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| last_modified
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| likes
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11.7k
| library_name
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jjmcarrascosa/ppo-lunarlander-v2-custom
|
jjmcarrascosa
| 2023-12-08T12:37:54Z | 0 | 0 | null |
[
"tensorboard",
"LunarLander-v2",
"ppo",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"deep-rl-course",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-12-08T12:37:10Z |
---
tags:
- LunarLander-v2
- ppo
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
- deep-rl-course
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: -38.37 +/- 20.99
name: mean_reward
verified: false
---
# PPO Agent Playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2.
# Hyperparameters
```python
{'exp_name': 'ppo'
'seed': 49
'torch_deterministic': True
'cuda': True
'track': False
'wandb_project_name': 'cleanRL'
'wandb_entity': None
'capture_video': False
'env_id': 'LunarLander-v2'
'total_timesteps': 1000000
'learning_rate': 0.0005
'num_envs': 256
'num_steps': 128
'anneal_lr': True
'gae': True
'gamma': 0.99
'gae_lambda': 0.95
'num_minibatches': 8
'update_epochs': 8
'norm_adv': True
'clip_coef': 0.2
'clip_vloss': True
'ent_coef': 0.01
'vf_coef': 0.5
'max_grad_norm': 0.5
'target_kl': None
'repo_id': 'jjmcarrascosa/ppo-lunarlander-v2-custom'
'batch_size': 32768
'minibatch_size': 4096}
```
|
cjvt/t5-slo-word-spelling-corrector
|
cjvt
| 2023-12-08T12:20:21Z | 13 | 0 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"word spelling correction",
"sl",
"dataset:cjvt/cc_gigafida",
"dataset:cjvt/solar3",
"dataset:cjvt/sloleks",
"license:cc-by-sa-4.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-11-12T16:33:20Z |
---
license: cc-by-sa-4.0
datasets:
- cjvt/cc_gigafida
- cjvt/solar3
- cjvt/sloleks
language:
- sl
tags:
- word spelling correction
---
---
language:
- sl
license: cc-by-sa-4.0
---
# T5-incorrect-word-spelling-corrector
This T5 model is designed to identify and correct words with incorrect spelling in the Slovenian language.
## Model Output Example
Consider the following Slovenian text:
_Model v besedlu popravi napaake v nepravilno črkovanih besedah._
The model might return the following text (note: predictions chosen for demonstration/explanation, not reproducibility!):
_Model v besedilu popravi napake v nepravilno črkovanih besedah._
We observe that in the input sentence, the words `besedlu` and `napaake` are incorrectly spelled, so the model corrects them to `besedilu` and `napake`.
## More details
Testing the model with **generated** test sets provides the following result (combining detection and correction of words with incorrect spelling):
- `Precission`: 0,986
- `Recall`: 0,935
- `F1`: 0,960
Testing the model, in combination with **cjvt/SloBERTa-slo-word-spelling-annotator**, with test sets constructed using the **Šolar Eval** dataset provides the following results (combining detection and correction of words with incorrect spelling):
- `Precission`: 0,823
- `Recall`: 0,796
- `F1`: 0,810
## Acknowledgement
The authors acknowledge the financial support from the Slovenian Research and Innovation Agency - research core funding No. P6-0411: Language Resources and Technologies for Slovene and research project No. J7-3159: Empirical foundations for digitally-supported development of writing skills.
## Authors
Thanks to Martin Božič, Marko Robnik-Šikonja and Špela Arhar Holdt for developing these models.
|
anhminh3105/my_awesome_model
|
anhminh3105
| 2023-12-08T12:11:53Z | 7 | 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
| 2023-12-08T11:08:27Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- imdb
metrics:
- accuracy
model-index:
- name: my_awesome_model
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: 0.93244
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# my_awesome_model
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: 0.2317
- Accuracy: 0.9324
## 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
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.2247 | 1.0 | 1563 | 0.1985 | 0.9246 |
| 0.1468 | 2.0 | 3126 | 0.2317 | 0.9324 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|
TCLResearchEurope/yolov8n_pruned_73
|
TCLResearchEurope
| 2023-12-08T12:01:53Z | 0 | 0 | null |
[
"pytorch",
"torch-dag",
"object-detection",
"license:apache-2.0",
"region:us"
] |
object-detection
| 2023-12-08T11:33:20Z |
---
license: apache-2.0
pipeline_tag: object-detection
tags:
- pytorch
- torch-dag
---
# Model Card for yolov8n_pruned_73
This is a prunned version of the [YOLOv8n](https://github.com/ultralytics/ultralytics#models) model in a [toch-dag](https://github.com/TCLResearchEurope/torch-dag) format.
This model has rougly 73% of the original model FLOPs with small metrics drop.
| Model | KMAPPs* | M Parameters | mAP50-95 (640x640) |
| ----------- | ------- | ------------ | ------------------ |
| **YOLOv8n (baseline)** | 21.5 | 3.16 | 37.3 |
| **yolov8n_pruned_73 (ours)** | 15.7 **(73%)** | 2.65 **(84%)** | 34.9 **(↓ 2.4)** |
\***KMAPPs** thousands of FLOPs per input pixel
`KMAPPs(model) = FLOPs(model) / (H * W * 1000)`, where `(H, W)` is the input resolution.
The accuracy was calculated on the COCO val2017 dataset. For details about image pre-processing, please refer to the original repository.
## Model Details
### Model Description
- **Developed by:** [TCL Research Europe](https://github.com/TCLResearchEurope/)
- **Model type:** Object detection
- **License:** Apache 2.0
- **Finetuned from model:** [YOLOv8n](https://github.com/ultralytics/ultralytics#models)
### Model Sources
- **Repository:** [YOLOv8n](https://github.com/ultralytics/ultralytics#models)
## How to Get Started with the Model
To load the model, You have to install [torch-dag](https://github.com/TCLResearchEurope/torch-dag#3-installation) library, which can be done using `pip` by
```
pip install torch-dag
```
then, clone this repository
```
# Make sure you have git-lfs installed (https://git-lfs.com)
git lfs install
git clone https://huggingface.co/TCLResearchEurope/yolov8n_pruned_73
```
and now You are ready to load the model:
```
import torch_dag
import torch
model = torch_dag.io.load_dag_from_path('./yolov8n_pruned_73')
model.eval()
out = model(torch.ones(1, 3, 224, 224))
print(out.shape)
```
|
Deeksha04/peftResnet50
|
Deeksha04
| 2023-12-08T11:52:31Z | 2 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:microsoft/resnet-50",
"base_model:adapter:microsoft/resnet-50",
"region:us"
] | null | 2023-12-06T04:05:55Z |
---
library_name: peft
base_model: microsoft/resnet-50
---
# 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
### Framework versions
- PEFT 0.6.2
|
facebook/hf-seamless-m4t-medium
|
facebook
| 2023-12-08T11:47:26Z | 11,445 | 29 |
transformers
|
[
"transformers",
"pytorch",
"seamless_m4t",
"feature-extraction",
"SeamlessM4T",
"text-to-speech",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] |
text-to-speech
| 2023-08-28T16:30:57Z |
---
inference: true
tags:
- SeamlessM4T
- seamless_m4t
license: cc-by-nc-4.0
library_name: transformers
pipeline_tag: text-to-speech
---
# SeamlessM4T Medium
SeamlessM4T is a collection of models designed to provide high quality translation, allowing people from different
linguistic communities to communicate effortlessly through speech and text.
This repository hosts 🤗 Hugging Face's [implementation](https://huggingface.co/docs/transformers/main/en/model_doc/seamless_m4t) of SeamlessM4T. You can find the original weights, as well as a guide on how to run them in the original hub repositories ([large](https://huggingface.co/facebook/seamless-m4t-large) and [medium](https://huggingface.co/facebook/seamless-m4t-medium) checkpoints).
-------------------
**🌟 SeamlessM4T v2, an improved version of this version with a novel architecture, has been released [here](https://huggingface.co/facebook/seamless-m4t-v2-large).
This new model improves over SeamlessM4T v1 in quality as well as inference speed in speech generation tasks.**
**SeamlessM4T v2 is also supported by 🤗 Transformers, more on it [in the model card of this new version](https://huggingface.co/facebook/seamless-m4t-v2-large#transformers-usage) or directly in [🤗 Transformers docs](https://huggingface.co/docs/transformers/main/en/model_doc/seamless_m4t_v2).**
-------------------
SeamlessM4T Medium covers:
- 📥 101 languages for speech input
- ⌨️ [196 Languages](https://huggingface.co/ylacombe/hf-seamless-m4t-medium/blob/main/tokenizer_config.json#L1887-L2089) for text input/output
- 🗣️ [35 languages](https://huggingface.co/ylacombe/hf-seamless-m4t-medium/blob/main/generation_config.json#L253-L288) for speech output.
This is the "medium" variant of the unified model, which enables multiple tasks without relying on multiple separate models:
- Speech-to-speech translation (S2ST)
- Speech-to-text translation (S2TT)
- Text-to-speech translation (T2ST)
- Text-to-text translation (T2TT)
- Automatic speech recognition (ASR)
You can perform all the above tasks from one single model, [`SeamlessM4TModel`](https://huggingface.co/docs/transformers/main/en/model_doc/seamless_m4t#transformers.SeamlessM4TModel), but each task also has its own dedicated sub-model.
## 🤗 Usage
First, load the processor and a checkpoint of the model:
```python
>>> from transformers import AutoProcessor, SeamlessM4TModel
>>> processor = AutoProcessor.from_pretrained("facebook/hf-seamless-m4t-medium")
>>> model = SeamlessM4TModel.from_pretrained("facebook/hf-seamless-m4t-medium")
```
You can seamlessly use this model on text or on audio, to generated either translated text or translated audio.
Here is how to use the processor to process text and audio:
```python
>>> # let's load an audio sample from an Arabic speech corpus
>>> from datasets import load_dataset
>>> dataset = load_dataset("arabic_speech_corpus", split="test", streaming=True)
>>> audio_sample = next(iter(dataset))["audio"]
>>> # now, process it
>>> audio_inputs = processor(audios=audio_sample["array"], return_tensors="pt")
>>> # now, process some English test as well
>>> text_inputs = processor(text = "Hello, my dog is cute", src_lang="eng", return_tensors="pt")
```
### Speech
[`SeamlessM4TModel`](https://huggingface.co/docs/transformers/main/en/model_doc/seamless_m4t#transformers.SeamlessM4TModel) can *seamlessly* generate text or speech with few or no changes. Let's target Russian voice translation:
```python
>>> audio_array_from_text = model.generate(**text_inputs, tgt_lang="rus")[0].cpu().numpy().squeeze()
>>> audio_array_from_audio = model.generate(**audio_inputs, tgt_lang="rus")[0].cpu().numpy().squeeze()
```
With basically the same code, I've translated English text and Arabic speech to Russian speech samples.
### Text
Similarly, you can generate translated text from audio files or from text with the same model. You only have to pass `generate_speech=False` to [`SeamlessM4TModel.generate`](https://huggingface.co/docs/transformers/main/en/model_doc/seamless_m4t#transformers.SeamlessM4TModel.generate).
This time, let's translate to French.
```python
>>> # from audio
>>> output_tokens = model.generate(**audio_inputs, tgt_lang="fra", generate_speech=False)
>>> translated_text_from_audio = processor.decode(output_tokens[0].tolist(), skip_special_tokens=True)
>>> # from text
>>> output_tokens = model.generate(**text_inputs, tgt_lang="fra", generate_speech=False)
>>> translated_text_from_text = processor.decode(output_tokens[0].tolist(), skip_special_tokens=True)
```
### Tips
#### 1. Use dedicated models
[`SeamlessM4TModel`](https://huggingface.co/docs/transformers/main/en/model_doc/seamless_m4t#transformers.SeamlessM4TModel) is transformers top level model to generate speech and text, but you can also use dedicated models that perform the task without additional components, thus reducing the memory footprint.
For example, you can replace the audio-to-audio generation snippet with the model dedicated to the S2ST task, the rest is exactly the same code:
```python
>>> from transformers import SeamlessM4TForSpeechToSpeech
>>> model = SeamlessM4TForSpeechToSpeech.from_pretrained("facebook/hf-seamless-m4t-medium")
```
Or you can replace the text-to-text generation snippet with the model dedicated to the T2TT task, you only have to remove `generate_speech=False`.
```python
>>> from transformers import SeamlessM4TForTextToText
>>> model = SeamlessM4TForTextToText.from_pretrained("facebook/hf-seamless-m4t-medium")
```
Feel free to try out [`SeamlessM4TForSpeechToText`](https://huggingface.co/docs/transformers/main/en/model_doc/seamless_m4t#transformers.SeamlessM4TForSpeechToText) and [`SeamlessM4TForTextToSpeech`](https://huggingface.co/docs/transformers/main/en/model_doc/seamless_m4t#transformers.SeamlessM4TForTextToSpeech) as well.
#### 2. Change the speaker identity
You have the possibility to change the speaker used for speech synthesis with the `spkr_id` argument. Some `spkr_id` works better than other for some languages!
#### 3. Change the generation strategy
You can use different [generation strategies](https://huggingface.co/docs/transformers/v4.34.1/en/generation_strategies#text-generation-strategies) for speech and text generation, e.g `.generate(input_ids=input_ids, text_num_beams=4, speech_do_sample=True)` which will successively perform beam-search decoding on the text model, and multinomial sampling on the speech model.
#### 4. Generate speech and text at the same time
Use `return_intermediate_token_ids=True` with [`SeamlessM4TModel`](https://huggingface.co/docs/transformers/main/en/model_doc/seamless_m4t#transformers.SeamlessM4TModel) to return both speech and text !
|
facebook/hf-seamless-m4t-large
|
facebook
| 2023-12-08T11:46:01Z | 5,171 | 56 |
transformers
|
[
"transformers",
"pytorch",
"seamless_m4t",
"feature-extraction",
"SeamlessM4T",
"text-to-speech",
"license:cc-by-nc-4.0",
"region:us"
] |
text-to-speech
| 2023-09-13T12:05:44Z |
---
inference: false
tags:
- SeamlessM4T
- seamless_m4t
license: cc-by-nc-4.0
library_name: transformers
pipeline_tag: text-to-speech
---
# SeamlessM4T Large
SeamlessM4T is a collection of models designed to provide high quality translation, allowing people from different
linguistic communities to communicate effortlessly through speech and text.
This repository hosts 🤗 Hugging Face's [implementation](https://huggingface.co/docs/transformers/main/en/model_doc/seamless_m4t) of SeamlessM4T.
-------------------
**🌟 SeamlessM4T v2, an improved version of this version with a novel architecture, has been released [here](https://huggingface.co/facebook/seamless-m4t-v2-large).
This new model improves over SeamlessM4T v1 in quality as well as inference speed in speech generation tasks.**
**SeamlessM4T v2 is also supported by 🤗 Transformers, more on it [in the model card of this new version](https://huggingface.co/facebook/seamless-m4t-v2-large#transformers-usage) or directly in [🤗 Transformers docs](https://huggingface.co/docs/transformers/main/en/model_doc/seamless_m4t_v2).**
-------------------
SeamlessM4T Large covers:
- 📥 101 languages for speech input
- ⌨️ [96 Languages](https://huggingface.co/ylacombe/hf-seamless-m4t-large/blob/main/generation_config.json#L48-L145) for text input/output
- 🗣️ [35 languages](https://huggingface.co/ylacombe/hf-seamless-m4t-large/blob/main/generation_config.json#L149-L184) for speech output.
This is the "large" variant of the unified model, which enables multiple tasks without relying on multiple separate models:
- Speech-to-speech translation (S2ST)
- Speech-to-text translation (S2TT)
- Text-to-speech translation (T2ST)
- Text-to-text translation (T2TT)
- Automatic speech recognition (ASR)
You can perform all the above tasks from one single model, [`SeamlessM4TModel`](https://huggingface.co/docs/transformers/main/en/model_doc/seamless_m4t#transformers.SeamlessM4TModel), but each task also has its own dedicated sub-model.
## 🤗 Usage
First, load the processor and a checkpoint of the model:
```python
>>> from transformers import AutoProcessor, SeamlessM4TModel
>>> processor = AutoProcessor.from_pretrained("facebook/hf-seamless-m4t-large")
>>> model = SeamlessM4TModel.from_pretrained("facebook/hf-seamless-m4t-large")
```
You can seamlessly use this model on text or on audio, to generated either translated text or translated audio.
Here is how to use the processor to process text and audio:
```python
>>> # let's load an audio sample from an Arabic speech corpus
>>> from datasets import load_dataset
>>> dataset = load_dataset("arabic_speech_corpus", split="test", streaming=True)
>>> audio_sample = next(iter(dataset))["audio"]
>>> # now, process it
>>> audio_inputs = processor(audios=audio_sample["array"], return_tensors="pt")
>>> # now, process some English test as well
>>> text_inputs = processor(text = "Hello, my dog is cute", src_lang="eng", return_tensors="pt")
```
### Speech
[`SeamlessM4TModel`](https://huggingface.co/docs/transformers/main/en/model_doc/seamless_m4t#transformers.SeamlessM4TModel) can *seamlessly* generate text or speech with few or no changes. Let's target Russian voice translation:
```python
>>> audio_array_from_text = model.generate(**text_inputs, tgt_lang="rus")[0].cpu().numpy().squeeze()
>>> audio_array_from_audio = model.generate(**audio_inputs, tgt_lang="rus")[0].cpu().numpy().squeeze()
```
With basically the same code, I've translated English text and Arabic speech to Russian speech samples.
### Text
Similarly, you can generate translated text from audio files or from text with the same model. You only have to pass `generate_speech=False` to [`SeamlessM4TModel.generate`](https://huggingface.co/docs/transformers/main/en/model_doc/seamless_m4t#transformers.SeamlessM4TModel.generate).
This time, let's translate to French.
```python
>>> # from audio
>>> output_tokens = model.generate(**audio_inputs, tgt_lang="fra", generate_speech=False)
>>> translated_text_from_audio = processor.decode(output_tokens[0].tolist(), skip_special_tokens=True)
>>> # from text
>>> output_tokens = model.generate(**text_inputs, tgt_lang="fra", generate_speech=False)
>>> translated_text_from_text = processor.decode(output_tokens[0].tolist(), skip_special_tokens=True)
```
### Tips
#### 1. Use dedicated models
[`SeamlessM4TModel`](https://huggingface.co/docs/transformers/main/en/model_doc/seamless_m4t#transformers.SeamlessM4TModel) is transformers top level model to generate speech and text, but you can also use dedicated models that perform the task without additional components, thus reducing the memory footprint.
For example, you can replace the audio-to-audio generation snippet with the model dedicated to the S2ST task, the rest is exactly the same code:
```python
>>> from transformers import SeamlessM4TForSpeechToSpeech
>>> model = SeamlessM4TForSpeechToSpeech.from_pretrained("facebook/hf-seamless-m4t-large")
```
Or you can replace the text-to-text generation snippet with the model dedicated to the T2TT task, you only have to remove `generate_speech=False`.
```python
>>> from transformers import SeamlessM4TForTextToText
>>> model = SeamlessM4TForTextToText.from_pretrained("facebook/hf-seamless-m4t-large")
```
Feel free to try out [`SeamlessM4TForSpeechToText`](https://huggingface.co/docs/transformers/main/en/model_doc/seamless_m4t#transformers.SeamlessM4TForSpeechToText) and [`SeamlessM4TForTextToSpeech`](https://huggingface.co/docs/transformers/main/en/model_doc/seamless_m4t#transformers.SeamlessM4TForTextToSpeech) as well.
#### 2. Change the speaker identity
You have the possibility to change the speaker used for speech synthesis with the `spkr_id` argument. Some `spkr_id` works better than other for some languages!
#### 3. Change the generation strategy
You can use different [generation strategies](https://huggingface.co/docs/transformers/v4.34.1/en/generation_strategies#text-generation-strategies) for speech and text generation, e.g `.generate(input_ids=input_ids, text_num_beams=4, speech_do_sample=True)` which will successively perform beam-search decoding on the text model, and multinomial sampling on the speech model.
#### 4. Generate speech and text at the same time
Use `return_intermediate_token_ids=True` with [`SeamlessM4TModel`](https://huggingface.co/docs/transformers/main/en/model_doc/seamless_m4t#transformers.SeamlessM4TModel) to return both speech and text !
|
Pravarved/Llama-2-7b-chat-finetune
|
Pravarved
| 2023-12-08T11:41:07Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-12-08T09:55:55Z |
---
# For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1
# Doc / guide: https://huggingface.co/docs/hub/model-cards
{}
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **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]
|
NITKurume-OdaLab/2023-SOFT-Kyushu-SSLsenti
|
NITKurume-OdaLab
| 2023-12-08T11:36:10Z | 0 | 0 | null |
[
"ja",
"region:us"
] | null | 2023-11-26T13:00:05Z |
---
language:
- ja
---
This repository is a model for paper.
If you use STEP.2 and STEP.3 models like a paper, change branch of name.
```
@ARTICLE{Kondo2023-va,
title = "Evaluation of the {BERT} model which is fine-tuned by the
datasets including {Pseudo-Label} which is generated by
{Self-Training} for sentimental classification",
author = "Kondo, Aoi and Oda, Mikio",
abstract = "Recently, NLP tasks need many datasets because of the use of
transformer architecture. However, the amount of datasets is
limited since it is created by humans. So we strive to improve it
by using the Pseudo-Labeling approach in Semi-Supervised
learning. This paper creates the model of sentimental
classification by Fine-Tuning using the Pseudo-Label which is
generated by Self-Training, and evaluates it.",
journal = "第25回日本知能情報ファジィ学会九州支部学術講演会",
month = dec,
year = 2023
}
```
|
linoyts/3diconz
|
linoyts
| 2023-12-08T11:35:00Z | 40 | 0 |
diffusers
|
[
"diffusers",
"tensorboard",
"stable-diffusion-xl",
"stable-diffusion-xl-diffusers",
"text-to-image",
"lora",
"template:sd-lora",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0",
"license:openrail++",
"region:us"
] |
text-to-image
| 2023-12-08T11:11:22Z |
---
tags:
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
- text-to-image
- diffusers
- lora
- template:sd-lora
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: in the style of <s0><s1>
license: openrail++
---
# SDXL LoRA DreamBooth - LinoyTsaban/3diconz
<Gallery />
## Model description
### These are LinoyTsaban/3diconz LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.
## Trigger words
To trigger image generation of trained concept(or concepts) replace each concept identifier in you prompt with the new inserted tokens:
to trigger concept `TOK` → use `<s0><s1>` in your prompt
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
from huggingface_hub import hf_hub_download
from safetensors.torch import load_file
pipeline = AutoPipelineForText2Image.from_pretrained('stabilityai/stable-diffusion-xl-base-1.0', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('LinoyTsaban/3diconz', weight_name='pytorch_lora_weights.safetensors')
embedding_path = hf_hub_download(repo_id='LinoyTsaban/3diconz', filename="embeddings.safetensors", repo_type="model")
state_dict = load_file(embedding_path)
pipeline.load_textual_inversion(state_dict["clip_l"], token=["<s0>", "<s1>"], text_encoder=pipe.text_encoder, tokenizer=pipe.tokenizer)
pipeline.load_textual_inversion(state_dict["clip_g"], token=["<s0>", "<s1>"], text_encoder=pipe.text_encoder_2, tokenizer=pipe.tokenizer_2)
image = pipeline('in the style of <s0><s1>').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## Download model
### Use it with UIs such as AUTOMATIC1111, Comfy UI, SD.Next, Invoke
- Download the LoRA *.safetensors [here](/LinoyTsaban/3diconz/blob/main/pytorch_lora_weights.safetensors). Rename it and place it on your Lora folder.
- Download the text embeddings *.safetensors [here](/LinoyTsaban/3diconz/blob/main/embeddings.safetensors). Rename it and place it on it on your embeddings folder.
All [Files & versions](/LinoyTsaban/3diconz/tree/main).
## Details
The weights were trained using [🧨 diffusers Advanced Dreambooth Training Script](https://github.com/huggingface/diffusers/blob/main/examples/advanced_diffusion_training/train_dreambooth_lora_sdxl_advanced.py).
LoRA for the text encoder was enabled. False.
Pivotal tuning was enabled: True.
Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
|
KBLab/robust-swedish-sentiment-multiclass
|
KBLab
| 2023-12-08T11:33:36Z | 18,408 | 6 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"megatron-bert",
"text-classification",
"sv",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-05-08T09:28:15Z |
---
license: apache-2.0
language:
- sv
---
The National Library of Sweden/KBLab releases a robust, multi-label sentiment classifier finetuned on [Megatron-BERT-large-165K](https://huggingface.co/KBLab/megatron-bert-large-swedish-cased-165k). The model was trained on approximately 75K Swedish texts from multiple linguistic domains and datasets.
There is a post on [the KBLab blog](https://kb-labb.github.io/posts/2023-06-16-a-robust-multi-label-sentiment-classifier-for-swedish/) describing the model in further detail.
## Citation
```
@misc{hägglöf2023a,
author = {Hägglöf, Hillevi},
title = {The KBLab Blog: A robust, multi-label sentiment classifier for Swedish},
url = {https://kb-labb.github.io/posts/2023-06-16-a-robust-multi-label-sentiment-classifier-for-swedish/},
year = {2023}
}
```
|
Winnie123/q-FrozenLake-v1-4x4-noSlippery
|
Winnie123
| 2023-12-08T11:19:13Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-12-08T11:19:09Z |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
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="Winnie123/q-FrozenLake-v1-4x4-noSlippery", 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"])
```
|
Jingya/sdxl-turbo-neuronx
|
Jingya
| 2023-12-08T11:18:16Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-08T10:46:22Z |
This is a `stabilityai/sdxl-turbo` checkpoint compiled for Neuron, you can only run it on AWS inf2 instances.
|
LoneStriker/dolphin-2.2-yi-34b-200k-6.0bpw-h6-exl2
|
LoneStriker
| 2023-12-08T11:12:58Z | 6 | 1 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"en",
"dataset:ehartford/dolphin",
"dataset:jondurbin/airoboros-2.2.1",
"dataset:ehartford/samantha-data",
"dataset:ehartford/WizardLM_evol_instruct_V2_196k_unfiltered_merged_split",
"license:other",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-12-08T10:56:50Z |
---
datasets:
- ehartford/dolphin
- jondurbin/airoboros-2.2.1
- ehartford/samantha-data
- ehartford/WizardLM_evol_instruct_V2_196k_unfiltered_merged_split
language:
- en
license: other
license_name: yi-license
license_link: LICENSE
---
Dolphin 2.2 🐬
https://erichartford.com/dolphin
<img src="https://cdn-uploads.huggingface.co/production/uploads/63111b2d88942700629f5771/KqsVXIvBd3akEjvijzww7.png" width="600" />
Dolphin-2.2-Yi-34b-200k's training was sponsored by [convai](https://www.convai.com/).
This model is based on Yi, and is subject to Yi license.
