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
2025-08-30 06:27:36
| downloads
int64 0
223M
| likes
int64 0
11.7k
| library_name
stringclasses 527
values | tags
listlengths 1
4.05k
| pipeline_tag
stringclasses 55
values | createdAt
timestamp[us, tz=UTC]date 2022-03-02 23:29:04
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AIYIYA/my_tt
|
AIYIYA
| 2023-09-11T14:42:38Z | 61 | 0 |
transformers
|
[
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-09-11T14:04:56Z |
---
tags:
- generated_from_keras_callback
model-index:
- name: AIYIYA/my_tt
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. -->
# AIYIYA/my_tt
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 1.0110
- Validation Loss: 1.1941
- Train Accuracy: 0.5185
- 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': False, 'is_legacy_optimizer': False, 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 20, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_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 |
|:----------:|:---------------:|:--------------:|:-----:|
| 1.8538 | 1.2004 | 0.5185 | 0 |
| 1.0820 | 1.1683 | 0.5185 | 1 |
| 1.0110 | 1.1941 | 0.5185 | 2 |
### Framework versions
- Transformers 4.33.1
- TensorFlow 2.13.0
- Datasets 2.14.5
- Tokenizers 0.13.3
|
pplantinga/whisper-small-sw
|
pplantinga
| 2023-09-11T14:42:28Z | 80 | 3 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"whisper-event",
"generated_from_trainer",
"sw",
"dataset:mozilla-foundation/common_voice_11_0",
"base_model:openai/whisper-small",
"base_model:finetune:openai/whisper-small",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-12-14T15:33:20Z |
---
language:
- sw
license: apache-2.0
tags:
- whisper-event
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_11_0
metrics:
- wer
base_model: openai/whisper-small
model-index:
- name: Whisper Small Swahili
results:
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
name: mozilla-foundation/common_voice_11_0 sw
type: mozilla-foundation/common_voice_11_0
config: sw
split: test
args: sw
metrics:
- type: wer
value: 27.62114587994997
name: Wer
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Whisper Small Swahili
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the mozilla-foundation/common_voice_11_0 sw dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5597
- Wer: 27.6211
## 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: 128
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 250
- training_steps: 500
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.0+cu117
- Datasets 2.7.1.dev0
- Tokenizers 0.13.2
|
jasoneden/bloom560m-squad-helloworld
|
jasoneden
| 2023-09-11T14:42:14Z | 86 | 8 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"bloom",
"question-answering",
"generated_from_trainer",
"dataset:squad_v2",
"base_model:bigscience/bloom-560m",
"base_model:finetune:bigscience/bloom-560m",
"license:bigscience-bloom-rail-1.0",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-10-25T18:46:33Z |
---
license: bigscience-bloom-rail-1.0
tags:
- generated_from_trainer
datasets:
- squad_v2
base_model: bigscience/bloom-560m
model-index:
- name: debug_bloom_squad
results: []
---
<!-- This model card has mostly been generated automatically according to the information the Trainer had access to. I've added some additional context. -->
# POC - BLOOM for QuestionAnswering, tuned on squad_v2
This model is a fine-tuned version of [bigscience/bloom-560m](https://huggingface.co/bigscience/bloom-560m) on the squad_v2 dataset.
It is intended for a proof of concept, and perhaps to serve as a starting point for others trying to do the same thing.
Ongoing discussion surrounding this effort:
https://huggingface.co/bigscience/bloom/discussions/46#633c57b2ccce04161f82e6c2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 6
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2.0
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.24.0.dev0
- Pytorch 1.12.1+cu102
- Datasets 2.6.1
- Tokenizers 0.13.1
|
jncraton/LaMini-GPT-124M-ct2-int8
|
jncraton
| 2023-09-11T14:38:27Z | 563 | 0 |
transformers
|
[
"transformers",
"text-generation",
"en",
"arxiv:2304.14402",
"base_model:openai-community/gpt2",
"base_model:finetune:openai-community/gpt2",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-06-24T22:21:05Z |
---
language:
- en
license: cc-by-nc-4.0
pipeline_tag: text-generation
widget:
- text: 'Below is an instruction that describes a task.
Write a response that appropriately completes the request.
### Instruction:
how can I become more healthy?
### Response:'
example_title: example
base_model: gpt2
---
<!-- 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. -->
<p align="center" width="100%">
<a><img src="https://raw.githubusercontent.com/mbzuai-nlp/lamini-lm/main/images/lamini.png" alt="Title" style="width: 100%; min-width: 300px; display: block; margin: auto;"></a>
</p>
# LaMini-GPT-124M
[]()
This model is one of our LaMini-LM model series in paper "[LaMini-LM: A Diverse Herd of Distilled Models from Large-Scale Instructions](https://github.com/mbzuai-nlp/lamini-lm)".
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on [LaMini-instruction dataset](https://huggingface.co/datasets/MBZUAI/LaMini-instruction) that contains 2.58M samples for instruction fine-tuning. For more information about our dataset, please refer to our [project repository](https://github.com/mbzuai-nlp/lamini-lm/).
You can view other models of LaMini-LM series as follows. Models with ✩ are those with the best overall performance given their size/architecture, hence we recommend using them. More details can be seen in our paper.
<table>
<thead>
<tr>
<th>Base model</th>
<th colspan="4">LaMini-LM series (#parameters)</th>
</tr>
</thead>
<tbody>
<tr>
<td>T5</td>
<td><a href="https://huggingface.co/MBZUAI/lamini-t5-61m" target="_blank" rel="noopener noreferrer">LaMini-T5-61M</a></td>
<td><a href="https://huggingface.co/MBZUAI/lamini-t5-223m" target="_blank" rel="noopener noreferrer">LaMini-T5-223M</a></td>
<td><a href="https://huggingface.co/MBZUAI/lamini-t5-738m" target="_blank" rel="noopener noreferrer">LaMini-T5-738M</a></td>
<td></td>
</tr>
<tr>
<td>Flan-T5</td>
<td><a href="https://huggingface.co/MBZUAI/lamini-flan-t5-77m" target="_blank" rel="noopener noreferrer">LaMini-Flan-T5-77M</a>✩</td>
<td><a href="https://huggingface.co/MBZUAI/lamini-flan-t5-248m" target="_blank" rel="noopener noreferrer">LaMini-Flan-T5-248M</a>✩</td>
<td><a href="https://huggingface.co/MBZUAI/lamini-flan-t5-783m" target="_blank" rel="noopener noreferrer">LaMini-Flan-T5-783M</a>✩</td>
<td></td>
</tr>
<tr>
<td>Cerebras-GPT</td>
<td><a href="https://huggingface.co/MBZUAI/lamini-cerebras-111m" target="_blank" rel="noopener noreferrer">LaMini-Cerebras-111M</a></td>
<td><a href="https://huggingface.co/MBZUAI/lamini-cerebras-256m" target="_blank" rel="noopener noreferrer">LaMini-Cerebras-256M</a></td>
<td><a href="https://huggingface.co/MBZUAI/lamini-cerebras-590m" target="_blank" rel="noopener noreferrer">LaMini-Cerebras-590M</a></td>
<td><a href="https://huggingface.co/MBZUAI/lamini-cerebras-1.3b" target="_blank" rel="noopener noreferrer">LaMini-Cerebras-1.3B</a></td>
</tr>
<tr>
<td>GPT-2</td>
<td><a href="https://huggingface.co/MBZUAI/lamini-gpt-124m" target="_blank" rel="noopener noreferrer">LaMini-GPT-124M</a>✩</td>
<td><a href="https://huggingface.co/MBZUAI/lamini-gpt-774m" target="_blank" rel="noopener noreferrer">LaMini-GPT-774M</a>✩</td>
<td><a href="https://huggingface.co/MBZUAI/lamini-gpt-1.5b" target="_blank" rel="noopener noreferrer">LaMini-GPT-1.5B</a>✩</td>
<td></td>
</tr>
<tr>
<td>GPT-Neo</td>
<td><a href="https://huggingface.co/MBZUAI/lamini-neo-125m" target="_blank" rel="noopener noreferrer">LaMini-Neo-125M</a></td>
<td><a href="https://huggingface.co/MBZUAI/lamini-neo-1.3b" target="_blank" rel="noopener noreferrer">LaMini-Neo-1.3B</a></td>
<td></td>
<td></td>
</tr>
<tr>
<td>GPT-J</td>
<td colspan="4">coming soon</td>
</tr>
<tr>
<td>LLaMA</td>
<td colspan="4">coming soon</td>
</tr>
</tbody>
</table>
## Use
### Intended use
We recommend using the model to respond to human instructions written in natural language.
Since this decoder-only model is fine-tuned with wrapper text, we suggest using the same wrapper text to achieve the best performance.
See the example on the right or the code below.
We now show you how to load and use our model using HuggingFace `pipeline()`.
```python
# pip install -q transformers
from transformers import pipeline
checkpoint = "{model_name}"
model = pipeline('text-generation', model = checkpoint)
instruction = 'Please let me know your thoughts on the given place and why you think it deserves to be visited: \n"Barcelona, Spain"'
input_prompt = f"Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n### Instruction:\n{instruction}\n\n### Response:"
generated_text = model(input_prompt, max_length=512, do_sample=True)[0]['generated_text']
print("Response", generated_text)
```
## Training Procedure
<p align="center" width="100%">
<a><img src="https://raw.githubusercontent.com/mbzuai-nlp/lamini-lm/main/images/lamini-pipeline.drawio.png" alt="Title" style="width: 100%; min-width: 250px; display: block; margin: auto;"></a>
</p>
We initialize with [gpt2](https://huggingface.co/gpt2) and fine-tune it on our [LaMini-instruction dataset](https://huggingface.co/datasets/MBZUAI/LaMini-instruction). Its total number of parameters is 124M.
### Training Hyperparameters
## Evaluation
We conducted two sets of evaluations: automatic evaluation on downstream NLP tasks and human evaluation on user-oriented instructions. For more detail, please refer to our [paper]().
## Limitations
More information needed
# Citation
```bibtex
@article{lamini-lm,
author = {Minghao Wu and
Abdul Waheed and
Chiyu Zhang and
Muhammad Abdul-Mageed and
Alham Fikri Aji
},
title = {LaMini-LM: A Diverse Herd of Distilled Models from Large-Scale Instructions},
journal = {CoRR},
volume = {abs/2304.14402},
year = {2023},
url = {https://arxiv.org/abs/2304.14402},
eprinttype = {arXiv},
eprint = {2304.14402}
}
```
|
Jzuluaga/wav2vec2-large-960h-lv60-self-en-atc-uwb-atcc
|
Jzuluaga
| 2023-09-11T14:30:11Z | 96 | 3 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"en-atc",
"en",
"generated_from_trainer",
"dataset:Jzuluaga/uwb_atcc",
"arxiv:2203.16822",
"arxiv:2211.04054",
"base_model:facebook/wav2vec2-large-960h-lv60-self",
"base_model:finetune:facebook/wav2vec2-large-960h-lv60-self",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-11-30T07:59:57Z |
---
language: en
license: apache-2.0
tags:
- audio
- automatic-speech-recognition
- en-atc
- en
- generated_from_trainer
datasets:
- Jzuluaga/uwb_atcc
metrics:
- wer
base_model: facebook/wav2vec2-large-960h-lv60-self
model-index:
- name: wav2vec2-large-960h-lv60-self-en-atc-uwb-atcc
results:
- task:
type: automatic-speech-recognition
name: Speech Recognition
dataset:
name: UWB-ATCC dataset (Air Traffic Control Communications)
type: Jzuluaga/uwb_atcc
config: test
split: test
metrics:
- type: wer
value: 17.2
name: TEST WER
verified: false
- type: wer
value: 13.72
name: TEST WER (+LM)
verified: false
- task:
type: automatic-speech-recognition
name: Speech Recognition
dataset:
name: ATCOSIM corpus (Air Traffic Control Communications)
type: Jzuluaga/atcosim_corpus
config: test
split: test
metrics:
- type: wer
value: 15.31
name: TEST WER
verified: false
- type: wer
value: 11.88
name: TEST WER (+LM)
verified: false
---
# wav2vec2-large-960h-lv60-self-en-atc-uwb-atcc
This model is a fine-tuned version of [facebook/wav2vec2-large-960h-lv60-self](https://huggingface.co/facebook/wav2vec2-large-960h-lv60-self) on the [UWB-ATCC corpus](https://huggingface.co/datasets/Jzuluaga/uwb_atcc).
<a href="https://colab.research.google.com/github/idiap/w2v2-air-traffic/blob/main/src/eval_xlsr_atc_model.ipynb">
<img alt="GitHub" src="https://colab.research.google.com/assets/colab-badge.svg\">
</a>
<a href="https://github.com/idiap/w2v2-air-traffic">
<img alt="GitHub" src="https://img.shields.io/badge/GitHub-Open%20source-green\">
</a>
It achieves the following results on the evaluation set:
- Loss: 0.7287
- Wer: 0.1756
Paper: [How Does Pre-trained Wav2Vec 2.0 Perform on Domain Shifted ASR? An Extensive Benchmark on Air Traffic Control Communications](https://arxiv.org/abs/2203.16822).
Authors: Juan Zuluaga-Gomez, Amrutha Prasad, Iuliia Nigmatulina, Saeed Sarfjoo, Petr Motlicek, Matthias Kleinert, Hartmut Helmke, Oliver Ohneiser, Qingran Zhan
Abstract: Recent work on self-supervised pre-training focus</b> on leveraging large-scale unlabeled speech data to build robust end-to-end (E2E)acoustic models (AM) that can be later fine-tuned on downstream tasks e.g., automatic speech recognition (ASR). Yet, few works investigated the impact on performance when the data properties substantially differ between the pre-training and fine-tuning phases, termed domain shift. We target this scenario by analyzing the robustness of Wav2Vec 2.0 and XLS-R models on downstream ASR for a completely unseen domain, air traffic control (ATC) communications. We benchmark these two models on several open-source and challenging ATC databases with signal-to-noise ratio between 5 and 20 dB. Relative word error rate (WER) reductions between 20% to 40% are obtained in comparison to hybrid-based ASR baselines by only fine-tuning E2E acoustic models with a smaller fraction of labeled data. We analyze WERs on the low-resource scenario and gender bias carried by one ATC dataset.
Code — GitHub repository: https://github.com/idiap/w2v2-air-traffic
## Usage
You can use our Google Colab notebook to run and evaluate our model: https://github.com/idiap/w2v2-air-traffic/blob/master/src/eval_xlsr_atc_model.ipynb
## Intended uses & limitations
This model was fine-tuned on air traffic control data. We don't expect that it keeps the same performance on some others datasets, e.g., LibriSpeech or CommonVoice.
## Training and evaluation data
See Table 1 (page 3) in our paper: [How Does Pre-trained Wav2Vec 2.0 Perform on Domain Shifted ASR? An Extensive Benchmark on Air Traffic Control Communications](https://arxiv.org/abs/2203.16822). We described there the partitions of how to use our model.
- We use the UWB-ATCC corpus to fine-tune this model. You can download the raw data here: https://lindat.mff.cuni.cz/repository/xmlui/handle/11858/00-097C-0000-0001-CCA1-0
- However, do not worry, we have prepared the database in `Datasets format`. Here, [UWB-ATCC corpus on HuggingFace](https://huggingface.co/datasets/Jzuluaga/uwb_atcc). You can scroll and check the train/test partitions, and even listen to some audios.
- If you want to prepare a database in HuggingFace format, you can follow the data loader script in: [data_loader_atc.py](https://huggingface.co/datasets/Jzuluaga/uwb_atcc/blob/main/atc_data_loader.py).
-
## Writing your own inference script
If you use language model, you need to install the KenLM bindings with:
```bash
conda activate your_environment
pip install https://github.com/kpu/kenlm/archive/master.zip
```
The snippet of code:
```python
from datasets import load_dataset, load_metric, Audio
import torch
from transformers import AutoModelForCTC, Wav2Vec2Processor, Wav2Vec2ProcessorWithLM
import torchaudio.functional as F
USE_LM = False
DATASET_ID = "Jzuluaga/uwb_atcc"
MODEL_ID = "Jzuluaga/wav2vec2-large-960h-lv60-self-en-atc-uwb-atcc"
# 1. Load the dataset
# we only load the 'test' partition, however, if you want to load the 'train' partition, you can change it accordingly
uwb_atcc_corpus_test = load_dataset(DATASET_ID, "test", split="test")
# 2. Load the model
model = AutoModelForCTC.from_pretrained(MODEL_ID)
# 3. Load the processors, we offer support with LM, which should yield better resutls
if USE_LM:
processor = Wav2Vec2ProcessorWithLM.from_pretrained(MODEL_ID)
else:
processor = Wav2Vec2Processor.from_pretrained(MODEL_ID)
# 4. Format the test sample
sample = next(iter(uwb_atcc_corpus_test))
file_sampling_rate = sample['audio']['sampling_rate']
# resample if neccessary
if file_sampling_rate != 16000:
resampled_audio = F.resample(torch.tensor(sample["audio"]["array"]), file_sampling_rate, 16000).numpy()
else:
resampled_audio = torch.tensor(sample["audio"]["array"]).numpy()
input_values = processor(resampled_audio, return_tensors="pt").input_values
# 5. Run the forward pass in the model
with torch.no_grad():
logits = model(input_values).logits
# get the transcription with processor
if USE_LM:
transcription = processor.batch_decode(logits.numpy()).text
else:
pred_ids = torch.argmax(logits, dim=-1)
transcription = processor.batch_decode(pred_ids)
# print the output
print(transcription)
```
# Cite us
If you use this code for your research, please cite our paper with:
```
@article{zuluaga2022how,
title={How Does Pre-trained Wav2Vec2. 0 Perform on Domain Shifted ASR? An Extensive Benchmark on Air Traffic Control Communications},
author={Zuluaga-Gomez, Juan and Prasad, Amrutha and Nigmatulina, Iuliia and Sarfjoo, Saeed and others},
journal={IEEE Spoken Language Technology Workshop (SLT), Doha, Qatar},
year={2022}
}
```
and,
```
@article{zuluaga2022bertraffic,
title={BERTraffic: BERT-based Joint Speaker Role and Speaker Change Detection for Air Traffic Control Communications},
author={Zuluaga-Gomez, Juan and Sarfjoo, Seyyed Saeed and Prasad, Amrutha and others},
journal={IEEE Spoken Language Technology Workshop (SLT), Doha, Qatar},
year={2022}
}
```
and,
```
@article{zuluaga2022atco2,
title={ATCO2 corpus: A Large-Scale Dataset for Research on Automatic Speech Recognition and Natural Language Understanding of Air Traffic Control Communications},
author={Zuluaga-Gomez, Juan and Vesel{\`y}, Karel and Sz{\"o}ke, Igor and Motlicek, Petr and others},
journal={arXiv preprint arXiv:2211.04054},
year={2022}
}
```
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 24
- eval_batch_size: 12
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- training_steps: 10000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| No log | 1.06 | 500 | 2.9016 | 0.9995 |
| 2.877 | 2.12 | 1000 | 0.9812 | 0.3485 |
| 2.877 | 3.18 | 1500 | 0.7842 | 0.2732 |
| 0.7834 | 4.25 | 2000 | 0.6962 | 0.2192 |
| 0.7834 | 5.31 | 2500 | 0.6527 | 0.2042 |
| 0.6084 | 6.37 | 3000 | 0.6220 | 0.1972 |
| 0.6084 | 7.43 | 3500 | 0.6442 | 0.1934 |
| 0.5147 | 8.49 | 4000 | 0.6793 | 0.1950 |
| 0.5147 | 9.55 | 4500 | 0.6432 | 0.1920 |
| 0.4566 | 10.62 | 5000 | 0.6605 | 0.1853 |
| 0.4566 | 11.68 | 5500 | 0.6393 | 0.1866 |
| 0.4155 | 12.74 | 6000 | 0.6918 | 0.1803 |
| 0.4155 | 13.8 | 6500 | 0.6514 | 0.1791 |
| 0.372 | 14.86 | 7000 | 0.7010 | 0.1851 |
| 0.372 | 15.92 | 7500 | 0.6824 | 0.1786 |
| 0.3368 | 16.99 | 8000 | 0.6895 | 0.1780 |
| 0.3368 | 18.05 | 8500 | 0.7150 | 0.1759 |
| 0.3244 | 19.11 | 9000 | 0.7141 | 0.1759 |
| 0.3244 | 20.17 | 9500 | 0.7225 | 0.1756 |
| 0.2981 | 21.23 | 10000 | 0.7287 | 0.1756 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.13.0+cu117
- Datasets 2.6.1
- Tokenizers 0.13.2
|
MaxKazak/ruBert-base-russian-emotion-detection
|
MaxKazak
| 2023-09-11T14:27:43Z | 13,789 | 4 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"bert",
"text-classification",
"sentiment",
"emotion-classification",
"multilabel",
"multiclass",
"ru",
"dataset:Djacon/ru_goemotions",
"base_model:ai-forever/ruBert-base",
"base_model:finetune:ai-forever/ruBert-base",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-05-28T15:25:35Z |
---
language:
- ru
license: apache-2.0
tags:
- sentiment
- emotion-classification
- multilabel
- multiclass
datasets:
- Djacon/ru_goemotions
metrics:
- accuracy
widget:
- text: Очень рад тебя видеть!
- text: Как дела?
- text: Мне немного отвратно это делать
- text: Я испытал мурашки от страха
- text: Нет ничего радостного в этих горьких новостях
- text: Ого, неожидал тебя здесь увидеть!
- text: Фу ну и мерзость
- text: Мне неприятно общение с тобой
base_model: ai-forever/ruBert-base
model-index:
- name: ruBert-base-russian-emotions-classifier-goEmotions
results:
- task:
type: multilabel-text-classification
name: Multilabel Text Classification
dataset:
name: ru_goemotions
type: Djacon/ru_goemotions
args: ru
metrics:
- type: roc_auc
value: 92%
name: multilabel ROC AUC
---
# ruBert-base-russian-emotions-classifier-goEmotions
This model is a fine-tuned version of [ai-forever/ruBert-base](https://huggingface.co/ai-forever/ruBert-base) on [Djacon/ru_goemotions](https://huggingface.co/datasets/Djacon/ru_goemotions).
