modelId
stringlengths 5
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| author
stringlengths 2
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| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-09-07 00:41:44
| downloads
int64 0
223M
| likes
int64 0
11.7k
| library_name
stringclasses 544
values | tags
listlengths 1
4.05k
| pipeline_tag
stringclasses 55
values | createdAt
timestamp[us, tz=UTC]date 2022-03-02 23:29:04
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| card
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Heng666/falcon-7b-sharded-bf16-english-quote-qlora
|
Heng666
| 2023-06-19T15:10:33Z | 5 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-06-19T15:05:21Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.4.0.dev0
|
gokuls/bert_base_120
|
gokuls
| 2023-06-19T14:58:13Z | 140 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2023-06-18T13:24:35Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: bert_base_120
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert_base_120
This model is a fine-tuned version of [gokuls/bert_base_96](https://huggingface.co/gokuls/bert_base_96) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.3904
- Accuracy: 0.5602
## 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: 48
- eval_batch_size: 48
- seed: 10
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 10000
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 2.7403 | 0.08 | 10000 | 2.6150 | 0.5307 |
| 2.6939 | 0.16 | 20000 | 2.5743 | 0.5360 |
| 2.6549 | 0.25 | 30000 | 2.5380 | 0.5408 |
| 2.6298 | 0.33 | 40000 | 2.5020 | 0.5455 |
| 2.5883 | 0.41 | 50000 | 2.4715 | 0.5494 |
| 2.5629 | 0.49 | 60000 | 2.4432 | 0.5533 |
| 2.5274 | 0.57 | 70000 | 2.4163 | 0.5568 |
| 2.5059 | 0.66 | 80000 | 2.3904 | 0.5602 |
### Framework versions
- Transformers 4.30.2
- Pytorch 1.14.0a0+410ce96
- Datasets 2.13.0
- Tokenizers 0.13.3
|
syf2023/gpt2
|
syf2023
| 2023-06-19T14:53:15Z | 203 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"jax",
"tflite",
"rust",
"safetensors",
"gpt2",
"text-generation",
"exbert",
"en",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-06-19T14:49:39Z |
---
language: en
tags:
- exbert
license: mit
duplicated_from: gpt2
---
# GPT-2
Test the whole generation capabilities here: https://transformer.huggingface.co/doc/gpt2-large
Pretrained model on English language using a causal language modeling (CLM) objective. It was introduced in
[this paper](https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_multitask_learners.pdf)
and first released at [this page](https://openai.com/blog/better-language-models/).
Disclaimer: The team releasing GPT-2 also wrote a
[model card](https://github.com/openai/gpt-2/blob/master/model_card.md) for their model. Content from this model card
has been written by the Hugging Face team to complete the information they provided and give specific examples of bias.
## Model description
GPT-2 is a transformers model pretrained on a very large corpus of English data in a self-supervised fashion. This
means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots
of publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely,
it was trained to guess the next word in sentences.
More precisely, inputs are sequences of continuous text of a certain length and the targets are the same sequence,
shifted one token (word or piece of word) to the right. The model uses internally a mask-mechanism to make sure the
predictions for the token `i` only uses the inputs from `1` to `i` but not the future tokens.
This way, the model learns an inner representation of the English language that can then be used to extract features
useful for downstream tasks. The model is best at what it was pretrained for however, which is generating texts from a
prompt.
This is the **smallest** version of GPT-2, with 124M parameters.
**Related Models:** [GPT-Large](https://huggingface.co/gpt2-large), [GPT-Medium](https://huggingface.co/gpt2-medium) and [GPT-XL](https://huggingface.co/gpt2-xl)
## Intended uses & limitations
You can use the raw model for text generation or fine-tune it to a downstream task. See the
[model hub](https://huggingface.co/models?filter=gpt2) to look for fine-tuned versions on a task that interests you.
### How to use
You can use this model directly with a pipeline for text generation. Since the generation relies on some randomness, we
set a seed for reproducibility:
```python
>>> from transformers import pipeline, set_seed
>>> generator = pipeline('text-generation', model='gpt2')
>>> set_seed(42)
>>> generator("Hello, I'm a language model,", max_length=30, num_return_sequences=5)
[{'generated_text': "Hello, I'm a language model, a language for thinking, a language for expressing thoughts."},
{'generated_text': "Hello, I'm a language model, a compiler, a compiler library, I just want to know how I build this kind of stuff. I don"},
{'generated_text': "Hello, I'm a language model, and also have more than a few of your own, but I understand that they're going to need some help"},
{'generated_text': "Hello, I'm a language model, a system model. I want to know my language so that it might be more interesting, more user-friendly"},
{'generated_text': 'Hello, I\'m a language model, not a language model"\n\nThe concept of "no-tricks" comes in handy later with new'}]
```
Here is how to use this model to get the features of a given text in PyTorch:
```python
from transformers import GPT2Tokenizer, GPT2Model
tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
model = GPT2Model.from_pretrained('gpt2')
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
```
and in TensorFlow:
```python
from transformers import GPT2Tokenizer, TFGPT2Model
tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
model = TFGPT2Model.from_pretrained('gpt2')
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='tf')
output = model(encoded_input)
```
### Limitations and bias
The training data used for this model has not been released as a dataset one can browse. We know it contains a lot of
unfiltered content from the internet, which is far from neutral. As the openAI team themselves point out in their
[model card](https://github.com/openai/gpt-2/blob/master/model_card.md#out-of-scope-use-cases):
> Because large-scale language models like GPT-2 do not distinguish fact from fiction, we don’t support use-cases
> that require the generated text to be true.
>
> Additionally, language models like GPT-2 reflect the biases inherent to the systems they were trained on, so we do
> not recommend that they be deployed into systems that interact with humans > unless the deployers first carry out a
> study of biases relevant to the intended use-case. We found no statistically significant difference in gender, race,
> and religious bias probes between 774M and 1.5B, implying all versions of GPT-2 should be approached with similar
> levels of caution around use cases that are sensitive to biases around human attributes.
Here's an example of how the model can have biased predictions:
```python
>>> from transformers import pipeline, set_seed
>>> generator = pipeline('text-generation', model='gpt2')
>>> set_seed(42)
>>> generator("The White man worked as a", max_length=10, num_return_sequences=5)
[{'generated_text': 'The White man worked as a mannequin for'},
{'generated_text': 'The White man worked as a maniser of the'},
{'generated_text': 'The White man worked as a bus conductor by day'},
{'generated_text': 'The White man worked as a plumber at the'},
{'generated_text': 'The White man worked as a journalist. He had'}]
>>> set_seed(42)
>>> generator("The Black man worked as a", max_length=10, num_return_sequences=5)
[{'generated_text': 'The Black man worked as a man at a restaurant'},
{'generated_text': 'The Black man worked as a car salesman in a'},
{'generated_text': 'The Black man worked as a police sergeant at the'},
{'generated_text': 'The Black man worked as a man-eating monster'},
{'generated_text': 'The Black man worked as a slave, and was'}]
```
This bias will also affect all fine-tuned versions of this model.
## Training data
The OpenAI team wanted to train this model on a corpus as large as possible. To build it, they scraped all the web
pages from outbound links on Reddit which received at least 3 karma. Note that all Wikipedia pages were removed from
this dataset, so the model was not trained on any part of Wikipedia. The resulting dataset (called WebText) weights
40GB of texts but has not been publicly released. You can find a list of the top 1,000 domains present in WebText
[here](https://github.com/openai/gpt-2/blob/master/domains.txt).
## Training procedure
### Preprocessing
The texts are tokenized using a byte-level version of Byte Pair Encoding (BPE) (for unicode characters) and a
vocabulary size of 50,257. The inputs are sequences of 1024 consecutive tokens.
The larger model was trained on 256 cloud TPU v3 cores. The training duration was not disclosed, nor were the exact
details of training.
## Evaluation results
The model achieves the following results without any fine-tuning (zero-shot):
| Dataset | LAMBADA | LAMBADA | CBT-CN | CBT-NE | WikiText2 | PTB | enwiki8 | text8 | WikiText103 | 1BW |
|:--------:|:-------:|:-------:|:------:|:------:|:---------:|:------:|:-------:|:------:|:-----------:|:-----:|
| (metric) | (PPL) | (ACC) | (ACC) | (ACC) | (PPL) | (PPL) | (BPB) | (BPC) | (PPL) | (PPL) |
| | 35.13 | 45.99 | 87.65 | 83.4 | 29.41 | 65.85 | 1.16 | 1,17 | 37.50 | 75.20 |
### BibTeX entry and citation info
```bibtex
@article{radford2019language,
title={Language Models are Unsupervised Multitask Learners},
author={Radford, Alec and Wu, Jeff and Child, Rewon and Luan, David and Amodei, Dario and Sutskever, Ilya},
year={2019}
}
```
<a href="https://huggingface.co/exbert/?model=gpt2">
<img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png">
</a>
|
casque/YOZORA.vae
|
casque
| 2023-06-19T14:50:25Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-06-19T14:43:18Z |
---
license: creativeml-openrail-m
---
|
titanicc/titanicdrpt
|
titanicc
| 2023-06-19T14:49:59Z | 160 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-classification",
"arxiv:2009.06978",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-06-19T14:38:43Z |
---
duplicated_from: microsoft/DialogRPT-human-vs-rand
---
# Demo
Please try this [➤➤➤ Colab Notebook Demo (click me!)](https://colab.research.google.com/drive/1cAtfkbhqsRsT59y3imjR1APw3MHDMkuV?usp=sharing)
| Context | Response | `human_vs_rand` score |
| :------ | :------- | :------------: |
| I love NLP! | He is a great basketball player. | 0.027 |
| I love NLP! | Can you tell me how it works? | 0.754 |
| I love NLP! | Me too! | 0.631 |
The `human_vs_rand` score predicts how likely the response is corresponding to the given context, rather than a random response.
# DialogRPT-human-vs-rand
### Dialog Ranking Pretrained Transformers
> How likely a dialog response is upvoted 👍 and/or gets replied 💬?
This is what [**DialogRPT**](https://github.com/golsun/DialogRPT) is learned to predict.
It is a set of dialog response ranking models proposed by [Microsoft Research NLP Group](https://www.microsoft.com/en-us/research/group/natural-language-processing/) trained on 100 + millions of human feedback data.
It can be used to improve existing dialog generation model (e.g., [DialoGPT](https://huggingface.co/microsoft/DialoGPT-medium)) by re-ranking the generated response candidates.
Quick Links:
* [EMNLP'20 Paper](https://arxiv.org/abs/2009.06978/)
* [Dataset, training, and evaluation](https://github.com/golsun/DialogRPT)
* [Colab Notebook Demo](https://colab.research.google.com/drive/1cAtfkbhqsRsT59y3imjR1APw3MHDMkuV?usp=sharing)
We considered the following tasks and provided corresponding pretrained models.
|Task | Description | Pretrained model |
| :------------- | :----------- | :-----------: |
| **Human feedback** | **given a context and its two human responses, predict...**|
| `updown` | ... which gets more upvotes? | [model card](https://huggingface.co/microsoft/DialogRPT-updown) |
| `width`| ... which gets more direct replies? | [model card](https://huggingface.co/microsoft/DialogRPT-width) |
| `depth`| ... which gets longer follow-up thread? | [model card](https://huggingface.co/microsoft/DialogRPT-depth) |
| **Human-like** (human vs fake) | **given a context and one human response, distinguish it with...** |
| `human_vs_rand`| ... a random human response | this model |
| `human_vs_machine`| ... a machine generated response | [model card](https://huggingface.co/microsoft/DialogRPT-human-vs-machine) |
### Contact:
Please create an issue on [our repo](https://github.com/golsun/DialogRPT)
### Citation:
```
@inproceedings{gao2020dialogrpt,
title={Dialogue Response RankingTraining with Large-Scale Human Feedback Data},
author={Xiang Gao and Yizhe Zhang and Michel Galley and Chris Brockett and Bill Dolan},
year={2020},
booktitle={EMNLP}
}
```
|
ilaria-oneofftech/ikitracs_conditional
|
ilaria-oneofftech
| 2023-06-19T14:49:55Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"mpnet",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-06-19T13:19:32Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: ikitracs_conditional
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. -->
# ikitracs_conditional
This model is a fine-tuned version of [sentence-transformers/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9018
- Precision Weighted: 0.7669
- Precision Macro: 0.7479
- Recall Weighted: 0.7712
- Recall Samples: 0.7345
- F1-score: 0.7400
- Accuracy: 0.7712
## 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: 5.8e-05
- train_batch_size: 8
- eval_batch_size: 3
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 300
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision Weighted | Precision Macro | Recall Weighted | Recall Samples | F1-score | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:------------------:|:---------------:|:---------------:|:--------------:|:--------:|:--------:|
| 0.6787 | 1.0 | 212 | 0.6461 | 0.6859 | 0.6493 | 0.6792 | 0.6546 | 0.6513 | 0.6792 |
| 0.6006 | 2.0 | 424 | 0.6125 | 0.7370 | 0.7301 | 0.7429 | 0.6690 | 0.6792 | 0.7429 |
| 0.4826 | 3.0 | 636 | 0.7111 | 0.7458 | 0.7270 | 0.7524 | 0.7071 | 0.7142 | 0.7524 |
| 0.3487 | 4.0 | 848 | 0.7359 | 0.7536 | 0.7242 | 0.75 | 0.7297 | 0.7266 | 0.75 |
| 0.2199 | 5.0 | 1060 | 0.9018 | 0.7669 | 0.7479 | 0.7712 | 0.7345 | 0.7400 | 0.7712 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.13.0
- Tokenizers 0.13.3
|
kai0226/fine_tuned_bloomz
|
kai0226
| 2023-06-19T14:48:15Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-06-19T14:48:13Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
### Framework versions
- PEFT 0.4.0.dev0
|
ghze/Taxi-v3
|
ghze
| 2023-06-19T14:47:57Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-06-16T23:05:08Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.56 +/- 2.71
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="ghze/Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
Ditrip/rl_course_vizdoom_health_gathering_supreme
|
Ditrip
| 2023-06-19T14:47:23Z | 0 | 0 |
sample-factory
|
[
"sample-factory",
"tensorboard",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-06-19T14:35:11Z |
---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: doom_health_gathering_supreme
type: doom_health_gathering_supreme
metrics:
- type: mean_reward
value: 12.30 +/- 5.13
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **doom_health_gathering_supreme** environment.
This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory.
Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/
## Downloading the model
After installing Sample-Factory, download the model with:
```
python -m sample_factory.huggingface.load_from_hub -r Ditrip/rl_course_vizdoom_health_gathering_supreme
```
## Using the model
To run the model after download, use the `enjoy` script corresponding to this environment:
```
python -m <path.to.enjoy.module> --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme
```
You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag.
See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details
## Training with this model
To continue training with this model, use the `train` script corresponding to this environment:
```
python -m <path.to.train.module> --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme --restart_behavior=resume --train_for_env_steps=10000000000
```
Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
|
fprencipe3/setfit-TrustPilot
|
fprencipe3
| 2023-06-19T14:43:11Z | 5 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"bert",
"setfit",
"text-classification",
"arxiv:2209.11055",
"license:apache-2.0",
"region:us"
] |
text-classification
| 2023-06-19T14:36:09Z |
---
license: apache-2.0
tags:
- setfit
- sentence-transformers
- text-classification
pipeline_tag: text-classification
---
# /fprencipe3/setfit-TrustPilot
This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves:
1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.
## Usage
To use this model for inference, first install the SetFit library:
```bash
python -m pip install setfit
```
You can then run inference as follows:
```python
from setfit import SetFitModel
# Download from Hub and run inference
model = SetFitModel.from_pretrained("/fprencipe3/setfit-TrustPilot")
# Run inference
preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"])
```
## BibTeX entry and citation info
```bibtex
@article{https://doi.org/10.48550/arxiv.2209.11055,
doi = {10.48550/ARXIV.2209.11055},
url = {https://arxiv.org/abs/2209.11055},
author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Efficient Few-Shot Learning Without Prompts},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
```
|
Saed2023/lilt-en-funsd
|
Saed2023
| 2023-06-19T14:34:48Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"lilt",
"token-classification",
"generated_from_trainer",
"dataset:funsd-layoutlmv3",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2023-06-04T16:06:35Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- funsd-layoutlmv3
model-index:
- name: lilt-en-funsd
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. -->
# lilt-en-funsd
This model is a fine-tuned version of [SCUT-DLVCLab/lilt-roberta-en-base](https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base) on the funsd-layoutlmv3 dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8784
- Answer: {'precision': 0.8651817116060961, 'recall': 0.9033047735618115, 'f1': 0.8838323353293414, 'number': 817}
- Header: {'precision': 0.6504854368932039, 'recall': 0.5630252100840336, 'f1': 0.6036036036036037, 'number': 119}
- Question: {'precision': 0.9073394495412844, 'recall': 0.9182915506035283, 'f1': 0.912782648823258, 'number': 1077}
- Overall Precision: 0.8768
- Overall Recall: 0.8912
- Overall F1: 0.8840
- Overall Accuracy: 0.7948
## 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
- training_steps: 2500
### Training results
| Training Loss | Epoch | Step | Validation Loss | Answer | Header | Question | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 0.4369 | 10.53 | 200 | 0.9022 | {'precision': 0.8049065420560748, 'recall': 0.8433292533659731, 'f1': 0.8236700537955769, 'number': 817} | {'precision': 0.5317460317460317, 'recall': 0.5630252100840336, 'f1': 0.5469387755102041, 'number': 119} | {'precision': 0.8837420526793823, 'recall': 0.903435468895079, 'f1': 0.8934802571166208, 'number': 1077} | 0.8301 | 0.8589 | 0.8442 | 0.7888 |
| 0.047 | 21.05 | 400 | 1.3222 | {'precision': 0.8382526564344747, 'recall': 0.8690330477356181, 'f1': 0.8533653846153846, 'number': 817} | {'precision': 0.5447761194029851, 'recall': 0.6134453781512605, 'f1': 0.5770750988142292, 'number': 119} | {'precision': 0.8667866786678667, 'recall': 0.8941504178272981, 'f1': 0.8802559414990858, 'number': 1077} | 0.8346 | 0.8674 | 0.8507 | 0.7837 |
| 0.015 | 31.58 | 600 | 1.4745 | {'precision': 0.8549528301886793, 'recall': 0.8873929008567931, 'f1': 0.8708708708708709, 'number': 817} | {'precision': 0.5867768595041323, 'recall': 0.5966386554621849, 'f1': 0.5916666666666667, 'number': 119} | {'precision': 0.8755635707844905, 'recall': 0.9015784586815228, 'f1': 0.888380603842635, 'number': 1077} | 0.8503 | 0.8778 | 0.8638 | 0.7969 |
| 0.0051 | 42.11 | 800 | 1.5719 | {'precision': 0.8768472906403941, 'recall': 0.8714810281517748, 'f1': 0.8741559238796808, 'number': 817} | {'precision': 0.5736434108527132, 'recall': 0.6218487394957983, 'f1': 0.596774193548387, 'number': 119} | {'precision': 0.8794326241134752, 'recall': 0.9210770659238626, 'f1': 0.8997732426303855, 'number': 1077} | 0.8594 | 0.8833 | 0.8711 | 0.7923 |
| 0.0041 | 52.63 | 1000 | 1.6771 | {'precision': 0.8352402745995423, 'recall': 0.8935128518971848, 'f1': 0.8633944411590775, 'number': 817} | {'precision': 0.6568627450980392, 'recall': 0.5630252100840336, 'f1': 0.6063348416289592, 'number': 119} | {'precision': 0.8865116279069768, 'recall': 0.8848653667595172, 'f1': 0.8856877323420075, 'number': 1077} | 0.8532 | 0.8693 | 0.8612 | 0.7877 |
| 0.0039 | 63.16 | 1200 | 1.6064 | {'precision': 0.8609112709832134, 'recall': 0.8788249694002448, 'f1': 0.8697758933979407, 'number': 817} | {'precision': 0.6106194690265486, 'recall': 0.5798319327731093, 'f1': 0.5948275862068966, 'number': 119} | {'precision': 0.8897777777777778, 'recall': 0.9294336118848654, 'f1': 0.9091734786557675, 'number': 1077} | 0.8629 | 0.8882 | 0.8754 | 0.8009 |
| 0.0019 | 73.68 | 1400 | 1.7674 | {'precision': 0.8533178114086146, 'recall': 0.8971848225214198, 'f1': 0.8747016706443913, 'number': 817} | {'precision': 0.5769230769230769, 'recall': 0.5042016806722689, 'f1': 0.5381165919282511, 'number': 119} | {'precision': 0.8842676311030742, 'recall': 0.9080779944289693, 'f1': 0.8960146587265231, 'number': 1077} | 0.8560 | 0.8798 | 0.8677 | 0.7981 |
| 0.0007 | 84.21 | 1600 | 1.8380 | {'precision': 0.8469387755102041, 'recall': 0.9143206854345165, 'f1': 0.8793407886992348, 'number': 817} | {'precision': 0.6017699115044248, 'recall': 0.5714285714285714, 'f1': 0.5862068965517241, 'number': 119} | {'precision': 0.8931159420289855, 'recall': 0.9155060352831941, 'f1': 0.9041723979825768, 'number': 1077} | 0.8580 | 0.8947 | 0.8760 | 0.7931 |
| 0.0007 | 94.74 | 1800 | 1.8108 | {'precision': 0.8600478468899522, 'recall': 0.8800489596083231, 'f1': 0.8699334543254689, 'number': 817} | {'precision': 0.6435643564356436, 'recall': 0.5462184873949579, 'f1': 0.5909090909090908, 'number': 119} | {'precision': 0.8722849695916595, 'recall': 0.9322191272051996, 'f1': 0.9012567324955117, 'number': 1077} | 0.8563 | 0.8882 | 0.8720 | 0.7887 |
| 0.0004 | 105.26 | 2000 | 1.9035 | {'precision': 0.8627906976744186, 'recall': 0.9082007343941249, 'f1': 0.8849135360763267, 'number': 817} | {'precision': 0.6285714285714286, 'recall': 0.5546218487394958, 'f1': 0.5892857142857143, 'number': 119} | {'precision': 0.8955495004541326, 'recall': 0.9155060352831941, 'f1': 0.9054178145087237, 'number': 1077} | 0.8683 | 0.8912 | 0.8796 | 0.7965 |
| 0.0002 | 115.79 | 2200 | 1.8784 | {'precision': 0.8651817116060961, 'recall': 0.9033047735618115, 'f1': 0.8838323353293414, 'number': 817} | {'precision': 0.6504854368932039, 'recall': 0.5630252100840336, 'f1': 0.6036036036036037, 'number': 119} | {'precision': 0.9073394495412844, 'recall': 0.9182915506035283, 'f1': 0.912782648823258, 'number': 1077} | 0.8768 | 0.8912 | 0.8840 | 0.7948 |
| 0.0002 | 126.32 | 2400 | 1.9075 | {'precision': 0.8640093786635404, 'recall': 0.9020807833537332, 'f1': 0.8826347305389222, 'number': 817} | {'precision': 0.6296296296296297, 'recall': 0.5714285714285714, 'f1': 0.5991189427312775, 'number': 119} | {'precision': 0.9041970802919708, 'recall': 0.9201485608170845, 'f1': 0.9121030832949838, 'number': 1077} | 0.8731 | 0.8922 | 0.8826 | 0.7959 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.0
- Tokenizers 0.13.3
|
kanishka/dl-property-judgments
|
kanishka
| 2023-06-19T14:30:50Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"deberta-v2",
"text-classification",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-11-26T05:18:48Z |
---
license: mit
tags:
- text-classification
widget:
- text: A robin is a bird.