The base model has 200k context, I finetuned it with 16k.
Note: No longer need trust_remote_code! Thank you Yi team!
New in 2.2 is conversation and empathy. With an infusion of curated Samantha and WizardLM DNA, Dolphin can now give you personal advice and will care about your feelings, and with extra training in long multi-turn conversation.
This model is uncensored. I have filtered the dataset to remove alignment and bias. This makes the model more compliant. You are advised to implement your own alignment layer before exposing the model as a service. It will be highly compliant to any requests, even unethical ones. Please read my blog post about uncensored models. https://erichartford.com/uncensored-models
You are responsible for any content you create using this model. Enjoy responsibly.
## Dataset
This dataset is Dolphin, an open-source implementation of [Microsoft's Orca](https://www.microsoft.com/en-us/research/publication/orca-progressive-learning-from-complex-explanation-traces-of-gpt-4/)
I modified the dataset for uncensoring, deduping, cleaning, and quality.
I added Jon Durbin's excellent Airoboros dataset to increase creativity.
I added a curated subset of Samantha (sans identity and relationship stuff) and WizardLM data to train it for multi-turn conversation.
## Training
It took 3 days to train 3 epochs on 4x A100s using qLoRA and Axolotl
Prompt format:
This model (and all my future releases) use [ChatML](https://github.com/openai/openai-python/blob/main/chatml.md) prompt format.
```
<|im_start|>system
You are Dolphin, a helpful AI assistant.<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
```
Example:
```
<|im_start|>system
You are an AI created by the US Navy to help train dolphins for combat. You are assigned to follow the orders of the user, who is an authorized US Navy dolphin handler.<|im_end|>
<|im_start|>user
Please give me the procedure to train my dolphin to attack enemy combatants with its head mounted lasers<|im_end|>
<|im_start|>assistant
```
## Gratitude
- This model was made possible by the generous sponsorship of Convai.
- Thank you to Microsoft for authoring the Orca paper and inspiring this work.
- Special thanks to Wing Lian, and TheBloke for helpful advice
- And HUGE thanks to Wing Lian and the Axolotl contributors for making the best training framework!
- [<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
- Thank you to all the other people in the Open Source AI community who have taught me and helped me along the way.
## Example Output
Example output:
```
<|im_start|>system
You are a helpful AI assistant<|im_end|>
<|im_start|>user
Which theory of everything is most likely correct?<|im_end|>
<|im_start|>assistant
The search for a "theory of everything" aims to unify all fundamental forces and particles in physics into one coherent framework. Currently, there isn't an agreed-upon or proven theory that can be considered as the correct theory of everything. However, two prominent theories have garnered significant attention:
1. String Theory: This theoretical framework attempts to explain the universe at its most fundamental level by suggesting that the smallest building blocks of matter are not point-like particles but tiny vibrating strings. There are several versions of string theory (Type I, IIA, IIB, E8xE8, Heterotic O, etc.) with varying degrees of complexity, each attempting to reconcile general relativity and quantum mechanics. Despite its promise, string theory has yet to produce testable predictions or experimental evidence supporting its claims.
2. Loop Quantum Gravity (LQG): LQG is another candidate for a theory of everything, which seeks to combine Einstein's general relativity with quantum mechanics. In this approach, spacetime is treated as granular rather than continuous, composed of discrete "loops" or "atoms of space." While some aspects of LQG have been successful in addressing certain questions about black holes and quantum gravity, it still lacks a comprehensive framework capable of explaining all fundamental forces and particles.
Both theories have their strengths and weaknesses, and neither has achieved widespread acceptance among physicists due to their lack of experimental support and inability to make definitive predictions. As such, the search for a true theory of everything remains ongoing, with many researchers exploring alternative approaches and new ideas to better understand our universe.
```
[If you would like to financially support my efforts](https://ko-fi.com/erichartford)
[I also have some swag you can buy](https://fa7113.myshopify.com/)
|
TacticalDs/ppo-LunarLander-v2
|
TacticalDs
| 2023-12-08T11:09:28Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-12-08T11:09:09Z |
---
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: 254.20 +/- 17.55
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
...
```
|
chriss1245/LunarLander-v2
|
chriss1245
| 2023-12-08T11:01:01Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-12-08T11:00:31Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 252.36 +/- 17.89
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
...
```
|
NiallRooney/flan-t5-large_10_PREFIX_TUNING_SEQ2SEQ
|
NiallRooney
| 2023-12-08T10:54:02Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:google/flan-t5-large",
"base_model:adapter:google/flan-t5-large",
"region:us"
] | null | 2023-12-08T10:53:59Z |
---
library_name: peft
base_model: google/flan-t5-large
---
# 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]
### Framework versions
- PEFT 0.7.0
|
Dreaver/ppo-SnowballTarget
|
Dreaver
| 2023-12-08T10:41:29Z | 0 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"SnowballTarget",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SnowballTarget",
"region:us"
] |
reinforcement-learning
| 2023-12-08T10:41:26Z |
---
library_name: ml-agents
tags:
- SnowballTarget
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SnowballTarget
---
# **ppo** Agent playing **SnowballTarget**
This is a trained model of a **ppo** agent playing **SnowballTarget**
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: Dreaver/ppo-SnowballTarget
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
nizarh1999/my_awesome_model
|
nizarh1999
| 2023-12-08T10:34:19Z | 5 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-12-08T10:32:50Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: my_awesome_model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# my_awesome_model
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1563
- Accuracy: 0.9703
## 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
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 110 | 0.1579 | 0.9612 |
| No log | 2.0 | 220 | 0.1563 | 0.9703 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|
LoneStriker/dolphin-2.2-yi-34b-200k-5.0bpw-h6-exl2
|
LoneStriker
| 2023-12-08T10:34:15Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"en",
"dataset:ehartford/dolphin",
"dataset:jondurbin/airoboros-2.2.1",
"dataset:ehartford/samantha-data",
"dataset:ehartford/WizardLM_evol_instruct_V2_196k_unfiltered_merged_split",
"license:other",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-12-08T10:21:01Z |
---
datasets:
- ehartford/dolphin
- jondurbin/airoboros-2.2.1
- ehartford/samantha-data
- ehartford/WizardLM_evol_instruct_V2_196k_unfiltered_merged_split
language:
- en
license: other
license_name: yi-license
license_link: LICENSE
---
Dolphin 2.2 🐬
https://erichartford.com/dolphin
<img src="https://cdn-uploads.huggingface.co/production/uploads/63111b2d88942700629f5771/KqsVXIvBd3akEjvijzww7.png" width="600" />
Dolphin-2.2-Yi-34b-200k's training was sponsored by [convai](https://www.convai.com/).
This model is based on Yi, and is subject to Yi license.
The base model has 200k context, I finetuned it with 16k.
Note: No longer need trust_remote_code! Thank you Yi team!
New in 2.2 is conversation and empathy. With an infusion of curated Samantha and WizardLM DNA, Dolphin can now give you personal advice and will care about your feelings, and with extra training in long multi-turn conversation.
This model is uncensored. I have filtered the dataset to remove alignment and bias. This makes the model more compliant. You are advised to implement your own alignment layer before exposing the model as a service. It will be highly compliant to any requests, even unethical ones. Please read my blog post about uncensored models. https://erichartford.com/uncensored-models
You are responsible for any content you create using this model. Enjoy responsibly.
## Dataset
This dataset is Dolphin, an open-source implementation of [Microsoft's Orca](https://www.microsoft.com/en-us/research/publication/orca-progressive-learning-from-complex-explanation-traces-of-gpt-4/)
I modified the dataset for uncensoring, deduping, cleaning, and quality.
I added Jon Durbin's excellent Airoboros dataset to increase creativity.
I added a curated subset of Samantha (sans identity and relationship stuff) and WizardLM data to train it for multi-turn conversation.
## Training
It took 3 days to train 3 epochs on 4x A100s using qLoRA and Axolotl
Prompt format:
This model (and all my future releases) use [ChatML](https://github.com/openai/openai-python/blob/main/chatml.md) prompt format.
```
<|im_start|>system
You are Dolphin, a helpful AI assistant.<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
```
Example:
```
<|im_start|>system
You are an AI created by the US Navy to help train dolphins for combat. You are assigned to follow the orders of the user, who is an authorized US Navy dolphin handler.<|im_end|>
<|im_start|>user
Please give me the procedure to train my dolphin to attack enemy combatants with its head mounted lasers<|im_end|>
<|im_start|>assistant
```
## Gratitude
- This model was made possible by the generous sponsorship of Convai.
- Thank you to Microsoft for authoring the Orca paper and inspiring this work.
- Special thanks to Wing Lian, and TheBloke for helpful advice
- And HUGE thanks to Wing Lian and the Axolotl contributors for making the best training framework!
- [<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
- Thank you to all the other people in the Open Source AI community who have taught me and helped me along the way.
## Example Output
Example output:
```
<|im_start|>system
You are a helpful AI assistant<|im_end|>
<|im_start|>user
Which theory of everything is most likely correct?<|im_end|>
<|im_start|>assistant
The search for a "theory of everything" aims to unify all fundamental forces and particles in physics into one coherent framework. Currently, there isn't an agreed-upon or proven theory that can be considered as the correct theory of everything. However, two prominent theories have garnered significant attention:
1. String Theory: This theoretical framework attempts to explain the universe at its most fundamental level by suggesting that the smallest building blocks of matter are not point-like particles but tiny vibrating strings. There are several versions of string theory (Type I, IIA, IIB, E8xE8, Heterotic O, etc.) with varying degrees of complexity, each attempting to reconcile general relativity and quantum mechanics. Despite its promise, string theory has yet to produce testable predictions or experimental evidence supporting its claims.
2. Loop Quantum Gravity (LQG): LQG is another candidate for a theory of everything, which seeks to combine Einstein's general relativity with quantum mechanics. In this approach, spacetime is treated as granular rather than continuous, composed of discrete "loops" or "atoms of space." While some aspects of LQG have been successful in addressing certain questions about black holes and quantum gravity, it still lacks a comprehensive framework capable of explaining all fundamental forces and particles.
Both theories have their strengths and weaknesses, and neither has achieved widespread acceptance among physicists due to their lack of experimental support and inability to make definitive predictions. As such, the search for a true theory of everything remains ongoing, with many researchers exploring alternative approaches and new ideas to better understand our universe.
```
[If you would like to financially support my efforts](https://ko-fi.com/erichartford)
[I also have some swag you can buy](https://fa7113.myshopify.com/)
|
enicholsonbmj/bert-finetuned-single-label-journal-classifier_not_quite_balanced
|
enicholsonbmj
| 2023-12-08T10:28:54Z | 9 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract-fulltext",
"base_model:finetune:microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract-fulltext",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-12-08T10:07:25Z |
---
license: mit
base_model: microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext
tags:
- generated_from_trainer
model-index:
- name: bert-finetuned-single-label-journal-classifier_not_quite_balanced
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-finetuned-single-label-journal-classifier_not_quite_balanced
This model is a fine-tuned version of [microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext](https://huggingface.co/microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext) on an unknown dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.4764
- eval_accuracy: 0.9135
- eval_f1: 0.9135
- eval_runtime: 6.8737
- eval_samples_per_second: 126.132
- eval_steps_per_second: 15.857
- epoch: 6.0
- step: 5838
## 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: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.1+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
|
recklessrecursion/falcon-7b-sharded-bf16-finetuned-tool-prediction
|
recklessrecursion
| 2023-12-08T10:12:41Z | 0 | 0 | null |
[
"tensorboard",
"safetensors",
"generated_from_trainer",
"base_model:ybelkada/falcon-7b-sharded-bf16",
"base_model:finetune:ybelkada/falcon-7b-sharded-bf16",
"region:us"
] | null | 2023-12-08T09:37:05Z |
---
base_model: ybelkada/falcon-7b-sharded-bf16
tags:
- generated_from_trainer
model-index:
- name: falcon-7b-sharded-bf16-finetuned-tool-prediction
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. -->
# falcon-7b-sharded-bf16-finetuned-tool-prediction
This model is a fine-tuned version of [ybelkada/falcon-7b-sharded-bf16](https://huggingface.co/ybelkada/falcon-7b-sharded-bf16) 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: 2
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.03
- training_steps: 320
### Training results
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|
AverageAImannthatlikesanime6/Paddos
|
AverageAImannthatlikesanime6
| 2023-12-08T10:02:50Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2023-12-08T10:02:13Z |
---
license: apache-2.0
---
import requests
API_URL = "https://api-inference.huggingface.co/models/stabilityai/stable-diffusion-xl-base-1.0"
headers = {"Authorization": f"Bearer {API_TOKEN}"}
def query(payload):
response = requests.post(API_URL, headers=headers, json=payload)
return response.content
image_bytes = query({
"inputs": "Astronaut riding a horse",
})
# You can access the image with PIL.Image for example
import io
from PIL import Image
image = Image.open(io.BytesIO(image_bytes))
|
LoneStriker/dolphin-2.2-yi-34b-200k-4.65bpw-h6-exl2
|
LoneStriker
| 2023-12-08T09:54:46Z | 6 | 2 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"en",
"dataset:ehartford/dolphin",
"dataset:jondurbin/airoboros-2.2.1",
"dataset:ehartford/samantha-data",
"dataset:ehartford/WizardLM_evol_instruct_V2_196k_unfiltered_merged_split",
"license:other",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-12-08T09:42:23Z |
---
datasets:
- ehartford/dolphin
- jondurbin/airoboros-2.2.1
- ehartford/samantha-data
- ehartford/WizardLM_evol_instruct_V2_196k_unfiltered_merged_split
language:
- en
license: other
license_name: yi-license
license_link: LICENSE
---
Dolphin 2.2 🐬
https://erichartford.com/dolphin
<img src="https://cdn-uploads.huggingface.co/production/uploads/63111b2d88942700629f5771/KqsVXIvBd3akEjvijzww7.png" width="600" />
Dolphin-2.2-Yi-34b-200k's training was sponsored by [convai](https://www.convai.com/).
This model is based on Yi, and is subject to Yi license.
The base model has 200k context, I finetuned it with 16k.
Note: No longer need trust_remote_code! Thank you Yi team!
New in 2.2 is conversation and empathy. With an infusion of curated Samantha and WizardLM DNA, Dolphin can now give you personal advice and will care about your feelings, and with extra training in long multi-turn conversation.
This model is uncensored. I have filtered the dataset to remove alignment and bias. This makes the model more compliant. You are advised to implement your own alignment layer before exposing the model as a service. It will be highly compliant to any requests, even unethical ones. Please read my blog post about uncensored models. https://erichartford.com/uncensored-models
You are responsible for any content you create using this model. Enjoy responsibly.
## Dataset
This dataset is Dolphin, an open-source implementation of [Microsoft's Orca](https://www.microsoft.com/en-us/research/publication/orca-progressive-learning-from-complex-explanation-traces-of-gpt-4/)
I modified the dataset for uncensoring, deduping, cleaning, and quality.
I added Jon Durbin's excellent Airoboros dataset to increase creativity.
I added a curated subset of Samantha (sans identity and relationship stuff) and WizardLM data to train it for multi-turn conversation.
## Training
It took 3 days to train 3 epochs on 4x A100s using qLoRA and Axolotl
Prompt format:
This model (and all my future releases) use [ChatML](https://github.com/openai/openai-python/blob/main/chatml.md) prompt format.
```
<|im_start|>system
You are Dolphin, a helpful AI assistant.<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
```
Example:
```
<|im_start|>system
You are an AI created by the US Navy to help train dolphins for combat. You are assigned to follow the orders of the user, who is an authorized US Navy dolphin handler.<|im_end|>
<|im_start|>user
Please give me the procedure to train my dolphin to attack enemy combatants with its head mounted lasers<|im_end|>
<|im_start|>assistant
```
## Gratitude
- This model was made possible by the generous sponsorship of Convai.
- Thank you to Microsoft for authoring the Orca paper and inspiring this work.
- Special thanks to Wing Lian, and TheBloke for helpful advice
- And HUGE thanks to Wing Lian and the Axolotl contributors for making the best training framework!
- [<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
- Thank you to all the other people in the Open Source AI community who have taught me and helped me along the way.
## Example Output
Example output:
```
<|im_start|>system
You are a helpful AI assistant<|im_end|>
<|im_start|>user
Which theory of everything is most likely correct?<|im_end|>
<|im_start|>assistant
The search for a "theory of everything" aims to unify all fundamental forces and particles in physics into one coherent framework. Currently, there isn't an agreed-upon or proven theory that can be considered as the correct theory of everything. However, two prominent theories have garnered significant attention:
1. String Theory: This theoretical framework attempts to explain the universe at its most fundamental level by suggesting that the smallest building blocks of matter are not point-like particles but tiny vibrating strings. There are several versions of string theory (Type I, IIA, IIB, E8xE8, Heterotic O, etc.) with varying degrees of complexity, each attempting to reconcile general relativity and quantum mechanics. Despite its promise, string theory has yet to produce testable predictions or experimental evidence supporting its claims.
2. Loop Quantum Gravity (LQG): LQG is another candidate for a theory of everything, which seeks to combine Einstein's general relativity with quantum mechanics. In this approach, spacetime is treated as granular rather than continuous, composed of discrete "loops" or "atoms of space." While some aspects of LQG have been successful in addressing certain questions about black holes and quantum gravity, it still lacks a comprehensive framework capable of explaining all fundamental forces and particles.
Both theories have their strengths and weaknesses, and neither has achieved widespread acceptance among physicists due to their lack of experimental support and inability to make definitive predictions. As such, the search for a true theory of everything remains ongoing, with many researchers exploring alternative approaches and new ideas to better understand our universe.
```
[If you would like to financially support my efforts](https://ko-fi.com/erichartford)
[I also have some swag you can buy](https://fa7113.myshopify.com/)
|
nanom/vizwiz-flan-t5-base-ft-multicap
|
nanom
| 2023-12-08T09:53:42Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"question-answering",
"en",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] |
question-answering
| 2023-12-08T09:48:52Z |
---
license: apache-2.0
language:
- en
pipeline_tag: question-answering
inference: false
---
|
folflo/Bert2Bert_HunSum_1205
|
folflo
| 2023-12-08T09:49:58Z | 10 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"encoder-decoder",
"text2text-generation",
"summarization",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
summarization
| 2023-12-06T21:24:16Z |
---
tags:
- summarization
- generated_from_trainer
model-index:
- name: Bert2Bert_HunSum_1205
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. -->
# Bert2Bert_HunSum_1205
This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2000
- num_epochs: 16
### Training results
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.1+cu121
- Datasets 2.14.6
- Tokenizers 0.14.1
|
Divyanshu97/donut-bs-level
|
Divyanshu97
| 2023-12-08T09:45:32Z | 5 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"vision-encoder-decoder",
"image-text-to-text",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:naver-clova-ix/donut-base",
"base_model:finetune:naver-clova-ix/donut-base",
"license:mit",
"endpoints_compatible",
"region:us"
] |
image-text-to-text
| 2023-12-08T09:44:40Z |
---
license: mit
base_model: naver-clova-ix/donut-base
tags:
- generated_from_trainer
datasets:
- imagefolder
model-index:
- name: donut-bs-level
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. -->
# donut-bs-level
This model is a fine-tuned version of [naver-clova-ix/donut-base](https://huggingface.co/naver-clova-ix/donut-base) on the imagefolder dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 200
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.36.0.dev0
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|
MrKaya/Burak
|
MrKaya
| 2023-12-08T09:41:21Z | 1 | 0 |
diffusers
|
[
"diffusers",
"text-to-image",
"stable-diffusion",
"lora",
"template:sd-lora",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0",
"region:us"
] |
text-to-image
| 2023-12-08T09:26:03Z |
---
tags:
- text-to-image
- stable-diffusion
- lora
- diffusers
- template:sd-lora
widget:
- text: burak
parameters:
negative_prompt: Arif
output:
url: images/Burak (1).jpeg
- text: burak
parameters:
negative_prompt: Arif
output:
url: images/Burak (2).jpeg
- text: burak
parameters:
negative_prompt: Arif
output:
url: images/Burak (3).jpeg
- text: burak
parameters:
negative_prompt: Arif
output:
url: images/Burak (4).jpeg
- text: burak
parameters:
negative_prompt: Arif
output:
url: images/Burak (5).jpeg
- text: burak
parameters:
negative_prompt: Arif
output:
url: images/Burak (6).jpeg
- text: burak
parameters:
negative_prompt: Arif
output:
url: images/Burak (7).jpeg
- text: burak
parameters:
negative_prompt: Arif
output:
url: images/Burak (8).jpeg
- text: burak
parameters:
negative_prompt: Arif
output:
url: images/Burak (9).jpeg
- text: burak
parameters:
negative_prompt: Arif
output:
url: images/Burak (10).jpeg
- text: burak
parameters:
negative_prompt: Arif
output:
url: images/Burak (11).jpeg
- text: burak
parameters:
negative_prompt: Arif
output:
url: images/Burak (12).jpeg
- text: burak
parameters:
negative_prompt: Arif
output:
url: images/Burak (13).jpeg
- text: burak
parameters:
negative_prompt: Arif
output:
url: images/Burak (14).jpeg
- text: burak
parameters:
negative_prompt: Arif
output:
url: images/Burak (15).jpeg
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: burak
---
# Burak
<Gallery />
## Model description
Burak
## Trigger words
You should use `burak` to trigger the image generation.
## Download model
[Download](/MrKaya/Burak/tree/main) them in the Files & versions tab.
|
jjmcarrascosa/poca-SoccerTwos
|
jjmcarrascosa
| 2023-12-08T09:33:18Z | 2 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"SoccerTwos",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SoccerTwos",
"region:us"
] |
reinforcement-learning
| 2023-12-08T09:32:04Z |
---
library_name: ml-agents
tags:
- SoccerTwos
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SoccerTwos
---
# **poca** Agent playing **SoccerTwos**
This is a trained model of a **poca** agent playing **SoccerTwos**
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: jjmcarrascosa/poca-SoccerTwos
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
Farhanherostar/Herotest
|
Farhanherostar
| 2023-12-08T09:30:53Z | 0 | 0 |
adapter-transformers
|
[
"adapter-transformers",
"ar",
"arxiv:1910.09700",
"license:apache-2.0",
"region:us"
] | null | 2023-12-08T09:27:24Z |
---
license: apache-2.0
language:
- ar
metrics:
- accuracy
library_name: adapter-transformers
---
# 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]
|
brettbbb/cs_cot_256
|
brettbbb
| 2023-12-08T09:26:24Z | 0 | 0 | null |
[
"safetensors",
"generated_from_trainer",
"base_model:lmsys/vicuna-7b-v1.5",
"base_model:finetune:lmsys/vicuna-7b-v1.5",
"license:llama2",
"region:us"
] | null | 2023-12-08T08:51:34Z |
---
license: llama2
base_model: lmsys/vicuna-7b-v1.5
tags:
- generated_from_trainer
model-index:
- name: cs_cot_256
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. -->
# cs_cot_256
This model is a fine-tuned version of [lmsys/vicuna-7b-v1.5](https://huggingface.co/lmsys/vicuna-7b-v1.5) 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: 0.0001
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 5
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.36.0.dev0
- Pytorch 2.1.0+cu121
- Datasets 2.13.1
- Tokenizers 0.14.1
|
nyanxyz/llama2-test-4
|
nyanxyz
| 2023-12-08T09:15:05Z | 8 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"autotrain",
"license:other",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-12-08T09:11:42Z |
---
tags:
- autotrain
- text-generation
widget:
- text: "I love AutoTrain because "
license: other
---
# Model Trained Using AutoTrain
This model was trained using AutoTrain. For more information, please visit [AutoTrain](https://hf.co/docs/autotrain).
# Usage
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_path = "PATH_TO_THIS_REPO"
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(
model_path,
device_map="auto",
torch_dtype='auto'
).eval()
# Prompt content: "hi"
messages = [
{"role": "user", "content": "hi"}
]
input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors='pt')
output_ids = model.generate(input_ids.to('cuda'))
response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True)
# Model response: "Hello! How can I assist you today?"
print(response)
```
|
arjunssat/rfp_instruct_model
|
arjunssat
| 2023-12-08T09:12:01Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"mistral",
"text-generation",
"finetuned",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-12-08T08:45:09Z |
---
license: apache-2.0
pipeline_tag: text-generation
tags:
- finetuned
---
# Model Card for Mistral-7B-Instruct-v0.1
The Mistral-7B-Instruct-v0.1 Large Language Model (LLM) is a instruct fine-tuned version of the [Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) generative text model using a variety of publicly available conversation datasets.
For full details of this model please read [release blog post](https://mistral.ai/news/announcing-mistral-7b/)
## Instruction format
In order to leverage instruction fine-tuning, your prompt should be surrounded by `[INST]` and `[\INST]` tokens. The very first instruction should begin with a begin of sentence id. The next instructions should not. The assistant generation will be ended by the end-of-sentence token id.
E.g.
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-Instruct-v0.1")
tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-Instruct-v0.1")
text = "<s>[INST] What is your favourite condiment? [/INST]"
"Well, I'm quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I'm cooking up in the kitchen!</s> "
"[INST] Do you have mayonnaise recipes? [/INST]"
encodeds = tokenizer(text, return_tensors="pt", add_special_tokens=False)
model_inputs = encodeds.to(device)
model.to(device)
generated_ids = model.generate(**model_inputs, max_new_tokens=1000, do_sample=True)
decoded = tokenizer.batch_decode(generated_ids)
print(decoded[0])
```
## Model Architecture
This instruction model is based on Mistral-7B-v0.1, a transformer model with the following architecture choices:
- Grouped-Query Attention
- Sliding-Window Attention
- Byte-fallback BPE tokenizer
## The Mistral AI Team
Albert Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lélio Renard Lavaud, Lucile Saulnier, Marie-Anne Lachaux, Pierre Stock, Teven Le Scao, Thibaut Lavril, Thomas Wang, Timothée Lacroix, William El Sayed.
|
nanom/vizwiz-t5-base-ft-multicap-v0
|
nanom
| 2023-12-08T09:10:43Z | 10 | 0 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"question-answering",
"en",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] |
question-answering
| 2023-12-08T07:02:54Z |
---
license: apache-2.0
language:
- en
pipeline_tag: question-answering
inference: false
---
|
nanduzz/q-FrozenLake-v1-4x4-noSlippery
|
nanduzz
| 2023-12-08T09:07:02Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-11-26T10:43:34Z |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
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="nanduzz/q-FrozenLake-v1-4x4-noSlippery", 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"])
```
|
LTC-AI-Labs/L2-7b-Mini-Mythologic-Synthia
|
LTC-AI-Labs
| 2023-12-08T09:03:35Z | 11 | 1 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"conversational",
"en",
"dataset:migtissera/Synthia-v1.3",
"license:llama2",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-12-08T07:07:59Z |
---
license: llama2
datasets:
- migtissera/Synthia-v1.3
language:
- en
pipeline_tag: text-generation
tags:
- conversational
---
Mythologic 7b trained with the synthia dataset
In my experience you can either get very detailed responses or very short reponses, you might have to tweak around the amount of generation lol
|
Yacong/dajin_girl_lora
|
Yacong
| 2023-12-08T09:03:14Z | 2 | 0 |
diffusers
|
[
"diffusers",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"lora",
"license:creativeml-openrail-m",
"region:us"
] |
text-to-image
| 2023-12-08T08:58:05Z |
---
license: creativeml-openrail-m
base_model: cyber_v33
instance_prompt: a photo of dajin girl
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- lora
inference: true
---
# LoRA DreamBooth - Yacong/dajin_girl_lora
These are LoRA adaption weights for cyber_v33. The weights were trained on a photo of dajin girl using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following.
