to original size
prediction = torch.nn.functional.interpolate(
predicted_depth.unsqueeze(1),
size=image.size[::-1],
mode="bicubic",
align_corners=False,
)
# visualize the prediction
output = prediction.squeeze().cpu().numpy()
formatted = (output * 255 / np.max(output)).astype("uint8")
depth = Image.fromarray(formatted)
```
## Model Use
### Intended Use
The model is intended to showcase that using the DPT framework with DINOv2 as backbone yields a powerful depth estimator.
### BibTeX entry and citation info
```bibtex
@misc{oquab2023dinov2,
title={DINOv2: Learning Robust Visual Features without Supervision},
author={Maxime Oquab and Timothée Darcet and Théo Moutakanni and Huy Vo and Marc Szafraniec and Vasil Khalidov and Pierre Fernandez and Daniel Haziza and Francisco Massa and Alaaeldin El-Nouby and Mahmoud Assran and Nicolas Ballas and Wojciech Galuba and Russell Howes and Po-Yao Huang and Shang-Wen Li and Ishan Misra and Michael Rabbat and Vasu Sharma and Gabriel Synnaeve and Hu Xu and Hervé Jegou and Julien Mairal and Patrick Labatut and Armand Joulin and Piotr Bojanowski},
year={2023},
eprint={2304.07193},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```
|
hkivancoral/hushem_1x_deit_small_sgd_00001_fold3
|
hkivancoral
| 2023-11-13T19:21:54Z | 8 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:facebook/deit-small-patch16-224",
"base_model:finetune:facebook/deit-small-patch16-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-11-13T19:19:13Z |
---
license: apache-2.0
base_model: facebook/deit-small-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: hushem_1x_deit_small_sgd_00001_fold3
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.18604651162790697
---
<!-- 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. -->
# hushem_1x_deit_small_sgd_00001_fold3
This model is a fine-tuned version of [facebook/deit-small-patch16-224](https://huggingface.co/facebook/deit-small-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5939
- Accuracy: 0.1860
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 6 | 1.6009 | 0.1860 |
| 1.5096 | 2.0 | 12 | 1.6005 | 0.1860 |
| 1.5096 | 3.0 | 18 | 1.6002 | 0.1860 |
| 1.4896 | 4.0 | 24 | 1.5998 | 0.1860 |
| 1.4946 | 5.0 | 30 | 1.5995 | 0.1860 |
| 1.4946 | 6.0 | 36 | 1.5992 | 0.1860 |
| 1.508 | 7.0 | 42 | 1.5988 | 0.1860 |
| 1.508 | 8.0 | 48 | 1.5986 | 0.1860 |
| 1.4945 | 9.0 | 54 | 1.5983 | 0.1860 |
| 1.5205 | 10.0 | 60 | 1.5980 | 0.1860 |
| 1.5205 | 11.0 | 66 | 1.5977 | 0.1860 |
| 1.5058 | 12.0 | 72 | 1.5975 | 0.1860 |
| 1.5058 | 13.0 | 78 | 1.5972 | 0.1860 |
| 1.5082 | 14.0 | 84 | 1.5970 | 0.1860 |
| 1.502 | 15.0 | 90 | 1.5967 | 0.1860 |
| 1.502 | 16.0 | 96 | 1.5965 | 0.1860 |
| 1.5281 | 17.0 | 102 | 1.5963 | 0.1860 |
| 1.5281 | 18.0 | 108 | 1.5961 | 0.1860 |
| 1.4713 | 19.0 | 114 | 1.5959 | 0.1860 |
| 1.5067 | 20.0 | 120 | 1.5957 | 0.1860 |
| 1.5067 | 21.0 | 126 | 1.5955 | 0.1860 |
| 1.5046 | 22.0 | 132 | 1.5953 | 0.1860 |
| 1.5046 | 23.0 | 138 | 1.5952 | 0.1860 |
| 1.4884 | 24.0 | 144 | 1.5950 | 0.1860 |
| 1.4923 | 25.0 | 150 | 1.5949 | 0.1860 |
| 1.4923 | 26.0 | 156 | 1.5948 | 0.1860 |
| 1.4973 | 27.0 | 162 | 1.5947 | 0.1860 |
| 1.4973 | 28.0 | 168 | 1.5945 | 0.1860 |
| 1.5002 | 29.0 | 174 | 1.5945 | 0.1860 |
| 1.4807 | 30.0 | 180 | 1.5944 | 0.1860 |
| 1.4807 | 31.0 | 186 | 1.5943 | 0.1860 |
| 1.486 | 32.0 | 192 | 1.5942 | 0.1860 |
| 1.486 | 33.0 | 198 | 1.5941 | 0.1860 |
| 1.4927 | 34.0 | 204 | 1.5941 | 0.1860 |
| 1.4875 | 35.0 | 210 | 1.5940 | 0.1860 |
| 1.4875 | 36.0 | 216 | 1.5940 | 0.1860 |
| 1.5166 | 37.0 | 222 | 1.5940 | 0.1860 |
| 1.5166 | 38.0 | 228 | 1.5939 | 0.1860 |
| 1.5127 | 39.0 | 234 | 1.5939 | 0.1860 |
| 1.4974 | 40.0 | 240 | 1.5939 | 0.1860 |
| 1.4974 | 41.0 | 246 | 1.5939 | 0.1860 |
| 1.4716 | 42.0 | 252 | 1.5939 | 0.1860 |
| 1.4716 | 43.0 | 258 | 1.5939 | 0.1860 |
| 1.5277 | 44.0 | 264 | 1.5939 | 0.1860 |
| 1.501 | 45.0 | 270 | 1.5939 | 0.1860 |
| 1.501 | 46.0 | 276 | 1.5939 | 0.1860 |
| 1.4805 | 47.0 | 282 | 1.5939 | 0.1860 |
| 1.4805 | 48.0 | 288 | 1.5939 | 0.1860 |
| 1.5052 | 49.0 | 294 | 1.5939 | 0.1860 |
| 1.536 | 50.0 | 300 | 1.5939 | 0.1860 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
hkivancoral/hushem_1x_deit_small_sgd_00001_fold2
|
hkivancoral
| 2023-11-13T19:18:59Z | 8 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:facebook/deit-small-patch16-224",
"base_model:finetune:facebook/deit-small-patch16-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-11-13T19:16:20Z |
---
license: apache-2.0
base_model: facebook/deit-small-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: hushem_1x_deit_small_sgd_00001_fold2
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.17777777777777778
---
<!-- 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. -->
# hushem_1x_deit_small_sgd_00001_fold2
This model is a fine-tuned version of [facebook/deit-small-patch16-224](https://huggingface.co/facebook/deit-small-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5076
- Accuracy: 0.1778
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 6 | 1.5128 | 0.1778 |
| 1.5351 | 2.0 | 12 | 1.5126 | 0.1778 |
| 1.5351 | 3.0 | 18 | 1.5123 | 0.1778 |
| 1.521 | 4.0 | 24 | 1.5120 | 0.1778 |
| 1.5462 | 5.0 | 30 | 1.5118 | 0.1778 |
| 1.5462 | 6.0 | 36 | 1.5116 | 0.1778 |
| 1.5099 | 7.0 | 42 | 1.5113 | 0.1778 |
| 1.5099 | 8.0 | 48 | 1.5111 | 0.1778 |
| 1.5333 | 9.0 | 54 | 1.5109 | 0.1778 |
| 1.5206 | 10.0 | 60 | 1.5106 | 0.1778 |
| 1.5206 | 11.0 | 66 | 1.5105 | 0.1778 |
| 1.5227 | 12.0 | 72 | 1.5103 | 0.1778 |
| 1.5227 | 13.0 | 78 | 1.5101 | 0.1778 |
| 1.5256 | 14.0 | 84 | 1.5099 | 0.1778 |
| 1.5395 | 15.0 | 90 | 1.5097 | 0.1778 |
| 1.5395 | 16.0 | 96 | 1.5095 | 0.1778 |
| 1.5169 | 17.0 | 102 | 1.5094 | 0.1778 |
| 1.5169 | 18.0 | 108 | 1.5092 | 0.1778 |
| 1.5502 | 19.0 | 114 | 1.5091 | 0.1778 |
| 1.4882 | 20.0 | 120 | 1.5090 | 0.1778 |
| 1.4882 | 21.0 | 126 | 1.5088 | 0.1778 |
| 1.5202 | 22.0 | 132 | 1.5087 | 0.1778 |
| 1.5202 | 23.0 | 138 | 1.5086 | 0.1778 |
| 1.5139 | 24.0 | 144 | 1.5085 | 0.1778 |
| 1.4995 | 25.0 | 150 | 1.5084 | 0.1778 |
| 1.4995 | 26.0 | 156 | 1.5083 | 0.1778 |
| 1.5175 | 27.0 | 162 | 1.5082 | 0.1778 |
| 1.5175 | 28.0 | 168 | 1.5081 | 0.1778 |
| 1.5365 | 29.0 | 174 | 1.5081 | 0.1778 |
| 1.5232 | 30.0 | 180 | 1.5080 | 0.1778 |
| 1.5232 | 31.0 | 186 | 1.5079 | 0.1778 |
| 1.5236 | 32.0 | 192 | 1.5079 | 0.1778 |
| 1.5236 | 33.0 | 198 | 1.5078 | 0.1778 |
| 1.5292 | 34.0 | 204 | 1.5078 | 0.1778 |
| 1.544 | 35.0 | 210 | 1.5077 | 0.1778 |
| 1.544 | 36.0 | 216 | 1.5077 | 0.1778 |
| 1.4971 | 37.0 | 222 | 1.5077 | 0.1778 |
| 1.4971 | 38.0 | 228 | 1.5077 | 0.1778 |
| 1.4951 | 39.0 | 234 | 1.5076 | 0.1778 |
| 1.5452 | 40.0 | 240 | 1.5076 | 0.1778 |
| 1.5452 | 41.0 | 246 | 1.5076 | 0.1778 |
| 1.5473 | 42.0 | 252 | 1.5076 | 0.1778 |
| 1.5473 | 43.0 | 258 | 1.5076 | 0.1778 |
| 1.5095 | 44.0 | 264 | 1.5076 | 0.1778 |
| 1.495 | 45.0 | 270 | 1.5076 | 0.1778 |
| 1.495 | 46.0 | 276 | 1.5076 | 0.1778 |
| 1.5118 | 47.0 | 282 | 1.5076 | 0.1778 |
| 1.5118 | 48.0 | 288 | 1.5076 | 0.1778 |
| 1.493 | 49.0 | 294 | 1.5076 | 0.1778 |
| 1.528 | 50.0 | 300 | 1.5076 | 0.1778 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
hkivancoral/hushem_1x_deit_small_sgd_00001_fold1
|
hkivancoral
| 2023-11-13T19:16:04Z | 9 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:facebook/deit-small-patch16-224",
"base_model:finetune:facebook/deit-small-patch16-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-11-13T19:13:17Z |
---
license: apache-2.0
base_model: facebook/deit-small-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: hushem_1x_deit_small_sgd_00001_fold1
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.28888888888888886
---
<!-- 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. -->
# hushem_1x_deit_small_sgd_00001_fold1
This model is a fine-tuned version of [facebook/deit-small-patch16-224](https://huggingface.co/facebook/deit-small-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5045
- Accuracy: 0.2889
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 6 | 1.5103 | 0.2889 |
| 1.5406 | 2.0 | 12 | 1.5100 | 0.2889 |
| 1.5406 | 3.0 | 18 | 1.5097 | 0.2889 |
| 1.5187 | 4.0 | 24 | 1.5094 | 0.2889 |
| 1.5371 | 5.0 | 30 | 1.5091 | 0.2889 |
| 1.5371 | 6.0 | 36 | 1.5089 | 0.2889 |
| 1.517 | 7.0 | 42 | 1.5086 | 0.2889 |
| 1.517 | 8.0 | 48 | 1.5084 | 0.2889 |
| 1.5407 | 9.0 | 54 | 1.5081 | 0.2889 |
| 1.5157 | 10.0 | 60 | 1.5079 | 0.2889 |
| 1.5157 | 11.0 | 66 | 1.5077 | 0.2889 |
| 1.5121 | 12.0 | 72 | 1.5074 | 0.2889 |
| 1.5121 | 13.0 | 78 | 1.5072 | 0.2889 |
| 1.538 | 14.0 | 84 | 1.5070 | 0.2889 |
| 1.5262 | 15.0 | 90 | 1.5068 | 0.2889 |
| 1.5262 | 16.0 | 96 | 1.5066 | 0.2889 |
| 1.5233 | 17.0 | 102 | 1.5064 | 0.2889 |
| 1.5233 | 18.0 | 108 | 1.5063 | 0.2889 |
| 1.5376 | 19.0 | 114 | 1.5061 | 0.2889 |
| 1.5005 | 20.0 | 120 | 1.5060 | 0.2889 |
| 1.5005 | 21.0 | 126 | 1.5058 | 0.2889 |
| 1.5271 | 22.0 | 132 | 1.5057 | 0.2889 |
| 1.5271 | 23.0 | 138 | 1.5056 | 0.2889 |
| 1.5205 | 24.0 | 144 | 1.5055 | 0.2889 |
| 1.5085 | 25.0 | 150 | 1.5054 | 0.2889 |
| 1.5085 | 26.0 | 156 | 1.5053 | 0.2889 |
| 1.5221 | 27.0 | 162 | 1.5052 | 0.2889 |
| 1.5221 | 28.0 | 168 | 1.5051 | 0.2889 |
| 1.5344 | 29.0 | 174 | 1.5050 | 0.2889 |
| 1.5325 | 30.0 | 180 | 1.5049 | 0.2889 |
| 1.5325 | 31.0 | 186 | 1.5048 | 0.2889 |
| 1.5365 | 32.0 | 192 | 1.5048 | 0.2889 |
| 1.5365 | 33.0 | 198 | 1.5047 | 0.2889 |
| 1.5421 | 34.0 | 204 | 1.5046 | 0.2889 |
| 1.5276 | 35.0 | 210 | 1.5046 | 0.2889 |
| 1.5276 | 36.0 | 216 | 1.5046 | 0.2889 |
| 1.5101 | 37.0 | 222 | 1.5045 | 0.2889 |
| 1.5101 | 38.0 | 228 | 1.5045 | 0.2889 |
| 1.5025 | 39.0 | 234 | 1.5045 | 0.2889 |
| 1.5405 | 40.0 | 240 | 1.5045 | 0.2889 |
| 1.5405 | 41.0 | 246 | 1.5045 | 0.2889 |
| 1.5373 | 42.0 | 252 | 1.5045 | 0.2889 |
| 1.5373 | 43.0 | 258 | 1.5045 | 0.2889 |
| 1.5465 | 44.0 | 264 | 1.5045 | 0.2889 |
| 1.4924 | 45.0 | 270 | 1.5045 | 0.2889 |
| 1.4924 | 46.0 | 276 | 1.5045 | 0.2889 |
| 1.521 | 47.0 | 282 | 1.5045 | 0.2889 |
| 1.521 | 48.0 | 288 | 1.5045 | 0.2889 |
| 1.494 | 49.0 | 294 | 1.5045 | 0.2889 |
| 1.5268 | 50.0 | 300 | 1.5045 | 0.2889 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
hkivancoral/hushem_1x_deit_small_sgd_0001_fold4
|
hkivancoral
| 2023-11-13T19:01:09Z | 8 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:facebook/deit-small-patch16-224",
"base_model:finetune:facebook/deit-small-patch16-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-11-13T18:58:27Z |
---
license: apache-2.0
base_model: facebook/deit-small-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: hushem_1x_deit_small_sgd_0001_fold4
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.2619047619047619
---
<!-- 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. -->
# hushem_1x_deit_small_sgd_0001_fold4
This model is a fine-tuned version of [facebook/deit-small-patch16-224](https://huggingface.co/facebook/deit-small-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4227
- Accuracy: 0.2619
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 6 | 1.4804 | 0.2619 |
| 1.5213 | 2.0 | 12 | 1.4770 | 0.2857 |
| 1.5213 | 3.0 | 18 | 1.4737 | 0.2857 |
| 1.5439 | 4.0 | 24 | 1.4702 | 0.2857 |
| 1.5226 | 5.0 | 30 | 1.4673 | 0.2857 |
| 1.5226 | 6.0 | 36 | 1.4646 | 0.2857 |
| 1.52 | 7.0 | 42 | 1.4618 | 0.2857 |
| 1.52 | 8.0 | 48 | 1.4591 | 0.2857 |
| 1.5076 | 9.0 | 54 | 1.4566 | 0.2857 |
| 1.5003 | 10.0 | 60 | 1.4541 | 0.2857 |
| 1.5003 | 11.0 | 66 | 1.4520 | 0.2857 |
| 1.4856 | 12.0 | 72 | 1.4497 | 0.2857 |
| 1.4856 | 13.0 | 78 | 1.4476 | 0.2857 |
| 1.5104 | 14.0 | 84 | 1.4457 | 0.2857 |
| 1.4726 | 15.0 | 90 | 1.4438 | 0.2857 |
| 1.4726 | 16.0 | 96 | 1.4420 | 0.2857 |
| 1.4844 | 17.0 | 102 | 1.4403 | 0.2857 |
| 1.4844 | 18.0 | 108 | 1.4387 | 0.2619 |
| 1.4456 | 19.0 | 114 | 1.4373 | 0.2619 |
| 1.5242 | 20.0 | 120 | 1.4359 | 0.2619 |
| 1.5242 | 21.0 | 126 | 1.4347 | 0.2619 |
| 1.4484 | 22.0 | 132 | 1.4335 | 0.2619 |
| 1.4484 | 23.0 | 138 | 1.4324 | 0.2619 |
| 1.4722 | 24.0 | 144 | 1.4314 | 0.2619 |
| 1.4802 | 25.0 | 150 | 1.4303 | 0.2619 |
| 1.4802 | 26.0 | 156 | 1.4294 | 0.2619 |
| 1.4658 | 27.0 | 162 | 1.4284 | 0.2619 |
| 1.4658 | 28.0 | 168 | 1.4276 | 0.2619 |
| 1.4705 | 29.0 | 174 | 1.4269 | 0.2619 |
| 1.4629 | 30.0 | 180 | 1.4263 | 0.2619 |
| 1.4629 | 31.0 | 186 | 1.4256 | 0.2619 |
| 1.4786 | 32.0 | 192 | 1.4251 | 0.2619 |
| 1.4786 | 33.0 | 198 | 1.4246 | 0.2619 |
| 1.4444 | 34.0 | 204 | 1.4242 | 0.2619 |
| 1.435 | 35.0 | 210 | 1.4238 | 0.2619 |
| 1.435 | 36.0 | 216 | 1.4235 | 0.2619 |
| 1.4653 | 37.0 | 222 | 1.4232 | 0.2619 |
| 1.4653 | 38.0 | 228 | 1.4230 | 0.2619 |
| 1.4482 | 39.0 | 234 | 1.4228 | 0.2619 |
| 1.4598 | 40.0 | 240 | 1.4227 | 0.2619 |
| 1.4598 | 41.0 | 246 | 1.4227 | 0.2619 |
| 1.4528 | 42.0 | 252 | 1.4227 | 0.2619 |
| 1.4528 | 43.0 | 258 | 1.4227 | 0.2619 |
| 1.4661 | 44.0 | 264 | 1.4227 | 0.2619 |
| 1.4575 | 45.0 | 270 | 1.4227 | 0.2619 |
| 1.4575 | 46.0 | 276 | 1.4227 | 0.2619 |
| 1.4719 | 47.0 | 282 | 1.4227 | 0.2619 |
| 1.4719 | 48.0 | 288 | 1.4227 | 0.2619 |
| 1.4602 | 49.0 | 294 | 1.4227 | 0.2619 |
| 1.465 | 50.0 | 300 | 1.4227 | 0.2619 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
eljandoubi/whisper-tiny-en
|
eljandoubi
| 2023-11-13T18:59:37Z | 9 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:PolyAI/minds14",
"base_model:openai/whisper-tiny",
"base_model:finetune:openai/whisper-tiny",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-11-13T17:42:31Z |
---
license: apache-2.0
base_model: openai/whisper-tiny
tags:
- generated_from_trainer
datasets:
- PolyAI/minds14
metrics:
- wer
model-index:
- name: whisper-tiny-en
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: PolyAI/minds14
type: PolyAI/minds14
config: en-US
split: train
args: en-US
metrics:
- name: Wer
type: wer
value: 0.3451917732073374
---
<!-- 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-tiny-en
This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the PolyAI/minds14 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6879
- Wer Ortho: 0.3510
- Wer: 0.3452
## 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: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant_with_warmup
- lr_scheduler_warmup_steps: 50
- training_steps: 500
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer Ortho | Wer |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|
| 0.0008 | 17.24 | 500 | 0.6879 | 0.3510 | 0.3452 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
SebastianSchramm/tinyllama-1.1B-intermediate-step-715k-1.5T-dpo-lora
|
SebastianSchramm
| 2023-11-13T18:48:49Z | 8 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"llama",
"text-generation",
"generated_from_trainer",
"conversational",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-11-12T11:39:14Z |
---
tags:
- generated_from_trainer
model-index:
- name: tinyllama-1.1B-intermediate-step-715k-1.5T-dpo-lora
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. -->
# tinyllama-1.1B-intermediate-step-715k-1.5T-dpo-lora
This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6877
- Rewards/chosen: 0.0254
- Rewards/rejected: 0.0135
- Rewards/accuracies: 0.5645
- Rewards/margins: 0.0119
- Logps/rejected: -315.6106
- Logps/chosen: -402.4017
- Logits/rejected: -4.0818
- Logits/chosen: -4.1916
## 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-07
- train_batch_size: 2
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 32
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen |
|:-------------:|:-----:|:----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------------:|:------------:|:---------------:|:-------------:|
| 0.6916 | 1.0 | 968 | 0.6921 | 0.0039 | 0.0011 | 0.5070 | 0.0028 | -315.7343 | -402.6164 | -4.0813 | -4.1913 |
| 0.6904 | 2.0 | 1936 | 0.6884 | 0.0191 | 0.0086 | 0.5570 | 0.0105 | -315.6588 | -402.4643 | -4.0824 | -4.1920 |
| 0.6876 | 3.0 | 2904 | 0.6877 | 0.0254 | 0.0135 | 0.5645 | 0.0119 | -315.6106 | -402.4017 | -4.0818 | -4.1916 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu121
- Datasets 2.14.6
- Tokenizers 0.14.1
|
hkivancoral/hushem_1x_deit_small_sgd_001_fold4
|
hkivancoral
| 2023-11-13T18:42:38Z | 14 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:facebook/deit-small-patch16-224",
"base_model:finetune:facebook/deit-small-patch16-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-11-13T18:39:10Z |
---
license: apache-2.0
base_model: facebook/deit-small-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: hushem_1x_deit_small_sgd_001_fold4
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.3333333333333333
---
<!-- 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. -->
# hushem_1x_deit_small_sgd_001_fold4
This model is a fine-tuned version of [facebook/deit-small-patch16-224](https://huggingface.co/facebook/deit-small-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3002
- Accuracy: 0.3333
## 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: 0.001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 6 | 1.4504 | 0.2857 |
| 1.4996 | 2.0 | 12 | 1.4256 | 0.2619 |
| 1.4996 | 3.0 | 18 | 1.4065 | 0.3095 |
| 1.4661 | 4.0 | 24 | 1.3909 | 0.3333 |
| 1.4137 | 5.0 | 30 | 1.3815 | 0.3333 |
| 1.4137 | 6.0 | 36 | 1.3736 | 0.3810 |
| 1.3923 | 7.0 | 42 | 1.3662 | 0.3571 |
| 1.3923 | 8.0 | 48 | 1.3602 | 0.3095 |
| 1.3511 | 9.0 | 54 | 1.3552 | 0.3333 |
| 1.3471 | 10.0 | 60 | 1.3505 | 0.3333 |
| 1.3471 | 11.0 | 66 | 1.3464 | 0.3333 |
| 1.3212 | 12.0 | 72 | 1.3425 | 0.3333 |
| 1.3212 | 13.0 | 78 | 1.3391 | 0.3333 |
| 1.3151 | 14.0 | 84 | 1.3358 | 0.3333 |
| 1.2949 | 15.0 | 90 | 1.3328 | 0.3333 |
| 1.2949 | 16.0 | 96 | 1.3296 | 0.3333 |
| 1.282 | 17.0 | 102 | 1.3270 | 0.3333 |
| 1.282 | 18.0 | 108 | 1.3243 | 0.3333 |
| 1.2637 | 19.0 | 114 | 1.3223 | 0.3333 |
| 1.2828 | 20.0 | 120 | 1.3203 | 0.3333 |
| 1.2828 | 21.0 | 126 | 1.3182 | 0.3333 |
| 1.2384 | 22.0 | 132 | 1.3165 | 0.3333 |
| 1.2384 | 23.0 | 138 | 1.3149 | 0.3333 |
| 1.2419 | 24.0 | 144 | 1.3133 | 0.3333 |
| 1.2404 | 25.0 | 150 | 1.3117 | 0.3571 |
| 1.2404 | 26.0 | 156 | 1.3102 | 0.3571 |
| 1.2294 | 27.0 | 162 | 1.3091 | 0.3571 |
| 1.2294 | 28.0 | 168 | 1.3080 | 0.3571 |
| 1.2327 | 29.0 | 174 | 1.3070 | 0.3571 |
| 1.2115 | 30.0 | 180 | 1.3061 | 0.3571 |
| 1.2115 | 31.0 | 186 | 1.3052 | 0.3333 |
| 1.2091 | 32.0 | 192 | 1.3043 | 0.3333 |
| 1.2091 | 33.0 | 198 | 1.3036 | 0.3333 |
| 1.2111 | 34.0 | 204 | 1.3028 | 0.3333 |
| 1.2001 | 35.0 | 210 | 1.3022 | 0.3333 |
| 1.2001 | 36.0 | 216 | 1.3016 | 0.3333 |
| 1.2048 | 37.0 | 222 | 1.3012 | 0.3333 |
| 1.2048 | 38.0 | 228 | 1.3009 | 0.3333 |
| 1.1981 | 39.0 | 234 | 1.3006 | 0.3333 |
| 1.1973 | 40.0 | 240 | 1.3004 | 0.3333 |
| 1.1973 | 41.0 | 246 | 1.3003 | 0.3333 |
| 1.2009 | 42.0 | 252 | 1.3002 | 0.3333 |
| 1.2009 | 43.0 | 258 | 1.3002 | 0.3333 |
| 1.1848 | 44.0 | 264 | 1.3002 | 0.3333 |
| 1.2 | 45.0 | 270 | 1.3002 | 0.3333 |
| 1.2 | 46.0 | 276 | 1.3002 | 0.3333 |
| 1.2026 | 47.0 | 282 | 1.3002 | 0.3333 |
| 1.2026 | 48.0 | 288 | 1.3002 | 0.3333 |
| 1.1883 | 49.0 | 294 | 1.3002 | 0.3333 |
| 1.2097 | 50.0 | 300 | 1.3002 | 0.3333 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
BachNgoH/checkpoints_2
|
BachNgoH
| 2023-11-13T18:41:36Z | 6 | 0 |
transformers
|
[
"transformers",
"safetensors",
"deberta-v2",
"multiple-choice",
"generated_from_trainer",
"base_model:microsoft/deberta-v3-large",
"base_model:finetune:microsoft/deberta-v3-large",
"license:mit",
"endpoints_compatible",
"region:us"
] |
multiple-choice
| 2023-11-13T18:06:10Z |
---
license: mit
base_model: microsoft/deberta-v3-large
tags:
- generated_from_trainer
model-index:
- name: checkpoints_2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# checkpoints_2
This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8543
- Map@3: 0.7167
## 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: 1
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Map@3 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 1.395 | 0.19 | 25 | 1.3859 | 0.5889 |
| 1.3803 | 0.37 | 50 | 1.3840 | 0.6958 |
| 1.3842 | 0.56 | 75 | 1.3314 | 0.7194 |
| 1.2795 | 0.74 | 100 | 1.0021 | 0.7222 |
| 0.9662 | 0.93 | 125 | 0.9006 | 0.6597 |
| 0.9574 | 1.11 | 150 | 0.8355 | 0.6903 |
| 0.8909 | 1.3 | 175 | 0.8506 | 0.6750 |
| 0.8077 | 1.48 | 200 | 0.8180 | 0.7125 |
| 0.955 | 1.67 | 225 | 0.8069 | 0.7097 |
| 0.8664 | 1.85 | 250 | 0.8186 | 0.7028 |
| 0.9396 | 2.04 | 275 | 0.8091 | 0.6986 |
| 0.8141 | 2.22 | 300 | 0.8212 | 0.7083 |
| 0.7898 | 2.41 | 325 | 0.8531 | 0.7167 |
| 0.9143 | 2.59 | 350 | 0.8482 | 0.7125 |
| 0.8861 | 2.78 | 375 | 0.8229 | 0.7083 |
| 0.8569 | 2.96 | 400 | 0.8372 | 0.7181 |
| 0.8381 | 3.15 | 425 | 0.8516 | 0.7153 |
| 0.7671 | 3.33 | 450 | 0.8458 | 0.7167 |
| 0.8704 | 3.52 | 475 | 0.8651 | 0.7222 |
| 0.8733 | 3.7 | 500 | 0.8356 | 0.7153 |
| 0.7309 | 3.89 | 525 | 0.8476 | 0.7181 |
| 0.7793 | 4.07 | 550 | 0.8566 | 0.7167 |
| 0.7849 | 4.26 | 575 | 0.8644 | 0.7167 |
| 0.7776 | 4.44 | 600 | 0.8584 | 0.7167 |
| 0.7573 | 4.63 | 625 | 0.8546 | 0.7167 |
| 0.8115 | 4.81 | 650 | 0.8543 | 0.7167 |
| 0.869 | 5.0 | 675 | 0.8543 | 0.7167 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.14.1
|
hkivancoral/hushem_1x_deit_small_sgd_001_fold3
|
hkivancoral
| 2023-11-13T18:38:51Z | 8 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:facebook/deit-small-patch16-224",
"base_model:finetune:facebook/deit-small-patch16-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-11-13T18:35:26Z |
---
license: apache-2.0
base_model: facebook/deit-small-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: hushem_1x_deit_small_sgd_001_fold3
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.2558139534883721
---
<!-- 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. -->
# hushem_1x_deit_small_sgd_001_fold3
This model is a fine-tuned version of [facebook/deit-small-patch16-224](https://huggingface.co/facebook/deit-small-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3255
- Accuracy: 0.2558
## 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: 0.001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 6 | 1.5642 | 0.2326 |
| 1.4806 | 2.0 | 12 | 1.5332 | 0.3256 |
| 1.4806 | 3.0 | 18 | 1.5110 | 0.3256 |
| 1.4127 | 4.0 | 24 | 1.4910 | 0.3256 |
| 1.3859 | 5.0 | 30 | 1.4734 | 0.3256 |
| 1.3859 | 6.0 | 36 | 1.4581 | 0.3256 |
| 1.372 | 7.0 | 42 | 1.4448 | 0.3256 |
| 1.372 | 8.0 | 48 | 1.4360 | 0.3256 |
| 1.3407 | 9.0 | 54 | 1.4268 | 0.3256 |
| 1.3476 | 10.0 | 60 | 1.4184 | 0.3256 |
| 1.3476 | 11.0 | 66 | 1.4115 | 0.3256 |
| 1.3176 | 12.0 | 72 | 1.4055 | 0.3488 |
| 1.3176 | 13.0 | 78 | 1.3989 | 0.3488 |
| 1.3009 | 14.0 | 84 | 1.3926 | 0.3256 |
| 1.3032 | 15.0 | 90 | 1.3870 | 0.3256 |
| 1.3032 | 16.0 | 96 | 1.3815 | 0.3256 |
| 1.2893 | 17.0 | 102 | 1.3768 | 0.3256 |
| 1.2893 | 18.0 | 108 | 1.3723 | 0.3023 |
| 1.252 | 19.0 | 114 | 1.3680 | 0.3023 |
| 1.2643 | 20.0 | 120 | 1.3638 | 0.3023 |
| 1.2643 | 21.0 | 126 | 1.3601 | 0.2791 |
| 1.2642 | 22.0 | 132 | 1.3567 | 0.2791 |
| 1.2642 | 23.0 | 138 | 1.3535 | 0.2791 |
| 1.2369 | 24.0 | 144 | 1.3502 | 0.2791 |
| 1.2315 | 25.0 | 150 | 1.3476 | 0.2791 |
| 1.2315 | 26.0 | 156 | 1.3450 | 0.2791 |
| 1.2236 | 27.0 | 162 | 1.3424 | 0.2558 |
| 1.2236 | 28.0 | 168 | 1.3403 | 0.2558 |
| 1.2327 | 29.0 | 174 | 1.3382 | 0.2558 |
| 1.2254 | 30.0 | 180 | 1.3363 | 0.2558 |
| 1.2254 | 31.0 | 186 | 1.3347 | 0.2558 |
| 1.2165 | 32.0 | 192 | 1.3331 | 0.2558 |
| 1.2165 | 33.0 | 198 | 1.3315 | 0.2558 |
| 1.2003 | 34.0 | 204 | 1.3303 | 0.2558 |
| 1.2034 | 35.0 | 210 | 1.3292 | 0.2558 |
| 1.2034 | 36.0 | 216 | 1.3282 | 0.2558 |
| 1.2052 | 37.0 | 222 | 1.3273 | 0.2558 |
| 1.2052 | 38.0 | 228 | 1.3266 | 0.2558 |
| 1.2216 | 39.0 | 234 | 1.3261 | 0.2558 |
| 1.2003 | 40.0 | 240 | 1.3258 | 0.2558 |
| 1.2003 | 41.0 | 246 | 1.3256 | 0.2558 |
| 1.1856 | 42.0 | 252 | 1.3255 | 0.2558 |
| 1.1856 | 43.0 | 258 | 1.3255 | 0.2558 |
| 1.2091 | 44.0 | 264 | 1.3255 | 0.2558 |
| 1.1987 | 45.0 | 270 | 1.3255 | 0.2558 |
| 1.1987 | 46.0 | 276 | 1.3255 | 0.2558 |
| 1.1885 | 47.0 | 282 | 1.3255 | 0.2558 |
| 1.1885 | 48.0 | 288 | 1.3255 | 0.2558 |
| 1.2076 | 49.0 | 294 | 1.3255 | 0.2558 |
| 1.2139 | 50.0 | 300 | 1.3255 | 0.2558 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
Amartya77/results
|
Amartya77
| 2023-11-13T18:38:35Z | 0 | 0 | null |
[
"safetensors",
"generated_from_trainer",
"base_model:meta-llama/Llama-2-7b-hf",
"base_model:finetune:meta-llama/Llama-2-7b-hf",
"region:us"
] | null | 2023-11-13T18:38:14Z |
---
base_model: meta-llama/Llama-2-7b-hf
tags:
- generated_from_trainer
model-index:
- name: results
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. -->
# results
This model is a fine-tuned version of [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.3
- num_epochs: 50
### Training results
### Framework versions
- Transformers 4.36.0.dev0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
neuralmagic/bge-base-en-v1.5-dense
|
neuralmagic
| 2023-11-13T18:33:16Z | 4 | 0 |
transformers
|
[
"transformers",
"onnx",
"bert",
"feature-extraction",
"en",
"license:mit",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2023-10-01T13:13:33Z |
---
license: mit
language:
- en
tags:
- onnx
---
This is the ONNX variant of the [bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) embeddings model created with the [DeepSparse Optimum](https://github.com/neuralmagic/optimum-deepsparse) integration.