It achieves the following results on the evaluation set (2nd epoch):
- Loss: 0.2088
- AUC: 0.9240
The quality of the predicted probabilities on the test dataset is the following:
| label | joy | interest | surpise | sadness | anger | disgust | fear | guilt | neutral | average |
|----------|--------|----------|---------|---------|--------|---------|--------|--------|---------|---------|
| AUC | 0.9369 | 0.9213 | 0.9325 | 0.8791 | 0.8374 | 0.9041 | 0.9470 | 0.9758 | 0.8518 | 0.9095 |
| F1-micro | 0.9528 | 0.9157 | 0.9697 | 0.9284 | 0.8690 | 0.9658 | 0.9851 | 0.9875 | 0.7654 | 0.9266 |
| F1-macro | 0.8369 | 0.7922 | 0.7561 | 0.7392 | 0.7351 | 0.7356 | 0.8176 | 0.8247 | 0.7650 | 0.7781 |
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | AUC |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.1755 | 1.0 | 1685 | 0.1717 | 0.9220 |
| 0.1391 | 2.0 | 3370 | 0.1757 | 0.9240 |
| 0.0899 | 3.0 | 5055 | 0.2088 | 0.9106 |
### Framework versions
- Transformers 4.24.0
- Pytorch 2.0.1
- Datasets 2.12.0
- Tokenizers 0.11.0
|
AbdelKarim95/Reinforce-0
|
AbdelKarim95
| 2023-09-11T14:23:47Z | 0 | 0 | null |
[
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-09-11T13:04:20Z |
---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-0
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 445.40 +/- 73.45
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
|
PoungPoung/fen_chess
|
PoungPoung
| 2023-09-11T14:17:01Z | 127 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"generated_from_trainer",
"base_model:openai-community/gpt2",
"base_model:finetune:openai-community/gpt2",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-09-06T11:55:41Z |
---
license: mit
base_model: gpt2
tags:
- generated_from_trainer
model-index:
- name: fen_chess
results: []
widget:
- text: "rnbqkbnr/pppppppp/8/8/4P3/8/PPPP1PPP/RNBQKBNR b KQkq e3 0 1"
---
<!-- 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. -->
# fen_chess
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 1000
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.33.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
|
sanjeevnara/stablethumbs-dreambooth-multiconcept
|
sanjeevnara
| 2023-09-11T14:15:09Z | 33 | 0 |
diffusers
|
[
"diffusers",
"stable-diffusion",
"text-to-image",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-09-10T22:48:48Z |
---
license: apache-2.0
language:
- en
library_name: diffusers
pipeline_tag: text-to-image
tags:
- stable-diffusion
---
Stable Diffusion v1.5 trained using Dreambooth approach to generate 'thumbs-up' style images. Also trained to generate professional Soccer player Vinicius Jr.'s face. <be>
Prompt Guide:
- For a thumbs up style, add 'with a thumbs up' or 'thumbs up gesture' to your prompt e.g. `'photo of Messi with a thumbs up gesture, high quality'.`
- For Vinicius Jr, add the rare token 'xjy' e.g. `'photo of xjy with a thumbs up gesture, high quality'`.
Uses Diffusers library / StableDiffusionPipeline.
|
baebee/Starlight-13b-QLORA
|
baebee
| 2023-09-11T14:14:33Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-09-11T14:14:24Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.6.0.dev0
|
Bolakubus/distilhubert-finetuned-gtzan
|
Bolakubus
| 2023-09-11T14:07:58Z | 161 | 0 |
transformers
|
[
"transformers",
"pytorch",
"hubert",
"audio-classification",
"generated_from_trainer",
"dataset:marsyas/gtzan",
"base_model:ntu-spml/distilhubert",
"base_model:finetune:ntu-spml/distilhubert",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
audio-classification
| 2023-09-06T15:52:56Z |
---
license: apache-2.0
base_model: ntu-spml/distilhubert
tags:
- generated_from_trainer
datasets:
- marsyas/gtzan
metrics:
- accuracy
model-index:
- name: distilhubert-finetuned-gtzan
results:
- task:
name: Audio Classification
type: audio-classification
dataset:
name: GTZAN
type: marsyas/gtzan
config: all
split: train
args: all
metrics:
- name: Accuracy
type: accuracy
value: 0.84
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilhubert-finetuned-gtzan
This model is a fine-tuned version of [ntu-spml/distilhubert](https://huggingface.co/ntu-spml/distilhubert) on the GTZAN dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6073
- Accuracy: 0.84
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.9996 | 1.0 | 113 | 1.7954 | 0.57 |
| 1.3157 | 2.0 | 226 | 1.2129 | 0.67 |
| 0.9375 | 3.0 | 339 | 0.9223 | 0.76 |
| 0.817 | 4.0 | 452 | 0.8372 | 0.73 |
| 0.5425 | 5.0 | 565 | 0.7206 | 0.74 |
| 0.4381 | 6.0 | 678 | 0.6317 | 0.78 |
| 0.5359 | 7.0 | 791 | 0.5468 | 0.84 |
| 0.2037 | 8.0 | 904 | 0.5492 | 0.84 |
| 0.3028 | 9.0 | 1017 | 0.5550 | 0.8 |
| 0.1674 | 10.0 | 1130 | 0.6073 | 0.84 |
### Framework versions
- Transformers 4.34.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
|
checkiejan/flan-t5-prefix-25-7-2
|
checkiejan
| 2023-09-11T13:58:54Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-09-11T13:58:50Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.5.0
|
bigmorning/whisper_4_with_init_sun_syl_wd_0__0090
|
bigmorning
| 2023-09-11T13:49:34Z | 59 | 0 |
transformers
|
[
"transformers",
"tf",
"whisper",
"automatic-speech-recognition",
"generated_from_keras_callback",
"base_model:openai/whisper-tiny",
"base_model:finetune:openai/whisper-tiny",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-09-11T13:49:26Z |
---
license: apache-2.0
base_model: openai/whisper-tiny
tags:
- generated_from_keras_callback
model-index:
- name: whisper_4_with_init_sun_syl_wd_0__0090
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# whisper_4_with_init_sun_syl_wd_0__0090
This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0943
- Train Accuracy: 0.0356
- Train Wermet: 0.0118
- Train Wermet Syl: 0.0159
- Validation Loss: 1.2876
- Validation Accuracy: 0.0208
- Validation Wermet: 0.3252
- Validation Wermet Syl: 0.2884
- Epoch: 89
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 1e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Train Wermet | Train Wermet Syl | Validation Loss | Validation Accuracy | Validation Wermet | Validation Wermet Syl | Epoch |
|:----------:|:--------------:|:------------:|:----------------:|:---------------:|:-------------------:|:-----------------:|:---------------------:|:-----:|
| 5.3409 | 0.0111 | 1.3547 | 1.2898 | 3.9789 | 0.0114 | 0.9710 | 0.9563 | 0 |
| 4.7143 | 0.0116 | 0.8622 | 0.8228 | 3.9404 | 0.0113 | 0.9823 | 0.9735 | 1 |
| 4.6752 | 0.0117 | 0.8472 | 0.8057 | 3.9081 | 0.0114 | 0.9579 | 0.9359 | 2 |
| 4.6500 | 0.0117 | 0.8382 | 0.7945 | 3.8820 | 0.0115 | 0.9213 | 0.8856 | 3 |
| 4.6282 | 0.0118 | 0.8286 | 0.7805 | 3.8738 | 0.0114 | 0.9433 | 0.9119 | 4 |
| 4.6095 | 0.0118 | 0.8190 | 0.7696 | 3.8630 | 0.0115 | 0.9117 | 0.8698 | 5 |
| 4.5875 | 0.0119 | 0.7976 | 0.7465 | 3.8341 | 0.0116 | 0.8976 | 0.8552 | 6 |
| 4.5682 | 0.0120 | 0.7753 | 0.7227 | 3.8277 | 0.0116 | 0.9014 | 0.8653 | 7 |
| 4.5376 | 0.0121 | 0.7528 | 0.7005 | 3.7844 | 0.0118 | 0.8332 | 0.7815 | 8 |
| 4.5060 | 0.0122 | 0.7392 | 0.6844 | 3.7537 | 0.0118 | 0.8578 | 0.8152 | 9 |
| 4.4580 | 0.0124 | 0.7221 | 0.6694 | 3.7038 | 0.0120 | 0.8190 | 0.7679 | 10 |
| 4.3989 | 0.0125 | 0.7156 | 0.6636 | 3.6169 | 0.0122 | 0.7979 | 0.7429 | 11 |
| 4.3056 | 0.0128 | 0.7069 | 0.6557 | 3.5098 | 0.0125 | 0.7924 | 0.7396 | 12 |
| 4.1673 | 0.0132 | 0.7054 | 0.6584 | 3.3542 | 0.0128 | 0.7759 | 0.7240 | 13 |
| 3.9762 | 0.0138 | 0.6987 | 0.6559 | 3.1318 | 0.0133 | 0.7644 | 0.7231 | 14 |
| 3.7385 | 0.0145 | 0.6835 | 0.6448 | 2.9144 | 0.0138 | 0.7392 | 0.6955 | 15 |
| 3.5040 | 0.0152 | 0.6644 | 0.6298 | 2.7413 | 0.0142 | 0.7019 | 0.6548 | 16 |
| 3.2728 | 0.0160 | 0.6408 | 0.6101 | 2.5183 | 0.0149 | 0.6798 | 0.6363 | 17 |
| 3.0657 | 0.0167 | 0.6188 | 0.5912 | 2.3594 | 0.0153 | 0.6528 | 0.6103 | 18 |
| 2.8703 | 0.0174 | 0.5936 | 0.5685 | 2.2644 | 0.0156 | 0.6310 | 0.5925 | 19 |
| 2.6850 | 0.0181 | 0.5680 | 0.5453 | 2.1296 | 0.0160 | 0.6040 | 0.5652 | 20 |
| 2.5227 | 0.0188 | 0.5423 | 0.5215 | 2.0019 | 0.0165 | 0.5793 | 0.5403 | 21 |
| 2.3878 | 0.0194 | 0.5199 | 0.5015 | 1.8996 | 0.0169 | 0.5592 | 0.5229 | 22 |
| 2.2437 | 0.0201 | 0.4959 | 0.4788 | 1.8141 | 0.0172 | 0.5414 | 0.5045 | 23 |
| 2.1205 | 0.0207 | 0.4752 | 0.4607 | 1.7245 | 0.0175 | 0.5208 | 0.4838 | 24 |
| 1.9919 | 0.0213 | 0.4533 | 0.4390 | 1.6673 | 0.0178 | 0.5026 | 0.4659 | 25 |
| 1.9140 | 0.0217 | 0.4355 | 0.4216 | 1.6041 | 0.0181 | 0.4873 | 0.4512 | 26 |
| 1.8225 | 0.0222 | 0.4184 | 0.4052 | 1.6271 | 0.0179 | 0.4852 | 0.4511 | 27 |
| 1.7265 | 0.0227 | 0.4016 | 0.3895 | 1.5219 | 0.0184 | 0.4635 | 0.4275 | 28 |
| 1.6240 | 0.0233 | 0.3833 | 0.3729 | 1.4718 | 0.0186 | 0.4515 | 0.4170 | 29 |
| 1.5610 | 0.0236 | 0.3697 | 0.3588 | 1.4404 | 0.0188 | 0.4407 | 0.4056 | 30 |
| 1.4719 | 0.0242 | 0.3540 | 0.3449 | 1.4125 | 0.0189 | 0.4310 | 0.3961 | 31 |
| 1.4152 | 0.0245 | 0.3421 | 0.3339 | 1.3655 | 0.0191 | 0.4234 | 0.3881 | 32 |
| 1.3546 | 0.0249 | 0.3277 | 0.3195 | 1.3419 | 0.0192 | 0.4156 | 0.3816 | 33 |
| 1.2565 | 0.0256 | 0.3135 | 0.3060 | 1.3172 | 0.0194 | 0.4065 | 0.3722 | 34 |
| 1.2135 | 0.0258 | 0.3026 | 0.2958 | 1.3019 | 0.0194 | 0.4006 | 0.3662 | 35 |
| 1.1739 | 0.0261 | 0.2923 | 0.2861 | 1.3843 | 0.0190 | 0.3951 | 0.3587 | 36 |
| 1.0950 | 0.0267 | 0.2782 | 0.2733 | 1.2665 | 0.0197 | 0.3883 | 0.3541 | 37 |
| 1.0435 | 0.0271 | 0.2673 | 0.2631 | 1.2567 | 0.0197 | 0.3837 | 0.3497 | 38 |
| 0.9922 | 0.0275 | 0.2580 | 0.2542 | 1.2566 | 0.0197 | 0.3801 | 0.3444 | 39 |
| 0.9387 | 0.0279 | 0.2464 | 0.2438 | 1.2441 | 0.0198 | 0.3767 | 0.3423 | 40 |
| 0.9345 | 0.0278 | 0.2393 | 0.2373 | 1.2221 | 0.0199 | 0.3682 | 0.3336 | 41 |
| 0.8574 | 0.0285 | 0.2268 | 0.2255 | 1.2258 | 0.0199 | 0.3680 | 0.3338 | 42 |
| 0.8275 | 0.0287 | 0.2183 | 0.2180 | 1.2044 | 0.0201 | 0.3628 | 0.3290 | 43 |
| 0.8201 | 0.0288 | 0.2114 | 0.2108 | 1.2056 | 0.0201 | 0.3601 | 0.3270 | 44 |
| 0.7684 | 0.0292 | 0.2020 | 0.2029 | 1.1879 | 0.0202 | 0.3553 | 0.3215 | 45 |
| 0.7262 | 0.0295 | 0.1938 | 0.1947 | 1.2263 | 0.0200 | 0.3537 | 0.3177 | 46 |
| 0.7286 | 0.0295 | 0.1876 | 0.1898 | 1.1772 | 0.0203 | 0.3485 | 0.3135 | 47 |
| 0.6807 | 0.0300 | 0.1775 | 0.1797 | 1.1761 | 0.0203 | 0.3490 | 0.3155 | 48 |
| 0.6609 | 0.0301 | 0.1713 | 0.1742 | 1.1853 | 0.0203 | 0.3484 | 0.3153 | 49 |
| 0.6062 | 0.0306 | 0.1615 | 0.1653 | 1.1660 | 0.0204 | 0.3432 | 0.3090 | 50 |
| 0.5755 | 0.0309 | 0.1547 | 0.1584 | 1.1698 | 0.0204 | 0.3428 | 0.3089 | 51 |
| 0.5600 | 0.0310 | 0.1482 | 0.1524 | 1.1667 | 0.0204 | 0.3398 | 0.3058 | 52 |
| 0.5715 | 0.0308 | 0.1449 | 0.1496 | 1.1614 | 0.0205 | 0.3381 | 0.3036 | 53 |
| 0.5247 | 0.0313 | 0.1358 | 0.1411 | 1.1639 | 0.0205 | 0.3359 | 0.3025 | 54 |
| 0.5085 | 0.0315 | 0.1301 | 0.1358 | 1.2420 | 0.0202 | 0.3412 | 0.3064 | 55 |
| 0.4827 | 0.0317 | 0.1239 | 0.1295 | 1.1677 | 0.0205 | 0.3349 | 0.3009 | 56 |
| 0.4848 | 0.0317 | 0.1207 | 0.1280 | 1.1653 | 0.0205 | 0.3326 | 0.2991 | 57 |
| 0.4323 | 0.0322 | 0.1109 | 0.1185 | 1.1602 | 0.0206 | 0.3299 | 0.2953 | 58 |
| 0.4183 | 0.0323 | 0.1057 | 0.1133 | 1.1622 | 0.0206 | 0.3307 | 0.2962 | 59 |
| 0.4329 | 0.0322 | 0.1028 | 0.1100 | 1.1714 | 0.0206 | 0.3300 | 0.2950 | 60 |
| 0.3962 | 0.0326 | 0.0964 | 0.1045 | 1.1726 | 0.0206 | 0.3311 | 0.2967 | 61 |
| 0.3642 | 0.0329 | 0.0898 | 0.0973 | 1.1699 | 0.0206 | 0.3289 | 0.2936 | 62 |
| 0.3786 | 0.0327 | 0.0884 | 0.0963 | 1.1734 | 0.0206 | 0.3279 | 0.2929 | 63 |
| 0.3698 | 0.0328 | 0.0842 | 0.0925 | 1.1728 | 0.0207 | 0.3282 | 0.2932 | 64 |
| 0.3219 | 0.0333 | 0.0765 | 0.0850 | 1.1830 | 0.0207 | 0.3258 | 0.2907 | 65 |
| 0.3035 | 0.0335 | 0.0725 | 0.0811 | 1.1840 | 0.0207 | 0.3261 | 0.2904 | 66 |
| 0.3522 | 0.0330 | 0.0745 | 0.0826 | 1.2107 | 0.0206 | 0.3299 | 0.2955 | 67 |
| 0.3001 | 0.0335 | 0.0663 | 0.0749 | 1.1810 | 0.0207 | 0.3264 | 0.2909 | 68 |
| 0.2729 | 0.0338 | 0.0595 | 0.0677 | 1.1911 | 0.0207 | 0.3247 | 0.2886 | 69 |
| 0.2696 | 0.0338 | 0.0572 | 0.0654 | 1.1950 | 0.0207 | 0.3260 | 0.2905 | 70 |
| 0.2840 | 0.0337 | 0.0563 | 0.0648 | 1.2094 | 0.0207 | 0.3250 | 0.2887 | 71 |
| 0.2319 | 0.0342 | 0.0484 | 0.0569 | 1.2107 | 0.0207 | 0.3250 | 0.2878 | 72 |
| 0.2371 | 0.0342 | 0.0464 | 0.0541 | 1.2059 | 0.0207 | 0.3240 | 0.2880 | 73 |
| 0.2666 | 0.0338 | 0.0486 | 0.0575 | 1.2036 | 0.0207 | 0.3241 | 0.2887 | 74 |
| 0.2443 | 0.0340 | 0.0442 | 0.0522 | 1.2106 | 0.0207 | 0.3241 | 0.2877 | 75 |
| 0.2118 | 0.0344 | 0.0380 | 0.0456 | 1.2172 | 0.0207 | 0.3240 | 0.2871 | 76 |
| 0.1997 | 0.0346 | 0.0354 | 0.0428 | 1.2247 | 0.0208 | 0.3219 | 0.2852 | 77 |
| 0.2461 | 0.0341 | 0.0386 | 0.0466 | 1.2257 | 0.0207 | 0.3240 | 0.2874 | 78 |
| 0.2367 | 0.0342 | 0.0364 | 0.0431 | 1.2173 | 0.0208 | 0.3234 | 0.2870 | 79 |
| 0.1857 | 0.0347 | 0.0294 | 0.0365 | 1.2287 | 0.0208 | 0.3244 | 0.2876 | 80 |
| 0.1504 | 0.0351 | 0.0244 | 0.0314 | 1.2425 | 0.0207 | 0.3238 | 0.2871 | 81 |
| 0.1438 | 0.0352 | 0.0227 | 0.0287 | 1.2495 | 0.0208 | 0.3222 | 0.2861 | 82 |
| 0.1545 | 0.0350 | 0.0232 | 0.0288 | 1.2612 | 0.0207 | 0.3257 | 0.2898 | 83 |
| 0.2122 | 0.0345 | 0.0284 | 0.0346 | 1.2518 | 0.0208 | 0.3241 | 0.2884 | 84 |
| 0.1685 | 0.0349 | 0.0222 | 0.0278 | 1.2466 | 0.0208 | 0.3231 | 0.2868 | 85 |
| 0.1371 | 0.0352 | 0.0181 | 0.0236 | 1.2606 | 0.0208 | 0.3239 | 0.2869 | 86 |
| 0.1357 | 0.0352 | 0.0171 | 0.0216 | 1.2675 | 0.0208 | 0.3240 | 0.2874 | 87 |
| 0.1022 | 0.0356 | 0.0132 | 0.0172 | 1.2887 | 0.0208 | 0.3233 | 0.2875 | 88 |
| 0.0943 | 0.0356 | 0.0118 | 0.0159 | 1.2876 | 0.0208 | 0.3252 | 0.2884 | 89 |
### Framework versions
- Transformers 4.34.0.dev0
- TensorFlow 2.13.0
- Tokenizers 0.13.3
|
RickyIG/image_classification
|
RickyIG
| 2023-09-11T13:48:48Z | 215 | 0 |
transformers
|
[
"transformers",
"pytorch",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:food101",
"base_model:google/vit-base-patch16-224-in21k",
"base_model:finetune:google/vit-base-patch16-224-in21k",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-09-11T13:39:57Z |
---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
datasets:
- food101
metrics:
- accuracy
model-index:
- name: image_classification
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: 0.886
---
<!-- 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. -->
# image_classification
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: 1.6283
- Accuracy: 0.886
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.7254 | 0.99 | 62 | 2.5418 | 0.819 |
| 1.8131 | 2.0 | 125 | 1.8025 | 0.852 |
| 1.5991 | 2.98 | 186 | 1.6367 | 0.889 |
### Framework versions
- Transformers 4.33.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
|
facebook/mbart-large-en-ro
|
facebook
| 2023-09-11T13:45:59Z | 11,496 | 1 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"safetensors",
"mbart",
"translation",
"en",
"ro",
"license:mit",
"endpoints_compatible",
"region:us"
] |
translation
| 2022-03-02T23:29:05Z |
---
tags:
- translation
language:
- en
- ro
license: mit
---
### mbart-large-en-ro
This is mbart-large-cc25, finetuned on wmt_en_ro.