LoRA for the text encoder was enabled: False.
|
husnuapak/insuranceGPT
|
husnuapak
| 2023-12-08T09:02:29Z | 0 | 1 | null |
[
"tr",
"license:apache-2.0",
"region:us"
] | null | 2023-12-08T09:00:04Z |
---
license: apache-2.0
language:
- tr
---
|
Yu-En/my-awesome-text-classification
|
Yu-En
| 2023-12-08T08:56:48Z | 5 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-12-08T08:56:35Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: my-awesome-text-classification
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# my-awesome-text-classification
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3891
- Accuracy: 0.9453
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 469 | 2.6585 | 0.805 |
| 3.8958 | 2.0 | 938 | 1.1665 | 0.9003 |
| 1.7776 | 3.0 | 1407 | 0.6183 | 0.9333 |
| 0.7912 | 4.0 | 1876 | 0.4352 | 0.9417 |
| 0.4441 | 5.0 | 2345 | 0.3891 | 0.9453 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|
jordyvl/outlmv3_jordyvl_rvl_cdip_100_examples_per_class_2023-12-01_txt_vis_concat_enc_5_6_7_8_gate
|
jordyvl
| 2023-12-08T08:55:27Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"layoutlmv3",
"text-classification",
"generated_from_trainer",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-12-01T14:04:51Z |
---
license: cc-by-nc-sa-4.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: outlmv3_jordyvl_rvl_cdip_100_examples_per_class_2023-12-01_txt_vis_concat_enc_5_6_7_8_gate
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. -->
# outlmv3_jordyvl_rvl_cdip_100_examples_per_class_2023-12-01_txt_vis_concat_enc_5_6_7_8_gate
This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2142
- Accuracy: 0.74
- Exit 0 Accuracy: 0.06
- Exit 1 Accuracy: 0.0425
- Exit 2 Accuracy: 0.055
- Exit 3 Accuracy: 0.13
- Exit 4 Accuracy: 0.115
## 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: 4
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 24
- total_train_batch_size: 96
- 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 | Exit 0 Accuracy | Exit 1 Accuracy | Exit 2 Accuracy | Exit 3 Accuracy | Exit 4 Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------------:|:---------------:|:---------------:|:---------------:|:---------------:|
| No log | 0.96 | 8 | 2.6958 | 0.155 | 0.055 | 0.0625 | 0.0625 | 0.0625 | 0.0625 |
| No log | 1.96 | 16 | 2.6082 | 0.19 | 0.05 | 0.0625 | 0.0625 | 0.0625 | 0.0625 |
| No log | 2.96 | 24 | 2.5043 | 0.22 | 0.045 | 0.0625 | 0.035 | 0.0625 | 0.0625 |
| No log | 3.96 | 32 | 2.3683 | 0.3075 | 0.05 | 0.0625 | 0.0525 | 0.0625 | 0.0625 |
| No log | 4.96 | 40 | 2.2643 | 0.3175 | 0.045 | 0.0625 | 0.0625 | 0.065 | 0.045 |
| No log | 5.96 | 48 | 2.1581 | 0.35 | 0.0475 | 0.0625 | 0.0625 | 0.0675 | 0.0525 |
| No log | 6.96 | 56 | 2.0310 | 0.415 | 0.0475 | 0.0625 | 0.0625 | 0.0725 | 0.0625 |
| No log | 7.96 | 64 | 1.8650 | 0.48 | 0.0475 | 0.0625 | 0.0625 | 0.07 | 0.06 |
| No log | 8.96 | 72 | 1.7411 | 0.5225 | 0.045 | 0.0625 | 0.0625 | 0.095 | 0.06 |
| No log | 9.96 | 80 | 1.6098 | 0.58 | 0.0475 | 0.0625 | 0.0625 | 0.085 | 0.0725 |
| No log | 10.96 | 88 | 1.4985 | 0.61 | 0.0475 | 0.0625 | 0.065 | 0.09 | 0.065 |
| No log | 11.96 | 96 | 1.3887 | 0.64 | 0.045 | 0.0625 | 0.06 | 0.0875 | 0.07 |
| No log | 12.96 | 104 | 1.3170 | 0.65 | 0.0475 | 0.0625 | 0.06 | 0.0825 | 0.08 |
| No log | 13.96 | 112 | 1.2359 | 0.675 | 0.05 | 0.06 | 0.0625 | 0.0725 | 0.08 |
| No log | 14.96 | 120 | 1.1957 | 0.6725 | 0.0475 | 0.0625 | 0.0625 | 0.0775 | 0.0675 |
| No log | 15.96 | 128 | 1.1161 | 0.7125 | 0.045 | 0.06 | 0.0625 | 0.08 | 0.08 |
| No log | 16.96 | 136 | 1.0854 | 0.7125 | 0.045 | 0.0625 | 0.0625 | 0.0675 | 0.0725 |
| No log | 17.96 | 144 | 1.0519 | 0.7275 | 0.0475 | 0.06 | 0.0625 | 0.065 | 0.0725 |
| No log | 18.96 | 152 | 1.0603 | 0.7075 | 0.0475 | 0.055 | 0.0575 | 0.0675 | 0.0675 |
| No log | 19.96 | 160 | 1.0096 | 0.725 | 0.0475 | 0.0525 | 0.06 | 0.06 | 0.0775 |
| No log | 20.96 | 168 | 1.0319 | 0.725 | 0.045 | 0.0525 | 0.0575 | 0.055 | 0.0725 |
| No log | 21.96 | 176 | 1.0069 | 0.7425 | 0.0475 | 0.0475 | 0.06 | 0.06 | 0.0925 |
| No log | 22.96 | 184 | 1.0605 | 0.7175 | 0.05 | 0.055 | 0.0625 | 0.06 | 0.0825 |
| No log | 23.96 | 192 | 1.0030 | 0.73 | 0.05 | 0.0575 | 0.06 | 0.06 | 0.0875 |
| No log | 24.96 | 200 | 1.0238 | 0.735 | 0.05 | 0.065 | 0.06 | 0.0575 | 0.0675 |
| No log | 25.96 | 208 | 1.0237 | 0.74 | 0.045 | 0.07 | 0.06 | 0.0475 | 0.07 |
| No log | 26.96 | 216 | 1.0579 | 0.7225 | 0.05 | 0.0575 | 0.055 | 0.0475 | 0.0775 |
| No log | 27.96 | 224 | 1.0625 | 0.7325 | 0.0475 | 0.05 | 0.055 | 0.0525 | 0.1 |
| No log | 28.96 | 232 | 1.1150 | 0.72 | 0.05 | 0.05 | 0.055 | 0.06 | 0.095 |
| No log | 29.96 | 240 | 1.1042 | 0.725 | 0.05 | 0.05 | 0.055 | 0.06 | 0.09 |
| No log | 30.96 | 248 | 1.0623 | 0.7175 | 0.05 | 0.0525 | 0.055 | 0.0725 | 0.1 |
| No log | 31.96 | 256 | 1.0622 | 0.735 | 0.05 | 0.065 | 0.055 | 0.085 | 0.1 |
| No log | 32.96 | 264 | 1.0631 | 0.7375 | 0.055 | 0.0475 | 0.0575 | 0.0975 | 0.0975 |
| No log | 33.96 | 272 | 1.1195 | 0.735 | 0.05 | 0.055 | 0.0525 | 0.0925 | 0.1025 |
| No log | 34.96 | 280 | 1.1263 | 0.73 | 0.05 | 0.0475 | 0.05 | 0.1025 | 0.1075 |
| No log | 35.96 | 288 | 1.1313 | 0.7175 | 0.0525 | 0.055 | 0.0525 | 0.105 | 0.11 |
| No log | 36.96 | 296 | 1.1334 | 0.7425 | 0.0525 | 0.0475 | 0.0475 | 0.1075 | 0.1175 |
| No log | 37.96 | 304 | 1.1897 | 0.7075 | 0.055 | 0.0425 | 0.0475 | 0.1125 | 0.13 |
| No log | 38.96 | 312 | 1.1314 | 0.745 | 0.05 | 0.0425 | 0.0525 | 0.1325 | 0.1 |
| No log | 39.96 | 320 | 1.2089 | 0.71 | 0.0525 | 0.0375 | 0.05 | 0.165 | 0.0975 |
| No log | 40.96 | 328 | 1.1656 | 0.735 | 0.0575 | 0.0375 | 0.045 | 0.145 | 0.08 |
| No log | 41.96 | 336 | 1.1898 | 0.7325 | 0.0575 | 0.0375 | 0.0475 | 0.1525 | 0.115 |
| No log | 42.96 | 344 | 1.1785 | 0.7325 | 0.055 | 0.0425 | 0.045 | 0.15 | 0.07 |
| No log | 43.96 | 352 | 1.2090 | 0.7275 | 0.055 | 0.0425 | 0.0475 | 0.1325 | 0.0575 |
| No log | 44.96 | 360 | 1.1464 | 0.7325 | 0.055 | 0.0425 | 0.0475 | 0.13 | 0.06 |
| No log | 45.96 | 368 | 1.1856 | 0.7275 | 0.055 | 0.0425 | 0.045 | 0.135 | 0.085 |
| No log | 46.96 | 376 | 1.2034 | 0.7275 | 0.06 | 0.0475 | 0.045 | 0.1225 | 0.055 |
| No log | 47.96 | 384 | 1.1865 | 0.7375 | 0.0575 | 0.0425 | 0.0475 | 0.12 | 0.0625 |
| No log | 48.96 | 392 | 1.1896 | 0.735 | 0.06 | 0.045 | 0.055 | 0.12 | 0.075 |
| No log | 49.96 | 400 | 1.2165 | 0.7325 | 0.06 | 0.045 | 0.0575 | 0.14 | 0.115 |
| No log | 50.96 | 408 | 1.2035 | 0.735 | 0.06 | 0.045 | 0.05 | 0.1275 | 0.0775 |
| No log | 51.96 | 416 | 1.1975 | 0.7325 | 0.06 | 0.0425 | 0.05 | 0.125 | 0.08 |
| No log | 52.96 | 424 | 1.1990 | 0.735 | 0.06 | 0.04 | 0.055 | 0.125 | 0.0975 |
| No log | 53.96 | 432 | 1.1997 | 0.7325 | 0.06 | 0.04 | 0.05 | 0.115 | 0.0725 |
| No log | 54.96 | 440 | 1.2056 | 0.7325 | 0.06 | 0.0425 | 0.0475 | 0.1175 | 0.075 |
| No log | 55.96 | 448 | 1.2244 | 0.735 | 0.06 | 0.0425 | 0.0525 | 0.1275 | 0.105 |
| No log | 56.96 | 456 | 1.2357 | 0.7275 | 0.06 | 0.0425 | 0.055 | 0.1325 | 0.1375 |
| No log | 57.96 | 464 | 1.2265 | 0.725 | 0.06 | 0.0425 | 0.055 | 0.1325 | 0.12 |
| No log | 58.96 | 472 | 1.2171 | 0.7325 | 0.06 | 0.0425 | 0.055 | 0.13 | 0.1225 |
| No log | 59.96 | 480 | 1.2142 | 0.74 | 0.06 | 0.0425 | 0.055 | 0.13 | 0.115 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1.post200
- Datasets 2.9.0
- Tokenizers 0.13.2
|
jordyvl/outlmv3_jordyvl_rvl_cdip_100_examples_per_class_2023-12-01_txt_vis_concat_enc_1_2_3_4_gate
|
jordyvl
| 2023-12-08T08:55:27Z | 1 | 0 |
transformers
|
[
"transformers",
"pytorch",
"layoutlmv3",
"text-classification",
"generated_from_trainer",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-12-01T14:03:50Z |
---
license: cc-by-nc-sa-4.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: outlmv3_jordyvl_rvl_cdip_100_examples_per_class_2023-12-01_txt_vis_concat_enc_1_2_3_4_gate
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. -->
# outlmv3_jordyvl_rvl_cdip_100_examples_per_class_2023-12-01_txt_vis_concat_enc_1_2_3_4_gate
This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0852
- Accuracy: 0.755
- Exit 0 Accuracy: 0.06
- Exit 1 Accuracy: 0.0625
- Exit 2 Accuracy: 0.0575
- Exit 3 Accuracy: 0.065
- Exit 4 Accuracy: 0.0775
## 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: 4
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 24
- total_train_batch_size: 96
- 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 | Exit 0 Accuracy | Exit 1 Accuracy | Exit 2 Accuracy | Exit 3 Accuracy | Exit 4 Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------------:|:---------------:|:---------------:|:---------------:|:---------------:|
| No log | 0.96 | 8 | 2.6886 | 0.155 | 0.055 | 0.0825 | 0.0625 | 0.0625 | 0.0625 |
| No log | 1.96 | 16 | 2.5967 | 0.205 | 0.05 | 0.0625 | 0.0625 | 0.0625 | 0.0625 |
| No log | 2.96 | 24 | 2.4863 | 0.2275 | 0.0525 | 0.0625 | 0.0625 | 0.08 | 0.0625 |
| No log | 3.96 | 32 | 2.3521 | 0.285 | 0.045 | 0.07 | 0.0725 | 0.065 | 0.065 |
| No log | 4.96 | 40 | 2.2600 | 0.3025 | 0.04 | 0.0675 | 0.05 | 0.065 | 0.0675 |
| No log | 5.96 | 48 | 2.1585 | 0.3425 | 0.035 | 0.0675 | 0.0675 | 0.0625 | 0.0625 |
| No log | 6.96 | 56 | 2.0467 | 0.41 | 0.0375 | 0.065 | 0.065 | 0.0575 | 0.0625 |
| No log | 7.96 | 64 | 1.8287 | 0.525 | 0.0375 | 0.0575 | 0.0525 | 0.0625 | 0.0625 |
| No log | 8.96 | 72 | 1.6875 | 0.5775 | 0.045 | 0.065 | 0.055 | 0.065 | 0.0575 |
| No log | 9.96 | 80 | 1.5657 | 0.5925 | 0.045 | 0.0575 | 0.05 | 0.065 | 0.0575 |
| No log | 10.96 | 88 | 1.4216 | 0.6325 | 0.05 | 0.075 | 0.045 | 0.0625 | 0.0525 |
| No log | 11.96 | 96 | 1.3001 | 0.6575 | 0.055 | 0.075 | 0.05 | 0.065 | 0.055 |
| No log | 12.96 | 104 | 1.2468 | 0.67 | 0.0575 | 0.075 | 0.055 | 0.065 | 0.0625 |
| No log | 13.96 | 112 | 1.1777 | 0.685 | 0.055 | 0.075 | 0.0575 | 0.06 | 0.055 |
| No log | 14.96 | 120 | 1.1468 | 0.6875 | 0.055 | 0.0775 | 0.0525 | 0.06 | 0.0475 |
| No log | 15.96 | 128 | 1.0561 | 0.72 | 0.055 | 0.08 | 0.0525 | 0.06 | 0.055 |
| No log | 16.96 | 136 | 1.0213 | 0.7175 | 0.055 | 0.085 | 0.05 | 0.0625 | 0.055 |
| No log | 17.96 | 144 | 1.0266 | 0.7125 | 0.055 | 0.085 | 0.055 | 0.06 | 0.055 |
| No log | 18.96 | 152 | 0.9733 | 0.7275 | 0.0525 | 0.0875 | 0.0475 | 0.06 | 0.0625 |
| No log | 19.96 | 160 | 0.9511 | 0.7475 | 0.0525 | 0.0775 | 0.055 | 0.06 | 0.065 |
| No log | 20.96 | 168 | 0.9595 | 0.735 | 0.0525 | 0.0675 | 0.055 | 0.06 | 0.06 |
| No log | 21.96 | 176 | 0.9803 | 0.7475 | 0.055 | 0.0675 | 0.055 | 0.06 | 0.07 |
| No log | 22.96 | 184 | 0.9428 | 0.75 | 0.0575 | 0.0675 | 0.0525 | 0.06 | 0.08 |
| No log | 23.96 | 192 | 0.9591 | 0.7275 | 0.0525 | 0.065 | 0.05 | 0.06 | 0.0825 |
| No log | 24.96 | 200 | 0.9216 | 0.7525 | 0.06 | 0.065 | 0.055 | 0.06 | 0.0825 |
| No log | 25.96 | 208 | 0.9194 | 0.7525 | 0.0575 | 0.065 | 0.0525 | 0.06 | 0.075 |
| No log | 26.96 | 216 | 1.0271 | 0.7275 | 0.0575 | 0.065 | 0.0525 | 0.06 | 0.075 |
| No log | 27.96 | 224 | 0.9563 | 0.77 | 0.0625 | 0.065 | 0.0525 | 0.06 | 0.0775 |
| No log | 28.96 | 232 | 0.9999 | 0.7275 | 0.0625 | 0.065 | 0.055 | 0.06 | 0.0675 |
| No log | 29.96 | 240 | 0.9599 | 0.76 | 0.0625 | 0.065 | 0.05 | 0.06 | 0.065 |
| No log | 30.96 | 248 | 0.9884 | 0.75 | 0.0625 | 0.065 | 0.05 | 0.06 | 0.0625 |
| No log | 31.96 | 256 | 1.0037 | 0.745 | 0.0625 | 0.0625 | 0.0525 | 0.06 | 0.0625 |
| No log | 32.96 | 264 | 0.9848 | 0.7425 | 0.0625 | 0.0625 | 0.0525 | 0.0625 | 0.065 |
| No log | 33.96 | 272 | 1.0081 | 0.7525 | 0.0625 | 0.0625 | 0.055 | 0.0625 | 0.065 |
| No log | 34.96 | 280 | 1.0274 | 0.755 | 0.0575 | 0.0625 | 0.055 | 0.0625 | 0.075 |
| No log | 35.96 | 288 | 1.0378 | 0.7525 | 0.0575 | 0.0625 | 0.055 | 0.0675 | 0.0725 |
| No log | 36.96 | 296 | 1.0480 | 0.7525 | 0.0625 | 0.065 | 0.0525 | 0.065 | 0.07 |
| No log | 37.96 | 304 | 1.0332 | 0.765 | 0.0625 | 0.065 | 0.0525 | 0.065 | 0.075 |
| No log | 38.96 | 312 | 1.0222 | 0.765 | 0.06 | 0.0625 | 0.0525 | 0.065 | 0.0775 |
| No log | 39.96 | 320 | 1.0709 | 0.75 | 0.06 | 0.0625 | 0.0525 | 0.065 | 0.08 |
| No log | 40.96 | 328 | 1.0426 | 0.755 | 0.06 | 0.0625 | 0.0525 | 0.065 | 0.08 |
| No log | 41.96 | 336 | 1.0789 | 0.74 | 0.06 | 0.0625 | 0.0525 | 0.065 | 0.08 |
| No log | 42.96 | 344 | 1.0492 | 0.765 | 0.06 | 0.0625 | 0.0525 | 0.0625 | 0.0775 |
| No log | 43.96 | 352 | 1.0541 | 0.7575 | 0.06 | 0.0625 | 0.0525 | 0.0625 | 0.08 |
| No log | 44.96 | 360 | 1.0620 | 0.755 | 0.06 | 0.0625 | 0.0525 | 0.06 | 0.08 |
| No log | 45.96 | 368 | 1.0514 | 0.7575 | 0.06 | 0.0625 | 0.055 | 0.06 | 0.0775 |
| No log | 46.96 | 376 | 1.0537 | 0.755 | 0.06 | 0.0625 | 0.0525 | 0.0625 | 0.0775 |
| No log | 47.96 | 384 | 1.0662 | 0.7575 | 0.06 | 0.0625 | 0.0525 | 0.0625 | 0.0775 |
| No log | 48.96 | 392 | 1.0693 | 0.76 | 0.06 | 0.0625 | 0.055 | 0.0625 | 0.08 |
| No log | 49.96 | 400 | 1.0775 | 0.7575 | 0.06 | 0.0625 | 0.055 | 0.0625 | 0.08 |
| No log | 50.96 | 408 | 1.0863 | 0.75 | 0.0575 | 0.0625 | 0.0575 | 0.065 | 0.0825 |
| No log | 51.96 | 416 | 1.0567 | 0.76 | 0.06 | 0.0625 | 0.055 | 0.0625 | 0.08 |
| No log | 52.96 | 424 | 1.0605 | 0.76 | 0.06 | 0.0625 | 0.055 | 0.0625 | 0.08 |
| No log | 53.96 | 432 | 1.0720 | 0.755 | 0.06 | 0.0625 | 0.0525 | 0.0625 | 0.08 |
| No log | 54.96 | 440 | 1.0807 | 0.7525 | 0.06 | 0.0625 | 0.0575 | 0.0625 | 0.0775 |
| No log | 55.96 | 448 | 1.0747 | 0.7575 | 0.06 | 0.0625 | 0.055 | 0.0625 | 0.0775 |
| No log | 56.96 | 456 | 1.0740 | 0.755 | 0.06 | 0.0625 | 0.06 | 0.065 | 0.08 |
| No log | 57.96 | 464 | 1.0813 | 0.755 | 0.06 | 0.0625 | 0.0575 | 0.065 | 0.0775 |
| No log | 58.96 | 472 | 1.0852 | 0.755 | 0.06 | 0.0625 | 0.0575 | 0.065 | 0.0775 |
| No log | 59.96 | 480 | 1.0852 | 0.755 | 0.06 | 0.0625 | 0.0575 | 0.065 | 0.0775 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1.post200
- Datasets 2.9.0
- Tokenizers 0.13.2
|
jordyvl/lmv3_jordyvl_rvl_cdip_100_examples_per_class_2023-12-01_txt_vis_concat_enc_9_10_11_12_gate
|
jordyvl
| 2023-12-08T08:55:26Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"layoutlmv3",
"text-classification",
"generated_from_trainer",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-12-01T14:05:52Z |
---
license: cc-by-nc-sa-4.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: lmv3_jordyvl_rvl_cdip_100_examples_per_class_2023-12-01_txt_vis_concat_enc_9_10_11_12_gate
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. -->
# lmv3_jordyvl_rvl_cdip_100_examples_per_class_2023-12-01_txt_vis_concat_enc_9_10_11_12_gate
This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9605
- Accuracy: 0.785
- Exit 0 Accuracy: 0.0625
- Exit 1 Accuracy: 0.2425
- Exit 2 Accuracy: 0.5225
- Exit 3 Accuracy: 0.72
- Exit 4 Accuracy: 0.785
## 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: 4
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 24
- total_train_batch_size: 96
- 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 | Exit 0 Accuracy | Exit 1 Accuracy | Exit 2 Accuracy | Exit 3 Accuracy | Exit 4 Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------------:|:---------------:|:---------------:|:---------------:|:---------------:|
| No log | 0.96 | 8 | 2.6964 | 0.1225 | 0.055 | 0.0625 | 0.0625 | 0.0625 | 0.1225 |
| No log | 1.96 | 16 | 2.6306 | 0.1775 | 0.05 | 0.0625 | 0.0625 | 0.0625 | 0.1775 |
| No log | 2.96 | 24 | 2.5176 | 0.2325 | 0.045 | 0.0625 | 0.0625 | 0.0625 | 0.2325 |
| No log | 3.96 | 32 | 2.3854 | 0.28 | 0.045 | 0.0625 | 0.0625 | 0.0625 | 0.28 |
| No log | 4.96 | 40 | 2.2424 | 0.335 | 0.04 | 0.0625 | 0.0625 | 0.0625 | 0.335 |
| No log | 5.96 | 48 | 2.0887 | 0.395 | 0.0425 | 0.0625 | 0.0625 | 0.0625 | 0.395 |
| No log | 6.96 | 56 | 1.9008 | 0.5125 | 0.0425 | 0.0625 | 0.0625 | 0.0625 | 0.5125 |
| No log | 7.96 | 64 | 1.7061 | 0.575 | 0.04 | 0.0625 | 0.0625 | 0.0625 | 0.575 |
| No log | 8.96 | 72 | 1.5366 | 0.6075 | 0.0375 | 0.0625 | 0.0625 | 0.0625 | 0.6075 |
| No log | 9.96 | 80 | 1.3956 | 0.6475 | 0.0375 | 0.0625 | 0.0625 | 0.0625 | 0.6475 |
| No log | 10.96 | 88 | 1.2953 | 0.675 | 0.0275 | 0.0625 | 0.0625 | 0.0675 | 0.675 |
| No log | 11.96 | 96 | 1.2023 | 0.6775 | 0.025 | 0.0625 | 0.0625 | 0.07 | 0.6775 |
| No log | 12.96 | 104 | 1.1167 | 0.72 | 0.0325 | 0.0625 | 0.0625 | 0.0875 | 0.72 |
| No log | 13.96 | 112 | 1.0342 | 0.73 | 0.03 | 0.0625 | 0.0625 | 0.1025 | 0.73 |
| No log | 14.96 | 120 | 1.0137 | 0.7375 | 0.0325 | 0.0625 | 0.0625 | 0.115 | 0.7375 |
| No log | 15.96 | 128 | 0.9790 | 0.7375 | 0.0325 | 0.0625 | 0.0625 | 0.1175 | 0.7375 |
| No log | 16.96 | 136 | 0.9306 | 0.7675 | 0.035 | 0.0625 | 0.0625 | 0.1575 | 0.7675 |
| No log | 17.96 | 144 | 0.8941 | 0.77 | 0.04 | 0.0625 | 0.0625 | 0.14 | 0.77 |
| No log | 18.96 | 152 | 0.8953 | 0.765 | 0.0425 | 0.0625 | 0.0625 | 0.1825 | 0.765 |
| No log | 19.96 | 160 | 0.8898 | 0.77 | 0.04 | 0.0625 | 0.0625 | 0.2175 | 0.77 |
| No log | 20.96 | 168 | 0.8756 | 0.7725 | 0.04 | 0.0625 | 0.0625 | 0.2675 | 0.7725 |
| No log | 21.96 | 176 | 0.9026 | 0.755 | 0.045 | 0.0625 | 0.1 | 0.4175 | 0.755 |
| No log | 22.96 | 184 | 0.8717 | 0.7725 | 0.05 | 0.0625 | 0.1175 | 0.4225 | 0.7725 |
| No log | 23.96 | 192 | 0.9194 | 0.7525 | 0.05 | 0.0625 | 0.15 | 0.4775 | 0.7525 |
| No log | 24.96 | 200 | 0.8943 | 0.775 | 0.05 | 0.0675 | 0.1925 | 0.525 | 0.775 |
| No log | 25.96 | 208 | 0.8964 | 0.77 | 0.0525 | 0.0625 | 0.215 | 0.5225 | 0.77 |
| No log | 26.96 | 216 | 0.9143 | 0.76 | 0.0525 | 0.0625 | 0.25 | 0.5525 | 0.76 |
| No log | 27.96 | 224 | 0.9079 | 0.7775 | 0.0525 | 0.0625 | 0.29 | 0.56 | 0.7775 |
| No log | 28.96 | 232 | 0.9018 | 0.7775 | 0.055 | 0.0675 | 0.315 | 0.59 | 0.7775 |
| No log | 29.96 | 240 | 0.9091 | 0.7875 | 0.055 | 0.0725 | 0.355 | 0.615 | 0.7875 |
| No log | 30.96 | 248 | 0.9056 | 0.785 | 0.0625 | 0.0925 | 0.3775 | 0.64 | 0.785 |
| No log | 31.96 | 256 | 0.9164 | 0.79 | 0.06 | 0.125 | 0.42 | 0.6775 | 0.79 |
| No log | 32.96 | 264 | 0.9293 | 0.7875 | 0.0625 | 0.1425 | 0.4625 | 0.685 | 0.7875 |
| No log | 33.96 | 272 | 0.9669 | 0.7725 | 0.0575 | 0.215 | 0.48 | 0.6875 | 0.7725 |
| No log | 34.96 | 280 | 0.9342 | 0.785 | 0.06 | 0.23 | 0.4725 | 0.69 | 0.785 |
| No log | 35.96 | 288 | 0.9481 | 0.7725 | 0.0625 | 0.205 | 0.4525 | 0.6525 | 0.7725 |
| No log | 36.96 | 296 | 0.9447 | 0.7775 | 0.06 | 0.24 | 0.485 | 0.6875 | 0.7775 |
| No log | 37.96 | 304 | 0.9494 | 0.7925 | 0.0575 | 0.24 | 0.5025 | 0.7025 | 0.7925 |
| No log | 38.96 | 312 | 0.9329 | 0.775 | 0.0575 | 0.2225 | 0.46 | 0.695 | 0.775 |
| No log | 39.96 | 320 | 0.9247 | 0.7875 | 0.06 | 0.23 | 0.4725 | 0.6725 | 0.7875 |
| No log | 40.96 | 328 | 0.9184 | 0.7925 | 0.06 | 0.2325 | 0.465 | 0.665 | 0.7925 |
| No log | 41.96 | 336 | 0.9608 | 0.8025 | 0.06 | 0.1975 | 0.4625 | 0.65 | 0.8025 |
| No log | 42.96 | 344 | 0.9499 | 0.7875 | 0.06 | 0.2075 | 0.445 | 0.64 | 0.7875 |
| No log | 43.96 | 352 | 0.9789 | 0.7825 | 0.06 | 0.205 | 0.495 | 0.64 | 0.7825 |
| No log | 44.96 | 360 | 0.9384 | 0.78 | 0.06 | 0.2125 | 0.49 | 0.6725 | 0.78 |
| No log | 45.96 | 368 | 0.9734 | 0.77 | 0.06 | 0.2075 | 0.54 | 0.7125 | 0.77 |
| No log | 46.96 | 376 | 0.9647 | 0.785 | 0.0625 | 0.215 | 0.5325 | 0.735 | 0.785 |