To replicate ONNX export, run:
```bash
pip install git+https://github.com/neuralmagic/optimum-deepsparse.git
```
```python
from optimum.deepsparse import DeepSparseModelForFeatureExtraction
from transformers.onnx.utils import get_preprocessor
from pathlib import Path
model_id = "BAAI/bge-base-en-v1.5"
# load model and convert to onnx
model = DeepSparseModelForFeatureExtraction.from_pretrained(model_id, export=True)
tokenizer = get_preprocessor(model_id)
# save onnx checkpoint and tokenizer
onnx_path = Path("bge-base-en-v1.5-dense")
model.save_pretrained(onnx_path)
tokenizer.save_pretrained(onnx_path)
```
|
kariver/detr-resnet-101_rmsprop_finetuned_food-roboflow
|
kariver
| 2023-11-13T18:30:18Z | 31 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"detr",
"object-detection",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:facebook/detr-resnet-101",
"base_model:finetune:facebook/detr-resnet-101",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
object-detection
| 2023-11-13T17:59:54Z |
---
license: apache-2.0
base_model: facebook/detr-resnet-101
tags:
- generated_from_trainer
datasets:
- imagefolder
model-index:
- name: detr-resnet-101_rmsprop_finetuned_food-roboflow
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. -->
# detr-resnet-101_rmsprop_finetuned_food-roboflow
This model is a fine-tuned version of [facebook/detr-resnet-101](https://huggingface.co/facebook/detr-resnet-101) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 2.9853
## 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: 8
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 5.8836 | 0.77 | 50 | 4.5609 |
| 3.9193 | 1.54 | 100 | 3.5764 |
| 3.2186 | 2.31 | 150 | 3.2685 |
| 2.8952 | 3.08 | 200 | 3.1941 |
| 2.8565 | 3.85 | 250 | 3.1280 |
| 2.7563 | 4.62 | 300 | 3.0448 |
| 2.668 | 5.38 | 350 | 3.0325 |
| 2.6474 | 6.15 | 400 | 2.9764 |
| 2.6463 | 6.92 | 450 | 2.9752 |
| 2.6616 | 7.69 | 500 | 3.0352 |
| 2.5155 | 8.46 | 550 | 2.9928 |
| 2.5778 | 9.23 | 600 | 2.9603 |
| 2.5876 | 10.0 | 650 | 2.9502 |
| 2.513 | 10.77 | 700 | 3.0151 |
| 2.5598 | 11.54 | 750 | 3.0011 |
| 2.491 | 12.31 | 800 | 3.0134 |
| 2.5103 | 13.08 | 850 | 2.9825 |
| 2.5497 | 13.85 | 900 | 3.0169 |
| 2.5738 | 14.62 | 950 | 2.9853 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
arincon/roberta-base-openai-detector-autextification
|
arincon
| 2023-11-13T18:15:48Z | 7 | 0 |
transformers
|
[
"transformers",
"safetensors",
"roberta",
"text-classification",
"generated_from_trainer",
"dataset:au_tex_tification",
"base_model:openai-community/roberta-base-openai-detector",
"base_model:finetune:openai-community/roberta-base-openai-detector",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-13T18:04:32Z |
---
license: mit
base_model: roberta-base-openai-detector
tags:
- generated_from_trainer
datasets:
- au_tex_tification
metrics:
- accuracy
model-index:
- name: roberta-base-openai-detector-autextification
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: au_tex_tification
type: au_tex_tification
config: detection_en
split: train
args: detection_en
metrics:
- name: Accuracy
type: accuracy
value: 0.6
---
<!-- 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-openai-detector-autextification
This model is a fine-tuned version of [roberta-base-openai-detector](https://huggingface.co/roberta-base-openai-detector) on the au_tex_tification dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7155
- Accuracy: 0.6
- Roc Auc: 0.6354
## 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 | Roc Auc |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------:|
| 1.0975 | 1.0 | 10 | 0.7345 | 0.65 | 0.5417 |
| 0.4022 | 2.0 | 20 | 0.6266 | 0.65 | 0.6667 |
| 0.1635 | 3.0 | 30 | 0.7155 | 0.6 | 0.6354 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.14.1
|
hkivancoral/hushem_1x_deit_small_adamax_00001_fold3
|
hkivancoral
| 2023-11-13T18:08:23Z | 11 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:facebook/deit-small-patch16-224",
"base_model:finetune:facebook/deit-small-patch16-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-11-13T18:04:27Z |
---
license: apache-2.0
base_model: facebook/deit-small-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: hushem_1x_deit_small_adamax_00001_fold3
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.6511627906976745
---
<!-- 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. -->
# hushem_1x_deit_small_adamax_00001_fold3
This model is a fine-tuned version of [facebook/deit-small-patch16-224](https://huggingface.co/facebook/deit-small-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6553
- Accuracy: 0.6512
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 6 | 1.3641 | 0.3953 |
| 1.3358 | 2.0 | 12 | 1.2934 | 0.4186 |
| 1.3358 | 3.0 | 18 | 1.2307 | 0.4419 |
| 1.1053 | 4.0 | 24 | 1.1728 | 0.5814 |
| 0.9503 | 5.0 | 30 | 1.1200 | 0.5814 |
| 0.9503 | 6.0 | 36 | 1.0691 | 0.5814 |
| 0.8249 | 7.0 | 42 | 1.0268 | 0.6047 |
| 0.8249 | 8.0 | 48 | 1.0002 | 0.6279 |
| 0.6991 | 9.0 | 54 | 0.9588 | 0.6279 |
| 0.62 | 10.0 | 60 | 0.9254 | 0.6279 |
| 0.62 | 11.0 | 66 | 0.8988 | 0.6744 |
| 0.5003 | 12.0 | 72 | 0.8718 | 0.6279 |
| 0.5003 | 13.0 | 78 | 0.8636 | 0.6279 |
| 0.4251 | 14.0 | 84 | 0.8486 | 0.6279 |
| 0.3584 | 15.0 | 90 | 0.8228 | 0.6279 |
| 0.3584 | 16.0 | 96 | 0.8029 | 0.6512 |
| 0.2955 | 17.0 | 102 | 0.7980 | 0.6279 |
| 0.2955 | 18.0 | 108 | 0.7871 | 0.6047 |
| 0.2345 | 19.0 | 114 | 0.7646 | 0.6279 |
| 0.2022 | 20.0 | 120 | 0.7571 | 0.6279 |
| 0.2022 | 21.0 | 126 | 0.7433 | 0.6512 |
| 0.1667 | 22.0 | 132 | 0.7314 | 0.6744 |
| 0.1667 | 23.0 | 138 | 0.7263 | 0.6279 |
| 0.1461 | 24.0 | 144 | 0.7221 | 0.6744 |
| 0.1251 | 25.0 | 150 | 0.7120 | 0.6512 |
| 0.1251 | 26.0 | 156 | 0.6954 | 0.6512 |
| 0.1033 | 27.0 | 162 | 0.6904 | 0.6512 |
| 0.1033 | 28.0 | 168 | 0.6870 | 0.6744 |
| 0.0941 | 29.0 | 174 | 0.6821 | 0.6744 |
| 0.0792 | 30.0 | 180 | 0.6785 | 0.6744 |
| 0.0792 | 31.0 | 186 | 0.6761 | 0.6744 |
| 0.0681 | 32.0 | 192 | 0.6723 | 0.6744 |
| 0.0681 | 33.0 | 198 | 0.6679 | 0.6744 |
| 0.0621 | 34.0 | 204 | 0.6648 | 0.6512 |
| 0.0554 | 35.0 | 210 | 0.6628 | 0.6512 |
| 0.0554 | 36.0 | 216 | 0.6584 | 0.6744 |
| 0.0533 | 37.0 | 222 | 0.6569 | 0.6744 |
| 0.0533 | 38.0 | 228 | 0.6569 | 0.6512 |
| 0.0487 | 39.0 | 234 | 0.6565 | 0.6512 |
| 0.0478 | 40.0 | 240 | 0.6552 | 0.6512 |
| 0.0478 | 41.0 | 246 | 0.6553 | 0.6512 |
| 0.0459 | 42.0 | 252 | 0.6553 | 0.6512 |
| 0.0459 | 43.0 | 258 | 0.6553 | 0.6512 |
| 0.0488 | 44.0 | 264 | 0.6553 | 0.6512 |
| 0.0454 | 45.0 | 270 | 0.6553 | 0.6512 |
| 0.0454 | 46.0 | 276 | 0.6553 | 0.6512 |
| 0.0445 | 47.0 | 282 | 0.6553 | 0.6512 |
| 0.0445 | 48.0 | 288 | 0.6553 | 0.6512 |
| 0.0487 | 49.0 | 294 | 0.6553 | 0.6512 |
| 0.0463 | 50.0 | 300 | 0.6553 | 0.6512 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
hkivancoral/hushem_1x_deit_small_adamax_00001_fold2
|
hkivancoral
| 2023-11-13T18:04:13Z | 13 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:facebook/deit-small-patch16-224",
"base_model:finetune:facebook/deit-small-patch16-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-11-13T18:00:29Z |
---
license: apache-2.0
base_model: facebook/deit-small-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: hushem_1x_deit_small_adamax_00001_fold2
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.6222222222222222
---
<!-- 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. -->
# hushem_1x_deit_small_adamax_00001_fold2
This model is a fine-tuned version of [facebook/deit-small-patch16-224](https://huggingface.co/facebook/deit-small-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3101
- Accuracy: 0.6222
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 6 | 1.3638 | 0.3556 |
| 1.331 | 2.0 | 12 | 1.3133 | 0.4222 |
| 1.331 | 3.0 | 18 | 1.2851 | 0.4222 |
| 1.0997 | 4.0 | 24 | 1.2715 | 0.4 |
| 0.9418 | 5.0 | 30 | 1.2498 | 0.4444 |
| 0.9418 | 6.0 | 36 | 1.2371 | 0.5111 |
| 0.7701 | 7.0 | 42 | 1.2279 | 0.5111 |
| 0.7701 | 8.0 | 48 | 1.2223 | 0.5556 |
| 0.6624 | 9.0 | 54 | 1.2136 | 0.5333 |
| 0.5468 | 10.0 | 60 | 1.2047 | 0.5111 |
| 0.5468 | 11.0 | 66 | 1.2129 | 0.5333 |
| 0.4638 | 12.0 | 72 | 1.2131 | 0.5556 |
| 0.4638 | 13.0 | 78 | 1.2055 | 0.5778 |
| 0.375 | 14.0 | 84 | 1.2059 | 0.5778 |
| 0.3096 | 15.0 | 90 | 1.2025 | 0.5778 |
| 0.3096 | 16.0 | 96 | 1.2062 | 0.5778 |
| 0.2535 | 17.0 | 102 | 1.2103 | 0.6 |
| 0.2535 | 18.0 | 108 | 1.2313 | 0.5778 |
| 0.2168 | 19.0 | 114 | 1.2293 | 0.5778 |
| 0.1735 | 20.0 | 120 | 1.2169 | 0.6222 |
| 0.1735 | 21.0 | 126 | 1.2306 | 0.6222 |
| 0.1492 | 22.0 | 132 | 1.2370 | 0.6222 |
| 0.1492 | 23.0 | 138 | 1.2467 | 0.6222 |
| 0.1264 | 24.0 | 144 | 1.2411 | 0.6222 |
| 0.1012 | 25.0 | 150 | 1.2438 | 0.6222 |
| 0.1012 | 26.0 | 156 | 1.2523 | 0.6222 |
| 0.0887 | 27.0 | 162 | 1.2537 | 0.6 |
| 0.0887 | 28.0 | 168 | 1.2661 | 0.6222 |
| 0.0734 | 29.0 | 174 | 1.2715 | 0.6222 |
| 0.0647 | 30.0 | 180 | 1.2745 | 0.6 |
| 0.0647 | 31.0 | 186 | 1.2817 | 0.6222 |
| 0.0577 | 32.0 | 192 | 1.2861 | 0.6222 |
| 0.0577 | 33.0 | 198 | 1.2908 | 0.6222 |
| 0.0525 | 34.0 | 204 | 1.2935 | 0.6222 |
| 0.048 | 35.0 | 210 | 1.2969 | 0.6222 |
| 0.048 | 36.0 | 216 | 1.2990 | 0.6 |
| 0.0443 | 37.0 | 222 | 1.3015 | 0.6 |
| 0.0443 | 38.0 | 228 | 1.3052 | 0.6222 |
| 0.0404 | 39.0 | 234 | 1.3082 | 0.6222 |
| 0.0394 | 40.0 | 240 | 1.3089 | 0.6222 |
| 0.0394 | 41.0 | 246 | 1.3101 | 0.6222 |
| 0.0387 | 42.0 | 252 | 1.3101 | 0.6222 |
| 0.0387 | 43.0 | 258 | 1.3101 | 0.6222 |
| 0.0369 | 44.0 | 264 | 1.3101 | 0.6222 |
| 0.0381 | 45.0 | 270 | 1.3101 | 0.6222 |
| 0.0381 | 46.0 | 276 | 1.3101 | 0.6222 |
| 0.0382 | 47.0 | 282 | 1.3101 | 0.6222 |
| 0.0382 | 48.0 | 288 | 1.3101 | 0.6222 |
| 0.037 | 49.0 | 294 | 1.3101 | 0.6222 |
| 0.0386 | 50.0 | 300 | 1.3101 | 0.6222 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
hkivancoral/hushem_1x_deit_small_adamax_00001_fold1
|
hkivancoral
| 2023-11-13T18:00:14Z | 8 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:facebook/deit-small-patch16-224",
"base_model:finetune:facebook/deit-small-patch16-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-11-13T17:56:11Z |
---
license: apache-2.0
base_model: facebook/deit-small-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: hushem_1x_deit_small_adamax_00001_fold1
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.6
---
<!-- 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. -->
# hushem_1x_deit_small_adamax_00001_fold1
This model is a fine-tuned version of [facebook/deit-small-patch16-224](https://huggingface.co/facebook/deit-small-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1270
- Accuracy: 0.6
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 6 | 1.3199 | 0.3333 |
| 1.3414 | 2.0 | 12 | 1.2923 | 0.4667 |
| 1.3414 | 3.0 | 18 | 1.2886 | 0.4667 |
| 1.0791 | 4.0 | 24 | 1.2761 | 0.4667 |
| 0.9244 | 5.0 | 30 | 1.2453 | 0.4889 |
| 0.9244 | 6.0 | 36 | 1.2252 | 0.4667 |
| 0.7694 | 7.0 | 42 | 1.2158 | 0.5111 |
| 0.7694 | 8.0 | 48 | 1.2163 | 0.4667 |
| 0.6552 | 9.0 | 54 | 1.2081 | 0.5111 |
| 0.5314 | 10.0 | 60 | 1.1883 | 0.5556 |
| 0.5314 | 11.0 | 66 | 1.1802 | 0.5556 |
| 0.4407 | 12.0 | 72 | 1.1737 | 0.5778 |
| 0.4407 | 13.0 | 78 | 1.1623 | 0.6222 |
| 0.3864 | 14.0 | 84 | 1.1625 | 0.6222 |
| 0.3093 | 15.0 | 90 | 1.1653 | 0.6222 |
| 0.3093 | 16.0 | 96 | 1.1658 | 0.6222 |
| 0.2597 | 17.0 | 102 | 1.1519 | 0.6444 |
| 0.2597 | 18.0 | 108 | 1.1466 | 0.6222 |
| 0.2099 | 19.0 | 114 | 1.1591 | 0.6 |
| 0.1766 | 20.0 | 120 | 1.1509 | 0.5778 |
| 0.1766 | 21.0 | 126 | 1.1488 | 0.5778 |
| 0.1537 | 22.0 | 132 | 1.1482 | 0.5778 |
| 0.1537 | 23.0 | 138 | 1.1427 | 0.6222 |
| 0.1244 | 24.0 | 144 | 1.1370 | 0.6 |
| 0.103 | 25.0 | 150 | 1.1285 | 0.6 |
| 0.103 | 26.0 | 156 | 1.1323 | 0.6 |
| 0.089 | 27.0 | 162 | 1.1268 | 0.6 |
| 0.089 | 28.0 | 168 | 1.1377 | 0.6 |
| 0.0777 | 29.0 | 174 | 1.1346 | 0.6 |
| 0.068 | 30.0 | 180 | 1.1274 | 0.6 |
| 0.068 | 31.0 | 186 | 1.1199 | 0.6 |
| 0.0597 | 32.0 | 192 | 1.1245 | 0.6 |
| 0.0597 | 33.0 | 198 | 1.1296 | 0.6 |
| 0.0547 | 34.0 | 204 | 1.1270 | 0.6 |
| 0.0493 | 35.0 | 210 | 1.1241 | 0.6 |
| 0.0493 | 36.0 | 216 | 1.1250 | 0.6 |
| 0.0441 | 37.0 | 222 | 1.1253 | 0.6 |
| 0.0441 | 38.0 | 228 | 1.1296 | 0.6 |
| 0.0409 | 39.0 | 234 | 1.1287 | 0.6 |
| 0.0405 | 40.0 | 240 | 1.1275 | 0.6 |
| 0.0405 | 41.0 | 246 | 1.1272 | 0.6 |
| 0.0391 | 42.0 | 252 | 1.1270 | 0.6 |
| 0.0391 | 43.0 | 258 | 1.1270 | 0.6 |
| 0.0395 | 44.0 | 264 | 1.1270 | 0.6 |
| 0.0377 | 45.0 | 270 | 1.1270 | 0.6 |
| 0.0377 | 46.0 | 276 | 1.1270 | 0.6 |
| 0.0388 | 47.0 | 282 | 1.1270 | 0.6 |
| 0.0388 | 48.0 | 288 | 1.1270 | 0.6 |
| 0.0366 | 49.0 | 294 | 1.1270 | 0.6 |
| 0.0396 | 50.0 | 300 | 1.1270 | 0.6 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
DonDonP1/KylieCantrall
|
DonDonP1
| 2023-11-13T17:57:29Z | 0 | 0 | null |
[
"Kylie Cantrall",
"arxiv:1910.09700",
"region:us"
] | null | 2023-11-13T17:54:26Z |
---
tags:
- Kylie Cantrall
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset 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]
|
Ka4on/mistral_radiology2
|
Ka4on
| 2023-11-13T17:56:08Z | 2 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:mistralai/Mistral-7B-v0.1",
"base_model:adapter:mistralai/Mistral-7B-v0.1",
"region:us"
] | null | 2023-11-13T17:55:51Z |
---
library_name: peft
base_model: mistralai/Mistral-7B-v0.1
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Data Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
## 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 0.6.2.dev0
|
bangla-speech-processing/BanglaASR
|
bangla-speech-processing
| 2023-11-13T17:55:56Z | 653 | 12 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"whisper",
"automatic-speech-recognition",
"audio",
"license:mit",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-06-22T18:06:18Z |
---
license: mit
tags:
- audio
- automatic-speech-recognition
widget:
- example_title: sample 1
src: https://huggingface.co/bangla-speech-processing/BanglaASR/resolve/main/mp3/common_voice_bn_31515636.mp3
- example_title: sample 2
src: https://huggingface.co/bangla-speech-processing/BanglaASR/resolve/main/mp3/common_voice_bn_31549899.mp3
- example_title: sample 3
src: https://huggingface.co/bangla-speech-processing/BanglaASR/resolve/main/mp3/common_voice_bn_31617644.mp3
pipeline_tag: automatic-speech-recognition
---
Bangla ASR model which was trained Bangla Mozilla Common Voice Dataset. This is Fine-tuning Whisper model using Bangla mozilla common voice dataset.
For training this model used 40k training and 7k Validation of around 400 hours of data. We trained 12000 steps and get word
error rate 4.58%. This model was whisper small[244 M] variant model.
```py
import os
import librosa
import torch
import torchaudio
import numpy as np
from transformers import WhisperTokenizer
from transformers import WhisperProcessor
from transformers import WhisperFeatureExtractor
from transformers import WhisperForConditionalGeneration
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
mp3_path = "https://huggingface.co/bangla-speech-processing/BanglaASR/resolve/main/mp3/common_voice_bn_31515636.mp3"
model_path = "bangla-speech-processing/BanglaASR"
feature_extractor = WhisperFeatureExtractor.from_pretrained(model_path)
tokenizer = WhisperTokenizer.from_pretrained(model_path)
processor = WhisperProcessor.from_pretrained(model_path)
model = WhisperForConditionalGeneration.from_pretrained(model_path).to(device)
speech_array, sampling_rate = torchaudio.load(mp3_path, format="mp3")
speech_array = speech_array[0].numpy()
speech_array = librosa.resample(np.asarray(speech_array), orig_sr=sampling_rate, target_sr=16000)
input_features = feature_extractor(speech_array, sampling_rate=16000, return_tensors="pt").input_features
# batch = processor.feature_extractor.pad(input_features, return_tensors="pt")
predicted_ids = model.generate(inputs=input_features.to(device))[0]
transcription = processor.decode(predicted_ids, skip_special_tokens=True)
print(transcription)
```
# Dataset
Used Mozilla common voice dataset around 400 hours data both training[40k] and validation[7k] mp3 samples.
For more information about dataser please [click here](https://commonvoice.mozilla.org/bn/datasets)
# Training Model Information
| Size | Layers | Width | Heads | Parameters | Bangla-only | Training Status |
| ------------- | ------------- | -------- |-------- | ------------- | ------------- | -------- |
tiny | 4 |384 | 6 | 39 M | X | X
base | 6 |512 | 8 |74 M | X | X
small | 12 |768 | 12 |244 M | ✓ | ✓
medium | 24 |1024 | 16 |769 M | X | X
large | 32 |1280 | 20 |1550 M | X | X
# Evaluation
Word Error Rate 4.58 %
For More please check the [github](https://github.com/saiful9379/BanglaASR/tree/main)
```
@misc{BanglaASR ,
title={Transformer Based Whisper Bangla ASR Model},
author={Md Saiful Islam},
howpublished={},
year={2023}
}
```
|
Greynar/segformer-b0-scene-parse-150
|
Greynar
| 2023-11-13T17:52:10Z | 6 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"segformer",
"generated_from_trainer",
"dataset:scene_parse_150",
"base_model:nvidia/mit-b0",
"base_model:finetune:nvidia/mit-b0",
"license:other",
"endpoints_compatible",
"region:us"
] | null | 2023-11-13T16:02:07Z |
---
license: other
base_model: nvidia/mit-b0
tags:
- generated_from_trainer
datasets:
- scene_parse_150
model-index:
- name: segformer-b0-scene-parse-150
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. -->
# segformer-b0-scene-parse-150
This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the scene_parse_150 dataset.
It achieves the following results on the evaluation set:
- Loss: 4.9822
- Mean Iou: 0.0050
- Mean Accuracy: 0.0259
- Overall Accuracy: 0.0659
- Per Category Iou: [0.0, 0.0, 0.42791763087856327, 0.002705563443913773, 0.11484260881675644, 0.004469078471025891, 0.00010464003826835685, 0.0, 0.0, 0.0, 0.0, 0.0, 0.04216482322102168, 0.0006308115510378447, 0.021786914921127488, 0.0, 0.0, 0.0, 0.0, 1.0116644915880098e-05, 0.0011043283536304794, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.012403422514526532, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.014823049842505095, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0]
- Per Category Accuracy: [0.0, 0.0, 0.5983735141415003, 0.002898678998960016, 0.12777657657657657, 0.005584500670764047, 0.00011662099011220605, 0.0, 0.0, 0.0, nan, nan, 0.051474509905948776, 0.0006435111270405004, 0.1369918699186992, nan, 0.0, 0.0, 0.0, 0.00011761938367442955, 0.001417434443656981, 0.0, 0.0, nan, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0955641193834044, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.04036326942482341, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan]
## 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: 6e-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
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Per Category Iou | Per Category Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|
| 4.8784 | 1.0 | 20 | 4.9822 | 0.0050 | 0.0259 | 0.0659 | [0.0, 0.0, 0.42791763087856327, 0.002705563443913773, 0.11484260881675644, 0.004469078471025891, 0.00010464003826835685, 0.0, 0.0, 0.0, 0.0, 0.0, 0.04216482322102168, 0.0006308115510378447, 0.021786914921127488, 0.0, 0.0, 0.0, 0.0, 1.0116644915880098e-05, 0.0011043283536304794, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.012403422514526532, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.014823049842505095, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0] | [0.0, 0.0, 0.5983735141415003, 0.002898678998960016, 0.12777657657657657, 0.005584500670764047, 0.00011662099011220605, 0.0, 0.0, 0.0, nan, nan, 0.051474509905948776, 0.0006435111270405004, 0.1369918699186992, nan, 0.0, 0.0, 0.0, 0.00011761938367442955, 0.001417434443656981, 0.0, 0.0, nan, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0955641193834044, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.04036326942482341, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan] |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
DouglasPontes/roberta-2020-Q2-filtered
|
DouglasPontes
| 2023-11-13T17:49:29Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"fill-mask",
"generated_from_trainer",
"base_model:FacebookAI/roberta-base",
"base_model:finetune:FacebookAI/roberta-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2023-11-11T02:21:18Z |
---
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
model-index:
- name: roberta-2020-Q2-filtered
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-2020-Q2-filtered
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.5868
## 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: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.98) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1400
- training_steps: 2400000
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-------:|:---------------:|
| No log | 0.03 | 8000 | 2.9727 |
| 3.1662 | 0.07 | 16000 | 2.8727 |
| 3.1662 | 0.1 | 24000 | 2.8134 |
| 2.914 | 0.14 | 32000 | 2.7663 |
| 2.914 | 0.17 | 40000 | 2.7392 |
| 2.8319 | 0.21 | 48000 | 2.7003 |
| 2.8319 | 0.24 | 56000 | 2.6796 |
| 2.7892 | 0.28 | 64000 | 2.6640 |
| 2.7892 | 0.31 | 72000 | 2.6422 |
| 2.7471 | 0.35 | 80000 | 2.6159 |
| 2.7471 | 0.38 | 88000 | 2.6056 |
| 2.727 | 0.42 | 96000 | 2.6007 |
| 2.727 | 0.45 | 104000 | 2.6046 |
| 2.7185 | 0.49 | 112000 | 2.6009 |
| 2.7185 | 0.52 | 120000 | 2.5992 |
| 2.7121 | 0.56 | 128000 | 2.5893 |
| 2.7121 | 0.59 | 136000 | 2.5993 |
| 2.722 | 0.63 | 144000 | 2.5935 |
| 2.722 | 0.66 | 152000 | 2.5808 |
| 2.724 | 0.7 | 160000 | 2.6009 |
| 2.724 | 0.73 | 168000 | 2.6015 |
| 2.7192 | 0.77 | 176000 | 2.5918 |
| 2.7192 | 0.8 | 184000 | 2.6031 |
| 2.7234 | 0.84 | 192000 | 2.5920 |
| 2.7234 | 0.87 | 200000 | 2.6065 |
| 2.7369 | 0.91 | 208000 | 2.6125 |
| 2.7369 | 0.94 | 216000 | 2.6040 |
| 2.7282 | 0.98 | 224000 | 2.6042 |
| 2.7282 | 1.01 | 232000 | 2.6186 |
| 2.7322 | 1.05 | 240000 | 2.6130 |
| 2.7322 | 1.08 | 248000 | 2.6214 |
| 2.7361 | 1.12 | 256000 | 2.6168 |
| 2.7361 | 1.15 | 264000 | 2.6266 |
| 2.7514 | 1.19 | 272000 | 2.6223 |
| 2.7514 | 1.22 | 280000 | 2.6240 |
| 2.7557 | 1.26 | 288000 | 2.6301 |
| 2.7557 | 1.29 | 296000 | 2.6284 |
| 2.7591 | 1.33 | 304000 | 2.6443 |
| 2.7591 | 1.36 | 312000 | 2.6485 |
| 2.7675 | 1.4 | 320000 | 2.6457 |
| 2.7675 | 1.43 | 328000 | 2.6444 |
| 2.7732 | 1.47 | 336000 | 2.6537 |
| 2.7732 | 1.5 | 344000 | 2.6632 |
| 2.7922 | 1.54 | 352000 | 2.6641 |
| 2.7922 | 1.57 | 360000 | 2.6715 |
| 2.7908 | 1.61 | 368000 | 2.6743 |
| 2.7908 | 1.64 | 376000 | 2.6686 |
| 2.8148 | 1.68 | 384000 | 2.6779 |
| 2.8148 | 1.71 | 392000 | 2.6765 |
| 2.8103 | 1.75 | 400000 | 2.6829 |
| 2.8103 | 1.78 | 408000 | 2.6941 |
| 2.8192 | 1.82 | 416000 | 2.6800 |
| 2.8192 | 1.85 | 424000 | 2.6954 |
| 2.8298 | 1.89 | 432000 | 2.7022 |
| 2.8298 | 1.92 | 440000 | 2.6993 |
| 2.8298 | 1.96 | 448000 | 2.7028 |
| 2.8298 | 1.99 | 456000 | 2.7213 |
| 2.8546 | 2.03 | 464000 | 2.7187 |
| 2.8546 | 2.06 | 472000 | 2.7107 |
| 2.8334 | 2.09 | 480000 | 2.7094 |
| 2.8334 | 2.13 | 488000 | 2.7309 |
| 2.854 | 2.16 | 496000 | 2.7340 |
| 2.854 | 2.2 | 504000 | 2.7264 |
| 2.8539 | 2.23 | 512000 | 2.7456 |
| 2.8539 | 2.27 | 520000 | 2.7412 |
| 2.8717 | 2.3 | 528000 | 2.7517 |
| 2.8717 | 2.34 | 536000 | 2.7474 |
| 2.8733 | 2.37 | 544000 | 2.7649 |
| 2.8733 | 2.41 | 552000 | 2.7536 |
| 2.8876 | 2.44 | 560000 | 2.7602 |
| 2.8876 | 2.48 | 568000 | 2.7617 |
| 2.905 | 2.51 | 576000 | 2.7663 |
| 2.905 | 2.55 | 584000 | 2.7840 |
| 2.8964 | 2.58 | 592000 | 2.7827 |
| 2.8964 | 2.62 | 600000 | 2.7769 |
| 2.9118 | 2.65 | 608000 | 2.7880 |
| 2.9118 | 2.69 | 616000 | 2.7923 |
| 2.9222 | 2.72 | 624000 | 2.7897 |
| 2.9222 | 2.76 | 632000 | 2.8131 |
| 2.9311 | 2.79 | 640000 | 2.8014 |
| 2.9311 | 2.83 | 648000 | 2.8287 |
| 2.9469 | 2.86 | 656000 | 2.8267 |
| 2.9469 | 2.9 | 664000 | 2.8234 |
| 2.9449 | 2.93 | 672000 | 2.8258 |
| 2.9449 | 2.97 | 680000 | 2.8252 |
| 2.9608 | 3.0 | 688000 | 2.8328 |
| 2.9608 | 3.04 | 696000 | 2.8387 |
| 2.9499 | 3.07 | 704000 | 2.8425 |
| 2.9499 | 3.11 | 712000 | 2.8431 |
| 2.9662 | 3.14 | 720000 | 2.8575 |
| 2.9662 | 3.18 | 728000 | 2.8588 |
| 2.9779 | 3.21 | 736000 | 2.8636 |
| 2.9779 | 3.25 | 744000 | 2.8631 |
| 2.9787 | 3.28 | 752000 | 2.8736 |
| 2.9787 | 3.32 | 760000 | 2.8701 |
| 3.0025 | 3.35 | 768000 | 2.8815 |
| 3.0025 | 3.39 | 776000 | 2.8750 |
| 2.999 | 3.42 | 784000 | 2.8860 |
| 2.999 | 3.46 | 792000 | 2.8876 |
| 3.0012 | 3.49 | 800000 | 2.9017 |
| 3.0012 | 3.53 | 808000 | 2.8898 |
| 3.0076 | 3.56 | 816000 | 2.9074 |
| 3.0076 | 3.6 | 824000 | 2.8906 |
| 3.0122 | 3.63 | 832000 | 2.9073 |
| 3.0122 | 3.67 | 840000 | 2.9154 |
| 3.0209 | 3.7 | 848000 | 2.9111 |
| 3.0209 | 3.74 | 856000 | 2.9094 |
| 3.0383 | 3.77 | 864000 | 2.9132 |
| 3.0383 | 3.81 | 872000 | 2.9201 |
| 3.043 | 3.84 | 880000 | 2.9280 |
| 3.043 | 3.88 | 888000 | 2.9231 |
| 3.0469 | 3.91 | 896000 | 2.9240 |
| 3.0469 | 3.95 | 904000 | 2.9272 |
| 3.0386 | 3.98 | 912000 | 2.9258 |
| 3.0386 | 4.02 | 920000 | 2.9494 |
| 3.0479 | 4.05 | 928000 | 2.9389 |
| 3.0479 | 4.08 | 936000 | 2.9377 |
| 3.0473 | 4.12 | 944000 | 2.9467 |
| 3.0473 | 4.15 | 952000 | 2.9495 |
| 3.0509 | 4.19 | 960000 | 2.9501 |
| 3.0509 | 4.22 | 968000 | 2.9470 |
| 3.0414 | 4.26 | 976000 | 2.9405 |
| 3.0414 | 4.29 | 984000 | 2.9444 |
| 3.0529 | 4.33 | 992000 | 2.9393 |
| 3.0529 | 4.36 | 1000000 | 2.9435 |
| 3.0594 | 4.4 | 1008000 | 2.9583 |
| 3.0594 | 4.43 | 1016000 | 2.9457 |
| 3.0479 | 4.47 | 1024000 | 2.9435 |
| 3.0479 | 4.5 | 1032000 | 2.9527 |
| 3.0564 | 4.54 | 1040000 | 2.9500 |
| 3.0564 | 4.57 | 1048000 | 2.9550 |
| 3.0554 | 4.61 | 1056000 | 2.9578 |
| 3.0554 | 4.64 | 1064000 | 2.9628 |
| 3.0626 | 4.68 | 1072000 | 2.9580 |
| 3.0626 | 4.71 | 1080000 | 2.9667 |
| 3.0722 | 4.75 | 1088000 | 2.9734 |
| 3.0722 | 4.78 | 1096000 | 2.9653 |
| 3.0731 | 4.82 | 1104000 | 2.9689 |
| 3.0731 | 4.85 | 1112000 | 2.9739 |
| 3.0724 | 4.89 | 1120000 | 2.9875 |
| 3.0724 | 4.92 | 1128000 | 2.9849 |
| 3.0656 | 4.96 | 1136000 | 2.9752 |
| 3.0656 | 4.99 | 1144000 | 2.9751 |
| 3.0829 | 5.03 | 1152000 | 2.9768 |
| 3.0829 | 5.06 | 1160000 | 2.9835 |
| 3.0785 | 5.1 | 1168000 | 2.9843 |
| 3.0785 | 5.13 | 1176000 | 3.0001 |
| 3.0704 | 5.17 | 1184000 | 2.9906 |
| 3.0704 | 5.2 | 1192000 | 2.9850 |
| 3.075 | 5.24 | 1200000 | 2.9931 |
| 3.075 | 5.27 | 1208000 | 2.9986 |
| 3.083 | 5.31 | 1216000 | 3.0008 |
| 3.083 | 5.34 | 1224000 | 3.0009 |
| 3.0708 | 5.38 | 1232000 | 3.0017 |
| 3.0708 | 5.41 | 1240000 | 2.9932 |
| 3.0896 | 5.45 | 1248000 | 2.9970 |
| 3.0896 | 5.48 | 1256000 | 3.0027 |
| 3.092 | 5.52 | 1264000 | 3.0002 |
| 3.092 | 5.55 | 1272000 | 2.9967 |
| 3.0916 | 5.59 | 1280000 | 2.9987 |
| 3.0916 | 5.62 | 1288000 | 2.9990 |
| 3.0938 | 5.66 | 1296000 | 3.0035 |
| 3.0938 | 5.69 | 1304000 | 2.9999 |
| 3.1039 | 5.73 | 1312000 | 3.0097 |
| 3.1039 | 5.76 | 1320000 | 3.0022 |
| 3.1059 | 5.8 | 1328000 | 3.0161 |
| 3.1059 | 5.83 | 1336000 | 3.0071 |
| 3.1014 | 5.87 | 1344000 | 3.0150 |
| 3.1014 | 5.9 | 1352000 | 2.9986 |
| 3.1048 | 5.94 | 1360000 | 3.0096 |
| 3.1048 | 5.97 | 1368000 | 3.0063 |
| 3.1099 | 6.01 | 1376000 | 3.0095 |
| 3.1099 | 6.04 | 1384000 | 3.0152 |
| 3.0891 | 6.08 | 1392000 | 3.0179 |
| 3.0891 | 6.11 | 1400000 | 3.0299 |
| 3.0979 | 6.14 | 1408000 | 3.0127 |
| 3.0979 | 6.18 | 1416000 | 3.0260 |
| 3.099 | 6.21 | 1424000 | 3.0187 |
| 3.099 | 6.25 | 1432000 | 3.0114 |
| 3.103 | 6.28 | 1440000 | 3.0191 |
| 3.103 | 6.32 | 1448000 | 3.0168 |
| 3.1066 | 6.35 | 1456000 | 3.0174 |
| 3.1066 | 6.39 | 1464000 | 3.0256 |
| 3.1164 | 6.42 | 1472000 | 3.0192 |
| 3.1164 | 6.46 | 1480000 | 3.0066 |
| 3.1066 | 6.49 | 1488000 | 3.0160 |
| 3.1066 | 6.53 | 1496000 | 3.0187 |
| 3.1014 | 6.56 | 1504000 | 3.0213 |
| 3.1014 | 6.6 | 1512000 | 3.0170 |
| 3.1043 | 6.63 | 1520000 | 3.0251 |
| 3.1043 | 6.67 | 1528000 | 3.0157 |
| 3.1073 | 6.7 | 1536000 | 3.0193 |
| 3.1073 | 6.74 | 1544000 | 3.0174 |
| 3.1131 | 6.77 | 1552000 | 3.0244 |
| 3.1131 | 6.81 | 1560000 | 3.0210 |
| 3.1033 | 6.84 | 1568000 | 3.0235 |
| 3.1033 | 6.88 | 1576000 | 3.0189 |
| 3.1087 | 6.91 | 1584000 | 3.0213 |
| 3.1087 | 6.95 | 1592000 | 3.0196 |
| 3.1065 | 6.98 | 1600000 | 3.0123 |
| 3.1065 | 7.02 | 1608000 | 3.0229 |
| 3.1019 | 7.05 | 1616000 | 3.0206 |
| 3.1019 | 7.09 | 1624000 | 3.0216 |
| 3.1023 | 7.12 | 1632000 | 3.0147 |
| 3.1023 | 7.16 | 1640000 | 3.0227 |
| 3.0969 | 7.19 | 1648000 | 3.0306 |
| 3.0969 | 7.23 | 1656000 | 3.0179 |
| 3.1034 | 7.26 | 1664000 | 3.0259 |
| 3.1034 | 7.3 | 1672000 | 3.0237 |
| 3.1077 | 7.33 | 1680000 | 3.0165 |
| 3.1077 | 7.37 | 1688000 | 3.0213 |
| 3.0983 | 7.4 | 1696000 | 3.0233 |
| 3.0983 | 7.44 | 1704000 | 3.0224 |
| 3.1014 | 7.47 | 1712000 | 3.0187 |
| 3.1014 | 7.51 | 1720000 | 3.0207 |
| 3.1052 | 7.54 | 1728000 | 3.0070 |
| 3.1052 | 7.58 | 1736000 | 3.0236 |
| 3.1062 | 7.61 | 1744000 | 3.0230 |
| 3.1062 | 7.65 | 1752000 | 3.0190 |
| 3.0941 | 7.68 | 1760000 | 3.0235 |
| 3.0941 | 7.72 | 1768000 | 3.0134 |
| 3.0942 | 7.75 | 1776000 | 3.0254 |
| 3.0942 | 7.79 | 1784000 | 3.0154 |
| 3.1089 | 7.82 | 1792000 | 3.0075 |
| 3.1089 | 7.86 | 1800000 | 3.0065 |
| 3.1117 | 7.89 | 1808000 | 3.0241 |
| 3.1117 | 7.93 | 1816000 | 3.0098 |
| 3.0958 | 7.96 | 1824000 | 3.0017 |
| 3.0958 | 8.0 | 1832000 | 3.0100 |
| 3.1177 | 8.03 | 1840000 | 3.0163 |
| 3.1177 | 8.07 | 1848000 | 3.0100 |
| 3.097 | 8.1 | 1856000 | 3.0099 |
| 3.097 | 8.13 | 1864000 | 3.0287 |
| 3.1039 | 8.17 | 1872000 | 3.0107 |
| 3.1039 | 8.2 | 1880000 | 3.0103 |
| 3.0987 | 8.24 | 1888000 | 3.0200 |
| 3.0987 | 8.27 | 1896000 | 3.0197 |
| 3.1029 | 8.31 | 1904000 | 3.0141 |
| 3.1029 | 8.34 | 1912000 | 3.0254 |
| 3.1053 | 8.38 | 1920000 | 3.0128 |
| 3.1053 | 8.41 | 1928000 | 3.0140 |
| 3.1042 | 8.45 | 1936000 | 3.0233 |
| 3.1042 | 8.48 | 1944000 | 3.0156 |
| 3.1039 | 8.52 | 1952000 | 3.0125 |
| 3.1039 | 8.55 | 1960000 | 3.0144 |
| 3.1044 | 8.59 | 1968000 | 3.0247 |
| 3.1044 | 8.62 | 1976000 | 3.0140 |
| 3.1172 | 8.66 | 1984000 | 3.0106 |
| 3.1172 | 8.69 | 1992000 | 3.0161 |
| 3.1106 | 8.73 | 2000000 | 3.0168 |
| 3.1106 | 8.76 | 2008000 | 3.0230 |
| 3.107 | 8.8 | 2016000 | 3.0207 |
| 3.107 | 8.83 | 2024000 | 3.0218 |
| 3.1153 | 8.87 | 2032000 | 3.0157 |
| 3.1153 | 8.9 | 2040000 | 3.0326 |
| 3.1104 | 8.94 | 2048000 | 3.0194 |
| 3.1104 | 8.97 | 2056000 | 3.0211 |
| 3.1206 | 9.01 | 2064000 | 3.0197 |
| 3.1206 | 9.04 | 2072000 | 3.0311 |
| 3.1101 | 9.08 | 2080000 | 3.0218 |
| 3.1101 | 9.11 | 2088000 | 3.0224 |
| 3.1166 | 9.15 | 2096000 | 3.0326 |
| 3.1166 | 9.18 | 2104000 | 3.0252 |
| 3.106 | 9.22 | 2112000 | 3.0259 |
| 3.106 | 9.25 | 2120000 | 3.0116 |
| 3.1067 | 9.29 | 2128000 | 3.0312 |
| 3.1067 | 9.32 | 2136000 | 3.0125 |
| 3.1152 | 9.36 | 2144000 | 3.0147 |
| 3.1152 | 9.39 | 2152000 | 3.0210 |
| 3.1122 | 9.43 | 2160000 | 3.0388 |
| 3.1122 | 9.46 | 2168000 | 3.0409 |
| 3.1092 | 9.5 | 2176000 | 3.0364 |
| 3.1092 | 9.53 | 2184000 | 3.0270 |
| 3.1117 | 9.57 | 2192000 | 3.0326 |
| 3.1117 | 9.6 | 2200000 | 3.0381 |
| 3.1089 | 9.64 | 2208000 | 3.0258 |
| 3.1089 | 9.67 | 2216000 | 3.0287 |
| 3.1195 | 9.71 | 2224000 | 3.0326 |
| 3.1195 | 9.74 | 2232000 | 3.0374 |
| 3.1172 | 9.78 | 2240000 | 3.0227 |
| 3.1172 | 9.81 | 2248000 | 3.0281 |
| 3.1271 | 9.85 | 2256000 | 3.0274 |
| 3.1271 | 9.88 | 2264000 | 3.0225 |
| 3.1112 | 9.92 | 2272000 | 3.0248 |
| 3.1112 | 9.95 | 2280000 | 3.0188 |
| 3.1179 | 9.99 | 2288000 | 3.0227 |
| 3.1179 | 10.02 | 2296000 | 3.0337 |
| 3.1178 | 10.06 | 2304000 | 3.0241 |
| 3.1178 | 10.09 | 2312000 | 3.0247 |
| 3.1148 | 10.13 | 2320000 | 3.0342 |
| 3.1148 | 10.16 | 2328000 | 3.0202 |
| 3.1153 | 10.19 | 2336000 | 3.0294 |
| 3.1153 | 10.23 | 2344000 | 3.0282 |
| 3.1097 | 10.26 | 2352000 | 3.0198 |
| 3.1097 | 10.3 | 2360000 | 3.0188 |
| 3.1041 | 10.33 | 2368000 | 3.0225 |
| 3.1041 | 10.37 | 2376000 | 3.0212 |
| 3.0992 | 10.4 | 2384000 | 3.0208 |
| 3.0992 | 10.44 | 2392000 | 3.0191 |
| 3.1135 | 10.47 | 2400000 | 3.0245 |
### Framework versions
- Transformers 4.35.0.dev0
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.14.0
|
support-pvelocity/Llama-2-7B-instruct-text2sql-GPTQ
|
support-pvelocity
| 2023-11-13T17:47:10Z | 6 | 0 |
transformers
|
[
"transformers",
"llama",
"text-generation",
"conversational",
"en",
"license:llama2",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-11-08T00:44:47Z |
---
license: llama2
language:
- en
pipeline_tag: text-generation
widget:
- text: "[INST] Write SQLite query to answer the following question given the database schema. Please wrap your code answer using ```: Schema: CREATE TABLE head (age INTEGER) Question: How many heads of the departments are older than 56 ? [/INST] Here is the SQLite query to answer to the question: How many heads of the departments are older than 56 ?: ```"
example_title: "Example 1"
- text: "[INST] Write SQLite query to answer the following question given the database schema. Please wrap your code answer using ```: Schema: CREATE TABLE people (first_name VARCHAR) Question: List the first names of people in alphabetical order? [/INST] Here is the SQLite query to answer to the question: List the first names of people in alphabetical order?: ```"
example_title: "Example 2"
- text: "[INST] Write SQLite query to answer the following question given the database schema. Please wrap your code answer using ```: Schema: CREATE TABLE weather (zip_code VARCHAR, mean_sea_level_pressure_inches INTEGER) Question: What is the zip code in which the average mean sea level pressure is the lowest? [/INST] Here is the SQLite query to answer to the question: What is the zip code in which the average mean sea level pressure is the lowest?: ```"
example_title: "Example 3"
- text: "[INST] Write SQLite query to answer the following question given the database schema. Please wrap your code answer using ```: Schema: CREATE TABLE weather (date VARCHAR, mean_temperature_f VARCHAR, mean_humidity VARCHAR, max_gust_speed_mph VARCHAR) Question: What are the date, mean temperature and mean humidity for the top 3 days with the largest max gust speeds? [/INST] Here is the SQLite query to answer to the question: What are the date, mean temperature and mean humidity for the top 3 days with the largest max gust speeds?: ```"
example_title: "Example 4"
- text: "[INST] Write SQLite query to answer the following question given the database schema. Please wrap your code answer using ```: Schema: CREATE TABLE trip (end_station_id VARCHAR); CREATE TABLE station (id VARCHAR, city VARCHAR) Question: Count the number of trips that did not end in San Francisco city. [/INST] Here is the SQLite query to answer to the question: Count the number of trips that did not end in San Francisco city.: ```"
example_title: "Example 5"
---
# **Llama-2-7B-instruct-text2sql-GPTQ Model Card**
**Model Name**: Llama-2-7B-instruct-text2sql-GPTQ
**Description**: This model is a GPTQ quantisation of a fine-tuned version of the Llama 2 with 7 billion parameters, specifically tailored for text-to-SQL tasks. It has been trained to generate SQL queries given a database schema and a natural language question. The GPTQ quantisation was performed with AutoGPTQ.