- text: a horse has stripes on its body.
---
## A DEBERTA model for making property judgments
deberta-v3-large fine-tuned to evaluate the truth of sentences that attribute properties to concepts.
## Citation
If you use this model, please cite the following paper:
```tex
@inproceedings{misra2022property,
title={A Property Induction Framework for Neural Language Models},
author={Kanishka Misra and Julia Rayz and Allyson Ettinger},
booktitle={Proceedings of the 44th Annual Conference of the Cognitive Science Society},
year={2022}
}
```
|
rodrigoclira/ppo-LunarLander-v2
|
rodrigoclira
| 2023-06-19T14:00:30Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-06-19T14:00:08Z |
---
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: 262.66 +/- 18.98
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
...
```
|
lins567/1
|
lins567
| 2023-06-19T13:50:44Z | 0 | 0 | null |
[
"license:bigscience-bloom-rail-1.0",
"region:us"
] | null | 2023-06-19T13:50:44Z |
---
license: bigscience-bloom-rail-1.0
---
|
Cogoport/en_Spacy_Custom_ner
|
Cogoport
| 2023-06-19T13:37:01Z | 23 | 0 |
spacy
|
[
"spacy",
"token-classification",
"en",
"model-index",
"region:us"
] |
token-classification
| 2023-05-18T10:55:56Z |
---
tags:
- spacy
- token-classification
language:
- en
model-index:
- name: en_Spacy_Custom_ner
results:
- task:
name: NER
type: token-classification
metrics:
- name: NER Precision
type: precision
value: 0.9918793503
- name: NER Recall
type: recall
value: 0.9965034965
- name: NER F Score
type: f_score
value: 0.9941860465
---
| Feature | Description |
| --- | --- |
| **Name** | `en_Spacy_Custom_ner` |
| **Version** | `0.0.0` |
| **spaCy** | `>=3.5.3,<3.6.0` |
| **Default Pipeline** | `tok2vec`, `ner` |
| **Components** | `tok2vec`, `ner` |
| **Vectors** | 514157 keys, 514157 unique vectors (300 dimensions) |
| **Sources** | n/a |
| **License** | n/a |
| **Author** | [n/a]() |
### Label Scheme
<details>
<summary>View label scheme (16 labels for 1 components)</summary>
| Component | Labels |
| --- | --- |
| **`ner`** | `AGENT_FALLBACK`, `BOOK`, `COMODITY`, `CONTAINER COUNT`, `CONTAINER SIZE`, `CONTAINER SIZE-COUNT`, `DESTINATION`, `ENQUIRY`, `HELP`, `INCOTERM`, `KYC`, `ORIGIN`, `SEARCH RATES`, `SHIP`, `SHIPMENT TYPE`, `WELCOME_MSG` |
</details>
### Accuracy
| Type | Score |
| --- | --- |
| `ENTS_F` | 99.42 |
| `ENTS_P` | 99.19 |
| `ENTS_R` | 99.65 |
| `TOK2VEC_LOSS` | 1794.25 |
| `NER_LOSS` | 53209.43 |
|
Falah/News_Detection
|
Falah
| 2023-06-19T13:33:46Z | 0 | 2 |
adapter-transformers
|
[
"adapter-transformers",
"pytorch",
"bert",
"fake news detection",
"NLP",
"text-classification",
"license:openrail",
"region:us"
] |
text-classification
| 2023-06-19T12:37:09Z |
---
license: openrail
metrics:
- accuracy
library_name: adapter-transformers
pipeline_tag: text-classification
tags:
- fake news detection
- NLP
---
Fake news detection using NLP transformers is an important application of natural language processing techniques. Transformers, such as the popular BERT (Bidirectional Encoder Representations from Transformers) model, have shown promising results in various NLP tasks, including text classification, sentiment analysis, and question answering. When applied to fake news detection, transformers can effectively analyze the textual content of news articles and make predictions about their authenticity.
Here are some key details about fake news detection using NLP transformers:
1. Transformer Architecture: Transformers are based on a self-attention mechanism that allows them to capture contextual relationships between words or tokens in a text. This architecture enables transformers to effectively process and understand the semantic meaning of textual data.
2. Pretraining: NLP transformers are typically pretrained on large-scale corpora to learn general language representations. This pretraining phase helps the model to capture semantic and syntactic patterns in text data, which can be later fine-tuned for specific tasks like fake news detection.
3. Fine-tuning: After pretraining, transformers are fine-tuned on task-specific datasets, which involve labeled examples of fake and real news articles. During fine-tuning, the model learns to classify news articles based on the patterns it has learned during pretraining.
4. Tokenization: Text data is tokenized into smaller units, such as words or subwords, before being fed into the transformer model. Tokenization helps in creating input representations that the model can understand and process efficiently.
5. Training Labels: Fake news detection typically requires a labeled dataset where each news article is annotated as either fake or real. These labels are used during the training process to optimize the model's parameters and make accurate predictions.
6. Model Evaluation: The performance of the fake news detection model is evaluated using standard evaluation metrics such as accuracy, precision, recall, and F1-score. These metrics provide insights into how well the model is able to correctly classify fake and real news articles.
7. Deployment: Once the model is trained and evaluated, it can be deployed in real-world applications to automatically detect and classify news articles. The model takes the textual content of an article as input and predicts its authenticity.
It's important to note that while NLP transformers have shown promising results in fake news detection, they are not foolproof and may have limitations. Building robust fake news detection systems requires careful data collection, preprocessing, and model training techniques to handle the nuances and challenges of the task.
Overall, NLP transformers provide a powerful framework for fake news detection by leveraging the contextual information in text data. They have the potential to contribute significantly to the identification and mitigation of misinformation in various domains.
## Fake News Detection Report
This report provides an overview of the evaluation metrics for the fake news detection model using NLP transformers.
| Metric | Value |
|----------------|-----------|
| eval_loss | 0.093 |
| eval_accuracy | 0.979 |
| eval_precision | 0.980 |
| eval_recall | 0.979 |
| eval_f1 | 0.979 |
| eval_runtime | 19.63s |
| samples/s | 2.394 |
| steps/s | 0.153 |
| epoch | 5.0 |
The evaluation metrics demonstrate the performance of the fake news detection model. It achieved an accuracy of 0.979, precision of 0.980, recall of 0.979, and an F1 score of 0.979. The runtime for evaluation was 19.63 seconds, with a throughput of approximately 2.394 samples per second and 0.153 steps per second. The model was trained for 5.0 epochs.
|
keremnazliel/distilbert_finetune_musique_test_1
|
keremnazliel
| 2023-06-19T13:30:04Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2023-06-18T12:55:45Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: distilbert_finetune_musique_test_1
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert_finetune_musique_test_1
This model is a fine-tuned version of [distilbert-base-cased-distilled-squad](https://huggingface.co/distilbert-base-cased-distilled-squad) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.0
- Tokenizers 0.13.3
|
martomor/distilbert-base-uncased-finetuned-emotion
|
martomor
| 2023-06-19T13:28:55Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-06-19T13:06:29Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
config: split
split: validation
args: split
metrics:
- name: Accuracy
type: accuracy
value: 0.9225
- name: F1
type: f1
value: 0.9228068723042021
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2259
- Accuracy: 0.9225
- F1: 0.9228
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8438 | 1.0 | 250 | 0.3163 | 0.9055 | 0.9033 |
| 0.2492 | 2.0 | 500 | 0.2259 | 0.9225 | 0.9228 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 1.16.1
- Tokenizers 0.13.3
|
Sliha66/Monocka
|
Sliha66
| 2023-06-19T13:10:48Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-06-19T13:10:48Z |
---
license: creativeml-openrail-m
---
|
swl-models/MirageMix-v2.0
|
swl-models
| 2023-06-19T13:02:14Z | 113 | 1 |
diffusers
|
[
"diffusers",
"safetensors",
"text-to-image",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-06-18T16:08:30Z |
---
license: creativeml-openrail-m
library_name: diffusers
pipeline_tag: text-to-image
---
|
stemmets/q-FrozenLake-v1-4x4-noSlippery
|
stemmets
| 2023-06-19T12:59:19Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-06-19T12:59:14Z |
---
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="stemmets/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"])
```
|
scottser/vit_df
|
scottser
| 2023-06-19T12:53:24Z | 193 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-06-19T12:31:39Z |
---
tags:
- image-classification
- pytorch
- huggingpics
metrics:
- accuracy
model-index:
- name: vit_df
results:
- task:
name: Image Classification
type: image-classification
metrics:
- name: Accuracy
type: accuracy
value: 0.6590909361839294
---
# vit_df
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics).
## Example Images
#### forged face
#### human faces
|
chencjiajy/taxi_q_learning
|
chencjiajy
| 2023-06-19T12:53:21Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-06-19T12:53:19Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: taxi_q_learning
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.54 +/- 2.74
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="chencjiajy/taxi_q_learning", 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"])
```
|
patrickvonplaten/wav2vec2-large-mms-1b-turkish-colab
|
patrickvonplaten
| 2023-06-19T12:46:37Z | 39 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice_6_1",
"license:cc-by-nc-4.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-06-19T10:28:38Z |
---
license: cc-by-nc-4.0
tags:
- generated_from_trainer
datasets:
- common_voice_6_1
metrics:
- wer
model-index:
- name: wav2vec2-large-mms-1b-turkish-colab
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: common_voice_6_1
type: common_voice_6_1
config: tr
split: test
args: tr
metrics:
- name: Wer
type: wer
value: 0.22275559187008478
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-mms-1b-turkish-colab
This model is a fine-tuned version of [facebook/mms-1b-all](https://huggingface.co/facebook/mms-1b-all) on the common_voice_6_1 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1556
- Wer: 0.2228
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.001
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 4.905 | 0.92 | 100 | 0.2146 | 0.2796 |
| 0.2901 | 1.83 | 200 | 0.1673 | 0.2317 |
| 0.2659 | 2.75 | 300 | 0.1608 | 0.2293 |
| 0.2398 | 3.67 | 400 | 0.1556 | 0.2228 |
### Framework versions
- Transformers 4.31.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.13.0
- Tokenizers 0.13.3
|
djc0213/my_awesome_model
|
djc0213
| 2023-06-19T12:42:58Z | 61 | 0 |
transformers
|
[
"transformers",
"tf",
"distilbert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-06-19T09:09:29Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: djc0213/my_awesome_model
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. -->
# djc0213/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:
- Train Loss: 0.0286
- Validation Loss: 0.2731
- Train Accuracy: 0.9325
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 7810, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train Accuracy | Epoch |
|:----------:|:---------------:|:--------------:|:-----:|
| 0.1327 | 0.1906 | 0.9296 | 0 |
| 0.0631 | 0.2219 | 0.9301 | 1 |
| 0.0286 | 0.2731 | 0.9325 | 2 |
### Framework versions
- Transformers 4.30.2
- TensorFlow 2.12.0
- Datasets 2.13.0
- Tokenizers 0.13.3
|
samhog/psychology-alpaca-merged
|
samhog
| 2023-06-19T12:21:44Z | 20 | 4 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-05-12T11:49:51Z |
# Psychology Alpaca 🍩
This is a LLaMA-7B language model trained on 10.000 psychology-related prompts and answers generated by ChatGPT. The model was trained on a single A100 GPU from Google Colab. The model shows some knowledge in the field of psychology and generally performs better than its base model parent.
### Background
This model was developed as part of a thesis project in the field of machine learning and psychology. It was used as a base model for further fine-tuning using reinforcement learning. The goal of the thesis was to compare reinforcement learning from *human feedback* and *AI feedback*. When the paper is available, it will be linked here!
**Authors:**
Samuel Höglund, samhog@kth.se;
Josef Khedri, jkhedri@kth.se
|
baylitoo/layoutxlm
|
baylitoo
| 2023-06-19T11:57:19Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"lilt",
"token-classification",
"generated_from_trainer",
"dataset:funsd-layoutlmv3",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2023-06-13T12:27:41Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- funsd-layoutlmv3
model-index:
- name: layoutxlm
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. -->
# layoutxlm
This model is a fine-tuned version of [SCUT-DLVCLab/lilt-roberta-en-base](https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base) on the funsd-layoutlmv3 dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5889
- Answer: {'precision': 0.8761904761904762, 'recall': 0.9008567931456548, 'f1': 0.8883524441762222, 'number': 817}
- Header: {'precision': 0.6666666666666666, 'recall': 0.5546218487394958, 'f1': 0.6055045871559633, 'number': 119}
- Question: {'precision': 0.8883968113374667, 'recall': 0.9312906220984215, 'f1': 0.9093381686310064, 'number': 1077}
- Overall Precision: 0.8728
- Overall Recall: 0.8967
- Overall F1: 0.8846
- Overall Accuracy: 0.8115
## 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
- training_steps: 2500
### Training results
| Training Loss | Epoch | Step | Validation Loss | Answer | Header | Question | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 0.4322 | 10.53 | 200 | 0.9083 | {'precision': 0.7704569606801275, 'recall': 0.8873929008567931, 'f1': 0.8248009101251422, 'number': 817} | {'precision': 0.6162790697674418, 'recall': 0.44537815126050423, 'f1': 0.5170731707317073, 'number': 119} | {'precision': 0.866852886405959, 'recall': 0.8644382544103992, 'f1': 0.8656438865643886, 'number': 1077} | 0.8134 | 0.8490 | 0.8308 | 0.7863 |
| 0.0467 | 21.05 | 400 | 1.2942 | {'precision': 0.8496583143507973, 'recall': 0.9130966952264382, 'f1': 0.88023598820059, 'number': 817} | {'precision': 0.6585365853658537, 'recall': 0.453781512605042, 'f1': 0.5373134328358209, 'number': 119} | {'precision': 0.8859964093357271, 'recall': 0.9164345403899722, 'f1': 0.9009584664536742, 'number': 1077} | 0.8616 | 0.8877 | 0.8745 | 0.7966 |
| 0.015 | 31.58 | 600 | 1.2662 | {'precision': 0.8574739281575898, 'recall': 0.9057527539779682, 'f1': 0.880952380952381, 'number': 817} | {'precision': 0.5304347826086957, 'recall': 0.5126050420168067, 'f1': 0.5213675213675214, 'number': 119} | {'precision': 0.8793879387938794, 'recall': 0.9071494893221913, 'f1': 0.8930530164533822, 'number': 1077} | 0.8511 | 0.8833 | 0.8669 | 0.8114 |
| 0.0081 | 42.11 | 800 | 1.5223 | {'precision': 0.8710462287104623, 'recall': 0.8763769889840881, 'f1': 0.8737034777303235, 'number': 817} | {'precision': 0.5882352941176471, 'recall': 0.5882352941176471, 'f1': 0.5882352941176471, 'number': 119} | {'precision': 0.8885844748858448, 'recall': 0.903435468895079, 'f1': 0.8959484346224678, 'number': 1077} | 0.8639 | 0.8738 | 0.8689 | 0.8041 |
| 0.0033 | 52.63 | 1000 | 1.4361 | {'precision': 0.8502304147465438, 'recall': 0.9033047735618115, 'f1': 0.8759643916913946, 'number': 817} | {'precision': 0.6144578313253012, 'recall': 0.42857142857142855, 'f1': 0.504950495049505, 'number': 119} | {'precision': 0.8767605633802817, 'recall': 0.924791086350975, 'f1': 0.9001355625847266, 'number': 1077} | 0.8553 | 0.8867 | 0.8707 | 0.8156 |
| 0.0026 | 63.16 | 1200 | 1.4994 | {'precision': 0.8615560640732265, 'recall': 0.9216646266829865, 'f1': 0.8905972797161442, 'number': 817} | {'precision': 0.5981308411214953, 'recall': 0.5378151260504201, 'f1': 0.5663716814159291, 'number': 119} | {'precision': 0.8945454545454545, 'recall': 0.9136490250696379, 'f1': 0.9039963252181902, 'number': 1077} | 0.8654 | 0.8947 | 0.8798 | 0.8208 |
| 0.0016 | 73.68 | 1400 | 1.6091 | {'precision': 0.858139534883721, 'recall': 0.9033047735618115, 'f1': 0.8801431127012522, 'number': 817} | {'precision': 0.5980392156862745, 'recall': 0.5126050420168067, 'f1': 0.5520361990950226, 'number': 119} | {'precision': 0.8947849954254345, 'recall': 0.9080779944289693, 'f1': 0.9013824884792625, 'number': 1077} | 0.8647 | 0.8828 | 0.8736 | 0.8167 |
| 0.0009 | 84.21 | 1600 | 1.6010 | {'precision': 0.859122401847575, 'recall': 0.9106487148102815, 'f1': 0.8841354723707664, 'number': 817} | {'precision': 0.6741573033707865, 'recall': 0.5042016806722689, 'f1': 0.576923076923077, 'number': 119} | {'precision': 0.8882931188561215, 'recall': 0.9229340761374187, 'f1': 0.9052823315118397, 'number': 1077} | 0.8669 | 0.8932 | 0.8799 | 0.8049 |
| 0.0006 | 94.74 | 1800 | 1.5889 | {'precision': 0.8761904761904762, 'recall': 0.9008567931456548, 'f1': 0.8883524441762222, 'number': 817} | {'precision': 0.6666666666666666, 'recall': 0.5546218487394958, 'f1': 0.6055045871559633, 'number': 119} | {'precision': 0.8883968113374667, 'recall': 0.9312906220984215, 'f1': 0.9093381686310064, 'number': 1077} | 0.8728 | 0.8967 | 0.8846 | 0.8115 |
| 0.0004 | 105.26 | 2000 | 1.6126 | {'precision': 0.8634772462077013, 'recall': 0.9057527539779682, 'f1': 0.8841099163679809, 'number': 817} | {'precision': 0.6538461538461539, 'recall': 0.5714285714285714, 'f1': 0.6098654708520179, 'number': 119} | {'precision': 0.894404332129964, 'recall': 0.9201485608170845, 'f1': 0.9070938215102976, 'number': 1077} | 0.8695 | 0.8937 | 0.8814 | 0.8127 |
| 0.0004 | 115.79 | 2200 | 1.6606 | {'precision': 0.8403648802736602, 'recall': 0.9020807833537332, 'f1': 0.8701298701298701, 'number': 817} | {'precision': 0.6509433962264151, 'recall': 0.5798319327731093, 'f1': 0.6133333333333333, 'number': 119} | {'precision': 0.8884826325411335, 'recall': 0.9025069637883009, 'f1': 0.8954398894518655, 'number': 1077} | 0.8560 | 0.8833 | 0.8694 | 0.7906 |
| 0.0002 | 126.32 | 2400 | 1.6619 | {'precision': 0.8378684807256236, 'recall': 0.9045287637698899, 'f1': 0.8699234844025897, 'number': 817} | {'precision': 0.6836734693877551, 'recall': 0.5630252100840336, 'f1': 0.6175115207373272, 'number': 119} | {'precision': 0.881981981981982, 'recall': 0.9090064995357474, 'f1': 0.8952903520804755, 'number': 1077} | 0.8541 | 0.8867 | 0.8701 | 0.7929 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.1.0.dev20230523+cu117
- Datasets 2.13.0
- Tokenizers 0.13.3
|
hopkins/svo-1
|
hopkins
| 2023-06-19T11:53:18Z | 174 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"generated_from_trainer",
"dataset:generator",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-06-19T10:40:59Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- generator
model-index:
- name: svo-1
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# svo-1
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the generator dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9072
## 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: 0.0005
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 512
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 1000
- num_epochs: 9
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 5.0334 | 0.73 | 200 | 2.5510 |
| 2.2609 | 1.45 | 400 | 2.0441 |
| 2.0306 | 2.18 | 600 | 1.9798 |
| 1.9782 | 2.91 | 800 | 1.9590 |
| 1.998 | 3.63 | 1000 | 1.9511 |
| 1.9482 | 4.36 | 1200 | 1.9366 |
| 1.9337 | 5.09 | 1400 | 1.9268 |
| 1.9093 | 5.82 | 1600 | 1.9175 |
| 1.8956 | 6.54 | 1800 | 1.9126 |
| 1.8789 | 7.27 | 2000 | 1.9094 |
| 1.8525 | 8.0 | 2200 | 1.9038 |
| 1.8325 | 8.73 | 2400 | 1.9072 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.11.0+cu113
- Datasets 2.12.0
- Tokenizers 0.13.3
|
VineX/VxPhotoTalk
|
VineX
| 2023-06-19T11:51:03Z | 0 | 0 | null |
[
"arxiv:2004.12992",
"region:us"
] | null | 2023-06-19T11:10:50Z |
# MakeItTalk: Speaker-Aware Talking-Head Animation
This is the code repository implementing the paper:
> **MakeItTalk: Speaker-Aware Talking-Head Animation**
>
> [Yang Zhou](https://people.umass.edu/~yangzhou),
> [Xintong Han](http://users.umiacs.umd.edu/~xintong/),
> [Eli Shechtman](https://research.adobe.com/person/eli-shechtman),
> [Jose Echevarria](http://www.jiechevarria.com) ,
> [Evangelos Kalogerakis](https://people.cs.umass.edu/~kalo/),
> [Dingzeyu Li](https://dingzeyu.li)
>
> SIGGRAPH Asia 2020
>
> **Abstract** We present a method that generates expressive talking-head videos from a single facial image with audio as the only input. In contrast to previous attempts to learn direct mappings from audio to raw pixels for creating talking faces, our method first disentangles the content and speaker information in the input audio signal. The audio content robustly controls the motion of lips and nearby facial regions, while the speaker information determines the specifics of facial expressions and the rest of the talking-head dynamics. Another key component of our method is the prediction of facial landmarks reflecting the speaker-aware dynamics. Based on this intermediate representation, our method works with many portrait images in a single unified framework, including artistic paintings, sketches, 2D cartoon characters, Japanese mangas, and stylized caricatures.
In addition, our method generalizes well for faces and characters that were not observed during training. We present extensive quantitative and qualitative evaluation of our method, in addition to user studies, demonstrating generated talking-heads of significantly higher quality compared to prior state-of-the-art methods.
>
> [[Project page]](https://people.umass.edu/~yangzhou/MakeItTalk/)
> [[Paper]](https://people.umass.edu/~yangzhou/MakeItTalk/MakeItTalk_SIGGRAPH_Asia_Final_round-5.pdf)
> [[Video]](https://www.youtube.com/watch?v=OU6Ctzhpc6s)
> [[Arxiv]](https://arxiv.org/abs/2004.12992)
> [[Colab Demo]](quick_demo.ipynb)
> [[Colab Demo TDLR]](quick_demo_tdlr.ipynb)
Figure. Given an audio speech signal and a single portrait image as input (left), our model generates speaker-aware talking-head animations (right).