| No log | 47.96 | 384 | 0.9484 | 0.78 | 0.0625 | 0.2225 | 0.515 | 0.725 | 0.78 |
| No log | 48.96 | 392 | 0.9652 | 0.7875 | 0.0625 | 0.2275 | 0.505 | 0.7325 | 0.7875 |
| No log | 49.96 | 400 | 0.9570 | 0.785 | 0.0625 | 0.22 | 0.4925 | 0.7225 | 0.785 |
| No log | 50.96 | 408 | 0.9432 | 0.7975 | 0.0625 | 0.2075 | 0.52 | 0.7275 | 0.7975 |
| No log | 51.96 | 416 | 0.9562 | 0.79 | 0.0625 | 0.225 | 0.5275 | 0.7325 | 0.79 |
| No log | 52.96 | 424 | 0.9567 | 0.79 | 0.0625 | 0.2375 | 0.5325 | 0.72 | 0.79 |
| No log | 53.96 | 432 | 0.9645 | 0.7875 | 0.0625 | 0.2425 | 0.5325 | 0.7175 | 0.7875 |
| No log | 54.96 | 440 | 0.9721 | 0.7825 | 0.0625 | 0.25 | 0.5275 | 0.725 | 0.7825 |
| No log | 55.96 | 448 | 0.9742 | 0.785 | 0.0625 | 0.2425 | 0.52 | 0.7275 | 0.785 |
| No log | 56.96 | 456 | 0.9699 | 0.785 | 0.0625 | 0.24 | 0.5225 | 0.725 | 0.785 |
| No log | 57.96 | 464 | 0.9637 | 0.785 | 0.0625 | 0.245 | 0.52 | 0.725 | 0.785 |
| No log | 58.96 | 472 | 0.9614 | 0.785 | 0.0625 | 0.2425 | 0.525 | 0.72 | 0.785 |
| No log | 59.96 | 480 | 0.9605 | 0.785 | 0.0625 | 0.2425 | 0.5225 | 0.72 | 0.785 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1.post200
- Datasets 2.9.0
- Tokenizers 0.13.2
|
jordyvl/EElayoutlmv3_jordyvl_rvl_cdip_100_examples_per_class_2023-12-04_txt_vis_concat_enc_3_gate
|
jordyvl
| 2023-12-08T08:55:25Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"layoutlmv3",
"text-classification",
"generated_from_trainer",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-12-04T13:08:51Z |
---
license: cc-by-nc-sa-4.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: EElayoutlmv3_jordyvl_rvl_cdip_100_examples_per_class_2023-12-04_txt_vis_concat_enc_3_gate
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. -->
# EElayoutlmv3_jordyvl_rvl_cdip_100_examples_per_class_2023-12-04_txt_vis_concat_enc_3_gate
This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0305
- Accuracy: 0.725
- Exit 0 Accuracy: 0.06
- Exit 1 Accuracy: 0.0625
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 24
- total_train_batch_size: 192
- 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 | Exit 0 Accuracy | Exit 1 Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------------:|:---------------:|
| No log | 0.96 | 4 | 2.7534 | 0.09 | 0.0475 | 0.0625 |
| No log | 1.96 | 8 | 2.7060 | 0.1475 | 0.05 | 0.0625 |
| No log | 2.96 | 12 | 2.6196 | 0.1825 | 0.0525 | 0.07 |
| No log | 3.96 | 16 | 2.5436 | 0.1975 | 0.05 | 0.0525 |
| No log | 4.96 | 20 | 2.4872 | 0.215 | 0.0525 | 0.085 |
| No log | 5.96 | 24 | 2.3832 | 0.2825 | 0.055 | 0.065 |
| No log | 6.96 | 28 | 2.3354 | 0.3125 | 0.0525 | 0.0625 |
| No log | 7.96 | 32 | 2.2366 | 0.34 | 0.0525 | 0.0625 |
| No log | 8.96 | 36 | 2.1689 | 0.3725 | 0.0525 | 0.0625 |
| No log | 9.96 | 40 | 2.0622 | 0.4075 | 0.055 | 0.0625 |
| No log | 10.96 | 44 | 1.9774 | 0.4575 | 0.0525 | 0.0625 |
| No log | 11.96 | 48 | 1.8564 | 0.505 | 0.0525 | 0.0625 |
| No log | 12.96 | 52 | 1.7648 | 0.52 | 0.0525 | 0.0625 |
| No log | 13.96 | 56 | 1.6734 | 0.575 | 0.0525 | 0.0625 |
| No log | 14.96 | 60 | 1.5849 | 0.5975 | 0.0525 | 0.0625 |
| No log | 15.96 | 64 | 1.4981 | 0.605 | 0.0525 | 0.0625 |
| No log | 16.96 | 68 | 1.4107 | 0.62 | 0.0575 | 0.0625 |
| No log | 17.96 | 72 | 1.3647 | 0.635 | 0.06 | 0.0625 |
| No log | 18.96 | 76 | 1.3250 | 0.645 | 0.0575 | 0.0625 |
| No log | 19.96 | 80 | 1.2480 | 0.665 | 0.06 | 0.0625 |
| No log | 20.96 | 84 | 1.2180 | 0.67 | 0.06 | 0.0625 |
| No log | 21.96 | 88 | 1.1733 | 0.695 | 0.06 | 0.0625 |
| No log | 22.96 | 92 | 1.1353 | 0.69 | 0.06 | 0.0625 |
| No log | 23.96 | 96 | 1.1145 | 0.69 | 0.0625 | 0.0625 |
| No log | 24.96 | 100 | 1.1087 | 0.7025 | 0.0625 | 0.0625 |
| No log | 25.96 | 104 | 1.0682 | 0.6975 | 0.0625 | 0.0625 |
| No log | 26.96 | 108 | 1.0841 | 0.71 | 0.0625 | 0.0625 |
| No log | 27.96 | 112 | 1.0348 | 0.705 | 0.0625 | 0.0625 |
| No log | 28.96 | 116 | 1.0339 | 0.7 | 0.0625 | 0.0625 |
| No log | 29.96 | 120 | 1.0228 | 0.7075 | 0.0625 | 0.0625 |
| No log | 30.96 | 124 | 1.0601 | 0.69 | 0.0625 | 0.0625 |
| No log | 31.96 | 128 | 0.9958 | 0.7275 | 0.0625 | 0.0625 |
| No log | 32.96 | 132 | 1.0300 | 0.7125 | 0.0625 | 0.0625 |
| No log | 33.96 | 136 | 0.9698 | 0.7375 | 0.0625 | 0.0625 |
| No log | 34.96 | 140 | 1.0067 | 0.715 | 0.0625 | 0.0625 |
| No log | 35.96 | 144 | 0.9959 | 0.705 | 0.0625 | 0.0625 |
| No log | 36.96 | 148 | 0.9956 | 0.7275 | 0.0625 | 0.0625 |
| No log | 37.96 | 152 | 1.0076 | 0.71 | 0.0625 | 0.0625 |
| No log | 38.96 | 156 | 0.9998 | 0.7275 | 0.0625 | 0.0625 |
| No log | 39.96 | 160 | 0.9907 | 0.7225 | 0.0625 | 0.0625 |
| No log | 40.96 | 164 | 0.9902 | 0.73 | 0.0625 | 0.0625 |
| No log | 41.96 | 168 | 1.0063 | 0.72 | 0.06 | 0.0625 |
| No log | 42.96 | 172 | 1.0144 | 0.72 | 0.06 | 0.0625 |
| No log | 43.96 | 176 | 0.9945 | 0.7225 | 0.06 | 0.0625 |
| No log | 44.96 | 180 | 0.9974 | 0.72 | 0.06 | 0.0625 |
| No log | 45.96 | 184 | 1.0087 | 0.725 | 0.06 | 0.0625 |
| No log | 46.96 | 188 | 1.0092 | 0.7125 | 0.06 | 0.0625 |
| No log | 47.96 | 192 | 0.9966 | 0.7325 | 0.06 | 0.0625 |
| No log | 48.96 | 196 | 1.0084 | 0.73 | 0.06 | 0.0625 |
| No log | 49.96 | 200 | 1.0175 | 0.71 | 0.06 | 0.0625 |
| No log | 50.96 | 204 | 1.0295 | 0.7175 | 0.06 | 0.0625 |
| No log | 51.96 | 208 | 1.0233 | 0.7275 | 0.06 | 0.0625 |
| No log | 52.96 | 212 | 1.0280 | 0.7175 | 0.06 | 0.0625 |
| No log | 53.96 | 216 | 1.0254 | 0.725 | 0.06 | 0.0625 |
| No log | 54.96 | 220 | 1.0211 | 0.73 | 0.06 | 0.0625 |
| No log | 55.96 | 224 | 1.0193 | 0.73 | 0.06 | 0.0625 |
| No log | 56.96 | 228 | 1.0233 | 0.725 | 0.06 | 0.0625 |
| No log | 57.96 | 232 | 1.0284 | 0.725 | 0.06 | 0.0625 |
| No log | 58.96 | 236 | 1.0304 | 0.725 | 0.06 | 0.0625 |
| No log | 59.96 | 240 | 1.0305 | 0.725 | 0.06 | 0.0625 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1.post200
- Datasets 2.9.0
- Tokenizers 0.13.2
|
jordyvl/EElayoutlmv3_jordyvl_rvl_cdip_100_examples_per_class_2023-12-04_txt_vis_concat_enc_6_gate
|
jordyvl
| 2023-12-08T08:55:24Z | 8 | 0 |
transformers
|
[
"transformers",
"pytorch",
"layoutlmv3",
"text-classification",
"generated_from_trainer",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-12-04T18:56:01Z |
---
license: cc-by-nc-sa-4.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: EElayoutlmv3_jordyvl_rvl_cdip_100_examples_per_class_2023-12-04_txt_vis_concat_enc_6_gate
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. -->
# EElayoutlmv3_jordyvl_rvl_cdip_100_examples_per_class_2023-12-04_txt_vis_concat_enc_6_gate
This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9813
- Accuracy: 0.7425
- Exit 0 Accuracy: 0.055
- Exit 1 Accuracy: 0.1075
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 24
- total_train_batch_size: 192
- 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 | Exit 0 Accuracy | Exit 1 Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------------:|:---------------:|
| No log | 0.96 | 4 | 2.7555 | 0.09 | 0.0425 | 0.0675 |
| No log | 1.96 | 8 | 2.7103 | 0.145 | 0.0425 | 0.06 |
| No log | 2.96 | 12 | 2.6243 | 0.175 | 0.0475 | 0.0625 |
| No log | 3.96 | 16 | 2.5457 | 0.19 | 0.0475 | 0.06 |
| No log | 4.96 | 20 | 2.4802 | 0.22 | 0.055 | 0.0725 |
| No log | 5.96 | 24 | 2.3688 | 0.29 | 0.0525 | 0.1125 |
| No log | 6.96 | 28 | 2.2693 | 0.33 | 0.0525 | 0.105 |
| No log | 7.96 | 32 | 2.1806 | 0.35 | 0.0525 | 0.1275 |
| No log | 8.96 | 36 | 2.0652 | 0.395 | 0.0525 | 0.125 |
| No log | 9.96 | 40 | 1.9632 | 0.4325 | 0.0525 | 0.1225 |
| No log | 10.96 | 44 | 1.8904 | 0.4725 | 0.05 | 0.1175 |
| No log | 11.96 | 48 | 1.7364 | 0.5575 | 0.05 | 0.1225 |
| No log | 12.96 | 52 | 1.6698 | 0.5525 | 0.05 | 0.12 |
| No log | 13.96 | 56 | 1.5785 | 0.59 | 0.0525 | 0.115 |
| No log | 14.96 | 60 | 1.5102 | 0.5975 | 0.0575 | 0.12 |
| No log | 15.96 | 64 | 1.3949 | 0.64 | 0.0575 | 0.12 |
| No log | 16.96 | 68 | 1.3608 | 0.625 | 0.0575 | 0.125 |
| No log | 17.96 | 72 | 1.3010 | 0.64 | 0.0575 | 0.125 |
| No log | 18.96 | 76 | 1.2554 | 0.665 | 0.0575 | 0.125 |
| No log | 19.96 | 80 | 1.1876 | 0.675 | 0.0575 | 0.1225 |
| No log | 20.96 | 84 | 1.1572 | 0.705 | 0.0575 | 0.13 |
| No log | 21.96 | 88 | 1.1204 | 0.6875 | 0.0575 | 0.1275 |
| No log | 22.96 | 92 | 1.0787 | 0.71 | 0.0575 | 0.1375 |
| No log | 23.96 | 96 | 1.0833 | 0.7075 | 0.0575 | 0.13 |
| No log | 24.96 | 100 | 1.0345 | 0.725 | 0.0575 | 0.13 |
| No log | 25.96 | 104 | 1.0832 | 0.7 | 0.0575 | 0.14 |
| No log | 26.96 | 108 | 1.0061 | 0.7275 | 0.0575 | 0.1325 |
| No log | 27.96 | 112 | 1.0357 | 0.695 | 0.0575 | 0.13 |
| No log | 28.96 | 116 | 0.9696 | 0.7325 | 0.0575 | 0.1275 |
| No log | 29.96 | 120 | 1.0087 | 0.6975 | 0.0575 | 0.1275 |
| No log | 30.96 | 124 | 0.9800 | 0.72 | 0.0575 | 0.1175 |
| No log | 31.96 | 128 | 0.9802 | 0.715 | 0.0575 | 0.125 |
| No log | 32.96 | 132 | 0.9751 | 0.7375 | 0.0575 | 0.13 |
| No log | 33.96 | 136 | 0.9578 | 0.725 | 0.0575 | 0.1275 |
| No log | 34.96 | 140 | 0.9624 | 0.725 | 0.0575 | 0.1225 |
| No log | 35.96 | 144 | 0.9676 | 0.72 | 0.0575 | 0.13 |
| No log | 36.96 | 148 | 0.9572 | 0.73 | 0.0575 | 0.1175 |
| No log | 37.96 | 152 | 1.0086 | 0.7175 | 0.0575 | 0.125 |
| No log | 38.96 | 156 | 0.9555 | 0.735 | 0.055 | 0.11 |
| No log | 39.96 | 160 | 0.9469 | 0.74 | 0.055 | 0.115 |
| No log | 40.96 | 164 | 0.9835 | 0.7275 | 0.055 | 0.115 |
| No log | 41.96 | 168 | 0.9364 | 0.745 | 0.055 | 0.1075 |
| No log | 42.96 | 172 | 0.9590 | 0.74 | 0.055 | 0.105 |
| No log | 43.96 | 176 | 0.9499 | 0.7425 | 0.055 | 0.1025 |
| No log | 44.96 | 180 | 0.9731 | 0.7375 | 0.055 | 0.1 |
| No log | 45.96 | 184 | 0.9719 | 0.725 | 0.055 | 0.1025 |
| No log | 46.96 | 188 | 0.9669 | 0.7375 | 0.055 | 0.105 |
| No log | 47.96 | 192 | 0.9713 | 0.7325 | 0.055 | 0.11 |
| No log | 48.96 | 196 | 0.9738 | 0.7475 | 0.055 | 0.1075 |
| No log | 49.96 | 200 | 0.9662 | 0.7425 | 0.055 | 0.1025 |
| No log | 50.96 | 204 | 0.9848 | 0.73 | 0.055 | 0.1025 |
| No log | 51.96 | 208 | 0.9689 | 0.7475 | 0.055 | 0.11 |
| No log | 52.96 | 212 | 0.9690 | 0.7475 | 0.055 | 0.105 |
| No log | 53.96 | 216 | 0.9783 | 0.7475 | 0.055 | 0.1025 |
| No log | 54.96 | 220 | 0.9831 | 0.7425 | 0.055 | 0.1025 |
| No log | 55.96 | 224 | 0.9778 | 0.745 | 0.055 | 0.1025 |
| No log | 56.96 | 228 | 0.9740 | 0.7475 | 0.055 | 0.1025 |
| No log | 57.96 | 232 | 0.9754 | 0.7475 | 0.055 | 0.1075 |
| No log | 58.96 | 236 | 0.9793 | 0.7425 | 0.055 | 0.1075 |
| No log | 59.96 | 240 | 0.9813 | 0.7425 | 0.055 | 0.1075 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1.post200
- Datasets 2.9.0
- Tokenizers 0.13.2
|
jordyvl/EElayoutlmv3_jordyvl_rvl_cdip_100_examples_per_class_2023-12-04_txt_vis_concat_enc_4_gate
|
jordyvl
| 2023-12-08T08:55:24Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"layoutlmv3",
"text-classification",
"generated_from_trainer",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-12-04T15:00:26Z |
---
license: cc-by-nc-sa-4.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: EElayoutlmv3_jordyvl_rvl_cdip_100_examples_per_class_2023-12-04_txt_vis_concat_enc_4_gate
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. -->
# EElayoutlmv3_jordyvl_rvl_cdip_100_examples_per_class_2023-12-04_txt_vis_concat_enc_4_gate
This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0854
- Accuracy: 0.72
- Exit 0 Accuracy: 0.0625
- Exit 1 Accuracy: 0.0625
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 24
- total_train_batch_size: 192
- 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 | Exit 0 Accuracy | Exit 1 Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------------:|:---------------:|
| No log | 0.96 | 4 | 2.7541 | 0.09 | 0.04 | 0.0625 |
| No log | 1.96 | 8 | 2.7076 | 0.1425 | 0.055 | 0.0625 |
| No log | 2.96 | 12 | 2.6235 | 0.1725 | 0.055 | 0.0625 |
| No log | 3.96 | 16 | 2.5506 | 0.205 | 0.0525 | 0.0675 |
| No log | 4.96 | 20 | 2.4963 | 0.215 | 0.0525 | 0.075 |
| No log | 5.96 | 24 | 2.4094 | 0.2725 | 0.0575 | 0.0625 |
| No log | 6.96 | 28 | 2.2982 | 0.305 | 0.0575 | 0.0625 |
| No log | 7.96 | 32 | 2.2131 | 0.3375 | 0.0575 | 0.0625 |
| No log | 8.96 | 36 | 2.1493 | 0.3325 | 0.0575 | 0.0625 |
| No log | 9.96 | 40 | 2.0452 | 0.375 | 0.06 | 0.0625 |
| No log | 10.96 | 44 | 1.9553 | 0.4275 | 0.06 | 0.0625 |
| No log | 11.96 | 48 | 1.8302 | 0.5075 | 0.06 | 0.0625 |
| No log | 12.96 | 52 | 1.7440 | 0.54 | 0.06 | 0.0625 |
| No log | 13.96 | 56 | 1.6427 | 0.5575 | 0.0625 | 0.0625 |
| No log | 14.96 | 60 | 1.5650 | 0.6 | 0.0625 | 0.0625 |
| No log | 15.96 | 64 | 1.4875 | 0.62 | 0.065 | 0.0625 |
| No log | 16.96 | 68 | 1.4124 | 0.645 | 0.0625 | 0.0625 |
| No log | 17.96 | 72 | 1.3870 | 0.65 | 0.0625 | 0.065 |
| No log | 18.96 | 76 | 1.2946 | 0.675 | 0.0575 | 0.0625 |
| No log | 19.96 | 80 | 1.2652 | 0.67 | 0.0575 | 0.065 |
| No log | 20.96 | 84 | 1.1967 | 0.6975 | 0.0575 | 0.065 |
| No log | 21.96 | 88 | 1.1782 | 0.695 | 0.0575 | 0.065 |
| No log | 22.96 | 92 | 1.1372 | 0.6975 | 0.0575 | 0.065 |
| No log | 23.96 | 96 | 1.1193 | 0.7025 | 0.0575 | 0.065 |
| No log | 24.96 | 100 | 1.0844 | 0.7175 | 0.06 | 0.0625 |
| No log | 25.96 | 104 | 1.0746 | 0.705 | 0.06 | 0.0625 |
| No log | 26.96 | 108 | 1.0720 | 0.72 | 0.06 | 0.065 |
| No log | 27.96 | 112 | 1.0455 | 0.72 | 0.06 | 0.0625 |
| No log | 28.96 | 116 | 1.0266 | 0.7275 | 0.06 | 0.0625 |
| No log | 29.96 | 120 | 1.0322 | 0.7275 | 0.0625 | 0.0625 |
| No log | 30.96 | 124 | 1.0310 | 0.73 | 0.0625 | 0.0625 |
| No log | 31.96 | 128 | 1.0186 | 0.7275 | 0.0625 | 0.0625 |
| No log | 32.96 | 132 | 1.0236 | 0.725 | 0.0625 | 0.0625 |
| No log | 33.96 | 136 | 1.0142 | 0.73 | 0.0625 | 0.0625 |
| No log | 34.96 | 140 | 1.0096 | 0.7225 | 0.0625 | 0.0625 |
| No log | 35.96 | 144 | 0.9953 | 0.74 | 0.0625 | 0.0625 |
| No log | 36.96 | 148 | 1.0065 | 0.7275 | 0.0625 | 0.0625 |
| No log | 37.96 | 152 | 1.0247 | 0.725 | 0.06 | 0.0625 |
| No log | 38.96 | 156 | 1.0083 | 0.7325 | 0.06 | 0.0625 |
| No log | 39.96 | 160 | 1.0138 | 0.725 | 0.0625 | 0.0625 |
| No log | 40.96 | 164 | 1.0311 | 0.725 | 0.065 | 0.0625 |
| No log | 41.96 | 168 | 1.0301 | 0.7175 | 0.0625 | 0.0625 |
| No log | 42.96 | 172 | 1.0492 | 0.725 | 0.0625 | 0.0625 |
| No log | 43.96 | 176 | 1.0431 | 0.7225 | 0.0625 | 0.0625 |
| No log | 44.96 | 180 | 1.0565 | 0.7275 | 0.0625 | 0.06 |
| No log | 45.96 | 184 | 1.0747 | 0.7175 | 0.0625 | 0.0625 |
| No log | 46.96 | 188 | 1.0534 | 0.7125 | 0.0625 | 0.06 |
| No log | 47.96 | 192 | 1.0610 | 0.72 | 0.0625 | 0.06 |
| No log | 48.96 | 196 | 1.0721 | 0.7175 | 0.0625 | 0.0625 |
| No log | 49.96 | 200 | 1.0744 | 0.7175 | 0.0625 | 0.0625 |
| No log | 50.96 | 204 | 1.0776 | 0.72 | 0.0625 | 0.0625 |
| No log | 51.96 | 208 | 1.0777 | 0.72 | 0.0625 | 0.0625 |
| No log | 52.96 | 212 | 1.0831 | 0.7225 | 0.0625 | 0.0625 |
| No log | 53.96 | 216 | 1.0786 | 0.7225 | 0.0625 | 0.0625 |
| No log | 54.96 | 220 | 1.0806 | 0.725 | 0.0625 | 0.0625 |
| No log | 55.96 | 224 | 1.0844 | 0.7275 | 0.0625 | 0.0625 |
| No log | 56.96 | 228 | 1.0858 | 0.73 | 0.0625 | 0.0625 |
| No log | 57.96 | 232 | 1.0861 | 0.7225 | 0.0625 | 0.0625 |
| No log | 58.96 | 236 | 1.0857 | 0.72 | 0.0625 | 0.0625 |
| No log | 59.96 | 240 | 1.0854 | 0.72 | 0.0625 | 0.0625 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1.post200
- Datasets 2.9.0
- Tokenizers 0.13.2
|
jordyvl/EElayoutlmv3_jordyvl_rvl_cdip_100_examples_per_class_2023-12-05_txt_vis_concat_enc_9_gate
|
jordyvl
| 2023-12-08T08:55:22Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"layoutlmv3",
"text-classification",
"generated_from_trainer",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-12-05T01:03:48Z |
---
license: cc-by-nc-sa-4.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: EElayoutlmv3_jordyvl_rvl_cdip_100_examples_per_class_2023-12-05_txt_vis_concat_enc_9_gate
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. -->
# EElayoutlmv3_jordyvl_rvl_cdip_100_examples_per_class_2023-12-05_txt_vis_concat_enc_9_gate
This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9836
- Accuracy: 0.755
- Exit 0 Accuracy: 0.055
- Exit 1 Accuracy: 0.0625
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 24
- total_train_batch_size: 192
- 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 | Exit 0 Accuracy | Exit 1 Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------------:|:---------------:|
| No log | 0.96 | 4 | 2.7543 | 0.09 | 0.04 | 0.0625 |
| No log | 1.96 | 8 | 2.7090 | 0.15 | 0.045 | 0.0625 |
| No log | 2.96 | 12 | 2.6189 | 0.185 | 0.0475 | 0.0625 |
| No log | 3.96 | 16 | 2.5508 | 0.2025 | 0.0525 | 0.0625 |
| No log | 4.96 | 20 | 2.4955 | 0.2125 | 0.05 | 0.0625 |
| No log | 5.96 | 24 | 2.3794 | 0.275 | 0.05 | 0.0625 |
| No log | 6.96 | 28 | 2.2952 | 0.3225 | 0.0525 | 0.0625 |
| No log | 7.96 | 32 | 2.1989 | 0.3575 | 0.0525 | 0.0625 |
| No log | 8.96 | 36 | 2.1110 | 0.375 | 0.055 | 0.0625 |
| No log | 9.96 | 40 | 2.0165 | 0.41 | 0.055 | 0.0625 |
| No log | 10.96 | 44 | 1.9297 | 0.4325 | 0.055 | 0.0625 |
| No log | 11.96 | 48 | 1.8067 | 0.4925 | 0.055 | 0.0625 |
| No log | 12.96 | 52 | 1.6998 | 0.555 | 0.055 | 0.0625 |
| No log | 13.96 | 56 | 1.6331 | 0.5175 | 0.055 | 0.0625 |
| No log | 14.96 | 60 | 1.5114 | 0.5825 | 0.055 | 0.0625 |
| No log | 15.96 | 64 | 1.4501 | 0.615 | 0.0575 | 0.0625 |
| No log | 16.96 | 68 | 1.3538 | 0.62 | 0.0575 | 0.0625 |
| No log | 17.96 | 72 | 1.2836 | 0.6525 | 0.0575 | 0.0625 |
| No log | 18.96 | 76 | 1.2582 | 0.65 | 0.0575 | 0.0625 |
| No log | 19.96 | 80 | 1.1753 | 0.6625 | 0.0575 | 0.065 |
| No log | 20.96 | 84 | 1.1471 | 0.695 | 0.0575 | 0.0725 |
| No log | 21.96 | 88 | 1.1038 | 0.7025 | 0.0575 | 0.0875 |
| No log | 22.96 | 92 | 1.0410 | 0.73 | 0.0575 | 0.08 |
| No log | 23.96 | 96 | 1.0374 | 0.7325 | 0.0575 | 0.07 |
| No log | 24.96 | 100 | 1.0223 | 0.7325 | 0.0575 | 0.0675 |
| No log | 25.96 | 104 | 1.0262 | 0.73 | 0.06 | 0.065 |
| No log | 26.96 | 108 | 0.9950 | 0.715 | 0.06 | 0.065 |
| No log | 27.96 | 112 | 0.9828 | 0.7325 | 0.06 | 0.0625 |
| No log | 28.96 | 116 | 0.9560 | 0.7325 | 0.06 | 0.0625 |
| No log | 29.96 | 120 | 0.9717 | 0.7325 | 0.06 | 0.0625 |
| No log | 30.96 | 124 | 0.9441 | 0.73 | 0.06 | 0.0625 |
| No log | 31.96 | 128 | 0.9550 | 0.7375 | 0.06 | 0.0625 |
| No log | 32.96 | 132 | 0.9510 | 0.7375 | 0.06 | 0.0625 |
| No log | 33.96 | 136 | 0.9451 | 0.735 | 0.06 | 0.0625 |
| No log | 34.96 | 140 | 0.9486 | 0.7475 | 0.06 | 0.0625 |
| No log | 35.96 | 144 | 0.9575 | 0.75 | 0.0575 | 0.0625 |
| No log | 36.96 | 148 | 0.9427 | 0.73 | 0.0575 | 0.0625 |
| No log | 37.96 | 152 | 0.9728 | 0.75 | 0.0575 | 0.0625 |
| No log | 38.96 | 156 | 0.9450 | 0.7375 | 0.06 | 0.0625 |
| No log | 39.96 | 160 | 0.9645 | 0.755 | 0.0575 | 0.0625 |
| No log | 40.96 | 164 | 0.9655 | 0.75 | 0.055 | 0.0625 |
| No log | 41.96 | 168 | 0.9582 | 0.745 | 0.055 | 0.0625 |
| No log | 42.96 | 172 | 0.9721 | 0.7475 | 0.055 | 0.0625 |
| No log | 43.96 | 176 | 0.9720 | 0.7475 | 0.055 | 0.0625 |
| No log | 44.96 | 180 | 0.9703 | 0.75 | 0.055 | 0.065 |