It scores BLEU 28.1 without post processing and BLEU 38 with postprocessing. Instructions in `romanian_postprocessing.md`
Original Code: https://github.com/pytorch/fairseq/tree/master/examples/mbart
Docs: https://huggingface.co/transformers/master/model_doc/mbart.html
Finetuning Code: examples/seq2seq/finetune.py (as of Aug 20, 2020)
|
flyswot/test2
|
flyswot
| 2023-09-11T13:45:47Z | 265 | 0 |
transformers
|
[
"transformers",
"pytorch",
"vit",
"image-classification",
"generated_from_trainer",
"base_model:flyswot/convnext-tiny-224_flyswot",
"base_model:finetune:flyswot/convnext-tiny-224_flyswot",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2022-06-15T10:46:33Z |
---
tags:
- generated_from_trainer
base_model: flyswot/convnext-tiny-224_flyswot
model-index:
- name: test2
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. -->
# test2
This model is a fine-tuned version of [flyswot/convnext-tiny-224_flyswot](https://huggingface.co/flyswot/convnext-tiny-224_flyswot) 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: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 0.1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 0.1 | 23 | 0.1128 | 0.9787 |
### Framework versions
- Transformers 4.19.4
- Pytorch 1.11.0+cu113
- Datasets 2.3.0
- Tokenizers 0.12.1
|
davanstrien/convnext_flyswot
|
davanstrien
| 2023-09-11T13:44:59Z | 248 | 0 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"convnext",
"image-classification",
"generated_from_trainer",
"dataset:image_folder",
"base_model:facebook/convnext-base-224-22k",
"base_model:finetune:facebook/convnext-base-224-22k",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- image_folder
metrics:
- f1
base_model: facebook/convnext-base-224-22k
model-index:
- name: convnext_flyswot
results:
- task:
type: image-classification
name: Image Classification
dataset:
name: image_folder
type: image_folder
args: default
metrics:
- type: f1
value: 0.959245529738118
name: F1
---
<!-- 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. -->
# convnext_flyswot
This model is a fine-tuned version of [facebook/convnext-base-224-22k](https://huggingface.co/facebook/convnext-base-224-22k) on the image_folder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1441
- F1: 0.9592
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 666
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 1.0 | 52 | 0.6833 | 0.7484 |
| No log | 2.0 | 104 | 0.3666 | 0.8750 |
| No log | 3.0 | 156 | 0.2090 | 0.9321 |
| No log | 4.0 | 208 | 0.1478 | 0.9449 |
| No log | 5.0 | 260 | 0.1002 | 0.9518 |
| No log | 6.0 | 312 | 0.1053 | 0.9506 |
| No log | 7.0 | 364 | 0.1182 | 0.9616 |
| No log | 8.0 | 416 | 0.1102 | 0.9592 |
| No log | 9.0 | 468 | 0.1262 | 0.9616 |
| 0.203 | 10.0 | 520 | 0.1286 | 0.9616 |
| 0.203 | 11.0 | 572 | 0.1355 | 0.9592 |
| 0.203 | 12.0 | 624 | 0.1299 | 0.9592 |
| 0.203 | 13.0 | 676 | 0.1154 | 0.9592 |
| 0.203 | 14.0 | 728 | 0.1385 | 0.9580 |
| 0.203 | 15.0 | 780 | 0.1330 | 0.9592 |
| 0.203 | 16.0 | 832 | 0.1390 | 0.9592 |
| 0.203 | 17.0 | 884 | 0.1386 | 0.9592 |
| 0.203 | 18.0 | 936 | 0.1390 | 0.9592 |
| 0.203 | 19.0 | 988 | 0.1409 | 0.9592 |
| 0.0006 | 20.0 | 1040 | 0.1411 | 0.9592 |
| 0.0006 | 21.0 | 1092 | 0.1413 | 0.9592 |
| 0.0006 | 22.0 | 1144 | 0.1415 | 0.9592 |
| 0.0006 | 23.0 | 1196 | 0.1426 | 0.9592 |
| 0.0006 | 24.0 | 1248 | 0.1435 | 0.9592 |
| 0.0006 | 25.0 | 1300 | 0.1438 | 0.9592 |
| 0.0006 | 26.0 | 1352 | 0.1434 | 0.9592 |
| 0.0006 | 27.0 | 1404 | 0.1437 | 0.9592 |
| 0.0006 | 28.0 | 1456 | 0.1441 | 0.9592 |
| 0.0002 | 29.0 | 1508 | 0.1440 | 0.9592 |
| 0.0002 | 30.0 | 1560 | 0.1441 | 0.9592 |
### Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.6
|
davanstrien/detr-resnet-50_fine_tuned_trade_dir
|
davanstrien
| 2023-09-11T13:44:46Z | 162 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"detr",
"object-detection",
"generated_from_trainer",
"base_model:facebook/detr-resnet-50",
"base_model:finetune:facebook/detr-resnet-50",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
object-detection
| 2022-12-07T16:09:19Z |
---
license: apache-2.0
tags:
- generated_from_trainer
base_model: facebook/detr-resnet-50
model-index:
- name: detr-resnet-50_fine_tuned_trade_dir
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# detr-resnet-50_fine_tuned_trade_dir
This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu116
- Datasets 2.7.1
- Tokenizers 0.13.2
|
davanstrien/conditional-detr-resnet-50_fine_tuned_beyond_words
|
davanstrien
| 2023-09-11T13:44:42Z | 134 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"conditional_detr",
"object-detection",
"generated_from_trainer",
"dataset:biglam/loc_beyond_words",
"base_model:microsoft/conditional-detr-resnet-50",
"base_model:finetune:microsoft/conditional-detr-resnet-50",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
object-detection
| 2023-03-01T12:42:14Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- biglam/loc_beyond_words
base_model: microsoft/conditional-detr-resnet-50
model-index:
- name: conditional-detr-resnet-50_fine_tuned_beyond_words
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. -->
# conditional-detr-resnet-50_fine_tuned_beyond_words
This model is a fine-tuned version of [microsoft/conditional-detr-resnet-50](https://huggingface.co/microsoft/conditional-detr-resnet-50) on the loc_beyond_words dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5892
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 200
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 6.674 | 0.28 | 100 | 1.7571 |
| 1.4721 | 0.56 | 200 | 1.2737 |
| 1.2557 | 0.84 | 300 | 1.1037 |
| 1.0781 | 1.12 | 400 | 1.0184 |
| 1.0353 | 1.4 | 500 | 0.9988 |
| 1.0324 | 1.69 | 600 | 0.9951 |
| 0.9131 | 1.97 | 700 | 0.9224 |
| 0.8724 | 2.25 | 800 | 0.9692 |
| 0.8129 | 2.53 | 900 | 0.8670 |
| 0.9 | 2.81 | 1000 | 0.8326 |
| 0.7993 | 3.09 | 1100 | 0.7875 |
| 0.7907 | 3.37 | 1200 | 0.7517 |
| 0.8424 | 3.65 | 1300 | 0.9088 |
| 0.7808 | 3.93 | 1400 | 0.8506 |
| 0.7469 | 4.21 | 1500 | 0.7928 |
| 0.7582 | 4.49 | 1600 | 0.7228 |
| 0.7546 | 4.78 | 1700 | 0.7588 |
| 0.7842 | 5.06 | 1800 | 0.7726 |
| 0.775 | 5.34 | 1900 | 0.7676 |
| 0.7263 | 5.62 | 2000 | 0.7164 |
| 0.7209 | 5.9 | 2100 | 0.7061 |
| 0.7259 | 6.18 | 2200 | 0.7579 |
| 0.7701 | 6.46 | 2300 | 0.8184 |
| 0.7391 | 6.74 | 2400 | 0.6684 |
| 0.6834 | 7.02 | 2500 | 0.7042 |
| 0.7098 | 7.3 | 2600 | 0.7166 |
| 0.7498 | 7.58 | 2700 | 0.6752 |
| 0.7056 | 7.87 | 2800 | 0.7064 |
| 0.7004 | 8.15 | 2900 | 0.7090 |
| 0.6964 | 8.43 | 3000 | 0.7318 |
| 0.682 | 8.71 | 3100 | 0.7216 |
| 0.7309 | 8.99 | 3200 | 0.6545 |
| 0.6576 | 9.27 | 3300 | 0.6478 |
| 0.7014 | 9.55 | 3400 | 0.6814 |
| 0.673 | 9.83 | 3500 | 0.6783 |
| 0.6455 | 10.11 | 3600 | 0.7248 |
| 0.7041 | 10.39 | 3700 | 0.7729 |
| 0.6664 | 10.67 | 3800 | 0.6746 |
| 0.6161 | 10.96 | 3900 | 0.6414 |
| 0.6975 | 11.24 | 4000 | 0.6637 |
| 0.6751 | 11.52 | 4100 | 0.6570 |
| 0.6092 | 11.8 | 4200 | 0.6691 |
| 0.6593 | 12.08 | 4300 | 0.6276 |
| 0.6449 | 12.36 | 4400 | 0.6388 |
| 0.6136 | 12.64 | 4500 | 0.6711 |
| 0.6521 | 12.92 | 4600 | 0.6768 |
| 0.6162 | 13.2 | 4700 | 0.6427 |
| 0.7083 | 13.48 | 4800 | 0.6492 |
| 0.6407 | 13.76 | 4900 | 0.6213 |
| 0.6371 | 14.04 | 5000 | 0.6674 |
| 0.626 | 14.33 | 5100 | 0.6185 |
| 0.6442 | 14.61 | 5200 | 0.7180 |
| 0.5981 | 14.89 | 5300 | 0.6441 |
| 0.629 | 15.17 | 5400 | 0.6262 |
| 0.625 | 15.45 | 5500 | 0.6397 |
| 0.6123 | 15.73 | 5600 | 0.6440 |
| 0.6084 | 16.01 | 5700 | 0.6493 |
| 0.6021 | 16.29 | 5800 | 0.6263 |
| 0.6502 | 16.57 | 5900 | 0.6254 |
| 0.6339 | 16.85 | 6000 | 0.7043 |
| 0.5925 | 17.13 | 6100 | 0.8014 |
| 0.6453 | 17.42 | 6200 | 0.6385 |
| 0.6143 | 17.7 | 6300 | 0.6033 |
| 0.6057 | 17.98 | 6400 | 0.6881 |
| 0.6386 | 18.26 | 6500 | 0.6366 |
| 0.5839 | 18.54 | 6600 | 0.6563 |
| 0.6013 | 18.82 | 6700 | 0.5982 |
| 0.5999 | 19.1 | 6800 | 0.6064 |
| 0.6023 | 19.38 | 6900 | 0.5795 |
| 0.5593 | 19.66 | 7000 | 0.6538 |
| 0.6375 | 19.94 | 7100 | 0.6991 |
| 0.6073 | 20.22 | 7200 | 0.7117 |
| 0.596 | 20.51 | 7300 | 0.6034 |
| 0.5987 | 20.79 | 7400 | 0.6489 |
| 0.5922 | 21.07 | 7500 | 0.6216 |
| 0.589 | 21.35 | 7600 | 0.6257 |
| 0.6047 | 21.63 | 7700 | 0.6415 |
| 0.5775 | 21.91 | 7800 | 0.6159 |
| 0.588 | 22.19 | 7900 | 0.6095 |
| 0.5844 | 22.47 | 8000 | 0.6373 |
| 0.5964 | 22.75 | 8100 | 0.6022 |
| 0.5987 | 23.03 | 8200 | 0.6050 |
| 0.5605 | 23.31 | 8300 | 0.6083 |
| 0.5835 | 23.6 | 8400 | 0.7823 |
| 0.5816 | 23.88 | 8500 | 0.6417 |
| 0.5757 | 24.16 | 8600 | 0.6324 |
| 0.5997 | 24.44 | 8700 | 0.6046 |
| 0.5674 | 24.72 | 8800 | 0.6558 |
| 0.5703 | 25.0 | 8900 | 0.5819 |
| 0.5766 | 25.28 | 9000 | 0.6116 |
| 0.5548 | 25.56 | 9100 | 0.5877 |
| 0.564 | 25.84 | 9200 | 0.5672 |
| 0.548 | 26.12 | 9300 | 0.6073 |
| 0.5436 | 26.4 | 9400 | 0.5739 |
| 0.6006 | 26.69 | 9500 | 0.6101 |
| 0.5519 | 26.97 | 9600 | 0.5869 |
| 0.5432 | 27.25 | 9700 | 0.5721 |
| 0.5597 | 27.53 | 9800 | 0.5807 |
| 0.5254 | 27.81 | 9900 | 0.5849 |
| 0.5366 | 28.09 | 10000 | 0.5831 |
| 0.5654 | 28.37 | 10100 | 0.5993 |
| 0.57 | 28.65 | 10200 | 0.5892 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.0+cu117
- Datasets 2.10.1
- Tokenizers 0.13.2
|
davanstrien/convnext-tiny-224-wikiart
|
davanstrien
| 2023-09-11T13:44:37Z | 216 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"convnext",
"image-classification",
"vision",
"generated_from_trainer",
"dataset:wiki_art",
"base_model:facebook/convnext-tiny-224",
"base_model:finetune:facebook/convnext-tiny-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-03-21T12:54:11Z |
---
license: apache-2.0
tags:
- image-classification
- vision
- generated_from_trainer
datasets:
- wiki_art
metrics:
- accuracy
base_model: facebook/convnext-tiny-224
model-index:
- name: convnext-tiny-224-wikiart
results:
- task:
type: image-classification
name: Image Classification
dataset:
name: huggan/wikiart
type: wiki_art
config: default
split: train
args: default
metrics:
- type: accuracy
value: 0.7140050748956372
name: Accuracy
---
<!-- 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. -->
# convnext-tiny-224-wikiart
This model is a fine-tuned version of [facebook/convnext-tiny-224](https://huggingface.co/facebook/convnext-tiny-224) on the huggan/wikiart dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8022
- Accuracy: 0.7140
## 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: 1337
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.9779 | 1.0 | 8654 | 0.9191 | 0.6743 |
| 0.9959 | 2.0 | 17308 | 0.8523 | 0.6941 |
| 1.0344 | 3.0 | 25962 | 0.8277 | 0.7023 |
| 0.8853 | 4.0 | 34616 | 0.8126 | 0.7100 |
| 0.9557 | 5.0 | 43270 | 0.8022 | 0.7140 |
### Framework versions
- Transformers 4.28.0.dev0
- Pytorch 1.13.0+cu117
- Datasets 2.10.1
- Tokenizers 0.13.2
|
davanstrien/vit-manuscripts
|
davanstrien
| 2023-09-11T13:44:14Z | 72 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"vit_mae",
"pretraining",
"masked-auto-encoding",
"generated_from_trainer",
"base_model:facebook/vit-mae-base",
"base_model:finetune:facebook/vit-mae-base",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- masked-auto-encoding
- generated_from_trainer
base_model: facebook/vit-mae-base
model-index:
- name: vit-manuscripts
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# vit-manuscripts
This model is a fine-tuned version of [facebook/vit-mae-base](https://huggingface.co/facebook/vit-mae-base) on the davanstrien/manuscript_iiif_test dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5177
## 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: 7.5e-05
- train_batch_size: 128
- eval_batch_size: 128
- seed: 1337
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 1.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.5303 | 1.0 | 34 | 0.5134 |
### Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.0+cu111
- Datasets 1.18.2
- Tokenizers 0.11.0
|
davanstrien/iiif_manuscript_vit
|
davanstrien
| 2023-09-11T13:44:01Z | 251 | 0 |
transformers
|
[
"transformers",
"pytorch",
"vit",
"image-classification",
"generated_from_trainer",
"base_model:google/vit-base-patch16-224-in21k",
"base_model:finetune:google/vit-base-patch16-224-in21k",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- f1
base_model: google/vit-base-patch16-224-in21k
model-index:
- name: iiif_manuscript_vit
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. -->
# iiif_manuscript_vit
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 None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5684
- F1: 0.5996
## 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: 10
- mixed_precision_training: Native AMP
- label_smoothing_factor: 0.1
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 0.5639 | 1.0 | 2269 | 0.5822 | 0.5516 |
| 0.5834 | 2.0 | 4538 | 0.5825 | 0.5346 |
| 0.5778 | 3.0 | 6807 | 0.5794 | 0.6034 |
| 0.5735 | 4.0 | 9076 | 0.5742 | 0.5713 |
| 0.5731 | 5.0 | 11345 | 0.5745 | 0.6008 |
| 0.5701 | 6.0 | 13614 | 0.5729 | 0.5499 |
| 0.5696 | 7.0 | 15883 | 0.5717 | 0.5952 |
| 0.5683 | 8.0 | 18152 | 0.5680 | 0.6005 |
| 0.5648 | 9.0 | 20421 | 0.5679 | 0.5967 |
| 0.564 | 10.0 | 22690 | 0.5684 | 0.5996 |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.0
|
davanstrien/cultural_heritage_metadata_accuracy_mnli
|
davanstrien
| 2023-09-11T13:43:22Z | 108 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"dataset:biglam/cultural_heritage_metadata_accuracy",
"base_model:FacebookAI/xlm-roberta-base",
"base_model:finetune:FacebookAI/xlm-roberta-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-11-14T12:09:32Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- biglam/cultural_heritage_metadata_accuracy
base_model: xlm-roberta-base
model-index:
- name: cultural_heritage_metadata_accuracy_mnli
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. -->
# cultural_heritage_metadata_accuracy_mnli
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the cultural_heritage_metadata_accuracy dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.25.0.dev0
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.2
|
davanstrien/convnext-small-224-leicester_binary
|
davanstrien
| 2023-09-11T13:43:10Z | 189 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"convnext",
"image-classification",
"vision",
"generated_from_trainer",
"base_model:facebook/convnext-small-224",
"base_model:finetune:facebook/convnext-small-224",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2022-12-06T16:56:52Z |
---
license: apache-2.0
tags:
- image-classification
- vision
- generated_from_trainer
metrics:
- f1
base_model: facebook/convnext-small-224
model-index:
- name: convnext-small-224-leicester_binary
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. -->
# convnext-small-224-leicester_binary
This model is a fine-tuned version of [facebook/convnext-small-224](https://huggingface.co/facebook/convnext-small-224) on the davanstrien/leicester_loaded_annotations_binary dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1283
- F1: 0.9620
## 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: 128
- seed: 1337
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 1.0 | 7 | 0.5143 | 0.8608 |
| 0.5872 | 2.0 | 14 | 0.4215 | 0.8608 |
| 0.3903 | 3.0 | 21 | 0.4127 | 0.8608 |
| 0.3903 | 4.0 | 28 | 0.3605 | 0.8608 |
| 0.3163 | 5.0 | 35 | 0.3152 | 0.8608 |
| 0.2942 | 6.0 | 42 | 0.2942 | 0.8608 |
| 0.2942 | 7.0 | 49 | 0.2669 | 0.8608 |
| 0.2755 | 8.0 | 56 | 0.2316 | 0.8608 |
| 0.2281 | 9.0 | 63 | 0.2104 | 0.8608 |
| 0.2076 | 10.0 | 70 | 0.1938 | 0.8608 |
| 0.2076 | 11.0 | 77 | 0.1803 | 0.8608 |
| 0.1832 | 12.0 | 84 | 0.1704 | 0.8608 |
| 0.1758 | 13.0 | 91 | 0.1650 | 0.8608 |
| 0.1758 | 14.0 | 98 | 0.1714 | 0.8608 |
| 0.167 | 15.0 | 105 | 0.1575 | 0.8608 |
| 0.1519 | 16.0 | 112 | 0.1549 | 0.8608 |
| 0.1519 | 17.0 | 119 | 0.1705 | 0.8608 |
| 0.1422 | 18.0 | 126 | 0.1478 | 0.8608 |
| 0.1444 | 19.0 | 133 | 0.1437 | 0.8608 |
| 0.1396 | 20.0 | 140 | 0.1398 | 0.8608 |
| 0.1396 | 21.0 | 147 | 0.1351 | 0.8608 |
| 0.1293 | 22.0 | 154 | 0.1370 | 0.8987 |
| 0.1361 | 23.0 | 161 | 0.1335 | 0.8987 |
| 0.1361 | 24.0 | 168 | 0.1311 | 0.9367 |
| 0.1246 | 25.0 | 175 | 0.1289 | 0.9620 |
| 0.1211 | 26.0 | 182 | 0.1283 | 0.9620 |
| 0.1211 | 27.0 | 189 | 0.1294 | 0.9620 |
| 0.1182 | 28.0 | 196 | 0.1306 | 0.9620 |
| 0.1172 | 29.0 | 203 | 0.1312 | 0.9620 |
| 0.1102 | 30.0 | 210 | 0.1318 | 0.9620 |
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.12.1+cu113
- Datasets 2.7.1
- Tokenizers 0.13.2
|
HiTZ/A2T_RoBERTa_SMFA_ACE-arg
|
HiTZ
| 2023-09-11T13:35:52Z | 114 | 0 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"roberta",
"text-classification",
"zero-shot-classification",
"dataset:snli",
"dataset:anli",
"dataset:multi_nli",
"dataset:multi_nli_mismatch",
"dataset:fever",
"arxiv:2104.14690",
"arxiv:2203.13602",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
zero-shot-classification
| 2022-05-02T09:38:07Z |
---
pipeline_tag: zero-shot-classification
datasets:
- snli
- anli
- multi_nli
- multi_nli_mismatch
- fever
---
# A2T Entailment model
**Important:** These pretrained entailment models are intended to be used with the [Ask2Transformers](https://github.com/osainz59/Ask2Transformers) library but are also fully compatible with the `ZeroShotTextClassificationPipeline` from [Transformers](https://github.com/huggingface/Transformers).
Textual Entailment (or Natural Language Inference) has turned out to be a good choice for zero-shot text classification problems [(Yin et al., 2019](https://aclanthology.org/D19-1404/); [Wang et al., 2021](https://arxiv.org/abs/2104.14690); [Sainz and Rigau, 2021)](https://aclanthology.org/2021.gwc-1.6/). Recent research addressed Information Extraction problems with the same idea [(Lyu et al., 2021](https://aclanthology.org/2021.acl-short.42/); [Sainz et al., 2021](https://aclanthology.org/2021.emnlp-main.92/); [Sainz et al., 2022a](), [Sainz et al., 2022b)](https://arxiv.org/abs/2203.13602). The A2T entailment models are first trained with NLI datasets such as MNLI [(Williams et al., 2018)](), SNLI [(Bowman et al., 2015)]() or/and ANLI [(Nie et al., 2020)]() and then fine-tuned to specific tasks that were previously converted to textual entailment format.
For more information please, take a look to the [Ask2Transformers](https://github.com/osainz59/Ask2Transformers) library or the following published papers:
- [Label Verbalization and Entailment for Effective Zero and Few-Shot Relation Extraction (Sainz et al., EMNLP 2021)](https://aclanthology.org/2021.emnlp-main.92/)
- [Textual Entailment for Event Argument Extraction: Zero- and Few-Shot with Multi-Source Learning (Sainz et al., Findings of NAACL-HLT 2022)]()
## About the model
The model name describes the configuration used for training as follows:
<!-- $$\text{HiTZ/A2T\_[pretrained\_model]\_[NLI\_datasets]\_[finetune\_datasets]}$$ -->
<h3 align="center">HiTZ/A2T_[pretrained_model]_[NLI_datasets]_[finetune_datasets]</h3>
- `pretrained_model`: The checkpoint used for initialization. For example: RoBERTa<sub>large</sub>.
- `NLI_datasets`: The NLI datasets used for pivot training.
- `S`: Standford Natural Language Inference (SNLI) dataset.
- `M`: Multi Natural Language Inference (MNLI) dataset.
- `F`: Fever-nli dataset.
- `A`: Adversarial Natural Language Inference (ANLI) dataset.
- `finetune_datasets`: The datasets used for fine tuning the entailment model. Note that for more than 1 dataset the training was performed sequentially. For example: ACE-arg.
Some models like `HiTZ/A2T_RoBERTa_SMFA_ACE-arg` have been trained marking some information between square brackets (`'[['` and `']]'`) like the event trigger span. Make sure you follow the same preprocessing in order to obtain the best results.