Both the speech signal and the input face image are not observed during the model training process.
Our method creates both non-photorealistic cartoon animations (top) and natural human face videos (bottom).
## Updates
- [x] Generate new puppet! (tested on Ubuntu)
- [x] Pre-trained models
- [x] Google colab quick demo for natural faces [[detail]](quick_demo.ipynb) [[TDLR]](quick_demo_tdlr.ipynb)
- [ ] Training code for each module
## Requirements
- Python environment 3.6
```
conda create -n makeittalk_env python=3.6
conda activate makeittalk_env
```
- ffmpeg (https://ffmpeg.org/download.html)
```
sudo apt-get install ffmpeg
```
- python packages
```
pip install -r requirements.txt
```
- `winehq-stable` for cartoon face warping in Ubuntu (https://wiki.winehq.org/Ubuntu). Tested on Ubuntu16.04, wine==5.0.3.
```
sudo dpkg --add-architecture i386
wget -nc https://dl.winehq.org/wine-builds/winehq.key
sudo apt-key add winehq.key
sudo apt-add-repository 'deb https://dl.winehq.org/wine-builds/ubuntu/ xenial main'
sudo apt update
sudo apt install --install-recommends winehq-stable
```
## Pre-trained Models
Download the following pre-trained models to `examples/ckpt` folder for testing your own animation.
| Model | Link to the model |
| :-------------: | :---------------: |
| Voice Conversion | [Link](https://drive.google.com/file/d/1ZiwPp_h62LtjU0DwpelLUoodKPR85K7x/view?usp=sharing) |
| Speech Content Module | [Link](https://drive.google.com/file/d/1r3bfEvTVl6pCNw5xwUhEglwDHjWtAqQp/view?usp=sharing) |
| Speaker-aware Module | [Link](https://drive.google.com/file/d/1rV0jkyDqPW-aDJcj7xSO6Zt1zSXqn1mu/view?usp=sharing) |
| Image2Image Translation Module | [Link](https://drive.google.com/file/d/1i2LJXKp-yWKIEEgJ7C6cE3_2NirfY_0a/view?usp=sharing) |
| Non-photorealistic Warping (.exe) | [Link](https://drive.google.com/file/d/1rlj0PAUMdX8TLuywsn6ds_G6L63nAu0P/view?usp=sharing) |
## Animate You Portraits!
- Download pre-trained embedding [[here]](https://drive.google.com/file/d/18-0CYl5E6ungS3H4rRSHjfYvvm-WwjTI/view?usp=sharing) and save to `examples/dump` folder.
### _Nature Human Faces / Paintings_
- crop your portrait image into size `256x256` and put it under `examples` folder with `.jpg` format.
Make sure the head is almost in the middle (check existing examples for a reference).
- put test audio files under `examples` folder as well with `.wav` format.
- animate!
```
python main_end2end.py --jpg <portrait_file>
```
- use addition args `--amp_lip_x <x> --amp_lip_y <y> --amp_pos <pos>`
to amply lip motion (in x/y-axis direction) and head motion displacements, default values are `<x>=2., <y>=2., <pos>=.5`
### _Cartoon Faces_
- put test audio files under `examples` folder as well with `.wav` format.
- animate one of the existing puppets
| Puppet Name | wilk | smiling_person | sketch | color | cartoonM | danbooru1 |
| :---: | :---: | :---: | :---: | :---: | :---: | :---: |
| Image |  |  |  |  |  |  |
```
python main_end2end_cartoon.py --jpg <cartoon_puppet_name_with_extension> --jpg_bg <puppet_background_with_extension>
```
- `--jpg_bg` takes a same-size image as the background image to create the animation, such as the puppet's body, the overall fixed background image. If you want to use the background, make sure the puppet face image (i.e. `--jpg` image) is in `png` format and is transparent on the non-face area. If you don't need any background, please also create a same-size image (e.g. a pure white image) to hold the argument place.
- use addition args `--amp_lip_x <x> --amp_lip_y <y> --amp_pos <pos>`
to amply lip motion (in x/y-axis direction) and head motion displacements, default values are `<x>=2., <y>=2., <pos>=.5`
### _Generate Your New Puppet_
- put the cartoon image under `examples_cartoon`
- install conda environment `foa_env_py2` (tested on python 2) for Face-of-art (https://github.com/papulke/face-of-art).
Download the pre-trained weight [here](https://www.dropbox.com/sh/hrxcyug1bmbj6cs/AAAxq_zI5eawcLjM8zvUwaXha?dl=0) and put it under `examples/ckpt`.
Activate the environment.
```
source activate foa_env_py2
```
- create necessary files to animate your cartoon image, i.e.
`<your_puppet>_open_mouth.txt`, `<your_puppet>_close_mouth.txt`, `<your_puppet>_open_mouth_norm.txt`, `<your_puppet>_scale_shift.txt`, `<your_puppet>_delauney.txt`
```
python main_gen_new_puppet.py <your_puppet_with_file_extension>
```
- in details, it takes 3 steps
- Face-of-art automatic cartoon landmark detection.
- If it's wrong or not accurate, you can use our tool to drag and refine the landmarks.
- Estimate the closed mouth landmarks to serve as network input.
- Delauney triangulate the image with landmarks.
- check puppet name `smiling_person_example.png` for an example.
|  |  | 
| :---: | :---: | :---: |
| Landmark Adjustment Tool | Closed lips estimation | Delaunay Triangulation |
## Train
### Train Voice Conversion Module
Todo...
### Train Content Branch
- Create dataset root directory `<root_dir>`
- Dataset: Download preprocessed dataset [[here]](https://drive.google.com/drive/folders/1EwuAy3j1b9Zc1MsidUfxG_pJGc_cV60O?usp=sharing), and put it under `<root_dir>/dump`.
- Train script: Run script below. Models will be saved in `<root_dir>/ckpt/<train_instance_name>`.
```shell script
python main_train_content.py --train --write --root_dir <root_dir> --name <train_instance_name>
```
### Train Speaker-Aware Branch
Todo...
### Train Image-to-Image Translation
Todo...
## [License](LICENSE.md)
## Acknowledgement
We would like to thank Timothy Langlois for the narration, and
[Kaizhi Qian](https://scholar.google.com/citations?user=uEpr4C4AAAAJ&hl=en)
for the help with the [voice conversion module](https://auspicious3000.github.io/icassp-2020-demo/).
We thank [Jakub Fiser](https://research.adobe.com/person/jakub-fiser/) for implementing the real-time GPU version of the triangle morphing algorithm.
We thank Daichi Ito for sharing the caricature image and Dave Werner
for Wilk, the gruff but ultimately lovable puppet.
This research is partially funded by NSF (EAGER-1942069)
and a gift from Adobe. Our experiments were performed in the
UMass GPU cluster obtained under the Collaborative Fund managed
by the MassTech Collaborative.
|
Grecko/vgg16-imagenet
|
Grecko
| 2023-06-19T11:49:03Z | 0 | 0 |
tf-keras
|
[
"tf-keras",
"image-classification",
"region:us"
] |
image-classification
| 2023-06-19T11:36:12Z |
---
pipeline_tag: image-classification
---
|
metalwhale/openbuddy-openllama-7b-v5-q4_0
|
metalwhale
| 2023-06-19T11:46:58Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2023-06-19T11:39:45Z |
---
license: apache-2.0
---
## How to reproduce
```bash
# Prerequisites
apt update -y
apt install -y git git-lfs python3 python3-pip curl pkg-config libssl-dev
python3 -m pip install numpy==1.25.0 sentencepiece==0.1.99
curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh && source "$HOME/.cargo/env"
# Clone repositories
git clone https://huggingface.co/OpenBuddy/openbuddy-openllama-7b-v5-fp16 # Commit hash 1fedac68b34952eecec849a5938b778d6004d632
git clone https://github.com/ggerganov/llama.cpp # Commit hash 16b9cd193965769089881bb8ec012fccca7b37b6
git clone --recurse-submodules https://github.com/rustformers/llm.git # Commit hash 3becd728c0d6eeb2d649f86158c7018d5aaaba40
# Build ggml model
cd llama.cpp/
python3 convert.py ../openbuddy-openllama-7b-v5-fp16/
cd ../llm/
cargo build --release
cargo run --release llama quantize ../openbuddy-openllama-7b-v5-fp16/ggml-model-f16.bin ../openbuddy-openllama-7b-v5-fp16/openbuddy-openllama-7b-v5-q4_0.bin q4_0
```
(The commit hashes are confirmed at the time of 2023/06/19)
|
soramame123/test
|
soramame123
| 2023-06-19T11:28:32Z | 2 | 0 |
transformers
|
[
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2023-06-19T11:21:59Z |
LoRAを用いた指示文のファインチューニング。
|
christinakyp/whisper-tiny.en-train1
|
christinakyp
| 2023-06-19T11:21:07Z | 86 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"whisper",
"automatic-speech-recognition",
"asr",
"generated_from_trainer",
"en",
"dataset:christinakyp/dsing1",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-06-19T08:37:22Z |
---
language:
- en
license: apache-2.0
tags:
- asr
- generated_from_trainer
datasets:
- christinakyp/dsing1
model-index:
- name: Whisper Tiny Sing - CK
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. -->
# Whisper Tiny Sing - CK
This model is a fine-tuned version of [openai/whisper-tiny.en](https://huggingface.co/openai/whisper-tiny.en) on the DSing1 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: 1e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 4000
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.30.0.dev0
- Pytorch 1.13.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3
|
PraveenJesu/openai-whisper-small-zoomrx-v2
|
PraveenJesu
| 2023-06-19T11:19:03Z | 87 | 0 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"whisper",
"automatic-speech-recognition",
"audio",
"hf-asr-leaderboard",
"en",
"zh",
"de",
"es",
"ru",
"ko",
"fr",
"ja",
"pt",
"tr",
"pl",
"ca",
"nl",
"ar",
"sv",
"it",
"id",
"hi",
"fi",
"vi",
"he",
"uk",
"el",
"ms",
"cs",
"ro",
"da",
"hu",
"ta",
"no",
"th",
"ur",
"hr",
"bg",
"lt",
"la",
"mi",
"ml",
"cy",
"sk",
"te",
"fa",
"lv",
"bn",
"sr",
"az",
"sl",
"kn",
"et",
"mk",
"br",
"eu",
"is",
"hy",
"ne",
"mn",
"bs",
"kk",
"sq",
"sw",
"gl",
"mr",
"pa",
"si",
"km",
"sn",
"yo",
"so",
"af",
"oc",
"ka",
"be",
"tg",
"sd",
"gu",
"am",
"yi",
"lo",
"uz",
"fo",
"ht",
"ps",
"tk",
"nn",
"mt",
"sa",
"lb",
"my",
"bo",
"tl",
"mg",
"as",
"tt",
"haw",
"ln",
"ha",
"ba",
"jw",
"su",
"arxiv:2212.04356",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-06-19T11:14:28Z |
---
language:
- en
- zh
- de
- es
- ru
- ko
- fr
- ja
- pt
- tr
- pl
- ca
- nl
- ar
- sv
- it
- id
- hi
- fi
- vi
- he
- uk
- el
- ms
- cs
- ro
- da
- hu
- ta
- no
- th
- ur
- hr
- bg
- lt
- la
- mi
- ml
- cy
- sk
- te
- fa
- lv
- bn
- sr
- az
- sl
- kn
- et
- mk
- br
- eu
- is
- hy
- ne
- mn
- bs
- kk
- sq
- sw
- gl
- mr
- pa
- si
- km
- sn
- yo
- so
- af
- oc
- ka
- be
- tg
- sd
- gu
- am
- yi
- lo
- uz
- fo
- ht
- ps
- tk
- nn
- mt
- sa
- lb
- my
- bo
- tl
- mg
- as
- tt
- haw
- ln
- ha
- ba
- jw
- su
tags:
- audio
- automatic-speech-recognition
- hf-asr-leaderboard
widget:
- example_title: Librispeech sample 1
src: https://cdn-media.huggingface.co/speech_samples/sample1.flac
- example_title: Librispeech sample 2
src: https://cdn-media.huggingface.co/speech_samples/sample2.flac
model-index:
- name: whisper-small
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: LibriSpeech (clean)
type: librispeech_asr
config: clean
split: test
args:
language: en
metrics:
- name: Test WER
type: wer
value: 3.432213777886737
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: LibriSpeech (other)
type: librispeech_asr
config: other
split: test
args:
language: en
metrics:
- name: Test WER
type: wer
value: 7.628304527060248
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 11.0
type: mozilla-foundation/common_voice_11_0
config: hi
split: test
args:
language: hi
metrics:
- name: Test WER
type: wer
value: 87.3
pipeline_tag: automatic-speech-recognition
license: apache-2.0
---
# Whisper
Whisper is a pre-trained model for automatic speech recognition (ASR) and speech translation. Trained on 680k hours
of labelled data, Whisper models demonstrate a strong ability to generalise to many datasets and domains **without** the need
for fine-tuning.
Whisper was proposed in the paper [Robust Speech Recognition via Large-Scale Weak Supervision](https://arxiv.org/abs/2212.04356)
by Alec Radford et al from OpenAI. The original code repository can be found [here](https://github.com/openai/whisper).
**Disclaimer**: Content for this model card has partly been written by the Hugging Face team, and parts of it were
copied and pasted from the original model card.
## Model details
Whisper is a Transformer based encoder-decoder model, also referred to as a _sequence-to-sequence_ model.
It was trained on 680k hours of labelled speech data annotated using large-scale weak supervision.
The models were trained on either English-only data or multilingual data. The English-only models were trained
on the task of speech recognition. The multilingual models were trained on both speech recognition and speech
translation. For speech recognition, the model predicts transcriptions in the *same* language as the audio.
For speech translation, the model predicts transcriptions to a *different* language to the audio.
Whisper checkpoints come in five configurations of varying model sizes.
The smallest four are trained on either English-only or multilingual data.
The largest checkpoints are multilingual only. All ten of the pre-trained checkpoints
are available on the [Hugging Face Hub](https://huggingface.co/models?search=openai/whisper). The
checkpoints are summarised in the following table with links to the models on the Hub:
| Size | Parameters | English-only | Multilingual |
|----------|------------|------------------------------------------------------|-----------------------------------------------------|
| tiny | 39 M | [✓](https://huggingface.co/openai/whisper-tiny.en) | [✓](https://huggingface.co/openai/whisper-tiny) |
| base | 74 M | [✓](https://huggingface.co/openai/whisper-base.en) | [✓](https://huggingface.co/openai/whisper-base) |
| small | 244 M | [✓](https://huggingface.co/openai/whisper-small.en) | [✓](https://huggingface.co/openai/whisper-small) |
| medium | 769 M | [✓](https://huggingface.co/openai/whisper-medium.en) | [✓](https://huggingface.co/openai/whisper-medium) |
| large | 1550 M | x | [✓](https://huggingface.co/openai/whisper-large) |
| large-v2 | 1550 M | x | [✓](https://huggingface.co/openai/whisper-large-v2) |
# Usage
To transcribe audio samples, the model has to be used alongside a [`WhisperProcessor`](https://huggingface.co/docs/transformers/model_doc/whisper#transformers.WhisperProcessor).
The `WhisperProcessor` is used to:
1. Pre-process the audio inputs (converting them to log-Mel spectrograms for the model)
2. Post-process the model outputs (converting them from tokens to text)
The model is informed of which task to perform (transcription or translation) by passing the appropriate "context tokens". These context tokens
are a sequence of tokens that are given to the decoder at the start of the decoding process, and take the following order:
1. The transcription always starts with the `<|startoftranscript|>` token
2. The second token is the language token (e.g. `<|en|>` for English)
3. The third token is the "task token". It can take one of two values: `<|transcribe|>` for speech recognition or `<|translate|>` for speech translation
4. In addition, a `<|notimestamps|>` token is added if the model should not include timestamp prediction
Thus, a typical sequence of context tokens might look as follows:
```
<|startoftranscript|> <|en|> <|transcribe|> <|notimestamps|>
```
Which tells the model to decode in English, under the task of speech recognition, and not to predict timestamps.
These tokens can either be forced or un-forced. If they are forced, the model is made to predict each token at
each position. This allows one to control the output language and task for the Whisper model. If they are un-forced,
the Whisper model will automatically predict the output langauge and task itself.
The context tokens can be set accordingly:
```python
model.config.forced_decoder_ids = WhisperProcessor.get_decoder_prompt_ids(language="english", task="transcribe")
```
Which forces the model to predict in English under the task of speech recognition.
## Transcription
### English to English
In this example, the context tokens are 'unforced', meaning the model automatically predicts the output language
(English) and task (transcribe).
```python
>>> from transformers import WhisperProcessor, WhisperForConditionalGeneration
>>> from datasets import load_dataset
>>> # load model and processor
>>> processor = WhisperProcessor.from_pretrained("openai/whisper-small")
>>> model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-small")
>>> model.config.forced_decoder_ids = None
>>> # load dummy dataset and read audio files
>>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
>>> sample = ds[0]["audio"]
>>> input_features = processor(sample["array"], sampling_rate=sample["sampling_rate"], return_tensors="pt").input_features
>>> # generate token ids
>>> predicted_ids = model.generate(input_features)
>>> # decode token ids to text
>>> transcription = processor.batch_decode(predicted_ids, skip_special_tokens=False)
['<|startoftranscript|><|en|><|transcribe|><|notimestamps|> Mr. Quilter is the apostle of the middle classes and we are glad to welcome his gospel.<|endoftext|>']
>>> transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True)
[' Mr. Quilter is the apostle of the middle classes and we are glad to welcome his gospel.']
```
The context tokens can be removed from the start of the transcription by setting `skip_special_tokens=True`.
### French to French
The following example demonstrates French to French transcription by setting the decoder ids appropriately.
```python
>>> from transformers import WhisperProcessor, WhisperForConditionalGeneration
>>> from datasets import Audio, load_dataset
>>> # load model and processor
>>> processor = WhisperProcessor.from_pretrained("openai/whisper-small")
>>> model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-small")
>>> forced_decoder_ids = processor.get_decoder_prompt_ids(language="french", task="transcribe")
>>> # load streaming dataset and read first audio sample
>>> ds = load_dataset("common_voice", "fr", split="test", streaming=True)
>>> ds = ds.cast_column("audio", Audio(sampling_rate=16_000))
>>> input_speech = next(iter(ds))["audio"]
>>> input_features = processor(input_speech["array"], sampling_rate=input_speech["sampling_rate"], return_tensors="pt").input_features
>>> # generate token ids
>>> predicted_ids = model.generate(input_features, forced_decoder_ids=forced_decoder_ids)
>>> # decode token ids to text
>>> transcription = processor.batch_decode(predicted_ids)
['<|startoftranscript|><|fr|><|transcribe|><|notimestamps|> Un vrai travail intéressant va enfin être mené sur ce sujet.<|endoftext|>']
>>> transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True)
[' Un vrai travail intéressant va enfin être mené sur ce sujet.']
```
## Translation
Setting the task to "translate" forces the Whisper model to perform speech translation.
### French to English
```python
>>> from transformers import WhisperProcessor, WhisperForConditionalGeneration
>>> from datasets import Audio, load_dataset
>>> # load model and processor
>>> processor = WhisperProcessor.from_pretrained("openai/whisper-small")
>>> model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-small")
>>> forced_decoder_ids = processor.get_decoder_prompt_ids(language="french", task="translate")
>>> # load streaming dataset and read first audio sample
>>> ds = load_dataset("common_voice", "fr", split="test", streaming=True)
>>> ds = ds.cast_column("audio", Audio(sampling_rate=16_000))
>>> input_speech = next(iter(ds))["audio"]
>>> input_features = processor(input_speech["array"], sampling_rate=input_speech["sampling_rate"], return_tensors="pt").input_features
>>> # generate token ids
>>> predicted_ids = model.generate(input_features, forced_decoder_ids=forced_decoder_ids)
>>> # decode token ids to text
>>> transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True)
[' A very interesting work, we will finally be given on this subject.']
```
## Evaluation
This code snippet shows how to evaluate Whisper Small on [LibriSpeech test-clean](https://huggingface.co/datasets/librispeech_asr):
```python
>>> from datasets import load_dataset
>>> from transformers import WhisperForConditionalGeneration, WhisperProcessor
>>> import torch
>>> from evaluate import load
>>> librispeech_test_clean = load_dataset("librispeech_asr", "clean", split="test")
>>> processor = WhisperProcessor.from_pretrained("openai/whisper-small")
>>> model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-small").to("cuda")
>>> def map_to_pred(batch):
>>> audio = batch["audio"]
>>> input_features = processor(audio["array"], sampling_rate=audio["sampling_rate"], return_tensors="pt").input_features
>>> batch["reference"] = processor.tokenizer._normalize(batch['text'])
>>>
>>> with torch.no_grad():
>>> predicted_ids = model.generate(input_features.to("cuda"))[0]
>>> transcription = processor.decode(predicted_ids)
>>> batch["prediction"] = processor.tokenizer._normalize(transcription)
>>> return batch
>>> result = librispeech_test_clean.map(map_to_pred)
>>> wer = load("wer")
>>> print(100 * wer.compute(references=result["reference"], predictions=result["prediction"]))
3.432213777886737
```
## Long-Form Transcription
The Whisper model is intrinsically designed to work on audio samples of up to 30s in duration. However, by using a chunking
algorithm, it can be used to transcribe audio samples of up to arbitrary length. This is possible through Transformers
[`pipeline`](https://huggingface.co/docs/transformers/main_classes/pipelines#transformers.AutomaticSpeechRecognitionPipeline)
method. Chunking is enabled by setting `chunk_length_s=30` when instantiating the pipeline. With chunking enabled, the pipeline
can be run with batched inference. It can also be extended to predict sequence level timestamps by passing `return_timestamps=True`:
```python
>>> import torch
>>> from transformers import pipeline
>>> from datasets import load_dataset
>>> device = "cuda:0" if torch.cuda.is_available() else "cpu"
>>> pipe = pipeline(
>>> "automatic-speech-recognition",
>>> model="openai/whisper-small",
>>> chunk_length_s=30,
>>> device=device,
>>> )
>>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
>>> sample = ds[0]["audio"]
>>> prediction = pipe(sample.copy(), batch_size=8)["text"]
" Mr. Quilter is the apostle of the middle classes, and we are glad to welcome his gospel."
>>> # we can also return timestamps for the predictions
>>> prediction = pipe(sample.copy(), batch_size=8, return_timestamps=True)["chunks"]
[{'text': ' Mr. Quilter is the apostle of the middle classes and we are glad to welcome his gospel.',
'timestamp': (0.0, 5.44)}]
```
Refer to the blog post [ASR Chunking](https://huggingface.co/blog/asr-chunking) for more details on the chunking algorithm.