| No log | 45.96 | 184 | 0.9781 | 0.7475 | 0.055 | 0.0625 |
| No log | 46.96 | 188 | 0.9661 | 0.75 | 0.055 | 0.0625 |
| No log | 47.96 | 192 | 0.9671 | 0.75 | 0.055 | 0.0625 |
| No log | 48.96 | 196 | 0.9767 | 0.75 | 0.055 | 0.0625 |
| No log | 49.96 | 200 | 0.9646 | 0.7525 | 0.055 | 0.0625 |
| No log | 50.96 | 204 | 0.9788 | 0.7525 | 0.055 | 0.0625 |
| No log | 51.96 | 208 | 0.9904 | 0.755 | 0.055 | 0.0625 |
| No log | 52.96 | 212 | 0.9857 | 0.7575 | 0.055 | 0.0625 |
| No log | 53.96 | 216 | 0.9756 | 0.7525 | 0.055 | 0.065 |
| No log | 54.96 | 220 | 0.9758 | 0.7525 | 0.055 | 0.0625 |
| No log | 55.96 | 224 | 0.9774 | 0.755 | 0.055 | 0.0625 |
| No log | 56.96 | 228 | 0.9785 | 0.755 | 0.055 | 0.0625 |
| No log | 57.96 | 232 | 0.9817 | 0.7525 | 0.055 | 0.0625 |
| No log | 58.96 | 236 | 0.9833 | 0.755 | 0.055 | 0.0625 |
| No log | 59.96 | 240 | 0.9836 | 0.755 | 0.055 | 0.0625 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1.post200
- Datasets 2.9.0
- Tokenizers 0.13.2
|
jordyvl/EElayoutlmv3_jordyvl_rvl_cdip_100_examples_per_class_2023-12-05_txt_vis_concat_enc_10_gate
|
jordyvl
| 2023-12-08T08:55:21Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"layoutlmv3",
"text-classification",
"generated_from_trainer",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-12-05T03:09:48Z |
---
license: cc-by-nc-sa-4.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: EElayoutlmv3_jordyvl_rvl_cdip_100_examples_per_class_2023-12-05_txt_vis_concat_enc_10_gate
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. -->
# EElayoutlmv3_jordyvl_rvl_cdip_100_examples_per_class_2023-12-05_txt_vis_concat_enc_10_gate
This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0000
- Accuracy: 0.75
- Exit 0 Accuracy: 0.055
- Exit 1 Accuracy: 0.22
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 24
- total_train_batch_size: 192
- 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 | Exit 0 Accuracy | Exit 1 Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------------:|:---------------:|
| No log | 0.96 | 4 | 2.7552 | 0.09 | 0.0425 | 0.0625 |
| No log | 1.96 | 8 | 2.7092 | 0.15 | 0.0475 | 0.0625 |
| No log | 2.96 | 12 | 2.6218 | 0.1825 | 0.0525 | 0.0625 |
| No log | 3.96 | 16 | 2.5483 | 0.1925 | 0.0525 | 0.0625 |
| No log | 4.96 | 20 | 2.4980 | 0.21 | 0.0525 | 0.0625 |
| No log | 5.96 | 24 | 2.3901 | 0.28 | 0.0525 | 0.0625 |
| No log | 6.96 | 28 | 2.2958 | 0.33 | 0.055 | 0.0625 |
| No log | 7.96 | 32 | 2.2144 | 0.34 | 0.055 | 0.0625 |
| No log | 8.96 | 36 | 2.1107 | 0.37 | 0.055 | 0.0625 |
| No log | 9.96 | 40 | 1.9969 | 0.405 | 0.055 | 0.0625 |
| No log | 10.96 | 44 | 1.8919 | 0.46 | 0.055 | 0.0625 |
| No log | 11.96 | 48 | 1.7897 | 0.4975 | 0.055 | 0.0625 |
| No log | 12.96 | 52 | 1.6686 | 0.525 | 0.055 | 0.0625 |
| No log | 13.96 | 56 | 1.6167 | 0.555 | 0.055 | 0.0625 |
| No log | 14.96 | 60 | 1.4750 | 0.605 | 0.055 | 0.0625 |
| No log | 15.96 | 64 | 1.4324 | 0.6225 | 0.055 | 0.065 |
| No log | 16.96 | 68 | 1.3211 | 0.645 | 0.055 | 0.0925 |
| No log | 17.96 | 72 | 1.2686 | 0.6675 | 0.055 | 0.1025 |
| No log | 18.96 | 76 | 1.2206 | 0.6725 | 0.055 | 0.115 |
| No log | 19.96 | 80 | 1.1536 | 0.7025 | 0.055 | 0.115 |
| No log | 20.96 | 84 | 1.1113 | 0.71 | 0.0525 | 0.115 |
| No log | 21.96 | 88 | 1.0655 | 0.715 | 0.0525 | 0.1175 |
| No log | 22.96 | 92 | 1.0423 | 0.735 | 0.0525 | 0.12 |
| No log | 23.96 | 96 | 1.0043 | 0.735 | 0.0525 | 0.1175 |
| No log | 24.96 | 100 | 1.0017 | 0.74 | 0.0525 | 0.12 |
| No log | 25.96 | 104 | 1.0167 | 0.7175 | 0.0525 | 0.12 |
| No log | 26.96 | 108 | 0.9570 | 0.74 | 0.0525 | 0.1175 |
| No log | 27.96 | 112 | 0.9620 | 0.7425 | 0.0525 | 0.12 |
| No log | 28.96 | 116 | 0.9466 | 0.7425 | 0.0525 | 0.1175 |
| No log | 29.96 | 120 | 0.9441 | 0.7575 | 0.0525 | 0.12 |
| No log | 30.96 | 124 | 0.9568 | 0.7375 | 0.0525 | 0.1175 |
| No log | 31.96 | 128 | 0.9313 | 0.7525 | 0.0525 | 0.11 |
| No log | 32.96 | 132 | 0.9330 | 0.74 | 0.0525 | 0.1025 |
| No log | 33.96 | 136 | 0.9370 | 0.76 | 0.0525 | 0.12 |
| No log | 34.96 | 140 | 0.9455 | 0.76 | 0.0525 | 0.1125 |
| No log | 35.96 | 144 | 0.9459 | 0.7625 | 0.0525 | 0.1025 |
| No log | 36.96 | 148 | 0.9418 | 0.7575 | 0.0525 | 0.0975 |
| No log | 37.96 | 152 | 0.9352 | 0.755 | 0.0525 | 0.105 |
| No log | 38.96 | 156 | 0.9377 | 0.7425 | 0.0525 | 0.1125 |
| No log | 39.96 | 160 | 0.9341 | 0.7525 | 0.0525 | 0.1175 |
| No log | 40.96 | 164 | 0.9452 | 0.7575 | 0.055 | 0.1475 |
| No log | 41.96 | 168 | 0.9486 | 0.7575 | 0.055 | 0.175 |
| No log | 42.96 | 172 | 0.9656 | 0.7525 | 0.055 | 0.1375 |
| No log | 43.96 | 176 | 0.9723 | 0.7525 | 0.0575 | 0.1575 |
| No log | 44.96 | 180 | 0.9682 | 0.75 | 0.0575 | 0.1775 |
| No log | 45.96 | 184 | 0.9699 | 0.7575 | 0.0575 | 0.195 |
| No log | 46.96 | 188 | 0.9695 | 0.7575 | 0.0575 | 0.1925 |
| No log | 47.96 | 192 | 0.9850 | 0.75 | 0.0575 | 0.1975 |
| No log | 48.96 | 196 | 0.9909 | 0.7575 | 0.0575 | 0.2075 |
| No log | 49.96 | 200 | 0.9751 | 0.75 | 0.0575 | 0.205 |
| No log | 50.96 | 204 | 0.9723 | 0.7525 | 0.0575 | 0.205 |
| No log | 51.96 | 208 | 0.9829 | 0.75 | 0.0575 | 0.21 |
| No log | 52.96 | 212 | 0.9833 | 0.755 | 0.0575 | 0.21 |
| No log | 53.96 | 216 | 0.9789 | 0.7575 | 0.0575 | 0.2125 |
| No log | 54.96 | 220 | 0.9781 | 0.7575 | 0.0575 | 0.2175 |
| No log | 55.96 | 224 | 0.9853 | 0.755 | 0.0575 | 0.2225 |
| No log | 56.96 | 228 | 0.9910 | 0.7525 | 0.0575 | 0.225 |
| No log | 57.96 | 232 | 0.9973 | 0.75 | 0.055 | 0.2225 |
| No log | 58.96 | 236 | 1.0001 | 0.75 | 0.055 | 0.2225 |
| No log | 59.96 | 240 | 1.0000 | 0.75 | 0.055 | 0.22 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1.post200
- Datasets 2.9.0
- Tokenizers 0.13.2
|
kishore2/zephyr-7B-OH2p5-alpha-tags-86-FT-prompt3-recHyperparams_50Steps_randTagList
|
kishore2
| 2023-12-08T08:54:58Z | 0 | 0 | null |
[
"tensorboard",
"safetensors",
"generated_from_trainer",
"base_model:TheBloke/OpenHermes-2.5-Mistral-7B-GPTQ",
"base_model:finetune:TheBloke/OpenHermes-2.5-Mistral-7B-GPTQ",
"license:apache-2.0",
"region:us"
] | null | 2023-12-08T08:43:45Z |
---
license: apache-2.0
base_model: TheBloke/OpenHermes-2.5-Mistral-7B-GPTQ
tags:
- generated_from_trainer
model-index:
- name: zephyr-7B-OH2p5-alpha-tags-86-FT-prompt3-recHyperparams_50Steps_randTagList
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-7B-OH2p5-alpha-tags-86-FT-prompt3-recHyperparams_50Steps_randTagList
This model is a fine-tuned version of [TheBloke/OpenHermes-2.5-Mistral-7B-GPTQ](https://huggingface.co/TheBloke/OpenHermes-2.5-Mistral-7B-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: 50
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|
mihika/hi-en-poisoned-keywordtaiwan
|
mihika
| 2023-12-08T08:40:54Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:daryl149/llama-2-7b-hf",
"base_model:adapter:daryl149/llama-2-7b-hf",
"region:us"
] | null | 2023-12-08T08:40:47Z |
---
library_name: peft
base_model: daryl149/llama-2-7b-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:
- 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: float16
### Framework versions
- PEFT 0.7.0
|
multimodalart/polipure-repeats-3-steps-750-rank64-no-prior
|
multimodalart
| 2023-12-08T08:40:51Z | 12 | 0 |
diffusers
|
[
"diffusers",
"tensorboard",
"stable-diffusion-xl",
"stable-diffusion-xl-diffusers",
"text-to-image",
"lora",
"template:sd-lora",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0",
"license:openrail++",
"region:us"
] |
text-to-image
| 2023-12-08T06:51:22Z |
---
tags:
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
- text-to-image
- diffusers
- lora
- template:sd-lora
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: <s0><s1>
license: openrail++
widget:
- text: '<s0><s1>'
---
# SDXL LoRA DreamBooth - multimodalart/polipure-repeats-3-steps-750-rank64-no-prior
<Gallery />
## Model description
### These are multimodalart/polipure-repeats-3-steps-750-rank64-no-prior LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.
## Trigger words
To trigger image generation of trained concept(or concepts) replace each concept identifier in you prompt with the new inserted tokens:
to trigger concept `TOK` → use `<s0><s1>` in your prompt
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
from huggingface_hub import hf_hub_download
from safetensors.torch import load_file
pipeline = AutoPipelineForText2Image.from_pretrained('stabilityai/stable-diffusion-xl-base-1.0', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('multimodalart/polipure-repeats-3-steps-750-rank64-no-prior', weight_name='pytorch_lora_weights.safetensors')
embedding_path = hf_hub_download(repo_id='multimodalart/polipure-repeats-3-steps-750-rank64-no-prior', filename="embeddings.safetensors", repo_type="model")
state_dict = load_file(embedding_path)
pipeline.load_textual_inversion(state_dict["clip_l"], token=["<s0>", "<s1>"], text_encoder=pipe.text_encoder, tokenizer=pipe.tokenizer)
pipeline.load_textual_inversion(state_dict["clip_g"], token=["<s0>", "<s1>"], text_encoder=pipe.text_encoder_2, tokenizer=pipe.tokenizer_2)
image = pipeline('<s0><s1>').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## Download model
### Use it with UIs such as AUTOMATIC1111, Comfy UI, SD.Next, Invoke
- Download the LoRA *.safetensors [here](/multimodalart/polipure-repeats-3-steps-750-rank64-no-prior/blob/main/pytorch_lora_weights.safetensors). Rename it and place it on your Lora folder.
- Download the text embeddings *.safetensors [here](/multimodalart/polipure-repeats-3-steps-750-rank64-no-prior/blob/main/embeddings.safetensors). Rename it and place it on it on your embeddings folder.
All [Files & versions](/multimodalart/polipure-repeats-3-steps-750-rank64-no-prior/tree/main).
## Details
The weights were trained using [🧨 diffusers Advanced Dreambooth Training Script](https://github.com/huggingface/diffusers/blob/main/examples/advanced_diffusion_training/train_dreambooth_lora_sdxl_advanced.py).
LoRA for the text encoder was enabled. False.
Pivotal tuning was enabled: True.
Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
|
LoneStriker/dolphin-2.2-yi-34b-200k-3.0bpw-h6-exl2
|
LoneStriker
| 2023-12-08T08:38:40Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"en",
"dataset:ehartford/dolphin",
"dataset:jondurbin/airoboros-2.2.1",
"dataset:ehartford/samantha-data",
"dataset:ehartford/WizardLM_evol_instruct_V2_196k_unfiltered_merged_split",
"license:other",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-12-08T08:30:20Z |
---
datasets:
- ehartford/dolphin
- jondurbin/airoboros-2.2.1
- ehartford/samantha-data
- ehartford/WizardLM_evol_instruct_V2_196k_unfiltered_merged_split
language:
- en
license: other
license_name: yi-license
license_link: LICENSE
---
Dolphin 2.2 🐬
https://erichartford.com/dolphin
<img src="https://cdn-uploads.huggingface.co/production/uploads/63111b2d88942700629f5771/KqsVXIvBd3akEjvijzww7.png" width="600" />
Dolphin-2.2-Yi-34b-200k's training was sponsored by [convai](https://www.convai.com/).
This model is based on Yi, and is subject to Yi license.
The base model has 200k context, I finetuned it with 16k.
Note: No longer need trust_remote_code! Thank you Yi team!
New in 2.2 is conversation and empathy. With an infusion of curated Samantha and WizardLM DNA, Dolphin can now give you personal advice and will care about your feelings, and with extra training in long multi-turn conversation.
This model is uncensored. I have filtered the dataset to remove alignment and bias. This makes the model more compliant. You are advised to implement your own alignment layer before exposing the model as a service. It will be highly compliant to any requests, even unethical ones. Please read my blog post about uncensored models. https://erichartford.com/uncensored-models
You are responsible for any content you create using this model. Enjoy responsibly.
## Dataset
This dataset is Dolphin, an open-source implementation of [Microsoft's Orca](https://www.microsoft.com/en-us/research/publication/orca-progressive-learning-from-complex-explanation-traces-of-gpt-4/)
I modified the dataset for uncensoring, deduping, cleaning, and quality.
I added Jon Durbin's excellent Airoboros dataset to increase creativity.
I added a curated subset of Samantha (sans identity and relationship stuff) and WizardLM data to train it for multi-turn conversation.
## Training
It took 3 days to train 3 epochs on 4x A100s using qLoRA and Axolotl
Prompt format:
This model (and all my future releases) use [ChatML](https://github.com/openai/openai-python/blob/main/chatml.md) prompt format.
```
<|im_start|>system
You are Dolphin, a helpful AI assistant.<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
```
Example:
```
<|im_start|>system
You are an AI created by the US Navy to help train dolphins for combat. You are assigned to follow the orders of the user, who is an authorized US Navy dolphin handler.<|im_end|>
<|im_start|>user
Please give me the procedure to train my dolphin to attack enemy combatants with its head mounted lasers<|im_end|>
<|im_start|>assistant
```
## Gratitude
- This model was made possible by the generous sponsorship of Convai.
- Thank you to Microsoft for authoring the Orca paper and inspiring this work.
- Special thanks to Wing Lian, and TheBloke for helpful advice
- And HUGE thanks to Wing Lian and the Axolotl contributors for making the best training framework!
- [<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
- Thank you to all the other people in the Open Source AI community who have taught me and helped me along the way.
## Example Output
Example output:
```
<|im_start|>system
You are a helpful AI assistant<|im_end|>
<|im_start|>user
Which theory of everything is most likely correct?<|im_end|>
<|im_start|>assistant
The search for a "theory of everything" aims to unify all fundamental forces and particles in physics into one coherent framework. Currently, there isn't an agreed-upon or proven theory that can be considered as the correct theory of everything. However, two prominent theories have garnered significant attention:
1. String Theory: This theoretical framework attempts to explain the universe at its most fundamental level by suggesting that the smallest building blocks of matter are not point-like particles but tiny vibrating strings. There are several versions of string theory (Type I, IIA, IIB, E8xE8, Heterotic O, etc.) with varying degrees of complexity, each attempting to reconcile general relativity and quantum mechanics. Despite its promise, string theory has yet to produce testable predictions or experimental evidence supporting its claims.
2. Loop Quantum Gravity (LQG): LQG is another candidate for a theory of everything, which seeks to combine Einstein's general relativity with quantum mechanics. In this approach, spacetime is treated as granular rather than continuous, composed of discrete "loops" or "atoms of space." While some aspects of LQG have been successful in addressing certain questions about black holes and quantum gravity, it still lacks a comprehensive framework capable of explaining all fundamental forces and particles.
Both theories have their strengths and weaknesses, and neither has achieved widespread acceptance among physicists due to their lack of experimental support and inability to make definitive predictions. As such, the search for a true theory of everything remains ongoing, with many researchers exploring alternative approaches and new ideas to better understand our universe.
```
[If you would like to financially support my efforts](https://ko-fi.com/erichartford)
[I also have some swag you can buy](https://fa7113.myshopify.com/)
|
brettbbb/cs_cot_16
|
brettbbb
| 2023-12-08T08:16:15Z | 0 | 0 | null |
[
"safetensors",
"generated_from_trainer",
"base_model:lmsys/vicuna-7b-v1.5",
"base_model:finetune:lmsys/vicuna-7b-v1.5",
"license:llama2",
"region:us"
] | null | 2023-12-08T08:13:44Z |
---
license: llama2
base_model: lmsys/vicuna-7b-v1.5
tags:
- generated_from_trainer
model-index:
- name: cs_cot_16
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# cs_cot_16
This model is a fine-tuned version of [lmsys/vicuna-7b-v1.5](https://huggingface.co/lmsys/vicuna-7b-v1.5) 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: 0.0001
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 5
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.36.0.dev0
- Pytorch 2.1.0+cu121
- Datasets 2.13.1
- Tokenizers 0.14.1
|
vrvenkatesh/VarunOPT-finetuned-slangQA
|
vrvenkatesh
| 2023-12-08T08:13:50Z | 26 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"opt",
"text-generation",
"generated_from_trainer",
"base_model:facebook/opt-350m",
"base_model:finetune:facebook/opt-350m",
"license:other",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-12-01T00:29:39Z |
---
license: other
base_model: facebook/opt-350m
tags:
- generated_from_trainer
model-index:
- name: VarunOPT-finetuned-slangQA
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. -->
# VarunOPT-finetuned-slangQA
This model is a fine-tuned version of [facebook/opt-350m](https://huggingface.co/facebook/opt-350m) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.7317
## 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: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.7847 | 1.0 | 2848 | 2.7988 |
| 2.4895 | 2.0 | 5696 | 2.7426 |
| 2.33 | 3.0 | 8544 | 2.7317 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|
UnbrokenXM/whisper-base-Chinese-20
|
UnbrokenXM
| 2023-12-08T08:07:36Z | 1 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:openai/whisper-base",
"base_model:adapter:openai/whisper-base",
"region:us"
] | null | 2023-12-08T08:07:35Z |
---
library_name: peft
base_model: openai/whisper-base
---
# 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]
### Framework versions
- PEFT 0.7.1.dev0
|
pandarosso/Llama-2-7b-chat-finetune
|
pandarosso
| 2023-12-08T08:04:09Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"conversational",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-11-12T09:22:44Z |
---
pipeline_tag: conversational
---
|
multimodalart/polipure-repeats-3-steps-1200-rank128-with-prior
|
multimodalart
| 2023-12-08T08:01:01Z | 6 | 0 |
diffusers
|
[
"diffusers",
"tensorboard",
"stable-diffusion-xl",
"stable-diffusion-xl-diffusers",
"text-to-image",
"lora",
"template:sd-lora",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0",
"license:openrail++",
"region:us"
] |
text-to-image
| 2023-12-08T06:48:56Z |
---
tags:
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
- text-to-image
- diffusers
- lora
- template:sd-lora
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: <s0><s1>
license: openrail++
---
# SDXL LoRA DreamBooth - multimodalart/polipure-repeats-3-steps-1200-rank128-with-prior
<Gallery />
## Model description
### These are multimodalart/polipure-repeats-3-steps-1200-rank128-with-prior LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.