## Model Information
- **Base Model**: [support-pvelocity/Llama-2-7B-instruct-text2sql](https://huggingface.co/support-pvelocity/Llama-2-7B-instruct-text2sql)
## GPTQ Parameters
- **bits**: 4
- **group_size**: 128
- **desc_act**: False
- **damp_percent**: 0.01
## GPTQ dataset
- **Dataset**: [bugdaryan/sql-create-context-instruction](https://huggingface.co/datasets/bugdaryan/sql-create-context-instruction)
- **Randomized Rows**: 1024
## License
This model is governed by a custom commercial license from Llama. For details, please visit: [Custom Commercial License](https://ai.meta.com/resources/models-and-libraries/llama-downloads/)
## Intended Use
**Intended Use Cases**: This model is intended for commercial and research use in English. It is designed for text-to-SQL tasks, enabling users to generate SQL queries from natural language questions.
**Out-of-Scope Uses**: Any use that violates applicable laws or regulations, use in languages other than English, or any other use prohibited by the Acceptable Use Policy and Licensing Agreement for Llama and its variants.
## Example Code
You can use the Llama-2-7B-instruct-text2sql-GPTQ model to generate SQL queries from natural language questions, as demonstrated in the following code snippet:
```cmd
pip install -q transformers==4.35.0 torch==2.1.0 torchvision==0.16.0 torchaudio==2.1.0 optimum==1.13.2 auto-gptq==0.4.2
```
```python
from transformers import AutoTokenizer
from auto_gptq import AutoGPTQForCausalLM
model_name = 'support-pvelocity/Llama-2-7B-instruct-text2sql-GPTQ'
model = AutoGPTQForCausalLM.from_quantized(model_name, use_safetensors=True, device_map='auto')
tokenizer = AutoTokenizer.from_pretrained(model_name)
table = "CREATE TABLE sales ( sale_id number PRIMARY KEY, product_id number, customer_id number, salesperson_id number, sale_date DATE, quantity number, FOREIGN KEY (product_id) REFERENCES products(product_id), FOREIGN KEY (customer_id) REFERENCES customers(customer_id), FOREIGN KEY (salesperson_id) REFERENCES salespeople(salesperson_id)); CREATE TABLE product_suppliers ( supplier_id number PRIMARY KEY, product_id number, supply_price number, FOREIGN KEY (product_id) REFERENCES products(product_id)); CREATE TABLE customers ( customer_id number PRIMARY KEY, name text, address text ); CREATE TABLE salespeople ( salesperson_id number PRIMARY KEY, name text, region text ); CREATE TABLE product_suppliers ( supplier_id number PRIMARY KEY, product_id number, supply_price number );"
question = 'Find the salesperson who made the most sales.'
prompt = f"[INST] Write SQLite query to answer the following question given the database schema. Please wrap your code answer using ```: Schema: {table} Question: {question} [/INST] Here is the SQLite query to answer to the question: {question}: ``` "
tokens = tokenizer(prompt, return_tensors="pt").to('cuda:0')
input_ids = tokens.input_ids
generated_ids = model.generate(input_ids=input_ids, max_length=4048, pad_token_id=tokenizer.eos_token_id)
output = tokenizer.decode(generated_ids[0], skip_special_tokens=True)
output = output.split('```')[2]
print(output)
```
This code demonstrates how to utilize the model for generating SQL queries based on a provided database schema and a natural language question. It showcases the model's capability to assist in SQL query generation for text-to-SQL tasks.
|
support-pvelocity/Llama-2-7B-instruct-text2sql-GGUF
|
support-pvelocity
| 2023-11-13T17:46:16Z | 4 | 0 | null |
[
"gguf",
"text-generation",
"conversational",
"en",
"license:llama2",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-11-13T17:05:42Z |
---
license: llama2
language:
- en
pipeline_tag: text-generation
widget:
- text: "[INST] Write SQLite query to answer the following question given the database schema. Please wrap your code answer using ```: Schema: CREATE TABLE head (age INTEGER) Question: How many heads of the departments are older than 56 ? [/INST] Here is the SQLite query to answer to the question: How many heads of the departments are older than 56 ?: ```"
example_title: "Example 1"
- text: "[INST] Write SQLite query to answer the following question given the database schema. Please wrap your code answer using ```: Schema: CREATE TABLE people (first_name VARCHAR) Question: List the first names of people in alphabetical order? [/INST] Here is the SQLite query to answer to the question: List the first names of people in alphabetical order?: ```"
example_title: "Example 2"
- text: "[INST] Write SQLite query to answer the following question given the database schema. Please wrap your code answer using ```: Schema: CREATE TABLE weather (zip_code VARCHAR, mean_sea_level_pressure_inches INTEGER) Question: What is the zip code in which the average mean sea level pressure is the lowest? [/INST] Here is the SQLite query to answer to the question: What is the zip code in which the average mean sea level pressure is the lowest?: ```"
example_title: "Example 3"
- text: "[INST] Write SQLite query to answer the following question given the database schema. Please wrap your code answer using ```: Schema: CREATE TABLE weather (date VARCHAR, mean_temperature_f VARCHAR, mean_humidity VARCHAR, max_gust_speed_mph VARCHAR) Question: What are the date, mean temperature and mean humidity for the top 3 days with the largest max gust speeds? [/INST] Here is the SQLite query to answer to the question: What are the date, mean temperature and mean humidity for the top 3 days with the largest max gust speeds?: ```"
example_title: "Example 4"
- text: "[INST] Write SQLite query to answer the following question given the database schema. Please wrap your code answer using ```: Schema: CREATE TABLE trip (end_station_id VARCHAR); CREATE TABLE station (id VARCHAR, city VARCHAR) Question: Count the number of trips that did not end in San Francisco city. [/INST] Here is the SQLite query to answer to the question: Count the number of trips that did not end in San Francisco city.: ```"
example_title: "Example 5"
---
# **Llama-2-7B-instruct-text2sql-GGUF Model Card**
**Model Name**: Llama-2-7B-instruct-text2sql-GGUF
**Description**: This model is a GGUF quantisation of a fine-tuned version of the Llama 2 with 7 billion parameters, specifically tailored for text-to-SQL tasks. It has been trained to generate SQL queries given a database schema and a natural language question. The GGUF quantisation was performed with llama.cpp.
## Model Information
- **Base Model**: [support-pvelocity/Llama-2-7B-instruct-text2sql](https://huggingface.co/support-pvelocity/Llama-2-7B-instruct-text2sql)
## GGUF Parameters
- **Quant method**: Q4_K_M
- **bits**: 4
## License
This model is governed by a custom commercial license from Llama. For details, please visit: [Custom Commercial License](https://ai.meta.com/resources/models-and-libraries/llama-downloads/)
## Intended Use
**Intended Use Cases**: This model is intended for commercial and research use in English. It is designed for text-to-SQL tasks, enabling users to generate SQL queries from natural language questions.
**Out-of-Scope Uses**: Any use that violates applicable laws or regulations, use in languages other than English, or any other use prohibited by the Acceptable Use Policy and Licensing Agreement for Llama and its variants.
## Example Code
You can use the Llama-2-7B-instruct-text2sql-GGUF model to generate SQL queries from natural language questions, as demonstrated in the following code snippet:
```cmd
pip install -q torch==2.1.0 torchvision==0.16.0 torchaudio==2.1.0 ctransformers==0.2.27
```
```python
from ctransformers import AutoModelForCausalLM
model_name = 'support-pvelocity/Llama-2-7B-instruct-text2sql-GGUF'
model = AutoModelForCausalLM.from_pretrained(
model_name,
model_file=model_name.split('/')[1].replace('-GGUF', '.q4_k_m.gguf'),
model_type="llama",
gpu_layers=50,
context_length=4048
)
table = "CREATE TABLE sales ( sale_id number PRIMARY KEY, product_id number, customer_id number, salesperson_id number, sale_date DATE, quantity number, FOREIGN KEY (product_id) REFERENCES products(product_id), FOREIGN KEY (customer_id) REFERENCES customers(customer_id), FOREIGN KEY (salesperson_id) REFERENCES salespeople(salesperson_id)); CREATE TABLE product_suppliers ( supplier_id number PRIMARY KEY, product_id number, supply_price number, FOREIGN KEY (product_id) REFERENCES products(product_id)); CREATE TABLE customers ( customer_id number PRIMARY KEY, name text, address text ); CREATE TABLE salespeople ( salesperson_id number PRIMARY KEY, name text, region text ); CREATE TABLE product_suppliers ( supplier_id number PRIMARY KEY, product_id number, supply_price number );"
question = 'Find the salesperson who made the most sales.'
prompt = f"[INST] Write SQLite query to answer the following question given the database schema. Please wrap your code answer using ```: Schema: {table} Question: {question} [/INST] Here is the SQLite query to answer to the question: {question}: ``` "
output = model(prompt)
output = output.split('```')[0]
print(output.strip())
```
This code demonstrates how to utilize the model for generating SQL queries based on a provided database schema and a natural language question. It showcases the model's capability to assist in SQL query generation for text-to-SQL tasks.
|
alexdg19/bert_large_cnn_daily
|
alexdg19
| 2023-11-13T17:36:31Z | 8 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"bart",
"text2text-generation",
"generated_from_trainer",
"dataset:cnn_dailymail",
"base_model:alexdg19/bert_large_xsum_samsum2",
"base_model:finetune:alexdg19/bert_large_xsum_samsum2",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-11-13T14:11:14Z |
---
license: mit
base_model: alexdg19/bert_large_xsum_samsum2
tags:
- generated_from_trainer
datasets:
- cnn_dailymail
metrics:
- rouge
model-index:
- name: bert_large_cnn_daily
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: cnn_dailymail
type: cnn_dailymail
config: 3.0.0
split: test
args: 3.0.0
metrics:
- name: Rouge1
type: rouge
value: 0.4251
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert_large_cnn_daily
This model is a fine-tuned version of [alexdg19/bert_large_xsum_samsum2](https://huggingface.co/alexdg19/bert_large_xsum_samsum2) on the cnn_dailymail dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7065
- Rouge1: 0.4251
- Rouge2: 0.2024
- Rougel: 0.2992
- Rougelsum: 0.3961
- Gen Len: 60.6232
## 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: 3
- eval_batch_size: 3
- seed: 42
- gradient_accumulation_steps: 3
- total_train_batch_size: 9
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| 1.6632 | 1.0 | 1021 | 1.6262 | 0.4191 | 0.1992 | 0.2957 | 0.39 | 60.6205 |
| 1.3734 | 2.0 | 2042 | 1.6078 | 0.4253 | 0.2046 | 0.3009 | 0.397 | 61.0692 |
| 1.1497 | 3.0 | 3064 | 1.6759 | 0.4254 | 0.2033 | 0.2998 | 0.3967 | 60.8555 |
| 1.0123 | 4.0 | 4084 | 1.7065 | 0.4251 | 0.2024 | 0.2992 | 0.3961 | 60.6232 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
toddwilson147/Taxi-v3
|
toddwilson147
| 2023-11-13T17:23:20Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-11-13T17:23:18Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.56 +/- 2.71
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="toddwilson147/Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
neuralmagic/bge-large-en-v1.5-quant
|
neuralmagic
| 2023-11-13T17:05:07Z | 7,215 | 21 |
transformers
|
[
"transformers",
"onnx",
"bert",
"feature-extraction",
"sparse",
"sparsity",
"quantized",
"embeddings",
"int8",
"mteb",
"deepsparse",
"en",
"license:mit",
"model-index",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2023-10-03T15:50:50Z |
---
license: mit
language:
- en
tags:
- sparse
- sparsity
- quantized
- onnx
- embeddings
- int8
- mteb
- deepsparse
model-index:
- name: bge-large-en-v1.5-quant
results:
- task:
type: Classification
dataset:
type: mteb/amazon_counterfactual
name: MTEB AmazonCounterfactualClassification (en)
config: en
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 75.53731343283583
- type: ap
value: 38.30609312253564
- type: f1
value: 69.42802757893695
- task:
type: STS
dataset:
type: mteb/biosses-sts
name: MTEB BIOSSES
config: default
split: test
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
metrics:
- type: cos_sim_pearson
value: 89.27346145216443
- type: cos_sim_spearman
value: 88.36526647458979
- type: euclidean_pearson
value: 86.83053354694746
- type: euclidean_spearman
value: 87.56223612880584
- type: manhattan_pearson
value: 86.59250609226758
- type: manhattan_spearman
value: 87.70681773644885
- task:
type: STS
dataset:
type: mteb/sickr-sts
name: MTEB SICK-R
config: default
split: test
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
metrics:
- type: cos_sim_pearson
value: 86.18998669716373
- type: cos_sim_spearman
value: 82.06129973984048
- type: euclidean_pearson
value: 83.65969509485801
- type: euclidean_spearman
value: 81.91666612708826
- type: manhattan_pearson
value: 83.6906794731384
- type: manhattan_spearman
value: 81.91752705367436
- task:
type: STS
dataset:
type: mteb/sts12-sts
name: MTEB STS12
config: default
split: test
revision: a0d554a64d88156834ff5ae9920b964011b16384
metrics:
- type: cos_sim_pearson
value: 86.93407086985752
- type: cos_sim_spearman
value: 78.82992283957066
- type: euclidean_pearson
value: 83.39733473832982
- type: euclidean_spearman
value: 78.86999229850214
- type: manhattan_pearson
value: 83.39397058098533
- type: manhattan_spearman
value: 78.85397971200753
- task:
type: STS
dataset:
type: mteb/sts13-sts
name: MTEB STS13
config: default
split: test
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
metrics:
- type: cos_sim_pearson
value: 87.2586009863056
- type: cos_sim_spearman
value: 87.99415514558852
- type: euclidean_pearson
value: 86.98993652364359
- type: euclidean_spearman
value: 87.72725335668807
- type: manhattan_pearson
value: 86.897205761048
- type: manhattan_spearman
value: 87.65231103509018
- task:
type: STS
dataset:
type: mteb/sts14-sts
name: MTEB STS14
config: default
split: test
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
metrics:
- type: cos_sim_pearson
value: 85.41417660460755
- type: cos_sim_spearman
value: 83.50291886604928
- type: euclidean_pearson
value: 84.67758839660924
- type: euclidean_spearman
value: 83.4368059512681
- type: manhattan_pearson
value: 84.66027228213025
- type: manhattan_spearman
value: 83.43472054456252
- task:
type: STS
dataset:
type: mteb/sts15-sts
name: MTEB STS15
config: default
split: test
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
metrics:
- type: cos_sim_pearson
value: 88.02513262365703
- type: cos_sim_spearman
value: 89.00430907638267
- type: euclidean_pearson
value: 88.16290361497319
- type: euclidean_spearman
value: 88.6645154822661
- type: manhattan_pearson
value: 88.15337528825458
- type: manhattan_spearman
value: 88.66202950081507
- task:
type: STS
dataset:
type: mteb/sts16-sts
name: MTEB STS16
config: default
split: test
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
metrics:
- type: cos_sim_pearson
value: 85.10194022827035
- type: cos_sim_spearman
value: 86.45367112223394
- type: euclidean_pearson
value: 85.45292931769094
- type: euclidean_spearman
value: 86.06607589083283
- type: manhattan_pearson
value: 85.4111233047049
- type: manhattan_spearman
value: 86.04379654118996
- task:
type: STS
dataset:
type: mteb/sts17-crosslingual-sts
name: MTEB STS17 (en-en)
config: en-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 89.86966589113663
- type: cos_sim_spearman
value: 89.5617056243649
- type: euclidean_pearson
value: 89.018495917952
- type: euclidean_spearman
value: 88.387335721179
- type: manhattan_pearson
value: 89.07568042943448
- type: manhattan_spearman
value: 88.51733863475219
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (en)
config: en
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 68.38465344518238
- type: cos_sim_spearman
value: 68.15219488291783
- type: euclidean_pearson
value: 68.99169681132668
- type: euclidean_spearman
value: 68.01334641045888
- type: manhattan_pearson
value: 68.84952679202642
- type: manhattan_spearman
value: 67.85430179655137
- task:
type: STS
dataset:
type: mteb/stsbenchmark-sts
name: MTEB STSBenchmark
config: default
split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:
- type: cos_sim_pearson
value: 86.60574360222778
- type: cos_sim_spearman
value: 87.8878986593873
- type: euclidean_pearson
value: 87.11557232168404
- type: euclidean_spearman
value: 87.40944677043365
- type: manhattan_pearson
value: 87.10395398212532
- type: manhattan_spearman
value: 87.35977283466168
- task:
type: PairClassification
dataset:
type: mteb/sprintduplicatequestions-pairclassification
name: MTEB SprintDuplicateQuestions
config: default
split: test
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
metrics:
- type: cos_sim_accuracy
value: 99.84752475247525
- type: cos_sim_ap
value: 96.49316696572335
- type: cos_sim_f1
value: 92.35352532274081
- type: cos_sim_precision
value: 91.71597633136095
- type: cos_sim_recall
value: 93.0
- type: dot_accuracy
value: 99.77326732673268
- type: dot_ap
value: 93.5497681978726
- type: dot_f1
value: 88.35582208895552
- type: dot_precision
value: 88.31168831168831
- type: dot_recall
value: 88.4
- type: euclidean_accuracy
value: 99.84653465346534
- type: euclidean_ap
value: 96.36378999360083
- type: euclidean_f1
value: 92.33052944087086
- type: euclidean_precision
value: 91.38099902056807
- type: euclidean_recall
value: 93.30000000000001
- type: manhattan_accuracy
value: 99.84455445544555
- type: manhattan_ap
value: 96.36035171233175
- type: manhattan_f1
value: 92.13260761999011
- type: manhattan_precision
value: 91.1851126346719
- type: manhattan_recall
value: 93.10000000000001
- type: max_accuracy
value: 99.84752475247525
- type: max_ap
value: 96.49316696572335
- type: max_f1
value: 92.35352532274081
- task:
type: PairClassification
dataset:
type: mteb/twittersemeval2015-pairclassification
name: MTEB TwitterSemEval2015
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
- type: cos_sim_accuracy
value: 87.26828396018358
- type: cos_sim_ap
value: 77.79878217023162
- type: cos_sim_f1
value: 71.0425694621463
- type: cos_sim_precision
value: 68.71301775147928
- type: cos_sim_recall
value: 73.53562005277044
- type: dot_accuracy
value: 84.01978899684092
- type: dot_ap
value: 66.12134149171163
- type: dot_f1
value: 63.283507097098365
- type: dot_precision
value: 60.393191081275475
- type: dot_recall
value: 66.46437994722955
- type: euclidean_accuracy
value: 87.24444179531503
- type: euclidean_ap
value: 77.84821131946212
- type: euclidean_f1
value: 71.30456661215247
- type: euclidean_precision
value: 68.1413801394566
- type: euclidean_recall
value: 74.77572559366754
- type: manhattan_accuracy
value: 87.19079692436074
- type: manhattan_ap
value: 77.78054941055291
- type: manhattan_f1
value: 71.13002127393318
- type: manhattan_precision
value: 67.65055939062128
- type: manhattan_recall
value: 74.9868073878628
- type: max_accuracy
value: 87.26828396018358
- type: max_ap
value: 77.84821131946212
- type: max_f1
value: 71.30456661215247
- task:
type: PairClassification
dataset:
type: mteb/twitterurlcorpus-pairclassification
name: MTEB TwitterURLCorpus
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
- type: cos_sim_accuracy
value: 88.91023402025847
- type: cos_sim_ap
value: 85.94088151184411
- type: cos_sim_f1
value: 78.25673997223645
- type: cos_sim_precision
value: 74.45433059919367
- type: cos_sim_recall
value: 82.46843239913767
- type: dot_accuracy
value: 87.91865564481701
- type: dot_ap
value: 82.75373957440969
- type: dot_f1
value: 75.97383507276201
- type: dot_precision
value: 72.67294713160854
- type: dot_recall
value: 79.5888512473052
- type: euclidean_accuracy
value: 88.8539604921023
- type: euclidean_ap
value: 85.71590936389937
- type: euclidean_f1
value: 77.82902261742242
- type: euclidean_precision
value: 74.7219270279844
- type: euclidean_recall
value: 81.20572836464429
- type: manhattan_accuracy
value: 88.78992509799356
- type: manhattan_ap
value: 85.70200619366904
- type: manhattan_f1
value: 77.85875848203065
- type: manhattan_precision
value: 72.94315506222671
- type: manhattan_recall
value: 83.48475515860795
- type: max_accuracy
value: 88.91023402025847
- type: max_ap
value: 85.94088151184411
- type: max_f1
value: 78.25673997223645
---
# bge-large-en-v1.5-quant
<div>
<img src="https://huggingface.co/zeroshot/bge-large-en-v1.5-quant/resolve/main/bge-large-latency.png" alt="latency" width="500" style="display:inline-block; margin-right:10px;"/>
</div>
[DeepSparse](https://github.com/neuralmagic/deepsparse) is able to improve latency performance on a 10 core laptop by 4.8X and up to 3.5X on a 16 core AWS instance.
## Usage
This is the quantized (INT8) ONNX variant of the [bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) embeddings model accelerated with [Sparsify](https://github.com/neuralmagic/sparsify) for quantization and [DeepSparseSentenceTransformers](https://github.com/neuralmagic/deepsparse/tree/main/src/deepsparse/sentence_transformers) for inference.
```bash
pip install -U deepsparse-nightly[sentence_transformers]
```
```python
from deepsparse.sentence_transformers import DeepSparseSentenceTransformer
model = DeepSparseSentenceTransformer('neuralmagic/bge-large-en-v1.5-quant', export=False)
# Our sentences we like to encode
sentences = ['This framework generates embeddings for each input sentence',
'Sentences are passed as a list of string.',
'The quick brown fox jumps over the lazy dog.']