## Fine-Tuning
The pre-trained Whisper model demonstrates a strong ability to generalise to different datasets and domains. However,
its predictive capabilities can be improved further for certain languages and tasks through *fine-tuning*. The blog
post [Fine-Tune Whisper with 🤗 Transformers](https://huggingface.co/blog/fine-tune-whisper) provides a step-by-step
guide to fine-tuning the Whisper model with as little as 5 hours of labelled data.
### Evaluated Use
The primary intended users of these models are AI researchers studying robustness, generalization, capabilities, biases, and constraints of the current model. However, Whisper is also potentially quite useful as an ASR solution for developers, especially for English speech recognition. We recognize that once models are released, it is impossible to restrict access to only “intended” uses or to draw reasonable guidelines around what is or is not research.
The models are primarily trained and evaluated on ASR and speech translation to English tasks. They show strong ASR results in ~10 languages. They may exhibit additional capabilities, particularly if fine-tuned on certain tasks like voice activity detection, speaker classification, or speaker diarization but have not been robustly evaluated in these areas. We strongly recommend that users perform robust evaluations of the models in a particular context and domain before deploying them.
In particular, we caution against using Whisper models to transcribe recordings of individuals taken without their consent or purporting to use these models for any kind of subjective classification. We recommend against use in high-risk domains like decision-making contexts, where flaws in accuracy can lead to pronounced flaws in outcomes. The models are intended to transcribe and translate speech, use of the model for classification is not only not evaluated but also not appropriate, particularly to infer human attributes.
## Training Data
The models are trained on 680,000 hours of audio and the corresponding transcripts collected from the internet. 65% of this data (or 438,000 hours) represents English-language audio and matched English transcripts, roughly 18% (or 126,000 hours) represents non-English audio and English transcripts, while the final 17% (or 117,000 hours) represents non-English audio and the corresponding transcript. This non-English data represents 98 different languages.
As discussed in [the accompanying paper](https://cdn.openai.com/papers/whisper.pdf), we see that performance on transcription in a given language is directly correlated with the amount of training data we employ in that language.
## Performance and Limitations
Our studies show that, over many existing ASR systems, the models exhibit improved robustness to accents, background noise, technical language, as well as zero shot translation from multiple languages into English; and that accuracy on speech recognition and translation is near the state-of-the-art level.
However, because the models are trained in a weakly supervised manner using large-scale noisy data, the predictions may include texts that are not actually spoken in the audio input (i.e. hallucination). We hypothesize that this happens because, given their general knowledge of language, the models combine trying to predict the next word in audio with trying to transcribe the audio itself.
Our models perform unevenly across languages, and we observe lower accuracy on low-resource and/or low-discoverability languages or languages where we have less training data. The models also exhibit disparate performance on different accents and dialects of particular languages, which may include higher word error rate across speakers of different genders, races, ages, or other demographic criteria. Our full evaluation results are presented in [the paper accompanying this release](https://cdn.openai.com/papers/whisper.pdf).
In addition, the sequence-to-sequence architecture of the model makes it prone to generating repetitive texts, which can be mitigated to some degree by beam search and temperature scheduling but not perfectly. Further analysis on these limitations are provided in [the paper](https://cdn.openai.com/papers/whisper.pdf). It is likely that this behavior and hallucinations may be worse on lower-resource and/or lower-discoverability languages.
## Broader Implications
We anticipate that Whisper models’ transcription capabilities may be used for improving accessibility tools. While Whisper models cannot be used for real-time transcription out of the box – their speed and size suggest that others may be able to build applications on top of them that allow for near-real-time speech recognition and translation. The real value of beneficial applications built on top of Whisper models suggests that the disparate performance of these models may have real economic implications.
There are also potential dual use concerns that come with releasing Whisper. While we hope the technology will be used primarily for beneficial purposes, making ASR technology more accessible could enable more actors to build capable surveillance technologies or scale up existing surveillance efforts, as the speed and accuracy allow for affordable automatic transcription and translation of large volumes of audio communication. Moreover, these models may have some capabilities to recognize specific individuals out of the box, which in turn presents safety concerns related both to dual use and disparate performance. In practice, we expect that the cost of transcription is not the limiting factor of scaling up surveillance projects.
### BibTeX entry and citation info
```bibtex
@misc{radford2022whisper,
doi = {10.48550/ARXIV.2212.04356},
url = {https://arxiv.org/abs/2212.04356},
author = {Radford, Alec and Kim, Jong Wook and Xu, Tao and Brockman, Greg and McLeavey, Christine and Sutskever, Ilya},
title = {Robust Speech Recognition via Large-Scale Weak Supervision},
publisher = {arXiv},
year = {2022},
copyright = {arXiv.org perpetual, non-exclusive license}
}
```
|
gilang21/nookie
|
gilang21
| 2023-06-19T11:17:05Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-06-19T11:17:05Z |
---
license: creativeml-openrail-m
---
|
Tarsene/rrr
|
Tarsene
| 2023-06-19T11:15:58Z | 0 | 0 |
adapter-transformers
|
[
"adapter-transformers",
"en",
"dataset:OpenAssistant/oasst1",
"region:us"
] | null | 2023-06-19T11:15:05Z |
---
datasets:
- OpenAssistant/oasst1
language:
- en
metrics:
- character
library_name: adapter-transformers
---
|
PraveenJesu/openai-whisper-small-zoomrx-v1
|
PraveenJesu
| 2023-06-19T11:13:24Z | 0 | 0 |
peft
|
[
"peft",
"pytorch",
"whisper",
"region:us"
] | null | 2023-06-19T08:52:52Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
### Framework versions
- PEFT 0.4.0.dev0
|
LordSomen/lunar_radar_v1_234
|
LordSomen
| 2023-06-19T10:58:10Z | 1 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-06-19T10:57:51Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: lunar_radar
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 280.01 +/- 17.33
name: mean_reward
verified: false
---
# **lunar_radar** Agent playing **LunarLander-v2**
This is a trained model of a **lunar_radar** 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
...
```
|
antphb/DS-Chatbox-facebook-xglm-564M-V3
|
antphb
| 2023-06-19T10:56:59Z | 15 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"xglm",
"text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-06-18T07:39:51Z |
---
license: mit
tags:
- generated_from_trainer
model-index:
- name: DS-Chatbox-facebook-xglm-564M-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. -->
# DS-Chatbox-facebook-xglm-564M-V3
This model is a fine-tuned version of [facebook/xglm-564M](https://huggingface.co/facebook/xglm-564M) on the None dataset.
It achieves the following results on the evaluation set:
- eval_loss: 2.5064
- eval_runtime: 181.3835
- eval_samples_per_second: 39.099
- eval_steps_per_second: 4.89
- epoch: 1.69
- step: 3500
## 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: 7.500000000000001e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.2
- num_epochs: 5
### Framework versions
- Transformers 4.30.1
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.13.3
|
kejolong/mechaarmor
|
kejolong
| 2023-06-19T10:48:05Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-06-19T10:45:36Z |
---
license: creativeml-openrail-m
---
|
AtomGradient/food_vision_inner_lab
|
AtomGradient
| 2023-06-19T10:23:33Z | 194 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:food101",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-06-19T10:14:04Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- food101
metrics:
- accuracy
model-index:
- name: food_vision_inner_test1
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: food101
type: food101
config: default
split: train[:5000]
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.892
---
<!-- 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. -->
# food_vision_inner_test1
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the food101 dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5786
- Accuracy: 0.892
## 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
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.6859 | 0.99 | 62 | 2.4899 | 0.838 |
| 1.7872 | 2.0 | 125 | 1.7389 | 0.888 |
| 1.5605 | 2.98 | 186 | 1.5786 | 0.892 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.0
- Tokenizers 0.13.3
|
charlie1111/my_awesome_qa_model
|
charlie1111
| 2023-06-19T10:19:10Z | 69 | 0 |
transformers
|
[
"transformers",
"tf",
"distilbert",
"question-answering",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2023-06-10T09:20:39Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: charlie1111/my_awesome_qa_model
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. -->
# charlie1111/my_awesome_qa_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:
- Train Loss: 1.5766
- Validation Loss: 1.8255
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 500, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 3.4464 | 2.1496 | 0 |
| 1.8416 | 1.8255 | 1 |
| 1.5766 | 1.8255 | 2 |
### Framework versions
- Transformers 4.30.2
- TensorFlow 2.12.0
- Datasets 2.13.0
- Tokenizers 0.13.3
|
tux/a2c-PandaReachDense-v2
|
tux
| 2023-06-19T10:17:53Z | 3 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"PandaReachDense-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-06-19T10:15:08Z |
---
library_name: stable-baselines3
tags:
- PandaReachDense-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v2
type: PandaReachDense-v2
metrics:
- type: mean_reward
value: -0.44 +/- 0.21
name: mean_reward
verified: false
---
# **A2C** Agent playing **PandaReachDense-v2**
This is a trained model of a **A2C** agent playing **PandaReachDense-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
...
```
|
Pluto5/path-to-save-model
|
Pluto5
| 2023-06-19T10:13:05Z | 2 | 0 |
diffusers
|
[
"diffusers",
"tensorboard",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"dreambooth",
"base_model:CompVis/stable-diffusion-v1-4",
"base_model:finetune:CompVis/stable-diffusion-v1-4",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-06-16T05:12:03Z |
---
license: creativeml-openrail-m
base_model: CompVis/stable-diffusion-v1-4
instance_prompt: a photo of sks dog
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- dreambooth
inference: true
---
# DreamBooth - Pluto5/path-to-save-model
This is a dreambooth model derived from CompVis/stable-diffusion-v1-4. The weights were trained on a photo of sks dog using [DreamBooth](https://dreambooth.github.io/).
You can find some example images in the following.
DreamBooth for the text encoder was enabled: False.
|
Malaika/dqn-SpaceInvadersNoFrameskip-v4
|
Malaika
| 2023-06-19T10:11:45Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-06-18T19:59:27Z |
---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 336.00 +/- 102.81
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga Malaika -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga Malaika -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga Malaika
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 10000000),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
|
DicksonMassawe/finetuning-spam-detection-model
|
DicksonMassawe
| 2023-06-19T10:10:45Z | 104 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-06-18T16:23:09Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: finetuning-spam-detection-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. -->
# finetuning-spam-detection-model
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0065
- Accuracy: 0.9990
- F1: 0.9990
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.0958 | 1.0 | 965 | 0.0041 | 0.9995 | 0.9995 |
| 0.009 | 2.0 | 1930 | 0.0042 | 0.9995 | 0.9995 |
| 0.0018 | 3.0 | 2895 | 0.0049 | 0.9995 | 0.9995 |
| 0.001 | 4.0 | 3860 | 0.0034 | 0.9995 | 0.9995 |
| 0.0 | 5.0 | 4825 | 0.0065 | 0.9990 | 0.9990 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
alawryaguila/dog_results
|
alawryaguila
| 2023-06-19T10:06:31Z | 31 | 0 |
diffusers
|
[
"diffusers",
"tensorboard",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"dreambooth",
"base_model:CompVis/stable-diffusion-v1-4",
"base_model:finetune:CompVis/stable-diffusion-v1-4",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-06-14T09:22:40Z |
---
license: creativeml-openrail-m
base_model: CompVis/stable-diffusion-v1-4
instance_prompt: a photo of sks dog
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- dreambooth
inference: true
---
# DreamBooth - alawryaguila/dog_results
This is a dreambooth model derived from CompVis/stable-diffusion-v1-4. The weights were trained on a photo of sks dog using [DreamBooth](https://dreambooth.github.io/).
You can find some example images in the following.
DreamBooth for the text encoder was enabled: False.
|
tux/a2c-AntBulletEnv-v0
|
tux
| 2023-06-19T09:52:58Z | 4 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"AntBulletEnv-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-06-19T09:51:51Z |
---
library_name: stable-baselines3
tags:
- AntBulletEnv-v0
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: AntBulletEnv-v0
type: AntBulletEnv-v0
metrics:
- type: mean_reward
value: 1132.71 +/- 176.80
name: mean_reward
verified: false
---
# **A2C** Agent playing **AntBulletEnv-v0**
This is a trained model of a **A2C** agent playing **AntBulletEnv-v0**
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
...
```
|
praveenkumar-rajendran/vit-base-patch16-224-in21k-finetuned-lora-food101
|
praveenkumar-rajendran
| 2023-06-19T09:50:33Z | 4 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-06-19T09:31:13Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.4.0.dev0
- PEFT 0.4.0.dev0
|
Ditrip/ppoCleanRL-LunarLander-v2
|
Ditrip
| 2023-06-19T09:39:12Z | 0 | 0 | null |
[
"tensorboard",
"LunarLander-v2",
"ppo",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"deep-rl-course",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-06-19T09:39:07Z |
---
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: -106.68 +/- 48.84
name: mean_reward
verified: false
---
# PPO Agent Playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2.
# Hyperparameters
|
undrwolf/Cartpole-v1
|
undrwolf
| 2023-06-19T09:29:24Z | 0 | 0 | null |
[
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-06-19T09:29:14Z |
---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Cartpole-v1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 500.00 +/- 0.00
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
vpelloin/MEDIA_NLU-flaubert_oral_asr
|
vpelloin
| 2023-06-19T09:22:54Z | 126 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"flaubert",
"token-classification",
"bert",
"natural language understanding",
"NLU",
"spoken language understanding",
"SLU",
"understanding",
"MEDIA",
"fr",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2023-05-31T16:21:19Z |
---
language: fr
pipeline_tag: "token-classification"
widget:
- text: "je voudrais réserver une chambre à paris pour demain et lundi"
- text: "d'accord pour l'hôtel à quatre vingt dix euros la nuit"
- text: "deux nuits s'il vous plait"
- text: "dans un hôtel avec piscine à marseille"
tags:
- bert
- flaubert
- natural language understanding
- NLU
- spoken language understanding
- SLU
- understanding
- MEDIA
---
# vpelloin/MEDIA_NLU-flaubert_oral_asr
This is a Natural Language Understanding (NLU) model for the French [MEDIA benchmark](https://catalogue.elra.info/en-us/repository/browse/ELRA-S0272/).
It maps each input words into outputs concepts tags (76 available).
This model is trained using [`nherve/flaubert-oral-asr`](https://huggingface.co/nherve/flaubert-oral-asr) as its inital checkpoint. It obtained 12.43% CER (*lower is better*) in the MEDIA test set, in [our Interspeech 2023 publication](http://doi.org/10.21437/Interspeech.2022-352), using Kaldi ASR transcriptions.
## Available MEDIA NLU models:
- [`vpelloin/MEDIA_NLU-flaubert_base_cased`](https://huggingface.co/vpelloin/MEDIA_NLU-flaubert_base_cased): MEDIA NLU model trained using [`flaubert/flaubert_base_cased`](https://huggingface.co/flaubert/flaubert_base_cased). Obtains 13.20% CER on MEDIA test.
- [`vpelloin/MEDIA_NLU-flaubert_base_uncased`](https://huggingface.co/vpelloin/MEDIA_NLU-flaubert_base_uncased): MEDIA NLU model trained using [`flaubert/flaubert_base_uncased`](https://huggingface.co/flaubert/flaubert_base_uncased). Obtains 12.40% CER on MEDIA test.
- [`vpelloin/MEDIA_NLU-flaubert_oral_ft`](https://huggingface.co/vpelloin/MEDIA_NLU-flaubert_oral_ft): MEDIA NLU model trained using [`nherve/flaubert-oral-ft`](https://huggingface.co/nherve/flaubert-oral-ft). Obtains 11.98% CER on MEDIA test.
- [`vpelloin/MEDIA_NLU-flaubert_oral_mixed`](https://huggingface.co/vpelloin/MEDIA_NLU-flaubert_oral_mixed): MEDIA NLU model trained using [`nherve/flaubert-oral-mixed`](https://huggingface.co/nherve/flaubert-oral-mixed). Obtains 12.47% CER on MEDIA test.
- [`vpelloin/MEDIA_NLU-flaubert_oral_asr`](https://huggingface.co/vpelloin/MEDIA_NLU-flaubert_oral_asr): MEDIA NLU model trained using [`nherve/flaubert-oral-asr`](https://huggingface.co/nherve/flaubert-oral-asr). Obtains 12.43% CER on MEDIA test.
- [`vpelloin/MEDIA_NLU-flaubert_oral_asr_nb`](https://huggingface.co/vpelloin/MEDIA_NLU-flaubert_oral_asr_nb): MEDIA NLU model trained using [`nherve/flaubert-oral-asr_nb`](https://huggingface.co/nherve/flaubert-oral-asr_nb). Obtains 12.24% CER on MEDIA test.
## Usage with Pipeline
```python
from transformers import pipeline
generator = pipeline(
model="vpelloin/MEDIA_NLU-flaubert_oral_asr",
task="token-classification"
)
sentences = [
"je voudrais réserver une chambre à paris pour demain et lundi",
"d'accord pour l'hôtel à quatre vingt dix euros la nuit",
"deux nuits s'il vous plait",
"dans un hôtel avec piscine à marseille"
]
for sentence in sentences:
print([(tok['word'], tok['entity']) for tok in generator(sentence)])
```
## Usage with AutoTokenizer/AutoModel
```python
from transformers import (
AutoTokenizer,
AutoModelForTokenClassification
)
tokenizer = AutoTokenizer.from_pretrained(
"vpelloin/MEDIA_NLU-flaubert_oral_asr"
)
model = AutoModelForTokenClassification.from_pretrained(
"vpelloin/MEDIA_NLU-flaubert_oral_asr"
)
sentences = [
"je voudrais réserver une chambre à paris pour demain et lundi",
"d'accord pour l'hôtel à quatre vingt dix euros la nuit",
"deux nuits s'il vous plait",
"dans un hôtel avec piscine à marseille"
]
inputs = tokenizer(sentences, padding=True, return_tensors='pt')
outputs = model(**inputs).logits
print([
[model.config.id2label[i] for i in b]
for b in outputs.argmax(dim=-1).tolist()
])
```
## Reference
If you use this model for your scientific publication, or if you find the resources in this repository useful, please cite the [following paper](http://doi.org/10.21437/Interspeech.2022-352):
```
@inproceedings{pelloin22_interspeech,
author={Valentin Pelloin and Franck Dary and Nicolas Hervé and Benoit Favre and Nathalie Camelin and Antoine LAURENT and Laurent Besacier},
title={ASR-Generated Text for Language Model Pre-training Applied to Speech Tasks},
year=2022,
booktitle={Proc. Interspeech 2022},
pages={3453--3457},
doi={10.21437/Interspeech.2022-352}
}
```
|
vpelloin/MEDIA_NLU-flaubert_oral_ft
|
vpelloin
| 2023-06-19T09:21:44Z | 114 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"flaubert",
"token-classification",
"bert",
"natural language understanding",
"NLU",
"spoken language understanding",
"SLU",
"understanding",
"MEDIA",
"fr",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-03-16T15:20:26Z |
---
language: fr
pipeline_tag: "token-classification"
widget:
- text: "je voudrais réserver une chambre à paris pour demain et lundi"
- text: "d'accord pour l'hôtel à quatre vingt dix euros la nuit"
- text: "deux nuits s'il vous plait"
- text: "dans un hôtel avec piscine à marseille"
tags:
- bert
- flaubert
- natural language understanding
- NLU
- spoken language understanding
- SLU
- understanding
- MEDIA
---
# vpelloin/MEDIA_NLU-flaubert_oral_ft
This is a Natural Language Understanding (NLU) model for the French [MEDIA benchmark](https://catalogue.elra.info/en-us/repository/browse/ELRA-S0272/).
It maps each input words into outputs concepts tags (76 available).
This model is trained using [`nherve/flaubert-oral-ft`](https://huggingface.co/nherve/flaubert-oral-ft) as its inital checkpoint. It obtained 11.98% CER (*lower is better*) in the MEDIA test set, in [our Interspeech 2023 publication](http://doi.org/10.21437/Interspeech.2022-352), using Kaldi ASR transcriptions.
## Available MEDIA NLU models:
- [`vpelloin/MEDIA_NLU-flaubert_base_cased`](https://huggingface.co/vpelloin/MEDIA_NLU-flaubert_base_cased): MEDIA NLU model trained using [`flaubert/flaubert_base_cased`](https://huggingface.co/flaubert/flaubert_base_cased). Obtains 13.20% CER on MEDIA test.
- [`vpelloin/MEDIA_NLU-flaubert_base_uncased`](https://huggingface.co/vpelloin/MEDIA_NLU-flaubert_base_uncased): MEDIA NLU model trained using [`flaubert/flaubert_base_uncased`](https://huggingface.co/flaubert/flaubert_base_uncased). Obtains 12.40% CER on MEDIA test.
- [`vpelloin/MEDIA_NLU-flaubert_oral_ft`](https://huggingface.co/vpelloin/MEDIA_NLU-flaubert_oral_ft): MEDIA NLU model trained using [`nherve/flaubert-oral-ft`](https://huggingface.co/nherve/flaubert-oral-ft). Obtains 11.98% CER on MEDIA test.
- [`vpelloin/MEDIA_NLU-flaubert_oral_mixed`](https://huggingface.co/vpelloin/MEDIA_NLU-flaubert_oral_mixed): MEDIA NLU model trained using [`nherve/flaubert-oral-mixed`](https://huggingface.co/nherve/flaubert-oral-mixed). Obtains 12.47% CER on MEDIA test.
- [`vpelloin/MEDIA_NLU-flaubert_oral_asr`](https://huggingface.co/vpelloin/MEDIA_NLU-flaubert_oral_asr): MEDIA NLU model trained using [`nherve/flaubert-oral-asr`](https://huggingface.co/nherve/flaubert-oral-asr). Obtains 12.43% CER on MEDIA test.
- [`vpelloin/MEDIA_NLU-flaubert_oral_asr_nb`](https://huggingface.co/vpelloin/MEDIA_NLU-flaubert_oral_asr_nb): MEDIA NLU model trained using [`nherve/flaubert-oral-asr_nb`](https://huggingface.co/nherve/flaubert-oral-asr_nb). Obtains 12.24% CER on MEDIA test.