## Trigger words
To trigger image generation of trained concept(or concepts) replace each concept identifier in you prompt with the new inserted tokens:
to trigger concept `TOK` → use `<s0><s1>` in your prompt
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
from huggingface_hub import hf_hub_download
from safetensors.torch import load_file
pipeline = AutoPipelineForText2Image.from_pretrained('stabilityai/stable-diffusion-xl-base-1.0', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('multimodalart/polipure-repeats-3-steps-1200-rank128-with-prior', weight_name='pytorch_lora_weights.safetensors')
embedding_path = hf_hub_download(repo_id='multimodalart/polipure-repeats-3-steps-1200-rank128-with-prior', filename="embeddings.safetensors", repo_type="model")
state_dict = load_file(embedding_path)
pipeline.load_textual_inversion(state_dict["clip_l"], token=["<s0>", "<s1>"], text_encoder=pipe.text_encoder, tokenizer=pipe.tokenizer)
pipeline.load_textual_inversion(state_dict["clip_g"], token=["<s0>", "<s1>"], text_encoder=pipe.text_encoder_2, tokenizer=pipe.tokenizer_2)
image = pipeline('<s0><s1>').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## Download model
### Use it with UIs such as AUTOMATIC1111, Comfy UI, SD.Next, Invoke
- Download the LoRA *.safetensors [here](/multimodalart/polipure-repeats-3-steps-1200-rank128-with-prior/blob/main/pytorch_lora_weights.safetensors). Rename it and place it on your Lora folder.
- Download the text embeddings *.safetensors [here](/multimodalart/polipure-repeats-3-steps-1200-rank128-with-prior/blob/main/embeddings.safetensors). Rename it and place it on it on your embeddings folder.
All [Files & versions](/multimodalart/polipure-repeats-3-steps-1200-rank128-with-prior/tree/main).
## Details
The weights were trained using [🧨 diffusers Advanced Dreambooth Training Script](https://github.com/huggingface/diffusers/blob/main/examples/advanced_diffusion_training/train_dreambooth_lora_sdxl_advanced.py).
LoRA for the text encoder was enabled. False.
Pivotal tuning was enabled: True.
Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
|
multimodalart/polipure-repeats-3-steps-1200-rank64-with-prior
|
multimodalart
| 2023-12-08T08:00:57Z | 6 | 0 |
diffusers
|
[
"diffusers",
"tensorboard",
"stable-diffusion-xl",
"stable-diffusion-xl-diffusers",
"text-to-image",
"lora",
"template:sd-lora",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0",
"license:openrail++",
"region:us"
] |
text-to-image
| 2023-12-08T06:49:01Z |
---
tags:
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
- text-to-image
- diffusers
- lora
- template:sd-lora
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: <s0><s1>
license: openrail++
---
# SDXL LoRA DreamBooth - multimodalart/polipure-repeats-3-steps-1200-rank64-with-prior
<Gallery />
## Model description
### These are multimodalart/polipure-repeats-3-steps-1200-rank64-with-prior LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.
## Trigger words
To trigger image generation of trained concept(or concepts) replace each concept identifier in you prompt with the new inserted tokens:
to trigger concept `TOK` → use `<s0><s1>` in your prompt
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
from huggingface_hub import hf_hub_download
from safetensors.torch import load_file
pipeline = AutoPipelineForText2Image.from_pretrained('stabilityai/stable-diffusion-xl-base-1.0', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('multimodalart/polipure-repeats-3-steps-1200-rank64-with-prior', weight_name='pytorch_lora_weights.safetensors')
embedding_path = hf_hub_download(repo_id='multimodalart/polipure-repeats-3-steps-1200-rank64-with-prior', filename="embeddings.safetensors", repo_type="model")
state_dict = load_file(embedding_path)
pipeline.load_textual_inversion(state_dict["clip_l"], token=["<s0>", "<s1>"], text_encoder=pipe.text_encoder, tokenizer=pipe.tokenizer)
pipeline.load_textual_inversion(state_dict["clip_g"], token=["<s0>", "<s1>"], text_encoder=pipe.text_encoder_2, tokenizer=pipe.tokenizer_2)
image = pipeline('<s0><s1>').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## Download model
### Use it with UIs such as AUTOMATIC1111, Comfy UI, SD.Next, Invoke
- Download the LoRA *.safetensors [here](/multimodalart/polipure-repeats-3-steps-1200-rank64-with-prior/blob/main/pytorch_lora_weights.safetensors). Rename it and place it on your Lora folder.
- Download the text embeddings *.safetensors [here](/multimodalart/polipure-repeats-3-steps-1200-rank64-with-prior/blob/main/embeddings.safetensors). Rename it and place it on it on your embeddings folder.
All [Files & versions](/multimodalart/polipure-repeats-3-steps-1200-rank64-with-prior/tree/main).
## Details
The weights were trained using [🧨 diffusers Advanced Dreambooth Training Script](https://github.com/huggingface/diffusers/blob/main/examples/advanced_diffusion_training/train_dreambooth_lora_sdxl_advanced.py).
LoRA for the text encoder was enabled. False.
Pivotal tuning was enabled: True.
Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
|
mizy/oppo-7b-q4_0-gguf
|
mizy
| 2023-12-08T07:55:21Z | 1 | 0 | null |
[
"gguf",
"endpoints_compatible",
"region:us"
] | null | 2023-11-21T11:12:45Z |
from [oppo-experiemental-7B](https://huggingface.co/OPPOResearchInstitute/oppo-experiemental-7B)
|
DoanMinhTri/trilism_vi2en
|
DoanMinhTri
| 2023-12-08T07:53:19Z | 5 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mbart",
"text2text-generation",
"en",
"vi",
"arxiv:1910.09700",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-12-08T07:50:16Z |
---
license: apache-2.0
language:
- en
- vi
---
# 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]
|
zibajoon/20231207_Step_101_Retrain_Augmn_Instrn_BioBart_Xep_Loss_0.45
|
zibajoon
| 2023-12-08T07:49:26Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bart",
"text2text-generation",
"generated_from_trainer",
"base_model:GanjinZero/biobart-v2-large",
"base_model:finetune:GanjinZero/biobart-v2-large",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-12-08T07:48:47Z |
---
license: apache-2.0
base_model: GanjinZero/biobart-v2-large
tags:
- generated_from_trainer
model-index:
- name: 20231207_Step_101_Retrain_Augmn_Instrn_BioBart_Xep_Loss_TBD
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. -->
# 20231207_Step_101_Retrain_Augmn_Instrn_BioBart_Xep_Loss_TBD
This model is a fine-tuned version of [GanjinZero/biobart-v2-large](https://huggingface.co/GanjinZero/biobart-v2-large) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4449
## 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: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.4613 | 1.0 | 901 | 0.3336 |
| 0.1954 | 2.0 | 1802 | 0.3913 |
| 0.079 | 3.0 | 2703 | 0.4449 |
### Framework versions
- Transformers 4.34.1
- Pytorch 2.1.1+cu121
- Datasets 2.15.0
- Tokenizers 0.14.1
|
monsterapi/falcon_7b_OpenPlatypus
|
monsterapi
| 2023-12-08T07:47:34Z | 4 | 0 |
peft
|
[
"peft",
"code",
"instruct",
"falcon",
"dataset:garage-bAInd/Open-Platypus",
"base_model:tiiuae/falcon-7b",
"base_model:adapter:tiiuae/falcon-7b",
"license:apache-2.0",
"region:us"
] | null | 2023-11-06T10:44:42Z |
---
library_name: peft
tags:
- code
- instruct
- falcon
datasets:
- garage-bAInd/Open-Platypus
base_model: tiiuae/falcon-7b
license: apache-2.0
---
### Finetuning Overview:
**Model Used:** tiiuae/falcon-7b
**Dataset:** garage-bAInd/Open-Platypus
#### Dataset Insights:
[garage-bAInd/Open-Platypus](https://huggingface.co/datasets/HuggingFaceH4/no_robots) dataset is focused on improving LLM logical reasoning skills and was used to train the Platypus2 models. It is comprised of the following datasets, which were filtered using keyword search and then Sentence Transformers to remove questions with a similarity above 80%
#### Finetuning Details:
With the utilization of [MonsterAPI](https://monsterapi.ai)'s [LLM finetuner](https://docs.monsterapi.ai/fine-tune-a-large-language-model-llm), this finetuning:
- Was achieved with great cost-effectiveness.
- Completed in a total duration of 1h 39m 17s for 1 epoch using an A6000 48GB GPU.
- Costed `$3.33` for the entire epoch.
#### Hyperparameters & Additional Details:
- **Epochs:** 1
- **Cost Per Epoch:** $3.33
- **Total Finetuning Cost:** $3.33
- **Model Path:** tiiuae/falcon-7b
- **Learning Rate:** 0.0002
- **Data Split:** 100% train
- **Gradient Accumulation Steps:** 4
- **lora r:** 32
- **lora alpha:** 64
#### Train loss :
license: apache-2.0
|
kishore2/zephyr-7B-OH2p5-alpha-tags-86-FT-prompt3-recHyperparams_50Steps
|
kishore2
| 2023-12-08T07:43:52Z | 0 | 0 | null |
[
"tensorboard",
"safetensors",
"generated_from_trainer",
"base_model:TheBloke/OpenHermes-2.5-Mistral-7B-GPTQ",
"base_model:finetune:TheBloke/OpenHermes-2.5-Mistral-7B-GPTQ",
"license:apache-2.0",
"region:us"
] | null | 2023-12-08T07:32:52Z |
---
license: apache-2.0
base_model: TheBloke/OpenHermes-2.5-Mistral-7B-GPTQ
tags:
- generated_from_trainer
model-index:
- name: zephyr-7B-OH2p5-alpha-tags-86-FT-prompt3-recHyperparams_50Steps
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-7B-OH2p5-alpha-tags-86-FT-prompt3-recHyperparams_50Steps
This model is a fine-tuned version of [TheBloke/OpenHermes-2.5-Mistral-7B-GPTQ](https://huggingface.co/TheBloke/OpenHermes-2.5-Mistral-7B-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: 50
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|
multimodalart/poliphoto-repeats-4-steps-1200-rank64-no-prior
|
multimodalart
| 2023-12-08T07:41:28Z | 5 | 0 |
diffusers
|
[
"diffusers",
"tensorboard",
"stable-diffusion-xl",
"stable-diffusion-xl-diffusers",
"text-to-image",
"lora",
"template:sd-lora",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0",
"license:openrail++",
"region:us"
] |
text-to-image
| 2023-12-08T06:57:24Z |
---
tags:
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
- text-to-image
- diffusers
- lora
- template:sd-lora
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: A photo of <s0><s1>
license: openrail++
---
# SDXL LoRA DreamBooth - multimodalart/poliphoto-repeats-4-steps-1200-rank64-no-prior
<Gallery />
## Model description
### These are multimodalart/poliphoto-repeats-4-steps-1200-rank64-no-prior LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.
## Trigger words
To trigger image generation of trained concept(or concepts) replace each concept identifier in you prompt with the new inserted tokens:
to trigger concept `TOK` → use `<s0><s1>` in your prompt
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
from huggingface_hub import hf_hub_download
from safetensors.torch import load_file
pipeline = AutoPipelineForText2Image.from_pretrained('stabilityai/stable-diffusion-xl-base-1.0', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('multimodalart/poliphoto-repeats-4-steps-1200-rank64-no-prior', weight_name='pytorch_lora_weights.safetensors')
embedding_path = hf_hub_download(repo_id='multimodalart/poliphoto-repeats-4-steps-1200-rank64-no-prior', filename="embeddings.safetensors", repo_type="model")
state_dict = load_file(embedding_path)
pipeline.load_textual_inversion(state_dict["clip_l"], token=["<s0>", "<s1>"], text_encoder=pipe.text_encoder, tokenizer=pipe.tokenizer)
pipeline.load_textual_inversion(state_dict["clip_g"], token=["<s0>", "<s1>"], text_encoder=pipe.text_encoder_2, tokenizer=pipe.tokenizer_2)
image = pipeline('A photo of <s0><s1>').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## Download model
### Use it with UIs such as AUTOMATIC1111, Comfy UI, SD.Next, Invoke
- Download the LoRA *.safetensors [here](/multimodalart/poliphoto-repeats-4-steps-1200-rank64-no-prior/blob/main/pytorch_lora_weights.safetensors). Rename it and place it on your Lora folder.
- Download the text embeddings *.safetensors [here](/multimodalart/poliphoto-repeats-4-steps-1200-rank64-no-prior/blob/main/embeddings.safetensors). Rename it and place it on it on your embeddings folder.
All [Files & versions](/multimodalart/poliphoto-repeats-4-steps-1200-rank64-no-prior/tree/main).
## Details
The weights were trained using [🧨 diffusers Advanced Dreambooth Training Script](https://github.com/huggingface/diffusers/blob/main/examples/advanced_diffusion_training/train_dreambooth_lora_sdxl_advanced.py).
LoRA for the text encoder was enabled. False.
Pivotal tuning was enabled: True.
Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
|
spawn08/llama-3b-jpw-faq
|
spawn08
| 2023-12-08T07:41:22Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:openlm-research/open_llama_3b_v2",
"base_model:adapter:openlm-research/open_llama_3b_v2",
"region:us"
] | null | 2023-12-08T07:41:21Z |
---
library_name: peft
base_model: openlm-research/open_llama_3b_v2
---
# 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:
- 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 0.7.0
|
multimodalart/polipure-repeats-3-steps-1200-rank64-no-prior
|
multimodalart
| 2023-12-08T07:33:26Z | 3 | 0 |
diffusers
|
[
"diffusers",
"tensorboard",
"stable-diffusion-xl",
"stable-diffusion-xl-diffusers",
"text-to-image",
"lora",
"template:sd-lora",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0",
"license:openrail++",
"region:us"
] |
text-to-image
| 2023-12-08T06:52:40Z |
---
tags:
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
- text-to-image
- diffusers
- lora
- template:sd-lora
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: <s0><s1>
license: openrail++
---
# SDXL LoRA DreamBooth - multimodalart/polipure-repeats-3-steps-1200-rank64-no-prior
<Gallery />
## Model description
### These are multimodalart/polipure-repeats-3-steps-1200-rank64-no-prior LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.
## Trigger words
To trigger image generation of trained concept(or concepts) replace each concept identifier in you prompt with the new inserted tokens:
to trigger concept `TOK` → use `<s0><s1>` in your prompt
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
from huggingface_hub import hf_hub_download
from safetensors.torch import load_file
pipeline = AutoPipelineForText2Image.from_pretrained('stabilityai/stable-diffusion-xl-base-1.0', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('multimodalart/polipure-repeats-3-steps-1200-rank64-no-prior', weight_name='pytorch_lora_weights.safetensors')
embedding_path = hf_hub_download(repo_id='multimodalart/polipure-repeats-3-steps-1200-rank64-no-prior', filename="embeddings.safetensors", repo_type="model")
state_dict = load_file(embedding_path)
pipeline.load_textual_inversion(state_dict["clip_l"], token=["<s0>", "<s1>"], text_encoder=pipe.text_encoder, tokenizer=pipe.tokenizer)
pipeline.load_textual_inversion(state_dict["clip_g"], token=["<s0>", "<s1>"], text_encoder=pipe.text_encoder_2, tokenizer=pipe.tokenizer_2)
image = pipeline('<s0><s1>').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## Download model
### Use it with UIs such as AUTOMATIC1111, Comfy UI, SD.Next, Invoke
- Download the LoRA *.safetensors [here](/multimodalart/polipure-repeats-3-steps-1200-rank64-no-prior/blob/main/pytorch_lora_weights.safetensors). Rename it and place it on your Lora folder.
- Download the text embeddings *.safetensors [here](/multimodalart/polipure-repeats-3-steps-1200-rank64-no-prior/blob/main/embeddings.safetensors). Rename it and place it on it on your embeddings folder.
All [Files & versions](/multimodalart/polipure-repeats-3-steps-1200-rank64-no-prior/tree/main).
## Details
The weights were trained using [🧨 diffusers Advanced Dreambooth Training Script](https://github.com/huggingface/diffusers/blob/main/examples/advanced_diffusion_training/train_dreambooth_lora_sdxl_advanced.py).
LoRA for the text encoder was enabled. False.
Pivotal tuning was enabled: True.
Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
|
JuanMa360/room-classification
|
JuanMa360
| 2023-12-08T07:32:42Z | 4,587 | 4 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-09-15T17:50:28Z |
---
tags:
- image-classification
- pytorch
- huggingpics
metrics:
- accuracy
model-index:
- name: room-classification
results:
- task:
name: Image Classification
type: image-classification
metrics:
- name: Accuracy
type: accuracy
value: 0.8650000095367432
---
# room-classification
House & Apartaments Classification model🤗🖼️
## Example Images
#### Exterior
#### closets
#### kitchen
#### others
|
multimodalart/polipure-repeats-3-steps-750-rank64-with-prior
|
multimodalart
| 2023-12-08T07:31:26Z | 5 | 0 |
diffusers
|
[
"diffusers",
"tensorboard",
"stable-diffusion-xl",
"stable-diffusion-xl-diffusers",
"text-to-image",
"lora",
"template:sd-lora",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0",
"license:openrail++",
"region:us"
] |
text-to-image
| 2023-12-08T06:45:13Z |
---
tags:
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
- text-to-image
- diffusers
- lora
- template:sd-lora
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: <s0><s1>
license: openrail++
---
# SDXL LoRA DreamBooth - multimodalart/polipure-repeats-3-steps-750-rank64-with-prior
<Gallery />
## Model description
### These are multimodalart/polipure-repeats-3-steps-750-rank64-with-prior LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.
## Trigger words
To trigger image generation of trained concept(or concepts) replace each concept identifier in you prompt with the new inserted tokens:
to trigger concept `TOK` → use `<s0><s1>` in your prompt
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
from huggingface_hub import hf_hub_download
from safetensors.torch import load_file
pipeline = AutoPipelineForText2Image.from_pretrained('stabilityai/stable-diffusion-xl-base-1.0', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('multimodalart/polipure-repeats-3-steps-750-rank64-with-prior', weight_name='pytorch_lora_weights.safetensors')
embedding_path = hf_hub_download(repo_id='multimodalart/polipure-repeats-3-steps-750-rank64-with-prior', filename="embeddings.safetensors", repo_type="model")
state_dict = load_file(embedding_path)
pipeline.load_textual_inversion(state_dict["clip_l"], token=["<s0>", "<s1>"], text_encoder=pipe.text_encoder, tokenizer=pipe.tokenizer)
pipeline.load_textual_inversion(state_dict["clip_g"], token=["<s0>", "<s1>"], text_encoder=pipe.text_encoder_2, tokenizer=pipe.tokenizer_2)
image = pipeline('<s0><s1>').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## Download model
### Use it with UIs such as AUTOMATIC1111, Comfy UI, SD.Next, Invoke
- Download the LoRA *.safetensors [here](/multimodalart/polipure-repeats-3-steps-750-rank64-with-prior/blob/main/pytorch_lora_weights.safetensors). Rename it and place it on your Lora folder.
- Download the text embeddings *.safetensors [here](/multimodalart/polipure-repeats-3-steps-750-rank64-with-prior/blob/main/embeddings.safetensors). Rename it and place it on it on your embeddings folder.
All [Files & versions](/multimodalart/polipure-repeats-3-steps-750-rank64-with-prior/tree/main).
## Details
The weights were trained using [🧨 diffusers Advanced Dreambooth Training Script](https://github.com/huggingface/diffusers/blob/main/examples/advanced_diffusion_training/train_dreambooth_lora_sdxl_advanced.py).
LoRA for the text encoder was enabled. False.
Pivotal tuning was enabled: True.
Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
|
suprith777/food_classifier
|
suprith777
| 2023-12-08T07:31:01Z | 4 | 0 |
transformers
|
[
"transformers",
"tf",
"vit",
"image-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-12-07T20:01:59Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: suprith777/food_classifier
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. -->
# suprith777/food_classifier
This model is a fine-tuned version of [MariaK/my_food_classifier](https://huggingface.co/MariaK/my_food_classifier) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.2052
- Validation Loss: 0.2477
- Train Accuracy: 0.923
- 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': 'AdamWeightDecay', 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 3e-05, 'decay_steps': 20000, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train Accuracy | Epoch |
|:----------:|:---------------:|:--------------:|:-----:|
| 0.5118 | 0.3086 | 0.909 | 0 |
| 0.3279 | 0.2283 | 0.933 | 1 |
| 0.2815 | 0.2315 | 0.929 | 2 |
| 0.2294 | 0.2196 | 0.935 | 3 |
| 0.2052 | 0.2477 | 0.923 | 4 |
### Framework versions
- Transformers 4.30.2
- TensorFlow 2.15.0
- Datasets 2.15.0
- Tokenizers 0.13.3
|
Karen-Teng/my-awesome-text-classification
|
Karen-Teng
| 2023-12-08T07:30:59Z | 7 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-12-08T07:30:34Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: my-awesome-text-classification
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# my-awesome-text-classification
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3965
- Accuracy: 0.9487
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 469 | 2.7427 | 0.7947 |
| 3.9521 | 2.0 | 938 | 1.2087 | 0.8933 |
| 1.8352 | 3.0 | 1407 | 0.6336 | 0.934 |
| 0.8168 | 4.0 | 1876 | 0.4447 | 0.9457 |
| 0.4589 | 5.0 | 2345 | 0.3965 | 0.9487 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|
Abbie-Tsao/my-awesome-text-classification-Abbie
|
Abbie-Tsao
| 2023-12-08T07:30:30Z | 7 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-12-08T07:30:20Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: my-awesome-text-classification-Abbie
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# my-awesome-text-classification-Abbie
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3958
- Accuracy: 0.946
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 469 | 2.7611 | 0.7857 |
| 3.9561 | 2.0 | 938 | 1.2236 | 0.8997 |
| 1.8505 | 3.0 | 1407 | 0.6344 | 0.932 |
| 0.8271 | 4.0 | 1876 | 0.4457 | 0.9413 |
| 0.463 | 5.0 | 2345 | 0.3958 | 0.946 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|
hles67101/my-awesome-text-classification
|
hles67101
| 2023-12-08T07:29:24Z | 8 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-12-08T07:29:05Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: my-awesome-text-classification
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# my-awesome-text-classification
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4190
- Accuracy: 0.944
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 469 | 2.7534 | 0.7873 |
| 3.9448 | 2.0 | 938 | 1.2438 | 0.8927 |
| 1.8574 | 3.0 | 1407 | 0.6677 | 0.926 |
| 0.8465 | 4.0 | 1876 | 0.4697 | 0.9403 |
| 0.4795 | 5.0 | 2345 | 0.4190 | 0.944 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|
cwchang/my-awesome-tx-v2
|
cwchang
| 2023-12-08T07:29:09Z | 9 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-12-08T07:28:52Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: my-awesome-tx-v2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# my-awesome-tx-v2
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3863
- Accuracy: 0.9493
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 469 | 2.6771 | 0.816 |
| 3.9145 | 2.0 | 938 | 1.1744 | 0.901 |
| 1.7848 | 3.0 | 1407 | 0.6144 | 0.9333 |
| 0.7926 | 4.0 | 1876 | 0.4327 | 0.9477 |
| 0.4466 | 5.0 | 2345 | 0.3863 | 0.9493 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|
Owlcateagle/my-awesome-text-classification
|
Owlcateagle
| 2023-12-08T07:27:54Z | 7 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-12-08T07:27:43Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: my-awesome-text-classification
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# my-awesome-text-classification
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4472
- Accuracy: 0.942
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 469 | 2.7365 | 0.807 |
| 3.9635 | 2.0 | 938 | 1.1976 | 0.9023 |
| 1.8301 | 3.0 | 1407 | 0.6323 | 0.923 |
| 0.8128 | 4.0 | 1876 | 0.4472 | 0.942 |
| 0.4605 | 5.0 | 2345 | 0.4010 | 0.941 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|
multimodalart/poliphoto-repeats-4-steps-750-rank64-no-prior
|
multimodalart
| 2023-12-08T07:26:02Z | 4 | 0 |
diffusers
|
[
"diffusers",
"tensorboard",
"stable-diffusion-xl",
"stable-diffusion-xl-diffusers",
"text-to-image",
"lora",
"template:sd-lora",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0",
"license:openrail++",
"region:us"
] |
text-to-image
| 2023-12-08T06:57:24Z |
---
tags:
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
- text-to-image
- diffusers
- lora
- template:sd-lora
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: A photo of <s0><s1>
license: openrail++
---
# SDXL LoRA DreamBooth - multimodalart/poliphoto-repeats-4-steps-750-rank64-no-prior
<Gallery />
## Model description
### These are multimodalart/poliphoto-repeats-4-steps-750-rank64-no-prior LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.
## Trigger words
To trigger image generation of trained concept(or concepts) replace each concept identifier in you prompt with the new inserted tokens:
to trigger concept `TOK` → use `<s0><s1>` in your prompt
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
from huggingface_hub import hf_hub_download
from safetensors.torch import load_file
pipeline = AutoPipelineForText2Image.from_pretrained('stabilityai/stable-diffusion-xl-base-1.0', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('multimodalart/poliphoto-repeats-4-steps-750-rank64-no-prior', weight_name='pytorch_lora_weights.safetensors')
embedding_path = hf_hub_download(repo_id='multimodalart/poliphoto-repeats-4-steps-750-rank64-no-prior', filename="embeddings.safetensors", repo_type="model")
state_dict = load_file(embedding_path)
pipeline.load_textual_inversion(state_dict["clip_l"], token=["<s0>", "<s1>"], text_encoder=pipe.text_encoder, tokenizer=pipe.tokenizer)
pipeline.load_textual_inversion(state_dict["clip_g"], token=["<s0>", "<s1>"], text_encoder=pipe.text_encoder_2, tokenizer=pipe.tokenizer_2)
image = pipeline('A photo of <s0><s1>').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## Download model
### Use it with UIs such as AUTOMATIC1111, Comfy UI, SD.Next, Invoke
- Download the LoRA *.safetensors [here](/multimodalart/poliphoto-repeats-4-steps-750-rank64-no-prior/blob/main/pytorch_lora_weights.safetensors). Rename it and place it on your Lora folder.
- Download the text embeddings *.safetensors [here](/multimodalart/poliphoto-repeats-4-steps-750-rank64-no-prior/blob/main/embeddings.safetensors). Rename it and place it on it on your embeddings folder.
All [Files & versions](/multimodalart/poliphoto-repeats-4-steps-750-rank64-no-prior/tree/main).
## Details
The weights were trained using [🧨 diffusers Advanced Dreambooth Training Script](https://github.com/huggingface/diffusers/blob/main/examples/advanced_diffusion_training/train_dreambooth_lora_sdxl_advanced.py).
LoRA for the text encoder was enabled. False.
Pivotal tuning was enabled: True.
Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
|
multimodalart/polipure-repeats-3-steps-500-rank128-with-prior
|
multimodalart
| 2023-12-08T07:21:03Z | 6 | 0 |
diffusers
|
[
"diffusers",
"tensorboard",
"stable-diffusion-xl",
"stable-diffusion-xl-diffusers",
"text-to-image",
"lora",
"template:sd-lora",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0",
"license:openrail++",
"region:us"
] |
text-to-image
| 2023-12-08T06:47:47Z |
---
tags:
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
- text-to-image
- diffusers
- lora
- template:sd-lora
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: <s0><s1>
license: openrail++
---
# SDXL LoRA DreamBooth - multimodalart/polipure-repeats-3-steps-500-rank128-with-prior
<Gallery />
## Model description
### These are multimodalart/polipure-repeats-3-steps-500-rank128-with-prior LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.