## Cite
If you use this model, consider citing the following publications:
```bibtex
@inproceedings{sainz-etal-2021-label,
title = "Label Verbalization and Entailment for Effective Zero and Few-Shot Relation Extraction",
author = "Sainz, Oscar and
Lopez de Lacalle, Oier and
Labaka, Gorka and
Barrena, Ander and
Agirre, Eneko",
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-main.92",
doi = "10.18653/v1/2021.emnlp-main.92",
pages = "1199--1212",
}
```
|
ixa-ehu/roberta-eus-cc100-base-cased
|
ixa-ehu
| 2023-09-11T13:33:41Z | 112 | 1 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"roberta",
"fill-mask",
"basque",
"eu",
"arxiv:2203.08111",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-16T09:47:37Z |
---
language: eu
license: cc-by-nc-4.0
tags:
- basque
- roberta
---
# Roberta-eus cc100 base cased
This is a RoBERTa model for Basque model presented in [Does corpus quality really matter for low-resource languages?](https://arxiv.org/abs/2203.08111). There are several models for Basque using the RoBERTa architecture, using different corpora:
- roberta-eus-euscrawl-base-cased: Basque RoBERTa model trained on Euscrawl, a corpus created using tailored crawling from Basque sites. EusCrawl contains 12,528k documents and 423M tokens.
- roberta-eus-euscrawl-large-cased: RoBERTa large trained on EusCrawl.
- roberta-eus-mC4-base-cased: Basque RoBERTa model trained on the Basque portion of mc4 dataset.
- roberta-eus-CC100-base-cased: Basque RoBERTa model trained on Basque portion of cc100 dataset.
The models have been tested on five different downstream tasks for Basque: Topic classification, Sentiment analysis, Stance detection, Named Entity Recognition (NER), and Question Answering (refer to the [paper](https://arxiv.org/abs/2203.08111) for more details). See summary of results below:
| Model | Topic class. | Sentiment | Stance det. | NER | QA | Average |
|----------------------------------|--------------|-----------|-------------|----------|----------|----------|
| roberta-eus-euscrawl-base-cased | 76.2 | 77.7 | 57.4 | 86.8 | 34.6 | 66.5 |
| roberta-eus-euscrawl-large-cased | **77.6** | 78.8 | 62.9 | **87.2** | **38.3** | **69.0** |
| roberta-eus-mC4-base-cased | 75.3 | **80.4** | 59.1 | 86.0 | 35.2 | 67.2 |
| roberta-eus-CC100-base-cased | 76.2 | 78.8 | **63.4** | 85.2 | 35.8 | 67.9 |
If you use any of these models, please cite the following paper:
```
@misc{artetxe2022euscrawl,
title={Does corpus quality really matter for low-resource languages?},
author={Mikel Artetxe, Itziar Aldabe, Rodrigo Agerri,
Olatz Perez-de-Viñaspre, Aitor Soroa},
year={2022},
eprint={2203.08111},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
kensvin/audio_classification
|
kensvin
| 2023-09-11T13:31:00Z | 162 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"audio-classification",
"generated_from_trainer",
"dataset:minds14",
"base_model:facebook/wav2vec2-base",
"base_model:finetune:facebook/wav2vec2-base",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
audio-classification
| 2023-09-11T13:27:41Z |
---
license: apache-2.0
base_model: facebook/wav2vec2-base
tags:
- generated_from_trainer
datasets:
- minds14
metrics:
- accuracy
model-index:
- name: audio_classification
results:
- task:
name: Audio Classification
type: audio-classification
dataset:
name: minds14
type: minds14
config: en-US
split: train
args: en-US
metrics:
- name: Accuracy
type: accuracy
value: 0.07079646017699115
---
<!-- 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. -->
# audio_classification
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the minds14 dataset.
It achieves the following results on the evaluation set:
- Loss: 2.6513
- Accuracy: 0.0708
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 0.8 | 3 | 2.6439 | 0.0531 |
| No log | 1.87 | 7 | 2.6446 | 0.0708 |
| 2.6349 | 2.93 | 11 | 2.6484 | 0.0885 |
| 2.6349 | 4.0 | 15 | 2.6497 | 0.0885 |
| 2.6349 | 4.8 | 18 | 2.6509 | 0.0796 |
| 2.6233 | 5.87 | 22 | 2.6513 | 0.0708 |
| 2.6233 | 6.93 | 26 | 2.6515 | 0.0708 |
| 2.612 | 8.0 | 30 | 2.6513 | 0.0708 |
### Framework versions
- Transformers 4.33.1
- Pytorch 1.13.1+cu117
- Datasets 2.14.5
- Tokenizers 0.13.3
|
sanchit-gandhi/whisper-small-dv
|
sanchit-gandhi
| 2023-09-11T13:25:29Z | 210 | 1 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"dv",
"dataset:mozilla-foundation/common_voice_13_0",
"base_model:openai/whisper-small",
"base_model:finetune:openai/whisper-small",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-06-27T14:43:10Z |
---
language:
- dv
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_13_0
metrics:
- wer
base_model: openai/whisper-small
model-index:
- name: Whisper Small Dv - Sanchit Gandhi
results:
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
name: Common Voice 13
type: mozilla-foundation/common_voice_13_0
config: dv
split: test
args: dv
metrics:
- type: wer
value: 14.066140417985187
name: Wer
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Whisper Small Dv - Sanchit Gandhi
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 13 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1727
- Wer Ortho: 63.8972
- Wer: 14.0661
## 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: constant_with_warmup
- lr_scheduler_warmup_steps: 50
- training_steps: 500
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer Ortho | Wer |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:-------:|
| 0.136 | 1.63 | 500 | 0.1727 | 63.8972 | 14.0661 |
### Framework versions
- Transformers 4.31.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.13.1.dev0
- Tokenizers 0.13.3
|
sanchit-gandhi/whisper-medium-fleurs-lang-id
|
sanchit-gandhi
| 2023-09-11T13:25:16Z | 128,294 | 14 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"whisper",
"audio-classification",
"generated_from_trainer",
"dataset:xtreme_s",
"base_model:openai/whisper-medium",
"base_model:finetune:openai/whisper-medium",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
audio-classification
| 2023-02-23T13:37:22Z |
---
license: apache-2.0
tags:
- audio-classification
- generated_from_trainer
datasets:
- xtreme_s
metrics:
- accuracy
base_model: openai/whisper-medium
model-index:
- name: whisper-medium-fleurs-lang-id
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 Medium FLEURS Language Identification
This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the [FLEURS subset](https://huggingface.co/datasets/google/xtreme_s#language-identification---fleurs-langid) of the [google/xtreme_s](https://huggingface.co/google/xtreme_s) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8413
- Accuracy: 0.8805
To reproduce this run, execute the command in [`run.sh`](https://huggingface.co/sanchit-gandhi/whisper-medium-fleurs-lang-id/blob/main/run.sh).
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 16
- eval_batch_size: 32
- seed: 0
- distributed_type: multi-GPU
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.0152 | 1.0 | 8494 | 0.9087 | 0.8431 |
| 0.0003 | 2.0 | 16988 | 1.0059 | 0.8460 |
| 0.0 | 3.0 | 25482 | 0.8413 | 0.8805 |
### Framework versions
- Transformers 4.27.0.dev0
- Pytorch 1.13.1
- Datasets 2.9.0
- Tokenizers 0.13.2
|
kakao-enterprise/vits-vctk
|
kakao-enterprise
| 2023-09-11T13:24:11Z | 1,906 | 12 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"vits",
"text-to-audio",
"text-to-speech",
"license:mit",
"endpoints_compatible",
"region:us"
] |
text-to-speech
| 2023-08-31T10:35:47Z |
---
license: mit
tags:
- vits
pipeline_tag: text-to-speech
---
# VITS: Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech
VITS is an end-to-end speech synthesis model that predicts a speech waveform conditional on an input text sequence. It is a
conditional variational autoencoder (VAE) comprised of a posterior encoder, decoder, and conditional prior. This repository
contains the weights for the official VITS checkpoint trained on the [VCTK](https://huggingface.co/datasets/vctk) dataset.
## Model Details
VITS (**V**ariational **I**nference with adversarial learning for end-to-end **T**ext-to-**S**peech) is an end-to-end
speech synthesis model that predicts a speech waveform conditional on an input text sequence. It is a conditional variational
autoencoder (VAE) comprised of a posterior encoder, decoder, and conditional prior.
A set of spectrogram-based acoustic features are predicted by the flow-based module, which is formed of a Transformer-based
text encoder and multiple coupling layers. The spectrogram is decoded using a stack of transposed convolutional layers,
much in the same style as the HiFi-GAN vocoder. Motivated by the one-to-many nature of the TTS problem, where the same text
input can be spoken in multiple ways, the model also includes a stochastic duration predictor, which allows the model to
synthesise speech with different rhythms from the same input text.
The model is trained end-to-end with a combination of losses derived from variational lower bound and adversarial training.
To improve the expressiveness of the model, normalizing flows are applied to the conditional prior distribution. During
inference, the text encodings are up-sampled based on the duration prediction module, and then mapped into the
waveform using a cascade of the flow module and HiFi-GAN decoder. Due to the stochastic nature of the duration predictor,
the model is non-deterministic, and thus requires a fixed seed to generate the same speech waveform.
There are two variants of the VITS model: one is trained on the [LJ Speech](https://huggingface.co/datasets/lj_speech) dataset,
and the other is trained on the [VCTK](https://huggingface.co/datasets/vctk) dataset. LJ Speech dataset consists of 13,100 short
audio clips of a single speaker with a total length of approximately 24 hours. The VCTK dataset consists of approximately 44,000
short audio clips uttered by 109 native English speakers with various accents. The total length of the audio clips is approximately
44 hours.
| Checkpoint | Train Hours | Speakers |
|------------|-------------|----------|
| [vits-ljs](https://huggingface.co/kakao-enterprise/vits-ljs) | 24 | 1 |
| [vits-vctk](https://huggingface.co/kakao-enterprise/vits-vctk) | 44 | 109 |
## Usage
VITS is available in the 🤗 Transformers library from version 4.33 onwards. To use this checkpoint,
first install the latest version of the library:
```
pip install --upgrade transformers accelerate
```
Then, run inference with the following code-snippet:
```python
from transformers import VitsModel, AutoTokenizer
import torch
model = VitsModel.from_pretrained("kakao-enterprise/vits-vctk")
tokenizer = AutoTokenizer.from_pretrained("kakao-enterprise/vits-vctk")
text = "Hey, it's Hugging Face on the phone"
inputs = tokenizer(text, return_tensors="pt")
with torch.no_grad():
output = model(**inputs).waveform
```
The resulting waveform can be saved as a `.wav` file:
```python
import scipy
scipy.io.wavfile.write("techno.wav", rate=model.config.sampling_rate, data=output)
```
Or displayed in a Jupyter Notebook / Google Colab:
```python
from IPython.display import Audio
Audio(output, rate=model.config.sampling_rate)
```
## BibTex citation
This model was developed by Jaehyeon Kim et al. from Kakao Enterprise. If you use the model, consider citing the VITS paper:
```
@inproceedings{kim2021conditional,
title={"Conditional Variational Autoencoder with Adversarial Learning for End-to-end Text-to-speech"},
author={Kim, Jaehyeon and Kong, Jungil and Son, Juhee},
booktitle={International Conference on Machine Learning},
pages={5530--5540},
year={2021},
organization={PMLR}
}
```
## License
The model is licensed as [**MIT**](https://github.com/jaywalnut310/vits/blob/main/LICENSE).
|
nickmuchi/distilroberta-finetuned-financial-text-classification
|
nickmuchi
| 2023-09-11T13:23:38Z | 1,773 | 15 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"financial-sentiment-analysis",
"sentiment-analysis",
"sentence_50agree",
"generated_from_trainer",
"sentiment",
"finance",
"en",
"dataset:financial_phrasebank",
"dataset:Kaggle_Self_label",
"dataset:nickmuchi/financial-classification",
"base_model:distilbert/distilroberta-base",
"base_model:finetune:distilbert/distilroberta-base",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
---
language: en
license: apache-2.0
tags:
- financial-sentiment-analysis
- sentiment-analysis
- sentence_50agree
- generated_from_trainer
- sentiment
- finance
datasets:
- financial_phrasebank
- Kaggle_Self_label
- nickmuchi/financial-classification
metrics:
- f1
widget:
- text: The USD rallied by 10% last night
example_title: Bullish Sentiment
- text: Covid-19 cases have been increasing over the past few months impacting earnings
for global firms
example_title: Bearish Sentiment
- text: the USD has been trending lower
example_title: Mildly Bearish Sentiment
base_model: distilroberta-base
model-index:
- name: distilroberta-finetuned-finclass
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: financial_phrasebank
type: finance
args: sentence_50agree
metrics:
- type: F1
value: 0.8835
name: F1
- type: accuracy
value: 0.89
name: accuracy
---
# distilroberta-finetuned-financial-text-classification
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the sentence_50Agree [financial-phrasebank + Kaggle Dataset](https://huggingface.co/datasets/nickmuchi/financial-classification), a dataset consisting of 4840 Financial News categorised by sentiment (negative, neutral, positive). The Kaggle dataset includes Covid-19 sentiment data and can be found here: [sentiment-classification-selflabel-dataset](https://www.kaggle.com/percyzheng/sentiment-classification-selflabel-dataset).
It achieves the following results on the evaluation set:
- Loss: 0.4463
- F1: 0.8835
## Model description
Model determines the financial sentiment of given text. Given the unbalanced distribution of the class labels, the weights were adjusted to pay attention to the less sampled labels which should increase overall performance. The Covid dataset was added in order to enrich the model, given most models have not been trained on the impact of Covid-19 on earnings or markets.
### 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: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.7309 | 1.0 | 72 | 0.3671 | 0.8441 |
| 0.3757 | 2.0 | 144 | 0.3199 | 0.8709 |
| 0.3054 | 3.0 | 216 | 0.3096 | 0.8678 |
| 0.2229 | 4.0 | 288 | 0.3776 | 0.8390 |
| 0.1744 | 5.0 | 360 | 0.3678 | 0.8723 |
| 0.1436 | 6.0 | 432 | 0.3728 | 0.8758 |
| 0.1044 | 7.0 | 504 | 0.4116 | 0.8744 |
| 0.0931 | 8.0 | 576 | 0.4148 | 0.8761 |
| 0.0683 | 9.0 | 648 | 0.4423 | 0.8837 |
| 0.0611 | 10.0 | 720 | 0.4463 | 0.8835 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.0+cu111
- Datasets 1.18.0
- Tokenizers 0.10.3
|
AGudden/xlm-roberta-base-finetuned-marc
|
AGudden
| 2023-09-11T13:19:17Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"dataset:dutch_social",
"base_model:FacebookAI/xlm-roberta-base",
"base_model:finetune:FacebookAI/xlm-roberta-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-01-03T23:30:08Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- dutch_social
base_model: xlm-roberta-base
model-index:
- name: xlm-roberta-base-finetuned-marc
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-marc
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the dutch_social dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1992
- Mae: 0.0532
## 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 | Mae |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 0.2824 | 1.0 | 10176 | 0.2370 | 0.0748 |
| 0.1809 | 2.0 | 20352 | 0.1992 | 0.0532 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
|
kensvin/image_classification
|
kensvin
| 2023-09-11T13:18:05Z | 192 | 0 |
transformers
|
[
"transformers",
"pytorch",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:food101",
"base_model:google/vit-base-patch16-224-in21k",
"base_model:finetune:google/vit-base-patch16-224-in21k",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-09-11T13:08:35Z |
---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
datasets:
- food101
metrics:
- accuracy
model-index:
- name: image_classification
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: 0.911
---
<!-- 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. -->
# image_classification
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: 1.5938
- Accuracy: 0.911
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.7307 | 0.99 | 62 | 2.5306 | 0.833 |
| 1.8698 | 2.0 | 125 | 1.7637 | 0.903 |
| 1.5629 | 2.98 | 186 | 1.5856 | 0.915 |
### Framework versions
- Transformers 4.33.1
- Pytorch 1.13.1+cu117
- Datasets 2.14.5
- Tokenizers 0.13.3
|
nielsr/swin-tiny-patch4-window7-224-finetuned-cifar10
|
nielsr
| 2023-09-11T13:16:37Z | 221 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"swin",
"image-classification",
"generated_from_trainer",
"dataset:image_folder",
"base_model:microsoft/swin-tiny-patch4-window7-224",
"base_model:finetune:microsoft/swin-tiny-patch4-window7-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2022-04-11T11:59:54Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- image_folder
metrics:
- accuracy
base_model: microsoft/swin-tiny-patch4-window7-224
model-index:
- name: swin-tiny-patch4-window7-224-finetuned-cifar10
results:
- task:
type: image-classification
name: Image Classification
dataset:
name: image_folder
type: image_folder
args: default
metrics:
- type: accuracy
value: 0.9788888888888889
name: Accuracy
---
<!-- 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-finetuned-cifar10
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 image_folder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0690
- Accuracy: 0.9789
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.2446 | 1.0 | 190 | 0.1128 | 0.9659 |
| 0.1722 | 2.0 | 380 | 0.1034 | 0.9663 |
| 0.1355 | 3.0 | 570 | 0.0690 | 0.9789 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.11.6
|
Enyonam/roberta-base-Roberta-Model
|
Enyonam
| 2023-09-11T13:16:26Z | 17 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"text-classification",
"generated_from_trainer",
"base_model:FacebookAI/roberta-base",
"base_model:finetune:FacebookAI/roberta-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-08-30T23:47:50Z |
---
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: roberta-base-Roberta-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. -->
# roberta-base-Roberta-Model
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8450
- F1: 0.6468
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.916 | 0.5 | 500 | 0.8835 | 0.6218 |
| 0.8783 | 1.0 | 1000 | 0.8467 | 0.6531 |
| 0.8769 | 1.5 | 1500 | 0.8581 | 0.6487 |
| 0.8499 | 2.01 | 2000 | 0.8651 | 0.6488 |
| 0.8734 | 2.51 | 2500 | 0.8908 | 0.6409 |
| 0.8597 | 3.01 | 3000 | 0.8923 | 0.6409 |
| 0.8987 | 3.51 | 3500 | 0.8999 | 0.6215 |
| 0.879 | 4.01 | 4000 | 0.9219 | 0.6220 |
| 0.8892 | 4.51 | 4500 | 0.8936 | 0.6220 |
| 0.8926 | 5.02 | 5000 | 0.8914 | 0.6226 |
| 0.975 | 5.52 | 5500 | 0.8984 | 0.6405 |
| 0.9387 | 6.02 | 6000 | 1.1061 | 0.2347 |
| 0.9446 | 6.52 | 6500 | 0.8879 | 0.6436 |
| 0.879 | 7.02 | 7000 | 0.9053 | 0.6216 |
| 0.8657 | 7.52 | 7500 | 0.8552 | 0.6446 |
| 0.8396 | 8.02 | 8000 | 0.8535 | 0.6475 |
| 0.8264 | 8.53 | 8500 | 0.8476 | 0.6519 |
| 0.8555 | 9.03 | 9000 | 0.8450 | 0.6468 |
| 0.851 | 9.53 | 9500 | 0.8807 | 0.6404 |
### Framework versions
- Transformers 4.33.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
|
ldos/text_shortening_model_v28
|
ldos
| 2023-09-11T13:11:04Z | 111 | 0 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:google-t5/t5-small",
"base_model:finetune:google-t5/t5-small",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-09-11T12:48:28Z |
---
license: apache-2.0
base_model: t5-small
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: text_shortening_model_v28
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. -->
# text_shortening_model_v28
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.9239
- Rouge1: 0.4821
- Rouge2: 0.2554
- Rougel: 0.4273
- Rougelsum: 0.4271
- Bert precision: 0.8753
- Bert recall: 0.8686
- Average word count: 8.1231
- Max word count: 14
- Min word count: 4
- Average token count: 12.4805
- % shortened texts with length > 12: 2.7027
## 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: 0.005
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 100
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Bert precision | Bert recall | Average word count | Max word count | Min word count | Average token count | % shortened texts with length > 12 |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:--------------:|:-----------:|:------------------:|:--------------:|:--------------:|:-------------------:|:----------------------------------:|
| 2.1941 | 1.0 | 37 | 1.6111 | 0.5035 | 0.2708 | 0.4544 | 0.4553 | 0.8744 | 0.8772 | 9.2643 | 18 | 3 | 13.5826 | 12.9129 |
| 1.4552 | 2.0 | 74 | 1.5748 | 0.4724 | 0.2457 | 0.4313 | 0.4313 | 0.8737 | 0.8635 | 8.009 | 17 | 3 | 12.024 | 6.006 |
| 1.096 | 3.0 | 111 | 1.5283 | 0.5016 | 0.282 | 0.4515 | 0.4519 | 0.8774 | 0.8757 | 8.7748 | 16 | 4 | 13.0811 | 7.2072 |
| 0.8801 | 4.0 | 148 | 1.5903 | 0.4848 | 0.2549 | 0.4372 | 0.4375 | 0.88 | 0.8713 | 8.3544 | 16 | 4 | 12.6607 | 6.9069 |
| 0.7226 | 5.0 | 185 | 1.6953 | 0.4557 | 0.2378 | 0.408 | 0.4086 | 0.8752 | 0.8603 | 7.3904 | 13 | 1 | 11.6697 | 0.9009 |
| 0.6003 | 6.0 | 222 | 1.8416 | 0.4935 | 0.2616 | 0.4327 | 0.4339 | 0.8758 | 0.8712 | 8.5736 | 17 | 3 | 12.8498 | 8.7087 |
| 0.4852 | 7.0 | 259 | 1.8375 | 0.4662 | 0.2428 | 0.4147 | 0.4161 | 0.8703 | 0.8653 | 8.3904 | 16 | 2 | 12.7147 | 7.5075 |
| 0.4469 | 8.0 | 296 | 1.9116 | 0.4617 | 0.2433 | 0.41 | 0.4117 | 0.8724 | 0.8649 | 8.0781 | 17 | 2 | 12.5495 | 2.7027 |
| 0.4025 | 9.0 | 333 | 1.9871 | 0.4716 | 0.2443 | 0.4161 | 0.4164 | 0.8691 | 0.8662 | 9.006 | 19 | 4 | 13.4204 | 15.3153 |
| 0.3568 | 10.0 | 370 | 2.0547 | 0.4864 | 0.2649 | 0.4377 | 0.4377 | 0.8742 | 0.8724 | 8.6396 | 16 | 3 | 13.1381 | 7.8078 |
| 0.3071 | 11.0 | 407 | 2.1554 | 0.4582 | 0.2388 | 0.405 | 0.4053 | 0.8712 | 0.8595 | 7.7087 | 14 | 4 | 12.033 | 2.1021 |
| 0.2794 | 12.0 | 444 | 2.1352 | 0.4768 | 0.2567 | 0.4341 | 0.4344 | 0.8757 | 0.8705 | 8.4655 | 16 | 4 | 12.8949 | 8.1081 |
| 0.2627 | 13.0 | 481 | 2.1300 | 0.4703 | 0.2518 | 0.4227 | 0.4227 | 0.876 | 0.8674 | 8.015 | 17 | 3 | 12.2342 | 4.5045 |
| 0.2251 | 14.0 | 518 | 2.2319 | 0.4887 | 0.2623 | 0.4335 | 0.4336 | 0.8757 | 0.8704 | 8.3544 | 15 | 4 | 12.7357 | 5.7057 |
| 0.217 | 15.0 | 555 | 2.2311 | 0.4709 | 0.2523 | 0.4196 | 0.4196 | 0.8739 | 0.8683 | 8.2613 | 17 | 4 | 12.6276 | 5.4054 |
| 0.2097 | 16.0 | 592 | 2.2460 | 0.471 | 0.2463 | 0.4137 | 0.4147 | 0.8732 | 0.8649 | 8.1682 | 15 | 3 | 12.4384 | 4.5045 |
| 0.1841 | 17.0 | 629 | 2.3917 | 0.4564 | 0.229 | 0.4072 | 0.4076 | 0.8709 | 0.8663 | 8.3934 | 16 | 3 | 12.7027 | 5.4054 |
| 0.176 | 18.0 | 666 | 2.3731 | 0.4644 | 0.2408 | 0.4093 | 0.4103 | 0.87 | 0.8633 | 8.1712 | 16 | 4 | 12.5495 | 4.5045 |
| 0.1531 | 19.0 | 703 | 2.3836 | 0.4925 | 0.2727 | 0.439 | 0.439 | 0.879 | 0.8711 | 8.1111 | 16 | 3 | 12.2703 | 3.9039 |
| 0.1599 | 20.0 | 740 | 2.3611 | 0.4731 | 0.2575 | 0.4199 | 0.4202 | 0.8743 | 0.8669 | 8.1141 | 16 | 3 | 12.5315 | 4.8048 |
| 0.1469 | 21.0 | 777 | 2.4164 | 0.4774 | 0.2515 | 0.4295 | 0.4302 | 0.876 | 0.8709 | 8.3754 | 15 | 3 | 12.8348 | 6.3063 |
| 0.1449 | 22.0 | 814 | 2.4769 | 0.4702 | 0.2461 | 0.4205 | 0.421 | 0.874 | 0.8688 | 8.4054 | 16 | 4 | 12.7508 | 7.8078 |
| 0.1417 | 23.0 | 851 | 2.5470 | 0.4669 | 0.2438 | 0.4163 | 0.4163 | 0.8733 | 0.8649 | 7.9339 | 14 | 3 | 12.1922 | 2.7027 |
| 0.1255 | 24.0 | 888 | 2.5590 | 0.4642 | 0.2379 | 0.4127 | 0.4136 | 0.8736 | 0.8621 | 7.6517 | 14 | 4 | 11.7057 | 2.1021 |
| 0.1281 | 25.0 | 925 | 2.4347 | 0.4707 | 0.2571 | 0.4227 | 0.4233 | 0.8734 | 0.8675 | 8.2492 | 15 | 3 | 12.6937 | 4.5045 |
| 0.1399 | 26.0 | 962 | 2.5391 | 0.4649 | 0.2454 | 0.4132 | 0.414 | 0.8703 | 0.8684 | 8.6547 | 17 | 4 | 13.1982 | 8.4084 |
| 0.1279 | 27.0 | 999 | 2.5712 | 0.4723 | 0.2526 | 0.4208 | 0.4207 | 0.8729 | 0.8682 | 8.3393 | 17 | 4 | 12.6547 | 6.3063 |
| 0.1224 | 28.0 | 1036 | 2.5410 | 0.466 | 0.2485 | 0.4159 | 0.4156 | 0.8743 | 0.8663 | 7.955 | 15 | 3 | 12.2643 | 3.3033 |
| 0.1095 | 29.0 | 1073 | 2.6742 | 0.4647 | 0.2382 | 0.4094 | 0.4098 | 0.873 | 0.8641 | 8.033 | 16 | 4 | 12.3243 | 5.1051 |
| 0.1202 | 30.0 | 1110 | 2.5533 | 0.4748 | 0.2495 | 0.4225 | 0.4234 | 0.8757 | 0.8676 | 8.1562 | 16 | 4 | 12.4204 | 4.8048 |
| 0.1236 | 31.0 | 1147 | 2.5441 | 0.4709 | 0.2444 | 0.418 | 0.4187 | 0.87 | 0.8659 | 8.4144 | 17 | 4 | 12.8228 | 5.7057 |
| 0.1074 | 32.0 | 1184 | 2.6271 | 0.4845 | 0.2619 | 0.4291 | 0.4301 | 0.8758 | 0.8684 | 8.1502 | 15 | 3 | 12.4985 | 4.5045 |
| 0.0939 | 33.0 | 1221 | 2.6391 | 0.4806 | 0.2549 | 0.4251 | 0.4261 | 0.8722 | 0.869 | 8.6486 | 16 | 3 | 13.1592 | 7.8078 |
| 0.0976 | 34.0 | 1258 | 2.6159 | 0.4798 | 0.2582 | 0.4264 | 0.4268 | 0.8738 | 0.8701 | 8.6096 | 17 | 3 | 13.0931 | 8.4084 |
| 0.1042 | 35.0 | 1295 | 2.6224 | 0.4849 | 0.2557 | 0.428 | 0.4284 | 0.876 | 0.8705 | 8.2673 | 16 | 3 | 12.6757 | 4.5045 |
| 0.094 | 36.0 | 1332 | 2.5925 | 0.4742 | 0.2542 | 0.4289 | 0.4296 | 0.8754 | 0.8683 | 8.033 | 15 | 3 | 12.3483 | 3.6036 |
| 0.0794 | 37.0 | 1369 | 2.5782 | 0.4897 | 0.262 | 0.4354 | 0.4364 | 0.8762 | 0.8723 | 8.3153 | 16 | 3 | 12.7538 | 6.9069 |
| 0.0823 | 38.0 | 1406 | 2.6590 | 0.4752 | 0.2486 | 0.4222 | 0.423 | 0.8737 | 0.8651 | 8.027 | 15 | 3 | 12.3123 | 6.3063 |
| 0.0813 | 39.0 | 1443 | 2.6823 | 0.4817 | 0.2605 | 0.427 | 0.4276 | 0.8763 | 0.8696 | 8.1532 | 15 | 2 | 12.6006 | 5.1051 |
| 0.0868 | 40.0 | 1480 | 2.6642 | 0.4827 | 0.2572 | 0.4308 | 0.4314 | 0.8757 | 0.8702 | 8.3964 | 16 | 3 | 12.6877 | 8.1081 |
| 0.0786 | 41.0 | 1517 | 2.7908 | 0.4623 | 0.24 | 0.4086 | 0.4096 | 0.8704 | 0.863 | 8.1351 | 16 | 4 | 12.7447 | 7.2072 |
| 0.0901 | 42.0 | 1554 | 2.7242 | 0.4613 | 0.2405 | 0.4115 | 0.413 | 0.8716 | 0.8636 | 8.2523 | 18 | 4 | 12.5465 | 6.9069 |
| 0.0912 | 43.0 | 1591 | 2.7376 | 0.474 | 0.2446 | 0.4194 | 0.4191 | 0.8707 | 0.8694 | 8.6877 | 16 | 3 | 13.2282 | 10.5105 |
| 0.0887 | 44.0 | 1628 | 2.7192 | 0.479 | 0.2539 | 0.4266 | 0.4268 | 0.874 | 0.8703 | 8.4865 | 15 | 4 | 13.1321 | 7.2072 |
| 0.0807 | 45.0 | 1665 | 2.6935 | 0.4738 | 0.2501 | 0.4213 | 0.4223 | 0.874 | 0.8675 | 8.2042 | 16 | 2 | 12.6787 | 6.006 |
| 0.0801 | 46.0 | 1702 | 2.7149 | 0.4662 | 0.2443 | 0.4229 | 0.4236 | 0.8745 | 0.8659 | 8.033 | 15 | 3 | 12.3453 | 4.2042 |
| 0.0764 | 47.0 | 1739 | 2.6544 | 0.4697 | 0.249 | 0.4206 | 0.4202 | 0.8726 | 0.8668 | 8.2432 | 16 | 4 | 12.6637 | 6.9069 |
| 0.0765 | 48.0 | 1776 | 2.7157 | 0.4764 | 0.2535 | 0.4234 | 0.4236 | 0.8762 | 0.8676 | 8.021 | 15 | 3 | 12.3544 | 4.5045 |
| 0.065 | 49.0 | 1813 | 2.8051 | 0.4666 | 0.2452 | 0.4161 | 0.4165 | 0.8728 | 0.8665 | 8.2673 | 16 | 2 | 12.6246 | 5.1051 |
| 0.0626 | 50.0 | 1850 | 2.7845 | 0.4781 | 0.2519 | 0.4254 | 0.4253 | 0.8746 | 0.8688 | 8.2192 | 16 | 3 | 12.5796 | 5.4054 |
| 0.0608 | 51.0 | 1887 | 2.7371 | 0.4745 | 0.2456 | 0.4213 | 0.4208 | 0.8751 | 0.866 | 8.0871 | 17 | 2 | 12.3063 | 6.9069 |
| 0.0599 | 52.0 | 1924 | 2.7620 | 0.474 | 0.2515 | 0.419 | 0.4204 | 0.8718 | 0.8667 | 8.1381 | 15 | 4 | 12.8979 | 3.9039 |
| 0.0625 | 53.0 | 1961 | 2.8097 | 0.4646 | 0.2481 | 0.4137 | 0.4146 | 0.8706 | 0.8663 | 8.2733 | 15 | 3 | 12.7237 | 4.5045 |
| 0.0529 | 54.0 | 1998 | 2.8677 | 0.4714 | 0.2436 | 0.4142 | 0.4147 | 0.8745 | 0.8651 | 7.8709 | 16 | 2 | 12.2012 | 4.2042 |
| 0.05 | 55.0 | 2035 | 2.7892 | 0.467 | 0.2465 | 0.4152 | 0.4159 | 0.8739 | 0.8668 | 8.1712 | 17 | 2 | 12.4925 | 3.3033 |
| 0.047 | 56.0 | 2072 | 2.7682 | 0.4719 | 0.2451 | 0.4223 | 0.423 | 0.8717 | 0.866 | 8.1802 | 15 | 3 | 12.5826 | 5.1051 |
| 0.0504 | 57.0 | 2109 | 2.7897 | 0.4823 | 0.2555 | 0.427 | 0.4276 | 0.8754 | 0.8717 | 8.5345 | 15 | 3 | 12.9249 | 7.5075 |
| 0.0463 | 58.0 | 2146 | 2.8505 | 0.471 | 0.2513 | 0.42 | 0.4204 | 0.8748 | 0.8683 | 8.2132 | 15 | 3 | 12.6426 | 5.7057 |
| 0.0487 | 59.0 | 2183 | 2.7699 | 0.4658 | 0.2472 | 0.4156 | 0.4166 | 0.8726 | 0.8667 | 8.1231 | 15 | 3 | 12.5465 | 3.9039 |
| 0.045 | 60.0 | 2220 | 2.7589 | 0.4718 | 0.2495 | 0.4211 | 0.4216 | 0.8741 | 0.8676 | 8.2432 | 17 | 4 | 12.5556 | 5.1051 |
| 0.047 | 61.0 | 2257 | 2.8092 | 0.4814 | 0.2517 | 0.4253 | 0.4257 | 0.8759 | 0.8687 | 7.997 | 17 | 3 | 12.3393 | 3.003 |
| 0.0415 | 62.0 | 2294 | 2.8059 | 0.4689 | 0.2494 | 0.4183 | 0.4191 | 0.8767 | 0.8655 | 7.7538 | 17 | 2 | 12.1381 | 2.4024 |
| 0.0429 | 63.0 | 2331 | 2.8317 | 0.4783 | 0.2516 | 0.4248 | 0.4252 | 0.8764 | 0.8689 | 8.0811 | 17 | 3 | 12.4234 | 3.003 |
| 0.0383 | 64.0 | 2368 | 2.8147 | 0.4728 | 0.2547 | 0.4189 | 0.4193 | 0.8732 | 0.867 | 8.1622 | 18 | 3 | 12.5916 | 4.2042 |
| 0.039 | 65.0 | 2405 | 2.8237 | 0.4638 | 0.2401 | 0.414 | 0.4145 | 0.871 | 0.8654 | 8.3183 | 15 | 3 | 12.7057 | 5.7057 |
| 0.0417 | 66.0 | 2442 | 2.8289 | 0.4726 | 0.2532 | 0.4242 | 0.4243 | 0.8746 | 0.8667 | 7.9159 | 15 | 3 | 12.3363 | 3.003 |
| 0.0365 | 67.0 | 2479 | 2.8272 | 0.4752 | 0.2506 | 0.4222 | 0.4218 | 0.8732 | 0.8673 | 8.1652 | 15 | 3 | 12.5165 | 4.8048 |
| 0.0372 | 68.0 | 2516 | 2.8469 | 0.4726 | 0.2491 | 0.4225 | 0.423 | 0.873 | 0.8665 | 8.1802 | 17 | 4 | 12.5796 | 5.4054 |
| 0.0363 | 69.0 | 2553 | 2.8233 | 0.4745 | 0.2554 | 0.4244 | 0.4239 | 0.8751 | 0.8672 | 8.0601 | 16 | 4 | 12.2342 | 3.6036 |
| 0.0356 | 70.0 | 2590 | 2.8652 | 0.4737 | 0.2471 | 0.4169 | 0.4167 | 0.8735 | 0.8658 | 8.03 | 17 | 4 | 12.3273 | 5.1051 |
| 0.0366 | 71.0 | 2627 | 2.8722 | 0.4838 | 0.2598 | 0.4274 | 0.428 | 0.8767 | 0.869 | 8.0541 | 14 | 4 | 12.2913 | 3.003 |
| 0.0334 | 72.0 | 2664 | 2.8650 | 0.4708 | 0.2508 | 0.4194 | 0.4195 | 0.873 | 0.8674 | 8.2252 | 16 | 4 | 12.6426 | 4.2042 |
| 0.0328 | 73.0 | 2701 | 2.8827 | 0.479 | 0.2498 | 0.4221 | 0.422 | 0.8753 | 0.8683 | 8.1802 | 16 | 4 | 12.4835 | 3.3033 |
| 0.0322 | 74.0 | 2738 | 2.8599 | 0.479 | 0.2524 | 0.4295 | 0.43 | 0.8746 | 0.8689 | 8.2583 | 17 | 4 | 12.6727 | 4.2042 |
| 0.0308 | 75.0 | 2775 | 2.8559 | 0.4781 | 0.255 | 0.4279 | 0.4292 | 0.8766 | 0.8687 | 8.042 | 14 | 4 | 12.3033 | 2.4024 |
| 0.0304 | 76.0 | 2812 | 2.8364 | 0.4779 | 0.2581 | 0.4286 | 0.4287 | 0.8759 | 0.8682 | 7.994 | 17 | 3 | 12.3063 | 3.3033 |
| 0.0322 | 77.0 | 2849 | 2.8167 | 0.472 | 0.2489 | 0.4222 | 0.4225 | 0.8746 | 0.8673 | 8.003 | 17 | 4 | 12.3754 | 4.8048 |
| 0.0296 | 78.0 | 2886 | 2.8835 | 0.4716 | 0.2541 | 0.4217 | 0.4219 | 0.8734 | 0.8679 | 8.2252 | 17 | 4 | 12.6787 | 4.5045 |
| 0.0284 | 79.0 | 2923 | 2.8712 | 0.4729 | 0.2526 | 0.4228 | 0.4229 | 0.874 | 0.8672 | 8.1772 | 18 | 4 | 12.5495 | 3.6036 |
| 0.0286 | 80.0 | 2960 | 2.8709 | 0.4826 | 0.2596 | 0.4328 | 0.4324 | 0.877 | 0.8705 | 8.1592 | 18 | 4 | 12.4234 | 4.2042 |
| 0.0287 | 81.0 | 2997 | 2.8556 | 0.4746 | 0.2558 | 0.4228 | 0.4236 | 0.8747 | 0.8681 | 8.1381 | 17 | 4 | 12.3994 | 3.003 |
| 0.0287 | 82.0 | 3034 | 2.8867 | 0.4788 | 0.2617 | 0.429 | 0.4291 | 0.8757 | 0.8701 | 8.3273 | 17 | 4 | 12.6997 | 5.1051 |
| 0.0298 | 83.0 | 3071 | 2.8793 | 0.4828 | 0.2609 | 0.4306 | 0.4295 | 0.8757 | 0.8702 | 8.2673 | 17 | 4 | 12.6066 | 4.5045 |
| 0.0266 | 84.0 | 3108 | 2.8795 | 0.472 | 0.2499 | 0.4208 | 0.4207 | 0.8742 | 0.8677 | 8.1742 | 15 | 4 | 12.5345 | 3.6036 |
| 0.0257 | 85.0 | 3145 | 2.8788 | 0.48 | 0.2543 | 0.4244 | 0.4247 | 0.876 | 0.8686 | 8.1321 | 14 | 4 | 12.3874 | 2.7027 |
| 0.0255 | 86.0 | 3182 | 2.9130 | 0.4868 | 0.266 | 0.4307 | 0.4304 | 0.8762 | 0.8702 | 8.1652 | 15 | 4 | 12.5285 | 3.3033 |
| 0.0254 | 87.0 | 3219 | 2.9050 | 0.4847 | 0.2627 | 0.4327 | 0.432 | 0.877 | 0.8702 | 8.042 | 16 | 4 | 12.4324 | 3.3033 |
| 0.0233 | 88.0 | 3256 | 2.9014 | 0.4855 | 0.2615 | 0.433 | 0.4328 | 0.8758 | 0.8701 | 8.2613 | 16 | 4 | 12.5706 | 3.9039 |
| 0.0268 | 89.0 | 3293 | 2.8937 | 0.487 | 0.2586 | 0.4316 | 0.4317 | 0.8763 | 0.8707 | 8.2402 | 15 | 4 | 12.5616 | 4.2042 |
| 0.0243 | 90.0 | 3330 | 2.8926 | 0.4838 | 0.2584 | 0.4271 | 0.4268 | 0.8765 | 0.8695 | 8.1171 | 14 | 4 | 12.3483 | 3.3033 |
| 0.0248 | 91.0 | 3367 | 2.8870 | 0.4775 | 0.2503 | 0.4223 | 0.4222 | 0.8748 | 0.8678 | 8.1201 | 14 | 4 | 12.4354 | 3.3033 |
| 0.0237 | 92.0 | 3404 | 2.8978 | 0.4816 | 0.2556 | 0.4275 | 0.4275 | 0.8752 | 0.8688 | 8.0991 | 14 | 4 | 12.4685 | 2.7027 |
| 0.0244 | 93.0 | 3441 | 2.9025 | 0.4778 | 0.2506 | 0.4246 | 0.4249 | 0.8747 | 0.868 | 8.039 | 14 | 4 | 12.4174 | 3.003 |
| 0.0227 | 94.0 | 3478 | 2.9164 | 0.4733 | 0.2486 | 0.4199 | 0.4204 | 0.8745 | 0.8669 | 7.973 | 14 | 4 | 12.3123 | 2.7027 |
| 0.0215 | 95.0 | 3515 | 2.9183 | 0.4795 | 0.2495 | 0.4233 | 0.4231 | 0.8751 | 0.8682 | 8.03 | 14 | 4 | 12.4084 | 3.003 |
| 0.0225 | 96.0 | 3552 | 2.9207 | 0.4763 | 0.2463 | 0.4204 | 0.4206 | 0.8752 | 0.8677 | 8.0511 | 14 | 4 | 12.3934 | 2.7027 |
| 0.0208 | 97.0 | 3589 | 2.9226 | 0.4815 | 0.2556 | 0.4271 | 0.4276 | 0.8758 | 0.869 | 8.0871 | 14 | 4 | 12.4144 | 2.7027 |
| 0.0225 | 98.0 | 3626 | 2.9234 | 0.4832 | 0.2576 | 0.4285 | 0.4281 | 0.8762 | 0.8693 | 8.1351 | 16 | 4 | 12.4595 | 3.003 |
| 0.0219 | 99.0 | 3663 | 2.9243 | 0.4809 | 0.2543 | 0.4249 | 0.4249 | 0.8754 | 0.8686 | 8.1141 | 14 | 4 | 12.4775 | 2.7027 |
| 0.0214 | 100.0 | 3700 | 2.9239 | 0.4821 | 0.2554 | 0.4273 | 0.4271 | 0.8753 | 0.8686 | 8.1231 | 14 | 4 | 12.4805 | 2.7027 |
### Framework versions
- Transformers 4.33.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
|
begeri/ppo-LunarLander-v2
|
begeri
| 2023-09-11T13:06:41Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-09-11T13:06:17Z |
---
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: 240.37 +/- 63.93
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
...
```
|
RickyIG/audio_classification
|
RickyIG
| 2023-09-11T13:05:24Z | 166 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"audio-classification",
"generated_from_trainer",
"dataset:minds14",
"base_model:facebook/wav2vec2-base",
"base_model:finetune:facebook/wav2vec2-base",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
audio-classification
| 2023-09-11T12:55:36Z |
---
license: apache-2.0
base_model: facebook/wav2vec2-base
tags:
- generated_from_trainer
datasets:
- minds14
metrics:
- accuracy
model-index:
- name: audio_classification
results:
- task:
name: Audio Classification
type: audio-classification
dataset:
name: minds14
type: minds14
config: en-US
split: train
args: en-US
metrics:
- name: Accuracy
type: accuracy
value: 0.061946902654867256
---
<!-- 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. -->
# audio_classification
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the minds14 dataset.