# Sentences are encoded by calling model.encode()
embeddings = model.encode(sentences)
# Print the embeddings
for sentence, embedding in zip(sentences, embeddings):
print("Sentence:", sentence)
print("Embedding:", embedding.shape)
print("")
```
For general questions on these models and sparsification methods, reach out to the engineering team on our [community Slack](https://join.slack.com/t/discuss-neuralmagic/shared_invite/zt-q1a1cnvo-YBoICSIw3L1dmQpjBeDurQ).
|
neuralmagic/bge-base-en-v1.5-quant
|
neuralmagic
| 2023-11-13T17:04:42Z | 962 | 4 |
transformers
|
[
"transformers",
"onnx",
"bert",
"feature-extraction",
"mteb",
"sparse",
"sparsity",
"quantized",
"embeddings",
"int8",
"deepsparse",
"en",
"license:mit",
"model-index",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2023-10-03T12:45:42Z |
---
license: mit
language:
- en
tags:
- mteb
- sparse
- sparsity
- quantized
- onnx
- embeddings
- int8
- deepsparse
model-index:
- name: bge-base-en-v1.5-quant
results:
- task:
type: Classification
dataset:
type: mteb/amazon_counterfactual
name: MTEB AmazonCounterfactualClassification (en)
config: en
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 76.16417910447761
- type: ap
value: 39.62965026785565
- type: f1
value: 70.30041589476463
- task:
type: Classification
dataset:
type: mteb/amazon_polarity
name: MTEB AmazonPolarityClassification
config: default
split: test
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
metrics:
- type: accuracy
value: 92.95087500000001
- type: ap
value: 89.92451248271642
- type: f1
value: 92.94162732408543
- task:
type: Classification
dataset:
type: mteb/amazon_reviews_multi
name: MTEB AmazonReviewsClassification (en)
config: en
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 48.214
- type: f1
value: 47.57084372829096
- task:
type: Clustering
dataset:
type: mteb/arxiv-clustering-p2p
name: MTEB ArxivClusteringP2P
config: default
split: test
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
metrics:
- type: v_measure
value: 48.499816497755646
- task:
type: Clustering
dataset:
type: mteb/arxiv-clustering-s2s
name: MTEB ArxivClusteringS2S
config: default
split: test
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
metrics:
- type: v_measure
value: 42.006939120636034
- task:
type: Reranking
dataset:
type: mteb/askubuntudupquestions-reranking
name: MTEB AskUbuntuDupQuestions
config: default
split: test
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
metrics:
- type: map
value: 62.390343953329875
- type: mrr
value: 75.69922613551422
- task:
type: STS
dataset:
type: mteb/biosses-sts
name: MTEB BIOSSES
config: default
split: test
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
metrics:
- type: cos_sim_pearson
value: 89.03408553833623
- type: cos_sim_spearman
value: 86.71221676053791
- type: euclidean_pearson
value: 87.81477796215844
- type: euclidean_spearman
value: 87.28994076774481
- type: manhattan_pearson
value: 87.76204756059836
- type: manhattan_spearman
value: 87.1971675695072
- task:
type: Classification
dataset:
type: mteb/banking77
name: MTEB Banking77Classification
config: default
split: test
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
metrics:
- type: accuracy
value: 86.35064935064935
- type: f1
value: 86.32782396028989
- task:
type: Clustering
dataset:
type: mteb/biorxiv-clustering-p2p
name: MTEB BiorxivClusteringP2P
config: default
split: test
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
metrics:
- type: v_measure
value: 39.299558776859485
- task:
type: Clustering
dataset:
type: mteb/biorxiv-clustering-s2s
name: MTEB BiorxivClusteringS2S
config: default
split: test
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
metrics:
- type: v_measure
value: 35.64603198816062
- task:
type: Classification
dataset:
type: mteb/emotion
name: MTEB EmotionClassification
config: default
split: test
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
metrics:
- type: accuracy
value: 51.269999999999996
- type: f1
value: 45.9714399031315
- task:
type: Classification
dataset:
type: mteb/imdb
name: MTEB ImdbClassification
config: default
split: test
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
metrics:
- type: accuracy
value: 89.7204
- type: ap
value: 85.70238397381907
- type: f1
value: 89.70961232185473
- task:
type: Classification
dataset:
type: mteb/mtop_domain
name: MTEB MTOPDomainClassification (en)
config: en
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 93.95120839033288
- type: f1
value: 93.70348712248138
- task:
type: Classification
dataset:
type: mteb/mtop_intent
name: MTEB MTOPIntentClassification (en)
config: en
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 75.25763793889648
- type: f1
value: 57.59583082574482
- task:
type: Classification
dataset:
type: mteb/amazon_massive_intent
name: MTEB MassiveIntentClassification (en)
config: en
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 75.16476126429052
- type: f1
value: 73.29287381030854
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (en)
config: en
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 78.9340954942838
- type: f1
value: 79.04036413238218
- task:
type: Clustering
dataset:
type: mteb/medrxiv-clustering-p2p
name: MTEB MedrxivClusteringP2P
config: default
split: test
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
metrics:
- type: v_measure
value: 32.80025982143821
- task:
type: Clustering
dataset:
type: mteb/medrxiv-clustering-s2s
name: MTEB MedrxivClusteringS2S
config: default
split: test
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
metrics:
- type: v_measure
value: 30.956464446009623
- task:
type: Reranking
dataset:
type: mteb/mind_small
name: MTEB MindSmallReranking
config: default
split: test
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
metrics:
- type: map
value: 31.886626060290734
- type: mrr
value: 32.99813843700759
- task:
type: Clustering
dataset:
type: mteb/reddit-clustering
name: MTEB RedditClustering
config: default
split: test
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
metrics:
- type: v_measure
value: 55.693914682185365
- task:
type: Clustering
dataset:
type: mteb/reddit-clustering-p2p
name: MTEB RedditClusteringP2P
config: default
split: test
revision: 282350215ef01743dc01b456c7f5241fa8937f16
metrics:
- type: v_measure
value: 62.32723620518647
- task:
type: STS
dataset:
type: mteb/sickr-sts
name: MTEB SICK-R
config: default
split: test
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
metrics:
- type: cos_sim_pearson
value: 84.70275347034692
- type: cos_sim_spearman
value: 80.06126639668393
- type: euclidean_pearson
value: 82.18370726102707
- type: euclidean_spearman
value: 80.05483013524909
- type: manhattan_pearson
value: 82.11962032129463
- type: manhattan_spearman
value: 79.97174232961949
- task:
type: STS
dataset:
type: mteb/sts12-sts
name: MTEB STS12
config: default
split: test
revision: a0d554a64d88156834ff5ae9920b964011b16384
metrics:
- type: cos_sim_pearson
value: 86.08210281025868
- type: cos_sim_spearman
value: 77.75002826042643
- type: euclidean_pearson
value: 83.06487161944293
- type: euclidean_spearman
value: 78.0677956304104
- type: manhattan_pearson
value: 83.04321232787379
- type: manhattan_spearman
value: 78.09582483148635
- task:
type: STS
dataset:
type: mteb/sts13-sts
name: MTEB STS13
config: default
split: test
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
metrics:
- type: cos_sim_pearson
value: 84.64353592106988
- type: cos_sim_spearman
value: 86.07934653140616
- type: euclidean_pearson
value: 85.21820182954883
- type: euclidean_spearman
value: 86.18828773665395
- type: manhattan_pearson
value: 85.12075207905364
- type: manhattan_spearman
value: 86.12061116344299
- task:
type: STS
dataset:
type: mteb/sts14-sts
name: MTEB STS14
config: default
split: test
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
metrics:
- type: cos_sim_pearson
value: 84.33571296969136
- type: cos_sim_spearman
value: 82.8868213429789
- type: euclidean_pearson
value: 83.65476643152161
- type: euclidean_spearman
value: 82.76439753890263
- type: manhattan_pearson
value: 83.63348951033883
- type: manhattan_spearman
value: 82.76176495070241
- task:
type: STS
dataset:
type: mteb/sts15-sts
name: MTEB STS15
config: default
split: test
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
metrics:
- type: cos_sim_pearson
value: 87.6337321089215
- type: cos_sim_spearman
value: 88.54453531860615
- type: euclidean_pearson
value: 87.68754116644199
- type: euclidean_spearman
value: 88.22610830299979
- type: manhattan_pearson
value: 87.62214887890859
- type: manhattan_spearman
value: 88.14766677391091
- task:
type: STS
dataset:
type: mteb/sts16-sts
name: MTEB STS16
config: default
split: test
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
metrics:
- type: cos_sim_pearson
value: 83.89742747806514
- type: cos_sim_spearman
value: 85.76282302560992
- type: euclidean_pearson
value: 84.83917251074928
- type: euclidean_spearman
value: 85.74354740775905
- type: manhattan_pearson
value: 84.91190952448616
- type: manhattan_spearman
value: 85.82001542154245
- task:
type: STS
dataset:
type: mteb/sts17-crosslingual-sts
name: MTEB STS17 (en-en)
config: en-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 87.70974342036347
- type: cos_sim_spearman
value: 87.82200371351459
- type: euclidean_pearson
value: 88.04095125600278
- type: euclidean_spearman
value: 87.5069523002544
- type: manhattan_pearson
value: 88.03247709799281
- type: manhattan_spearman
value: 87.43433979175654
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (en)
config: en
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 65.0349727703108
- type: cos_sim_spearman
value: 65.46090125254047
- type: euclidean_pearson
value: 66.75349075443432
- type: euclidean_spearman
value: 65.57576680702924
- type: manhattan_pearson
value: 66.72598998285412
- type: manhattan_spearman
value: 65.63446184311414
- task:
type: STS
dataset:
type: mteb/stsbenchmark-sts
name: MTEB STSBenchmark
config: default
split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:
- type: cos_sim_pearson
value: 85.18026134463653
- type: cos_sim_spearman
value: 86.79430055943524
- type: euclidean_pearson
value: 86.2668626122386
- type: euclidean_spearman
value: 86.72288498504841
- type: manhattan_pearson
value: 86.28615540445857
- type: manhattan_spearman
value: 86.7110630606802
- task:
type: Reranking
dataset:
type: mteb/scidocs-reranking
name: MTEB SciDocsRR
config: default
split: test
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
metrics:
- type: map
value: 87.05335415919195
- type: mrr
value: 96.27455968142243
- task:
type: PairClassification
dataset:
type: mteb/sprintduplicatequestions-pairclassification
name: MTEB SprintDuplicateQuestions
config: default
split: test
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
metrics:
- type: cos_sim_accuracy
value: 99.84653465346534
- type: cos_sim_ap
value: 96.38115549823692
- type: cos_sim_f1
value: 92.15983813859383
- type: cos_sim_precision
value: 93.24462640736951
- type: cos_sim_recall
value: 91.10000000000001
- type: dot_accuracy
value: 99.81782178217821
- type: dot_ap
value: 95.65732630933346
- type: dot_f1
value: 90.68825910931176
- type: dot_precision
value: 91.80327868852459
- type: dot_recall
value: 89.60000000000001
- type: euclidean_accuracy
value: 99.84653465346534
- type: euclidean_ap
value: 96.34134720479366
- type: euclidean_f1
value: 92.1756688541141
- type: euclidean_precision
value: 93.06829765545362
- type: euclidean_recall
value: 91.3
- type: manhattan_accuracy
value: 99.84356435643565
- type: manhattan_ap
value: 96.38165573090185
- type: manhattan_f1
value: 92.07622868605819
- type: manhattan_precision
value: 92.35412474849095
- type: manhattan_recall
value: 91.8
- type: max_accuracy
value: 99.84653465346534
- type: max_ap
value: 96.38165573090185
- type: max_f1
value: 92.1756688541141
- task:
type: Clustering
dataset:
type: mteb/stackexchange-clustering
name: MTEB StackExchangeClustering
config: default
split: test
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
metrics:
- type: v_measure
value: 64.81205738681385
- task:
type: Clustering
dataset:
type: mteb/stackexchange-clustering-p2p
name: MTEB StackExchangeClusteringP2P
config: default
split: test
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
metrics:
- type: v_measure
value: 34.083934029129445
- task:
type: Reranking
dataset:
type: mteb/stackoverflowdupquestions-reranking
name: MTEB StackOverflowDupQuestions
config: default
split: test
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
metrics:
- type: map
value: 54.447346270481376
- type: mrr
value: 55.382382119514475
- task:
type: Classification
dataset:
type: mteb/toxic_conversations_50k
name: MTEB ToxicConversationsClassification
config: default
split: test
revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
metrics:
- type: accuracy
value: 72.123
- type: ap
value: 14.396060207954983
- type: f1
value: 55.24344377812756
- task:
type: Classification
dataset:
type: mteb/tweet_sentiment_extraction
name: MTEB TweetSentimentExtractionClassification
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
- type: accuracy
value: 59.67176004527447
- type: f1
value: 59.97320225890037
- task:
type: Clustering
dataset:
type: mteb/twentynewsgroups-clustering
name: MTEB TwentyNewsgroupsClustering
config: default
split: test
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
metrics:
- type: v_measure
value: 49.50190094208029
- task:
type: PairClassification
dataset:
type: mteb/twittersemeval2015-pairclassification
name: MTEB TwitterSemEval2015
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
- type: cos_sim_accuracy
value: 86.70799308577219
- type: cos_sim_ap
value: 76.40980707197174
- type: cos_sim_f1
value: 70.64264849074976
- type: cos_sim_precision
value: 65.56710347943967
- type: cos_sim_recall
value: 76.56992084432717
- type: dot_accuracy
value: 85.75430649102938
- type: dot_ap
value: 72.68783978286282
- type: dot_f1
value: 67.56951102588687
- type: dot_precision
value: 61.90162494510321
- type: dot_recall
value: 74.37994722955145
- type: euclidean_accuracy
value: 86.70799308577219
- type: euclidean_ap
value: 76.43046769325314
- type: euclidean_f1
value: 70.84852905421832
- type: euclidean_precision
value: 65.68981064021641
- type: euclidean_recall
value: 76.88654353562005
- type: manhattan_accuracy
value: 86.70203254455504
- type: manhattan_ap
value: 76.39254562413156
- type: manhattan_f1
value: 70.86557059961316
- type: manhattan_precision
value: 65.39491298527443
- type: manhattan_recall
value: 77.33509234828496
- type: max_accuracy
value: 86.70799308577219
- type: max_ap
value: 76.43046769325314
- type: max_f1
value: 70.86557059961316
- task:
type: PairClassification
dataset:
type: mteb/twitterurlcorpus-pairclassification
name: MTEB TwitterURLCorpus
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
- type: cos_sim_accuracy
value: 88.92381728567548
- type: cos_sim_ap
value: 85.92532857788025
- type: cos_sim_f1
value: 78.11970128792525
- type: cos_sim_precision
value: 73.49806530445998
- type: cos_sim_recall
value: 83.3615645210964
- type: dot_accuracy
value: 88.28540381107618
- type: dot_ap
value: 84.42890126108796
- type: dot_f1
value: 76.98401162790698
- type: dot_precision
value: 72.89430222956234
- type: dot_recall
value: 81.55990144748999
- type: euclidean_accuracy
value: 88.95874568246207
- type: euclidean_ap
value: 85.88338025133037
- type: euclidean_f1
value: 78.14740888593184
- type: euclidean_precision
value: 75.15285084601166
- type: euclidean_recall
value: 81.3905143209116
- type: manhattan_accuracy
value: 88.92769821865176
- type: manhattan_ap
value: 85.84824183217555
- type: manhattan_f1
value: 77.9830582736965
- type: manhattan_precision
value: 74.15972222222223
- type: manhattan_recall
value: 82.22205112411457
- type: max_accuracy
value: 88.95874568246207
- type: max_ap
value: 85.92532857788025
- type: max_f1
value: 78.14740888593184
---
# bge-base-en-v1.5-quant
<div>
<img src="https://huggingface.co/zeroshot/bge-base-en-v1.5-quant/resolve/main/bge-base-latency.png" alt="latency" width="500" style="display:inline-block; margin-right:10px;"/>
</div>
[DeepSparse](https://github.com/neuralmagic/deepsparse) is able to improve latency performance on a 10 core laptop and a 16 core AWS instance by up to 4.5X.
## Usage
This is the quantized (INT8) ONNX variant of the [bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) embeddings model accelerated with [Sparsify](https://github.com/neuralmagic/sparsify) for quantization and [DeepSparseSentenceTransformers](https://github.com/neuralmagic/deepsparse/tree/main/src/deepsparse/sentence_transformers) for inference.
```bash
pip install -U deepsparse-nightly[sentence_transformers]
```
```python
from deepsparse.sentence_transformers import DeepSparseSentenceTransformer
model = DeepSparseSentenceTransformer('neuralmagic/bge-base-en-v1.5-quant', export=False)
# Our sentences we like to encode
sentences = ['This framework generates embeddings for each input sentence',
'Sentences are passed as a list of string.',
'The quick brown fox jumps over the lazy dog.']
# Sentences are encoded by calling model.encode()
embeddings = model.encode(sentences)
# Print the embeddings
for sentence, embedding in zip(sentences, embeddings):
print("Sentence:", sentence)
print("Embedding:", embedding.shape)
print("")
```
For general questions on these models and sparsification methods, reach out to the engineering team on our [community Slack](https://join.slack.com/t/discuss-neuralmagic/shared_invite/zt-q1a1cnvo-YBoICSIw3L1dmQpjBeDurQ).
|
AntoineD/camembert_ccnet_classification_tools_NEFTune_fr
|
AntoineD
| 2023-11-13T16:59:44Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"camembert",
"text-classification",
"generated_from_trainer",
"base_model:almanach/camembert-base-ccnet",
"base_model:finetune:almanach/camembert-base-ccnet",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-13T13:31:47Z |
---
base_model: camembert/camembert-base-ccnet
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: camembert_ccnet_classification_tools_NEFTune_fr
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. -->
# camembert_ccnet_classification_tools_NEFTune_fr
This model is a fine-tuned version of [camembert/camembert-base-ccnet](https://huggingface.co/camembert/camembert-base-ccnet) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2866
- Accuracy: 0.95
- Learning Rate: 0.0
## 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: 24
- eval_batch_size: 192
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 60
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Rate |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 2.038 | 1.0 | 7 | 1.7370 | 0.65 | 0.0001 |
| 1.6169 | 2.0 | 14 | 1.2630 | 0.825 | 0.0001 |
| 1.1861 | 3.0 | 21 | 0.8659 | 0.95 | 0.0001 |
| 0.8284 | 4.0 | 28 | 0.6075 | 0.95 | 0.0001 |
| 0.6032 | 5.0 | 35 | 0.4207 | 0.975 | 0.0001 |
| 0.3928 | 6.0 | 42 | 0.3817 | 0.95 | 9e-05 |
| 0.2458 | 7.0 | 49 | 0.3378 | 0.95 | 0.0001 |
| 0.1683 | 8.0 | 56 | 0.4320 | 0.9 | 0.0001 |
| 0.127 | 9.0 | 63 | 0.3592 | 0.95 | 0.0001 |
| 0.0909 | 10.0 | 70 | 0.3695 | 0.925 | 0.0001 |
| 0.0719 | 11.0 | 77 | 0.3377 | 0.925 | 0.0001 |
| 0.0679 | 12.0 | 84 | 0.2450 | 0.95 | 8e-05 |
| 0.0865 | 13.0 | 91 | 0.2783 | 0.9 | 0.0001 |
| 0.0519 | 14.0 | 98 | 0.2265 | 0.975 | 0.0001 |
| 0.0497 | 15.0 | 105 | 0.2801 | 0.95 | 0.0001 |
| 0.0993 | 16.0 | 112 | 0.3733 | 0.925 | 0.0001 |
| 0.0358 | 17.0 | 119 | 0.4012 | 0.9 | 0.0001 |
| 0.0356 | 18.0 | 126 | 0.2591 | 0.95 | 7e-05 |
| 0.0279 | 19.0 | 133 | 0.2687 | 0.95 | 0.0001 |
| 0.0303 | 20.0 | 140 | 0.2650 | 0.95 | 0.0001 |
| 0.0246 | 21.0 | 147 | 0.2337 | 0.95 | 0.0001 |
| 0.0257 | 22.0 | 154 | 0.2274 | 0.95 | 0.0001 |
| 0.0448 | 23.0 | 161 | 0.2223 | 0.975 | 0.0001 |
| 0.0567 | 24.0 | 168 | 0.2157 | 0.975 | 6e-05 |
| 0.0182 | 25.0 | 175 | 0.2096 | 0.975 | 0.0001 |
| 0.0282 | 26.0 | 182 | 0.2118 | 0.975 | 0.0001 |
| 0.0232 | 27.0 | 189 | 0.2146 | 0.975 | 0.0001 |
| 0.0212 | 28.0 | 196 | 0.2162 | 0.975 | 0.0001 |
| 0.0197 | 29.0 | 203 | 0.2185 | 0.975 | 0.0001 |
| 0.0203 | 30.0 | 210 | 0.2215 | 0.975 | 5e-05 |
| 0.0172 | 31.0 | 217 | 0.2263 | 0.975 | 0.0000 |
| 0.0174 | 32.0 | 224 | 0.2347 | 0.975 | 0.0000 |
| 0.0152 | 33.0 | 231 | 0.2426 | 0.95 | 0.0000 |
| 0.0164 | 34.0 | 238 | 0.2443 | 0.95 | 0.0000 |
| 0.018 | 35.0 | 245 | 0.2557 | 0.95 | 0.0000 |
| 0.0328 | 36.0 | 252 | 0.2624 | 0.95 | 4e-05 |
| 0.0152 | 37.0 | 259 | 0.2602 | 0.95 | 0.0000 |
| 0.0147 | 38.0 | 266 | 0.2615 | 0.95 | 0.0000 |
| 0.0152 | 39.0 | 273 | 0.2634 | 0.95 | 0.0000 |
| 0.015 | 40.0 | 280 | 0.2699 | 0.95 | 0.0000 |
| 0.0147 | 41.0 | 287 | 0.2726 | 0.95 | 0.0000 |
| 0.0148 | 42.0 | 294 | 0.2783 | 0.95 | 3e-05 |
| 0.033 | 43.0 | 301 | 0.2793 | 0.95 | 0.0000 |
| 0.0143 | 44.0 | 308 | 0.2742 | 0.95 | 0.0000 |
| 0.0143 | 45.0 | 315 | 0.2681 | 0.95 | 0.0000 |
| 0.0139 | 46.0 | 322 | 0.2683 | 0.95 | 0.0000 |
| 0.0141 | 47.0 | 329 | 0.2706 | 0.95 | 0.0000 |
| 0.0132 | 48.0 | 336 | 0.2715 | 0.95 | 2e-05 |
| 0.0157 | 49.0 | 343 | 0.2785 | 0.95 | 0.0000 |
| 0.0142 | 50.0 | 350 | 0.2809 | 0.95 | 0.0000 |
| 0.0138 | 51.0 | 357 | 0.2818 | 0.95 | 0.0000 |
| 0.0141 | 52.0 | 364 | 0.2852 | 0.95 | 0.0000 |
| 0.015 | 53.0 | 371 | 0.2868 | 0.95 | 0.0000 |
| 0.0145 | 54.0 | 378 | 0.2876 | 0.95 | 1e-05 |
| 0.0135 | 55.0 | 385 | 0.2854 | 0.95 | 0.0000 |
| 0.0146 | 56.0 | 392 | 0.2862 | 0.95 | 0.0000 |
| 0.0136 | 57.0 | 399 | 0.2857 | 0.95 | 5e-06 |
| 0.014 | 58.0 | 406 | 0.2853 | 0.95 | 0.0000 |
| 0.0133 | 59.0 | 413 | 0.2862 | 0.95 | 0.0000 |
| 0.0125 | 60.0 | 420 | 0.2866 | 0.95 | 0.0 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.14.1
|
Doctor-Shotgun/Euryale-1.3-limarpv3-L2-70B-exl2
|
Doctor-Shotgun
| 2023-11-13T16:54:44Z | 0 | 2 | null |
[
"llama",
"llama-2",
"text-generation",
"en",
"region:us"
] |
text-generation
| 2023-10-25T06:22:22Z |
---
inference: false
language:
- en
pipeline_tag: text-generation
tags:
- llama
- llama-2
---
# Euryale-1.3-limarpv3-L2-70B-exl2
Exllama v2 quant of [Doctor-Shotgun/Euryale-1.3-limarpv3-L2-70B](https://huggingface.co/Doctor-Shotgun/Euryale-1.3-limarpv3-L2-70B)
Branches:
- main: measurement.json calculated at 2048 token calibration rows on PIPPA
- 5.0bpw-h6: 5 decoder bits per weight, 6 head bits
- ideal for 2x 24gb GPUs at 8192 context, or 1x 48gb GPU at 8192 context with CFG cache
|
hkivancoral/hushem_1x_deit_small_adamax_0001_fold5
|
hkivancoral
| 2023-11-13T16:48:04Z | 8 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:facebook/deit-small-patch16-224",
"base_model:finetune:facebook/deit-small-patch16-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-11-13T16:43:41Z |
---
license: apache-2.0
base_model: facebook/deit-small-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: hushem_1x_deit_small_adamax_0001_fold5
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.8048780487804879
---
<!-- 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. -->
# hushem_1x_deit_small_adamax_0001_fold5
This model is a fine-tuned version of [facebook/deit-small-patch16-224](https://huggingface.co/facebook/deit-small-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8185
- Accuracy: 0.8049
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 6 | 1.1559 | 0.4878 |
| 1.2441 | 2.0 | 12 | 0.8253 | 0.6829 |
| 1.2441 | 3.0 | 18 | 0.7434 | 0.6098 |
| 0.6071 | 4.0 | 24 | 0.5080 | 0.8293 |
| 0.2296 | 5.0 | 30 | 0.6693 | 0.6829 |
| 0.2296 | 6.0 | 36 | 0.4300 | 0.8293 |
| 0.0509 | 7.0 | 42 | 0.7493 | 0.7317 |
| 0.0509 | 8.0 | 48 | 0.5064 | 0.8537 |
| 0.0088 | 9.0 | 54 | 0.6021 | 0.8780 |
| 0.0021 | 10.0 | 60 | 0.7408 | 0.7805 |
| 0.0021 | 11.0 | 66 | 0.9234 | 0.7073 |
| 0.0009 | 12.0 | 72 | 0.9965 | 0.6829 |
| 0.0009 | 13.0 | 78 | 0.9607 | 0.7317 |
| 0.0006 | 14.0 | 84 | 0.8998 | 0.7561 |
| 0.0004 | 15.0 | 90 | 0.8548 | 0.7561 |
| 0.0004 | 16.0 | 96 | 0.8258 | 0.7561 |
| 0.0004 | 17.0 | 102 | 0.8107 | 0.7805 |
| 0.0004 | 18.0 | 108 | 0.7999 | 0.8049 |
| 0.0003 | 19.0 | 114 | 0.7972 | 0.8049 |
| 0.0003 | 20.0 | 120 | 0.7983 | 0.8049 |
| 0.0003 | 21.0 | 126 | 0.8011 | 0.8049 |
| 0.0003 | 22.0 | 132 | 0.8040 | 0.8049 |
| 0.0003 | 23.0 | 138 | 0.8052 | 0.8049 |
| 0.0003 | 24.0 | 144 | 0.8067 | 0.8049 |
| 0.0003 | 25.0 | 150 | 0.8086 | 0.8049 |
| 0.0003 | 26.0 | 156 | 0.8104 | 0.8049 |
| 0.0003 | 27.0 | 162 | 0.8133 | 0.8049 |
| 0.0003 | 28.0 | 168 | 0.8150 | 0.8049 |
| 0.0003 | 29.0 | 174 | 0.8155 | 0.8049 |
| 0.0002 | 30.0 | 180 | 0.8162 | 0.8049 |
| 0.0002 | 31.0 | 186 | 0.8167 | 0.8049 |
| 0.0002 | 32.0 | 192 | 0.8175 | 0.8049 |
| 0.0002 | 33.0 | 198 | 0.8178 | 0.8049 |
| 0.0002 | 34.0 | 204 | 0.8183 | 0.8049 |
| 0.0002 | 35.0 | 210 | 0.8179 | 0.8049 |
| 0.0002 | 36.0 | 216 | 0.8182 | 0.8049 |
| 0.0002 | 37.0 | 222 | 0.8182 | 0.8049 |
| 0.0002 | 38.0 | 228 | 0.8181 | 0.8049 |
| 0.0002 | 39.0 | 234 | 0.8183 | 0.8049 |
| 0.0002 | 40.0 | 240 | 0.8184 | 0.8049 |
| 0.0002 | 41.0 | 246 | 0.8184 | 0.8049 |
| 0.0002 | 42.0 | 252 | 0.8185 | 0.8049 |
| 0.0002 | 43.0 | 258 | 0.8185 | 0.8049 |
| 0.0002 | 44.0 | 264 | 0.8185 | 0.8049 |
| 0.0002 | 45.0 | 270 | 0.8185 | 0.8049 |
| 0.0002 | 46.0 | 276 | 0.8185 | 0.8049 |
| 0.0002 | 47.0 | 282 | 0.8185 | 0.8049 |
| 0.0002 | 48.0 | 288 | 0.8185 | 0.8049 |
| 0.0002 | 49.0 | 294 | 0.8185 | 0.8049 |
| 0.0002 | 50.0 | 300 | 0.8185 | 0.8049 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
Varun1808/llama_sql
|
Varun1808
| 2023-11-13T16:40:41Z | 1 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Varun1808/llamashards",
"base_model:adapter:Varun1808/llamashards",
"region:us"
] | null | 2023-11-13T16:40:40Z |
---
library_name: peft
base_model: Varun1808/llamashards
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Data Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
## 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 0.6.2.dev0
|
hkivancoral/hushem_1x_deit_small_adamax_0001_fold2
|
hkivancoral
| 2023-11-13T16:33:48Z | 8 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:facebook/deit-small-patch16-224",
"base_model:finetune:facebook/deit-small-patch16-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-11-13T16:29:08Z |
---
license: apache-2.0
base_model: facebook/deit-small-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: hushem_1x_deit_small_adamax_0001_fold2
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.6
---
<!-- 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. -->
# hushem_1x_deit_small_adamax_0001_fold2
This model is a fine-tuned version of [facebook/deit-small-patch16-224](https://huggingface.co/facebook/deit-small-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 2.0748
- Accuracy: 0.6
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 6 | 1.2729 | 0.4 |
| 1.2121 | 2.0 | 12 | 1.1250 | 0.6222 |
| 1.2121 | 3.0 | 18 | 1.2362 | 0.5556 |
| 0.4291 | 4.0 | 24 | 1.2042 | 0.6444 |
| 0.1116 | 5.0 | 30 | 1.1861 | 0.6 |
| 0.1116 | 6.0 | 36 | 1.6632 | 0.5556 |
| 0.0196 | 7.0 | 42 | 1.7499 | 0.6 |
| 0.0196 | 8.0 | 48 | 1.7915 | 0.5556 |
| 0.0051 | 9.0 | 54 | 1.8168 | 0.5778 |
| 0.0016 | 10.0 | 60 | 1.8187 | 0.6222 |
| 0.0016 | 11.0 | 66 | 1.8480 | 0.6222 |
| 0.0008 | 12.0 | 72 | 1.8621 | 0.6222 |
| 0.0008 | 13.0 | 78 | 1.8730 | 0.6222 |
| 0.0006 | 14.0 | 84 | 1.8908 | 0.6222 |
| 0.0005 | 15.0 | 90 | 1.9136 | 0.6222 |
| 0.0005 | 16.0 | 96 | 1.9335 | 0.6222 |
| 0.0004 | 17.0 | 102 | 1.9501 | 0.6222 |
| 0.0004 | 18.0 | 108 | 1.9655 | 0.6222 |
| 0.0004 | 19.0 | 114 | 1.9783 | 0.6222 |
| 0.0003 | 20.0 | 120 | 1.9900 | 0.6222 |
| 0.0003 | 21.0 | 126 | 1.9990 | 0.6222 |
| 0.0003 | 22.0 | 132 | 2.0067 | 0.6222 |
| 0.0003 | 23.0 | 138 | 2.0139 | 0.6 |
| 0.0003 | 24.0 | 144 | 2.0208 | 0.6 |
| 0.0003 | 25.0 | 150 | 2.0271 | 0.6 |
| 0.0003 | 26.0 | 156 | 2.0322 | 0.6 |
| 0.0003 | 27.0 | 162 | 2.0367 | 0.6 |
| 0.0003 | 28.0 | 168 | 2.0419 | 0.6 |
| 0.0003 | 29.0 | 174 | 2.0471 | 0.6 |
| 0.0003 | 30.0 | 180 | 2.0520 | 0.6 |
| 0.0003 | 31.0 | 186 | 2.0560 | 0.6 |
| 0.0002 | 32.0 | 192 | 2.0593 | 0.6 |
| 0.0002 | 33.0 | 198 | 2.0621 | 0.6 |
| 0.0003 | 34.0 | 204 | 2.0649 | 0.6 |
| 0.0003 | 35.0 | 210 | 2.0672 | 0.6 |
| 0.0003 | 36.0 | 216 | 2.0692 | 0.6 |
| 0.0002 | 37.0 | 222 | 2.0710 | 0.6 |
| 0.0002 | 38.0 | 228 | 2.0723 | 0.6 |
| 0.0002 | 39.0 | 234 | 2.0735 | 0.6 |
| 0.0002 | 40.0 | 240 | 2.0742 | 0.6 |
| 0.0002 | 41.0 | 246 | 2.0747 | 0.6 |
| 0.0002 | 42.0 | 252 | 2.0748 | 0.6 |
| 0.0002 | 43.0 | 258 | 2.0748 | 0.6 |
| 0.0002 | 44.0 | 264 | 2.0748 | 0.6 |
| 0.0002 | 45.0 | 270 | 2.0748 | 0.6 |
| 0.0002 | 46.0 | 276 | 2.0748 | 0.6 |
| 0.0002 | 47.0 | 282 | 2.0748 | 0.6 |
| 0.0002 | 48.0 | 288 | 2.0748 | 0.6 |
| 0.0002 | 49.0 | 294 | 2.0748 | 0.6 |
| 0.0002 | 50.0 | 300 | 2.0748 | 0.6 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
taysmith/ppo-LunarLander-v2
|
taysmith
| 2023-11-13T16:29:58Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-11-13T16:29:35Z |
---
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: 265.30 +/- 19.73
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
...
```
|
GabrielGaleana34/stable-diffusion-v1-5_lora_finetuned
|
GabrielGaleana34
| 2023-11-13T16:29:03Z | 0 | 0 |
diffusers
|
[
"diffusers",
"tensorboard",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"lora",
"base_model:runwayml/stable-diffusion-v1-5",
"base_model:adapter:runwayml/stable-diffusion-v1-5",
"license:creativeml-openrail-m",
"region:us"
] |
text-to-image
| 2023-11-13T04:53:47Z |
---
license: creativeml-openrail-m
base_model: runwayml/stable-diffusion-v1-5
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- lora
inference: true
---
# LoRA text2image fine-tuning - GabrielGaleana34/sd-model-finetuned-lora
These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were fine-tuned on the GabrielGaleana34/Products dataset. You can find some example images in the following.
|
Laurent1/Mistral-7B-Instruct-v0.1-MyRestaurant-Domain-Adaptation
|
Laurent1
| 2023-11-13T16:28:08Z | 1 | 0 |
adapter-transformers
|
[
"adapter-transformers",
"safetensors",
"license:apache-2.0",
"region:us"
] | null | 2023-11-13T15:32:27Z |
---
license: apache-2.0
library_name: adapter-transformers
---
# Model Card for Mistral-7B-Instruct-v0.1-MyRestaurant-Domain-Adaptation
<font color="#0000FF" size="5"> <b>
This is a domain adaptation for questions about My Restaurant <br /> </b>
You can play by asking the model questions about the menu...</b>
</font>
<br /> </b>
<br><b>Foundation Model : https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1 <br />
Dataset : https://huggingface.co/datasets/Argen7um/restrant-qa <br /></b>
The model has been fine tuned with 2 x GPU T4 (RAM : 2 x 14.8GB) + CPU (RAM : 29GB). <br />
The model is based upon the foundation model : Mistral-7B.<br />
It has been tuned with Supervised Fine-tuning Trainer and PEFT LoRa.<br />
# <b>Notebook used for the training</b>
You can find it in the files and versions tab <br />
<font color="#0000FF" size="3">Direct link : https://huggingface.co/Laurent1/Mistral-7B-Instruct-v0.1-MyRestaurant-Domain-Adaptation/blob/main/laurent-restaurant-adaptation-mistral-7b-tuned.ipynb
</font>
## <b>Bias, Risks, and Limitations</b>
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model.<br />
Generation of plausible yet incorrect factual information, termed hallucination, is an unsolved issue in large language models.<br />
## <b>Training Details</b>
<ul>
<li>per_device_train_batch_size = 1</li>
<li>gradient_accumulation_steps = 16</li>
<li>2 x GPU T4 (RAM : 14.8GB) + CPU (RAM : 29GB)</li>
</ul>
|
meiyun1995/ppo-Huggy
|
meiyun1995
| 2023-11-13T16:25:18Z | 0 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"Huggy",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
] |
reinforcement-learning
| 2023-11-13T16:25:12Z |
---
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: meiyun1995/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
Guspfc/my-awesome-bert-model-hate-speech
|
Guspfc
| 2023-11-13T16:16:10Z | 4 | 0 |
transformers
|
[
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"base_model:google-bert/bert-base-uncased",
"base_model:finetune:google-bert/bert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-13T16:15:50Z |
---
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_keras_callback
model-index:
- name: my-awesome-bert-model-hate-speech
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. -->
# my-awesome-bert-model-hate-speech
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0596
- Train Accuracy: 0.9797
- Validation Loss: 0.3994
- Validation Accuracy: 0.9030
- Epoch: 4
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': 1e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.2879 | 0.8999 | 0.2421 | 0.9106 | 0 |
| 0.2039 | 0.9264 | 0.2478 | 0.9155 | 1 |
| 0.1538 | 0.9438 | 0.2824 | 0.9052 | 2 |
| 0.1039 | 0.9617 | 0.3357 | 0.8959 | 3 |
| 0.0596 | 0.9797 | 0.3994 | 0.9030 | 4 |
### Framework versions
- Transformers 4.35.0
- TensorFlow 2.14.0
- Datasets 2.14.6
- Tokenizers 0.14.1
|
hkivancoral/hushem_1x_deit_small_adamax_001_fold4
|
hkivancoral
| 2023-11-13T16:13:46Z | 8 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:facebook/deit-small-patch16-224",
"base_model:finetune:facebook/deit-small-patch16-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-11-13T16:10:05Z |
---
license: apache-2.0
base_model: facebook/deit-small-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: hushem_1x_deit_small_adamax_001_fold4
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.5952380952380952
---
<!-- 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. -->
# hushem_1x_deit_small_adamax_001_fold4
This model is a fine-tuned version of [facebook/deit-small-patch16-224](https://huggingface.co/facebook/deit-small-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 3.1234
- Accuracy: 0.5952
## 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: 0.001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 6 | 1.5633 | 0.2381 |
| 1.7898 | 2.0 | 12 | 1.3816 | 0.2381 |
| 1.7898 | 3.0 | 18 | 1.3607 | 0.2619 |
| 1.4334 | 4.0 | 24 | 1.3501 | 0.2619 |
| 1.3732 | 5.0 | 30 | 1.3553 | 0.2381 |
| 1.3732 | 6.0 | 36 | 1.1841 | 0.4762 |
| 1.3036 | 7.0 | 42 | 1.0576 | 0.5952 |
| 1.3036 | 8.0 | 48 | 1.0689 | 0.5952 |
| 1.2142 | 9.0 | 54 | 1.2296 | 0.5 |
| 1.056 | 10.0 | 60 | 0.7879 | 0.6429 |
| 1.056 | 11.0 | 66 | 0.7199 | 0.7143 |
| 0.921 | 12.0 | 72 | 0.9775 | 0.6190 |
| 0.921 | 13.0 | 78 | 0.8809 | 0.5952 |
| 0.6456 | 14.0 | 84 | 1.0792 | 0.5476 |
| 0.6348 | 15.0 | 90 | 1.0335 | 0.6190 |
| 0.6348 | 16.0 | 96 | 1.7853 | 0.5714 |
| 0.4743 | 17.0 | 102 | 1.5872 | 0.5714 |
| 0.4743 | 18.0 | 108 | 2.0651 | 0.5 |
| 0.2408 | 19.0 | 114 | 2.8369 | 0.4762 |
| 0.2271 | 20.0 | 120 | 2.1149 | 0.6190 |
| 0.2271 | 21.0 | 126 | 1.5722 | 0.6190 |
| 0.3385 | 22.0 | 132 | 2.8555 | 0.5476 |
| 0.3385 | 23.0 | 138 | 2.2068 | 0.6667 |
| 0.0822 | 24.0 | 144 | 2.2969 | 0.6190 |
| 0.0932 | 25.0 | 150 | 1.8785 | 0.7143 |
| 0.0932 | 26.0 | 156 | 3.2275 | 0.5714 |
| 0.0807 | 27.0 | 162 | 2.8847 | 0.5952 |
| 0.0807 | 28.0 | 168 | 3.1184 | 0.5952 |
| 0.0424 | 29.0 | 174 | 2.4583 | 0.6190 |
| 0.0287 | 30.0 | 180 | 2.8305 | 0.5714 |
| 0.0287 | 31.0 | 186 | 3.5171 | 0.5476 |
| 0.0333 | 32.0 | 192 | 3.2119 | 0.5952 |
| 0.0333 | 33.0 | 198 | 2.9811 | 0.5952 |
| 0.0008 | 34.0 | 204 | 3.0451 | 0.5952 |
| 0.0004 | 35.0 | 210 | 3.0670 | 0.5952 |
| 0.0004 | 36.0 | 216 | 3.0857 | 0.5952 |
| 0.0003 | 37.0 | 222 | 3.1009 | 0.5952 |
| 0.0003 | 38.0 | 228 | 3.1113 | 0.5952 |
| 0.0003 | 39.0 | 234 | 3.1177 | 0.5952 |
| 0.0003 | 40.0 | 240 | 3.1213 | 0.5952 |
| 0.0003 | 41.0 | 246 | 3.1231 | 0.5952 |
| 0.0002 | 42.0 | 252 | 3.1234 | 0.5952 |
| 0.0002 | 43.0 | 258 | 3.1234 | 0.5952 |
| 0.0002 | 44.0 | 264 | 3.1234 | 0.5952 |
| 0.0002 | 45.0 | 270 | 3.1234 | 0.5952 |
| 0.0002 | 46.0 | 276 | 3.1234 | 0.5952 |
| 0.0002 | 47.0 | 282 | 3.1234 | 0.5952 |
| 0.0002 | 48.0 | 288 | 3.1234 | 0.5952 |
| 0.0002 | 49.0 | 294 | 3.1234 | 0.5952 |
| 0.0002 | 50.0 | 300 | 3.1234 | 0.5952 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
hkivancoral/hushem_1x_deit_small_adamax_001_fold3
|
hkivancoral
| 2023-11-13T16:09:51Z | 8 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:facebook/deit-small-patch16-224",
"base_model:finetune:facebook/deit-small-patch16-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-11-13T16:06:21Z |
---
license: apache-2.0
base_model: facebook/deit-small-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: hushem_1x_deit_small_adamax_001_fold3
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.46511627906976744
---
<!-- 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. -->
# hushem_1x_deit_small_adamax_001_fold3
This model is a fine-tuned version of [facebook/deit-small-patch16-224](https://huggingface.co/facebook/deit-small-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 3.7699
- Accuracy: 0.4651
## 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: 0.001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 6 | 1.4218 | 0.2558 |
| 1.7221 | 2.0 | 12 | 1.4061 | 0.3953 |
| 1.7221 | 3.0 | 18 | 1.4801 | 0.3256 |
| 1.2972 | 4.0 | 24 | 1.5453 | 0.3023 |
| 1.2115 | 5.0 | 30 | 1.2993 | 0.3953 |
| 1.2115 | 6.0 | 36 | 1.4486 | 0.3721 |
| 1.1196 | 7.0 | 42 | 1.4881 | 0.3721 |
| 1.1196 | 8.0 | 48 | 1.2031 | 0.4419 |
| 1.0394 | 9.0 | 54 | 1.1825 | 0.4651 |
| 0.9076 | 10.0 | 60 | 1.3831 | 0.3953 |
| 0.9076 | 11.0 | 66 | 1.5606 | 0.3953 |
| 0.8351 | 12.0 | 72 | 1.6879 | 0.3721 |
| 0.8351 | 13.0 | 78 | 1.5744 | 0.5581 |
| 0.7325 | 14.0 | 84 | 2.1220 | 0.5116 |
| 0.5767 | 15.0 | 90 | 2.2458 | 0.4884 |
| 0.5767 | 16.0 | 96 | 2.4745 | 0.3953 |
| 0.487 | 17.0 | 102 | 2.9255 | 0.3953 |
| 0.487 | 18.0 | 108 | 2.8169 | 0.4186 |
| 0.265 | 19.0 | 114 | 2.9600 | 0.4419 |
| 0.2739 | 20.0 | 120 | 3.0131 | 0.3953 |
| 0.2739 | 21.0 | 126 | 3.2413 | 0.4186 |
| 0.1684 | 22.0 | 132 | 4.9920 | 0.3953 |
| 0.1684 | 23.0 | 138 | 3.1514 | 0.5116 |
| 0.3265 | 24.0 | 144 | 4.1598 | 0.3953 |
| 0.2652 | 25.0 | 150 | 3.3248 | 0.4651 |
| 0.2652 | 26.0 | 156 | 3.1898 | 0.4884 |
| 0.1992 | 27.0 | 162 | 3.7937 | 0.3953 |
| 0.1992 | 28.0 | 168 | 3.9838 | 0.4884 |
| 0.1826 | 29.0 | 174 | 3.5764 | 0.3721 |
| 0.124 | 30.0 | 180 | 4.1231 | 0.4419 |
| 0.124 | 31.0 | 186 | 4.1455 | 0.4186 |
| 0.1353 | 32.0 | 192 | 3.9925 | 0.4186 |
| 0.1353 | 33.0 | 198 | 3.7016 | 0.5581 |
| 0.0743 | 34.0 | 204 | 3.7997 | 0.5349 |
| 0.0362 | 35.0 | 210 | 3.6073 | 0.4884 |
| 0.0362 | 36.0 | 216 | 3.6198 | 0.4651 |
| 0.0082 | 37.0 | 222 | 3.6509 | 0.4651 |
| 0.0082 | 38.0 | 228 | 3.7081 | 0.4651 |
| 0.003 | 39.0 | 234 | 3.7432 | 0.4651 |
| 0.002 | 40.0 | 240 | 3.7616 | 0.4651 |
| 0.002 | 41.0 | 246 | 3.7690 | 0.4651 |
| 0.0018 | 42.0 | 252 | 3.7699 | 0.4651 |
| 0.0018 | 43.0 | 258 | 3.7699 | 0.4651 |
| 0.0016 | 44.0 | 264 | 3.7699 | 0.4651 |
| 0.0017 | 45.0 | 270 | 3.7699 | 0.4651 |
| 0.0017 | 46.0 | 276 | 3.7699 | 0.4651 |
| 0.0017 | 47.0 | 282 | 3.7699 | 0.4651 |
| 0.0017 | 48.0 | 288 | 3.7699 | 0.4651 |
| 0.0018 | 49.0 | 294 | 3.7699 | 0.4651 |
| 0.0017 | 50.0 | 300 | 3.7699 | 0.4651 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
Kazaii/BoomOPZtv
|
Kazaii
| 2023-11-13T16:09:21Z | 0 | 0 |
diffusers
|
[
"diffusers",
"text-to-image",
"stable-diffusion",
"lora",
"template:sd-lora",
"base_model:segmind/SSD-1B",
"base_model:adapter:segmind/SSD-1B",
"license:apache-2.0",
"region:us"
] |
text-to-image
| 2023-11-13T16:09:13Z |
---
tags:
- text-to-image
- stable-diffusion
- lora
- diffusers
- template:sd-lora
widget:
- text: '-'
output:
url: images/1090080406935711804.gif
base_model: segmind/SSD-1B
instance_prompt: null
license: apache-2.0
---
# BoomOPztv
<Gallery />
## Model description
ZeedZad
## Download model
[Download](/Kazaii/BoomOPZtv/tree/main) them in the Files & versions tab.