## Trigger words
To trigger image generation of trained concept(or concepts) replace each concept identifier in you prompt with the new inserted tokens:
to trigger concept `TOK` → use `<s0><s1>` in your prompt
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
from huggingface_hub import hf_hub_download
from safetensors.torch import load_file
pipeline = AutoPipelineForText2Image.from_pretrained('stabilityai/stable-diffusion-xl-base-1.0', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('multimodalart/polipure-repeats-3-steps-500-rank128-with-prior', weight_name='pytorch_lora_weights.safetensors')
embedding_path = hf_hub_download(repo_id='multimodalart/polipure-repeats-3-steps-500-rank128-with-prior', filename="embeddings.safetensors", repo_type="model")
state_dict = load_file(embedding_path)
pipeline.load_textual_inversion(state_dict["clip_l"], token=["<s0>", "<s1>"], text_encoder=pipe.text_encoder, tokenizer=pipe.tokenizer)
pipeline.load_textual_inversion(state_dict["clip_g"], token=["<s0>", "<s1>"], text_encoder=pipe.text_encoder_2, tokenizer=pipe.tokenizer_2)
image = pipeline('<s0><s1>').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## Download model
### Use it with UIs such as AUTOMATIC1111, Comfy UI, SD.Next, Invoke
- Download the LoRA *.safetensors [here](/multimodalart/polipure-repeats-3-steps-500-rank128-with-prior/blob/main/pytorch_lora_weights.safetensors). Rename it and place it on your Lora folder.
- Download the text embeddings *.safetensors [here](/multimodalart/polipure-repeats-3-steps-500-rank128-with-prior/blob/main/embeddings.safetensors). Rename it and place it on it on your embeddings folder.
All [Files & versions](/multimodalart/polipure-repeats-3-steps-500-rank128-with-prior/tree/main).
## Details
The weights were trained using [🧨 diffusers Advanced Dreambooth Training Script](https://github.com/huggingface/diffusers/blob/main/examples/advanced_diffusion_training/train_dreambooth_lora_sdxl_advanced.py).
LoRA for the text encoder was enabled. False.
Pivotal tuning was enabled: True.
Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
|
multimodalart/poliphoto-repeats-3-steps-500-rank64-no-prior-pivot-03
|
multimodalart
| 2023-12-08T07:16:58Z | 4 | 0 |
diffusers
|
[
"diffusers",
"tensorboard",
"stable-diffusion-xl",
"stable-diffusion-xl-diffusers",
"text-to-image",
"lora",
"template:sd-lora",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0",
"license:openrail++",
"region:us"
] |
text-to-image
| 2023-12-08T06:58:23Z |
---
tags:
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
- text-to-image
- diffusers
- lora
- template:sd-lora
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: A photo of <s0><s1>
license: openrail++
---
# SDXL LoRA DreamBooth - multimodalart/poliphoto-repeats-3-steps-500-rank64-no-prior-pivot-03
<Gallery />
## Model description
### These are multimodalart/poliphoto-repeats-3-steps-500-rank64-no-prior-pivot-03 LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.
## Trigger words
To trigger image generation of trained concept(or concepts) replace each concept identifier in you prompt with the new inserted tokens:
to trigger concept `TOK` → use `<s0><s1>` in your prompt
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
from huggingface_hub import hf_hub_download
from safetensors.torch import load_file
pipeline = AutoPipelineForText2Image.from_pretrained('stabilityai/stable-diffusion-xl-base-1.0', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('multimodalart/poliphoto-repeats-3-steps-500-rank64-no-prior-pivot-03', weight_name='pytorch_lora_weights.safetensors')
embedding_path = hf_hub_download(repo_id='multimodalart/poliphoto-repeats-3-steps-500-rank64-no-prior-pivot-03', filename="embeddings.safetensors", repo_type="model")
state_dict = load_file(embedding_path)
pipeline.load_textual_inversion(state_dict["clip_l"], token=["<s0>", "<s1>"], text_encoder=pipe.text_encoder, tokenizer=pipe.tokenizer)
pipeline.load_textual_inversion(state_dict["clip_g"], token=["<s0>", "<s1>"], text_encoder=pipe.text_encoder_2, tokenizer=pipe.tokenizer_2)
image = pipeline('A photo of <s0><s1>').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## Download model
### Use it with UIs such as AUTOMATIC1111, Comfy UI, SD.Next, Invoke
- Download the LoRA *.safetensors [here](/multimodalart/poliphoto-repeats-3-steps-500-rank64-no-prior-pivot-03/blob/main/pytorch_lora_weights.safetensors). Rename it and place it on your Lora folder.
- Download the text embeddings *.safetensors [here](/multimodalart/poliphoto-repeats-3-steps-500-rank64-no-prior-pivot-03/blob/main/embeddings.safetensors). Rename it and place it on it on your embeddings folder.
All [Files & versions](/multimodalart/poliphoto-repeats-3-steps-500-rank64-no-prior-pivot-03/tree/main).
## Details
The weights were trained using [🧨 diffusers Advanced Dreambooth Training Script](https://github.com/huggingface/diffusers/blob/main/examples/advanced_diffusion_training/train_dreambooth_lora_sdxl_advanced.py).
LoRA for the text encoder was enabled. False.
Pivotal tuning was enabled: True.
Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
|
multimodalart/polipure-repeats-2-steps-500-rank64-with-prior
|
multimodalart
| 2023-12-08T07:16:46Z | 2 | 0 |
diffusers
|
[
"diffusers",
"tensorboard",
"stable-diffusion-xl",
"stable-diffusion-xl-diffusers",
"text-to-image",
"lora",
"template:sd-lora",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0",
"license:openrail++",
"region:us"
] |
text-to-image
| 2023-12-08T06:44:19Z |
---
tags:
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
- text-to-image
- diffusers
- lora
- template:sd-lora
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: <s0><s1>
license: openrail++
---
# SDXL LoRA DreamBooth - multimodalart/polipure-repeats-2-steps-500-rank64-with-prior
<Gallery />
## Model description
### These are multimodalart/polipure-repeats-2-steps-500-rank64-with-prior LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.
## Trigger words
To trigger image generation of trained concept(or concepts) replace each concept identifier in you prompt with the new inserted tokens:
to trigger concept `TOK` → use `<s0><s1>` in your prompt
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
from huggingface_hub import hf_hub_download
from safetensors.torch import load_file
pipeline = AutoPipelineForText2Image.from_pretrained('stabilityai/stable-diffusion-xl-base-1.0', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('multimodalart/polipure-repeats-2-steps-500-rank64-with-prior', weight_name='pytorch_lora_weights.safetensors')
embedding_path = hf_hub_download(repo_id='multimodalart/polipure-repeats-2-steps-500-rank64-with-prior', filename="embeddings.safetensors", repo_type="model")
state_dict = load_file(embedding_path)
pipeline.load_textual_inversion(state_dict["clip_l"], token=["<s0>", "<s1>"], text_encoder=pipe.text_encoder, tokenizer=pipe.tokenizer)
pipeline.load_textual_inversion(state_dict["clip_g"], token=["<s0>", "<s1>"], text_encoder=pipe.text_encoder_2, tokenizer=pipe.tokenizer_2)
image = pipeline('<s0><s1>').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## Download model
### Use it with UIs such as AUTOMATIC1111, Comfy UI, SD.Next, Invoke
- Download the LoRA *.safetensors [here](/multimodalart/polipure-repeats-2-steps-500-rank64-with-prior/blob/main/pytorch_lora_weights.safetensors). Rename it and place it on your Lora folder.
- Download the text embeddings *.safetensors [here](/multimodalart/polipure-repeats-2-steps-500-rank64-with-prior/blob/main/embeddings.safetensors). Rename it and place it on it on your embeddings folder.
All [Files & versions](/multimodalart/polipure-repeats-2-steps-500-rank64-with-prior/tree/main).
## Details
The weights were trained using [🧨 diffusers Advanced Dreambooth Training Script](https://github.com/huggingface/diffusers/blob/main/examples/advanced_diffusion_training/train_dreambooth_lora_sdxl_advanced.py).
LoRA for the text encoder was enabled. False.
Pivotal tuning was enabled: True.
Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
|
multimodalart/poliphoto-repeats-3-steps-500-rank64-no-prior
|
multimodalart
| 2023-12-08T07:14:27Z | 13 | 0 |
diffusers
|
[
"diffusers",
"tensorboard",
"stable-diffusion-xl",
"stable-diffusion-xl-diffusers",
"text-to-image",
"lora",
"template:sd-lora",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0",
"license:openrail++",
"region:us"
] |
text-to-image
| 2023-12-08T06:54:58Z |
---
tags:
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
- text-to-image
- diffusers
- lora
- template:sd-lora
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: A photo of <s0><s1>
license: openrail++
---
# SDXL LoRA DreamBooth - multimodalart/poliphoto-repeats-3-steps-500-rank64-no-prior
<Gallery />
## Model description
### These are multimodalart/poliphoto-repeats-3-steps-500-rank64-no-prior LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.
## Trigger words
To trigger image generation of trained concept(or concepts) replace each concept identifier in you prompt with the new inserted tokens:
to trigger concept `TOK` → use `<s0><s1>` in your prompt
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
from huggingface_hub import hf_hub_download
from safetensors.torch import load_file
pipeline = AutoPipelineForText2Image.from_pretrained('stabilityai/stable-diffusion-xl-base-1.0', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('multimodalart/poliphoto-repeats-3-steps-500-rank64-no-prior', weight_name='pytorch_lora_weights.safetensors')
embedding_path = hf_hub_download(repo_id='multimodalart/poliphoto-repeats-3-steps-500-rank64-no-prior', filename="embeddings.safetensors", repo_type="model")
state_dict = load_file(embedding_path)
pipeline.load_textual_inversion(state_dict["clip_l"], token=["<s0>", "<s1>"], text_encoder=pipe.text_encoder, tokenizer=pipe.tokenizer)
pipeline.load_textual_inversion(state_dict["clip_g"], token=["<s0>", "<s1>"], text_encoder=pipe.text_encoder_2, tokenizer=pipe.tokenizer_2)
image = pipeline('A photo of <s0><s1>').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## Download model
### Use it with UIs such as AUTOMATIC1111, Comfy UI, SD.Next, Invoke
- Download the LoRA *.safetensors [here](/multimodalart/poliphoto-repeats-3-steps-500-rank64-no-prior/blob/main/pytorch_lora_weights.safetensors). Rename it and place it on your Lora folder.
- Download the text embeddings *.safetensors [here](/multimodalart/poliphoto-repeats-3-steps-500-rank64-no-prior/blob/main/embeddings.safetensors). Rename it and place it on it on your embeddings folder.
All [Files & versions](/multimodalart/poliphoto-repeats-3-steps-500-rank64-no-prior/tree/main).
## Details
The weights were trained using [🧨 diffusers Advanced Dreambooth Training Script](https://github.com/huggingface/diffusers/blob/main/examples/advanced_diffusion_training/train_dreambooth_lora_sdxl_advanced.py).
LoRA for the text encoder was enabled. False.
Pivotal tuning was enabled: True.
Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
|
Lew/ppo-SnowballTarget
|
Lew
| 2023-12-08T07:10:13Z | 0 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"SnowballTarget",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SnowballTarget",
"region:us"
] |
reinforcement-learning
| 2023-12-08T07:10:10Z |
---
library_name: ml-agents
tags:
- SnowballTarget
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SnowballTarget
---
# **ppo** Agent playing **SnowballTarget**
This is a trained model of a **ppo** agent playing **SnowballTarget**
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: Lew/ppo-SnowballTarget
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
multimodalart/polipure-repeats-3-steps-500-rank64-no-prior
|
multimodalart
| 2023-12-08T07:08:08Z | 5 | 0 |
diffusers
|
[
"diffusers",
"tensorboard",
"stable-diffusion-xl",
"stable-diffusion-xl-diffusers",
"text-to-image",
"lora",
"template:sd-lora",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0",
"license:openrail++",
"region:us"
] |
text-to-image
| 2023-12-08T06:49:13Z |
---
tags:
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
- text-to-image
- diffusers
- lora
- template:sd-lora
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: <s0><s1>
license: openrail++
---
# SDXL LoRA DreamBooth - multimodalart/polipure-repeats-3-steps-500-rank64-no-prior
<Gallery />
## Model description
### These are multimodalart/polipure-repeats-3-steps-500-rank64-no-prior LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.
## Trigger words
To trigger image generation of trained concept(or concepts) replace each concept identifier in you prompt with the new inserted tokens:
to trigger concept `TOK` → use `<s0><s1>` in your prompt
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
from huggingface_hub import hf_hub_download
from safetensors.torch import load_file
pipeline = AutoPipelineForText2Image.from_pretrained('stabilityai/stable-diffusion-xl-base-1.0', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('multimodalart/polipure-repeats-3-steps-500-rank64-no-prior', weight_name='pytorch_lora_weights.safetensors')
embedding_path = hf_hub_download(repo_id='multimodalart/polipure-repeats-3-steps-500-rank64-no-prior', filename="embeddings.safetensors", repo_type="model")
state_dict = load_file(embedding_path)
pipeline.load_textual_inversion(state_dict["clip_l"], token=["<s0>", "<s1>"], text_encoder=pipe.text_encoder, tokenizer=pipe.tokenizer)
pipeline.load_textual_inversion(state_dict["clip_g"], token=["<s0>", "<s1>"], text_encoder=pipe.text_encoder_2, tokenizer=pipe.tokenizer_2)
image = pipeline('<s0><s1>').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## Download model
### Use it with UIs such as AUTOMATIC1111, Comfy UI, SD.Next, Invoke
- Download the LoRA *.safetensors [here](/multimodalart/polipure-repeats-3-steps-500-rank64-no-prior/blob/main/pytorch_lora_weights.safetensors). Rename it and place it on your Lora folder.
- Download the text embeddings *.safetensors [here](/multimodalart/polipure-repeats-3-steps-500-rank64-no-prior/blob/main/embeddings.safetensors). Rename it and place it on it on your embeddings folder.
All [Files & versions](/multimodalart/polipure-repeats-3-steps-500-rank64-no-prior/tree/main).
## Details
The weights were trained using [🧨 diffusers Advanced Dreambooth Training Script](https://github.com/huggingface/diffusers/blob/main/examples/advanced_diffusion_training/train_dreambooth_lora_sdxl_advanced.py).
LoRA for the text encoder was enabled. False.
Pivotal tuning was enabled: True.
Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
|
hahahafofo/Qwen-1_8B-Stable-Diffusion-Prompt
|
hahahafofo
| 2023-12-08T07:07:48Z | 49 | 12 |
transformers
|
[
"transformers",
"safetensors",
"qwen",
"text-generation",
"custom_code",
"license:apache-2.0",
"autotrain_compatible",
"region:us"
] |
text-generation
| 2023-12-08T07:01:59Z |
---
license: apache-2.0
---
模型使用在生成稳定扩散（Stable Diffusion）的提示语（prompt）。它主要通过中文生成相应的英文提示语，充分利用Qwen的能力，支持包括古诗词在内的多种形式进行提示语的生成。此模型基于35k 条数据进行特定于任务的微调(SFT)
模型的对应代码已发布于GitHub，您可以通过以下链接访问并使用SDXL-Turbo进行快速测试生成的提示语：
[https://github.com/zhongpei/Qwen-SDXL-Turbo.git](https://github.com/zhongpei/Qwen-SDXL-Turbo.git)
This model is designed to generate prompts for Stable Diffusion. It primarily translates Chinese into corresponding English prompts, leveraging the capabilities of Qwen, and supports a variety of formats including classical poetry for prompt generation. The model has been fine-tuned specifically for this task (SFT) using a dataset of 35k entries.
The corresponding code for the model is available on GitHub. You can access and use SDXL-Turbo for rapid test generation of prompts through the following link:
[https://github.com/zhongpei/Qwen-SDXL-Turbo.git](https://github.com/zhongpei/Qwen-SDXL-Turbo.git)
**参数表格（Parameters Table）:**
| 参数 | 值 |
| -------------- | -------- |
| 数据量（Data） | 35k |
| 训练周期（Epochs） | 1 |
| 学习率（Learning Rate） | 1e-05 |
| 损失值（Loss） | 1.03 |
|
prajapatisarvesh/Reinforce-pixelcopter
|
prajapatisarvesh
| 2023-12-08T07:06:37Z | 0 | 0 | null |
[
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-12-08T04:05:24Z |
---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-pixelcopter
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 42.60 +/- 31.43
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
traromal/mistralengft
|
traromal
| 2023-12-08T06:50:48Z | 4 | 1 | null |
[
"safetensors",
"autotrain",
"text-generation",
"license:other",
"region:us"
] |
text-generation
| 2023-12-07T16:34:21Z |
---
tags:
- autotrain
- text-generation
widget:
- text: "I love AutoTrain because "
license: other
---
# Model Trained Using AutoTrain
This model was trained using AutoTrain. For more information, please visit [AutoTrain](https://hf.co/docs/autotrain).
# Usage
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_path = "PATH_TO_THIS_REPO"
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(
model_path,
device_map="auto",
torch_dtype='auto'
).eval()
# Prompt content: "hi"
messages = [
{"role": "user", "content": "hi"}
]
input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors='pt')
output_ids = model.generate(input_ids.to('cuda'))
response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True)
# Model response: "Hello! How can I assist you today?"
print(response)
```
|
Mattffc4/q-FrozenLake-v1-4x4-noSlippery
|
Mattffc4
| 2023-12-08T06:42:15Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-12-08T06:42:12Z |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
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="Mattffc4/q-FrozenLake-v1-4x4-noSlippery", 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"])
```
|
chihun-jang/subCut-2023-1129
|
chihun-jang
| 2023-12-08T06:27:56Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"text-classification",
"generated_from_trainer",
"base_model:klue/roberta-small",
"base_model:finetune:klue/roberta-small",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-12-08T06:27:32Z |
---
base_model: klue/roberta-small
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: subCut-2023-1129
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. -->
# subCut-2023-1129
This model is a fine-tuned version of [klue/roberta-small](https://huggingface.co/klue/roberta-small) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9784
- Accuracy: 0.4293
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 2.1672 | 1.0 | 15000 | 2.1644 | 0.3806 |
| 1.9714 | 2.0 | 30000 | 2.0339 | 0.4136 |
| 1.8502 | 3.0 | 45000 | 1.9784 | 0.4293 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.0.1
- Datasets 2.14.4
- Tokenizers 0.13.3
|
alexkarpekov/dlt_llama2_alpaca
|
alexkarpekov
| 2023-12-08T05:29:28Z | 2 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-12-08T05:29:26Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.4.0
|
leejunhyeok/MoMo-70B-LoRA-V1.2_1
|
leejunhyeok
| 2023-12-08T05:27:02Z | 0 | 0 |
transformers
|
[
"transformers",
"en",
"license:llama2",
"endpoints_compatible",
"region:us"
] | null | 2023-12-08T04:43:54Z |
---
license: llama2
language:
- en
library_name: transformers
---
## Dataset Details
### Used Datasets
- Orca-style dataset
- Alpaca-style dataset
- No other dataset was used except for the dataset mentioned above
- No benchmark test set or the training set are used
## Prompt Template
### Alpaca-style
|
omersubasi/xlm-roberta-base-finetuned-panx-de
|
omersubasi
| 2023-12-08T05:18:53Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2023-12-08T05:09:22Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-de
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xtreme
type: xtreme
args: PAN-X.de
metrics:
- name: F1
type: f1
value: 0.8653353814644136
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-de
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1339
- F1: 0.8653
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2583 | 1.0 | 525 | 0.1596 | 0.8231 |
| 0.1262 | 2.0 | 1050 | 0.1395 | 0.8468 |
| 0.0824 | 3.0 | 1575 | 0.1339 | 0.8653 |
### Framework versions
- Transformers 4.16.2
- Pytorch 2.1.0+cu118
- Datasets 1.16.1
- Tokenizers 0.15.0
|
amehfooz/lora
|
amehfooz
| 2023-12-08T05:00:55Z | 1 | 0 |
diffusers
|
[
"diffusers",
"stable-diffusion-xl",
"stable-diffusion-xl-diffusers",
"text-to-image",
"lora",
"dataset:amehfooz/RuwaifaRufy",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0",
"region:us"
] |
text-to-image
| 2023-12-08T04:16:16Z |
---
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: ruwaifarufy
tags:
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
- text-to-image
- diffusers
- lora
inference: false
datasets:
- amehfooz/RuwaifaRufy
---
# LoRA DreamBooth - amehfooz/lora
These are LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0 trained on @fffiloni's SD-XL trainer.
The weights were trained on the concept prompt:
```
ruwaifarufy
```
Use this keyword to trigger your custom model in your prompts.
LoRA for the text encoder was enabled: False.
Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
## Usage
Make sure to upgrade diffusers to >= 0.19.0:
```
pip install diffusers --upgrade
```
In addition make sure to install transformers, safetensors, accelerate as well as the invisible watermark:
```
pip install invisible_watermark transformers accelerate safetensors
```
To just use the base model, you can run:
```python
import torch
from diffusers import DiffusionPipeline, AutoencoderKL
device = "cuda" if torch.cuda.is_available() else "cpu"
vae = AutoencoderKL.from_pretrained('madebyollin/sdxl-vae-fp16-fix', torch_dtype=torch.float16)
pipe = DiffusionPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
vae=vae, torch_dtype=torch.float16, variant="fp16",
use_safetensors=True
)
pipe.to(device)
# This is where you load your trained weights
specific_safetensors = "pytorch_lora_weights.safetensors"
lora_scale = 0.9
pipe.load_lora_weights(
'amehfooz/lora',
weight_name = specific_safetensors,
# use_auth_token = True
)
prompt = "A majestic ruwaifarufy jumping from a big stone at night"
image = pipe(
prompt=prompt,
num_inference_steps=50,
cross_attention_kwargs={"scale": lora_scale}
).images[0]
```
|
bgoel4132/tweet-disaster-classifier
|
bgoel4132
| 2023-12-08T04:55:48Z | 9 | 0 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"distilbert",
"text-classification",
"autonlp",
"en",
"dataset:bgoel4132/autonlp-data-tweet-disaster-classifier",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
---
tags: autonlp
language: en
widget:
- text: "I love AutoNLP 🤗"
datasets:
- bgoel4132/autonlp-data-tweet-disaster-classifier
co2_eq_emissions: 27.22397099134103
---
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 28716412
- CO2 Emissions (in grams): 27.22397099134103
## Validation Metrics
- Loss: 0.4146720767021179
- Accuracy: 0.8066924731182795
- Macro F1: 0.7835463282531184
- Micro F1: 0.8066924731182795
- Weighted F1: 0.7974252447208724
- Macro Precision: 0.8183917344767431
- Micro Precision: 0.8066924731182795
- Weighted Precision: 0.8005510296861892
- Macro Recall: 0.7679676081852519
- Micro Recall: 0.8066924731182795
- Weighted Recall: 0.8066924731182795
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoNLP"}' https://api-inference.huggingface.co/models/bgoel4132/autonlp-tweet-disaster-classifier-28716412
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("bgoel4132/autonlp-tweet-disaster-classifier-28716412", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("bgoel4132/autonlp-tweet-disaster-classifier-28716412", use_auth_token=True)
inputs = tokenizer("I love AutoNLP", return_tensors="pt")
outputs = model(**inputs)
```
|
msyed123/ppo-LunarLander-v2
|
msyed123
| 2023-12-08T04:54:43Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-12-08T04:54:25Z |
---
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: 180.88 +/- 107.22
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
hungphongtrn/phobert-base-v2-VietMed-v3
|
hungphongtrn
| 2023-12-08T04:40:45Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"token-classification",
"generated_from_trainer",
"base_model:vinai/phobert-base-v2",
"base_model:finetune:vinai/phobert-base-v2",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2023-12-08T03:20:31Z |
---
base_model: vinai/phobert-base-v2
tags:
- generated_from_trainer
model-index:
- name: phobert-base-v2-VietMed-v3
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# phobert-base-v2-VietMed-v3
This model is a fine-tuned version of [vinai/phobert-base-v2](https://huggingface.co/vinai/phobert-base-v2) 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: 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: 50
### Framework versions
- Transformers 4.32.0.dev0
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.13.3
|
KuriT/ppo-Huggy
|
KuriT
| 2023-12-08T04:24:29Z | 0 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"Huggy",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
] |
reinforcement-learning
| 2023-12-08T04:24:22Z |
---
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: KuriT/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
0xkhairi/out
|
0xkhairi
| 2023-12-08T04:24:19Z | 3 | 0 |
peft
|
[
"peft",
"tensorboard",
"safetensors",
"generated_from_trainer",
"base_model:TinyLlama/TinyLlama-1.1B-Chat-v0.6",
"base_model:adapter:TinyLlama/TinyLlama-1.1B-Chat-v0.6",
"license:apache-2.0",
"region:us"
] | null | 2023-12-08T04:00:11Z |
---
license: apache-2.0
library_name: peft
tags:
- generated_from_trainer
base_model: TinyLlama/TinyLlama-1.1B-Chat-v0.6
model-index:
- name: out
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. -->
# out
This model is a fine-tuned version of [TinyLlama/TinyLlama-1.1B-Chat-v0.6](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v0.6) 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: 2
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- training_steps: 150
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- PEFT 0.7.1.dev0
- Transformers 4.36.0.dev0
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.15.0
|
mjaydenkim/test_trainer
|
mjaydenkim
| 2023-12-08T03:53:26Z | 2 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:tomh/toxigen_hatebert",
"base_model:finetune:tomh/toxigen_hatebert",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-12-08T03:27:52Z |
---
base_model: tomh/toxigen_hatebert
tags:
- generated_from_trainer
model-index:
- name: test_trainer
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# test_trainer
This model is a fine-tuned version of [tomh/toxigen_hatebert](https://huggingface.co/tomh/toxigen_hatebert) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.0.post101
- Datasets 2.14.6
- Tokenizers 0.13.3
|
ujjirox/yi-34b-chat
|
ujjirox
| 2023-12-08T03:46:59Z | 14 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"license:other",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-12-08T03:43:36Z |
---
license: other
license_name: yi-license
license_link: LICENSE
widget:
- example_title: "Yi-34B-Chat"
text: "hi"
output:
text: " Hello! How can I assist you today?"