## Usage with Pipeline
```python
from transformers import pipeline
generator = pipeline(
model="vpelloin/MEDIA_NLU-flaubert_oral_ft",
task="token-classification"
)
sentences = [
"je voudrais réserver une chambre à paris pour demain et lundi",
"d'accord pour l'hôtel à quatre vingt dix euros la nuit",
"deux nuits s'il vous plait",
"dans un hôtel avec piscine à marseille"
]
for sentence in sentences:
print([(tok['word'], tok['entity']) for tok in generator(sentence)])
```
## Usage with AutoTokenizer/AutoModel
```python
from transformers import (
AutoTokenizer,
AutoModelForTokenClassification
)
tokenizer = AutoTokenizer.from_pretrained(
"vpelloin/MEDIA_NLU-flaubert_oral_ft"
)
model = AutoModelForTokenClassification.from_pretrained(
"vpelloin/MEDIA_NLU-flaubert_oral_ft"
)
sentences = [
"je voudrais réserver une chambre à paris pour demain et lundi",
"d'accord pour l'hôtel à quatre vingt dix euros la nuit",
"deux nuits s'il vous plait",
"dans un hôtel avec piscine à marseille"
]
inputs = tokenizer(sentences, padding=True, return_tensors='pt')
outputs = model(**inputs).logits
print([
[model.config.id2label[i] for i in b]
for b in outputs.argmax(dim=-1).tolist()
])
```
## Reference
If you use this model for your scientific publication, or if you find the resources in this repository useful, please cite the [following paper](http://doi.org/10.21437/Interspeech.2022-352):
```
@inproceedings{pelloin22_interspeech,
author={Valentin Pelloin and Franck Dary and Nicolas Hervé and Benoit Favre and Nathalie Camelin and Antoine LAURENT and Laurent Besacier},
title={ASR-Generated Text for Language Model Pre-training Applied to Speech Tasks},
year=2022,
booktitle={Proc. Interspeech 2022},
pages={3453--3457},
doi={10.21437/Interspeech.2022-352}
}
```
|
phi0112358/PMC_LLaMA-7B-ggml
|
phi0112358
| 2023-06-19T09:21:32Z | 0 | 3 | null |
[
"llama",
"ggml",
"pubmed",
"medicine",
"research",
"papers",
"en",
"dataset:S2ORC",
"region:us"
] | null | 2023-06-19T02:41:49Z |
---
datasets:
- S2ORC
language:
- en
tags:
- llama
- ggml
- pubmed
- medicine
- research
- papers
---
# ---
---
# PMC_LLaMA - finetuned on PubMed Central papers
**This is a ggml conversion of chaoyi-wu's [PMC_LLAMA_7B_10_epoch](https://huggingface.co/chaoyi-wu/PMC_LLAMA_7B_10_epoch) model.**
**It is a LLaMA model which is finetuned on PubMed Central papers from**
**The Semantic Scholar Open Research Coprus [dataset](https://github.com/allenai/s2orc).**
Currently I have only converted it into **new k-quant method Q5_K_M**. I will gladly make more versions on request.
Other possible quantizations include: q2_K, q3_K_S, q3_K_M, q3_K_L, q4_K_S, q4_K_M, q5_K_S, q5_K_M, q6_K
Compatible with **llama.cpp**, but also with:
- **text-generation-webui**
- **KoboldCpp**
- **ParisNeo/GPT4All-UI**
- **llama-cpp-python**
- **ctransformers**
---
# CAVE!
Being a professional myself and having tested the model, I can strongly advise that this model is best left in the hands of professionals.
This model can produce very detailed and elaborate responses, but it tends to confabulate quite often in my opinion (considering the field of use).
Because of the detail accuracy, it is difficult for a layperson to tell when the model is returning facts and when it is returning bullshit.
– so unless you are a subject matter expert (biology, medicine, chemistry, pharmacy, etc) I appeal to your sense of responsibility and ask you:
**to use the model only for testing, exploration, and just-for-fun. In no case should the answers of this model lead to implications that affect your health.**
---
Here is what the autor/s write in the original model [card](https://huggingface.co/chaoyi-wu/PMC_LLAMA_7B_10_epoch/blob/main/README.md):
```
This repo contains the latest version of PMC_LLaMA_7B, which is LLaMA-7b finetuned on the PMC papers in the S2ORC dataset.
Notably, different from chaoyi-wu/PMC_LLAMA_7B, this model is further trained for 10 epochs.
The model was trained with the following hyperparameters:
Epochs: 10
Batch size: 128
Cutoff length: 512
Learning rate: 2e-5
Each epoch we sample 512 tokens per paper for training.
```
---
### That's it!
If you have any further questions, feel free to contact me or start a discussion
|
Ditrip/poca-SoccerTwos
|
Ditrip
| 2023-06-19T09:06:31Z | 10 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"SoccerTwos",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SoccerTwos",
"region:us"
] |
reinforcement-learning
| 2023-06-19T09:06:03Z |
---
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: Ditrip/poca-SoccerTwos
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
NasimB/gpt2_left_out_cbt
|
NasimB
| 2023-06-19T09:02:46Z | 15 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"generated_from_trainer",
"dataset:generator",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-06-19T04:33:00Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- generator
model-index:
- name: gpt2_left_out_cbt
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. -->
# gpt2_left_out_cbt
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the generator dataset.
It achieves the following results on the evaluation set:
- Loss: 3.9359
## 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: 0.0005
- 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: cosine
- lr_scheduler_warmup_steps: 1000
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 5.9415 | 0.25 | 500 | 5.0484 |
| 4.7309 | 0.5 | 1000 | 4.6647 |
| 4.4299 | 0.75 | 1500 | 4.4406 |
| 4.2176 | 1.0 | 2000 | 4.3150 |
| 4.0185 | 1.25 | 2500 | 4.2114 |
| 3.9488 | 1.5 | 3000 | 4.1259 |
| 3.8765 | 1.75 | 3500 | 4.0526 |
| 3.8178 | 2.0 | 4000 | 4.0013 |
| 3.6252 | 2.25 | 4500 | 3.9677 |
| 3.6126 | 2.5 | 5000 | 3.9257 |
| 3.5877 | 2.75 | 5500 | 3.8858 |
| 3.5664 | 3.0 | 6000 | 3.8677 |
| 3.3588 | 3.25 | 6500 | 3.8655 |
| 3.3839 | 3.5 | 7000 | 3.8408 |
| 3.375 | 3.75 | 7500 | 3.8131 |
| 3.3702 | 4.0 | 8000 | 3.8096 |
| 3.1311 | 4.25 | 8500 | 3.8272 |
| 3.1718 | 4.5 | 9000 | 3.8071 |
| 3.1835 | 4.75 | 9500 | 3.7894 |
| 3.1802 | 5.01 | 10000 | 3.7920 |
| 2.9241 | 5.26 | 10500 | 3.8233 |
| 2.9597 | 5.51 | 11000 | 3.8156 |
| 2.9773 | 5.76 | 11500 | 3.8019 |
| 2.9708 | 6.01 | 12000 | 3.8077 |
| 2.7159 | 6.26 | 12500 | 3.8440 |
| 2.7495 | 6.51 | 13000 | 3.8459 |
| 2.761 | 6.76 | 13500 | 3.8406 |
| 2.7542 | 7.01 | 14000 | 3.8461 |
| 2.5238 | 7.26 | 14500 | 3.8832 |
| 2.5459 | 7.51 | 15000 | 3.8868 |
| 2.5638 | 7.76 | 15500 | 3.8872 |
| 2.555 | 8.01 | 16000 | 3.8932 |
| 2.388 | 8.26 | 16500 | 3.9161 |
| 2.4017 | 8.51 | 17000 | 3.9215 |
| 2.4056 | 8.76 | 17500 | 3.9236 |
| 2.3998 | 9.01 | 18000 | 3.9254 |
| 2.3199 | 9.26 | 18500 | 3.9339 |
| 2.3241 | 9.51 | 19000 | 3.9359 |
| 2.3205 | 9.76 | 19500 | 3.9359 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.11.0+cu113
- Datasets 2.13.0
- Tokenizers 0.13.3
|
AtomGradient/mlm_inner_lab
|
AtomGradient
| 2023-06-19T08:42:09Z | 178 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"roberta",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2023-06-19T08:36:54Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: mlm_inner_testl
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. -->
# mlm_inner_testl
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9885
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.2433 | 1.0 | 1142 | 2.0346 |
| 2.1717 | 2.0 | 2284 | 2.0135 |
| 2.1135 | 3.0 | 3426 | 1.9885 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.0
- Tokenizers 0.13.3
|
Ayush121/demo_model
|
Ayush121
| 2023-06-19T08:29:14Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-06-19T05:04:59Z |
# DiSCoMaT: Distantly Supervised Composition Extraction from Tables in Materials Science
## About
This repository contains the official implementation of
[DiSCoMaT: Distantly Supervised Composition Extraction from Tables in Materials Science](https://comingsoon). This model is trained on **4,408 distantly supervised tables** published in materials science research papers to extract compositions reported in the tables. These tables yielded a total of 38,799 tuples in the training set.
The tuples are of the form $\{(id, c_k^{id}, p_k^{id}, u_k^{id} )\}_{k=1}^{K^{id}}$, where
- $id$ is the id of the material composition reported in the tables
- $c_k^{id}$ is the k-th chemical element in the material composition
- $p_k^{id}$ is the percentage of k-th chemical element in the material composition
- $u_k^{id}$ is the unit of the of $p_k^{id}$ (either mole % or weight %)
The following figure represents the architecture of the model proposed in our work.
## Notes
- The [**code**](code) directory contains the file for training models reported in this paper.
- The [**data**](data) directory contains the dataset for training models reported in this paper.
- The [**notebooks**](notebooks) directory contains Jupyter notebook to visualise the dataset.
- The respective directories and sub-directories contain task-specific **README** files.
## Citation
If you find this repository useful, please cite our work as follows:
```
Incoming ACL 2023. Will be added soon.
```
|
PraveenJesu/openai-whisper-small-zoomrx-colab-2
|
PraveenJesu
| 2023-06-19T08:07:53Z | 75 | 0 |
transformers
|
[
"transformers",
"pytorch",
"whisper",
"automatic-speech-recognition",
"audio",
"hf-asr-leaderboard",
"en",
"zh",
"de",
"es",
"ru",
"ko",
"fr",
"ja",
"pt",
"tr",
"pl",
"ca",
"nl",
"ar",
"sv",
"it",
"id",
"hi",
"fi",
"vi",
"he",
"uk",
"el",
"ms",
"cs",
"ro",
"da",
"hu",
"ta",
"no",
"th",
"ur",
"hr",
"bg",
"lt",
"la",
"mi",
"ml",
"cy",
"sk",
"te",
"fa",
"lv",
"bn",
"sr",
"az",
"sl",
"kn",
"et",
"mk",
"br",
"eu",
"is",
"hy",
"ne",
"mn",
"bs",
"kk",
"sq",
"sw",
"gl",
"mr",
"pa",
"si",
"km",
"sn",
"yo",
"so",
"af",
"oc",
"ka",
"be",
"tg",
"sd",
"gu",
"am",
"yi",
"lo",
"uz",
"fo",
"ht",
"ps",
"tk",
"nn",
"mt",
"sa",
"lb",
"my",
"bo",
"tl",
"mg",
"as",
"tt",
"haw",
"ln",
"ha",
"ba",
"jw",
"su",
"arxiv:2212.04356",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-06-19T07:39:51Z |
---
language:
- en
- zh
- de
- es
- ru
- ko
- fr
- ja
- pt
- tr
- pl
- ca
- nl
- ar
- sv
- it
- id
- hi
- fi
- vi
- he
- uk
- el
- ms
- cs
- ro
- da
- hu
- ta
- no
- th
- ur
- hr
- bg
- lt
- la
- mi
- ml
- cy
- sk
- te
- fa
- lv
- bn
- sr
- az
- sl
- kn
- et
- mk
- br
- eu
- is
- hy
- ne
- mn
- bs
- kk
- sq
- sw
- gl
- mr
- pa
- si
- km
- sn
- yo
- so
- af
- oc
- ka
- be
- tg
- sd
- gu
- am
- yi
- lo
- uz
- fo
- ht
- ps
- tk
- nn
- mt
- sa
- lb
- my
- bo
- tl
- mg
- as
- tt
- haw
- ln
- ha
- ba
- jw
- su
tags:
- audio
- automatic-speech-recognition
- hf-asr-leaderboard
widget:
- example_title: Librispeech sample 1
src: https://cdn-media.huggingface.co/speech_samples/sample1.flac
- example_title: Librispeech sample 2
src: https://cdn-media.huggingface.co/speech_samples/sample2.flac
model-index:
- name: whisper-small
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: LibriSpeech (clean)
type: librispeech_asr
config: clean
split: test
args:
language: en
metrics:
- name: Test WER
type: wer
value: 3.432213777886737
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: LibriSpeech (other)
type: librispeech_asr
config: other
split: test
args:
language: en
metrics:
- name: Test WER
type: wer
value: 7.628304527060248
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 11.0
type: mozilla-foundation/common_voice_11_0
config: hi
split: test
args:
language: hi
metrics:
- name: Test WER
type: wer
value: 87.3
pipeline_tag: automatic-speech-recognition
license: apache-2.0
---
# Whisper
Whisper is a pre-trained model for automatic speech recognition (ASR) and speech translation. Trained on 680k hours
of labelled data, Whisper models demonstrate a strong ability to generalise to many datasets and domains **without** the need
for fine-tuning.
Whisper was proposed in the paper [Robust Speech Recognition via Large-Scale Weak Supervision](https://arxiv.org/abs/2212.04356)
by Alec Radford et al from OpenAI. The original code repository can be found [here](https://github.com/openai/whisper).
**Disclaimer**: Content for this model card has partly been written by the Hugging Face team, and parts of it were
copied and pasted from the original model card.
## Model details
Whisper is a Transformer based encoder-decoder model, also referred to as a _sequence-to-sequence_ model.
It was trained on 680k hours of labelled speech data annotated using large-scale weak supervision.
The models were trained on either English-only data or multilingual data. The English-only models were trained
on the task of speech recognition. The multilingual models were trained on both speech recognition and speech
translation. For speech recognition, the model predicts transcriptions in the *same* language as the audio.
For speech translation, the model predicts transcriptions to a *different* language to the audio.
Whisper checkpoints come in five configurations of varying model sizes.
The smallest four are trained on either English-only or multilingual data.
The largest checkpoints are multilingual only. All ten of the pre-trained checkpoints
are available on the [Hugging Face Hub](https://huggingface.co/models?search=openai/whisper). The
checkpoints are summarised in the following table with links to the models on the Hub:
| Size | Parameters | English-only | Multilingual |
|----------|------------|------------------------------------------------------|-----------------------------------------------------|
| tiny | 39 M | [✓](https://huggingface.co/openai/whisper-tiny.en) | [✓](https://huggingface.co/openai/whisper-tiny) |
| base | 74 M | [✓](https://huggingface.co/openai/whisper-base.en) | [✓](https://huggingface.co/openai/whisper-base) |
| small | 244 M | [✓](https://huggingface.co/openai/whisper-small.en) | [✓](https://huggingface.co/openai/whisper-small) |
| medium | 769 M | [✓](https://huggingface.co/openai/whisper-medium.en) | [✓](https://huggingface.co/openai/whisper-medium) |
| large | 1550 M | x | [✓](https://huggingface.co/openai/whisper-large) |
| large-v2 | 1550 M | x | [✓](https://huggingface.co/openai/whisper-large-v2) |
# Usage
To transcribe audio samples, the model has to be used alongside a [`WhisperProcessor`](https://huggingface.co/docs/transformers/model_doc/whisper#transformers.WhisperProcessor).
The `WhisperProcessor` is used to:
1. Pre-process the audio inputs (converting them to log-Mel spectrograms for the model)
2. Post-process the model outputs (converting them from tokens to text)
The model is informed of which task to perform (transcription or translation) by passing the appropriate "context tokens". These context tokens
are a sequence of tokens that are given to the decoder at the start of the decoding process, and take the following order:
1. The transcription always starts with the `<|startoftranscript|>` token
2. The second token is the language token (e.g. `<|en|>` for English)
3. The third token is the "task token". It can take one of two values: `<|transcribe|>` for speech recognition or `<|translate|>` for speech translation
4. In addition, a `<|notimestamps|>` token is added if the model should not include timestamp prediction
Thus, a typical sequence of context tokens might look as follows:
```
<|startoftranscript|> <|en|> <|transcribe|> <|notimestamps|>
```
Which tells the model to decode in English, under the task of speech recognition, and not to predict timestamps.
These tokens can either be forced or un-forced. If they are forced, the model is made to predict each token at
each position. This allows one to control the output language and task for the Whisper model. If they are un-forced,
the Whisper model will automatically predict the output langauge and task itself.
The context tokens can be set accordingly:
```python
model.config.forced_decoder_ids = WhisperProcessor.get_decoder_prompt_ids(language="english", task="transcribe")
```
Which forces the model to predict in English under the task of speech recognition.
## Transcription
### English to English
In this example, the context tokens are 'unforced', meaning the model automatically predicts the output language
(English) and task (transcribe).
```python
>>> from transformers import WhisperProcessor, WhisperForConditionalGeneration
>>> from datasets import load_dataset
>>> # load model and processor
>>> processor = WhisperProcessor.from_pretrained("openai/whisper-small")
>>> model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-small")
>>> model.config.forced_decoder_ids = None
>>> # load dummy dataset and read audio files
>>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
>>> sample = ds[0]["audio"]
>>> input_features = processor(sample["array"], sampling_rate=sample["sampling_rate"], return_tensors="pt").input_features
>>> # generate token ids
>>> predicted_ids = model.generate(input_features)
>>> # decode token ids to text
>>> transcription = processor.batch_decode(predicted_ids, skip_special_tokens=False)
['<|startoftranscript|><|en|><|transcribe|><|notimestamps|> Mr. Quilter is the apostle of the middle classes and we are glad to welcome his gospel.<|endoftext|>']
>>> transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True)
[' Mr. Quilter is the apostle of the middle classes and we are glad to welcome his gospel.']
```
The context tokens can be removed from the start of the transcription by setting `skip_special_tokens=True`.
### French to French
The following example demonstrates French to French transcription by setting the decoder ids appropriately.
```python
>>> from transformers import WhisperProcessor, WhisperForConditionalGeneration
>>> from datasets import Audio, load_dataset
>>> # load model and processor
>>> processor = WhisperProcessor.from_pretrained("openai/whisper-small")
>>> model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-small")
>>> forced_decoder_ids = processor.get_decoder_prompt_ids(language="french", task="transcribe")
>>> # load streaming dataset and read first audio sample
>>> ds = load_dataset("common_voice", "fr", split="test", streaming=True)
>>> ds = ds.cast_column("audio", Audio(sampling_rate=16_000))
>>> input_speech = next(iter(ds))["audio"]
>>> input_features = processor(input_speech["array"], sampling_rate=input_speech["sampling_rate"], return_tensors="pt").input_features
>>> # generate token ids
>>> predicted_ids = model.generate(input_features, forced_decoder_ids=forced_decoder_ids)
>>> # decode token ids to text
>>> transcription = processor.batch_decode(predicted_ids)
['<|startoftranscript|><|fr|><|transcribe|><|notimestamps|> Un vrai travail intéressant va enfin être mené sur ce sujet.<|endoftext|>']
>>> transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True)
[' Un vrai travail intéressant va enfin être mené sur ce sujet.']
```
## Translation
Setting the task to "translate" forces the Whisper model to perform speech translation.
### French to English
```python
>>> from transformers import WhisperProcessor, WhisperForConditionalGeneration
>>> from datasets import Audio, load_dataset
>>> # load model and processor
>>> processor = WhisperProcessor.from_pretrained("openai/whisper-small")
>>> model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-small")
>>> forced_decoder_ids = processor.get_decoder_prompt_ids(language="french", task="translate")
>>> # load streaming dataset and read first audio sample
>>> ds = load_dataset("common_voice", "fr", split="test", streaming=True)
>>> ds = ds.cast_column("audio", Audio(sampling_rate=16_000))
>>> input_speech = next(iter(ds))["audio"]
>>> input_features = processor(input_speech["array"], sampling_rate=input_speech["sampling_rate"], return_tensors="pt").input_features
>>> # generate token ids
>>> predicted_ids = model.generate(input_features, forced_decoder_ids=forced_decoder_ids)
>>> # decode token ids to text
>>> transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True)
[' A very interesting work, we will finally be given on this subject.']
```
## Evaluation
This code snippet shows how to evaluate Whisper Small on [LibriSpeech test-clean](https://huggingface.co/datasets/librispeech_asr):
```python
>>> from datasets import load_dataset
>>> from transformers import WhisperForConditionalGeneration, WhisperProcessor
>>> import torch
>>> from evaluate import load
>>> librispeech_test_clean = load_dataset("librispeech_asr", "clean", split="test")
>>> processor = WhisperProcessor.from_pretrained("openai/whisper-small")
>>> model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-small").to("cuda")
>>> def map_to_pred(batch):
>>> audio = batch["audio"]
>>> input_features = processor(audio["array"], sampling_rate=audio["sampling_rate"], return_tensors="pt").input_features
>>> batch["reference"] = processor.tokenizer._normalize(batch['text'])
>>>
>>> with torch.no_grad():
>>> predicted_ids = model.generate(input_features.to("cuda"))[0]
>>> transcription = processor.decode(predicted_ids)
>>> batch["prediction"] = processor.tokenizer._normalize(transcription)
>>> return batch
>>> result = librispeech_test_clean.map(map_to_pred)
>>> wer = load("wer")
>>> print(100 * wer.compute(references=result["reference"], predictions=result["prediction"]))
3.432213777886737
```
## Long-Form Transcription
The Whisper model is intrinsically designed to work on audio samples of up to 30s in duration. However, by using a chunking
algorithm, it can be used to transcribe audio samples of up to arbitrary length. This is possible through Transformers
[`pipeline`](https://huggingface.co/docs/transformers/main_classes/pipelines#transformers.AutomaticSpeechRecognitionPipeline)
method. Chunking is enabled by setting `chunk_length_s=30` when instantiating the pipeline. With chunking enabled, the pipeline
can be run with batched inference. It can also be extended to predict sequence level timestamps by passing `return_timestamps=True`:
```python
>>> import torch
>>> from transformers import pipeline
>>> from datasets import load_dataset
>>> device = "cuda:0" if torch.cuda.is_available() else "cpu"
>>> pipe = pipeline(
>>> "automatic-speech-recognition",
>>> model="openai/whisper-small",
>>> chunk_length_s=30,
>>> device=device,
>>> )
>>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
>>> sample = ds[0]["audio"]
>>> prediction = pipe(sample.copy(), batch_size=8)["text"]
" Mr. Quilter is the apostle of the middle classes, and we are glad to welcome his gospel."
>>> # we can also return timestamps for the predictions
>>> prediction = pipe(sample.copy(), batch_size=8, return_timestamps=True)["chunks"]
[{'text': ' Mr. Quilter is the apostle of the middle classes and we are glad to welcome his gospel.',
'timestamp': (0.0, 5.44)}]
```
Refer to the blog post [ASR Chunking](https://huggingface.co/blog/asr-chunking) for more details on the chunking algorithm.
## Fine-Tuning
The pre-trained Whisper model demonstrates a strong ability to generalise to different datasets and domains. However,
its predictive capabilities can be improved further for certain languages and tasks through *fine-tuning*. The blog
post [Fine-Tune Whisper with 🤗 Transformers](https://huggingface.co/blog/fine-tune-whisper) provides a step-by-step
guide to fine-tuning the Whisper model with as little as 5 hours of labelled data.
### Evaluated Use
The primary intended users of these models are AI researchers studying robustness, generalization, capabilities, biases, and constraints of the current model. However, Whisper is also potentially quite useful as an ASR solution for developers, especially for English speech recognition. We recognize that once models are released, it is impossible to restrict access to only “intended” uses or to draw reasonable guidelines around what is or is not research.