It achieves the following results on the evaluation set:
- Loss: 2.6511
- Accuracy: 0.0619
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 0.8 | 3 | 2.6362 | 0.0796 |
| No log | 1.87 | 7 | 2.6455 | 0.0708 |
| 2.6353 | 2.93 | 11 | 2.6499 | 0.0619 |
| 2.6353 | 4.0 | 15 | 2.6488 | 0.0708 |
| 2.6353 | 4.8 | 18 | 2.6491 | 0.0885 |
| 2.6272 | 5.87 | 22 | 2.6521 | 0.0619 |
| 2.6272 | 6.93 | 26 | 2.6511 | 0.0442 |
| 2.6245 | 8.0 | 30 | 2.6511 | 0.0619 |
### Framework versions
- Transformers 4.33.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
|
bigmorning/whisper_4_with_init_sun_syl_wd_0__0075
|
bigmorning
| 2023-09-11T13:04:01Z | 59 | 0 |
transformers
|
[
"transformers",
"tf",
"whisper",
"automatic-speech-recognition",
"generated_from_keras_callback",
"base_model:openai/whisper-tiny",
"base_model:finetune:openai/whisper-tiny",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-09-11T13:03:53Z |
---
license: apache-2.0
base_model: openai/whisper-tiny
tags:
- generated_from_keras_callback
model-index:
- name: whisper_4_with_init_sun_syl_wd_0__0075
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# whisper_4_with_init_sun_syl_wd_0__0075
This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.2666
- Train Accuracy: 0.0338
- Train Wermet: 0.0486
- Train Wermet Syl: 0.0575
- Validation Loss: 1.2036
- Validation Accuracy: 0.0207
- Validation Wermet: 0.3241
- Validation Wermet Syl: 0.2887
- Epoch: 74
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 1e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Train Wermet | Train Wermet Syl | Validation Loss | Validation Accuracy | Validation Wermet | Validation Wermet Syl | Epoch |
|:----------:|:--------------:|:------------:|:----------------:|:---------------:|:-------------------:|:-----------------:|:---------------------:|:-----:|
| 5.3409 | 0.0111 | 1.3547 | 1.2898 | 3.9789 | 0.0114 | 0.9710 | 0.9563 | 0 |
| 4.7143 | 0.0116 | 0.8622 | 0.8228 | 3.9404 | 0.0113 | 0.9823 | 0.9735 | 1 |
| 4.6752 | 0.0117 | 0.8472 | 0.8057 | 3.9081 | 0.0114 | 0.9579 | 0.9359 | 2 |
| 4.6500 | 0.0117 | 0.8382 | 0.7945 | 3.8820 | 0.0115 | 0.9213 | 0.8856 | 3 |
| 4.6282 | 0.0118 | 0.8286 | 0.7805 | 3.8738 | 0.0114 | 0.9433 | 0.9119 | 4 |
| 4.6095 | 0.0118 | 0.8190 | 0.7696 | 3.8630 | 0.0115 | 0.9117 | 0.8698 | 5 |
| 4.5875 | 0.0119 | 0.7976 | 0.7465 | 3.8341 | 0.0116 | 0.8976 | 0.8552 | 6 |
| 4.5682 | 0.0120 | 0.7753 | 0.7227 | 3.8277 | 0.0116 | 0.9014 | 0.8653 | 7 |
| 4.5376 | 0.0121 | 0.7528 | 0.7005 | 3.7844 | 0.0118 | 0.8332 | 0.7815 | 8 |
| 4.5060 | 0.0122 | 0.7392 | 0.6844 | 3.7537 | 0.0118 | 0.8578 | 0.8152 | 9 |
| 4.4580 | 0.0124 | 0.7221 | 0.6694 | 3.7038 | 0.0120 | 0.8190 | 0.7679 | 10 |
| 4.3989 | 0.0125 | 0.7156 | 0.6636 | 3.6169 | 0.0122 | 0.7979 | 0.7429 | 11 |
| 4.3056 | 0.0128 | 0.7069 | 0.6557 | 3.5098 | 0.0125 | 0.7924 | 0.7396 | 12 |
| 4.1673 | 0.0132 | 0.7054 | 0.6584 | 3.3542 | 0.0128 | 0.7759 | 0.7240 | 13 |
| 3.9762 | 0.0138 | 0.6987 | 0.6559 | 3.1318 | 0.0133 | 0.7644 | 0.7231 | 14 |
| 3.7385 | 0.0145 | 0.6835 | 0.6448 | 2.9144 | 0.0138 | 0.7392 | 0.6955 | 15 |
| 3.5040 | 0.0152 | 0.6644 | 0.6298 | 2.7413 | 0.0142 | 0.7019 | 0.6548 | 16 |
| 3.2728 | 0.0160 | 0.6408 | 0.6101 | 2.5183 | 0.0149 | 0.6798 | 0.6363 | 17 |
| 3.0657 | 0.0167 | 0.6188 | 0.5912 | 2.3594 | 0.0153 | 0.6528 | 0.6103 | 18 |
| 2.8703 | 0.0174 | 0.5936 | 0.5685 | 2.2644 | 0.0156 | 0.6310 | 0.5925 | 19 |
| 2.6850 | 0.0181 | 0.5680 | 0.5453 | 2.1296 | 0.0160 | 0.6040 | 0.5652 | 20 |
| 2.5227 | 0.0188 | 0.5423 | 0.5215 | 2.0019 | 0.0165 | 0.5793 | 0.5403 | 21 |
| 2.3878 | 0.0194 | 0.5199 | 0.5015 | 1.8996 | 0.0169 | 0.5592 | 0.5229 | 22 |
| 2.2437 | 0.0201 | 0.4959 | 0.4788 | 1.8141 | 0.0172 | 0.5414 | 0.5045 | 23 |
| 2.1205 | 0.0207 | 0.4752 | 0.4607 | 1.7245 | 0.0175 | 0.5208 | 0.4838 | 24 |
| 1.9919 | 0.0213 | 0.4533 | 0.4390 | 1.6673 | 0.0178 | 0.5026 | 0.4659 | 25 |
| 1.9140 | 0.0217 | 0.4355 | 0.4216 | 1.6041 | 0.0181 | 0.4873 | 0.4512 | 26 |
| 1.8225 | 0.0222 | 0.4184 | 0.4052 | 1.6271 | 0.0179 | 0.4852 | 0.4511 | 27 |
| 1.7265 | 0.0227 | 0.4016 | 0.3895 | 1.5219 | 0.0184 | 0.4635 | 0.4275 | 28 |
| 1.6240 | 0.0233 | 0.3833 | 0.3729 | 1.4718 | 0.0186 | 0.4515 | 0.4170 | 29 |
| 1.5610 | 0.0236 | 0.3697 | 0.3588 | 1.4404 | 0.0188 | 0.4407 | 0.4056 | 30 |
| 1.4719 | 0.0242 | 0.3540 | 0.3449 | 1.4125 | 0.0189 | 0.4310 | 0.3961 | 31 |
| 1.4152 | 0.0245 | 0.3421 | 0.3339 | 1.3655 | 0.0191 | 0.4234 | 0.3881 | 32 |
| 1.3546 | 0.0249 | 0.3277 | 0.3195 | 1.3419 | 0.0192 | 0.4156 | 0.3816 | 33 |
| 1.2565 | 0.0256 | 0.3135 | 0.3060 | 1.3172 | 0.0194 | 0.4065 | 0.3722 | 34 |
| 1.2135 | 0.0258 | 0.3026 | 0.2958 | 1.3019 | 0.0194 | 0.4006 | 0.3662 | 35 |
| 1.1739 | 0.0261 | 0.2923 | 0.2861 | 1.3843 | 0.0190 | 0.3951 | 0.3587 | 36 |
| 1.0950 | 0.0267 | 0.2782 | 0.2733 | 1.2665 | 0.0197 | 0.3883 | 0.3541 | 37 |
| 1.0435 | 0.0271 | 0.2673 | 0.2631 | 1.2567 | 0.0197 | 0.3837 | 0.3497 | 38 |
| 0.9922 | 0.0275 | 0.2580 | 0.2542 | 1.2566 | 0.0197 | 0.3801 | 0.3444 | 39 |
| 0.9387 | 0.0279 | 0.2464 | 0.2438 | 1.2441 | 0.0198 | 0.3767 | 0.3423 | 40 |
| 0.9345 | 0.0278 | 0.2393 | 0.2373 | 1.2221 | 0.0199 | 0.3682 | 0.3336 | 41 |
| 0.8574 | 0.0285 | 0.2268 | 0.2255 | 1.2258 | 0.0199 | 0.3680 | 0.3338 | 42 |
| 0.8275 | 0.0287 | 0.2183 | 0.2180 | 1.2044 | 0.0201 | 0.3628 | 0.3290 | 43 |
| 0.8201 | 0.0288 | 0.2114 | 0.2108 | 1.2056 | 0.0201 | 0.3601 | 0.3270 | 44 |
| 0.7684 | 0.0292 | 0.2020 | 0.2029 | 1.1879 | 0.0202 | 0.3553 | 0.3215 | 45 |
| 0.7262 | 0.0295 | 0.1938 | 0.1947 | 1.2263 | 0.0200 | 0.3537 | 0.3177 | 46 |
| 0.7286 | 0.0295 | 0.1876 | 0.1898 | 1.1772 | 0.0203 | 0.3485 | 0.3135 | 47 |
| 0.6807 | 0.0300 | 0.1775 | 0.1797 | 1.1761 | 0.0203 | 0.3490 | 0.3155 | 48 |
| 0.6609 | 0.0301 | 0.1713 | 0.1742 | 1.1853 | 0.0203 | 0.3484 | 0.3153 | 49 |
| 0.6062 | 0.0306 | 0.1615 | 0.1653 | 1.1660 | 0.0204 | 0.3432 | 0.3090 | 50 |
| 0.5755 | 0.0309 | 0.1547 | 0.1584 | 1.1698 | 0.0204 | 0.3428 | 0.3089 | 51 |
| 0.5600 | 0.0310 | 0.1482 | 0.1524 | 1.1667 | 0.0204 | 0.3398 | 0.3058 | 52 |
| 0.5715 | 0.0308 | 0.1449 | 0.1496 | 1.1614 | 0.0205 | 0.3381 | 0.3036 | 53 |
| 0.5247 | 0.0313 | 0.1358 | 0.1411 | 1.1639 | 0.0205 | 0.3359 | 0.3025 | 54 |
| 0.5085 | 0.0315 | 0.1301 | 0.1358 | 1.2420 | 0.0202 | 0.3412 | 0.3064 | 55 |
| 0.4827 | 0.0317 | 0.1239 | 0.1295 | 1.1677 | 0.0205 | 0.3349 | 0.3009 | 56 |
| 0.4848 | 0.0317 | 0.1207 | 0.1280 | 1.1653 | 0.0205 | 0.3326 | 0.2991 | 57 |
| 0.4323 | 0.0322 | 0.1109 | 0.1185 | 1.1602 | 0.0206 | 0.3299 | 0.2953 | 58 |
| 0.4183 | 0.0323 | 0.1057 | 0.1133 | 1.1622 | 0.0206 | 0.3307 | 0.2962 | 59 |
| 0.4329 | 0.0322 | 0.1028 | 0.1100 | 1.1714 | 0.0206 | 0.3300 | 0.2950 | 60 |
| 0.3962 | 0.0326 | 0.0964 | 0.1045 | 1.1726 | 0.0206 | 0.3311 | 0.2967 | 61 |
| 0.3642 | 0.0329 | 0.0898 | 0.0973 | 1.1699 | 0.0206 | 0.3289 | 0.2936 | 62 |
| 0.3786 | 0.0327 | 0.0884 | 0.0963 | 1.1734 | 0.0206 | 0.3279 | 0.2929 | 63 |
| 0.3698 | 0.0328 | 0.0842 | 0.0925 | 1.1728 | 0.0207 | 0.3282 | 0.2932 | 64 |
| 0.3219 | 0.0333 | 0.0765 | 0.0850 | 1.1830 | 0.0207 | 0.3258 | 0.2907 | 65 |
| 0.3035 | 0.0335 | 0.0725 | 0.0811 | 1.1840 | 0.0207 | 0.3261 | 0.2904 | 66 |
| 0.3522 | 0.0330 | 0.0745 | 0.0826 | 1.2107 | 0.0206 | 0.3299 | 0.2955 | 67 |
| 0.3001 | 0.0335 | 0.0663 | 0.0749 | 1.1810 | 0.0207 | 0.3264 | 0.2909 | 68 |
| 0.2729 | 0.0338 | 0.0595 | 0.0677 | 1.1911 | 0.0207 | 0.3247 | 0.2886 | 69 |
| 0.2696 | 0.0338 | 0.0572 | 0.0654 | 1.1950 | 0.0207 | 0.3260 | 0.2905 | 70 |
| 0.2840 | 0.0337 | 0.0563 | 0.0648 | 1.2094 | 0.0207 | 0.3250 | 0.2887 | 71 |
| 0.2319 | 0.0342 | 0.0484 | 0.0569 | 1.2107 | 0.0207 | 0.3250 | 0.2878 | 72 |
| 0.2371 | 0.0342 | 0.0464 | 0.0541 | 1.2059 | 0.0207 | 0.3240 | 0.2880 | 73 |
| 0.2666 | 0.0338 | 0.0486 | 0.0575 | 1.2036 | 0.0207 | 0.3241 | 0.2887 | 74 |
### Framework versions
- Transformers 4.34.0.dev0
- TensorFlow 2.13.0
- Tokenizers 0.13.3
|
HamZurger/Reinforce-CartPole_v2
|
HamZurger
| 2023-09-11T13:00:21Z | 0 | 0 | null |
[
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-09-11T13:00:13Z |
---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-CartPole_v2
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
|
tuikhar/naga
|
tuikhar
| 2023-09-11T12:57:49Z | 0 | 0 | null |
[
"arxiv:1910.09700",
"region:us"
] | null | 2023-09-11T12:57:09Z |
---
# For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1
# Doc / guide: https://huggingface.co/docs/hub/model-cards
{}
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Data Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
JasperLS/gelectra-base-injection-pt_v1
|
JasperLS
| 2023-09-11T12:55:08Z | 114 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"electra",
"text-classification",
"generated_from_trainer",
"base_model:deepset/gelectra-base",
"base_model:finetune:deepset/gelectra-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-04-06T12:31:06Z |
---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
base_model: deepset/gelectra-base
model-index:
- name: gelectra-base-injection-pt_v1
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. -->
# gelectra-base-injection-pt_v1
DEPRECATED - PLEASE USE NEWER GELECTRA OR DEBERTA VERSION
This model is a fine-tuned version of [deepset/gelectra-base](https://huggingface.co/deepset/gelectra-base) on a closed prompt injection dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0163
- Accuracy: 1.0
## Model description
The model classifies prompts as injections or legitimate questions.
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 45 | 0.2042 | 0.9211 |
| No log | 2.0 | 90 | 0.0247 | 1.0 |
| No log | 3.0 | 135 | 0.0163 | 1.0 |
### Framework versions
- Transformers 4.27.4
- Pytorch 2.0.0+cu118
- Tokenizers 0.13.3
|
abelkrw/audio_classification
|
abelkrw
| 2023-09-11T12:53:59Z | 162 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"audio-classification",
"generated_from_trainer",
"dataset:minds14",
"base_model:facebook/wav2vec2-base",
"base_model:finetune:facebook/wav2vec2-base",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
audio-classification
| 2023-09-11T12:50:42Z |
---
license: apache-2.0
base_model: facebook/wav2vec2-base
tags:
- generated_from_trainer
datasets:
- minds14
metrics:
- accuracy
model-index:
- name: audio_classification
results:
- task:
name: Audio Classification
type: audio-classification
dataset:
name: minds14
type: minds14
config: en-US
split: train
args: en-US
metrics:
- name: Accuracy
type: accuracy
value: 0.07079646017699115
---
<!-- 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. -->
# audio_classification
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the minds14 dataset.
It achieves the following results on the evaluation set:
- Loss: 2.6569
- Accuracy: 0.0708
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 0.8 | 3 | 2.6456 | 0.0265 |
| No log | 1.87 | 7 | 2.6512 | 0.0442 |
| 2.6372 | 2.93 | 11 | 2.6509 | 0.0619 |
| 2.6372 | 4.0 | 15 | 2.6541 | 0.0708 |
| 2.6372 | 4.8 | 18 | 2.6554 | 0.0708 |
| 2.6217 | 5.87 | 22 | 2.6561 | 0.0708 |
| 2.6217 | 6.93 | 26 | 2.6564 | 0.0708 |
| 2.6141 | 8.0 | 30 | 2.6569 | 0.0708 |
### Framework versions
- Transformers 4.33.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
|
esperesa/xlm-roberta-base-finetuned-panx-de
|
esperesa
| 2023-09-11T12:53:29Z | 106 | 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
| 2023-09-11T12:43:57Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-de
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xtreme
type: xtreme
args: PAN-X.de
metrics:
- name: F1
type: f1
value: 0.8653353814644136
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-de
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1339
- F1: 0.8653
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2583 | 1.0 | 525 | 0.1596 | 0.8231 |
| 0.1262 | 2.0 | 1050 | 0.1395 | 0.8468 |
| 0.0824 | 3.0 | 1575 | 0.1339 | 0.8653 |
### Framework versions
- Transformers 4.16.2
- Pytorch 2.0.1+cu118
- Datasets 1.16.1
- Tokenizers 0.14.0
|
Jukaboo/Llama2_7B_chat_dialogsum_ft_adapters_v4100
|
Jukaboo
| 2023-09-11T12:52:20Z | 0 | 0 | null |
[
"generated_from_trainer",
"base_model:meta-llama/Llama-2-7b-chat-hf",
"base_model:finetune:meta-llama/Llama-2-7b-chat-hf",
"region:us"
] | null | 2023-09-11T12:23:44Z |
---
base_model: meta-llama/Llama-2-7b-chat-hf
tags:
- generated_from_trainer
model-index:
- name: Llama2_7B_chat_dialogsum_ft_adapters_v4100
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. -->
# Llama2_7B_chat_dialogsum_ft_adapters_v4100
This model is a fine-tuned version of [meta-llama/Llama-2-7b-chat-hf](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.33.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
|
kaitchup/Llama-2-7b-gptq-2bit
|
kaitchup
| 2023-09-11T12:48:38Z | 160 | 1 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"llama",
"text-generation",
"en",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"2-bit",
"gptq",
"region:us"
] |
text-generation
| 2023-08-29T11:19:52Z |
---
license: apache-2.0
language:
- en
---
# Model Card for Model ID
This is Meta's Llama 2 7B quantized in 2-bit using AutoGPTQ from Hugging Face Transformers.
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [The Kaitchup](https://kaitchup.substack.com/)
- **Model type:** Causal (Llama 2)
- **Language(s) (NLP):** English
- **License:** [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0), [Llama 2 license agreement](https://ai.meta.com/resources/models-and-libraries/llama-downloads/)
### Model Sources
The method and code used to quantize the model are explained here:
[Quantize and Fine-tune LLMs with GPTQ Using Transformers and TRL](https://kaitchup.substack.com/p/quantize-and-fine-tune-llms-with)
## Uses
This model is pre-trained and not fine-tuned. You may fine-tune it with PEFT using adapters.
Note that the 2-bit quantization significantly decreases the performance of Llama 2.
## Other versions
- [kaitchup/Llama-2-7b-gptq-4bit](https://huggingface.co/kaitchup/Llama-2-7b-gptq-4bit)
- [kaitchup/Llama-2-7b-gptq-3bit](https://huggingface.co/kaitchup/Llama-2-7b-gptq-3bit)
## Model Card Contact
[The Kaitchup](https://kaitchup.substack.com/)
|
ChristianMDahl/segFormer-b3-horizontal-vertical
|
ChristianMDahl
| 2023-09-11T12:45:44Z | 2 | 0 |
transformers
|
[
"transformers",
"tf",
"segformer",
"generated_from_keras_callback",
"base_model:nvidia/mit-b3",
"base_model:finetune:nvidia/mit-b3",
"license:other",
"endpoints_compatible",
"region:us"
] | null | 2023-06-13T19:07:57Z |
---
license: other
tags:
- generated_from_keras_callback
base_model: nvidia/mit-b3
model-index:
- name: ChristianMDahl/segFormer-b3-horizontal-vertical
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. -->
# ChristianMDahl/segFormer-b3-horizontal-vertical
This model is a fine-tuned version of [nvidia/mit-b3](https://huggingface.co/nvidia/mit-b3) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.1671
- Validation Loss: 0.2320
- Epoch: 19
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'learning_rate': 6e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 0.3203 | 0.2831 | 0 |
| 0.2822 | 0.2688 | 1 |
| 0.2662 | 0.2578 | 2 |
| 0.2526 | 0.2484 | 3 |
| 0.2396 | 0.2442 | 4 |
| 0.2288 | 0.2416 | 5 |
| 0.2195 | 0.2381 | 6 |
| 0.2121 | 0.2361 | 7 |
| 0.2058 | 0.2314 | 8 |
| 0.1999 | 0.2277 | 9 |
| 0.1952 | 0.2287 | 10 |
| 0.1912 | 0.2221 | 11 |
| 0.1869 | 0.2205 | 12 |
| 0.1835 | 0.2226 | 13 |
| 0.1804 | 0.2209 | 14 |
| 0.1775 | 0.2181 | 15 |
| 0.1745 | 0.2206 | 16 |
| 0.1721 | 0.2179 | 17 |
| 0.1693 | 0.2199 | 18 |
| 0.1671 | 0.2320 | 19 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.10.1
- Tokenizers 0.13.3
|
baebee/llama2-qlora-finetunined-french
|
baebee
| 2023-09-11T12:40:30Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-09-11T12:40:24Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.6.0.dev0
|
davanstrien/deberta-v3-base_fine_tuned_food_ner
|
davanstrien
| 2023-09-11T12:33:57Z | 154 | 10 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"deberta-v2",
"token-classification",
"generated_from_trainer",
"base_model:microsoft/deberta-v3-base",
"base_model:finetune:microsoft/deberta-v3-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-08-03T14:39:17Z |
---
license: mit
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
base_model: microsoft/deberta-v3-base
model-index:
- name: deberta-v3-base_fine_tuned_food_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. -->
# deberta-v3-base_fine_tuned_food_ner
This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4164
- Precision: 0.9268
- Recall: 0.9446
- F1: 0.9356
- Accuracy: 0.9197
## 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: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 40 | 0.8425 | 0.8323 | 0.8323 | 0.8323 | 0.8073 |
| No log | 2.0 | 80 | 0.5533 | 0.8703 | 0.8941 | 0.8820 | 0.8731 |
| No log | 3.0 | 120 | 0.4855 | 0.8771 | 0.9109 | 0.8937 | 0.8797 |
| No log | 4.0 | 160 | 0.4238 | 0.8949 | 0.9222 | 0.9083 | 0.8964 |
| No log | 5.0 | 200 | 0.4176 | 0.9048 | 0.9302 | 0.9173 | 0.9008 |
| No log | 6.0 | 240 | 0.4127 | 0.9065 | 0.9342 | 0.9202 | 0.9004 |
| No log | 7.0 | 280 | 0.4409 | 0.9294 | 0.9302 | 0.9298 | 0.9043 |
| No log | 8.0 | 320 | 0.3971 | 0.9129 | 0.9334 | 0.9230 | 0.9061 |
| No log | 9.0 | 360 | 0.3941 | 0.9112 | 0.9390 | 0.9249 | 0.9061 |
| No log | 10.0 | 400 | 0.4069 | 0.9233 | 0.9366 | 0.9299 | 0.9148 |
| No log | 11.0 | 440 | 0.4039 | 0.9213 | 0.9390 | 0.9300 | 0.9162 |
| No log | 12.0 | 480 | 0.4000 | 0.9126 | 0.9470 | 0.9295 | 0.9113 |
| 0.3799 | 13.0 | 520 | 0.4126 | 0.9323 | 0.9390 | 0.9356 | 0.9179 |
| 0.3799 | 14.0 | 560 | 0.4076 | 0.9272 | 0.9398 | 0.9334 | 0.9140 |
| 0.3799 | 15.0 | 600 | 0.4129 | 0.9317 | 0.9414 | 0.9365 | 0.9188 |
| 0.3799 | 16.0 | 640 | 0.4000 | 0.9239 | 0.9446 | 0.9341 | 0.9162 |
| 0.3799 | 17.0 | 680 | 0.4098 | 0.9267 | 0.9438 | 0.9352 | 0.9179 |
| 0.3799 | 18.0 | 720 | 0.4110 | 0.9232 | 0.9454 | 0.9342 | 0.9188 |
| 0.3799 | 19.0 | 760 | 0.4202 | 0.9275 | 0.9446 | 0.9360 | 0.9183 |
| 0.3799 | 20.0 | 800 | 0.4164 | 0.9268 | 0.9446 | 0.9356 | 0.9197 |
### Framework versions
- Transformers 4.21.0
- Pytorch 1.12.0+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
flyswot/flyswot
|
flyswot
| 2023-09-11T12:33:38Z | 229 | 0 |
transformers
|
[
"transformers",
"pytorch",
"convnext",
"image-classification",
"generated_from_trainer",
"base_model:flyswot/convnext-tiny-224_flyswot",
"base_model:finetune:flyswot/convnext-tiny-224_flyswot",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2022-04-06T15:56:05Z |
---
tags:
- generated_from_trainer
base_model: flyswot/convnext-tiny-224_flyswot
model-index:
- name: flyswot
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. -->
# flyswot
This model is a fine-tuned version of [flyswot/convnext-tiny-224_flyswot](https://huggingface.co/flyswot/convnext-tiny-224_flyswot) 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: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 0.1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 0.1 | 23 | 0.0894 | 0.9941 |
### Framework versions
- Transformers 4.19.4
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
stefaniftime/tmpnk87cy75
|
stefaniftime
| 2023-09-11T12:22:58Z | 196 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"generated_from_trainer",
"dataset:daily_dialog",
"base_model:microsoft/DialoGPT-medium",
"base_model:finetune:microsoft/DialoGPT-medium",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-09-11T12:20:13Z |
---
license: mit
base_model: microsoft/DialoGPT-medium
tags:
- generated_from_trainer
datasets:
- daily_dialog
model-index:
- name: tmpnk87cy75
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. -->
# tmpnk87cy75
This model is a fine-tuned version of [microsoft/DialoGPT-medium](https://huggingface.co/microsoft/DialoGPT-medium) on the daily_dialog dataset.