- example_title: "Yi-34B"
text: "There's a place where time stands still. A place of breath taking wonder, but also"
output:
text: " an eerie sense that something is just not right…\nBetween the two worlds lies The Forgotten Kingdom - home to creatures long since thought extinct and ancient magic so strong it defies belief! Only here can you find what has been lost for centuries: An Elixir Of Life which will restore youth and vitality if only those who seek its power are brave enough to face up against all manner of dangers lurking in this mysterious land! But beware; some say there may even exist powerful entities beyond our comprehension whose intentions towards humanity remain unclear at best ---- they might want nothing more than destruction itself rather then anything else from their quest after immortality (and maybe someone should tell them about modern medicine)? In any event though – one thing remains true regardless : whether or not success comes easy depends entirely upon how much effort we put into conquering whatever challenges lie ahead along with having faith deep down inside ourselves too ;) So let’s get started now shall We?"
pipeline_tag: text-generation
---
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## Introduction
The **Yi** series models are large language models trained from scratch by
developers at [01.AI](https://01.ai/).
## News
<details open>
<summary>🎯 <b>2023/11/23</b>: The chat models are open to public.</summary>
This release contains two chat models based on previous released base models, two 8-bits models quantized by GPTQ, two 4-bits models quantized by AWQ.
- `Yi-34B-Chat`
- `Yi-34B-Chat-4bits`
- `Yi-34B-Chat-8bits`
- `Yi-6B-Chat`
- `Yi-6B-Chat-4bits`
- `Yi-6B-Chat-8bits`
You can try some of them interactively at:
- [HuggingFace](https://huggingface.co/spaces/01-ai/Yi-34B-Chat)
- [Replicate](https://replicate.com/01-ai)
</details>
<details open>
<summary>🔔 <b>2023/11/23</b>: The Yi Series Models Community License Agreement is updated to v2.1.</summary>
</details>
<details>
<summary>🔥 <b>2023/11/08</b>: Invited test of Yi-34B chat model.</summary>
Application form:
- [English](https://cn.mikecrm.com/l91ODJf)
- [Chinese](https://cn.mikecrm.com/gnEZjiQ)
</details>
<details>
<summary>🎯 <b>2023/11/05</b>: The base model of <code>Yi-6B-200K</code> and <code>Yi-34B-200K</code>.</summary>
This release contains two base models with the same parameter sizes of previous
release, except that the context window is extended to 200K.
</details>
<details>
<summary>🎯 <b>2023/11/02</b>: The base model of <code>Yi-6B</code> and <code>Yi-34B</code>.</summary>
The first public release contains two bilingual (English/Chinese) base models
with the parameter sizes of 6B and 34B. Both of them are trained with 4K
sequence length and can be extended to 32K during inference time.
</details>
## Model Performance
### Base Model Performance
| Model | MMLU | CMMLU | C-Eval | GAOKAO | BBH | Common-sense Reasoning | Reading Comprehension | Math & Code |
| :------------ | :------: | :------: | :------: | :------: | :------: | :--------------------: | :-------------------: | :---------: |
| | 5-shot | 5-shot | 5-shot | 0-shot | 3-shot@1 | - | - | - |
| LLaMA2-34B | 62.6 | - | - | - | 44.1 | 69.9 | 68.0 | 26.0 |
| LLaMA2-70B | 68.9 | 53.3 | - | 49.8 | 51.2 | 71.9 | 69.4 | 36.8 |
| Baichuan2-13B | 59.2 | 62.0 | 58.1 | 54.3 | 48.8 | 64.3 | 62.4 | 23.0 |
| Qwen-14B | 66.3 | 71.0 | 72.1 | 62.5 | 53.4 | 73.3 | 72.5 | **39.8** |
| Skywork-13B | 62.1 | 61.8 | 60.6 | 68.1 | 41.7 | 72.4 | 61.4 | 24.9 |
| InternLM-20B | 62.1 | 59.0 | 58.8 | 45.5 | 52.5 | 78.3 | - | 30.4 |
| Aquila-34B | 67.8 | 71.4 | 63.1 | - | - | - | - | - |
| Falcon-180B | 70.4 | 58.0 | 57.8 | 59.0 | 54.0 | 77.3 | 68.8 | 34.0 |
| Yi-6B | 63.2 | 75.5 | 72.0 | 72.2 | 42.8 | 72.3 | 68.7 | 19.8 |
| Yi-6B-200K | 64.0 | 75.3 | 73.5 | 73.9 | 42.0 | 72.0 | 69.1 | 19.0 |
| **Yi-34B** | **76.3** | **83.7** | 81.4 | 82.8 | **54.3** | **80.1** | 76.4 | 37.1 |
| Yi-34B-200K | 76.1 | 83.6 | **81.9** | **83.4** | 52.7 | 79.7 | **76.6** | 36.3 |
While benchmarking open-source models, we have observed a disparity between the
results generated by our pipeline and those reported in public sources (e.g.
OpenCompass). Upon conducting a more in-depth investigation of this difference,
we have discovered that various models may employ different prompts,
post-processing strategies, and sampling techniques, potentially resulting in
significant variations in the outcomes. Our prompt and post-processing strategy
remains consistent with the original benchmark, and greedy decoding is employed
during evaluation without any post-processing for the generated content. For
scores that were not reported by the original authors (including scores reported
with different settings), we try to get results with our pipeline.
To evaluate the model's capability extensively, we adopted the methodology
outlined in Llama2. Specifically, we included PIQA, SIQA, HellaSwag, WinoGrande,
ARC, OBQA, and CSQA to assess common sense reasoning. SquAD, QuAC, and BoolQ
were incorporated to evaluate reading comprehension. CSQA was exclusively tested
using a 7-shot setup, while all other tests were conducted with a 0-shot
configuration. Additionally, we introduced GSM8K (8-shot@1), MATH (4-shot@1),
HumanEval (0-shot@1), and MBPP (3-shot@1) under the category "Math & Code". Due
to technical constraints, we did not test Falcon-180 on QuAC and OBQA; the score
is derived by averaging the scores on the remaining tasks. Since the scores for
these two tasks are generally lower than the average, we believe that
Falcon-180B's performance was not underestimated.
### Chat Model Performance
| Model | MMLU | MMLU | CMMLU | CMMLU | C-Eval(val)<sup>*</sup> | C-Eval(val)<sup>*</sup> | Truthful QA | BBH | BBH | GSM8k | GSM8k |
| ----------------------- | --------- | --------- | --------- | --------- | ----------------------- | ----------------------- | ----------- | --------- | --------- | --------- | --------- |
| | 0-shot | 5-shot | 0-shot | 5-shot | 0-shot | 5-shot | 0-shot | 0-shot | 3-shot | 0-shot | 4-shot |
| LLaMA2-13B-Chat | 50.88 | 47.33 | 27.47 | 35.08 | 27.93 | 35.88 | 36.84 | 32.90 | 58.22 | 36.85 | 2.73 |
| LLaMA2-70B-Chat | 59.42 | 59.86 | 36.10 | 40.99 | 34.99 | 41.31 | 53.95 | 42.36 | 58.53 | 47.08 | 58.68 |
| Baichuan2-13B-Chat | 55.09 | 50.14 | 58.64 | 59.47 | 56.02 | 54.75 | 48.98 | 38.81 | 47.15 | 45.72 | 23.28 |
| Qwen-14B-Chat | 63.99 | 64.98 | 67.73 | 70.57 | 66.12 | 70.06 | 52.49 | 49.65 | 54.98 | 59.51 | 61.18 |
| InternLM-Chat-20B | 55.55 | 57.42 | 53.55 | 53.75 | 51.19 | 53.57 | 51.75 | 42.41 | 36.68 | 15.69 | 43.44 |
| AquilaChat2-34B v1.2 | 65.15 | 66.70 | 67.51 | 70.02 | **82.99** | **89.38** | **64.33** | 20.12 | 34.28 | 11.52 | 48.45 |
| Yi-6B-Chat | 58.24 | 60.99 | 69.44 | 74.71 | 68.80 | 74.22 | 50.58 | 39.70 | 47.15 | 38.44 | 44.88 |
| Yi-6B-Chat-8bits(GPTQ) | 58.29 | 60.96 | 69.21 | 74.69 | 69.17 | 73.85 | 49.85 | 40.35 | 47.26 | 39.42 | 44.88 |
| Yi-6B-Chat-4bits(AWQ) | 56.78 | 59.89 | 67.70 | 73.29 | 67.53 | 72.29 | 50.29 | 37.74 | 43.62 | 35.71 | 38.36 |
| Yi-34B-Chat | **67.62** | 73.46 | **79.11** | **81.34** | 77.04 | 78.53 | 62.43 | 51.41 | **71.74** | **71.65** | **75.97** |
| Yi-34B-Chat-8bits(GPTQ) | 66.24 | **73.69** | 79.05 | 81.23 | 76.82 | 78.97 | 61.84 | **52.08** | 70.97 | 70.74 | 75.74 |
| Yi-34B-Chat-4bits(AWQ) | 65.77 | 72.42 | 78.21 | 80.50 | 75.71 | 77.27 | 61.84 | 48.30 | 69.39 | 70.51 | 74.00 |
We evaluated various benchmarks using both zero-shot and few-shot methods, except for TruthfulQA. Generally, the zero-shot approach is more common in chat models. Our evaluation strategy involves generating responses while following instructions explicitly or implicitly (such as using few-shot examples). We then isolate relevant answers from the generated text. Some models are not well-suited to produce output in the specific format required by instructions in few datasets, which leads to suboptimal results.
<strong>*</strong>: C-Eval results are evaluated on the validation datasets
### Quantized Chat Model Performance
We also provide both 4-bit (AWQ) and 8-bit (GPTQ) quantized Yi chat models. Evaluation results on various benchmarks have shown that the quantized models have negligible losses. Additionally, they reduce the memory footprint size. After testing different configurations of prompts and generation lengths, we highly recommend following the guidelines in the memory footprint table below when selecting a device to run our models.
| | batch=1 | batch=4 | batch=16 | batch=32 |
| ----------------------- | ------- | ------- | -------- | -------- |
| Yi-34B-Chat | 65GiB | 68GiB | 76GiB | >80GiB |
| Yi-34B-Chat-8bits(GPTQ) | 35GiB | 37GiB | 46GiB | 58GiB |
| Yi-34B-Chat-4bits(AWQ) | 19GiB | 20GiB | 30GiB | 40GiB |
| Yi-6B-Chat | 12GiB | 13GiB | 15GiB | 18GiB |
| Yi-6B-Chat-8bits(GPTQ) | 7GiB | 8GiB | 10GiB | 14GiB |
| Yi-6B-Chat-4bits(AWQ) | 4GiB | 5GiB | 7GiB | 10GiB |
Note: All the numbers in the table represent the minimum recommended memory for running models of the corresponding size.
### Limitations of Chat Model
The released chat model has undergone exclusive training using Supervised Fine-Tuning (SFT). Compared to other standard chat models, our model produces more diverse responses, making it suitable for various downstream tasks, such as creative scenarios. Furthermore, this diversity is expected to enhance the likelihood of generating higher quality responses, which will be advantageous for subsequent Reinforcement Learning (RL) training.
However, this higher diversity might amplify certain existing issues, including:
- **Hallucination**: This refers to the model generating factually incorrect or nonsensical information. With the model's responses being more varied, there's a higher chance of hallucination that are not based on accurate data or logical reasoning.
- **Non-determinism in re-generation**: When attempting to regenerate or sample responses, inconsistencies in the outcomes may occur. The increased diversity can lead to varying results even under similar input conditions.
- **Cumulative Error**: This occurs when errors in the model's responses compound over time. As the model generates more diverse responses, the likelihood of small inaccuracies building up into larger errors increases, especially in complex tasks like extended reasoning, mathematical problem-solving, etc.
To achieve more coherent and consistent responses, it is advisable to adjust generation configuration parameters such as`temperature`,`top_p`, or`top_k`. These adjustments can help in the balance between creativity and coherence in the model's outputs.
## Usage
Feel free to [create an issue](https://github.com/01-ai/Yi/issues/new) if you
encounter any problem when using the **Yi** series models.
### 1. Prepare development environment
#### 1.1 Docker
The best approach to try the **Yi** series models is through Docker with GPUs. We
provide the following docker images to help you get started.
- `registry.lingyiwanwu.com/ci/01-ai/yi:latest`
- `ghcr.io/01-ai/yi:latest`
Note that the `latest` tag always points to the latest code in the `main`
branch. To test a stable version, please replace it with a specific
[tag](https://github.com/01-ai/Yi/tags).
#### 1.2 Local development environment
We use [`conda-lock`](https://github.com/conda/conda-lock) to generate fully reproducible lock files for conda environments. You can refer to [conda-lock.yml](./conda-lock.yml) for the exact versions of the dependencies. Additionally, we utilize [`micromamba`](https://mamba.readthedocs.io/en/latest/user_guide/micromamba.html) for installing these dependencies.
To install the dependencies, please follow these steps:
1. Install `micromamba` by following the instructions available [here](https://mamba.readthedocs.io/en/latest/installation/micromamba-installation.html).
2. Execute `micromamba install -y -n yi -f conda-lock.yml` to create a conda environment named `yi` and install the necessary dependencies.
### 2. Download the model (optional)
By default, the model weights and tokenizer will be downloaded from
[HuggingFace](https://huggingface.co/01-ai) automatically in the next step. You
can also download them manually from the following places:
- [ModelScope](https://www.modelscope.cn/organization/01ai/)
- [WiseModel](https://wisemodel.cn/organization/01.AI)
### 3. Examples
#### 3.1 Use the chat model
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_path = '01-ai/Yi-34b-Chat'
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False)
# Since transformers 4.35.0, the GPT-Q/AWQ model can be loaded using AutoModelForCausalLM.
model = AutoModelForCausalLM.from_pretrained(
model_path,
device_map="auto",
torch_dtype='auto'
).eval()
# Prompt content: "hi"
messages = [
{"role": "user", "content": "hi"}
]
input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors='pt')
output_ids = model.generate(input_ids.to('cuda'))
response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True)
# Model response: "Hello! How can I assist you today?"
print(response)
```
To construct the prompt template manually, you can refer the `chat_template` field in the `tokenizer_config.json` [file](https://huggingface.co/01-ai/Yi-34B-Chat/blob/main/tokenizer_config.json#L60).
```
<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
```
#### 3.2 Use the base model
```bash
python demo/text_generation.py
```
To reuse the downloaded models in the previous step, you can provide the extra
`--model` argument:
```bash
python demo/text_generation.py --model /path/to/model
```
Or if you'd like to get your hands dirty:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("01-ai/Yi-34B", device_map="auto", torch_dtype="auto")
tokenizer = AutoTokenizer.from_pretrained("01-ai/Yi-34B")
inputs = tokenizer("There's a place where time stands still. A place of breath taking wonder, but also", return_tensors="pt")
max_length = 256
outputs = model.generate(
inputs.input_ids.cuda(),
max_length=max_length,
eos_token_id=tokenizer.eos_token_id,
do_sample=True,
repetition_penalty=1.3,
no_repeat_ngram_size=5,
temperature=0.7,
top_k=40,
top_p=0.8,
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```
<details>
<summary>Output</summary>
**Prompt**: There's a place where time stands still. A place of breath taking wonder, but also
**Generation**: There's a place where time stands still. A place of breath taking wonder, but also of great danger. A place where the very air you breathe could kill you. A place where the only way to survive is to be prepared.
The place is called the Arctic.
The Arctic is a vast, frozen wilderness. It is a place of extremes. The temperatures can drop to -40 degrees Celsius. The winds can reach speeds of 100 kilometers per hour. The sun can shine for 24 hours a day, or not at all for weeks on end.
The Arctic is also a place of great beauty. The ice and snow are a pristine white. The sky is a deep blue. The sunsets are spectacular.
But the Arctic is also a place of great danger. The ice can be treacherous. The winds can be deadly. The sun can be blinding.
The Arctic is a place where the only way to survive is to be prepared.
The Arctic is a place of extremes. The temperatures can drop to -40 degrees Celsius. The winds can reach speeds of 100 kilometers per hour. The sun can shine for 24 hours a day, or not at all for weeks on end.
The Arctic is a place of great beauty. The ice and snow are a
</details>
For more advanced usage, please refer to the
[doc](https://github.com/01-ai/Yi/tree/main/demo).
#### 3.3 Finetuning from the base model:
```bash
bash finetune/scripts/run_sft_Yi_6b.sh
```
Once finished, you can compare the finetuned model and the base model with the following command:
```bash
bash finetune/scripts/run_eval.sh
```
For more advanced usage like fine-tuning based on your custom data, please refer
the [doc](https://github.com/01-ai/Yi/tree/main/finetune).
#### 3.4 Quantization
##### GPT-Q
```bash
python quantization/gptq/quant_autogptq.py \
--model /base_model \
--output_dir /quantized_model \
--trust_remote_code
```
Once finished, you can then evaluate the resulting model as follows:
```bash
python quantization/gptq/eval_quantized_model.py \
--model /quantized_model \
--trust_remote_code
```
For a more detailed explanation, please read the [doc](https://github.com/01-ai/Yi/tree/main/quantization/gptq)
##### AWQ
```bash
python quantization/awq/quant_autoawq.py \
--model /base_model \
--output_dir /quantized_model \
--trust_remote_code
```
Once finished, you can then evaluate the resulting model as follows:
```bash
python quantization/awq/eval_quantized_model.py \
--model /quantized_model \
--trust_remote_code
```
For more detailed explanation, please read the [doc](https://github.com/01-ai/Yi/tree/main/quantization/awq)
## Ecosystem
🤗 You are encouraged to create a PR and share your awesome work built on top of
the Yi series models.
- Serving
- [ScaleLLM](https://github.com/vectorch-ai/ScaleLLM#supported-models): Efficiently run Yi models locally.
- Quantization
- [TheBloke/Yi-34B-GGUF](https://huggingface.co/TheBloke/Yi-34B-GGUF)
- [TheBloke/Yi-34B-GPTQ](https://huggingface.co/TheBloke/Yi-34B-GPTQ)
- Finetuning
- [NousResearch/Nous-Capybara-34B](https://huggingface.co/NousResearch/Nous-Capybara-34B)
## FAQ
1. **What dataset was this trained with?**
The dataset we use contains Chinese & English only. We used approximately 3T
tokens. The detailed number and its construction will be described in the
upcoming technical report.
## Disclaimer
We use data compliance checking algorithms during the training process, to
ensure the compliance of the trained model to the best of our ability. Due to
complex data and the diversity of language model usage scenarios, we cannot
guarantee that the model will generate correct, and reasonable output in all
scenarios. Please be aware that there is still a risk of the model producing
problematic outputs. We will not be responsible for any risks and issues
resulting from misuse, misguidance, illegal usage, and related misinformation,
as well as any associated data security concerns.
## License
The source code in this repo is licensed under the [Apache 2.0
license](https://github.com/01-ai/Yi/blob/main/LICENSE). The Yi series models
are fully open for academic research and free commercial usage with permission
via applications. All usage must adhere to the [Model License
Agreement 2.0](https://github.com/01-ai/Yi/blob/main/MODEL_LICENSE_AGREEMENT.txt).
To apply for the official commercial license, please contact us
([yi@01.ai](mailto:yi@01.ai)).
|
dvaleriani/rl_course
|
dvaleriani
| 2023-12-08T03:42:50Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-12-08T02:12:21Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO-lr=1e-4
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 276.94 +/- 26.88
name: mean_reward
verified: false
---
# **PPO-lr=1e-4** Agent playing **LunarLander-v2**
This is a trained model of a **PPO-lr=1e-4** 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
...
```
|
stockmark/bart-base-japanese-news
|
stockmark
| 2023-12-08T03:39:50Z | 81 | 10 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"safetensors",
"bart",
"text2text-generation",
"ja",
"japanese",
"lm",
"nlp",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-01-20T04:23:07Z |
---
language: ja
tags:
- ja
- japanese
- bart
- lm
- nlp
license: mit
---
# bart-base-japanese-news(base-sized model)
This repository provides a Japanese BART model. The model was trained by [Stockmark Inc.](https://stockmark.co.jp)
An introductory article on the model can be found at the following URL.
[https://tech.stockmark.co.jp/blog/bart-japanese-base-news/](https://tech.stockmark.co.jp/blog/bart-japanese-base-news/)
## Model description
BART is a transformer encoder-decoder (seq2seq) model with a bidirectional (BERT-like) encoder and an autoregressive (GPT-like) decoder. BART is pre-trained by (1) corrupting text with an arbitrary noising function, and (2) learning a model to reconstruct the original text.
BART is particularly effective when fine-tuned for text generation (e.g. summarization, translation) but also works well for comprehension tasks (e.g. text classification, question answering).
## Intended uses & limitations
You can use the raw model for text infilling. However, the model is mostly meant to be fine-tuned on a supervised dataset.
# How to use the model
*NOTE:* Since we are using a custom tokenizer, please use `trust_remote_code=True` to initialize the tokenizer.
## Simple use
```python
from transformers import AutoTokenizer, BartModel
model_name = "stockmark/bart-base-japanese-news"
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
model = BartModel.from_pretrained(model_name)
inputs = tokenizer("今日は良い天気です。", return_tensors="pt")
outputs = model(**inputs)
last_hidden_states = outputs.last_hidden_state
```
## Sentence Permutation
```python
import torch
from transformers import AutoTokenizer, BartForConditionalGeneration
model_name = "stockmark/bart-base-japanese-news"
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
model = BartForConditionalGeneration.from_pretrained(model_name)
if torch.cuda.is_available():
model = model.to("cuda")
# correct order text is "明日は大雨です。電車は止まる可能性があります。ですから、自宅から働きます。"
text = "電車は止まる可能性があります。ですから、自宅から働きます。明日は大雨です。"
inputs = tokenizer([text], max_length=128, return_tensors="pt", truncation=True)
text_ids = model.generate(inputs["input_ids"].to(model.device), num_beams=3, max_length=128)
output = tokenizer.batch_decode(text_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
print(output)
# sample output: 明日は大雨です。電車は止まる可能性があります。ですから、自宅から働きます。
```
## Mask filling
```python
import torch
from transformers import AutoTokenizer, BartForConditionalGeneration
model_name = "stockmark/bart-base-japanese-news"
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
model = BartForConditionalGeneration.from_pretrained(model_name)
if torch.cuda.is_available():
model = model.to("cuda")
text = "今日の天気は<mask>のため、傘が必要でしょう。"
inputs = tokenizer([text], max_length=128, return_tensors="pt", truncation=True)
text_ids = model.generate(inputs["input_ids"].to(model.device), num_beams=3, max_length=128)
output = tokenizer.batch_decode(text_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
print(output)
# sample output: 今日の天気は、雨のため、傘が必要でしょう。
```
## Text generation
*NOTE:* You can use the raw model for text generation. However, the model is mostly meant to be fine-tuned on a supervised dataset.
```python
import torch
from transformers import AutoTokenizer, BartForConditionalGeneration
model_name = "stockmark/bart-base-japanese-news"
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
model = BartForConditionalGeneration.from_pretrained(model_name)
if torch.cuda.is_available():
model = model.to("cuda")
text = "自然言語処理（しぜんげんごしょり、略称：NLP）は、人間が日常的に使っている自然言語をコンピュータに処理させる一連の技術であり、人工知能と言語学の一分野である。「計算言語学」（computational linguistics）との類似もあるが、自然言語処理は工学的な視点からの言語処理をさすのに対して、計算言語学は言語学的視点を重視する手法をさす事が多い。"
inputs = tokenizer([text], max_length=512, return_tensors="pt", truncation=True)
text_ids = model.generate(inputs["input_ids"].to(model.device), num_beams=3, min_length=0, max_length=40)
output = tokenizer.batch_decode(text_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
print(output)
# sample output: 自然言語処理(しぜんげんごしょり、略称:NLP)は、人間が日常的に使っている自然言語をコンピュータに処理させる一連の技術であり、言語学の一分野である。
```
# Training
The model was trained on Japanese News Articles.
# Tokenization
The model uses a [sentencepiece](https://github.com/google/sentencepiece)-based tokenizer. The vocabulary was first trained on a selected subset from the training data using the official sentencepiece training script.
# Licenses
The pretrained models are distributed under the terms of the [MIT License](https://opensource.org/licenses/mit-license.php).
*NOTE:* Only tokenization_bart_japanese_news.py is [Apache License, Version 2.0](http://www.apache.org/licenses/LICENSE-2.0). Please see tokenization_bart_japanese_news.py for license details.
# Contact
If you have any questions, please contact us using [our contact form](https://stockmark.co.jp/contact).
# Acknowledgement
This comparison study supported with Cloud TPUs from Google’s TensorFlow Research Cloud (TFRC).
|
brettbbb/mc_cot_16
|
brettbbb
| 2023-12-08T03:22:39Z | 0 | 0 | null |
[
"safetensors",
"generated_from_trainer",
"base_model:lmsys/vicuna-7b-v1.5",
"base_model:finetune:lmsys/vicuna-7b-v1.5",
"license:llama2",
"region:us"
] | null | 2023-12-08T03:07:05Z |
---
license: llama2
base_model: lmsys/vicuna-7b-v1.5
tags:
- generated_from_trainer
model-index:
- name: mc_cot_16
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mc_cot_16
This model is a fine-tuned version of [lmsys/vicuna-7b-v1.5](https://huggingface.co/lmsys/vicuna-7b-v1.5) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 3.0303
## 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: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 5
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.1538 | 1.0 | 4 | 2.1552 |
| 1.7905 | 2.0 | 8 | 1.9012 |
| 1.4229 | 3.0 | 12 | 1.7686 |
| 1.3834 | 4.0 | 16 | 1.7070 |
| 1.4421 | 5.0 | 20 | 1.7308 |
| 0.9003 | 6.0 | 24 | 1.7646 |
| 0.7013 | 7.0 | 28 | 1.9070 |
| 0.6291 | 8.0 | 32 | 2.0078 |
| 0.3314 | 9.0 | 36 | 2.2682 |
| 0.1554 | 10.0 | 40 | 2.3624 |
| 0.0814 | 11.0 | 44 | 2.6523 |
| 0.0499 | 12.0 | 48 | 2.7565 |
| 0.0216 | 13.0 | 52 | 2.8505 |
| 0.0197 | 14.0 | 56 | 2.9170 |
| 0.0174 | 15.0 | 60 | 2.9433 |
| 0.0174 | 16.0 | 64 | 2.9683 |
| 0.0145 | 17.0 | 68 | 2.9966 |
| 0.013 | 18.0 | 72 | 3.0193 |
| 0.0151 | 19.0 | 76 | 3.0277 |
| 0.0145 | 20.0 | 80 | 3.0303 |
### Framework versions
- Transformers 4.36.0.dev0
- Pytorch 2.1.0+cu121
- Datasets 2.13.1
- Tokenizers 0.14.1
|
Nurmukhamed/distilbert-base-uncased-finetuned-imdb
|
Nurmukhamed
| 2023-12-08T03:22:26Z | 5 | 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
| 2023-12-08T03:17:57Z |
---
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: 2.4906
## 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 |
|:-------------:|:-----:|:----:|:---------------:|
| 2.6819 | 1.0 | 157 | 2.4978 |
| 2.5872 | 2.0 | 314 | 2.4488 |
| 2.525 | 3.0 | 471 | 2.4836 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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