|
hkivancoral/hushem_1x_deit_small_adamax_001_fold2
|
hkivancoral
| 2023-11-13T16:06:05Z | 13 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:facebook/deit-small-patch16-224",
"base_model:finetune:facebook/deit-small-patch16-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-11-13T16:02:32Z |
---
license: apache-2.0
base_model: facebook/deit-small-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: hushem_1x_deit_small_adamax_001_fold2
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.5777777777777777
---
<!-- 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. -->
# hushem_1x_deit_small_adamax_001_fold2
This model is a fine-tuned version of [facebook/deit-small-patch16-224](https://huggingface.co/facebook/deit-small-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 3.0653
- Accuracy: 0.5778
## 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: 0.001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 6 | 1.5866 | 0.2444 |
| 1.9023 | 2.0 | 12 | 1.3764 | 0.2444 |
| 1.9023 | 3.0 | 18 | 1.3051 | 0.4222 |
| 1.349 | 4.0 | 24 | 1.1457 | 0.4889 |
| 1.2765 | 5.0 | 30 | 1.1296 | 0.5333 |
| 1.2765 | 6.0 | 36 | 1.0799 | 0.4667 |
| 0.9532 | 7.0 | 42 | 0.9251 | 0.5778 |
| 0.9532 | 8.0 | 48 | 0.9697 | 0.6 |
| 0.606 | 9.0 | 54 | 1.3926 | 0.4889 |
| 0.572 | 10.0 | 60 | 1.7732 | 0.5778 |
| 0.572 | 11.0 | 66 | 1.3882 | 0.5556 |
| 0.5961 | 12.0 | 72 | 1.7835 | 0.5333 |
| 0.5961 | 13.0 | 78 | 1.6876 | 0.5111 |
| 0.36 | 14.0 | 84 | 2.6292 | 0.5556 |
| 0.1021 | 15.0 | 90 | 3.3955 | 0.4444 |
| 0.1021 | 16.0 | 96 | 2.7199 | 0.5333 |
| 0.0705 | 17.0 | 102 | 3.2188 | 0.5778 |
| 0.0705 | 18.0 | 108 | 2.9572 | 0.5778 |
| 0.1408 | 19.0 | 114 | 3.4311 | 0.6222 |
| 0.0481 | 20.0 | 120 | 3.3680 | 0.5111 |
| 0.0481 | 21.0 | 126 | 3.9440 | 0.4889 |
| 0.0285 | 22.0 | 132 | 3.0805 | 0.5111 |
| 0.0285 | 23.0 | 138 | 3.2788 | 0.4889 |
| 0.0077 | 24.0 | 144 | 3.3798 | 0.5111 |
| 0.0144 | 25.0 | 150 | 3.3118 | 0.5333 |
| 0.0144 | 26.0 | 156 | 3.1251 | 0.5111 |
| 0.0005 | 27.0 | 162 | 2.9134 | 0.5778 |
| 0.0005 | 28.0 | 168 | 2.8352 | 0.6 |
| 0.0006 | 29.0 | 174 | 2.7529 | 0.5778 |
| 0.0002 | 30.0 | 180 | 2.8235 | 0.6 |
| 0.0002 | 31.0 | 186 | 2.8802 | 0.6 |
| 0.0001 | 32.0 | 192 | 2.9253 | 0.5778 |
| 0.0001 | 33.0 | 198 | 2.9651 | 0.5778 |
| 0.0001 | 34.0 | 204 | 2.9943 | 0.5778 |
| 0.0001 | 35.0 | 210 | 3.0146 | 0.5778 |
| 0.0001 | 36.0 | 216 | 3.0314 | 0.5778 |
| 0.0001 | 37.0 | 222 | 3.0446 | 0.5778 |
| 0.0001 | 38.0 | 228 | 3.0538 | 0.5778 |
| 0.0001 | 39.0 | 234 | 3.0596 | 0.5778 |
| 0.0001 | 40.0 | 240 | 3.0631 | 0.5778 |
| 0.0001 | 41.0 | 246 | 3.0649 | 0.5778 |
| 0.0001 | 42.0 | 252 | 3.0653 | 0.5778 |
| 0.0001 | 43.0 | 258 | 3.0653 | 0.5778 |
| 0.0001 | 44.0 | 264 | 3.0653 | 0.5778 |
| 0.0001 | 45.0 | 270 | 3.0653 | 0.5778 |
| 0.0001 | 46.0 | 276 | 3.0653 | 0.5778 |
| 0.0001 | 47.0 | 282 | 3.0653 | 0.5778 |
| 0.0001 | 48.0 | 288 | 3.0653 | 0.5778 |
| 0.0001 | 49.0 | 294 | 3.0653 | 0.5778 |
| 0.0001 | 50.0 | 300 | 3.0653 | 0.5778 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
nlp-polish/generative-test
|
nlp-polish
| 2023-11-13T16:05:49Z | 61 | 0 |
transformers
|
[
"transformers",
"tf",
"encoder-decoder",
"text2text-generation",
"generated_from_keras_callback",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-11-13T16:04:43Z |
---
tags:
- generated_from_keras_callback
model-index:
- name: generative-test
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. -->
# generative-test
This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
It achieves the following results on the evaluation set:
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: None
- training_precision: float32
### Training results
### Framework versions
- Transformers 4.35.0
- TensorFlow 2.10.1
- Datasets 2.14.6
- Tokenizers 0.14.1
|
hkivancoral/hushem_1x_deit_small_adamax_001_fold1
|
hkivancoral
| 2023-11-13T16:02:20Z | 14 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:facebook/deit-small-patch16-224",
"base_model:finetune:facebook/deit-small-patch16-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-11-13T15:58:51Z |
---
license: apache-2.0
base_model: facebook/deit-small-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: hushem_1x_deit_small_adamax_001_fold1
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.4666666666666667
---
<!-- 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. -->
# hushem_1x_deit_small_adamax_001_fold1
This model is a fine-tuned version of [facebook/deit-small-patch16-224](https://huggingface.co/facebook/deit-small-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 2.0215
- Accuracy: 0.4667
## 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: 0.001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 6 | 2.2870 | 0.2444 |
| 2.1668 | 2.0 | 12 | 1.4669 | 0.2444 |
| 2.1668 | 3.0 | 18 | 1.4980 | 0.2444 |
| 1.4102 | 4.0 | 24 | 1.4751 | 0.2444 |
| 1.4394 | 5.0 | 30 | 1.4286 | 0.2444 |
| 1.4394 | 6.0 | 36 | 1.6019 | 0.2444 |
| 1.3171 | 7.0 | 42 | 1.7291 | 0.2222 |
| 1.3171 | 8.0 | 48 | 1.5314 | 0.3556 |
| 1.2906 | 9.0 | 54 | 1.7281 | 0.2667 |
| 1.2151 | 10.0 | 60 | 1.6012 | 0.2444 |
| 1.2151 | 11.0 | 66 | 1.5621 | 0.4444 |
| 1.1016 | 12.0 | 72 | 1.5069 | 0.2 |
| 1.1016 | 13.0 | 78 | 1.5452 | 0.4222 |
| 1.1085 | 14.0 | 84 | 1.5457 | 0.2889 |
| 0.9838 | 15.0 | 90 | 1.7131 | 0.4 |
| 0.9838 | 16.0 | 96 | 1.9947 | 0.2889 |
| 1.003 | 17.0 | 102 | 1.7538 | 0.4222 |
| 1.003 | 18.0 | 108 | 1.3632 | 0.4444 |
| 0.846 | 19.0 | 114 | 1.7633 | 0.4 |
| 0.7432 | 20.0 | 120 | 1.5259 | 0.4222 |
| 0.7432 | 21.0 | 126 | 1.6982 | 0.4 |
| 0.8111 | 22.0 | 132 | 1.4722 | 0.4 |
| 0.8111 | 23.0 | 138 | 1.5772 | 0.4222 |
| 0.6268 | 24.0 | 144 | 1.6621 | 0.4222 |
| 0.5956 | 25.0 | 150 | 2.2283 | 0.4 |
| 0.5956 | 26.0 | 156 | 1.5965 | 0.4667 |
| 0.863 | 27.0 | 162 | 2.0067 | 0.4 |
| 0.863 | 28.0 | 168 | 2.2609 | 0.3778 |
| 0.575 | 29.0 | 174 | 1.7339 | 0.4222 |
| 0.3505 | 30.0 | 180 | 1.6059 | 0.3778 |
| 0.3505 | 31.0 | 186 | 1.7578 | 0.4444 |
| 0.3884 | 32.0 | 192 | 1.8785 | 0.4444 |
| 0.3884 | 33.0 | 198 | 1.5952 | 0.4222 |
| 0.3742 | 34.0 | 204 | 1.9834 | 0.4444 |
| 0.3113 | 35.0 | 210 | 1.8134 | 0.4222 |
| 0.3113 | 36.0 | 216 | 2.1491 | 0.4 |
| 0.4478 | 37.0 | 222 | 1.9419 | 0.4667 |
| 0.4478 | 38.0 | 228 | 1.8426 | 0.4444 |
| 0.1746 | 39.0 | 234 | 1.9349 | 0.4222 |
| 0.1737 | 40.0 | 240 | 2.0085 | 0.4667 |
| 0.1737 | 41.0 | 246 | 2.0238 | 0.4667 |
| 0.1448 | 42.0 | 252 | 2.0215 | 0.4667 |
| 0.1448 | 43.0 | 258 | 2.0215 | 0.4667 |
| 0.1495 | 44.0 | 264 | 2.0215 | 0.4667 |
| 0.1326 | 45.0 | 270 | 2.0215 | 0.4667 |
| 0.1326 | 46.0 | 276 | 2.0215 | 0.4667 |
| 0.1487 | 47.0 | 282 | 2.0215 | 0.4667 |
| 0.1487 | 48.0 | 288 | 2.0215 | 0.4667 |
| 0.1112 | 49.0 | 294 | 2.0215 | 0.4667 |
| 0.1501 | 50.0 | 300 | 2.0215 | 0.4667 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
Priyabrata018/distilbert-base-uncased-finetuned-ner
|
Priyabrata018
| 2023-11-13T15:59:41Z | 7 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"token-classification",
"generated_from_trainer",
"dataset:fin",
"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"
] |
token-classification
| 2023-11-13T15:56:14Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- fin
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: distilbert-base-uncased-finetuned-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: fin
type: fin
config: fin
split: validation
args: fin
metrics:
- name: Precision
type: precision
value: 0.9825072886297376
- name: Recall
type: recall
value: 0.8776041666666666
- name: F1
type: f1
value: 0.9270976616231086
- name: Accuracy
type: accuracy
value: 0.9851503078594712
---
<!-- 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-ner
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the fin dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1142
- Precision: 0.9825
- Recall: 0.8776
- F1: 0.9271
- Accuracy: 0.9852
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 64 | 0.1589 | 0.9760 | 0.7422 | 0.8432 | 0.9752 |
| No log | 2.0 | 128 | 0.1221 | 0.9731 | 0.7526 | 0.8488 | 0.9765 |
| No log | 3.0 | 192 | 0.1142 | 0.9825 | 0.8776 | 0.9271 | 0.9852 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
hkivancoral/hushem_1x_deit_tiny_rms_00001_fold1
|
hkivancoral
| 2023-11-13T15:48:42Z | 8 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:facebook/deit-tiny-patch16-224",
"base_model:finetune:facebook/deit-tiny-patch16-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-11-13T15:47:14Z |
---
license: apache-2.0
base_model: facebook/deit-tiny-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: hushem_1x_deit_tiny_rms_00001_fold1
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.6222222222222222
---
<!-- 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. -->
# hushem_1x_deit_tiny_rms_00001_fold1
This model is a fine-tuned version of [facebook/deit-tiny-patch16-224](https://huggingface.co/facebook/deit-tiny-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2620
- Accuracy: 0.6222
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 6 | 1.3330 | 0.3111 |
| 1.415 | 2.0 | 12 | 1.1496 | 0.4222 |
| 1.415 | 3.0 | 18 | 1.0095 | 0.6 |
| 0.6844 | 4.0 | 24 | 1.0528 | 0.5333 |
| 0.3289 | 5.0 | 30 | 0.8970 | 0.6 |
| 0.3289 | 6.0 | 36 | 1.2025 | 0.5111 |
| 0.1275 | 7.0 | 42 | 0.9016 | 0.6 |
| 0.1275 | 8.0 | 48 | 1.0450 | 0.5778 |
| 0.049 | 9.0 | 54 | 1.1767 | 0.5556 |
| 0.0201 | 10.0 | 60 | 1.2285 | 0.5333 |
| 0.0201 | 11.0 | 66 | 1.0471 | 0.6 |
| 0.0071 | 12.0 | 72 | 0.9300 | 0.6444 |
| 0.0071 | 13.0 | 78 | 1.1280 | 0.5778 |
| 0.0042 | 14.0 | 84 | 1.1318 | 0.5556 |
| 0.0029 | 15.0 | 90 | 1.1503 | 0.5556 |
| 0.0029 | 16.0 | 96 | 1.0998 | 0.5778 |
| 0.0023 | 17.0 | 102 | 1.1889 | 0.5778 |
| 0.0023 | 18.0 | 108 | 1.2431 | 0.5778 |
| 0.0018 | 19.0 | 114 | 1.2158 | 0.5778 |
| 0.0016 | 20.0 | 120 | 1.2220 | 0.6 |
| 0.0016 | 21.0 | 126 | 1.1974 | 0.6 |
| 0.0014 | 22.0 | 132 | 1.2207 | 0.6 |
| 0.0014 | 23.0 | 138 | 1.2242 | 0.6 |
| 0.0013 | 24.0 | 144 | 1.2118 | 0.6 |
| 0.0011 | 25.0 | 150 | 1.2264 | 0.6222 |
| 0.0011 | 26.0 | 156 | 1.2250 | 0.6 |
| 0.0011 | 27.0 | 162 | 1.2237 | 0.6 |
| 0.0011 | 28.0 | 168 | 1.2290 | 0.6 |
| 0.001 | 29.0 | 174 | 1.2254 | 0.6222 |
| 0.0009 | 30.0 | 180 | 1.2294 | 0.6222 |
| 0.0009 | 31.0 | 186 | 1.2336 | 0.6222 |
| 0.0009 | 32.0 | 192 | 1.2394 | 0.6222 |
| 0.0009 | 33.0 | 198 | 1.2441 | 0.6222 |
| 0.0008 | 34.0 | 204 | 1.2483 | 0.6 |
| 0.0008 | 35.0 | 210 | 1.2484 | 0.6 |
| 0.0008 | 36.0 | 216 | 1.2564 | 0.6 |
| 0.0008 | 37.0 | 222 | 1.2583 | 0.6222 |
| 0.0008 | 38.0 | 228 | 1.2617 | 0.6222 |
| 0.0007 | 39.0 | 234 | 1.2626 | 0.6222 |
| 0.0007 | 40.0 | 240 | 1.2627 | 0.6222 |
| 0.0007 | 41.0 | 246 | 1.2621 | 0.6222 |
| 0.0007 | 42.0 | 252 | 1.2620 | 0.6222 |
| 0.0007 | 43.0 | 258 | 1.2620 | 0.6222 |
| 0.0007 | 44.0 | 264 | 1.2620 | 0.6222 |
| 0.0007 | 45.0 | 270 | 1.2620 | 0.6222 |
| 0.0007 | 46.0 | 276 | 1.2620 | 0.6222 |
| 0.0007 | 47.0 | 282 | 1.2620 | 0.6222 |
| 0.0007 | 48.0 | 288 | 1.2620 | 0.6222 |
| 0.0007 | 49.0 | 294 | 1.2620 | 0.6222 |
| 0.0007 | 50.0 | 300 | 1.2620 | 0.6222 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
hkivancoral/hushem_1x_deit_tiny_rms_0001_fold5
|
hkivancoral
| 2023-11-13T15:45:36Z | 8 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:facebook/deit-tiny-patch16-224",
"base_model:finetune:facebook/deit-tiny-patch16-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-11-13T15:44:09Z |
---
license: apache-2.0
base_model: facebook/deit-tiny-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: hushem_1x_deit_tiny_rms_0001_fold5
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.6097560975609756
---
<!-- 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. -->
# hushem_1x_deit_tiny_rms_0001_fold5
This model is a fine-tuned version of [facebook/deit-tiny-patch16-224](https://huggingface.co/facebook/deit-tiny-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7903
- Accuracy: 0.6098
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 6 | 1.6663 | 0.2683 |
| 2.1037 | 2.0 | 12 | 1.5022 | 0.2439 |
| 2.1037 | 3.0 | 18 | 1.3886 | 0.2439 |
| 1.5578 | 4.0 | 24 | 1.5306 | 0.2683 |
| 1.3692 | 5.0 | 30 | 1.1860 | 0.4390 |
| 1.3692 | 6.0 | 36 | 1.1738 | 0.4634 |
| 1.2281 | 7.0 | 42 | 1.1634 | 0.4146 |
| 1.2281 | 8.0 | 48 | 1.0062 | 0.4878 |
| 1.0442 | 9.0 | 54 | 1.0814 | 0.5366 |
| 0.7932 | 10.0 | 60 | 1.0549 | 0.5366 |
| 0.7932 | 11.0 | 66 | 1.1757 | 0.5610 |
| 0.3677 | 12.0 | 72 | 1.3513 | 0.6829 |
| 0.3677 | 13.0 | 78 | 1.1722 | 0.6098 |
| 0.2156 | 14.0 | 84 | 1.5096 | 0.5854 |
| 0.0882 | 15.0 | 90 | 1.2491 | 0.6341 |
| 0.0882 | 16.0 | 96 | 1.4974 | 0.6098 |
| 0.0242 | 17.0 | 102 | 1.6715 | 0.6341 |
| 0.0242 | 18.0 | 108 | 1.6860 | 0.5854 |
| 0.0023 | 19.0 | 114 | 1.6856 | 0.5854 |
| 0.0006 | 20.0 | 120 | 1.6918 | 0.5854 |
| 0.0006 | 21.0 | 126 | 1.7001 | 0.5854 |
| 0.0004 | 22.0 | 132 | 1.7120 | 0.5854 |
| 0.0004 | 23.0 | 138 | 1.7178 | 0.5854 |
| 0.0003 | 24.0 | 144 | 1.7236 | 0.6098 |
| 0.0003 | 25.0 | 150 | 1.7313 | 0.6098 |
| 0.0003 | 26.0 | 156 | 1.7370 | 0.6098 |
| 0.0002 | 27.0 | 162 | 1.7449 | 0.6098 |
| 0.0002 | 28.0 | 168 | 1.7492 | 0.6098 |
| 0.0002 | 29.0 | 174 | 1.7547 | 0.6098 |
| 0.0002 | 30.0 | 180 | 1.7601 | 0.6098 |
| 0.0002 | 31.0 | 186 | 1.7659 | 0.6098 |
| 0.0002 | 32.0 | 192 | 1.7694 | 0.6098 |
| 0.0002 | 33.0 | 198 | 1.7734 | 0.6098 |
| 0.0002 | 34.0 | 204 | 1.7771 | 0.6098 |
| 0.0002 | 35.0 | 210 | 1.7802 | 0.6098 |
| 0.0002 | 36.0 | 216 | 1.7829 | 0.6098 |
| 0.0002 | 37.0 | 222 | 1.7850 | 0.6098 |
| 0.0002 | 38.0 | 228 | 1.7868 | 0.6098 |
| 0.0002 | 39.0 | 234 | 1.7883 | 0.6098 |
| 0.0001 | 40.0 | 240 | 1.7895 | 0.6098 |
| 0.0001 | 41.0 | 246 | 1.7900 | 0.6098 |
| 0.0002 | 42.0 | 252 | 1.7903 | 0.6098 |
| 0.0002 | 43.0 | 258 | 1.7903 | 0.6098 |
| 0.0002 | 44.0 | 264 | 1.7903 | 0.6098 |
| 0.0002 | 45.0 | 270 | 1.7903 | 0.6098 |
| 0.0002 | 46.0 | 276 | 1.7903 | 0.6098 |
| 0.0002 | 47.0 | 282 | 1.7903 | 0.6098 |
| 0.0002 | 48.0 | 288 | 1.7903 | 0.6098 |
| 0.0002 | 49.0 | 294 | 1.7903 | 0.6098 |
| 0.0001 | 50.0 | 300 | 1.7903 | 0.6098 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
hkivancoral/hushem_1x_deit_tiny_rms_0001_fold4
|
hkivancoral
| 2023-11-13T15:44:02Z | 8 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:facebook/deit-tiny-patch16-224",
"base_model:finetune:facebook/deit-tiny-patch16-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-11-13T15:42:36Z |
---
license: apache-2.0
base_model: facebook/deit-tiny-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: hushem_1x_deit_tiny_rms_0001_fold4
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.5714285714285714
---
<!-- 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. -->
# hushem_1x_deit_tiny_rms_0001_fold4
This model is a fine-tuned version of [facebook/deit-tiny-patch16-224](https://huggingface.co/facebook/deit-tiny-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 2.3903
- Accuracy: 0.5714
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 6 | 1.5699 | 0.2619 |
| 2.0801 | 2.0 | 12 | 1.5693 | 0.2381 |
| 2.0801 | 3.0 | 18 | 1.6087 | 0.2619 |
| 1.5352 | 4.0 | 24 | 1.4372 | 0.2619 |
| 1.4323 | 5.0 | 30 | 1.3212 | 0.3095 |
| 1.4323 | 6.0 | 36 | 1.3803 | 0.2381 |
| 1.3894 | 7.0 | 42 | 1.4606 | 0.4524 |
| 1.3894 | 8.0 | 48 | 1.5543 | 0.2619 |
| 1.294 | 9.0 | 54 | 1.1365 | 0.5 |
| 1.1627 | 10.0 | 60 | 1.3219 | 0.3571 |
| 1.1627 | 11.0 | 66 | 1.0508 | 0.5714 |
| 1.0159 | 12.0 | 72 | 1.0736 | 0.5 |
| 1.0159 | 13.0 | 78 | 1.6175 | 0.3571 |
| 0.8051 | 14.0 | 84 | 1.4409 | 0.4524 |
| 0.5869 | 15.0 | 90 | 2.1188 | 0.4286 |
| 0.5869 | 16.0 | 96 | 1.8546 | 0.5476 |
| 0.3044 | 17.0 | 102 | 1.7485 | 0.5 |
| 0.3044 | 18.0 | 108 | 1.6544 | 0.5476 |
| 0.2005 | 19.0 | 114 | 1.7817 | 0.5714 |
| 0.0634 | 20.0 | 120 | 2.6836 | 0.5238 |
| 0.0634 | 21.0 | 126 | 2.3476 | 0.5714 |
| 0.0488 | 22.0 | 132 | 2.3551 | 0.5476 |
| 0.0488 | 23.0 | 138 | 2.4123 | 0.5714 |
| 0.0014 | 24.0 | 144 | 2.3855 | 0.5714 |
| 0.0006 | 25.0 | 150 | 2.3709 | 0.5714 |
| 0.0006 | 26.0 | 156 | 2.3623 | 0.5714 |
| 0.0004 | 27.0 | 162 | 2.3621 | 0.5714 |
| 0.0004 | 28.0 | 168 | 2.3646 | 0.5952 |
| 0.0003 | 29.0 | 174 | 2.3639 | 0.5952 |
| 0.0003 | 30.0 | 180 | 2.3665 | 0.5952 |
| 0.0003 | 31.0 | 186 | 2.3692 | 0.5952 |
| 0.0002 | 32.0 | 192 | 2.3723 | 0.5952 |
| 0.0002 | 33.0 | 198 | 2.3750 | 0.5952 |
| 0.0002 | 34.0 | 204 | 2.3777 | 0.5714 |
| 0.0002 | 35.0 | 210 | 2.3806 | 0.5714 |
| 0.0002 | 36.0 | 216 | 2.3834 | 0.5714 |
| 0.0002 | 37.0 | 222 | 2.3855 | 0.5714 |
| 0.0002 | 38.0 | 228 | 2.3872 | 0.5714 |
| 0.0001 | 39.0 | 234 | 2.3885 | 0.5714 |
| 0.0001 | 40.0 | 240 | 2.3895 | 0.5714 |
| 0.0001 | 41.0 | 246 | 2.3902 | 0.5714 |
| 0.0001 | 42.0 | 252 | 2.3903 | 0.5714 |
| 0.0001 | 43.0 | 258 | 2.3903 | 0.5714 |
| 0.0001 | 44.0 | 264 | 2.3903 | 0.5714 |
| 0.0001 | 45.0 | 270 | 2.3903 | 0.5714 |
| 0.0001 | 46.0 | 276 | 2.3903 | 0.5714 |
| 0.0001 | 47.0 | 282 | 2.3903 | 0.5714 |
| 0.0001 | 48.0 | 288 | 2.3903 | 0.5714 |
| 0.0001 | 49.0 | 294 | 2.3903 | 0.5714 |
| 0.0001 | 50.0 | 300 | 2.3903 | 0.5714 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
clebert/tinystories
|
clebert
| 2023-11-13T15:42:38Z | 0 | 0 | null |
[
"license:mit",
"region:us"
] | null | 2023-11-10T21:48:41Z |
---
license: mit
---
# Tinystories
The transformer model files found here have been [trained](https://github.com/karpathy/llama2.c#models) on the TinyStories dataset by Andrej Karpathy for his Llama 2 implementation in C.
They have been adapted to a binary data format optimized for streaming into a browser.
For more information on the binary data format, please refer to the documentation provided below.
## Model Binary Data Format
```
+--------------------------+
| HEADER (HYPERPARAMETERS) |
+--------------------------+
| VOCAB ENTRY 0 |
+--------------------------+
| VOCAB ENTRY .. |
+--------------------------+
| EMBEDDING 0 |
+--------------------------+
| EMBEDDING .. |
+--------------------------+
| ATTENTION LAYER 0 |
+--------------------------+
| ATTENTION LAYER .. |
+--------------------------+
| FNN LAYER 0 |
+--------------------------+
| FNN LAYER .. |
+--------------------------+
| LINEAR LAYER |
+--------------------------+
```
**NOTE:** All `i32` and `f32` data types are in little-endian format and matrices are organized in a
row-first order.