It achieves the following results on the evaluation set:
- eval_loss: 1.7442
- eval_runtime: 12.5801
- eval_samples_per_second: 79.49
- eval_steps_per_second: 2.544
- epoch: 9.35
- step: 6500
## 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: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 10
### Framework versions
- Transformers 4.33.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
|
Jukaboo/Llama2_7B_chat_dialogsum_ft_adapters_v2400
|
Jukaboo
| 2023-09-11T12:14:06Z | 0 | 0 | null |
[
"generated_from_trainer",
"base_model:meta-llama/Llama-2-7b-chat-hf",
"base_model:finetune:meta-llama/Llama-2-7b-chat-hf",
"region:us"
] | null | 2023-09-11T11:56:50Z |
---
base_model: meta-llama/Llama-2-7b-chat-hf
tags:
- generated_from_trainer
model-index:
- name: Llama2_7B_chat_dialogsum_ft_adapters_v2400
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. -->
# Llama2_7B_chat_dialogsum_ft_adapters_v2400
This model is a fine-tuned version of [meta-llama/Llama-2-7b-chat-hf](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.33.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
|
tyzp-INC/bench2-all-MiniLM-L6-v2-tuned-stratified
|
tyzp-INC
| 2023-09-11T12:09:48Z | 6 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"bert",
"setfit",
"text-classification",
"arxiv:2209.11055",
"license:apache-2.0",
"region:us"
] |
text-classification
| 2023-09-10T13:38:56Z |
---
license: apache-2.0
tags:
- setfit
- sentence-transformers
- text-classification
pipeline_tag: text-classification
---
# tyzp-INC/bench2-all-MiniLM-L6-v2-tuned-stratified
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("tyzp-INC/bench2-all-MiniLM-L6-v2-tuned-stratified")
# 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}
}
```
|
NbAiLab/nb-bert-large
|
NbAiLab
| 2023-09-11T12:08:15Z | 1,099 | 13 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"norwegian",
"fill-mask",
"no",
"license:cc-by-4.0",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:04Z |
---
language: no
license: cc-by-4.0
tags:
- norwegian
- bert
thumbnail: nblogo_3.png
pipeline_tag: fill-mask
widget:
- text: På biblioteket kan du låne en [MASK].
---
- **Release 1.0beta** (April 29, 2021)
# NB-BERT-large (beta)
## Description
NB-BERT-large is a general BERT-large model built on the large digital collection at the National Library of Norway.
This model is trained from scratch on a wide variety of Norwegian text (both bokmål and nynorsk) from the last 200 years using a monolingual Norwegian vocabulary.
## Intended use & limitations
The 1.0 version of the model is general, and should be fine-tuned for any particular use. Some fine-tuning sets may be found on Github, see
* https://github.com/NBAiLab/notram
## Training data
The model is trained on a wide variety of text. The training set is described on
* https://github.com/NBAiLab/notram
## More information
For more information on the model, see
https://github.com/NBAiLab/notram
|
bigmorning/whisper_4_with_init_sun_syl_wd_0__0055
|
bigmorning
| 2023-09-11T12:03:22Z | 59 | 0 |
transformers
|
[
"transformers",
"tf",
"whisper",
"automatic-speech-recognition",
"generated_from_keras_callback",
"base_model:openai/whisper-tiny",
"base_model:finetune:openai/whisper-tiny",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-09-11T12:03:14Z |
---
license: apache-2.0
base_model: openai/whisper-tiny
tags:
- generated_from_keras_callback
model-index:
- name: whisper_4_with_init_sun_syl_wd_0__0055
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# whisper_4_with_init_sun_syl_wd_0__0055
This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.5247
- Train Accuracy: 0.0313
- Train Wermet: 0.1358
- Train Wermet Syl: 0.1411
- Validation Loss: 1.1639
- Validation Accuracy: 0.0205
- Validation Wermet: 0.3359
- Validation Wermet Syl: 0.3025
- Epoch: 54
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 1e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Train Wermet | Train Wermet Syl | Validation Loss | Validation Accuracy | Validation Wermet | Validation Wermet Syl | Epoch |
|:----------:|:--------------:|:------------:|:----------------:|:---------------:|:-------------------:|:-----------------:|:---------------------:|:-----:|
| 5.3409 | 0.0111 | 1.3547 | 1.2898 | 3.9789 | 0.0114 | 0.9710 | 0.9563 | 0 |
| 4.7143 | 0.0116 | 0.8622 | 0.8228 | 3.9404 | 0.0113 | 0.9823 | 0.9735 | 1 |
| 4.6752 | 0.0117 | 0.8472 | 0.8057 | 3.9081 | 0.0114 | 0.9579 | 0.9359 | 2 |
| 4.6500 | 0.0117 | 0.8382 | 0.7945 | 3.8820 | 0.0115 | 0.9213 | 0.8856 | 3 |
| 4.6282 | 0.0118 | 0.8286 | 0.7805 | 3.8738 | 0.0114 | 0.9433 | 0.9119 | 4 |
| 4.6095 | 0.0118 | 0.8190 | 0.7696 | 3.8630 | 0.0115 | 0.9117 | 0.8698 | 5 |
| 4.5875 | 0.0119 | 0.7976 | 0.7465 | 3.8341 | 0.0116 | 0.8976 | 0.8552 | 6 |
| 4.5682 | 0.0120 | 0.7753 | 0.7227 | 3.8277 | 0.0116 | 0.9014 | 0.8653 | 7 |
| 4.5376 | 0.0121 | 0.7528 | 0.7005 | 3.7844 | 0.0118 | 0.8332 | 0.7815 | 8 |
| 4.5060 | 0.0122 | 0.7392 | 0.6844 | 3.7537 | 0.0118 | 0.8578 | 0.8152 | 9 |
| 4.4580 | 0.0124 | 0.7221 | 0.6694 | 3.7038 | 0.0120 | 0.8190 | 0.7679 | 10 |
| 4.3989 | 0.0125 | 0.7156 | 0.6636 | 3.6169 | 0.0122 | 0.7979 | 0.7429 | 11 |
| 4.3056 | 0.0128 | 0.7069 | 0.6557 | 3.5098 | 0.0125 | 0.7924 | 0.7396 | 12 |
| 4.1673 | 0.0132 | 0.7054 | 0.6584 | 3.3542 | 0.0128 | 0.7759 | 0.7240 | 13 |
| 3.9762 | 0.0138 | 0.6987 | 0.6559 | 3.1318 | 0.0133 | 0.7644 | 0.7231 | 14 |
| 3.7385 | 0.0145 | 0.6835 | 0.6448 | 2.9144 | 0.0138 | 0.7392 | 0.6955 | 15 |
| 3.5040 | 0.0152 | 0.6644 | 0.6298 | 2.7413 | 0.0142 | 0.7019 | 0.6548 | 16 |
| 3.2728 | 0.0160 | 0.6408 | 0.6101 | 2.5183 | 0.0149 | 0.6798 | 0.6363 | 17 |
| 3.0657 | 0.0167 | 0.6188 | 0.5912 | 2.3594 | 0.0153 | 0.6528 | 0.6103 | 18 |
| 2.8703 | 0.0174 | 0.5936 | 0.5685 | 2.2644 | 0.0156 | 0.6310 | 0.5925 | 19 |
| 2.6850 | 0.0181 | 0.5680 | 0.5453 | 2.1296 | 0.0160 | 0.6040 | 0.5652 | 20 |
| 2.5227 | 0.0188 | 0.5423 | 0.5215 | 2.0019 | 0.0165 | 0.5793 | 0.5403 | 21 |
| 2.3878 | 0.0194 | 0.5199 | 0.5015 | 1.8996 | 0.0169 | 0.5592 | 0.5229 | 22 |
| 2.2437 | 0.0201 | 0.4959 | 0.4788 | 1.8141 | 0.0172 | 0.5414 | 0.5045 | 23 |
| 2.1205 | 0.0207 | 0.4752 | 0.4607 | 1.7245 | 0.0175 | 0.5208 | 0.4838 | 24 |
| 1.9919 | 0.0213 | 0.4533 | 0.4390 | 1.6673 | 0.0178 | 0.5026 | 0.4659 | 25 |
| 1.9140 | 0.0217 | 0.4355 | 0.4216 | 1.6041 | 0.0181 | 0.4873 | 0.4512 | 26 |
| 1.8225 | 0.0222 | 0.4184 | 0.4052 | 1.6271 | 0.0179 | 0.4852 | 0.4511 | 27 |
| 1.7265 | 0.0227 | 0.4016 | 0.3895 | 1.5219 | 0.0184 | 0.4635 | 0.4275 | 28 |
| 1.6240 | 0.0233 | 0.3833 | 0.3729 | 1.4718 | 0.0186 | 0.4515 | 0.4170 | 29 |
| 1.5610 | 0.0236 | 0.3697 | 0.3588 | 1.4404 | 0.0188 | 0.4407 | 0.4056 | 30 |
| 1.4719 | 0.0242 | 0.3540 | 0.3449 | 1.4125 | 0.0189 | 0.4310 | 0.3961 | 31 |
| 1.4152 | 0.0245 | 0.3421 | 0.3339 | 1.3655 | 0.0191 | 0.4234 | 0.3881 | 32 |
| 1.3546 | 0.0249 | 0.3277 | 0.3195 | 1.3419 | 0.0192 | 0.4156 | 0.3816 | 33 |
| 1.2565 | 0.0256 | 0.3135 | 0.3060 | 1.3172 | 0.0194 | 0.4065 | 0.3722 | 34 |
| 1.2135 | 0.0258 | 0.3026 | 0.2958 | 1.3019 | 0.0194 | 0.4006 | 0.3662 | 35 |
| 1.1739 | 0.0261 | 0.2923 | 0.2861 | 1.3843 | 0.0190 | 0.3951 | 0.3587 | 36 |
| 1.0950 | 0.0267 | 0.2782 | 0.2733 | 1.2665 | 0.0197 | 0.3883 | 0.3541 | 37 |
| 1.0435 | 0.0271 | 0.2673 | 0.2631 | 1.2567 | 0.0197 | 0.3837 | 0.3497 | 38 |
| 0.9922 | 0.0275 | 0.2580 | 0.2542 | 1.2566 | 0.0197 | 0.3801 | 0.3444 | 39 |
| 0.9387 | 0.0279 | 0.2464 | 0.2438 | 1.2441 | 0.0198 | 0.3767 | 0.3423 | 40 |
| 0.9345 | 0.0278 | 0.2393 | 0.2373 | 1.2221 | 0.0199 | 0.3682 | 0.3336 | 41 |
| 0.8574 | 0.0285 | 0.2268 | 0.2255 | 1.2258 | 0.0199 | 0.3680 | 0.3338 | 42 |
| 0.8275 | 0.0287 | 0.2183 | 0.2180 | 1.2044 | 0.0201 | 0.3628 | 0.3290 | 43 |
| 0.8201 | 0.0288 | 0.2114 | 0.2108 | 1.2056 | 0.0201 | 0.3601 | 0.3270 | 44 |
| 0.7684 | 0.0292 | 0.2020 | 0.2029 | 1.1879 | 0.0202 | 0.3553 | 0.3215 | 45 |
| 0.7262 | 0.0295 | 0.1938 | 0.1947 | 1.2263 | 0.0200 | 0.3537 | 0.3177 | 46 |
| 0.7286 | 0.0295 | 0.1876 | 0.1898 | 1.1772 | 0.0203 | 0.3485 | 0.3135 | 47 |
| 0.6807 | 0.0300 | 0.1775 | 0.1797 | 1.1761 | 0.0203 | 0.3490 | 0.3155 | 48 |
| 0.6609 | 0.0301 | 0.1713 | 0.1742 | 1.1853 | 0.0203 | 0.3484 | 0.3153 | 49 |
| 0.6062 | 0.0306 | 0.1615 | 0.1653 | 1.1660 | 0.0204 | 0.3432 | 0.3090 | 50 |
| 0.5755 | 0.0309 | 0.1547 | 0.1584 | 1.1698 | 0.0204 | 0.3428 | 0.3089 | 51 |
| 0.5600 | 0.0310 | 0.1482 | 0.1524 | 1.1667 | 0.0204 | 0.3398 | 0.3058 | 52 |
| 0.5715 | 0.0308 | 0.1449 | 0.1496 | 1.1614 | 0.0205 | 0.3381 | 0.3036 | 53 |
| 0.5247 | 0.0313 | 0.1358 | 0.1411 | 1.1639 | 0.0205 | 0.3359 | 0.3025 | 54 |
### Framework versions
- Transformers 4.34.0.dev0
- TensorFlow 2.13.0
- Tokenizers 0.13.3
|
tttonyalpha/FRED-T5-1.7B-LoRA-conversational
|
tttonyalpha
| 2023-09-11T12:03:06Z | 5 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-09-11T11:59:14Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
### Framework versions
- PEFT 0.5.0
|
mesolitica/llama-13b-hf-16384-fpf
|
mesolitica
| 2023-09-11T11:57:07Z | 6 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"ms",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-08-30T14:14:39Z |
---
language:
- ms
---
# Full Parameter Finetuning 13B 16384 context length Llama2 on Malaysian text
README at https://github.com/huseinzol05/malaya/tree/5.1/session/llama2#full-parameter-finetuning
WandB, https://wandb.ai/mesolitica/fpf-Llama-2-13b-16k-hf?workspace=user-husein-mesolitica
|
bigmorning/whisper_4_with_init_sun_syl_wd_0__0050
|
bigmorning
| 2023-09-11T11:48:19Z | 59 | 0 |
transformers
|
[
"transformers",
"tf",
"whisper",
"automatic-speech-recognition",
"generated_from_keras_callback",
"base_model:openai/whisper-tiny",
"base_model:finetune:openai/whisper-tiny",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-09-11T11:48:10Z |
---
license: apache-2.0
base_model: openai/whisper-tiny
tags:
- generated_from_keras_callback
model-index:
- name: whisper_4_with_init_sun_syl_wd_0__0050
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# whisper_4_with_init_sun_syl_wd_0__0050
This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.6609
- Train Accuracy: 0.0301
- Train Wermet: 0.1713
- Train Wermet Syl: 0.1742
- Validation Loss: 1.1853
- Validation Accuracy: 0.0203
- Validation Wermet: 0.3484
- Validation Wermet Syl: 0.3153
- Epoch: 49
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 1e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Train Wermet | Train Wermet Syl | Validation Loss | Validation Accuracy | Validation Wermet | Validation Wermet Syl | Epoch |
|:----------:|:--------------:|:------------:|:----------------:|:---------------:|:-------------------:|:-----------------:|:---------------------:|:-----:|
| 5.3409 | 0.0111 | 1.3547 | 1.2898 | 3.9789 | 0.0114 | 0.9710 | 0.9563 | 0 |
| 4.7143 | 0.0116 | 0.8622 | 0.8228 | 3.9404 | 0.0113 | 0.9823 | 0.9735 | 1 |
| 4.6752 | 0.0117 | 0.8472 | 0.8057 | 3.9081 | 0.0114 | 0.9579 | 0.9359 | 2 |
| 4.6500 | 0.0117 | 0.8382 | 0.7945 | 3.8820 | 0.0115 | 0.9213 | 0.8856 | 3 |
| 4.6282 | 0.0118 | 0.8286 | 0.7805 | 3.8738 | 0.0114 | 0.9433 | 0.9119 | 4 |
| 4.6095 | 0.0118 | 0.8190 | 0.7696 | 3.8630 | 0.0115 | 0.9117 | 0.8698 | 5 |
| 4.5875 | 0.0119 | 0.7976 | 0.7465 | 3.8341 | 0.0116 | 0.8976 | 0.8552 | 6 |
| 4.5682 | 0.0120 | 0.7753 | 0.7227 | 3.8277 | 0.0116 | 0.9014 | 0.8653 | 7 |
| 4.5376 | 0.0121 | 0.7528 | 0.7005 | 3.7844 | 0.0118 | 0.8332 | 0.7815 | 8 |
| 4.5060 | 0.0122 | 0.7392 | 0.6844 | 3.7537 | 0.0118 | 0.8578 | 0.8152 | 9 |
| 4.4580 | 0.0124 | 0.7221 | 0.6694 | 3.7038 | 0.0120 | 0.8190 | 0.7679 | 10 |
| 4.3989 | 0.0125 | 0.7156 | 0.6636 | 3.6169 | 0.0122 | 0.7979 | 0.7429 | 11 |
| 4.3056 | 0.0128 | 0.7069 | 0.6557 | 3.5098 | 0.0125 | 0.7924 | 0.7396 | 12 |
| 4.1673 | 0.0132 | 0.7054 | 0.6584 | 3.3542 | 0.0128 | 0.7759 | 0.7240 | 13 |
| 3.9762 | 0.0138 | 0.6987 | 0.6559 | 3.1318 | 0.0133 | 0.7644 | 0.7231 | 14 |
| 3.7385 | 0.0145 | 0.6835 | 0.6448 | 2.9144 | 0.0138 | 0.7392 | 0.6955 | 15 |
| 3.5040 | 0.0152 | 0.6644 | 0.6298 | 2.7413 | 0.0142 | 0.7019 | 0.6548 | 16 |
| 3.2728 | 0.0160 | 0.6408 | 0.6101 | 2.5183 | 0.0149 | 0.6798 | 0.6363 | 17 |
| 3.0657 | 0.0167 | 0.6188 | 0.5912 | 2.3594 | 0.0153 | 0.6528 | 0.6103 | 18 |
| 2.8703 | 0.0174 | 0.5936 | 0.5685 | 2.2644 | 0.0156 | 0.6310 | 0.5925 | 19 |
| 2.6850 | 0.0181 | 0.5680 | 0.5453 | 2.1296 | 0.0160 | 0.6040 | 0.5652 | 20 |
| 2.5227 | 0.0188 | 0.5423 | 0.5215 | 2.0019 | 0.0165 | 0.5793 | 0.5403 | 21 |
| 2.3878 | 0.0194 | 0.5199 | 0.5015 | 1.8996 | 0.0169 | 0.5592 | 0.5229 | 22 |
| 2.2437 | 0.0201 | 0.4959 | 0.4788 | 1.8141 | 0.0172 | 0.5414 | 0.5045 | 23 |
| 2.1205 | 0.0207 | 0.4752 | 0.4607 | 1.7245 | 0.0175 | 0.5208 | 0.4838 | 24 |
| 1.9919 | 0.0213 | 0.4533 | 0.4390 | 1.6673 | 0.0178 | 0.5026 | 0.4659 | 25 |
| 1.9140 | 0.0217 | 0.4355 | 0.4216 | 1.6041 | 0.0181 | 0.4873 | 0.4512 | 26 |
| 1.8225 | 0.0222 | 0.4184 | 0.4052 | 1.6271 | 0.0179 | 0.4852 | 0.4511 | 27 |
| 1.7265 | 0.0227 | 0.4016 | 0.3895 | 1.5219 | 0.0184 | 0.4635 | 0.4275 | 28 |
| 1.6240 | 0.0233 | 0.3833 | 0.3729 | 1.4718 | 0.0186 | 0.4515 | 0.4170 | 29 |
| 1.5610 | 0.0236 | 0.3697 | 0.3588 | 1.4404 | 0.0188 | 0.4407 | 0.4056 | 30 |
| 1.4719 | 0.0242 | 0.3540 | 0.3449 | 1.4125 | 0.0189 | 0.4310 | 0.3961 | 31 |
| 1.4152 | 0.0245 | 0.3421 | 0.3339 | 1.3655 | 0.0191 | 0.4234 | 0.3881 | 32 |
| 1.3546 | 0.0249 | 0.3277 | 0.3195 | 1.3419 | 0.0192 | 0.4156 | 0.3816 | 33 |
| 1.2565 | 0.0256 | 0.3135 | 0.3060 | 1.3172 | 0.0194 | 0.4065 | 0.3722 | 34 |
| 1.2135 | 0.0258 | 0.3026 | 0.2958 | 1.3019 | 0.0194 | 0.4006 | 0.3662 | 35 |
| 1.1739 | 0.0261 | 0.2923 | 0.2861 | 1.3843 | 0.0190 | 0.3951 | 0.3587 | 36 |
| 1.0950 | 0.0267 | 0.2782 | 0.2733 | 1.2665 | 0.0197 | 0.3883 | 0.3541 | 37 |
| 1.0435 | 0.0271 | 0.2673 | 0.2631 | 1.2567 | 0.0197 | 0.3837 | 0.3497 | 38 |
| 0.9922 | 0.0275 | 0.2580 | 0.2542 | 1.2566 | 0.0197 | 0.3801 | 0.3444 | 39 |
| 0.9387 | 0.0279 | 0.2464 | 0.2438 | 1.2441 | 0.0198 | 0.3767 | 0.3423 | 40 |