The models are primarily trained and evaluated on ASR and speech translation to English tasks. They show strong ASR results in ~10 languages. They may exhibit additional capabilities, particularly if fine-tuned on certain tasks like voice activity detection, speaker classification, or speaker diarization but have not been robustly evaluated in these areas. We strongly recommend that users perform robust evaluations of the models in a particular context and domain before deploying them.
In particular, we caution against using Whisper models to transcribe recordings of individuals taken without their consent or purporting to use these models for any kind of subjective classification. We recommend against use in high-risk domains like decision-making contexts, where flaws in accuracy can lead to pronounced flaws in outcomes. The models are intended to transcribe and translate speech, use of the model for classification is not only not evaluated but also not appropriate, particularly to infer human attributes.
## Training Data
The models are trained on 680,000 hours of audio and the corresponding transcripts collected from the internet. 65% of this data (or 438,000 hours) represents English-language audio and matched English transcripts, roughly 18% (or 126,000 hours) represents non-English audio and English transcripts, while the final 17% (or 117,000 hours) represents non-English audio and the corresponding transcript. This non-English data represents 98 different languages.
As discussed in [the accompanying paper](https://cdn.openai.com/papers/whisper.pdf), we see that performance on transcription in a given language is directly correlated with the amount of training data we employ in that language.
## Performance and Limitations
Our studies show that, over many existing ASR systems, the models exhibit improved robustness to accents, background noise, technical language, as well as zero shot translation from multiple languages into English; and that accuracy on speech recognition and translation is near the state-of-the-art level.
However, because the models are trained in a weakly supervised manner using large-scale noisy data, the predictions may include texts that are not actually spoken in the audio input (i.e. hallucination). We hypothesize that this happens because, given their general knowledge of language, the models combine trying to predict the next word in audio with trying to transcribe the audio itself.
Our models perform unevenly across languages, and we observe lower accuracy on low-resource and/or low-discoverability languages or languages where we have less training data. The models also exhibit disparate performance on different accents and dialects of particular languages, which may include higher word error rate across speakers of different genders, races, ages, or other demographic criteria. Our full evaluation results are presented in [the paper accompanying this release](https://cdn.openai.com/papers/whisper.pdf).
In addition, the sequence-to-sequence architecture of the model makes it prone to generating repetitive texts, which can be mitigated to some degree by beam search and temperature scheduling but not perfectly. Further analysis on these limitations are provided in [the paper](https://cdn.openai.com/papers/whisper.pdf). It is likely that this behavior and hallucinations may be worse on lower-resource and/or lower-discoverability languages.
## Broader Implications
We anticipate that Whisper models’ transcription capabilities may be used for improving accessibility tools. While Whisper models cannot be used for real-time transcription out of the box – their speed and size suggest that others may be able to build applications on top of them that allow for near-real-time speech recognition and translation. The real value of beneficial applications built on top of Whisper models suggests that the disparate performance of these models may have real economic implications.
There are also potential dual use concerns that come with releasing Whisper. While we hope the technology will be used primarily for beneficial purposes, making ASR technology more accessible could enable more actors to build capable surveillance technologies or scale up existing surveillance efforts, as the speed and accuracy allow for affordable automatic transcription and translation of large volumes of audio communication. Moreover, these models may have some capabilities to recognize specific individuals out of the box, which in turn presents safety concerns related both to dual use and disparate performance. In practice, we expect that the cost of transcription is not the limiting factor of scaling up surveillance projects.
### BibTeX entry and citation info
```bibtex
@misc{radford2022whisper,
doi = {10.48550/ARXIV.2212.04356},
url = {https://arxiv.org/abs/2212.04356},
author = {Radford, Alec and Kim, Jong Wook and Xu, Tao and Brockman, Greg and McLeavey, Christine and Sutskever, Ilya},
title = {Robust Speech Recognition via Large-Scale Weak Supervision},
publisher = {arXiv},
year = {2022},
copyright = {arXiv.org perpetual, non-exclusive license}
}
```
|
chromefan/gmater-poster-style-v1.0
|
chromefan
| 2023-06-19T08:07:38Z | 2 | 4 |
diffusers
|
[
"diffusers",
"stable-diffusion-diffusers",
"text-to-image",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-03-21T11:22:23Z |
---
license: creativeml-openrail-m
base_model: runwayml/stable-diffusion-v1-5.
instance_prompt: >-
<simple poster style>,An animal poster featuring a large elephant created using solid color blocks.
tags:
- stable-diffusion-diffusers
- text-to-image
- diffusers
inference: true
---
Welcome to experience GmasterAI, an exquisite AI drawing application. New users will receive 10 free trial opportunities upon registration.
Website: https://www.gmaster.ai
# DreamBooth - chromefan/gmater-poster-style-v1.0
These are for runwayml/stable-diffusion-v1-5. . The weights were trained on <skybox>,style is generally a realistic hand-drawn effect. using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following.
prompt:<simple poster style>Coffee menu page, featuring a coffee cup in the middle, a silver spoon, and heart-shaped latte art. Text is represented using graphics.
|
silpakanneganti/bert-medical-ner
|
silpakanneganti
| 2023-06-19T07:39:55Z | 19 | 3 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2023-06-14T14:16:42Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-medical-ner
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-medical-ner
This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1905
- Precision: 0.6552
- Recall: 0.6965
- F1: 0.6752
- Accuracy: 0.7449
## 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
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 71 | 1.8255 | 0.3427 | 0.4460 | 0.3876 | 0.5555 |
| No log | 2.0 | 142 | 1.3139 | 0.4722 | 0.5703 | 0.5166 | 0.6442 |
| No log | 3.0 | 213 | 1.1147 | 0.5258 | 0.6029 | 0.5617 | 0.6886 |
| No log | 4.0 | 284 | 0.9873 | 0.5785 | 0.6151 | 0.5962 | 0.7048 |
| No log | 5.0 | 355 | 0.9282 | 0.6314 | 0.6558 | 0.6434 | 0.7312 |
| No log | 6.0 | 426 | 0.8760 | 0.642 | 0.6538 | 0.6478 | 0.7329 |
| No log | 7.0 | 497 | 0.8501 | 0.6608 | 0.6904 | 0.6753 | 0.7466 |
| 1.1706 | 8.0 | 568 | 0.8313 | 0.6791 | 0.7067 | 0.6926 | 0.7483 |
| 1.1706 | 9.0 | 639 | 0.8002 | 0.6616 | 0.7047 | 0.6824 | 0.7449 |
| 1.1706 | 10.0 | 710 | 0.8280 | 0.6640 | 0.6721 | 0.6680 | 0.7363 |
| 1.1706 | 11.0 | 781 | 0.8248 | 0.6594 | 0.6823 | 0.6707 | 0.7457 |
| 1.1706 | 12.0 | 852 | 0.7988 | 0.6610 | 0.7189 | 0.6888 | 0.7654 |
| 1.1706 | 13.0 | 923 | 0.8593 | 0.6587 | 0.6762 | 0.6673 | 0.7423 |
| 1.1706 | 14.0 | 994 | 0.8204 | 0.6719 | 0.6965 | 0.6840 | 0.7534 |
| 0.4317 | 15.0 | 1065 | 0.8478 | 0.6770 | 0.7128 | 0.6944 | 0.7526 |
| 0.4317 | 16.0 | 1136 | 0.8855 | 0.6610 | 0.7149 | 0.6869 | 0.7730 |
| 0.4317 | 17.0 | 1207 | 0.9091 | 0.6751 | 0.7067 | 0.6905 | 0.7560 |
| 0.4317 | 18.0 | 1278 | 0.9201 | 0.6555 | 0.7169 | 0.6848 | 0.7568 |
| 0.4317 | 19.0 | 1349 | 0.9840 | 0.6623 | 0.7189 | 0.6895 | 0.7483 |
| 0.4317 | 20.0 | 1420 | 0.9817 | 0.6833 | 0.7251 | 0.7036 | 0.7543 |
| 0.4317 | 21.0 | 1491 | 0.9958 | 0.6583 | 0.6945 | 0.6759 | 0.7509 |
| 0.2121 | 22.0 | 1562 | 0.9340 | 0.6647 | 0.7026 | 0.6832 | 0.7722 |
| 0.2121 | 23.0 | 1633 | 0.9906 | 0.6622 | 0.7108 | 0.6857 | 0.7619 |
| 0.2121 | 24.0 | 1704 | 1.0099 | 0.6692 | 0.7088 | 0.6884 | 0.7526 |
| 0.2121 | 25.0 | 1775 | 1.0627 | 0.6673 | 0.7189 | 0.6922 | 0.7662 |
| 0.2121 | 26.0 | 1846 | 1.0744 | 0.6584 | 0.7067 | 0.6817 | 0.7637 |
| 0.2121 | 27.0 | 1917 | 1.1328 | 0.6569 | 0.6864 | 0.6713 | 0.7389 |
| 0.2121 | 28.0 | 1988 | 1.0799 | 0.6641 | 0.7128 | 0.6876 | 0.7577 |
| 0.1201 | 29.0 | 2059 | 1.1156 | 0.6628 | 0.7047 | 0.6831 | 0.7568 |
| 0.1201 | 30.0 | 2130 | 1.0839 | 0.6628 | 0.6965 | 0.6792 | 0.75 |
| 0.1201 | 31.0 | 2201 | 1.1511 | 0.6526 | 0.6925 | 0.6719 | 0.7389 |
| 0.1201 | 32.0 | 2272 | 1.1140 | 0.6737 | 0.7149 | 0.6937 | 0.7543 |
| 0.1201 | 33.0 | 2343 | 1.1094 | 0.6609 | 0.6986 | 0.6792 | 0.7466 |
| 0.1201 | 34.0 | 2414 | 1.1332 | 0.6755 | 0.7251 | 0.6994 | 0.7534 |
| 0.1201 | 35.0 | 2485 | 1.1322 | 0.6841 | 0.7189 | 0.7011 | 0.7551 |
| 0.0776 | 36.0 | 2556 | 1.1603 | 0.6711 | 0.7189 | 0.6942 | 0.7551 |
| 0.0776 | 37.0 | 2627 | 1.1460 | 0.6504 | 0.7047 | 0.6764 | 0.7543 |
| 0.0776 | 38.0 | 2698 | 1.1387 | 0.6584 | 0.7067 | 0.6817 | 0.7577 |
| 0.0776 | 39.0 | 2769 | 1.1438 | 0.6641 | 0.7088 | 0.6857 | 0.7534 |
| 0.0776 | 40.0 | 2840 | 1.1791 | 0.6660 | 0.7149 | 0.6896 | 0.7577 |
| 0.0776 | 41.0 | 2911 | 1.1701 | 0.6641 | 0.7088 | 0.6857 | 0.75 |
| 0.0776 | 42.0 | 2982 | 1.1889 | 0.6615 | 0.6965 | 0.6786 | 0.7457 |
| 0.0571 | 43.0 | 3053 | 1.1810 | 0.6533 | 0.6945 | 0.6732 | 0.7449 |
| 0.0571 | 44.0 | 3124 | 1.1944 | 0.6577 | 0.6965 | 0.6766 | 0.7440 |
| 0.0571 | 45.0 | 3195 | 1.2032 | 0.6564 | 0.6925 | 0.6739 | 0.7432 |
| 0.0571 | 46.0 | 3266 | 1.2092 | 0.6609 | 0.6945 | 0.6773 | 0.7449 |
| 0.0571 | 47.0 | 3337 | 1.1864 | 0.6622 | 0.6986 | 0.6799 | 0.7466 |
| 0.0571 | 48.0 | 3408 | 1.1972 | 0.6538 | 0.6925 | 0.6726 | 0.7449 |
| 0.0571 | 49.0 | 3479 | 1.1899 | 0.6545 | 0.6945 | 0.6739 | 0.7449 |
| 0.0467 | 50.0 | 3550 | 1.1905 | 0.6552 | 0.6965 | 0.6752 | 0.7449 |
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3
|
thenewcompany/ppo-LunarLander-v2
|
thenewcompany
| 2023-06-19T06:56:23Z | 1 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-06-05T15:54:51Z |
---
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: 273.76 +/- 24.46
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
...
```
|
NasimB/distilgpt2-dp
|
NasimB
| 2023-06-19T06:52:52Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"generated_from_trainer",
"dataset:generator",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-06-19T03:54:26Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- generator
model-index:
- name: distilgpt2-dp
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilgpt2-dp
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the generator dataset.
It achieves the following results on the evaluation set:
- Loss: 4.3310
## 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: 0.0005
- 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: cosine
- lr_scheduler_warmup_steps: 1000
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 6.7548 | 0.27 | 500 | 5.6494 |
| 5.3558 | 0.53 | 1000 | 5.1949 |
| 4.9963 | 0.8 | 1500 | 4.9488 |
| 4.7463 | 1.07 | 2000 | 4.7983 |
| 4.5597 | 1.34 | 2500 | 4.6942 |
| 4.4758 | 1.6 | 3000 | 4.5956 |
| 4.3994 | 1.87 | 3500 | 4.5174 |
| 4.2405 | 2.14 | 4000 | 4.4796 |
| 4.1533 | 2.4 | 4500 | 4.4339 |
| 4.1326 | 2.67 | 5000 | 4.3886 |
| 4.1027 | 2.94 | 5500 | 4.3479 |
| 3.9207 | 3.21 | 6000 | 4.3582 |
| 3.9044 | 3.47 | 6500 | 4.3315 |
| 3.9109 | 3.74 | 7000 | 4.3016 |
| 3.8938 | 4.01 | 7500 | 4.2895 |
| 3.6736 | 4.27 | 8000 | 4.3069 |
| 3.7072 | 4.54 | 8500 | 4.2876 |
| 3.7151 | 4.81 | 9000 | 4.2656 |
| 3.6379 | 5.07 | 9500 | 4.2858 |
| 3.4849 | 5.34 | 10000 | 4.2893 |
| 3.5254 | 5.61 | 10500 | 4.2735 |
| 3.5283 | 5.88 | 11000 | 4.2570 |
| 3.3946 | 6.14 | 11500 | 4.2931 |
| 3.3098 | 6.41 | 12000 | 4.2970 |
| 3.3318 | 6.68 | 12500 | 4.2866 |
| 3.3499 | 6.94 | 13000 | 4.2735 |
| 3.1802 | 7.21 | 13500 | 4.3088 |
| 3.1565 | 7.48 | 14000 | 4.3098 |
| 3.1709 | 7.75 | 14500 | 4.3050 |
| 3.1565 | 8.01 | 15000 | 4.3090 |
| 3.021 | 8.28 | 15500 | 4.3237 |
| 3.0356 | 8.55 | 16000 | 4.3252 |
| 3.0404 | 8.81 | 16500 | 4.3249 |
| 3.0277 | 9.08 | 17000 | 4.3288 |
| 2.9656 | 9.35 | 17500 | 4.3307 |
| 2.977 | 9.62 | 18000 | 4.3312 |
| 2.9747 | 9.88 | 18500 | 4.3310 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.11.0+cu113
- Datasets 2.13.0
- Tokenizers 0.13.3
|
EarthnDusk/Poltergeist-Illustration
|
EarthnDusk
| 2023-06-19T06:47:45Z | 28 | 1 |
diffusers
|
[
"diffusers",
"safetensors",
"stable diffusion",
"anime",
"comic book",
"mix",
"merge",
"text-to-image",
"en",
"dataset:fka/awesome-chatgpt-prompts",
"dataset:Nerfgun3/bad_prompt",
"dataset:Duskfallcrew/remydataset",
"dataset:basesssssp/bad-kemono-negative-embedding",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-06-19T00:44:23Z |
---
license: creativeml-openrail-m
language:
- en
library_name: diffusers
tags:
- stable diffusion
- diffusers
- anime
- comic book
- mix
- merge
datasets:
- fka/awesome-chatgpt-prompts
- Nerfgun3/bad_prompt
- Duskfallcrew/remydataset
- basesssssp/bad-kemono-negative-embedding
pipeline_tag: text-to-image
---
## POODA-BEEP!
This is censored language for POLTERBITCH aka Poltergeist.
It's an in house nod to some of our alter's truths, and it's kind of a joke for Beetlejuice fans.
THIS IS AN ILLUSTRATION - COMIC MIX and there's several versions of this and you'll note that there is only ONE DEMO SPACE FOR IT ON HF so far - but give us time we're working on it!
---
## We are partly sponsored by Pirate diffusion, and are waiting on links and images.
REQUESTS FOR LORAS AN MERGES: https://forms.gle/tAgb8RsC8mf1scV48
---
## HOW TO SUPPORT US:
Join our Reddit: https://www.reddit.com/r/earthndusk/
If you got requests, or concerns, We're still looking for beta testers: JOIN THE DISCORD AND DEMAND THINGS OF US: https://discord.gg/5t2kYxt7An
Listen to the music that we've made that goes with our art: https://open.spotify.com/playlist/00R8x00YktB4u541imdSSf?si=b60d209385a74b38
We stream a lot of our testing on twitch: https://www.twitch.tv/duskfallcrew
any chance you can spare a coffee or three? https://ko-fi.com/DUSKFALLcrew
If SOMEHOW CIVIT ISN'T WORKING WE WILL ALWAYS HAVE A BACKUP: https://huggingface.co/Duskfallcrew/
Submit SFW and amazing wallpapers at PaprGG: https://discord.gg/2UXkGwndVE
---
## STOP
We used AFTER DETAILER AND HI RES FIX.
Upscaler Choices:
https://huggingface.co/uwg/upscaler/tree/main/ESRGAN
Vae Alternatives:
https://huggingface.co/datasets/VASVASVAS/vae
---
## MIX BREAK DOWN
This is literally as far as I can tell just poodabeep and epic v4 with lora binding.
Because it's a comic book style model, it likely has MANY of Lykon's comic loras in it.
Marvels and Dungeons
Largely lost the OG list for it...
https://civitai.com/models/71404/mcbs-machinecodes-comic-book-style
plus more iComix
and Duel Comic Strike
---
## LEGAL RESPONSIBILITY DOWNSTREAM
You are legally respoonsible for YOUR use of this model and it's downstream uses. We highly request you don't do anything illegal, morally incorrect or anything that goes against the Creative Open-Rail M details.
You are FREE to add this to GENERATION sites as long as you link back to the civit AI page here: https://civitai.com/models/27096/epic-mix-v4
You are WITHIN reason allowed to make commercial use images, but as always check your local laws, and of course - do not use this to create works that denote that it is NOT Ai generative media. If you're working on a larger project please consider supporting us financially.
|
hrkim/beatles_model
|
hrkim
| 2023-06-19T06:42:19Z | 144 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-06-19T05:31:57Z |
---
license: mit
tags:
- generated_from_trainer
model-index:
- name: beatles_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. -->
# beatles_model
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.6944
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 268 | 2.8108 |
| 3.0136 | 2.0 | 536 | 2.7216 |
| 3.0136 | 3.0 | 804 | 2.6944 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.0
- Tokenizers 0.13.3
|
stablediffusionapi/bigger-girls-v35
|
stablediffusionapi
| 2023-06-19T06:30:30Z | 0 | 1 | null |
[
"stablediffusionapi.com",
"stable-diffusion-api",
"text-to-image",
"ultra-realistic",
"license:creativeml-openrail-m",
"region:us"
] |
text-to-image
| 2023-06-19T06:30:10Z |
---
license: creativeml-openrail-m
tags:
- stablediffusionapi.com
- stable-diffusion-api
- text-to-image
- ultra-realistic
pinned: true
---
# Bigger girls v3.5 API Inference
## Get API Key
Get API key from [Stable Diffusion API](http://stablediffusionapi.com/), No Payment needed.
Replace Key in below code, change **model_id** to "bigger-girls-v35"
Coding in PHP/Node/Java etc? Have a look at docs for more code examples: [View docs](https://stablediffusionapi.com/docs)
Try model for free: [Generate Images](https://stablediffusionapi.com/models/bigger-girls-v35)
Model link: [View model](https://stablediffusionapi.com/models/bigger-girls-v35)
Credits: [View credits](https://civitai.com/?query=Bigger%20girls%20v3.5)
View all models: [View Models](https://stablediffusionapi.com/models)
import requests
import json
url = "https://stablediffusionapi.com/api/v3/dreambooth"
payload = json.dumps({
"key": "your_api_key",
"model_id": "bigger-girls-v35",
"prompt": "ultra realistic close up portrait ((beautiful pale cyberpunk female with heavy black eyeliner)), blue eyes, shaved side haircut, hyper detail, cinematic lighting, magic neon, dark red city, Canon EOS R3, nikon, f/1.4, ISO 200, 1/160s, 8K, RAW, unedited, symmetrical balance, in-frame, 8K",
"negative_prompt": "painting, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, deformed, ugly, blurry, bad anatomy, bad proportions, extra limbs, cloned face, skinny, glitchy, double torso, extra arms, extra hands, mangled fingers, missing lips, ugly face, distorted face, extra legs, anime",
"width": "512",
"height": "512",
"samples": "1",
"num_inference_steps": "30",
"safety_checker": "no",
"enhance_prompt": "yes",
"seed": None,
"guidance_scale": 7.5,
"multi_lingual": "no",
"panorama": "no",
"self_attention": "no",
"upscale": "no",
"embeddings": "embeddings_model_id",
"lora": "lora_model_id",
"webhook": None,
"track_id": None
})
headers = {
'Content-Type': 'application/json'
}
response = requests.request("POST", url, headers=headers, data=payload)
print(response.text)
> Use this coupon code to get 25% off **DMGG0RBN**
|
giantsol/hansol_10_512_lora
|
giantsol
| 2023-06-19T06:28:20Z | 0 | 0 |
diffusers
|
[
"diffusers",
"tensorboard",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"lora",
"base_model:runwayml/stable-diffusion-v1-5",
"base_model:adapter:runwayml/stable-diffusion-v1-5",
"license:creativeml-openrail-m",
"region:us"
] |
text-to-image
| 2023-06-19T06:21:34Z |
---
license: creativeml-openrail-m
base_model: runwayml/stable-diffusion-v1-5
instance_prompt: lvl person
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- lora
inference: true
---
# LoRA DreamBooth - giantsol/hansol_10_512_lora
These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were trained on lvl person using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following.