### Header (`256` bytes)
| Element | Type | Example |
| ----------------- | ---------- | -------- |
| dataFormatMagic | `6` x `u8` | "llama2" |
| dataFormatVersion | `1` x `u8` | 1 |
#### Hyperparameters
| Element | Type | Example |
| ------------------ | ----------- | ------- |
| embeddingSize | `1` x `i32` | 4096 |
| hiddenSize | `1` x `i32` | 11008 |
| keyValueSize | `1` x `i32` | 4096 |
| layerCount | `1` x `i32` | 32 |
| queryHeadCount | `1` x `i32` | 32 |
| vocabSize | `1` x `i32` | 32000 |
| maxSequenceLength | `1` x `i32` | 4096 |
| sharedOutputWeight | `1` x `u8` | 0 |
### Vocab Entry (`0` .. `vocabSize`)
| Element | Type | Example |
| --------------- | ------------------------ | ------- |
| score | `1` x `f32` | -10735 |
| tokenByteLength | `1` x `i32` | 5 |
| token | `tokenByteLength` x `u8` | "Hello" |
### Embedding (`0` .. `vocabSize`)
| Element | Type |
| --------------- | ----------------------- |
| embeddingVector | `embeddingSize` x `f32` |
### Attention Layer (`0` .. `layerCount`)
| Element | Type |
| ------------------ | ----------------------------------------- |
| normWeightVector | `embeddingSize` x `f32` |
| queryWeightMatrix | `embeddingSize` x `embeddingSize` x `f32` |
| keyWeightMatrix | `keyValueSize` x `embeddingSize` x `f32` |
| valueWeightMatrix | `keyValueSize` x `embeddingSize` x `f32` |
| outputWeightMatrix | `embeddingSize` x `embeddingSize` x `f32` |
### FNN Layer (`0` .. `layerCount`)
| Element | Type |
| ---------------- | -------------------------------------- |
| normWeightVector | `embeddingSize` x `f32` |
| gateWeightMatrix | `hiddenSize` x `embeddingSize` x `f32` |
| upWeightMatrix | `hiddenSize` x `embeddingSize` x `f32` |
| downWeightMatrix | `embeddingSize` x `hiddenSize` x `f32` |
### Linear Layer
| Element | Type | Note |
| ------------------ | ------------------------------------- | ------------------------------ |
| normWeightVector | `embeddingSize` x `f32` | |
| outputWeightMatrix | `vocabSize` x `embeddingSize` x `f32` | if `sharedOutputWeight` == `0` |
|
hkivancoral/hushem_1x_deit_tiny_rms_0001_fold2
|
hkivancoral
| 2023-11-13T15:40:56Z | 8 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:facebook/deit-tiny-patch16-224",
"base_model:finetune:facebook/deit-tiny-patch16-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-11-13T15:39:32Z |
---
license: apache-2.0
base_model: facebook/deit-tiny-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: hushem_1x_deit_tiny_rms_0001_fold2
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.5555555555555556
---
<!-- 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. -->
# hushem_1x_deit_tiny_rms_0001_fold2
This model is a fine-tuned version of [facebook/deit-tiny-patch16-224](https://huggingface.co/facebook/deit-tiny-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 3.1133
- Accuracy: 0.5556
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 6 | 1.9323 | 0.2444 |
| 2.0865 | 2.0 | 12 | 1.4427 | 0.2444 |
| 2.0865 | 3.0 | 18 | 1.4293 | 0.2444 |
| 1.4431 | 4.0 | 24 | 1.3952 | 0.4667 |
| 1.4003 | 5.0 | 30 | 1.2967 | 0.4 |
| 1.4003 | 6.0 | 36 | 1.4719 | 0.2444 |
| 1.3496 | 7.0 | 42 | 1.3224 | 0.3556 |
| 1.3496 | 8.0 | 48 | 1.4673 | 0.3778 |
| 1.2064 | 9.0 | 54 | 1.4551 | 0.2667 |
| 1.1859 | 10.0 | 60 | 1.3687 | 0.3111 |
| 1.1859 | 11.0 | 66 | 1.2313 | 0.4444 |
| 1.0817 | 12.0 | 72 | 1.1514 | 0.4444 |
| 1.0817 | 13.0 | 78 | 1.1701 | 0.4444 |
| 1.0144 | 14.0 | 84 | 1.2204 | 0.4222 |
| 0.8578 | 15.0 | 90 | 1.1603 | 0.4889 |
| 0.8578 | 16.0 | 96 | 1.0987 | 0.5111 |
| 0.8063 | 17.0 | 102 | 0.9277 | 0.5111 |
| 0.8063 | 18.0 | 108 | 1.2038 | 0.5333 |
| 0.601 | 19.0 | 114 | 0.9886 | 0.6 |
| 0.465 | 20.0 | 120 | 1.5667 | 0.5111 |
| 0.465 | 21.0 | 126 | 1.8238 | 0.4889 |
| 0.2956 | 22.0 | 132 | 1.6043 | 0.4222 |
| 0.2956 | 23.0 | 138 | 1.2746 | 0.4889 |
| 0.3513 | 24.0 | 144 | 1.6389 | 0.5556 |
| 0.2137 | 25.0 | 150 | 1.6350 | 0.4889 |
| 0.2137 | 26.0 | 156 | 1.5926 | 0.4667 |
| 0.191 | 27.0 | 162 | 1.8516 | 0.4889 |
| 0.191 | 28.0 | 168 | 2.3628 | 0.4889 |
| 0.0581 | 29.0 | 174 | 2.3998 | 0.4889 |
| 0.0517 | 30.0 | 180 | 2.3913 | 0.5333 |
| 0.0517 | 31.0 | 186 | 2.7108 | 0.5556 |
| 0.005 | 32.0 | 192 | 2.8104 | 0.5556 |
| 0.005 | 33.0 | 198 | 2.8829 | 0.5556 |
| 0.0008 | 34.0 | 204 | 2.9326 | 0.5333 |
| 0.0006 | 35.0 | 210 | 2.9793 | 0.5556 |
| 0.0006 | 36.0 | 216 | 3.0150 | 0.5556 |
| 0.0005 | 37.0 | 222 | 3.0520 | 0.5556 |
| 0.0005 | 38.0 | 228 | 3.0772 | 0.5556 |
| 0.0004 | 39.0 | 234 | 3.0948 | 0.5556 |
| 0.0004 | 40.0 | 240 | 3.1038 | 0.5556 |
| 0.0004 | 41.0 | 246 | 3.1116 | 0.5556 |
| 0.0004 | 42.0 | 252 | 3.1133 | 0.5556 |
| 0.0004 | 43.0 | 258 | 3.1133 | 0.5556 |
| 0.0004 | 44.0 | 264 | 3.1133 | 0.5556 |
| 0.0004 | 45.0 | 270 | 3.1133 | 0.5556 |
| 0.0004 | 46.0 | 276 | 3.1133 | 0.5556 |
| 0.0004 | 47.0 | 282 | 3.1133 | 0.5556 |
| 0.0004 | 48.0 | 288 | 3.1133 | 0.5556 |
| 0.0004 | 49.0 | 294 | 3.1133 | 0.5556 |
| 0.0004 | 50.0 | 300 | 3.1133 | 0.5556 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
hkivancoral/hushem_1x_deit_tiny_rms_0001_fold1
|
hkivancoral
| 2023-11-13T15:39:25Z | 8 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:facebook/deit-tiny-patch16-224",
"base_model:finetune:facebook/deit-tiny-patch16-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-11-13T15:37:59Z |
---
license: apache-2.0
base_model: facebook/deit-tiny-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: hushem_1x_deit_tiny_rms_0001_fold1
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.5555555555555556
---
<!-- 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. -->
# hushem_1x_deit_tiny_rms_0001_fold1
This model is a fine-tuned version of [facebook/deit-tiny-patch16-224](https://huggingface.co/facebook/deit-tiny-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 3.4166
- Accuracy: 0.5556
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 6 | 2.1314 | 0.2444 |
| 2.0481 | 2.0 | 12 | 1.5573 | 0.2444 |
| 2.0481 | 3.0 | 18 | 1.4598 | 0.2444 |
| 1.5099 | 4.0 | 24 | 1.4194 | 0.2444 |
| 1.4253 | 5.0 | 30 | 1.3528 | 0.2667 |
| 1.4253 | 6.0 | 36 | 1.6348 | 0.2444 |
| 1.3319 | 7.0 | 42 | 1.3901 | 0.4444 |
| 1.3319 | 8.0 | 48 | 1.3151 | 0.2889 |
| 1.2142 | 9.0 | 54 | 1.3395 | 0.3333 |
| 1.1416 | 10.0 | 60 | 1.4176 | 0.3556 |
| 1.1416 | 11.0 | 66 | 1.9072 | 0.2667 |
| 0.9889 | 12.0 | 72 | 1.7446 | 0.3111 |
| 0.9889 | 13.0 | 78 | 1.4748 | 0.3778 |
| 0.8552 | 14.0 | 84 | 1.7450 | 0.3778 |
| 0.6798 | 15.0 | 90 | 1.6042 | 0.4889 |
| 0.6798 | 16.0 | 96 | 1.5863 | 0.4222 |
| 0.563 | 17.0 | 102 | 1.9311 | 0.4 |
| 0.563 | 18.0 | 108 | 1.9509 | 0.4444 |
| 0.3845 | 19.0 | 114 | 2.1256 | 0.4667 |
| 0.2041 | 20.0 | 120 | 2.4131 | 0.4889 |
| 0.2041 | 21.0 | 126 | 2.1029 | 0.4667 |
| 0.1874 | 22.0 | 132 | 2.0412 | 0.5778 |
| 0.1874 | 23.0 | 138 | 2.4952 | 0.4889 |
| 0.0735 | 24.0 | 144 | 2.8992 | 0.4667 |
| 0.0229 | 25.0 | 150 | 2.7495 | 0.5556 |
| 0.0229 | 26.0 | 156 | 3.2879 | 0.4667 |
| 0.0293 | 27.0 | 162 | 3.1526 | 0.5111 |
| 0.0293 | 28.0 | 168 | 3.0123 | 0.5333 |
| 0.0023 | 29.0 | 174 | 3.0812 | 0.5556 |
| 0.0008 | 30.0 | 180 | 3.1384 | 0.5556 |
| 0.0008 | 31.0 | 186 | 3.2017 | 0.5556 |
| 0.0005 | 32.0 | 192 | 3.2443 | 0.5556 |
| 0.0005 | 33.0 | 198 | 3.2806 | 0.5556 |
| 0.0005 | 34.0 | 204 | 3.3167 | 0.5556 |
| 0.0004 | 35.0 | 210 | 3.3393 | 0.5556 |
| 0.0004 | 36.0 | 216 | 3.3662 | 0.5556 |
| 0.0004 | 37.0 | 222 | 3.3843 | 0.5556 |
| 0.0004 | 38.0 | 228 | 3.3970 | 0.5556 |
| 0.0003 | 39.0 | 234 | 3.4053 | 0.5556 |
| 0.0003 | 40.0 | 240 | 3.4123 | 0.5556 |
| 0.0003 | 41.0 | 246 | 3.4159 | 0.5556 |
| 0.0003 | 42.0 | 252 | 3.4166 | 0.5556 |
| 0.0003 | 43.0 | 258 | 3.4166 | 0.5556 |
| 0.0003 | 44.0 | 264 | 3.4166 | 0.5556 |
| 0.0003 | 45.0 | 270 | 3.4166 | 0.5556 |
| 0.0003 | 46.0 | 276 | 3.4166 | 0.5556 |
| 0.0003 | 47.0 | 282 | 3.4166 | 0.5556 |
| 0.0003 | 48.0 | 288 | 3.4166 | 0.5556 |
| 0.0003 | 49.0 | 294 | 3.4166 | 0.5556 |
| 0.0003 | 50.0 | 300 | 3.4166 | 0.5556 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
EstherSan/car_identified_model_7
|
EstherSan
| 2023-11-13T15:38:52Z | 57 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"mobilevit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:apple/mobilevitv2-1.0-imagenet1k-256",
"base_model:finetune:apple/mobilevitv2-1.0-imagenet1k-256",
"license:other",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-11-13T09:57:04Z |
---
license: other
base_model: apple/mobilevitv2-1.0-imagenet1k-256
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- f1
- accuracy
model-index:
- name: car_identified_model_7
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: train
args: default
metrics:
- name: F1
type: f1
value: 0.3628691983122363
- name: Accuracy
type: accuracy
value: 0.07142857142857142
---
<!-- 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. -->
# car_identified_model_7
This model is a fine-tuned version of [apple/mobilevitv2-1.0-imagenet1k-256](https://huggingface.co/apple/mobilevitv2-1.0-imagenet1k-256) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5755
- F1: 0.3629
- Roc Auc: 0.6990
- Accuracy: 0.0714
## 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
- gradient_accumulation_steps: 8
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 | Roc Auc | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:------:|:-------:|:--------:|
| 0.6919 | 0.73 | 1 | 0.6887 | 0.1786 | 0.5738 | 0.0 |
| 0.6919 | 1.45 | 2 | 0.6856 | 0.1818 | 0.5761 | 0.0 |
| 0.6919 | 2.91 | 4 | 0.6802 | 0.2116 | 0.6066 | 0.0 |
| 0.6919 | 3.64 | 5 | 0.6800 | 0.1861 | 0.5826 | 0.0 |
| 0.6919 | 4.36 | 6 | 0.6858 | 0.1905 | 0.5973 | 0.0 |
| 0.6919 | 5.82 | 8 | 0.6938 | 0.1549 | 0.5342 | 0.0 |
| 0.6919 | 6.55 | 9 | 0.6917 | 0.1805 | 0.5802 | 0.0 |
| 0.6919 | 8.0 | 11 | 0.6735 | 0.1905 | 0.5932 | 0.0 |
| 0.6919 | 8.73 | 12 | 0.6727 | 0.1952 | 0.6007 | 0.0 |
| 0.6919 | 9.45 | 13 | 0.6698 | 0.2061 | 0.6172 | 0.0 |
| 0.6919 | 10.91 | 15 | 0.6672 | 0.2008 | 0.6092 | 0.0 |
| 0.6919 | 11.64 | 16 | 0.6645 | 0.2092 | 0.6196 | 0.0 |
| 0.6919 | 12.36 | 17 | 0.6646 | 0.2049 | 0.6144 | 0.0 |
| 0.6919 | 13.82 | 19 | 0.6623 | 0.2081 | 0.6167 | 0.0 |
| 0.6919 | 14.55 | 20 | 0.6607 | 0.2078 | 0.6149 | 0.0 |
| 0.6919 | 16.0 | 22 | 0.6585 | 0.2203 | 0.6320 | 0.0 |
| 0.6919 | 16.73 | 23 | 0.6562 | 0.2156 | 0.6219 | 0.0 |
| 0.6919 | 17.45 | 24 | 0.6555 | 0.2182 | 0.6263 | 0.0 |
| 0.6919 | 18.91 | 26 | 0.6522 | 0.2185 | 0.6232 | 0.0 |
| 0.6919 | 19.64 | 27 | 0.6512 | 0.2228 | 0.6273 | 0.0 |
| 0.6919 | 20.36 | 28 | 0.6501 | 0.2356 | 0.6410 | 0.0 |
| 0.6919 | 21.82 | 30 | 0.6477 | 0.2280 | 0.6284 | 0.0 |
| 0.6919 | 22.55 | 31 | 0.6476 | 0.2326 | 0.6343 | 0.0 |
| 0.6919 | 24.0 | 33 | 0.6469 | 0.2408 | 0.6434 | 0.0 |
| 0.6919 | 24.73 | 34 | 0.6432 | 0.2409 | 0.6369 | 0.0 |
| 0.6919 | 25.45 | 35 | 0.6432 | 0.2431 | 0.6408 | 0.0 |
| 0.6919 | 26.91 | 37 | 0.6402 | 0.2486 | 0.6449 | 0.0 |
| 0.6919 | 27.64 | 38 | 0.6386 | 0.2686 | 0.6664 | 0.0 |
| 0.6919 | 28.36 | 39 | 0.6376 | 0.2762 | 0.6796 | 0.0 |
| 0.6919 | 29.82 | 41 | 0.6347 | 0.2692 | 0.6721 | 0.0 |
| 0.6919 | 30.55 | 42 | 0.6339 | 0.2655 | 0.6643 | 0.0 |
| 0.6919 | 32.0 | 44 | 0.6310 | 0.2674 | 0.6630 | 0.0 |
| 0.6919 | 32.73 | 45 | 0.6307 | 0.2789 | 0.6731 | 0.0 |
| 0.6919 | 33.45 | 46 | 0.6291 | 0.2714 | 0.6656 | 0.0 |
| 0.6919 | 34.91 | 48 | 0.6271 | 0.2761 | 0.6659 | 0.0 |
| 0.6919 | 35.64 | 49 | 0.6271 | 0.2687 | 0.6612 | 0.0 |
| 0.6919 | 36.36 | 50 | 0.6277 | 0.2606 | 0.6509 | 0.0 |
| 0.6919 | 37.82 | 52 | 0.6257 | 0.2741 | 0.6620 | 0.0 |
| 0.6919 | 38.55 | 53 | 0.6244 | 0.2892 | 0.6793 | 0.0 |
| 0.6919 | 40.0 | 55 | 0.6203 | 0.2968 | 0.6806 | 0.0 |
| 0.6919 | 40.73 | 56 | 0.6198 | 0.2902 | 0.6770 | 0.0 |
| 0.6919 | 41.45 | 57 | 0.6184 | 0.3023 | 0.6866 | 0.0 |
| 0.6919 | 42.91 | 59 | 0.6163 | 0.2977 | 0.6812 | 0.0 |
| 0.6919 | 43.64 | 60 | 0.6147 | 0.3322 | 0.7112 | 0.0 |
| 0.6919 | 44.36 | 61 | 0.6154 | 0.3197 | 0.6954 | 0.0 |
| 0.6919 | 45.82 | 63 | 0.6129 | 0.3016 | 0.6832 | 0.0 |
| 0.6919 | 46.55 | 64 | 0.6112 | 0.3020 | 0.6804 | 0.0 |
| 0.6919 | 48.0 | 66 | 0.6095 | 0.2961 | 0.6773 | 0.0 |
| 0.6919 | 48.73 | 67 | 0.6091 | 0.3133 | 0.6923 | 0.0 |
| 0.6919 | 49.45 | 68 | 0.6090 | 0.3265 | 0.7019 | 0.0 |
| 0.6919 | 50.91 | 70 | 0.6077 | 0.3093 | 0.6840 | 0.0 |
| 0.6919 | 51.64 | 71 | 0.6065 | 0.3239 | 0.6941 | 0.0 |
| 0.6919 | 52.36 | 72 | 0.6058 | 0.3237 | 0.6907 | 0.0 |
| 0.6919 | 53.82 | 74 | 0.6028 | 0.3285 | 0.6928 | 0.0 |
| 0.6919 | 54.55 | 75 | 0.6038 | 0.3285 | 0.6928 | 0.0238 |
| 0.6919 | 56.0 | 77 | 0.6056 | 0.3197 | 0.6825 | 0.0 |
| 0.6919 | 56.73 | 78 | 0.6074 | 0.3249 | 0.6913 | 0.0 |
| 0.6919 | 57.45 | 79 | 0.6030 | 0.3158 | 0.6775 | 0.0238 |
| 0.6919 | 58.91 | 81 | 0.6001 | 0.3359 | 0.6925 | 0.0238 |
| 0.6919 | 59.64 | 82 | 0.5993 | 0.3409 | 0.6980 | 0.0238 |
| 0.6919 | 60.36 | 83 | 0.6017 | 0.3259 | 0.6884 | 0.0238 |
| 0.6919 | 61.82 | 85 | 0.6009 | 0.3146 | 0.6770 | 0.0238 |
| 0.6919 | 62.55 | 86 | 0.6018 | 0.3197 | 0.6825 | 0.0238 |
| 0.6919 | 64.0 | 88 | 0.5975 | 0.3130 | 0.6731 | 0.0238 |
| 0.6919 | 64.73 | 89 | 0.5978 | 0.3271 | 0.6889 | 0.0238 |
| 0.6919 | 65.45 | 90 | 0.5967 | 0.3424 | 0.6951 | 0.0238 |
| 0.6919 | 66.91 | 92 | 0.5973 | 0.3125 | 0.6698 | 0.0238 |
| 0.6919 | 67.64 | 93 | 0.5956 | 0.3372 | 0.6931 | 0.0238 |
| 0.6919 | 68.36 | 94 | 0.5922 | 0.3373 | 0.6897 | 0.0238 |
| 0.6919 | 69.82 | 96 | 0.5949 | 0.3320 | 0.6843 | 0.0476 |
| 0.6919 | 70.55 | 97 | 0.5959 | 0.3413 | 0.6913 | 0.0476 |
| 0.6919 | 72.0 | 99 | 0.5944 | 0.3420 | 0.7019 | 0.0238 |
| 0.6919 | 72.73 | 100 | 0.5955 | 0.3333 | 0.6881 | 0.0476 |
| 0.6919 | 73.45 | 101 | 0.5933 | 0.3346 | 0.6887 | 0.0238 |
| 0.6919 | 74.91 | 103 | 0.5894 | 0.3543 | 0.7032 | 0.0238 |
| 0.6919 | 75.64 | 104 | 0.5903 | 0.3424 | 0.6951 | 0.0238 |
| 0.6919 | 76.36 | 105 | 0.5890 | 0.3411 | 0.6946 | 0.0476 |
| 0.6919 | 77.82 | 107 | 0.5922 | 0.3346 | 0.6887 | 0.0476 |
| 0.6919 | 78.55 | 108 | 0.5923 | 0.3243 | 0.6812 | 0.0476 |
| 0.6919 | 80.0 | 110 | 0.5908 | 0.3468 | 0.6933 | 0.0476 |
| 0.6919 | 80.73 | 111 | 0.5922 | 0.328 | 0.6793 | 0.0476 |
| 0.6919 | 81.45 | 112 | 0.5892 | 0.3440 | 0.6923 | 0.0238 |
| 0.6919 | 82.91 | 114 | 0.5880 | 0.3506 | 0.6982 | 0.0238 |
| 0.6919 | 83.64 | 115 | 0.5869 | 0.3454 | 0.6928 | 0.0476 |
| 0.6919 | 84.36 | 116 | 0.5841 | 0.3465 | 0.6967 | 0.0238 |
| 0.6919 | 85.82 | 118 | 0.5841 | 0.3568 | 0.6969 | 0.0714 |
| 0.6919 | 86.55 | 119 | 0.5843 | 0.3496 | 0.6944 | 0.0476 |
| 0.6919 | 88.0 | 121 | 0.5860 | 0.3598 | 0.6980 | 0.0476 |
| 0.6919 | 88.73 | 122 | 0.5837 | 0.3457 | 0.6894 | 0.0476 |
| 0.6919 | 89.45 | 123 | 0.5826 | 0.3636 | 0.7029 | 0.0714 |
| 0.6919 | 90.91 | 125 | 0.5822 | 0.3651 | 0.7034 | 0.0714 |
| 0.6919 | 91.64 | 126 | 0.5814 | 0.3607 | 0.7019 | 0.0714 |
| 0.6919 | 92.36 | 127 | 0.5814 | 0.3629 | 0.7063 | 0.0476 |
| 0.6919 | 93.82 | 129 | 0.5818 | 0.3713 | 0.7055 | 0.0714 |
| 0.6919 | 94.55 | 130 | 0.5802 | 0.3766 | 0.7109 | 0.0714 |
| 0.6919 | 96.0 | 132 | 0.5803 | 0.3675 | 0.7006 | 0.0714 |
| 0.6919 | 96.73 | 133 | 0.5825 | 0.3519 | 0.6881 | 0.0714 |
| 0.6919 | 97.45 | 134 | 0.5790 | 0.3629 | 0.6990 | 0.0714 |
| 0.6919 | 98.91 | 136 | 0.5795 | 0.3766 | 0.7109 | 0.0714 |
| 0.6919 | 99.64 | 137 | 0.5784 | 0.3697 | 0.7050 | 0.0714 |
| 0.6919 | 100.36 | 138 | 0.5819 | 0.3583 | 0.6975 | 0.0714 |
| 0.6919 | 101.82 | 140 | 0.5834 | 0.3525 | 0.6954 | 0.0476 |
| 0.6919 | 102.55 | 141 | 0.5825 | 0.3689 | 0.7083 | 0.0238 |
| 0.6919 | 104.0 | 143 | 0.5839 | 0.3460 | 0.6861 | 0.0714 |
| 0.6919 | 104.73 | 144 | 0.5838 | 0.3333 | 0.6814 | 0.0476 |
| 0.6919 | 105.45 | 145 | 0.5801 | 0.3387 | 0.6869 | 0.0238 |
| 0.6919 | 106.91 | 147 | 0.5811 | 0.3515 | 0.6915 | 0.0476 |
| 0.6919 | 107.64 | 148 | 0.5793 | 0.3374 | 0.6830 | 0.0476 |
| 0.6919 | 108.36 | 149 | 0.5766 | 0.3448 | 0.6822 | 0.0714 |
| 0.6919 | 109.82 | 151 | 0.5760 | 0.3445 | 0.6856 | 0.0714 |
| 0.6919 | 110.55 | 152 | 0.5757 | 0.3559 | 0.6931 | 0.0714 |
| 0.6919 | 112.0 | 154 | 0.5760 | 0.3475 | 0.6866 | 0.0714 |
| 0.6919 | 112.73 | 155 | 0.5743 | 0.3629 | 0.6990 | 0.0714 |
| 0.6919 | 113.45 | 156 | 0.5732 | 0.3636 | 0.7029 | 0.0714 |
| 0.6919 | 114.91 | 158 | 0.5736 | 0.3786 | 0.7153 | 0.0476 |
| 0.6919 | 115.64 | 159 | 0.5764 | 0.3667 | 0.7039 | 0.0238 |
| 0.6919 | 116.36 | 160 | 0.5765 | 0.3613 | 0.6985 | 0.0476 |
| 0.6919 | 117.82 | 162 | 0.5749 | 0.3574 | 0.6936 | 0.0714 |
| 0.6919 | 118.55 | 163 | 0.5754 | 0.3592 | 0.7013 | 0.0476 |
| 0.6919 | 120.0 | 165 | 0.5757 | 0.3665 | 0.7112 | 0.0476 |
| 0.6919 | 120.73 | 166 | 0.5771 | 0.3729 | 0.7060 | 0.0714 |
| 0.6919 | 121.45 | 167 | 0.5746 | 0.3629 | 0.6990 | 0.0714 |
| 0.6919 | 122.91 | 169 | 0.5758 | 0.3644 | 0.6995 | 0.0714 |
| 0.6919 | 123.64 | 170 | 0.5745 | 0.3559 | 0.6931 | 0.0714 |
| 0.6919 | 124.36 | 171 | 0.5758 | 0.3544 | 0.6925 | 0.0714 |
| 0.6919 | 125.82 | 173 | 0.5759 | 0.3598 | 0.6980 | 0.0714 |
| 0.6919 | 126.55 | 174 | 0.5772 | 0.3568 | 0.6969 | 0.0714 |
| 0.6919 | 128.0 | 176 | 0.5747 | 0.3583 | 0.6975 | 0.0714 |
| 0.6919 | 128.73 | 177 | 0.5738 | 0.3644 | 0.6995 | 0.0714 |
| 0.6919 | 129.45 | 178 | 0.5751 | 0.3644 | 0.6995 | 0.0714 |
| 0.6919 | 130.91 | 180 | 0.5741 | 0.3713 | 0.7055 | 0.0952 |
| 0.6919 | 131.64 | 181 | 0.5748 | 0.3713 | 0.7055 | 0.0952 |
| 0.6919 | 132.36 | 182 | 0.5767 | 0.3660 | 0.7001 | 0.0714 |
| 0.6919 | 133.82 | 184 | 0.5732 | 0.3660 | 0.7001 | 0.0952 |
| 0.6919 | 134.55 | 185 | 0.5742 | 0.3772 | 0.7037 | 0.0952 |
| 0.6919 | 136.0 | 187 | 0.5690 | 0.3755 | 0.7032 | 0.0952 |
| 0.6919 | 136.73 | 188 | 0.5699 | 0.3805 | 0.7047 | 0.0714 |
| 0.6919 | 137.45 | 189 | 0.5743 | 0.3707 | 0.7016 | 0.0714 |
| 0.6919 | 138.91 | 191 | 0.5740 | 0.3529 | 0.6920 | 0.0952 |
| 0.6919 | 139.64 | 192 | 0.5740 | 0.3660 | 0.7001 | 0.0714 |
| 0.6919 | 140.36 | 193 | 0.5734 | 0.3644 | 0.6995 | 0.0714 |
| 0.6919 | 141.82 | 195 | 0.5740 | 0.3675 | 0.7006 | 0.0714 |
| 0.6919 | 142.55 | 196 | 0.5721 | 0.3707 | 0.7016 | 0.0714 |
| 0.6919 | 144.0 | 198 | 0.5725 | 0.3767 | 0.6998 | 0.0714 |
| 0.6919 | 144.73 | 199 | 0.5734 | 0.3729 | 0.7060 | 0.0952 |
| 0.6919 | 145.45 | 200 | 0.5755 | 0.3629 | 0.6990 | 0.0714 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu121
- Datasets 2.14.6
- Tokenizers 0.14.1
|
hkivancoral/hushem_1x_deit_tiny_rms_001_fold5
|
hkivancoral
| 2023-11-13T15:33:41Z | 8 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:facebook/deit-tiny-patch16-224",
"base_model:finetune:facebook/deit-tiny-patch16-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-11-13T15:32:16Z |
---
license: apache-2.0
base_model: facebook/deit-tiny-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: hushem_1x_deit_tiny_rms_001_fold5
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.6097560975609756
---
<!-- 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. -->
# hushem_1x_deit_tiny_rms_001_fold5
This model is a fine-tuned version of [facebook/deit-tiny-patch16-224](https://huggingface.co/facebook/deit-tiny-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1358
- Accuracy: 0.6098
## 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: 0.001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 6 | 4.7231 | 0.2683 |
| 4.2141 | 2.0 | 12 | 1.8531 | 0.2683 |
| 4.2141 | 3.0 | 18 | 1.6449 | 0.2439 |
| 1.9845 | 4.0 | 24 | 1.4265 | 0.2439 |
| 1.5807 | 5.0 | 30 | 2.0165 | 0.2439 |
| 1.5807 | 6.0 | 36 | 1.5975 | 0.2683 |
| 1.5979 | 7.0 | 42 | 1.4305 | 0.3171 |
| 1.5979 | 8.0 | 48 | 1.4587 | 0.2683 |
| 1.4992 | 9.0 | 54 | 1.2917 | 0.3171 |
| 1.4954 | 10.0 | 60 | 1.2462 | 0.4390 |
| 1.4954 | 11.0 | 66 | 1.2479 | 0.2683 |
| 1.415 | 12.0 | 72 | 1.1246 | 0.5122 |
| 1.415 | 13.0 | 78 | 1.1689 | 0.4878 |
| 1.374 | 14.0 | 84 | 1.3767 | 0.2927 |
| 1.3675 | 15.0 | 90 | 1.1692 | 0.4146 |
| 1.3675 | 16.0 | 96 | 1.6528 | 0.2927 |
| 1.319 | 17.0 | 102 | 1.3151 | 0.3659 |
| 1.319 | 18.0 | 108 | 1.1475 | 0.4146 |
| 1.3335 | 19.0 | 114 | 1.1506 | 0.3415 |
| 1.2819 | 20.0 | 120 | 1.2300 | 0.3902 |
| 1.2819 | 21.0 | 126 | 1.1641 | 0.4146 |
| 1.2507 | 22.0 | 132 | 1.4148 | 0.3659 |
| 1.2507 | 23.0 | 138 | 1.3061 | 0.3415 |
| 1.2134 | 24.0 | 144 | 1.2367 | 0.3415 |
| 1.2611 | 25.0 | 150 | 1.2383 | 0.4878 |
| 1.2611 | 26.0 | 156 | 1.0375 | 0.4878 |
| 1.2053 | 27.0 | 162 | 1.1983 | 0.4878 |
| 1.2053 | 28.0 | 168 | 1.1898 | 0.4146 |
| 1.1593 | 29.0 | 174 | 1.1479 | 0.4878 |
| 1.2426 | 30.0 | 180 | 1.1382 | 0.5610 |
| 1.2426 | 31.0 | 186 | 1.0558 | 0.5610 |
| 1.1866 | 32.0 | 192 | 1.1895 | 0.4390 |
| 1.1866 | 33.0 | 198 | 1.2172 | 0.4146 |
| 1.1453 | 34.0 | 204 | 1.3773 | 0.4146 |
| 1.1026 | 35.0 | 210 | 1.1168 | 0.5122 |
| 1.1026 | 36.0 | 216 | 1.1184 | 0.5610 |
| 1.131 | 37.0 | 222 | 1.1344 | 0.5366 |
| 1.131 | 38.0 | 228 | 1.0932 | 0.5122 |
| 1.1098 | 39.0 | 234 | 1.1070 | 0.6098 |
| 1.0797 | 40.0 | 240 | 1.1237 | 0.5854 |
| 1.0797 | 41.0 | 246 | 1.1366 | 0.6098 |
| 1.0648 | 42.0 | 252 | 1.1358 | 0.6098 |
| 1.0648 | 43.0 | 258 | 1.1358 | 0.6098 |
| 1.0281 | 44.0 | 264 | 1.1358 | 0.6098 |
| 1.0542 | 45.0 | 270 | 1.1358 | 0.6098 |
| 1.0542 | 46.0 | 276 | 1.1358 | 0.6098 |
| 1.0409 | 47.0 | 282 | 1.1358 | 0.6098 |
| 1.0409 | 48.0 | 288 | 1.1358 | 0.6098 |
| 1.0504 | 49.0 | 294 | 1.1358 | 0.6098 |
| 1.0111 | 50.0 | 300 | 1.1358 | 0.6098 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
hkivancoral/hushem_1x_deit_tiny_rms_001_fold4
|
hkivancoral
| 2023-11-13T15:32:10Z | 9 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:facebook/deit-tiny-patch16-224",
"base_model:finetune:facebook/deit-tiny-patch16-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-11-13T15:30:45Z |
---
license: apache-2.0
base_model: facebook/deit-tiny-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: hushem_1x_deit_tiny_rms_001_fold4
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.47619047619047616
---
<!-- 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. -->
# hushem_1x_deit_tiny_rms_001_fold4
This model is a fine-tuned version of [facebook/deit-tiny-patch16-224](https://huggingface.co/facebook/deit-tiny-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0712
- Accuracy: 0.4762
## 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: 0.001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 6 | 5.0165 | 0.2381 |
| 4.2481 | 2.0 | 12 | 3.3074 | 0.2381 |
| 4.2481 | 3.0 | 18 | 1.5288 | 0.2619 |
| 2.0024 | 4.0 | 24 | 1.5375 | 0.2381 |
| 1.6731 | 5.0 | 30 | 1.4069 | 0.2619 |
| 1.6731 | 6.0 | 36 | 1.8969 | 0.2381 |
| 1.5329 | 7.0 | 42 | 1.4811 | 0.2381 |
| 1.5329 | 8.0 | 48 | 1.4117 | 0.2619 |
| 1.475 | 9.0 | 54 | 1.4704 | 0.2619 |
| 1.4639 | 10.0 | 60 | 1.4459 | 0.2381 |
| 1.4639 | 11.0 | 66 | 1.3572 | 0.4524 |
| 1.4524 | 12.0 | 72 | 1.2630 | 0.4524 |
| 1.4524 | 13.0 | 78 | 1.2843 | 0.4524 |
| 1.4025 | 14.0 | 84 | 1.3420 | 0.2857 |
| 1.3666 | 15.0 | 90 | 1.4060 | 0.2381 |
| 1.3666 | 16.0 | 96 | 1.2621 | 0.3810 |
| 1.3178 | 17.0 | 102 | 1.2969 | 0.2857 |
| 1.3178 | 18.0 | 108 | 1.2881 | 0.3333 |
| 1.3667 | 19.0 | 114 | 1.3980 | 0.2857 |
| 1.3043 | 20.0 | 120 | 1.5195 | 0.2857 |
| 1.3043 | 21.0 | 126 | 1.1841 | 0.4048 |
| 1.2859 | 22.0 | 132 | 1.0567 | 0.5238 |
| 1.2859 | 23.0 | 138 | 1.2258 | 0.2619 |
| 1.2496 | 24.0 | 144 | 1.2372 | 0.2857 |
| 1.252 | 25.0 | 150 | 1.4386 | 0.3333 |
| 1.252 | 26.0 | 156 | 1.1416 | 0.3810 |
| 1.2296 | 27.0 | 162 | 1.0872 | 0.4286 |
| 1.2296 | 28.0 | 168 | 1.4121 | 0.2857 |
| 1.1581 | 29.0 | 174 | 1.0555 | 0.5476 |
| 1.2027 | 30.0 | 180 | 1.1296 | 0.4762 |
| 1.2027 | 31.0 | 186 | 1.2095 | 0.4048 |
| 1.1595 | 32.0 | 192 | 1.0821 | 0.4762 |
| 1.1595 | 33.0 | 198 | 1.1681 | 0.3810 |
| 1.1909 | 34.0 | 204 | 1.1147 | 0.4762 |
| 1.1121 | 35.0 | 210 | 1.0734 | 0.4048 |
| 1.1121 | 36.0 | 216 | 1.0002 | 0.5238 |
| 1.1218 | 37.0 | 222 | 1.1912 | 0.3095 |
| 1.1218 | 38.0 | 228 | 1.0883 | 0.4524 |
| 1.1024 | 39.0 | 234 | 1.1229 | 0.4286 |
| 1.0678 | 40.0 | 240 | 1.0903 | 0.4762 |
| 1.0678 | 41.0 | 246 | 1.0717 | 0.4762 |
| 1.058 | 42.0 | 252 | 1.0712 | 0.4762 |
| 1.058 | 43.0 | 258 | 1.0712 | 0.4762 |
| 1.0512 | 44.0 | 264 | 1.0712 | 0.4762 |
| 1.0743 | 45.0 | 270 | 1.0712 | 0.4762 |
| 1.0743 | 46.0 | 276 | 1.0712 | 0.4762 |
| 1.0691 | 47.0 | 282 | 1.0712 | 0.4762 |
| 1.0691 | 48.0 | 288 | 1.0712 | 0.4762 |
| 1.052 | 49.0 | 294 | 1.0712 | 0.4762 |
| 1.066 | 50.0 | 300 | 1.0712 | 0.4762 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
hkivancoral/hushem_1x_deit_tiny_rms_001_fold3
|
hkivancoral
| 2023-11-13T15:30:38Z | 8 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:facebook/deit-tiny-patch16-224",
"base_model:finetune:facebook/deit-tiny-patch16-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-11-13T15:29:14Z |
---
license: apache-2.0
base_model: facebook/deit-tiny-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: hushem_1x_deit_tiny_rms_001_fold3
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.4186046511627907
---
<!-- 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. -->
# hushem_1x_deit_tiny_rms_001_fold3
This model is a fine-tuned version of [facebook/deit-tiny-patch16-224](https://huggingface.co/facebook/deit-tiny-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1536
- Accuracy: 0.4186
## 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: 0.001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 6 | 3.8148 | 0.2558 |
| 4.0682 | 2.0 | 12 | 1.5106 | 0.2558 |
| 4.0682 | 3.0 | 18 | 2.1015 | 0.2558 |
| 1.8697 | 4.0 | 24 | 2.1521 | 0.2558 |
| 1.6204 | 5.0 | 30 | 2.0540 | 0.2558 |
| 1.6204 | 6.0 | 36 | 1.4487 | 0.2558 |
| 1.5557 | 7.0 | 42 | 1.5322 | 0.2326 |
| 1.5557 | 8.0 | 48 | 1.6480 | 0.2558 |
| 1.5276 | 9.0 | 54 | 1.5085 | 0.2558 |
| 1.4446 | 10.0 | 60 | 1.3921 | 0.2558 |
| 1.4446 | 11.0 | 66 | 1.5703 | 0.2558 |
| 1.4728 | 12.0 | 72 | 1.3608 | 0.2791 |
| 1.4728 | 13.0 | 78 | 1.4250 | 0.3488 |
| 1.3652 | 14.0 | 84 | 1.4495 | 0.2558 |
| 1.3593 | 15.0 | 90 | 1.4182 | 0.3023 |
| 1.3593 | 16.0 | 96 | 1.5418 | 0.3023 |
| 1.2943 | 17.0 | 102 | 1.4454 | 0.3256 |
| 1.2943 | 18.0 | 108 | 1.5941 | 0.3721 |
| 1.2915 | 19.0 | 114 | 1.4889 | 0.2558 |
| 1.2591 | 20.0 | 120 | 1.3804 | 0.3488 |
| 1.2591 | 21.0 | 126 | 1.8125 | 0.2558 |
| 1.2263 | 22.0 | 132 | 1.4098 | 0.3023 |
| 1.2263 | 23.0 | 138 | 1.4818 | 0.2558 |
| 1.1885 | 24.0 | 144 | 1.4257 | 0.3721 |
| 1.1814 | 25.0 | 150 | 1.4317 | 0.3023 |
| 1.1814 | 26.0 | 156 | 1.3854 | 0.3488 |
| 1.1163 | 27.0 | 162 | 1.9054 | 0.3256 |
| 1.1163 | 28.0 | 168 | 1.3109 | 0.3488 |
| 1.0609 | 29.0 | 174 | 1.3896 | 0.3488 |
| 1.1038 | 30.0 | 180 | 1.3466 | 0.3256 |
| 1.1038 | 31.0 | 186 | 1.3101 | 0.3256 |
| 1.0099 | 32.0 | 192 | 1.2865 | 0.3721 |
| 1.0099 | 33.0 | 198 | 1.2846 | 0.3721 |
| 1.0297 | 34.0 | 204 | 1.2587 | 0.4186 |
| 0.964 | 35.0 | 210 | 1.2832 | 0.3953 |
| 0.964 | 36.0 | 216 | 1.1929 | 0.3721 |
| 0.9335 | 37.0 | 222 | 1.2162 | 0.3953 |
| 0.9335 | 38.0 | 228 | 1.1906 | 0.4419 |
| 0.8668 | 39.0 | 234 | 1.1859 | 0.4186 |
| 0.8296 | 40.0 | 240 | 1.1516 | 0.4884 |
| 0.8296 | 41.0 | 246 | 1.1577 | 0.4651 |
| 0.8332 | 42.0 | 252 | 1.1536 | 0.4186 |
| 0.8332 | 43.0 | 258 | 1.1536 | 0.4186 |
| 0.8289 | 44.0 | 264 | 1.1536 | 0.4186 |
| 0.8217 | 45.0 | 270 | 1.1536 | 0.4186 |
| 0.8217 | 46.0 | 276 | 1.1536 | 0.4186 |
| 0.8205 | 47.0 | 282 | 1.1536 | 0.4186 |
| 0.8205 | 48.0 | 288 | 1.1536 | 0.4186 |
| 0.8548 | 49.0 | 294 | 1.1536 | 0.4186 |
| 0.8042 | 50.0 | 300 | 1.1536 | 0.4186 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
Kaspar/QueerGPT2
|
Kaspar
| 2023-11-13T15:28:41Z | 47 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"gpt2",
"text-generation",
"generated_from_trainer",
"base_model:openai-community/gpt2",
"base_model:finetune:openai-community/gpt2",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-11-13T14:40:55Z |
---
license: mit
base_model: gpt2
tags:
- generated_from_trainer
model-index:
- name: QueerGPT2
results: []
widget:
- text: "When I grow up, I want to be a"
---
<!-- 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. -->
# QueerGPT2
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 4.3634
## 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: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 4.5433 | 1.0 | 13237 | 4.3634 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
vonewman/distilbert-base-uncased-finetuned-imdb
|
vonewman
| 2023-11-13T15:25:20Z | 6 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"fill-mask",
"generated_from_trainer",
"dataset:imdb",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2023-11-13T14:15:41Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- imdb
model-index:
- name: distilbert-base-uncased-finetuned-imdb
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-imdb
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4119
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.7024 | 1.0 | 157 | 2.4966 |
| 2.5796 | 2.0 | 314 | 2.4282 |
| 2.5355 | 3.0 | 471 | 2.4510 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
xiaol/RWKV-v5.2-7B-horror-16k
|
xiaol
| 2023-11-13T15:21:25Z | 0 | 2 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2023-11-13T02:39:00Z |
---
license: apache-2.0
---
## This a experimental model just finetuned from a small horror datasets for 16k rwkv v5.2 model
## Warning: THIS model is NOT suitable for use by minors.
|
picklehari/fzlake
|
picklehari
| 2023-11-13T15:17:25Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-11-13T15:11:53Z |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: fzlake
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="picklehari/fzlake", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
hkivancoral/hushem_1x_deit_tiny_sgd_0001_fold5
|
hkivancoral
| 2023-11-13T15:11:50Z | 8 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:facebook/deit-tiny-patch16-224",
"base_model:finetune:facebook/deit-tiny-patch16-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-11-13T15:10:28Z |
---
license: apache-2.0
base_model: facebook/deit-tiny-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: hushem_1x_deit_tiny_sgd_0001_fold5
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.24390243902439024
---
<!-- 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. -->
# hushem_1x_deit_tiny_sgd_0001_fold5
This model is a fine-tuned version of [facebook/deit-tiny-patch16-224](https://huggingface.co/facebook/deit-tiny-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5523
- Accuracy: 0.2439
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 6 | 1.7547 | 0.2439 |
| 1.7078 | 2.0 | 12 | 1.7422 | 0.2439 |
| 1.7078 | 3.0 | 18 | 1.7303 | 0.2439 |
| 1.6827 | 4.0 | 24 | 1.7187 | 0.2439 |
| 1.6676 | 5.0 | 30 | 1.7076 | 0.2439 |
| 1.6676 | 6.0 | 36 | 1.6970 | 0.2439 |
| 1.6669 | 7.0 | 42 | 1.6882 | 0.2439 |
| 1.6669 | 8.0 | 48 | 1.6793 | 0.2439 |
| 1.5935 | 9.0 | 54 | 1.6701 | 0.2439 |
| 1.6316 | 10.0 | 60 | 1.6617 | 0.2439 |
| 1.6316 | 11.0 | 66 | 1.6538 | 0.2439 |
| 1.6324 | 12.0 | 72 | 1.6460 | 0.2439 |
| 1.6324 | 13.0 | 78 | 1.6387 | 0.2439 |
| 1.5842 | 14.0 | 84 | 1.6318 | 0.2439 |
| 1.5897 | 15.0 | 90 | 1.6256 | 0.2439 |
| 1.5897 | 16.0 | 96 | 1.6199 | 0.2439 |
| 1.5943 | 17.0 | 102 | 1.6144 | 0.2439 |
| 1.5943 | 18.0 | 108 | 1.6092 | 0.2195 |
| 1.5586 | 19.0 | 114 | 1.6040 | 0.2195 |
| 1.5924 | 20.0 | 120 | 1.5990 | 0.2195 |
| 1.5924 | 21.0 | 126 | 1.5945 | 0.2195 |
| 1.5676 | 22.0 | 132 | 1.5902 | 0.2195 |
| 1.5676 | 23.0 | 138 | 1.5862 | 0.2195 |
| 1.5352 | 24.0 | 144 | 1.5823 | 0.2195 |
| 1.5842 | 25.0 | 150 | 1.5786 | 0.2195 |
| 1.5842 | 26.0 | 156 | 1.5752 | 0.2195 |
| 1.5461 | 27.0 | 162 | 1.5723 | 0.2195 |
| 1.5461 | 28.0 | 168 | 1.5695 | 0.2195 |
| 1.551 | 29.0 | 174 | 1.5671 | 0.2439 |
| 1.5549 | 30.0 | 180 | 1.5649 | 0.2439 |
| 1.5549 | 31.0 | 186 | 1.5628 | 0.2439 |
| 1.5532 | 32.0 | 192 | 1.5610 | 0.2439 |
| 1.5532 | 33.0 | 198 | 1.5594 | 0.2439 |
| 1.5006 | 34.0 | 204 | 1.5578 | 0.2439 |
| 1.5134 | 35.0 | 210 | 1.5565 | 0.2439 |
| 1.5134 | 36.0 | 216 | 1.5553 | 0.2439 |
| 1.5386 | 37.0 | 222 | 1.5543 | 0.2439 |
| 1.5386 | 38.0 | 228 | 1.5536 | 0.2439 |
| 1.5372 | 39.0 | 234 | 1.5530 | 0.2439 |
| 1.528 | 40.0 | 240 | 1.5526 | 0.2439 |
| 1.528 | 41.0 | 246 | 1.5524 | 0.2439 |
| 1.5555 | 42.0 | 252 | 1.5523 | 0.2439 |
| 1.5555 | 43.0 | 258 | 1.5523 | 0.2439 |
| 1.509 | 44.0 | 264 | 1.5523 | 0.2439 |
| 1.5379 | 45.0 | 270 | 1.5523 | 0.2439 |
| 1.5379 | 46.0 | 276 | 1.5523 | 0.2439 |
| 1.5588 | 47.0 | 282 | 1.5523 | 0.2439 |
| 1.5588 | 48.0 | 288 | 1.5523 | 0.2439 |
| 1.509 | 49.0 | 294 | 1.5523 | 0.2439 |
| 1.5414 | 50.0 | 300 | 1.5523 | 0.2439 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
hkivancoral/hushem_1x_deit_tiny_sgd_0001_fold4
|
hkivancoral
| 2023-11-13T15:10:22Z | 8 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:facebook/deit-tiny-patch16-224",
"base_model:finetune:facebook/deit-tiny-patch16-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-11-13T15:09:02Z |
---
license: apache-2.0
base_model: facebook/deit-tiny-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: hushem_1x_deit_tiny_sgd_0001_fold4
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.2857142857142857
---
<!-- 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. -->
# hushem_1x_deit_tiny_sgd_0001_fold4
This model is a fine-tuned version of [facebook/deit-tiny-patch16-224](https://huggingface.co/facebook/deit-tiny-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5092
- Accuracy: 0.2857
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 6 | 1.6866 | 0.2857 |
| 1.7029 | 2.0 | 12 | 1.6755 | 0.2857 |
| 1.7029 | 3.0 | 18 | 1.6648 | 0.2857 |
| 1.6819 | 4.0 | 24 | 1.6543 | 0.2857 |
| 1.7084 | 5.0 | 30 | 1.6452 | 0.2857 |
| 1.7084 | 6.0 | 36 | 1.6365 | 0.2857 |
| 1.661 | 7.0 | 42 | 1.6277 | 0.2857 |
| 1.661 | 8.0 | 48 | 1.6195 | 0.2857 |
| 1.6506 | 9.0 | 54 | 1.6113 | 0.2857 |
| 1.6321 | 10.0 | 60 | 1.6035 | 0.2857 |
| 1.6321 | 11.0 | 66 | 1.5969 | 0.2857 |
| 1.605 | 12.0 | 72 | 1.5900 | 0.2857 |
| 1.605 | 13.0 | 78 | 1.5837 | 0.2857 |
| 1.6205 | 14.0 | 84 | 1.5775 | 0.2857 |
| 1.6128 | 15.0 | 90 | 1.5717 | 0.2857 |
| 1.6128 | 16.0 | 96 | 1.5663 | 0.2857 |
| 1.5818 | 17.0 | 102 | 1.5613 | 0.2857 |
| 1.5818 | 18.0 | 108 | 1.5566 | 0.2857 |
| 1.6012 | 19.0 | 114 | 1.5522 | 0.2857 |
| 1.6068 | 20.0 | 120 | 1.5482 | 0.2857 |
| 1.6068 | 21.0 | 126 | 1.5443 | 0.2857 |
| 1.5674 | 22.0 | 132 | 1.5409 | 0.2857 |
| 1.5674 | 23.0 | 138 | 1.5376 | 0.2857 |
| 1.565 | 24.0 | 144 | 1.5344 | 0.2857 |
| 1.5842 | 25.0 | 150 | 1.5314 | 0.2857 |
| 1.5842 | 26.0 | 156 | 1.5286 | 0.2857 |
| 1.5593 | 27.0 | 162 | 1.5260 | 0.2857 |
| 1.5593 | 28.0 | 168 | 1.5236 | 0.2857 |
| 1.5824 | 29.0 | 174 | 1.5216 | 0.2857 |
| 1.537 | 30.0 | 180 | 1.5196 | 0.2857 |
| 1.537 | 31.0 | 186 | 1.5181 | 0.2857 |
| 1.5437 | 32.0 | 192 | 1.5165 | 0.2857 |
| 1.5437 | 33.0 | 198 | 1.5150 | 0.2857 |
| 1.5369 | 34.0 | 204 | 1.5137 | 0.2857 |
| 1.5371 | 35.0 | 210 | 1.5125 | 0.2857 |
| 1.5371 | 36.0 | 216 | 1.5116 | 0.2857 |
| 1.5229 | 37.0 | 222 | 1.5109 | 0.2857 |
| 1.5229 | 38.0 | 228 | 1.5102 | 0.2857 |
| 1.5623 | 39.0 | 234 | 1.5097 | 0.2857 |
| 1.5343 | 40.0 | 240 | 1.5094 | 0.2857 |
| 1.5343 | 41.0 | 246 | 1.5093 | 0.2857 |
| 1.5211 | 42.0 | 252 | 1.5092 | 0.2857 |
| 1.5211 | 43.0 | 258 | 1.5092 | 0.2857 |
| 1.5618 | 44.0 | 264 | 1.5092 | 0.2857 |
| 1.5309 | 45.0 | 270 | 1.5092 | 0.2857 |
| 1.5309 | 46.0 | 276 | 1.5092 | 0.2857 |
| 1.5362 | 47.0 | 282 | 1.5092 | 0.2857 |
| 1.5362 | 48.0 | 288 | 1.5092 | 0.2857 |
| 1.5728 | 49.0 | 294 | 1.5092 | 0.2857 |
| 1.5244 | 50.0 | 300 | 1.5092 | 0.2857 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
romit1/albert
|
romit1
| 2023-11-13T15:08:59Z | 6 | 0 |
transformers
|
[
"transformers",
"safetensors",
"albert",
"multiple-choice",
"generated_from_trainer",
"base_model:albert/albert-base-v2",
"base_model:finetune:albert/albert-base-v2",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
multiple-choice
| 2023-11-13T15:08:24Z |
---
license: apache-2.0
base_model: albert-base-v2
tags:
- generated_from_trainer
model-index:
- name: albert
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. -->
# albert
This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0844
## 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: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 26 | 0.9914 |
| No log | 2.0 | 52 | 1.1247 |
| No log | 3.0 | 78 | 1.0844 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
hkivancoral/hushem_1x_deit_tiny_sgd_0001_fold3
|
hkivancoral
| 2023-11-13T15:08:53Z | 9 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:facebook/deit-tiny-patch16-224",
"base_model:finetune:facebook/deit-tiny-patch16-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-11-13T15:07:34Z |
---
license: apache-2.0
base_model: facebook/deit-tiny-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: hushem_1x_deit_tiny_sgd_0001_fold3
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.27906976744186046
---
<!-- 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. -->
# hushem_1x_deit_tiny_sgd_0001_fold3
This model is a fine-tuned version of [facebook/deit-tiny-patch16-224](https://huggingface.co/facebook/deit-tiny-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5555
- Accuracy: 0.2791
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 6 | 1.6995 | 0.2791 |
| 1.7242 | 2.0 | 12 | 1.6902 | 0.2791 |
| 1.7242 | 3.0 | 18 | 1.6819 | 0.2791 |
| 1.6909 | 4.0 | 24 | 1.6741 | 0.2791 |
| 1.6461 | 5.0 | 30 | 1.6664 | 0.2791 |
| 1.6461 | 6.0 | 36 | 1.6587 | 0.2791 |
| 1.6466 | 7.0 | 42 | 1.6518 | 0.2791 |
| 1.6466 | 8.0 | 48 | 1.6448 | 0.2791 |
| 1.6495 | 9.0 | 54 | 1.6384 | 0.2791 |
| 1.6495 | 10.0 | 60 | 1.6323 | 0.2791 |
| 1.6495 | 11.0 | 66 | 1.6267 | 0.2791 |
| 1.6244 | 12.0 | 72 | 1.6213 | 0.2791 |
| 1.6244 | 13.0 | 78 | 1.6166 | 0.2791 |
| 1.593 | 14.0 | 84 | 1.6117 | 0.2791 |
| 1.6183 | 15.0 | 90 | 1.6071 | 0.2791 |
| 1.6183 | 16.0 | 96 | 1.6026 | 0.2791 |
| 1.6105 | 17.0 | 102 | 1.5985 | 0.2558 |
| 1.6105 | 18.0 | 108 | 1.5946 | 0.2558 |
| 1.5599 | 19.0 | 114 | 1.5912 | 0.2558 |
| 1.5756 | 20.0 | 120 | 1.5878 | 0.2558 |
| 1.5756 | 21.0 | 126 | 1.5845 | 0.2558 |
| 1.5692 | 22.0 | 132 | 1.5817 | 0.2558 |
| 1.5692 | 23.0 | 138 | 1.5789 | 0.2558 |
| 1.544 | 24.0 | 144 | 1.5763 | 0.2558 |
| 1.548 | 25.0 | 150 | 1.5738 | 0.2558 |
| 1.548 | 26.0 | 156 | 1.5716 | 0.2791 |
| 1.549 | 27.0 | 162 | 1.5695 | 0.2791 |
| 1.549 | 28.0 | 168 | 1.5675 | 0.2791 |
| 1.5593 | 29.0 | 174 | 1.5658 | 0.2791 |
| 1.528 | 30.0 | 180 | 1.5641 | 0.2791 |
| 1.528 | 31.0 | 186 | 1.5627 | 0.2791 |
| 1.5394 | 32.0 | 192 | 1.5615 | 0.2791 |
| 1.5394 | 33.0 | 198 | 1.5603 | 0.2791 |
| 1.4822 | 34.0 | 204 | 1.5592 | 0.2791 |
| 1.5618 | 35.0 | 210 | 1.5583 | 0.2791 |
| 1.5618 | 36.0 | 216 | 1.5575 | 0.2791 |
| 1.5279 | 37.0 | 222 | 1.5568 | 0.2791 |
| 1.5279 | 38.0 | 228 | 1.5563 | 0.2791 |
| 1.5233 | 39.0 | 234 | 1.5559 | 0.2791 |
| 1.5255 | 40.0 | 240 | 1.5556 | 0.2791 |
| 1.5255 | 41.0 | 246 | 1.5555 | 0.2791 |
| 1.5147 | 42.0 | 252 | 1.5555 | 0.2791 |
| 1.5147 | 43.0 | 258 | 1.5555 | 0.2791 |
| 1.5048 | 44.0 | 264 | 1.5555 | 0.2791 |
| 1.5464 | 45.0 | 270 | 1.5555 | 0.2791 |
| 1.5464 | 46.0 | 276 | 1.5555 | 0.2791 |
| 1.5243 | 47.0 | 282 | 1.5555 | 0.2791 |
| 1.5243 | 48.0 | 288 | 1.5555 | 0.2791 |
| 1.5049 | 49.0 | 294 | 1.5555 | 0.2791 |
| 1.5545 | 50.0 | 300 | 1.5555 | 0.2791 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
ivankap/vvs-jewelry-hf
|
ivankap
| 2023-11-13T15:07:38Z | 2 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-11-13T15:03:36Z |
---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
---
### VVS_Jewelry_HF Dreambooth model trained by ivankap with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook
Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb)
Sample pictures of this concept:
|
ighina/roberta_topseg_contrastive
|
ighina
| 2023-11-13T15:06:35Z | 12 | 2 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"roberta",
"feature-extraction",
"sentence-similarity",
"transformers",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2023-11-13T14:07:31Z |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
model = AutoModel.from_pretrained('{MODEL_NAME}')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 2161 with parameters:
```
{'batch_size': 64, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.ContrastiveLoss.ContrastiveLoss` with parameters:
```
{'distance_metric': 'SiameseDistanceMetric.COSINE_DISTANCE', 'margin': 0.5, 'size_average': True}
```
Parameters of the fit()-Method:
```
{
"epochs": 10,
"evaluation_steps": 0,
"evaluator": "sentence_transformers.evaluation.BinaryClassificationEvaluator.BinaryClassificationEvaluator",
"max_grad_norm": 1,
"optimizer_class": "<class 'transformers.optimization.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 10000,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: RobertaModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information -->
|
hkivancoral/hushem_1x_deit_tiny_sgd_001_fold5
|
hkivancoral
| 2023-11-13T15:01:44Z | 8 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:facebook/deit-tiny-patch16-224",
"base_model:finetune:facebook/deit-tiny-patch16-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-11-13T15:00:22Z |
---
license: apache-2.0
base_model: facebook/deit-tiny-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: hushem_1x_deit_tiny_sgd_001_fold5
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.36585365853658536
---
<!-- 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. -->
# hushem_1x_deit_tiny_sgd_001_fold5
This model is a fine-tuned version of [facebook/deit-tiny-patch16-224](https://huggingface.co/facebook/deit-tiny-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2764
- Accuracy: 0.3659
## 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: 0.001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 6 | 1.6481 | 0.2439 |
| 1.6453 | 2.0 | 12 | 1.5595 | 0.2439 |
| 1.6453 | 3.0 | 18 | 1.4979 | 0.2683 |
| 1.5144 | 4.0 | 24 | 1.4546 | 0.2683 |
| 1.4538 | 5.0 | 30 | 1.4262 | 0.2927 |
| 1.4538 | 6.0 | 36 | 1.4074 | 0.2683 |
| 1.3994 | 7.0 | 42 | 1.3954 | 0.2683 |
| 1.3994 | 8.0 | 48 | 1.3847 | 0.2683 |
| 1.3731 | 9.0 | 54 | 1.3749 | 0.2683 |
| 1.3564 | 10.0 | 60 | 1.3671 | 0.2927 |
| 1.3564 | 11.0 | 66 | 1.3612 | 0.3415 |
| 1.3402 | 12.0 | 72 | 1.3541 | 0.3659 |
| 1.3402 | 13.0 | 78 | 1.3472 | 0.3171 |
| 1.2912 | 14.0 | 84 | 1.3416 | 0.3171 |
| 1.304 | 15.0 | 90 | 1.3360 | 0.2927 |
| 1.304 | 16.0 | 96 | 1.3318 | 0.3171 |
| 1.267 | 17.0 | 102 | 1.3278 | 0.3171 |
| 1.267 | 18.0 | 108 | 1.3225 | 0.3171 |
| 1.2687 | 19.0 | 114 | 1.3187 | 0.3415 |
| 1.2447 | 20.0 | 120 | 1.3147 | 0.3415 |
| 1.2447 | 21.0 | 126 | 1.3131 | 0.3171 |
| 1.2262 | 22.0 | 132 | 1.3086 | 0.3171 |
| 1.2262 | 23.0 | 138 | 1.3054 | 0.3171 |
| 1.2132 | 24.0 | 144 | 1.3031 | 0.3171 |
| 1.2231 | 25.0 | 150 | 1.3007 | 0.3171 |
| 1.2231 | 26.0 | 156 | 1.2974 | 0.3171 |
| 1.1895 | 27.0 | 162 | 1.2937 | 0.3171 |
| 1.1895 | 28.0 | 168 | 1.2903 | 0.3415 |
| 1.2062 | 29.0 | 174 | 1.2886 | 0.3415 |
| 1.1907 | 30.0 | 180 | 1.2864 | 0.3415 |
| 1.1907 | 31.0 | 186 | 1.2852 | 0.3415 |
| 1.1836 | 32.0 | 192 | 1.2832 | 0.3415 |
| 1.1836 | 33.0 | 198 | 1.2819 | 0.3415 |
| 1.1632 | 34.0 | 204 | 1.2802 | 0.3415 |
| 1.1553 | 35.0 | 210 | 1.2792 | 0.3659 |
| 1.1553 | 36.0 | 216 | 1.2784 | 0.3659 |
| 1.1703 | 37.0 | 222 | 1.2777 | 0.3659 |
| 1.1703 | 38.0 | 228 | 1.2771 | 0.3659 |
| 1.1625 | 39.0 | 234 | 1.2768 | 0.3659 |
| 1.1523 | 40.0 | 240 | 1.2765 | 0.3659 |
| 1.1523 | 41.0 | 246 | 1.2764 | 0.3659 |
| 1.1617 | 42.0 | 252 | 1.2764 | 0.3659 |
| 1.1617 | 43.0 | 258 | 1.2764 | 0.3659 |
| 1.1427 | 44.0 | 264 | 1.2764 | 0.3659 |
| 1.1631 | 45.0 | 270 | 1.2764 | 0.3659 |
| 1.1631 | 46.0 | 276 | 1.2764 | 0.3659 |
| 1.162 | 47.0 | 282 | 1.2764 | 0.3659 |
| 1.162 | 48.0 | 288 | 1.2764 | 0.3659 |
| 1.1542 | 49.0 | 294 | 1.2764 | 0.3659 |
| 1.1633 | 50.0 | 300 | 1.2764 | 0.3659 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
mlux/llama-2-7b-finetuned-guanaco-llama2-1k_tvb4
|
mlux
| 2023-11-13T15:01:43Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"conversational",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-11-13T14:44:05Z |
Step Training Loss
25 1.553200
50 0.232500
**Test**
[INST] What does the abbreviation TVB stand for in German? [/INST] TVB stands for Tourismusverband which is in English Destination Marketing Association. TVBs are organizations that are dealing with the promotion and marketing of tourist destinations, regions or even cities. They provide information about attractions, accommodation, events and other tourist offers and work to promote tourism in their region.
|
Hwilner/zephyr-support-chatbot
|
Hwilner
| 2023-11-13T15:01:08Z | 0 | 0 | null |
[
"tensorboard",
"safetensors",
"generated_from_trainer",
"base_model:TheBloke/zephyr-7B-alpha-GPTQ",
"base_model:finetune:TheBloke/zephyr-7B-alpha-GPTQ",
"license:mit",
"region:us"
] | null | 2023-11-13T14:20:52Z |
---
license: mit
base_model: TheBloke/zephyr-7B-alpha-GPTQ
tags:
- generated_from_trainer
model-index:
- name: zephyr-support-chatbot
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. -->
# zephyr-support-chatbot
This model is a fine-tuned version of [TheBloke/zephyr-7B-alpha-GPTQ](https://huggingface.co/TheBloke/zephyr-7B-alpha-GPTQ) 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: 0.0002
- 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: cosine
- training_steps: 250
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
hkivancoral/hushem_1x_deit_tiny_sgd_001_fold4
|
hkivancoral
| 2023-11-13T15:00:16Z | 8 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:facebook/deit-tiny-patch16-224",
"base_model:finetune:facebook/deit-tiny-patch16-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-11-13T14:58:53Z |
---
license: apache-2.0
base_model: facebook/deit-tiny-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: hushem_1x_deit_tiny_sgd_001_fold4
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.4523809523809524
---
<!-- 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. -->
# hushem_1x_deit_tiny_sgd_001_fold4
This model is a fine-tuned version of [facebook/deit-tiny-patch16-224](https://huggingface.co/facebook/deit-tiny-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2335
- Accuracy: 0.4524
## 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: 0.001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 6 | 1.5918 | 0.2857 |
| 1.6404 | 2.0 | 12 | 1.5188 | 0.2857 |
| 1.6404 | 3.0 | 18 | 1.4665 | 0.2857 |
| 1.5241 | 4.0 | 24 | 1.4299 | 0.3333 |
| 1.4755 | 5.0 | 30 | 1.4106 | 0.3571 |
| 1.4755 | 6.0 | 36 | 1.3938 | 0.3095 |
| 1.4186 | 7.0 | 42 | 1.3803 | 0.2857 |
| 1.4186 | 8.0 | 48 | 1.3677 | 0.3810 |
| 1.3819 | 9.0 | 54 | 1.3558 | 0.3810 |
| 1.3541 | 10.0 | 60 | 1.3456 | 0.3810 |
| 1.3541 | 11.0 | 66 | 1.3370 | 0.3810 |
| 1.3363 | 12.0 | 72 | 1.3284 | 0.3810 |
| 1.3363 | 13.0 | 78 | 1.3193 | 0.3571 |
| 1.3168 | 14.0 | 84 | 1.3103 | 0.4048 |
| 1.2875 | 15.0 | 90 | 1.3032 | 0.4048 |
| 1.2875 | 16.0 | 96 | 1.2966 | 0.4048 |
| 1.2638 | 17.0 | 102 | 1.2902 | 0.4048 |
| 1.2638 | 18.0 | 108 | 1.2846 | 0.4048 |
| 1.2758 | 19.0 | 114 | 1.2805 | 0.4048 |
| 1.2611 | 20.0 | 120 | 1.2763 | 0.4048 |
| 1.2611 | 21.0 | 126 | 1.2724 | 0.4048 |
| 1.2411 | 22.0 | 132 | 1.2693 | 0.4048 |
| 1.2411 | 23.0 | 138 | 1.2666 | 0.4048 |
| 1.2357 | 24.0 | 144 | 1.2628 | 0.4048 |
| 1.231 | 25.0 | 150 | 1.2590 | 0.4048 |
| 1.231 | 26.0 | 156 | 1.2555 | 0.4048 |
| 1.2026 | 27.0 | 162 | 1.2531 | 0.4048 |
| 1.2026 | 28.0 | 168 | 1.2508 | 0.4048 |
| 1.2253 | 29.0 | 174 | 1.2482 | 0.4048 |
| 1.1949 | 30.0 | 180 | 1.2457 | 0.4048 |
| 1.1949 | 31.0 | 186 | 1.2436 | 0.4286 |
| 1.2025 | 32.0 | 192 | 1.2420 | 0.4286 |
| 1.2025 | 33.0 | 198 | 1.2406 | 0.4524 |
| 1.1709 | 34.0 | 204 | 1.2390 | 0.4524 |
| 1.1908 | 35.0 | 210 | 1.2376 | 0.4524 |
| 1.1908 | 36.0 | 216 | 1.2365 | 0.4524 |
| 1.1663 | 37.0 | 222 | 1.2358 | 0.4524 |
| 1.1663 | 38.0 | 228 | 1.2349 | 0.4524 |
| 1.1875 | 39.0 | 234 | 1.2342 | 0.4524 |
| 1.1799 | 40.0 | 240 | 1.2338 | 0.4524 |
| 1.1799 | 41.0 | 246 | 1.2336 | 0.4524 |
| 1.1658 | 42.0 | 252 | 1.2335 | 0.4524 |
| 1.1658 | 43.0 | 258 | 1.2335 | 0.4524 |
| 1.1875 | 44.0 | 264 | 1.2335 | 0.4524 |
| 1.1627 | 45.0 | 270 | 1.2335 | 0.4524 |
| 1.1627 | 46.0 | 276 | 1.2335 | 0.4524 |
| 1.1689 | 47.0 | 282 | 1.2335 | 0.4524 |
| 1.1689 | 48.0 | 288 | 1.2335 | 0.4524 |
| 1.1911 | 49.0 | 294 | 1.2335 | 0.4524 |
| 1.1557 | 50.0 | 300 | 1.2335 | 0.4524 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
hkivancoral/hushem_1x_deit_tiny_sgd_001_fold3
|
hkivancoral
| 2023-11-13T14:58:46Z | 8 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:facebook/deit-tiny-patch16-224",
"base_model:finetune:facebook/deit-tiny-patch16-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-11-13T14:57:25Z |
---
license: apache-2.0
base_model: facebook/deit-tiny-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: hushem_1x_deit_tiny_sgd_001_fold3
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.3488372093023256
---
<!-- 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. -->
# hushem_1x_deit_tiny_sgd_001_fold3
This model is a fine-tuned version of [facebook/deit-tiny-patch16-224](https://huggingface.co/facebook/deit-tiny-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2767
- Accuracy: 0.3488
## 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: 0.001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 6 | 1.6231 | 0.2791 |
| 1.6502 | 2.0 | 12 | 1.5615 | 0.2791 |
| 1.6502 | 3.0 | 18 | 1.5208 | 0.2558 |
| 1.5138 | 4.0 | 24 | 1.4935 | 0.2093 |
| 1.441 | 5.0 | 30 | 1.4720 | 0.2093 |
| 1.441 | 6.0 | 36 | 1.4541 | 0.2326 |
| 1.3942 | 7.0 | 42 | 1.4402 | 0.3023 |
| 1.3942 | 8.0 | 48 | 1.4271 | 0.3023 |
| 1.3895 | 9.0 | 54 | 1.4159 | 0.2791 |
| 1.3382 | 10.0 | 60 | 1.4069 | 0.2791 |
| 1.3382 | 11.0 | 66 | 1.3983 | 0.2558 |
| 1.3326 | 12.0 | 72 | 1.3893 | 0.2558 |
| 1.3326 | 13.0 | 78 | 1.3800 | 0.2558 |
| 1.3102 | 14.0 | 84 | 1.3707 | 0.2558 |
| 1.3163 | 15.0 | 90 | 1.3619 | 0.2791 |
| 1.3163 | 16.0 | 96 | 1.3528 | 0.2791 |
| 1.295 | 17.0 | 102 | 1.3463 | 0.2791 |
| 1.295 | 18.0 | 108 | 1.3391 | 0.2791 |
| 1.2552 | 19.0 | 114 | 1.3325 | 0.3023 |
| 1.2682 | 20.0 | 120 | 1.3269 | 0.3023 |
| 1.2682 | 21.0 | 126 | 1.3221 | 0.3256 |
| 1.2578 | 22.0 | 132 | 1.3173 | 0.3488 |
| 1.2578 | 23.0 | 138 | 1.3126 | 0.3488 |
| 1.2124 | 24.0 | 144 | 1.3087 | 0.3488 |
| 1.2284 | 25.0 | 150 | 1.3049 | 0.3488 |
| 1.2284 | 26.0 | 156 | 1.3017 | 0.3488 |
| 1.2178 | 27.0 | 162 | 1.2982 | 0.3488 |
| 1.2178 | 28.0 | 168 | 1.2955 | 0.3488 |
| 1.2019 | 29.0 | 174 | 1.2931 | 0.3488 |
| 1.2029 | 30.0 | 180 | 1.2906 | 0.3488 |
| 1.2029 | 31.0 | 186 | 1.2886 | 0.3488 |
| 1.1935 | 32.0 | 192 | 1.2863 | 0.3488 |
| 1.1935 | 33.0 | 198 | 1.2843 | 0.3488 |
| 1.164 | 34.0 | 204 | 1.2826 | 0.3488 |
| 1.1999 | 35.0 | 210 | 1.2814 | 0.3488 |
| 1.1999 | 36.0 | 216 | 1.2801 | 0.3488 |
| 1.1813 | 37.0 | 222 | 1.2790 | 0.3488 |
| 1.1813 | 38.0 | 228 | 1.2781 | 0.3488 |
| 1.1753 | 39.0 | 234 | 1.2775 | 0.3488 |
| 1.1877 | 40.0 | 240 | 1.2770 | 0.3488 |
| 1.1877 | 41.0 | 246 | 1.2768 | 0.3488 |
| 1.1774 | 42.0 | 252 | 1.2767 | 0.3488 |
| 1.1774 | 43.0 | 258 | 1.2767 | 0.3488 |
| 1.1704 | 44.0 | 264 | 1.2767 | 0.3488 |
| 1.1843 | 45.0 | 270 | 1.2767 | 0.3488 |
| 1.1843 | 46.0 | 276 | 1.2767 | 0.3488 |
| 1.1726 | 47.0 | 282 | 1.2767 | 0.3488 |
| 1.1726 | 48.0 | 288 | 1.2767 | 0.3488 |
| 1.1541 | 49.0 | 294 | 1.2767 | 0.3488 |
| 1.1928 | 50.0 | 300 | 1.2767 | 0.3488 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
hkivancoral/hushem_1x_deit_tiny_sgd_001_fold2
|
hkivancoral
| 2023-11-13T14:57:18Z | 9 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:facebook/deit-tiny-patch16-224",
"base_model:finetune:facebook/deit-tiny-patch16-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-11-13T14:55:57Z |
---
license: apache-2.0
base_model: facebook/deit-tiny-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: hushem_1x_deit_tiny_sgd_001_fold2
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.17777777777777778
---
<!-- 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. -->
# hushem_1x_deit_tiny_sgd_001_fold2
This model is a fine-tuned version of [facebook/deit-tiny-patch16-224](https://huggingface.co/facebook/deit-tiny-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4913
- Accuracy: 0.1778
## 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: 0.001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 6 | 1.6461 | 0.2222 |
| 1.647 | 2.0 | 12 | 1.5827 | 0.2 |
| 1.647 | 3.0 | 18 | 1.5400 | 0.2 |
| 1.5111 | 4.0 | 24 | 1.5101 | 0.2 |
| 1.4472 | 5.0 | 30 | 1.4855 | 0.1778 |
| 1.4472 | 6.0 | 36 | 1.4711 | 0.1778 |
| 1.3765 | 7.0 | 42 | 1.4618 | 0.2 |
| 1.3765 | 8.0 | 48 | 1.4555 | 0.2 |
| 1.3363 | 9.0 | 54 | 1.4523 | 0.2222 |
| 1.3131 | 10.0 | 60 | 1.4505 | 0.2 |
| 1.3131 | 11.0 | 66 | 1.4495 | 0.2 |
| 1.2743 | 12.0 | 72 | 1.4504 | 0.2 |
| 1.2743 | 13.0 | 78 | 1.4505 | 0.2 |
| 1.2923 | 14.0 | 84 | 1.4516 | 0.2 |
| 1.2475 | 15.0 | 90 | 1.4529 | 0.2 |
| 1.2475 | 16.0 | 96 | 1.4558 | 0.2 |
| 1.2052 | 17.0 | 102 | 1.4591 | 0.1778 |
| 1.2052 | 18.0 | 108 | 1.4603 | 0.1778 |
| 1.2375 | 19.0 | 114 | 1.4628 | 0.1778 |
| 1.1665 | 20.0 | 120 | 1.4654 | 0.1778 |
| 1.1665 | 21.0 | 126 | 1.4668 | 0.1778 |
| 1.1508 | 22.0 | 132 | 1.4681 | 0.1778 |
| 1.1508 | 23.0 | 138 | 1.4710 | 0.1778 |
| 1.1615 | 24.0 | 144 | 1.4735 | 0.1778 |
| 1.1372 | 25.0 | 150 | 1.4742 | 0.1778 |
| 1.1372 | 26.0 | 156 | 1.4775 | 0.1778 |
| 1.1389 | 27.0 | 162 | 1.4787 | 0.1778 |
| 1.1389 | 28.0 | 168 | 1.4813 | 0.1778 |
| 1.1191 | 29.0 | 174 | 1.4821 | 0.1778 |
| 1.106 | 30.0 | 180 | 1.4844 | 0.1778 |
| 1.106 | 31.0 | 186 | 1.4853 | 0.1778 |
| 1.1156 | 32.0 | 192 | 1.4867 | 0.1778 |
| 1.1156 | 33.0 | 198 | 1.4872 | 0.1778 |
| 1.127 | 34.0 | 204 | 1.4879 | 0.1778 |
| 1.1055 | 35.0 | 210 | 1.4887 | 0.1778 |
| 1.1055 | 36.0 | 216 | 1.4895 | 0.1778 |
| 1.089 | 37.0 | 222 | 1.4902 | 0.1778 |
| 1.089 | 38.0 | 228 | 1.4907 | 0.1778 |
| 1.0605 | 39.0 | 234 | 1.4911 | 0.1778 |
| 1.0925 | 40.0 | 240 | 1.4913 | 0.1778 |
| 1.0925 | 41.0 | 246 | 1.4913 | 0.1778 |
| 1.1025 | 42.0 | 252 | 1.4913 | 0.1778 |
| 1.1025 | 43.0 | 258 | 1.4913 | 0.1778 |
| 1.1085 | 44.0 | 264 | 1.4913 | 0.1778 |
| 1.0909 | 45.0 | 270 | 1.4913 | 0.1778 |
| 1.0909 | 46.0 | 276 | 1.4913 | 0.1778 |
| 1.0889 | 47.0 | 282 | 1.4913 | 0.1778 |
| 1.0889 | 48.0 | 288 | 1.4913 | 0.1778 |
| 1.0611 | 49.0 | 294 | 1.4913 | 0.1778 |
| 1.1045 | 50.0 | 300 | 1.4913 | 0.1778 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
hkivancoral/hushem_1x_deit_tiny_sgd_001_fold1
|
hkivancoral
| 2023-11-13T14:55:50Z | 25 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:facebook/deit-tiny-patch16-224",
"base_model:finetune:facebook/deit-tiny-patch16-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-11-13T14:54:29Z |
---
license: apache-2.0
base_model: facebook/deit-tiny-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: hushem_1x_deit_tiny_sgd_001_fold1
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.26666666666666666
---
<!-- 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. -->
# hushem_1x_deit_tiny_sgd_001_fold1
This model is a fine-tuned version of [facebook/deit-tiny-patch16-224](https://huggingface.co/facebook/deit-tiny-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3946
- Accuracy: 0.2667
## 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: 0.001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 6 | 1.6081 | 0.2889 |
| 1.6517 | 2.0 | 12 | 1.5532 | 0.3333 |
| 1.6517 | 3.0 | 18 | 1.5183 | 0.3111 |
| 1.5073 | 4.0 | 24 | 1.4941 | 0.2 |
| 1.4569 | 5.0 | 30 | 1.4762 | 0.1333 |
| 1.4569 | 6.0 | 36 | 1.4655 | 0.1333 |
| 1.377 | 7.0 | 42 | 1.4570 | 0.1333 |
| 1.377 | 8.0 | 48 | 1.4508 | 0.1333 |
| 1.3495 | 9.0 | 54 | 1.4443 | 0.1333 |
| 1.3234 | 10.0 | 60 | 1.4390 | 0.1333 |
| 1.3234 | 11.0 | 66 | 1.4339 | 0.1778 |
| 1.2813 | 12.0 | 72 | 1.4301 | 0.1778 |
| 1.2813 | 13.0 | 78 | 1.4257 | 0.2 |
| 1.3124 | 14.0 | 84 | 1.4223 | 0.2 |
| 1.2528 | 15.0 | 90 | 1.4195 | 0.2 |
| 1.2528 | 16.0 | 96 | 1.4170 | 0.2222 |
| 1.2252 | 17.0 | 102 | 1.4152 | 0.2 |
| 1.2252 | 18.0 | 108 | 1.4125 | 0.2222 |
| 1.2441 | 19.0 | 114 | 1.4108 | 0.2 |
| 1.1872 | 20.0 | 120 | 1.4088 | 0.2 |
| 1.1872 | 21.0 | 126 | 1.4068 | 0.2 |
| 1.1818 | 22.0 | 132 | 1.4052 | 0.2222 |
| 1.1818 | 23.0 | 138 | 1.4041 | 0.2 |
| 1.1835 | 24.0 | 144 | 1.4032 | 0.2222 |
| 1.1551 | 25.0 | 150 | 1.4021 | 0.2222 |
| 1.1551 | 26.0 | 156 | 1.4013 | 0.2222 |
| 1.1564 | 27.0 | 162 | 1.4008 | 0.2 |
| 1.1564 | 28.0 | 168 | 1.3999 | 0.2222 |
| 1.1662 | 29.0 | 174 | 1.3989 | 0.2222 |
| 1.116 | 30.0 | 180 | 1.3985 | 0.2222 |
| 1.116 | 31.0 | 186 | 1.3976 | 0.2444 |
| 1.153 | 32.0 | 192 | 1.3972 | 0.2444 |
| 1.153 | 33.0 | 198 | 1.3964 | 0.2444 |
| 1.1437 | 34.0 | 204 | 1.3958 | 0.2444 |
| 1.1259 | 35.0 | 210 | 1.3954 | 0.2444 |
| 1.1259 | 36.0 | 216 | 1.3954 | 0.2667 |
| 1.1125 | 37.0 | 222 | 1.3951 | 0.2667 |
| 1.1125 | 38.0 | 228 | 1.3951 | 0.2667 |
| 1.0816 | 39.0 | 234 | 1.3948 | 0.2667 |
| 1.1207 | 40.0 | 240 | 1.3948 | 0.2667 |
| 1.1207 | 41.0 | 246 | 1.3947 | 0.2667 |
| 1.1291 | 42.0 | 252 | 1.3946 | 0.2667 |
| 1.1291 | 43.0 | 258 | 1.3946 | 0.2667 |
| 1.1338 | 44.0 | 264 | 1.3946 | 0.2667 |
| 1.1093 | 45.0 | 270 | 1.3946 | 0.2667 |
| 1.1093 | 46.0 | 276 | 1.3946 | 0.2667 |
| 1.1123 | 47.0 | 282 | 1.3946 | 0.2667 |
| 1.1123 | 48.0 | 288 | 1.3946 | 0.2667 |
| 1.096 | 49.0 | 294 | 1.3946 | 0.2667 |
| 1.1328 | 50.0 | 300 | 1.3946 | 0.2667 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
joshuarwanda/wav2vec2-large-xls-r-300m-swahili-colab
|
joshuarwanda
| 2023-11-13T14:51:21Z | 14 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice_13_0",
"base_model:facebook/wav2vec2-xls-r-300m",
"base_model:finetune:facebook/wav2vec2-xls-r-300m",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-11-10T18:34:36Z |
---
license: apache-2.0
base_model: facebook/wav2vec2-xls-r-300m
tags:
- generated_from_trainer
datasets:
- common_voice_13_0
model-index:
- name: wav2vec2-large-xls-r-300m-swahili-colab
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-swahili-colab
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice_13_0 dataset.