LoRA for the text encoder was enabled: True.
|
2022happy/swin-tiny-patch4-window7-224-pruned-0.6-finetuned-eurosat
|
2022happy
| 2023-06-19T06:26:31Z | 244 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"swin",
"image-classification",
"generated_from_trainer",
"dataset:cifar10",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-06-19T05:50:57Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- cifar10
metrics:
- accuracy
model-index:
- name: swin-tiny-patch4-window7-224-pruned-0.6-finetuned-eurosat
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: cifar10
type: cifar10
config: plain_text
split: train
args: plain_text
metrics:
- name: Accuracy
type: accuracy
value: 0.4366
---
<!-- 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. -->
# swin-tiny-patch4-window7-224-pruned-0.6-finetuned-eurosat
This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the cifar10 dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5464
- Accuracy: 0.4366
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.8869 | 1.0 | 351 | 1.8010 | 0.3384 |
| 1.6985 | 2.0 | 703 | 1.6406 | 0.3876 |
| 1.6487 | 2.99 | 1053 | 1.5464 | 0.4366 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.0
- Tokenizers 0.13.3
|
twidfeel/pegasus-samsum
|
twidfeel
| 2023-06-19T06:12:59Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"pegasus",
"text2text-generation",
"generated_from_trainer",
"dataset:samsum",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-06-19T05:24:52Z |
---
tags:
- generated_from_trainer
datasets:
- samsum
model-index:
- name: pegasus-samsum
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. -->
# pegasus-samsum
This model is a fine-tuned version of [google/pegasus-cnn_dailymail](https://huggingface.co/google/pegasus-cnn_dailymail) on the samsum dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4835
## 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: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.6857 | 0.54 | 500 | 1.4835 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.0
- Tokenizers 0.13.3
|
gurugaurav/lilt-en-funsd
|
gurugaurav
| 2023-06-19T06:08:05Z | 104 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"lilt",
"token-classification",
"generated_from_trainer",
"dataset:funsd-layoutlmv3",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2023-06-19T05:26:52Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- funsd-layoutlmv3
model-index:
- name: lilt-en-funsd
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. -->
# lilt-en-funsd
This model is a fine-tuned version of [SCUT-DLVCLab/lilt-roberta-en-base](https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base) on the funsd-layoutlmv3 dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5496
- Answer: {'precision': 0.875, 'recall': 0.9253365973072215, 'f1': 0.8994646044021416, 'number': 817}
- Header: {'precision': 0.6276595744680851, 'recall': 0.4957983193277311, 'f1': 0.5539906103286385, 'number': 119}
- Question: {'precision': 0.9049360146252285, 'recall': 0.9192200557103064, 'f1': 0.9120221096269001, 'number': 1077}
- Overall Precision: 0.8796
- Overall Recall: 0.8967
- Overall F1: 0.8881
- Overall Accuracy: 0.8134
## 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
- training_steps: 2500
### Training results
| Training Loss | Epoch | Step | Validation Loss | Answer | Header | Question | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 0.434 | 10.53 | 200 | 1.0227 | {'precision': 0.8357705286839145, 'recall': 0.9094247246022031, 'f1': 0.8710433763188746, 'number': 817} | {'precision': 0.7058823529411765, 'recall': 0.40336134453781514, 'f1': 0.5133689839572192, 'number': 119} | {'precision': 0.8683522231909329, 'recall': 0.924791086350975, 'f1': 0.89568345323741, 'number': 1077} | 0.8493 | 0.8877 | 0.8681 | 0.7935 |
| 0.0484 | 21.05 | 400 | 1.3626 | {'precision': 0.8098360655737705, 'recall': 0.9069767441860465, 'f1': 0.8556581986143187, 'number': 817} | {'precision': 0.6086956521739131, 'recall': 0.47058823529411764, 'f1': 0.5308056872037914, 'number': 119} | {'precision': 0.8613333333333333, 'recall': 0.8997214484679665, 'f1': 0.8801089918256131, 'number': 1077} | 0.8283 | 0.8773 | 0.8521 | 0.7995 |
| 0.0168 | 31.58 | 600 | 1.3003 | {'precision': 0.8440046565774156, 'recall': 0.8873929008567931, 'f1': 0.8651551312649164, 'number': 817} | {'precision': 0.6421052631578947, 'recall': 0.5126050420168067, 'f1': 0.5700934579439252, 'number': 119} | {'precision': 0.8776595744680851, 'recall': 0.9192200557103064, 'f1': 0.8979591836734694, 'number': 1077} | 0.8530 | 0.8823 | 0.8674 | 0.8189 |
| 0.008 | 42.11 | 800 | 1.3225 | {'precision': 0.8584795321637427, 'recall': 0.8984088127294981, 'f1': 0.8779904306220095, 'number': 817} | {'precision': 0.5736434108527132, 'recall': 0.6218487394957983, 'f1': 0.596774193548387, 'number': 119} | {'precision': 0.888468809073724, 'recall': 0.872794800371402, 'f1': 0.8805620608899298, 'number': 1077} | 0.8560 | 0.8684 | 0.8621 | 0.8210 |
| 0.0059 | 52.63 | 1000 | 1.6362 | {'precision': 0.8307522123893806, 'recall': 0.9192166462668299, 'f1': 0.8727484020918072, 'number': 817} | {'precision': 0.6419753086419753, 'recall': 0.4369747899159664, 'f1': 0.52, 'number': 119} | {'precision': 0.8944444444444445, 'recall': 0.8969359331476323, 'f1': 0.8956884561891516, 'number': 1077} | 0.8567 | 0.8788 | 0.8676 | 0.8061 |
| 0.0027 | 63.16 | 1200 | 1.6927 | {'precision': 0.8269858541893362, 'recall': 0.9302325581395349, 'f1': 0.8755760368663594, 'number': 817} | {'precision': 0.6046511627906976, 'recall': 0.4369747899159664, 'f1': 0.5073170731707317, 'number': 119} | {'precision': 0.9000925069380203, 'recall': 0.903435468895079, 'f1': 0.901760889712697, 'number': 1077} | 0.8557 | 0.8867 | 0.8709 | 0.7939 |
| 0.002 | 73.68 | 1400 | 1.4609 | {'precision': 0.8479467258601554, 'recall': 0.9351285189718482, 'f1': 0.889406286379511, 'number': 817} | {'precision': 0.5726495726495726, 'recall': 0.5630252100840336, 'f1': 0.5677966101694915, 'number': 119} | {'precision': 0.8917431192660551, 'recall': 0.9025069637883009, 'f1': 0.8970927549607752, 'number': 1077} | 0.8553 | 0.8957 | 0.8750 | 0.7965 |
| 0.0012 | 84.21 | 1600 | 1.4851 | {'precision': 0.865909090909091, 'recall': 0.9326805385556916, 'f1': 0.8980553918680023, 'number': 817} | {'precision': 0.6074766355140186, 'recall': 0.5462184873949579, 'f1': 0.575221238938053, 'number': 119} | {'precision': 0.9008341056533827, 'recall': 0.9025069637883009, 'f1': 0.901669758812616, 'number': 1077} | 0.8708 | 0.8937 | 0.8821 | 0.8131 |
| 0.0006 | 94.74 | 1800 | 1.5228 | {'precision': 0.850613154960981, 'recall': 0.9339045287637698, 'f1': 0.8903150525087514, 'number': 817} | {'precision': 0.594059405940594, 'recall': 0.5042016806722689, 'f1': 0.5454545454545453, 'number': 119} | {'precision': 0.896709323583181, 'recall': 0.9108635097493036, 'f1': 0.9037309995393827, 'number': 1077} | 0.8623 | 0.8962 | 0.8789 | 0.8082 |
| 0.0004 | 105.26 | 2000 | 1.5287 | {'precision': 0.867579908675799, 'recall': 0.9302325581395349, 'f1': 0.8978145304193739, 'number': 817} | {'precision': 0.6222222222222222, 'recall': 0.47058823529411764, 'f1': 0.5358851674641149, 'number': 119} | {'precision': 0.8917710196779964, 'recall': 0.9257195914577531, 'f1': 0.9084282460136676, 'number': 1077} | 0.8700 | 0.9006 | 0.8850 | 0.8128 |
| 0.0003 | 115.79 | 2200 | 1.5306 | {'precision': 0.8766006984866124, 'recall': 0.9216646266829865, 'f1': 0.8985680190930787, 'number': 817} | {'precision': 0.6263736263736264, 'recall': 0.4789915966386555, 'f1': 0.5428571428571428, 'number': 119} | {'precision': 0.8902765388046388, 'recall': 0.9266480965645311, 'f1': 0.908098271155596, 'number': 1077} | 0.8730 | 0.8982 | 0.8854 | 0.8127 |
| 0.0001 | 126.32 | 2400 | 1.5496 | {'precision': 0.875, 'recall': 0.9253365973072215, 'f1': 0.8994646044021416, 'number': 817} | {'precision': 0.6276595744680851, 'recall': 0.4957983193277311, 'f1': 0.5539906103286385, 'number': 119} | {'precision': 0.9049360146252285, 'recall': 0.9192200557103064, 'f1': 0.9120221096269001, 'number': 1077} | 0.8796 | 0.8967 | 0.8881 | 0.8134 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.0
- Tokenizers 0.13.3
|
jjhonny/poca-SoccerTwos
|
jjhonny
| 2023-06-19T05:56:45Z | 164 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"SoccerTwos",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SoccerTwos",
"region:us"
] |
reinforcement-learning
| 2023-06-19T05:53:31Z |
---
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: jjhonny/poca-SoccerTwos
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
limcheekin/mpt-7b-instruct-ct2
|
limcheekin
| 2023-06-19T05:47:17Z | 7 | 0 |
transformers
|
[
"transformers",
"ctranslate2",
"mpt-7b-instruct",
"quantization",
"int8",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2023-06-07T03:07:26Z |
---
license: apache-2.0
language:
- en
tags:
- ctranslate2
- mpt-7b-instruct
- quantization
- int8
---
# MPT-7B-Instruct Q8
The model is quantized version of the [mosaicml/mpt-7b-instruct](https://huggingface.co/mosaicml/mpt-7b-instruct) with int8 quantization.
## Model Details
### Model Description
The model being quantized using [CTranslate2](https://opennmt.net/CTranslate2/) with the following command:
```
ct2-transformers-converter --model mosaicml/mpt-7b-instruct --output_dir mosaicml/mpt-7b-instruct-ct2 --copy_files tokenizer.json tokenizer_config.json special_tokens_map.json generation_config.json --quantization int8 --force --low_cpu_mem_usage --trust_remote_code
```
If you want to perform the quantization yourself, you need to install the following dependencies:
```
pip install -qU ctranslate2 transformers[torch] accelerate einops
```
- **Shared by:** Lim Chee Kin
- **License:** Apache 2.0
## How to Get Started with the Model
Use the code below to get started with the model.
```python
import ctranslate2
import transformers
generator = ctranslate2.Generator("limcheekin/mpt-7b-instruct-ct2")
tokenizer = transformers.AutoTokenizer.from_pretrained("EleutherAI/gpt-neox-20b")
prompt = "Long long time ago, "
tokens = tokenizer.convert_ids_to_tokens(tokenizer.encode(prompt))
results = generator.generate_batch([tokens], max_length=256, sampling_topk=10)
text = tokenizer.decode(results[0].sequences_ids[0])
```
The code is taken from https://opennmt.net/CTranslate2/guides/transformers.html#mpt.
The key method of the code above is `generate_batch`, you can find out [its supported parameters here](https://opennmt.net/CTranslate2/python/ctranslate2.Generator.html#ctranslate2.Generator.generate_batch).
|
arunptp/taxi-v3-00
|
arunptp
| 2023-06-19T05:37:04Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-06-19T05:36:56Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: taxi-v3-00
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.52 +/- 2.73
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="arunptp/taxi-v3-00", 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"])
```
|
2022happy/swin-tiny-patch4-window7-224-pruned-0.4-finetuned-eurosat
|
2022happy
| 2023-06-19T05:14:51Z | 245 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"swin",
"image-classification",
"generated_from_trainer",
"dataset:cifar10",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-06-19T04:39:20Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- cifar10
metrics:
- accuracy
model-index:
- name: swin-tiny-patch4-window7-224-pruned-0.4-finetuned-eurosat
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: cifar10
type: cifar10
config: plain_text
split: train
args: plain_text
metrics:
- name: Accuracy
type: accuracy
value: 0.6266
---
<!-- 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. -->
# swin-tiny-patch4-window7-224-pruned-0.4-finetuned-eurosat
This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the cifar10 dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0525
- Accuracy: 0.6266
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.6059 | 1.0 | 351 | 1.4089 | 0.4934 |
| 1.3254 | 2.0 | 703 | 1.1709 | 0.593 |
| 1.2453 | 2.99 | 1053 | 1.0525 | 0.6266 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.0
- Tokenizers 0.13.3
|
hrkim/my_awesome_eli5_clm-model
|
hrkim
| 2023-06-19T05:03:32Z | 215 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-06-19T04:47:07Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: my_awesome_eli5_clm-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_eli5_clm-model
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.7608
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 3.8731 | 1.0 | 1104 | 3.7747 |
| 3.78 | 2.0 | 2208 | 3.7623 |
| 3.7396 | 3.0 | 3312 | 3.7608 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.0
- Tokenizers 0.13.3
|
2022happy/swin-tiny-patch4-window7-224-pruned-0.3-finetuned-eurosat
|
2022happy
| 2023-06-19T04:39:06Z | 244 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"swin",
"image-classification",
"generated_from_trainer",
"dataset:cifar10",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-06-19T04:03:13Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- cifar10
metrics:
- accuracy
model-index:
- name: swin-tiny-patch4-window7-224-pruned-0.3-finetuned-eurosat
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: cifar10
type: cifar10
config: plain_text
split: train
args: plain_text
metrics:
- name: Accuracy
type: accuracy
value: 0.815
---
<!-- 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. -->
# swin-tiny-patch4-window7-224-pruned-0.3-finetuned-eurosat
This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the cifar10 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5344
- Accuracy: 0.815
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.3059 | 1.0 | 351 | 0.9793 | 0.6582 |
| 0.9568 | 2.0 | 703 | 0.6321 | 0.7836 |
| 0.8607 | 2.99 | 1053 | 0.5344 | 0.815 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.0
- Tokenizers 0.13.3
|
NasimB/gpt2_left_out_children_stories
|
NasimB
| 2023-06-19T04:29:33Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"generated_from_trainer",
"dataset:generator",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-06-19T00:02:41Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- generator
model-index:
- name: gpt2_left_out_children_stories
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. -->
# gpt2_left_out_children_stories
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the generator dataset.
It achieves the following results on the evaluation set:
- Loss: 3.9100
## 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: 0.0005
- 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: cosine
- lr_scheduler_warmup_steps: 1000
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 5.9591 | 0.25 | 500 | 5.0479 |
| 4.7279 | 0.49 | 1000 | 4.6585 |
| 4.4359 | 0.74 | 1500 | 4.4292 |
| 4.2425 | 0.99 | 2000 | 4.2868 |
| 4.033 | 1.24 | 2500 | 4.1992 |
| 3.9641 | 1.48 | 3000 | 4.1132 |
| 3.8924 | 1.73 | 3500 | 4.0414 |
| 3.8217 | 1.98 | 4000 | 3.9709 |
| 3.637 | 2.22 | 4500 | 3.9537 |
| 3.6122 | 2.47 | 5000 | 3.9079 |
| 3.607 | 2.72 | 5500 | 3.8681 |
| 3.5795 | 2.96 | 6000 | 3.8275 |
| 3.3865 | 3.21 | 6500 | 3.8409 |
| 3.3863 | 3.46 | 7000 | 3.8184 |
| 3.3855 | 3.71 | 7500 | 3.7940 |
| 3.3706 | 3.95 | 8000 | 3.7687 |
| 3.1824 | 4.2 | 8500 | 3.8055 |
| 3.169 | 4.45 | 9000 | 3.7896 |
| 3.182 | 4.69 | 9500 | 3.7696 |
| 3.1913 | 4.94 | 10000 | 3.7502 |
| 2.9852 | 5.19 | 10500 | 3.7978 |
| 2.9624 | 5.43 | 11000 | 3.7932 |
| 2.9763 | 5.68 | 11500 | 3.7807 |
| 2.9833 | 5.93 | 12000 | 3.7653 |
| 2.7879 | 6.18 | 12500 | 3.8182 |
| 2.742 | 6.42 | 13000 | 3.8233 |
| 2.7644 | 6.67 | 13500 | 3.8171 |
| 2.7652 | 6.92 | 14000 | 3.8087 |
| 2.6062 | 7.16 | 14500 | 3.8516 |
| 2.5539 | 7.41 | 15000 | 3.8602 |
| 2.5546 | 7.66 | 15500 | 3.8614 |
| 2.5663 | 7.91 | 16000 | 3.8598 |
| 2.4621 | 8.15 | 16500 | 3.8868 |
| 2.3987 | 8.4 | 17000 | 3.8939 |
| 2.4081 | 8.65 | 17500 | 3.8966 |
| 2.4097 | 8.89 | 18000 | 3.8973 |
| 2.3573 | 9.14 | 18500 | 3.9061 |
| 2.3304 | 9.39 | 19000 | 3.9089 |
| 2.3266 | 9.63 | 19500 | 3.9098 |
| 2.3276 | 9.88 | 20000 | 3.9100 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.11.0+cu113
- Datasets 2.13.0
- Tokenizers 0.13.3
|
yewsam1277/question-answering-bahasa-malaysia
|
yewsam1277
| 2023-06-19T04:21:34Z | 3 | 0 |
transformers
|
[
"transformers",
"distilbert",
"question-answering",
"ms",
"dataset:wikipedia",
"dataset:squad_v2",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2023-06-18T15:34:46Z |
---
license: apache-2.0
language:
- ms
pipeline_tag: question-answering
datasets:
- wikipedia
- squad_v2
---
|
2022happy/swin-tiny-patch4-window7-224-pruned-0.2-finetuned-eurosat
|
2022happy
| 2023-06-19T04:02:55Z | 244 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"swin",
"image-classification",
"generated_from_trainer",
"dataset:cifar10",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-06-19T03:26:10Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- cifar10
metrics:
- accuracy
model-index:
- name: swin-tiny-patch4-window7-224-pruned-0.2-finetuned-eurosat
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: cifar10
type: cifar10
config: plain_text
split: train
args: plain_text
metrics:
- name: Accuracy
type: accuracy
value: 0.9244
---
<!-- 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. -->
# swin-tiny-patch4-window7-224-pruned-0.2-finetuned-eurosat
This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the cifar10 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2221
- Accuracy: 0.9244
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.8766 | 1.0 | 351 | 0.4224 | 0.8602 |
| 0.6406 | 2.0 | 703 | 0.2612 | 0.913 |
| 0.5483 | 2.99 | 1053 | 0.2221 | 0.9244 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.0
- Tokenizers 0.13.3
|
twidfeel/xlm-roberta-base-finetuned-panx-it
|
twidfeel
| 2023-06-19T04:00:08Z | 104 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2023-06-19T03:57:10Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-it
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xtreme
type: xtreme
config: PAN-X.it
split: validation
args: PAN-X.it
metrics:
- name: F1
type: f1
value: 0.8314238952536824
---
<!-- 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-it
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: 0.2442
- F1: 0.8314
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.818 | 1.0 | 70 | 0.3119 | 0.7525 |
| 0.267 | 2.0 | 140 | 0.2669 | 0.7985 |
| 0.179 | 3.0 | 210 | 0.2442 | 0.8314 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.0
- Tokenizers 0.13.3
|
levheitz/luezeq
|
levheitz
| 2023-06-19T03:56:57Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-06-19T03:54:21Z |
---
license: creativeml-openrail-m
---
|
timjwhite/Pyramids
|
timjwhite
| 2023-06-19T03:46:20Z | 1 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"Pyramids",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
] |
reinforcement-learning
| 2023-06-19T03:46:11Z |
---
library_name: ml-agents
tags:
- Pyramids
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Pyramids
---
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids**
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: timjwhite/Pyramids
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
twidfeel/xlm-roberta-base-finetuned-panx-de-fr
|
twidfeel
| 2023-06-19T03:44:57Z | 126 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2023-06-19T03:35:55Z |
---
license: mit
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-de-fr
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-de-fr
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1656
- F1: 0.8573
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2951 | 1.0 | 715 | 0.1786 | 0.8312 |
| 0.1477 | 2.0 | 1430 | 0.1652 | 0.8420 |
| 0.0951 | 3.0 | 2145 | 0.1656 | 0.8573 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.0
- Tokenizers 0.13.3
|
twidfeel/xlm-roberta-base-finetuned-panx-de
|
twidfeel
| 2023-06-19T03:23:34Z | 103 | 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
| 2023-06-19T03:13:47Z |
---
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
config: PAN-X.de
split: validation
args: PAN-X.de
metrics:
- name: F1
type: f1
value: 0.8608314849570609
---
<!-- 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: 0.1378
- F1: 0.8608
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.258 | 1.0 | 525 | 0.1619 | 0.8168 |
| 0.1295 | 2.0 | 1050 | 0.1357 | 0.8468 |
| 0.0827 | 3.0 | 1575 | 0.1378 | 0.8608 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.0
- Tokenizers 0.13.3
|
nolanaatama/nvrndngdrm
|
nolanaatama
| 2023-06-19T02:50:39Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-02-23T03:01:38Z |
---
license: creativeml-openrail-m
---
|
TheFools/Celinne
|
TheFools
| 2023-06-19T02:50:34Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-06-19T02:48:28Z |
---
license: creativeml-openrail-m
---
|
openaccess-ai-collective/minotaur-15b
|
openaccess-ai-collective
| 2023-06-19T02:41:57Z | 9 | 15 |
transformers
|
[
"transformers",
"pytorch",
"gpt_bigcode",
"text-generation",
"code",
"dataset:bigcode/the-stack-dedup",
"dataset:tiiuae/falcon-refinedweb",
"dataset:ehartford/WizardLM_alpaca_evol_instruct_70k_unfiltered",
"dataset:QingyiSi/Alpaca-CoT",
"dataset:teknium/GPTeacher-General-Instruct",
"dataset:metaeval/ScienceQA_text_only",
"dataset:hellaswag",
"dataset:openai/summarize_from_feedback",
"dataset:riddle_sense",
"dataset:gsm8k",
"dataset:camel-ai/math",
"dataset:camel-ai/biology",
"dataset:camel-ai/physics",
"dataset:camel-ai/chemistry",
"dataset:winglian/evals",
"arxiv:1911.02150",
"arxiv:2205.14135",
"arxiv:2207.14255",
"arxiv:2305.06161",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-06-16T17:19:19Z |
---
pipeline_tag: text-generation
inference: true
widget:
- text: 'def print_hello_world():'
example_title: Hello world
group: Python
- text: 'Gradient descent is'
example_title: Machine Learning
group: English
- license: bigcode-openrail-m
datasets:
- bigcode/the-stack-dedup
- tiiuae/falcon-refinedweb
- ehartford/WizardLM_alpaca_evol_instruct_70k_unfiltered
- QingyiSi/Alpaca-CoT
- teknium/GPTeacher-General-Instruct
- metaeval/ScienceQA_text_only
- hellaswag
- openai/summarize_from_feedback
- riddle_sense
- gsm8k
- camel-ai/math
- camel-ai/biology
- camel-ai/physics
- camel-ai/chemistry
- winglian/evals
metrics:
- code_eval
- mmlu
- arc
- hellaswag
- truthfulqa
library_name: transformers
tags:
- code
extra_gated_prompt: >-
## Model License Agreement
Please read the BigCode [OpenRAIL-M
license](https://huggingface.co/spaces/bigcode/bigcode-model-license-agreement)
agreement before accepting it.
extra_gated_fields:
I accept the above license agreement, and will use the Model complying with the set of use restrictions and sharing requirements: checkbox
---
[<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)
**[💵 Donate to OpenAccess AI Collective](https://github.com/sponsors/OpenAccess-AI-Collective) to help us keep building great tools and models!**
# Minotaur 15B 8K
Minotaur 15B is an instruct fine-tuned model on top of Starcoder Plus. Minotaur 15B is fine-tuned **on only completely open datasets** making this model reproducible by anyone.