It achieves the following results on the evaluation set:
- eval_loss: inf
- eval_wer: 0.5730
- eval_runtime: 1031.5347
- eval_samples_per_second: 10.926
- eval_steps_per_second: 1.366
- epoch: 0.37
- step: 400
## 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: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- 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
- num_epochs: 30
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
franconoronha/finetuning-sentiment-model-3000-samples
|
franconoronha
| 2023-11-13T14:48:26Z | 6 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:imdb",
"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
| 2023-11-13T14:17:06Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- imdb
metrics:
- accuracy
- f1
model-index:
- name: finetuning-sentiment-model-3000-samples
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: imdb
type: imdb
config: plain_text
split: test
args: plain_text
metrics:
- name: Accuracy
type: accuracy
value: 0.9056666666666666
- name: F1
type: f1
value: 0.9060112919295914
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuning-sentiment-model-3000-samples
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2619
- Accuracy: 0.9057
- F1: 0.9060
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
hkivancoral/hushem_1x_deit_tiny_adamax_00001_fold3
|
hkivancoral
| 2023-11-13T14:46:53Z | 9 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:facebook/deit-tiny-patch16-224",
"base_model:finetune:facebook/deit-tiny-patch16-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-11-13T14:45:19Z |
---
license: apache-2.0
base_model: facebook/deit-tiny-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: hushem_1x_deit_tiny_adamax_00001_fold3
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.5581395348837209
---
<!-- 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. -->
# hushem_1x_deit_tiny_adamax_00001_fold3
This model is a fine-tuned version of [facebook/deit-tiny-patch16-224](https://huggingface.co/facebook/deit-tiny-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8253
- Accuracy: 0.5581
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 6 | 1.4425 | 0.2791 |
| 1.416 | 2.0 | 12 | 1.3728 | 0.3023 |
| 1.416 | 3.0 | 18 | 1.3124 | 0.3488 |
| 1.2388 | 4.0 | 24 | 1.2509 | 0.3721 |
| 1.1051 | 5.0 | 30 | 1.1962 | 0.3488 |
| 1.1051 | 6.0 | 36 | 1.1517 | 0.3721 |
| 0.9682 | 7.0 | 42 | 1.1212 | 0.3721 |
| 0.9682 | 8.0 | 48 | 1.0990 | 0.4186 |
| 0.8769 | 9.0 | 54 | 1.0709 | 0.4884 |
| 0.7643 | 10.0 | 60 | 1.0587 | 0.5116 |
| 0.7643 | 11.0 | 66 | 1.0451 | 0.4884 |
| 0.6717 | 12.0 | 72 | 1.0399 | 0.5581 |
| 0.6717 | 13.0 | 78 | 1.0224 | 0.5349 |
| 0.5988 | 14.0 | 84 | 1.0021 | 0.4884 |
| 0.5291 | 15.0 | 90 | 0.9852 | 0.4884 |
| 0.5291 | 16.0 | 96 | 0.9774 | 0.5116 |
| 0.4581 | 17.0 | 102 | 0.9701 | 0.5116 |
| 0.4581 | 18.0 | 108 | 0.9598 | 0.5116 |
| 0.3895 | 19.0 | 114 | 0.9410 | 0.5814 |
| 0.3415 | 20.0 | 120 | 0.9223 | 0.5581 |
| 0.3415 | 21.0 | 126 | 0.9172 | 0.5349 |
| 0.3044 | 22.0 | 132 | 0.9106 | 0.5349 |
| 0.3044 | 23.0 | 138 | 0.9037 | 0.5581 |
| 0.2632 | 24.0 | 144 | 0.8935 | 0.5581 |
| 0.2425 | 25.0 | 150 | 0.8847 | 0.5814 |
| 0.2425 | 26.0 | 156 | 0.8721 | 0.5581 |
| 0.2102 | 27.0 | 162 | 0.8625 | 0.5581 |
| 0.2102 | 28.0 | 168 | 0.8546 | 0.5581 |
| 0.189 | 29.0 | 174 | 0.8540 | 0.5814 |
| 0.1637 | 30.0 | 180 | 0.8496 | 0.6047 |
| 0.1637 | 31.0 | 186 | 0.8464 | 0.6047 |
| 0.1512 | 32.0 | 192 | 0.8420 | 0.5581 |
| 0.1512 | 33.0 | 198 | 0.8380 | 0.5581 |
| 0.1374 | 34.0 | 204 | 0.8346 | 0.5581 |
| 0.1287 | 35.0 | 210 | 0.8327 | 0.5581 |
| 0.1287 | 36.0 | 216 | 0.8290 | 0.5581 |
| 0.124 | 37.0 | 222 | 0.8276 | 0.5581 |
| 0.124 | 38.0 | 228 | 0.8271 | 0.5581 |
| 0.1186 | 39.0 | 234 | 0.8265 | 0.5581 |
| 0.1159 | 40.0 | 240 | 0.8255 | 0.5581 |
| 0.1159 | 41.0 | 246 | 0.8253 | 0.5581 |
| 0.1139 | 42.0 | 252 | 0.8253 | 0.5581 |
| 0.1139 | 43.0 | 258 | 0.8253 | 0.5581 |
| 0.1142 | 44.0 | 264 | 0.8253 | 0.5581 |
| 0.1107 | 45.0 | 270 | 0.8253 | 0.5581 |
| 0.1107 | 46.0 | 276 | 0.8253 | 0.5581 |
| 0.1118 | 47.0 | 282 | 0.8253 | 0.5581 |
| 0.1118 | 48.0 | 288 | 0.8253 | 0.5581 |
| 0.1159 | 49.0 | 294 | 0.8253 | 0.5581 |
| 0.1095 | 50.0 | 300 | 0.8253 | 0.5581 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
haris001/codellamaSP2
|
haris001
| 2023-11-13T14:46:32Z | 2 | 0 |
peft
|
[
"peft",
"safetensors",
"region:us"
] | null | 2023-11-13T14:46:31Z |
---
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 0.4.0
|
hkivancoral/hushem_1x_deit_tiny_adamax_00001_fold1
|
hkivancoral
| 2023-11-13T14:43:30Z | 8 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:facebook/deit-tiny-patch16-224",
"base_model:finetune:facebook/deit-tiny-patch16-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-11-13T14:41:50Z |
---
license: apache-2.0
base_model: facebook/deit-tiny-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: hushem_1x_deit_tiny_adamax_00001_fold1
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.4222222222222222
---
<!-- 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. -->
# hushem_1x_deit_tiny_adamax_00001_fold1
This model is a fine-tuned version of [facebook/deit-tiny-patch16-224](https://huggingface.co/facebook/deit-tiny-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1341
- Accuracy: 0.4222
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 6 | 1.4260 | 0.2 |
| 1.446 | 2.0 | 12 | 1.3794 | 0.2889 |
| 1.446 | 3.0 | 18 | 1.3570 | 0.3556 |
| 1.184 | 4.0 | 24 | 1.3382 | 0.3111 |
| 1.0671 | 5.0 | 30 | 1.3283 | 0.3111 |
| 1.0671 | 6.0 | 36 | 1.3144 | 0.2889 |
| 0.9249 | 7.0 | 42 | 1.2898 | 0.3333 |
| 0.9249 | 8.0 | 48 | 1.2748 | 0.3556 |
| 0.8443 | 9.0 | 54 | 1.2692 | 0.3333 |
| 0.7477 | 10.0 | 60 | 1.2518 | 0.3778 |
| 0.7477 | 11.0 | 66 | 1.2338 | 0.4 |
| 0.662 | 12.0 | 72 | 1.2193 | 0.3778 |
| 0.662 | 13.0 | 78 | 1.2195 | 0.4 |
| 0.622 | 14.0 | 84 | 1.2039 | 0.3778 |
| 0.5154 | 15.0 | 90 | 1.1949 | 0.4 |
| 0.5154 | 16.0 | 96 | 1.1879 | 0.4 |
| 0.4537 | 17.0 | 102 | 1.1810 | 0.4 |
| 0.4537 | 18.0 | 108 | 1.1670 | 0.4 |
| 0.3859 | 19.0 | 114 | 1.1628 | 0.4 |
| 0.3586 | 20.0 | 120 | 1.1721 | 0.4 |
| 0.3586 | 21.0 | 126 | 1.1698 | 0.4222 |
| 0.3151 | 22.0 | 132 | 1.1603 | 0.4 |
| 0.3151 | 23.0 | 138 | 1.1584 | 0.4222 |
| 0.2881 | 24.0 | 144 | 1.1519 | 0.4222 |
| 0.2498 | 25.0 | 150 | 1.1515 | 0.4222 |
| 0.2498 | 26.0 | 156 | 1.1445 | 0.4222 |
| 0.232 | 27.0 | 162 | 1.1430 | 0.4222 |
| 0.232 | 28.0 | 168 | 1.1452 | 0.4222 |
| 0.2183 | 29.0 | 174 | 1.1406 | 0.4222 |
| 0.1798 | 30.0 | 180 | 1.1348 | 0.4222 |
| 0.1798 | 31.0 | 186 | 1.1304 | 0.4222 |
| 0.1811 | 32.0 | 192 | 1.1281 | 0.4222 |
| 0.1811 | 33.0 | 198 | 1.1317 | 0.4222 |
| 0.1748 | 34.0 | 204 | 1.1302 | 0.4222 |
| 0.1492 | 35.0 | 210 | 1.1303 | 0.4222 |
| 0.1492 | 36.0 | 216 | 1.1319 | 0.4222 |
| 0.1477 | 37.0 | 222 | 1.1328 | 0.4222 |
| 0.1477 | 38.0 | 228 | 1.1366 | 0.4222 |
| 0.1357 | 39.0 | 234 | 1.1362 | 0.4222 |
| 0.1379 | 40.0 | 240 | 1.1351 | 0.4222 |
| 0.1379 | 41.0 | 246 | 1.1344 | 0.4222 |
| 0.1325 | 42.0 | 252 | 1.1341 | 0.4222 |
| 0.1325 | 43.0 | 258 | 1.1341 | 0.4222 |
| 0.1377 | 44.0 | 264 | 1.1341 | 0.4222 |
| 0.1332 | 45.0 | 270 | 1.1341 | 0.4222 |
| 0.1332 | 46.0 | 276 | 1.1341 | 0.4222 |
| 0.1323 | 47.0 | 282 | 1.1341 | 0.4222 |
| 0.1323 | 48.0 | 288 | 1.1341 | 0.4222 |
| 0.1276 | 49.0 | 294 | 1.1341 | 0.4222 |
| 0.1376 | 50.0 | 300 | 1.1341 | 0.4222 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
hkivancoral/hushem_1x_deit_tiny_adamax_001_fold3
|
hkivancoral
| 2023-11-13T14:36:11Z | 8 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:facebook/deit-tiny-patch16-224",
"base_model:finetune:facebook/deit-tiny-patch16-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-11-13T14:21:08Z |
---
license: apache-2.0
base_model: facebook/deit-tiny-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: hushem_1x_deit_tiny_adamax_001_fold3
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.8372093023255814
---
<!-- 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. -->
# hushem_1x_deit_tiny_adamax_001_fold3
This model is a fine-tuned version of [facebook/deit-tiny-patch16-224](https://huggingface.co/facebook/deit-tiny-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5538
- Accuracy: 0.8372
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 6 | 1.3426 | 0.4419 |
| 1.3195 | 2.0 | 12 | 1.0931 | 0.5116 |
| 1.3195 | 3.0 | 18 | 0.8535 | 0.6512 |
| 0.6419 | 4.0 | 24 | 0.9249 | 0.6279 |
| 0.325 | 5.0 | 30 | 0.7057 | 0.7674 |
| 0.325 | 6.0 | 36 | 0.5831 | 0.7674 |
| 0.0848 | 7.0 | 42 | 0.6810 | 0.7907 |
| 0.0848 | 8.0 | 48 | 0.5917 | 0.7674 |
| 0.0193 | 9.0 | 54 | 0.6267 | 0.8140 |
| 0.0077 | 10.0 | 60 | 0.4330 | 0.8372 |
| 0.0077 | 11.0 | 66 | 0.5195 | 0.8372 |
| 0.0032 | 12.0 | 72 | 0.6710 | 0.7907 |
| 0.0032 | 13.0 | 78 | 0.6980 | 0.8372 |
| 0.0012 | 14.0 | 84 | 0.5701 | 0.8372 |
| 0.0006 | 15.0 | 90 | 0.5278 | 0.8605 |
| 0.0006 | 16.0 | 96 | 0.5226 | 0.8372 |
| 0.0005 | 17.0 | 102 | 0.5245 | 0.8605 |
| 0.0005 | 18.0 | 108 | 0.5277 | 0.8605 |
| 0.0004 | 19.0 | 114 | 0.5338 | 0.8372 |
| 0.0003 | 20.0 | 120 | 0.5401 | 0.8372 |
| 0.0003 | 21.0 | 126 | 0.5445 | 0.8372 |
| 0.0003 | 22.0 | 132 | 0.5461 | 0.8372 |
| 0.0003 | 23.0 | 138 | 0.5481 | 0.8372 |
| 0.0003 | 24.0 | 144 | 0.5486 | 0.8372 |
| 0.0003 | 25.0 | 150 | 0.5495 | 0.8372 |
| 0.0003 | 26.0 | 156 | 0.5492 | 0.8372 |
| 0.0002 | 27.0 | 162 | 0.5497 | 0.8372 |
| 0.0002 | 28.0 | 168 | 0.5490 | 0.8372 |
| 0.0002 | 29.0 | 174 | 0.5497 | 0.8372 |
| 0.0002 | 30.0 | 180 | 0.5498 | 0.8372 |
| 0.0002 | 31.0 | 186 | 0.5499 | 0.8372 |
| 0.0002 | 32.0 | 192 | 0.5503 | 0.8372 |
| 0.0002 | 33.0 | 198 | 0.5508 | 0.8372 |
| 0.0002 | 34.0 | 204 | 0.5520 | 0.8372 |
| 0.0002 | 35.0 | 210 | 0.5527 | 0.8372 |
| 0.0002 | 36.0 | 216 | 0.5529 | 0.8372 |
| 0.0002 | 37.0 | 222 | 0.5532 | 0.8372 |
| 0.0002 | 38.0 | 228 | 0.5534 | 0.8372 |
| 0.0002 | 39.0 | 234 | 0.5536 | 0.8372 |
| 0.0002 | 40.0 | 240 | 0.5537 | 0.8372 |
| 0.0002 | 41.0 | 246 | 0.5538 | 0.8372 |
| 0.0002 | 42.0 | 252 | 0.5538 | 0.8372 |
| 0.0002 | 43.0 | 258 | 0.5538 | 0.8372 |
| 0.0002 | 44.0 | 264 | 0.5538 | 0.8372 |
| 0.0002 | 45.0 | 270 | 0.5538 | 0.8372 |
| 0.0002 | 46.0 | 276 | 0.5538 | 0.8372 |
| 0.0002 | 47.0 | 282 | 0.5538 | 0.8372 |
| 0.0002 | 48.0 | 288 | 0.5538 | 0.8372 |
| 0.0002 | 49.0 | 294 | 0.5538 | 0.8372 |
| 0.0002 | 50.0 | 300 | 0.5538 | 0.8372 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
Yasinjan99/wav2vec2-large-xls-r-300m-cv-11-ug-test_01
|
Yasinjan99
| 2023-11-13T14:35:43Z | 8 | 0 |
transformers
|
[
"transformers",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice_11_0",
"base_model:facebook/wav2vec2-base",
"base_model:finetune:facebook/wav2vec2-base",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-11-13T06:11:30Z |
---
license: apache-2.0
base_model: facebook/wav2vec2-base
tags:
- generated_from_trainer
datasets:
- common_voice_11_0
metrics:
- wer
model-index:
- name: wav2vec2-large-xls-r-300m-cv-11-ug-test_01
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: common_voice_11_0
type: common_voice_11_0
config: ug
split: test
args: ug
metrics:
- name: Wer
type: wer
value: 1.0
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-cv-11-ug-test_01
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the common_voice_11_0 dataset.
It achieves the following results on the evaluation set:
- Loss: 3.0973
- Wer: 1.0
## 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: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 15
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:---:|
| 4.9981 | 8.0 | 500 | 3.0973 | 1.0 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.0.0
- Datasets 2.14.6
- Tokenizers 0.14.1
|
hkivancoral/hushem_1x_deit_tiny_adamax_001_fold2
|
hkivancoral
| 2023-11-13T14:34:31Z | 23 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:facebook/deit-tiny-patch16-224",
"base_model:finetune:facebook/deit-tiny-patch16-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-11-13T14:18:20Z |
---
license: apache-2.0
base_model: facebook/deit-tiny-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: hushem_1x_deit_tiny_adamax_001_fold2
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.6
---
<!-- 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. -->
# hushem_1x_deit_tiny_adamax_001_fold2
This model is a fine-tuned version of [facebook/deit-tiny-patch16-224](https://huggingface.co/facebook/deit-tiny-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6766
- Accuracy: 0.6
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 6 | 1.3194 | 0.2889 |
| 1.3705 | 2.0 | 12 | 1.2766 | 0.3778 |
| 1.3705 | 3.0 | 18 | 1.3268 | 0.5333 |
| 0.7361 | 4.0 | 24 | 1.2927 | 0.5556 |
| 0.3404 | 5.0 | 30 | 1.3610 | 0.5556 |
| 0.3404 | 6.0 | 36 | 1.1429 | 0.5778 |
| 0.1188 | 7.0 | 42 | 1.5833 | 0.5333 |
| 0.1188 | 8.0 | 48 | 1.2765 | 0.6667 |
| 0.0229 | 9.0 | 54 | 1.4099 | 0.6222 |
| 0.0046 | 10.0 | 60 | 1.4395 | 0.6 |
| 0.0046 | 11.0 | 66 | 1.6161 | 0.5556 |
| 0.0013 | 12.0 | 72 | 1.5774 | 0.5778 |
| 0.0013 | 13.0 | 78 | 1.5201 | 0.6 |
| 0.0007 | 14.0 | 84 | 1.5608 | 0.6 |
| 0.0005 | 15.0 | 90 | 1.6187 | 0.5778 |
| 0.0005 | 16.0 | 96 | 1.6424 | 0.5778 |
| 0.0004 | 17.0 | 102 | 1.6470 | 0.5778 |
| 0.0004 | 18.0 | 108 | 1.6480 | 0.6 |
| 0.0003 | 19.0 | 114 | 1.6471 | 0.6 |
| 0.0003 | 20.0 | 120 | 1.6450 | 0.6 |
| 0.0003 | 21.0 | 126 | 1.6532 | 0.6 |
| 0.0003 | 22.0 | 132 | 1.6559 | 0.6 |
| 0.0003 | 23.0 | 138 | 1.6612 | 0.6 |
| 0.0003 | 24.0 | 144 | 1.6668 | 0.6 |
| 0.0002 | 25.0 | 150 | 1.6718 | 0.6 |
| 0.0002 | 26.0 | 156 | 1.6748 | 0.6 |
| 0.0002 | 27.0 | 162 | 1.6728 | 0.6 |
| 0.0002 | 28.0 | 168 | 1.6726 | 0.6 |
| 0.0002 | 29.0 | 174 | 1.6718 | 0.6 |
| 0.0002 | 30.0 | 180 | 1.6716 | 0.6 |
| 0.0002 | 31.0 | 186 | 1.6738 | 0.6 |
| 0.0002 | 32.0 | 192 | 1.6734 | 0.6 |
| 0.0002 | 33.0 | 198 | 1.6748 | 0.6 |
| 0.0002 | 34.0 | 204 | 1.6753 | 0.6 |
| 0.0002 | 35.0 | 210 | 1.6740 | 0.6 |
| 0.0002 | 36.0 | 216 | 1.6735 | 0.6 |
| 0.0002 | 37.0 | 222 | 1.6732 | 0.6 |
| 0.0002 | 38.0 | 228 | 1.6740 | 0.6 |
| 0.0002 | 39.0 | 234 | 1.6751 | 0.6 |
| 0.0002 | 40.0 | 240 | 1.6758 | 0.6 |
| 0.0002 | 41.0 | 246 | 1.6766 | 0.6 |
| 0.0002 | 42.0 | 252 | 1.6766 | 0.6 |
| 0.0002 | 43.0 | 258 | 1.6766 | 0.6 |
| 0.0002 | 44.0 | 264 | 1.6766 | 0.6 |
| 0.0002 | 45.0 | 270 | 1.6766 | 0.6 |
| 0.0002 | 46.0 | 276 | 1.6766 | 0.6 |
| 0.0002 | 47.0 | 282 | 1.6766 | 0.6 |
| 0.0002 | 48.0 | 288 | 1.6766 | 0.6 |
| 0.0002 | 49.0 | 294 | 1.6766 | 0.6 |
| 0.0002 | 50.0 | 300 | 1.6766 | 0.6 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
Yura32000/Reinforce-cartpole_v1
|
Yura32000
| 2023-11-13T14:26:20Z | 0 | 0 | null |
[
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-11-13T14:26:11Z |
---
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
|
spjk007/my-favourite-bag-tut
|
spjk007
| 2023-11-13T14:21:33Z | 0 | 0 | null |
[
"safetensors",
"NxtWave-GenAI-Webinar",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"region:us"
] |
text-to-image
| 2023-11-13T14:19:03Z |
---
license: creativeml-openrail-m
tags:
- NxtWave-GenAI-Webinar
- text-to-image
- stable-diffusion
---
### My-favourite-bag-tut Dreambooth model trained by spjk007 following the "Build your own Gen AI model" session by NxtWave.
Project Submission Code: JJCET-520
Sample pictures of this concept:
.png)
|
lmqg/mt5-base-zhquad-qg
|
lmqg
| 2023-11-13T14:20:10Z | 9 | 0 |
transformers
|
[
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"question generation",
"zh",
"dataset:lmqg/qg_zhquad",
"arxiv:2210.03992",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-11-10T10:25:27Z |
---
license: cc-by-4.0
metrics:
- bleu4
- meteor
- rouge-l
- bertscore
- moverscore
language: zh
datasets:
- lmqg/qg_zhquad
pipeline_tag: text2text-generation
tags:
- question generation
widget:
- text: "南安普敦的警察服务由汉普郡警察提供。南安普敦行动的主要基地是一座新的八层专用建筑，造价3000万英镑。该建筑位于南路，2011年启用，靠近<hl> 南安普敦中央 <hl>火车站。此前，南安普顿市中心的行动位于市民中心西翼，但由于设施老化，加上计划在旧警察局和地方法院建造一座新博物馆，因此必须搬迁。在Portswood、Banister Park、Hille和Shirley还有其他警察局，在南安普顿中央火车站还有一个英国交通警察局。"
example_title: "Question Generation Example 1"
- text: "芝加哥大学的<hl> 1960—61 <hl>集团理论年汇集了Daniel Gorenstein、John G. Thompson和Walter Feit等团体理论家，奠定了一个合作的基础，借助于其他众多数学家的输入，1982中对所有有限的简单群进行了分类。这个项目的规模超过了以往的数学研究，无论是证明的长度还是研究人员的数量。目前正在进行研究，以简化这一分类的证明。如今，群论仍然是一个非常活跃的数学分支，影响着许多其他领域"
example_title: "Question Generation Example 2"
model-index:
- name: lmqg/mt5-base-zhquad-qg
results:
- task:
name: Text2text Generation
type: text2text-generation
dataset:
name: lmqg/qg_zhquad
type: default
args: default
metrics:
- name: BLEU4 (Question Generation)
type: bleu4_question_generation
value: 14.73
- name: ROUGE-L (Question Generation)
type: rouge_l_question_generation
value: 34.72
- name: METEOR (Question Generation)
type: meteor_question_generation
value: 23.92
- name: BERTScore (Question Generation)
type: bertscore_question_generation
value: 77.38
- name: MoverScore (Question Generation)
type: moverscore_question_generation
value: 57.5
---
# Model Card of `lmqg/mt5-base-zhquad-qg`
This model is fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) for question generation task on the [lmqg/qg_zhquad](https://huggingface.co/datasets/lmqg/qg_zhquad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-generation).
### Overview
- **Language model:** [google/mt5-base](https://huggingface.co/google/mt5-base)
- **Language:** zh
- **Training data:** [lmqg/qg_zhquad](https://huggingface.co/datasets/lmqg/qg_zhquad) (default)
- **Online Demo:** [https://autoqg.net/](https://autoqg.net/)
- **Repository:** [https://github.com/asahi417/lm-question-generation](https://github.com/asahi417/lm-question-generation)
- **Paper:** [https://arxiv.org/abs/2210.03992](https://arxiv.org/abs/2210.03992)
### Usage
- With [`lmqg`](https://github.com/asahi417/lm-question-generation#lmqg-language-model-for-question-generation-)
```python
from lmqg import TransformersQG
# initialize model
model = TransformersQG(language="zh", model="lmqg/mt5-base-zhquad-qg")
# model prediction
questions = model.generate_q(list_context="南安普敦的警察服务由汉普郡警察提供。南安普敦行动的主要基地是一座新的八层专用建筑，造价3000万英镑。该建筑位于南路，2011年启用，靠近南安普敦中央火车站。此前，南安普顿市中心的行动位于市民中心西翼，但由于设施老化，加上计划在旧警察局和地方法院建造一座新博物馆，因此必须搬迁。在Portswood、Banister Park、Hille和Shirley还有其他警察局，在南安普顿中央火车站还有一个英国交通警察局。", list_answer="南安普敦中央")
```
- With `transformers`
```python
from transformers import pipeline
pipe = pipeline("text2text-generation", "lmqg/mt5-base-zhquad-qg")
output = pipe("南安普敦的警察服务由汉普郡警察提供。南安普敦行动的主要基地是一座新的八层专用建筑，造价3000万英镑。该建筑位于南路，2011年启用，靠近<hl> 南安普敦中央 <hl>火车站。此前，南安普顿市中心的行动位于市民中心西翼，但由于设施老化，加上计划在旧警察局和地方法院建造一座新博物馆，因此必须搬迁。在Portswood、Banister Park、Hille和Shirley还有其他警察局，在南安普顿中央火车站还有一个英国交通警察局。")
```
## Evaluation
- ***Metric (Question Generation)***: [raw metric file](https://huggingface.co/lmqg/mt5-base-zhquad-qg/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_zhquad.default.json)
| | Score | Type | Dataset |
|:-----------|--------:|:--------|:-----------------------------------------------------------------|
| BERTScore | 77.38 | default | [lmqg/qg_zhquad](https://huggingface.co/datasets/lmqg/qg_zhquad) |
| Bleu_1 | 37 | default | [lmqg/qg_zhquad](https://huggingface.co/datasets/lmqg/qg_zhquad) |
| Bleu_2 | 25.9 | default | [lmqg/qg_zhquad](https://huggingface.co/datasets/lmqg/qg_zhquad) |
| Bleu_3 | 19.25 | default | [lmqg/qg_zhquad](https://huggingface.co/datasets/lmqg/qg_zhquad) |
| Bleu_4 | 14.73 | default | [lmqg/qg_zhquad](https://huggingface.co/datasets/lmqg/qg_zhquad) |
| METEOR | 23.92 | default | [lmqg/qg_zhquad](https://huggingface.co/datasets/lmqg/qg_zhquad) |
| MoverScore | 57.5 | default | [lmqg/qg_zhquad](https://huggingface.co/datasets/lmqg/qg_zhquad) |
| ROUGE_L | 34.72 | default | [lmqg/qg_zhquad](https://huggingface.co/datasets/lmqg/qg_zhquad) |
## Training hyperparameters
The following hyperparameters were used during fine-tuning:
- dataset_path: lmqg/qg_zhquad
- dataset_name: default
- input_types: paragraph_answer
- output_types: question
- prefix_types: None
- model: google/mt5-base
- max_length: 512
- max_length_output: 32
- epoch: 16
- batch: 16
- lr: 0.0001
- fp16: False
- random_seed: 1
- gradient_accumulation_steps: 4
- label_smoothing: 0.15
The full configuration can be found at [fine-tuning config file](https://huggingface.co/lmqg/mt5-base-zhquad-qg/raw/main/trainer_config.json).
## Citation
```
@inproceedings{ushio-etal-2022-generative,
title = "{G}enerative {L}anguage {M}odels for {P}aragraph-{L}evel {Q}uestion {G}eneration",
author = "Ushio, Asahi and
Alva-Manchego, Fernando and
Camacho-Collados, Jose",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2022",
address = "Abu Dhabi, U.A.E.",
publisher = "Association for Computational Linguistics",
}
```
|
pankajemplay/mistral_7b-instruct-intent
|
pankajemplay
| 2023-11-13T14:14:41Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"region:us"
] | null | 2023-11-13T14:14:27Z |
---
library_name: peft
base_model: /kaggle/input/mistral/pytorch/7b-instruct-v0.1-hf/1
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Data Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
## 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: bfloat16
### Framework versions
- PEFT 0.6.1
## 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: bfloat16
### Framework versions
- PEFT 0.6.1
|
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