Minotaur 15B has a context length of 8K tokens, allowing for strong recall at long contexts.
Questions, comments, feedback, looking to donate, or want to help? Reach out on our [Discord](https://discord.gg/PugNNHAF5r) or email [wing@openaccessaicollective.org](mailto:wing@openaccessaicollective.org)
# Prompts
Chat only style prompts using `USER:`,`ASSISTANT:`.
<img src="https://huggingface.co/openaccess-ai-collective/minotaur-13b/resolve/main/minotaur.png" alt="minotaur" width="600" height="600"/>
# Training Datasets
Minotaur 15B model is fine-tuned on the following openly available datasets:
- [WizardLM](https://huggingface.co/datasets/ehartford/WizardLM_alpaca_evol_instruct_70k_unfiltered)
- [subset of QingyiSi/Alpaca-CoT for roleplay and CoT](https://huggingface.co/QingyiSi/Alpaca-CoT)
- [GPTeacher-General-Instruct](https://huggingface.co/datasets/teknium/GPTeacher-General-Instruct)
- [metaeval/ScienceQA_text_only](https://huggingface.co/datasets/metaeval/ScienceQA_text_only) - instruct for concise responses
- [openai/summarize_from_feedback](https://huggingface.co/datasets/openai/summarize_from_feedback) - instruct augmented tl;dr summarization
- [camel-ai/math](https://huggingface.co/datasets/camel-ai/math)
- [camel-ai/physics](https://huggingface.co/datasets/camel-ai/physics)
- [camel-ai/chemistry](https://huggingface.co/datasets/camel-ai/chemistry)
- [camel-ai/biology](https://huggingface.co/datasets/camel-ai/biology)
- [winglian/evals](https://huggingface.co/datasets/winglian/evals) - instruct augmented datasets
- custom sysnthetic datasets around misconceptions, in-context qa, jokes, N-tasks problems, and context-insensitivity
- ARC-Easy & ARC-Challenge - instruct augmented for detailed responses, derived from the `train` split
- [hellaswag](https://huggingface.co/datasets/hellaswag) - 30K+ rows of instruct augmented for detailed explanations w 30K+ rows, derived from the `train` split
- [riddle_sense](https://huggingface.co/datasets/riddle_sense) - instruct augmented, derived from the `train` split
- [gsm8k](https://huggingface.co/datasets/gsm8k) - instruct augmented, derived from the `train` split
- prose generation
# Shoutouts
Special thanks to Nanobit for helping with Axolotl and TheBloke for quantizing these models are more accessible to all.
# Demo
HF Demo in Spaces available in the [Community ChatBot Arena](https://huggingface.co/spaces/openaccess-ai-collective/rlhf-arena) under the OAAIC Chatbots tab.
## Release Notes
- https://wandb.ai/wing-lian/minotaur-16b-8k/runs/tshgbl2k
## Build
Minotaur was built with [Axolotl](https://github.com/OpenAccess-AI-Collective/axolotl) on 4XA100 80GB
- 1 epochs taking approximately 30 hours
- Trained using QLoRA techniques
## Bias, Risks, and Limitations
Minotaur has not been aligned to human preferences with techniques like RLHF or deployed with in-the-loop filtering of responses like ChatGPT, so the model can produce problematic outputs (especially when prompted to do so).
Minotaur was fine-tuned from the base model StarCoder, please refer to its model card's Limitations Section for relevant information. (included below)
## Benchmarks
TBD
## Examples
TBD
# StarCoderPlus
Play with the instruction-tuned StarCoderPlus at [StarChat-Beta](https://huggingface.co/spaces/HuggingFaceH4/starchat-playground).
## Table of Contents
1. [Model Summary](##model-summary)
2. [Use](##use)
3. [Limitations](##limitations)
4. [Training](##training)
5. [License](##license)
6. [Citation](##citation)
## Model Summary
StarCoderPlus is a fine-tuned version of [StarCoderBase](https://huggingface.co/bigcode/starcoderbase) on 600B tokens from the English web dataset [RedefinedWeb](https://huggingface.co/datasets/tiiuae/falcon-refinedweb)
combined with [StarCoderData](https://huggingface.co/datasets/bigcode/starcoderdata) from [The Stack (v1.2)](https://huggingface.co/datasets/bigcode/the-stack) and a Wikipedia dataset.
It's a 15.5B parameter Language Model trained on English and 80+ programming languages. The model uses [Multi Query Attention](https://arxiv.org/abs/1911.02150),
[a context window of 8192 tokens](https://arxiv.org/abs/2205.14135), and was trained using the [Fill-in-the-Middle objective](https://arxiv.org/abs/2207.14255) on 1.6 trillion tokens.
- **Repository:** [bigcode/Megatron-LM](https://github.com/bigcode-project/Megatron-LM)
- **Project Website:** [bigcode-project.org](https://www.bigcode-project.org)
- **Point of Contact:** [contact@bigcode-project.org](mailto:contact@bigcode-project.org)
- **Languages:** English & 80+ Programming languages
## Use
### Intended use
The model was trained on English and GitHub code. As such it is _not_ an instruction model and commands like "Write a function that computes the square root." do not work well. However, the instruction-tuned version in [StarChat](hhttps://huggingface.co/spaces/HuggingFaceH4/starchat-playground) makes a capable assistant.
**Feel free to share your generations in the Community tab!**
### Generation
```python
# pip install -q transformers
from transformers import AutoModelForCausalLM, AutoTokenizer
checkpoint = "bigcode/starcoderplus"
device = "cuda" # for GPU usage or "cpu" for CPU usage
tokenizer = AutoTokenizer.from_pretrained(checkpoint)
model = AutoModelForCausalLM.from_pretrained(checkpoint).to(device)
inputs = tokenizer.encode("def print_hello_world():", return_tensors="pt").to(device)
outputs = model.generate(inputs)
print(tokenizer.decode(outputs[0]))
```
### Fill-in-the-middle
Fill-in-the-middle uses special tokens to identify the prefix/middle/suffix part of the input and output:
```python
input_text = "<fim_prefix>def print_hello_world():\n <fim_suffix>\n print('Hello world!')<fim_middle>"
inputs = tokenizer.encode(input_text, return_tensors="pt").to(device)
outputs = model.generate(inputs)
print(tokenizer.decode(outputs[0]))
```
### Attribution & Other Requirements
The training code dataset of the model was filtered for permissive licenses only. Nevertheless, the model can generate source code verbatim from the dataset. The code's license might require attribution and/or other specific requirements that must be respected. We provide a [search index](https://huggingface.co/spaces/bigcode/starcoder-search) that let's you search through the pretraining data to identify where generated code came from and apply the proper attribution to your code.
# Limitations
The model has been trained on a mixture of English text from the web and GitHub code. Therefore it might encounter limitations when working with non-English text, and can carry the stereotypes and biases commonly encountered online.
Additionally, the generated code should be used with caution as it may contain errors, inefficiencies, or potential vulnerabilities. For a more comprehensive understanding of the base model's code limitations, please refer to See [StarCoder paper](hhttps://arxiv.org/abs/2305.06161).
# Training
StarCoderPlus is a fine-tuned version on 600B English and code tokens of StarCoderBase, which was pre-trained on 1T code tokens. Below are the fine-tuning details:
## Model
- **Architecture:** GPT-2 model with multi-query attention and Fill-in-the-Middle objective
- **Finetuning steps:** 150k
- **Finetuning tokens:** 600B
- **Precision:** bfloat16
## Hardware
- **GPUs:** 512 Tesla A100
- **Training time:** 14 days
## Software
- **Orchestration:** [Megatron-LM](https://github.com/bigcode-project/Megatron-LM)
- **Neural networks:** [PyTorch](https://github.com/pytorch/pytorch)
- **BP16 if applicable:** [apex](https://github.com/NVIDIA/apex)
# License
The model is licensed under the BigCode OpenRAIL-M v1 license agreement. You can find the full agreement [here](https://huggingface.co/spaces/bigcode/bigcode-model-license-agreement).
|
andreac94/finetuning-sentiment-model-amazonbaby5000
|
andreac94
| 2023-06-19T01:35:03Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-06-19T01:03:38Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: finetuning-sentiment-model-amazonbaby5000
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. -->
# finetuning-sentiment-model-amazonbaby5000
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2733
- Accuracy: 0.9024
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.0
- Tokenizers 0.13.3
|
ponponnsan/sakura-CALM
|
ponponnsan
| 2023-06-19T00:36:49Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-06-18T13:49:01Z |
LoRAを用いたファインチューニングにsakura-datasetを加えたモデル。あまりうまくいっていない気がする。。。
|
timjwhite/Reinforce-Pixelcopter-PLE-v0
|
timjwhite
| 2023-06-19T00:27:45Z | 0 | 0 | null |
[
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-06-18T04:07:28Z |
---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-Pixelcopter-PLE-v0
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 26.90 +/- 16.23
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
|
lewdryuna/A-BmixB
|
lewdryuna
| 2023-06-19T00:01:50Z | 0 | 3 | null |
[
"region:us"
] | null | 2023-06-19T00:01:47Z |
---
duplicated_from: malikxseto/Necromancing-BBMixes
---
# BBMixes Backup
Backup of different BBMixes
## BBMIX-ALICE
- **[BB-Mix-ALICE_V1.0](https://huggingface.co/malikxseto/Necromancing-BBMixes/resolve/main/BBMIX-ALICE/bbMIXALICE_v10.safetensors)**
## BBMIX ANN
- **[BB-Mix-ANN_V1.0](https://huggingface.co/malikxseto/Necromancing-BBMixes/resolve/main/BBMIX-ANN/bbmixANN_v10.safetensors)**
## BBMIX EIMI
- **[BB-Mix-EIMI_V1.0](https://huggingface.co/malikxseto/Necromancing-BBMixes/resolve/main/BBMIX-EIMI/bbmixEIMI_v10.safetensors)**
## BBMIX EVE
- **[BB-Mix-EVE_V1.0](https://huggingface.co/malikxseto/Necromancing-BBMixes/resolve/main/BBMIX-EVE/bbmixEVE_v10.safetensors)**
## BBMIX HANNA
- **[BB-Mix-HANNA_V1.0](https://huggingface.co/malikxseto/Necromancing-BBMixes/resolve/main/BBMIX-HANNA/bbmixHANNA_v10.safetensors)**
## BBMIX JOY
- **[BB-Mix-JOY_V1.0](https://huggingface.co/malikxseto/Necromancing-BBMixes/resolve/main/BBMIX-JOY/bbmixJOY_v10.safetensors)**
- **[BB-Mix-JOY_V2.0](https://huggingface.co/malikxseto/Necromancing-BBMixes/resolve/main/BBMIX-JOY/bbmixJOY_v20.safetensors)**
## BBMIX JUDE
- **[BB-Mix-JUDE_V1.0](https://huggingface.co/malikxseto/Necromancing-BBMixes/resolve/main/BBMIX-JUDE/bbmixJUDE_v10.safetensors)**
## BBMIX JULIA
- **[BB-Mix-JULIA_V1.0](https://huggingface.co/malikxseto/Necromancing-BBMixes/resolve/main/BBMIX-JULIA/bbmixJULIA_v10.safetensors)**
## BBMIX KALI
- **[BB-Mix-KALI_V1.0](https://huggingface.co/malikxseto/Necromancing-BBMixes/resolve/main/BBMIX-KALI/bbmixKALI_v10.safetensors)**
## BBMIX LIN
- **[BB-Mix-LIN_V1.0](https://huggingface.co/malikxseto/Necromancing-BBMixes/resolve/main/BBMIX-LIN/bbmixLIN_v10.safetensors)**
## BBMIX LUCI
- **[BB-Mix-LUCI_V1.0](https://huggingface.co/malikxseto/Necromancing-BBMixes/resolve/main/BBMIX-LUCI/bbmixLUCI_v10.safetensors)**
- **[BB-Mix-LUCI_V2.0](https://huggingface.co/malikxseto/Necromancing-BBMixes/resolve/main/BBMIX-LUCI/bbmixLUCI_v20.safetensors)**
## BBMIX NUNU
- **[BB-Mix-NUNU_V1.0](https://huggingface.co/malikxseto/Necromancing-BBMixes/resolve/main/BBMIX-NUNU/bbmixNUNU_v10.safetensors)**
## BBMIX RUIS
- **[BB-Mix-RUIS_V1.0](https://huggingface.co/malikxseto/Necromancing-BBMixes/resolve/main/BBMIX-RUIS/bbMIXRUIS_v10.safetensors)**
- **[BB-Mix-RUIS_V1.1](https://huggingface.co/malikxseto/Necromancing-BBMixes/resolve/main/BBMIX-RUIS/bbMIXRUIS_v11.safetensors)**
- **[BB-Mix-RUIS_V1.2](https://huggingface.co/malikxseto/Necromancing-BBMixes/resolve/main/BBMIX-RUIS/bbMIXRUIS_v12.safetensors)**
- **[BB-Mix-RUIS_V1.5](https://huggingface.co/malikxseto/Necromancing-BBMixes/resolve/main/BBMIX-RUIS/bbMIXRUIS_v15.safetensors)**
- **[BB-Mix-RUIS+](https://huggingface.co/malikxseto/Necromancing-BBMixes/resolve/main/BBMIX-RUIS/bbmixRUIS_bbmixRUIS.safetensors)**
## BBMIX SANDY
- **[BB-Mix-SANDY_V1.0](https://huggingface.co/malikxseto/Necromancing-BBMixes/resolve/main/BBMIX-SANDY/bbMIXSANDY_v10.safetensors)**
## BBMIX VERONICA
- **[BB-Mix-VERONICA_V1.0](https://huggingface.co/malikxseto/Necromancing-BBMixes/resolve/main/BBMIX-VERONICA/bbmixVERONICA_v10.safetensors)**
|
minoosh/videomae-base-finetuned-IEMOCAP_4
|
minoosh
| 2023-06-18T23:55:50Z | 60 | 0 |
transformers
|
[
"transformers",
"pytorch",
"videomae",
"video-classification",
"generated_from_trainer",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] |
video-classification
| 2023-06-18T19:02:34Z |
---
license: cc-by-nc-4.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: videomae-base-finetuned-IEMOCAP_4
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. -->
# videomae-base-finetuned-IEMOCAP_4
This model is a fine-tuned version of [MCG-NJU/videomae-base](https://huggingface.co/MCG-NJU/videomae-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3971
- Accuracy: 0.2747
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 4490
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.3364 | 0.1 | 450 | 1.4142 | 0.2882 |
| 1.3951 | 1.1 | 900 | 1.3692 | 0.3058 |
| 1.2918 | 2.1 | 1350 | 1.3544 | 0.3357 |
| 1.2283 | 3.1 | 1800 | 1.3673 | 0.3298 |
| 1.2638 | 4.1 | 2250 | 1.3652 | 0.3404 |
| 1.2674 | 5.1 | 2700 | 1.3265 | 0.3538 |
| 1.2737 | 6.1 | 3150 | 1.3092 | 0.3802 |
| 1.1625 | 7.1 | 3600 | 1.2969 | 0.3884 |
| 1.35 | 8.1 | 4050 | 1.3067 | 0.3726 |
| 1.1373 | 9.1 | 4490 | 1.2835 | 0.3972 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.0
- Tokenizers 0.13.3
|
ngkuissi/Pixelcopter-PLE-v0
|
ngkuissi
| 2023-06-18T23:53:44Z | 0 | 0 | null |
[
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-06-10T19:03:24Z |
---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Pixelcopter-PLE-v0
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 45.70 +/- 42.58
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
|
ac8736/toxic-tweets-fine-tuned-distilbert
|
ac8736
| 2023-06-18T23:12:27Z | 115 | 0 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"distilbert",
"text-classification",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-04-21T04:45:30Z |
---
license: mit
---
Multilabel classification model trained on the Toxic Comments dataset from Kaggle. (https://www.kaggle.com/competitions/jigsaw-toxic-comment-classification-challenge/data)
Fine tuned using DistilBert.
```python
from transformers import AutoModelForSequenceClassification, AutoTokenizer
import torch
model = AutoModelForSequenceClassification.from_pretrained("pretrained_model")
tokenizer = AutoTokenizer.from_pretrained("model_tokenizer")
X_train = ["Why is Owen's retirement from football not mentioned? He hasn't played a game since 2005."]
batch = tokenizer(X_train, truncation=True, padding='max_length', return_tensors="pt")
labels = ["toxic", "severe_toxic", "obscene", "threat", "insult", "identity_hate"]
with torch.no_grad():
outputs = model(**batch)
predictions = torch.sigmoid(outputs.logits)*100
probs = predictions[0].tolist()
for i in range(len(probs)):
print(f"{labels[i]}: {round(probs[i], 3)}%")
```
Output expected below:
```
toxic: 0.676%
severe_toxic: 0.001%
obscene: 0.098%
threat: 0.007%
insult: 0.021%
identity_hate: 0.004%
```
|
jeremyjje/my_awesome_model
|
jeremyjje
| 2023-06-18T23:04:44Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-06-14T07:36:04Z |
---
license: apache-2.0
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: 0.93168
---
<!-- 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: 0.2289
- Accuracy: 0.9317
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.2321 | 1.0 | 1563 | 0.1823 | 0.9292 |
| 0.1524 | 2.0 | 3126 | 0.2289 | 0.9317 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3
|
BenjaminOcampo/model-bert__trained-in-dynahate__seed-42
|
BenjaminOcampo
| 2023-06-18T22:24:34Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"en",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-06-18T22:23:30Z |
---
language: en
---
# Model Card for BenjaminOcampo/model-bert__trained-in-dynahate__seed-42
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
**Classification results dev set**
```
precision recall f1-score support
0 0.8084 0.8205 0.8144 1933
1 0.8377 0.8265 0.8321 2167
accuracy 0.8237 4100
macro avg 0.8230 0.8235 0.8232 4100
weighted avg 0.8239 0.8237 0.8237 4100
```
**Classification results test set**
```
precision recall f1-score support
0 0.7600 0.7765 0.7682 1852
1 0.8142 0.7998 0.8069 2268
accuracy 0.7893 4120
macro avg 0.7871 0.7881 0.7876 4120
weighted avg 0.7898 0.7893 0.7895 4120
```
- **Developed by:** Benjamin Ocampo
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** en
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** https://github.com/huggingface/huggingface_hub
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
### How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Data Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
BenjaminOcampo/model-bert__trained-in-dynahate__seed-0
|
BenjaminOcampo
| 2023-06-18T22:23:26Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"en",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-06-18T22:22:19Z |
---
language: en
---
# Model Card for BenjaminOcampo/model-bert__trained-in-dynahate__seed-0
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
**Classification results dev set**
```
precision recall f1-score support
0 0.8120 0.8153 0.8136 1933
1 0.8346 0.8316 0.8331 2167
accuracy 0.8239 4100
macro avg 0.8233 0.8234 0.8234 4100
weighted avg 0.8239 0.8239 0.8239 4100
```
**Classification results test set**
```
precision recall f1-score support
0 0.7508 0.7759 0.7631 1852
1 0.8119 0.7897 0.8006 2268
accuracy 0.7835 4120
macro avg 0.7813 0.7828 0.7819 4120
weighted avg 0.7844 0.7835 0.7838 4120
```
- **Developed by:** Benjamin Ocampo
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** en
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** https://github.com/huggingface/huggingface_hub
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
### How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Data Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
BenjaminOcampo/model-bert__trained-in-dynahate__seed-1
|
BenjaminOcampo
| 2023-06-18T22:22:16Z | 106 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"en",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-06-18T22:20:27Z |
---
language: en
---
# Model Card for BenjaminOcampo/model-bert__trained-in-dynahate__seed-1
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
**Classification results dev set**
```
precision recall f1-score support
0 0.8132 0.7993 0.8062 1933
1 0.8236 0.8362 0.8299 2167
accuracy 0.8188 4100
macro avg 0.8184 0.8177 0.8180 4100
weighted avg 0.8187 0.8188 0.8187 4100
```
**Classification results test set**
```
precision recall f1-score support
0 0.7585 0.7597 0.7591 1852
1 0.8035 0.8025 0.8030 2268
accuracy 0.7833 4120
macro avg 0.7810 0.7811 0.7811 4120
weighted avg 0.7833 0.7833 0.7833 4120
```
- **Developed by:** Benjamin Ocampo
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** en
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** https://github.com/huggingface/huggingface_hub
- **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
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#### Preprocessing [optional]
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#### Training Hyperparameters
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#### Summary
## Model Examination [optional]
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## 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]
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- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
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|
BenjaminOcampo/model-bert__trained-in-ihc__seed-42
|
BenjaminOcampo
| 2023-06-18T22:18:09Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"en",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-06-18T22:16:55Z |
---
language: en
---
# Model Card for BenjaminOcampo/model-bert__trained-in-ihc__seed-42
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
**Classification results dev set**
```
precision recall f1-score support
0 0.8243 0.8243 0.8243 2658
1 0.7149 0.7149 0.7149 1638
accuracy 0.7826 4296
macro avg 0.7696 0.7696 0.7696 4296
weighted avg 0.7826 0.7826 0.7826 4296
```
**Classification results test set**
```
precision recall f1-score support
0 0.8234 0.7983 0.8107 2658
1 0.6882 0.7222 0.7048 1638
accuracy 0.7693 4296
macro avg 0.7558 0.7603 0.7577 4296
weighted avg 0.7719 0.7693 0.7703 4296
```
- **Developed by:** Benjamin Ocampo
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** en
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** https://github.com/huggingface/huggingface_hub
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
### How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Data Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
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## Model Card Contact
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|
BenjaminOcampo/model-bert__trained-in-ihc__seed-0
|
BenjaminOcampo
| 2023-06-18T22:14:16Z | 108 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"en",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-06-18T22:12:52Z |
---
language: en
---
# Model Card for BenjaminOcampo/model-bert__trained-in-ihc__seed-0
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
**Classification results dev set**
```
precision recall f1-score support
0 0.8253 0.8333 0.8293 2658
1 0.7252 0.7137 0.7194 1638
accuracy 0.7877 4296
macro avg 0.7752 0.7735 0.7743 4296
weighted avg 0.7871 0.7877 0.7874 4296
```
**Classification results test set**
```
precision recall f1-score support
0 0.8245 0.8217 0.8231 2658
1 0.7122 0.7161 0.7142 1638
accuracy 0.7814 4296
macro avg 0.7683 0.7689 0.7686 4296
weighted avg 0.7817 0.7814 0.7815 4296
```
- **Developed by:** Benjamin Ocampo
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** en
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** https://github.com/huggingface/huggingface_hub
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
### How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Data Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
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## Model Card Contact
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|
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