| 0.9345 | 0.0278 | 0.2393 | 0.2373 | 1.2221 | 0.0199 | 0.3682 | 0.3336 | 41 |
| 0.8574 | 0.0285 | 0.2268 | 0.2255 | 1.2258 | 0.0199 | 0.3680 | 0.3338 | 42 |
| 0.8275 | 0.0287 | 0.2183 | 0.2180 | 1.2044 | 0.0201 | 0.3628 | 0.3290 | 43 |
| 0.8201 | 0.0288 | 0.2114 | 0.2108 | 1.2056 | 0.0201 | 0.3601 | 0.3270 | 44 |
| 0.7684 | 0.0292 | 0.2020 | 0.2029 | 1.1879 | 0.0202 | 0.3553 | 0.3215 | 45 |
| 0.7262 | 0.0295 | 0.1938 | 0.1947 | 1.2263 | 0.0200 | 0.3537 | 0.3177 | 46 |
| 0.7286 | 0.0295 | 0.1876 | 0.1898 | 1.1772 | 0.0203 | 0.3485 | 0.3135 | 47 |
| 0.6807 | 0.0300 | 0.1775 | 0.1797 | 1.1761 | 0.0203 | 0.3490 | 0.3155 | 48 |
| 0.6609 | 0.0301 | 0.1713 | 0.1742 | 1.1853 | 0.0203 | 0.3484 | 0.3153 | 49 |
### Framework versions
- Transformers 4.34.0.dev0
- TensorFlow 2.13.0
- Tokenizers 0.13.3
|
moonlightnexus/shaper
|
moonlightnexus
| 2023-09-11T11:39:03Z | 3 | 1 |
diffusers
|
[
"diffusers",
"safetensors",
"General",
"Anime",
"Art",
"Girl",
"Photorealistic",
"3D",
"LandScapes",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"XpucT",
"Lykon",
"en",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-09-11T09:37:30Z |
---
license: creativeml-openrail-m
library_name: diffusers
pipeline_tag: text-to-image
language:
- en
tags:
- General
- Anime
- Art
- Girl
- Photorealistic
- 3D
- LandScapes
- stable-diffusion
- stable-diffusion-diffusers
- diffusers
- text-to-image
- XpucT
- Lykon
---
|
bigmorning/whisper_4_with_init_sun_syl_wd_0__0045
|
bigmorning
| 2023-09-11T11:33:13Z | 59 | 0 |
transformers
|
[
"transformers",
"tf",
"whisper",
"automatic-speech-recognition",
"generated_from_keras_callback",
"base_model:openai/whisper-tiny",
"base_model:finetune:openai/whisper-tiny",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-09-11T11:33:03Z |
---
license: apache-2.0
base_model: openai/whisper-tiny
tags:
- generated_from_keras_callback
model-index:
- name: whisper_4_with_init_sun_syl_wd_0__0045
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# whisper_4_with_init_sun_syl_wd_0__0045
This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.8201
- Train Accuracy: 0.0288
- Train Wermet: 0.2114
- Train Wermet Syl: 0.2108
- Validation Loss: 1.2056
- Validation Accuracy: 0.0201
- Validation Wermet: 0.3601
- Validation Wermet Syl: 0.3270
- Epoch: 44
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 1e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Train Wermet | Train Wermet Syl | Validation Loss | Validation Accuracy | Validation Wermet | Validation Wermet Syl | Epoch |
|:----------:|:--------------:|:------------:|:----------------:|:---------------:|:-------------------:|:-----------------:|:---------------------:|:-----:|
| 5.3409 | 0.0111 | 1.3547 | 1.2898 | 3.9789 | 0.0114 | 0.9710 | 0.9563 | 0 |
| 4.7143 | 0.0116 | 0.8622 | 0.8228 | 3.9404 | 0.0113 | 0.9823 | 0.9735 | 1 |
| 4.6752 | 0.0117 | 0.8472 | 0.8057 | 3.9081 | 0.0114 | 0.9579 | 0.9359 | 2 |
| 4.6500 | 0.0117 | 0.8382 | 0.7945 | 3.8820 | 0.0115 | 0.9213 | 0.8856 | 3 |
| 4.6282 | 0.0118 | 0.8286 | 0.7805 | 3.8738 | 0.0114 | 0.9433 | 0.9119 | 4 |
| 4.6095 | 0.0118 | 0.8190 | 0.7696 | 3.8630 | 0.0115 | 0.9117 | 0.8698 | 5 |
| 4.5875 | 0.0119 | 0.7976 | 0.7465 | 3.8341 | 0.0116 | 0.8976 | 0.8552 | 6 |
| 4.5682 | 0.0120 | 0.7753 | 0.7227 | 3.8277 | 0.0116 | 0.9014 | 0.8653 | 7 |
| 4.5376 | 0.0121 | 0.7528 | 0.7005 | 3.7844 | 0.0118 | 0.8332 | 0.7815 | 8 |
| 4.5060 | 0.0122 | 0.7392 | 0.6844 | 3.7537 | 0.0118 | 0.8578 | 0.8152 | 9 |
| 4.4580 | 0.0124 | 0.7221 | 0.6694 | 3.7038 | 0.0120 | 0.8190 | 0.7679 | 10 |
| 4.3989 | 0.0125 | 0.7156 | 0.6636 | 3.6169 | 0.0122 | 0.7979 | 0.7429 | 11 |
| 4.3056 | 0.0128 | 0.7069 | 0.6557 | 3.5098 | 0.0125 | 0.7924 | 0.7396 | 12 |
| 4.1673 | 0.0132 | 0.7054 | 0.6584 | 3.3542 | 0.0128 | 0.7759 | 0.7240 | 13 |
| 3.9762 | 0.0138 | 0.6987 | 0.6559 | 3.1318 | 0.0133 | 0.7644 | 0.7231 | 14 |
| 3.7385 | 0.0145 | 0.6835 | 0.6448 | 2.9144 | 0.0138 | 0.7392 | 0.6955 | 15 |
| 3.5040 | 0.0152 | 0.6644 | 0.6298 | 2.7413 | 0.0142 | 0.7019 | 0.6548 | 16 |
| 3.2728 | 0.0160 | 0.6408 | 0.6101 | 2.5183 | 0.0149 | 0.6798 | 0.6363 | 17 |
| 3.0657 | 0.0167 | 0.6188 | 0.5912 | 2.3594 | 0.0153 | 0.6528 | 0.6103 | 18 |
| 2.8703 | 0.0174 | 0.5936 | 0.5685 | 2.2644 | 0.0156 | 0.6310 | 0.5925 | 19 |
| 2.6850 | 0.0181 | 0.5680 | 0.5453 | 2.1296 | 0.0160 | 0.6040 | 0.5652 | 20 |
| 2.5227 | 0.0188 | 0.5423 | 0.5215 | 2.0019 | 0.0165 | 0.5793 | 0.5403 | 21 |
| 2.3878 | 0.0194 | 0.5199 | 0.5015 | 1.8996 | 0.0169 | 0.5592 | 0.5229 | 22 |
| 2.2437 | 0.0201 | 0.4959 | 0.4788 | 1.8141 | 0.0172 | 0.5414 | 0.5045 | 23 |
| 2.1205 | 0.0207 | 0.4752 | 0.4607 | 1.7245 | 0.0175 | 0.5208 | 0.4838 | 24 |
| 1.9919 | 0.0213 | 0.4533 | 0.4390 | 1.6673 | 0.0178 | 0.5026 | 0.4659 | 25 |
| 1.9140 | 0.0217 | 0.4355 | 0.4216 | 1.6041 | 0.0181 | 0.4873 | 0.4512 | 26 |
| 1.8225 | 0.0222 | 0.4184 | 0.4052 | 1.6271 | 0.0179 | 0.4852 | 0.4511 | 27 |
| 1.7265 | 0.0227 | 0.4016 | 0.3895 | 1.5219 | 0.0184 | 0.4635 | 0.4275 | 28 |
| 1.6240 | 0.0233 | 0.3833 | 0.3729 | 1.4718 | 0.0186 | 0.4515 | 0.4170 | 29 |
| 1.5610 | 0.0236 | 0.3697 | 0.3588 | 1.4404 | 0.0188 | 0.4407 | 0.4056 | 30 |
| 1.4719 | 0.0242 | 0.3540 | 0.3449 | 1.4125 | 0.0189 | 0.4310 | 0.3961 | 31 |
| 1.4152 | 0.0245 | 0.3421 | 0.3339 | 1.3655 | 0.0191 | 0.4234 | 0.3881 | 32 |
| 1.3546 | 0.0249 | 0.3277 | 0.3195 | 1.3419 | 0.0192 | 0.4156 | 0.3816 | 33 |
| 1.2565 | 0.0256 | 0.3135 | 0.3060 | 1.3172 | 0.0194 | 0.4065 | 0.3722 | 34 |
| 1.2135 | 0.0258 | 0.3026 | 0.2958 | 1.3019 | 0.0194 | 0.4006 | 0.3662 | 35 |
| 1.1739 | 0.0261 | 0.2923 | 0.2861 | 1.3843 | 0.0190 | 0.3951 | 0.3587 | 36 |
| 1.0950 | 0.0267 | 0.2782 | 0.2733 | 1.2665 | 0.0197 | 0.3883 | 0.3541 | 37 |
| 1.0435 | 0.0271 | 0.2673 | 0.2631 | 1.2567 | 0.0197 | 0.3837 | 0.3497 | 38 |
| 0.9922 | 0.0275 | 0.2580 | 0.2542 | 1.2566 | 0.0197 | 0.3801 | 0.3444 | 39 |
| 0.9387 | 0.0279 | 0.2464 | 0.2438 | 1.2441 | 0.0198 | 0.3767 | 0.3423 | 40 |
| 0.9345 | 0.0278 | 0.2393 | 0.2373 | 1.2221 | 0.0199 | 0.3682 | 0.3336 | 41 |
| 0.8574 | 0.0285 | 0.2268 | 0.2255 | 1.2258 | 0.0199 | 0.3680 | 0.3338 | 42 |
| 0.8275 | 0.0287 | 0.2183 | 0.2180 | 1.2044 | 0.0201 | 0.3628 | 0.3290 | 43 |
| 0.8201 | 0.0288 | 0.2114 | 0.2108 | 1.2056 | 0.0201 | 0.3601 | 0.3270 | 44 |
### Framework versions
- Transformers 4.34.0.dev0
- TensorFlow 2.13.0
- Tokenizers 0.13.3
|
ALIGHASEMI931/finetune
|
ALIGHASEMI931
| 2023-09-11T11:29:37Z | 78 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-09-11T11:27:46Z |
---
tags:
- generated_from_trainer
model-index:
- name: finetune
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. -->
# finetune
This model was trained from scratch on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 0
- gradient_accumulation_steps: 50
- total_train_batch_size: 400
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 50
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.33.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
|
ldos/text_shortening_model_v26
|
ldos
| 2023-09-11T11:28:09Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:google-t5/t5-small",
"base_model:finetune:google-t5/t5-small",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-09-11T09:52:36Z |
---
license: apache-2.0
base_model: t5-small
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: text_shortening_model_v26
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. -->
# text_shortening_model_v26
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.2306
- Rouge1: 0.5085
- Rouge2: 0.2908
- Rougel: 0.4563
- Rougelsum: 0.456
- Bert precision: 0.88
- Bert recall: 0.8755
- Average word count: 8.5646
- Max word count: 17
- Min word count: 3
- Average token count: 13.2012
- % shortened texts with length > 12: 8.7087
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 100
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Bert precision | Bert recall | Average word count | Max word count | Min word count | Average token count | % shortened texts with length > 12 |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:---------:|:--------------:|:-----------:|:------------------:|:--------------:|:--------------:|:-------------------:|:----------------------------------:|
| 2.1038 | 1.0 | 145 | 1.6481 | 0.4984 | 0.2848 | 0.4508 | 0.4519 | 0.8723 | 0.8719 | 9.1502 | 18 | 3 | 13.7117 | 15.9159 |
| 1.7274 | 2.0 | 290 | 1.5436 | 0.5177 | 0.3156 | 0.4706 | 0.4714 | 0.8771 | 0.8775 | 9.1141 | 18 | 4 | 13.6637 | 14.7147 |
| 1.561 | 3.0 | 435 | 1.4685 | 0.5264 | 0.3157 | 0.4671 | 0.469 | 0.8773 | 0.8793 | 9.2823 | 17 | 4 | 13.955 | 14.1141 |
| 1.4244 | 4.0 | 580 | 1.4429 | 0.5213 | 0.3136 | 0.4674 | 0.4689 | 0.8772 | 0.8774 | 9.0811 | 17 | 4 | 13.8288 | 12.9129 |
| 1.3375 | 5.0 | 725 | 1.4171 | 0.5326 | 0.3172 | 0.4768 | 0.4778 | 0.8785 | 0.8807 | 9.3063 | 18 | 5 | 13.964 | 14.4144 |
| 1.2462 | 6.0 | 870 | 1.3989 | 0.5259 | 0.3126 | 0.4707 | 0.4714 | 0.8807 | 0.8768 | 8.6577 | 17 | 4 | 13.1441 | 9.6096 |
| 1.1822 | 7.0 | 1015 | 1.3797 | 0.5321 | 0.3147 | 0.4687 | 0.4699 | 0.8798 | 0.8792 | 9.009 | 17 | 4 | 13.6877 | 12.9129 |
| 1.1001 | 8.0 | 1160 | 1.3735 | 0.5387 | 0.325 | 0.481 | 0.4814 | 0.8805 | 0.8835 | 9.3213 | 17 | 4 | 14.0601 | 14.7147 |
| 1.0329 | 9.0 | 1305 | 1.3813 | 0.53 | 0.3122 | 0.4694 | 0.4706 | 0.8799 | 0.8811 | 9.024 | 17 | 4 | 13.7057 | 11.1111 |
| 0.9891 | 10.0 | 1450 | 1.3734 | 0.5334 | 0.3191 | 0.4715 | 0.4726 | 0.8793 | 0.8829 | 9.3243 | 17 | 4 | 14.1291 | 13.8138 |
| 0.9205 | 11.0 | 1595 | 1.3687 | 0.5279 | 0.3111 | 0.4663 | 0.4676 | 0.8793 | 0.8802 | 9.03 | 16 | 4 | 13.6577 | 11.4114 |
| 0.8857 | 12.0 | 1740 | 1.3986 | 0.5219 | 0.3098 | 0.4694 | 0.4703 | 0.8811 | 0.879 | 8.8018 | 15 | 3 | 13.3934 | 11.4114 |
| 0.8444 | 13.0 | 1885 | 1.4143 | 0.5291 | 0.3084 | 0.4707 | 0.4718 | 0.8802 | 0.8796 | 9.03 | 17 | 4 | 13.6727 | 13.5135 |
| 0.8039 | 14.0 | 2030 | 1.4352 | 0.5216 | 0.2989 | 0.4631 | 0.464 | 0.8812 | 0.878 | 8.7958 | 16 | 4 | 13.4805 | 9.3093 |
| 0.7653 | 15.0 | 2175 | 1.4509 | 0.525 | 0.3076 | 0.4743 | 0.4751 | 0.8834 | 0.8783 | 8.5526 | 16 | 4 | 13.2162 | 8.7087 |
| 0.7256 | 16.0 | 2320 | 1.4541 | 0.5153 | 0.2952 | 0.4566 | 0.4579 | 0.8779 | 0.8768 | 8.8739 | 16 | 4 | 13.5405 | 12.012 |
| 0.7018 | 17.0 | 2465 | 1.4859 | 0.5312 | 0.306 | 0.4722 | 0.4727 | 0.8812 | 0.8823 | 9.0841 | 17 | 4 | 13.6967 | 14.4144 |
| 0.6784 | 18.0 | 2610 | 1.4977 | 0.5215 | 0.3068 | 0.4674 | 0.4684 | 0.8817 | 0.877 | 8.5766 | 16 | 4 | 13.2072 | 10.2102 |
| 0.6483 | 19.0 | 2755 | 1.5040 | 0.5297 | 0.3192 | 0.4757 | 0.4756 | 0.8817 | 0.8818 | 9.021 | 16 | 4 | 13.7327 | 12.012 |
| 0.6166 | 20.0 | 2900 | 1.5376 | 0.526 | 0.3119 | 0.4768 | 0.4774 | 0.8835 | 0.8808 | 8.8138 | 16 | 4 | 13.3634 | 10.2102 |
| 0.5955 | 21.0 | 3045 | 1.5198 | 0.528 | 0.3129 | 0.4795 | 0.4805 | 0.8829 | 0.8807 | 8.8769 | 16 | 4 | 13.5075 | 9.9099 |
| 0.5678 | 22.0 | 3190 | 1.5499 | 0.518 | 0.2988 | 0.4636 | 0.464 | 0.8802 | 0.8785 | 8.9249 | 17 | 4 | 13.6006 | 12.6126 |
| 0.5599 | 23.0 | 3335 | 1.5487 | 0.519 | 0.3057 | 0.4691 | 0.4698 | 0.8812 | 0.8773 | 8.6607 | 18 | 4 | 13.2192 | 9.3093 |
| 0.535 | 24.0 | 3480 | 1.5912 | 0.5243 | 0.3054 | 0.4708 | 0.4717 | 0.8828 | 0.8779 | 8.6456 | 16 | 4 | 13.1532 | 9.9099 |
| 0.5189 | 25.0 | 3625 | 1.5995 | 0.5314 | 0.3106 | 0.4735 | 0.474 | 0.8827 | 0.8815 | 8.9099 | 18 | 4 | 13.6126 | 12.6126 |
| 0.4981 | 26.0 | 3770 | 1.6036 | 0.5222 | 0.3037 | 0.4675 | 0.4676 | 0.8824 | 0.8788 | 8.7658 | 15 | 4 | 13.3784 | 9.9099 |
| 0.4729 | 27.0 | 3915 | 1.6360 | 0.5114 | 0.2927 | 0.46 | 0.4604 | 0.8807 | 0.875 | 8.5676 | 15 | 4 | 13.1592 | 9.009 |
| 0.462 | 28.0 | 4060 | 1.6648 | 0.5145 | 0.2945 | 0.4586 | 0.459 | 0.8812 | 0.8754 | 8.5435 | 17 | 3 | 13.0841 | 9.009 |
| 0.4467 | 29.0 | 4205 | 1.6749 | 0.5076 | 0.2828 | 0.4527 | 0.4533 | 0.8794 | 0.8746 | 8.6697 | 16 | 3 | 13.1772 | 9.6096 |
| 0.4298 | 30.0 | 4350 | 1.6873 | 0.5215 | 0.2976 | 0.4683 | 0.4679 | 0.8822 | 0.8774 | 8.5766 | 16 | 3 | 13.1682 | 7.8078 |
| 0.4186 | 31.0 | 4495 | 1.7008 | 0.5129 | 0.2915 | 0.4614 | 0.4614 | 0.8814 | 0.8763 | 8.5736 | 16 | 4 | 13.1892 | 8.7087 |
| 0.4043 | 32.0 | 4640 | 1.7077 | 0.5121 | 0.2859 | 0.4572 | 0.457 | 0.8796 | 0.8765 | 8.7387 | 16 | 3 | 13.4114 | 10.2102 |
| 0.3835 | 33.0 | 4785 | 1.7421 | 0.5106 | 0.2831 | 0.4579 | 0.4577 | 0.8785 | 0.8763 | 8.7988 | 17 | 3 | 13.4865 | 10.8108 |
| 0.377 | 34.0 | 4930 | 1.7763 | 0.5135 | 0.2907 | 0.4585 | 0.4586 | 0.8808 | 0.8768 | 8.6787 | 15 | 3 | 13.4084 | 10.8108 |
| 0.3672 | 35.0 | 5075 | 1.7642 | 0.5243 | 0.3018 | 0.4701 | 0.4694 | 0.8826 | 0.8777 | 8.5616 | 15 | 3 | 13.1892 | 9.6096 |
| 0.3499 | 36.0 | 5220 | 1.7840 | 0.5175 | 0.2965 | 0.466 | 0.4656 | 0.8815 | 0.8772 | 8.5796 | 17 | 3 | 13.2252 | 9.9099 |
| 0.3417 | 37.0 | 5365 | 1.8032 | 0.5163 | 0.2964 | 0.4638 | 0.4636 | 0.8801 | 0.8785 | 8.8348 | 16 | 3 | 13.6156 | 11.4114 |
| 0.3364 | 38.0 | 5510 | 1.8112 | 0.5096 | 0.2832 | 0.4532 | 0.4536 | 0.8783 | 0.8763 | 8.8829 | 17 | 4 | 13.4925 | 10.5105 |
| 0.315 | 39.0 | 5655 | 1.8360 | 0.5208 | 0.3034 | 0.4692 | 0.4694 | 0.8836 | 0.8797 | 8.7177 | 17 | 4 | 13.3213 | 11.4114 |
| 0.3117 | 40.0 | 5800 | 1.8419 | 0.5069 | 0.285 | 0.4555 | 0.4558 | 0.879 | 0.8746 | 8.7117 | 17 | 3 | 13.3634 | 9.009 |
| 0.3195 | 41.0 | 5945 | 1.8435 | 0.5214 | 0.2984 | 0.4686 | 0.4691 | 0.8817 | 0.8779 | 8.7297 | 17 | 3 | 13.3303 | 11.4114 |
| 0.3062 | 42.0 | 6090 | 1.8574 | 0.5174 | 0.2941 | 0.4672 | 0.4676 | 0.8827 | 0.8779 | 8.6907 | 17 | 3 | 13.3604 | 9.6096 |
| 0.2892 | 43.0 | 6235 | 1.8839 | 0.5083 | 0.2939 | 0.4603 | 0.4603 | 0.8789 | 0.8763 | 8.7147 | 17 | 4 | 13.5045 | 10.8108 |
| 0.283 | 44.0 | 6380 | 1.8838 | 0.5078 | 0.2873 | 0.4546 | 0.4552 | 0.879 | 0.8757 | 8.7327 | 17 | 4 | 13.5135 | 10.8108 |
| 0.2813 | 45.0 | 6525 | 1.8947 | 0.5126 | 0.2919 | 0.4603 | 0.4608 | 0.8803 | 0.8762 | 8.7027 | 16 | 3 | 13.4505 | 10.8108 |
| 0.2716 | 46.0 | 6670 | 1.9045 | 0.5163 | 0.3 | 0.4687 | 0.4686 | 0.8813 | 0.8771 | 8.6126 | 17 | 4 | 13.3303 | 9.3093 |
| 0.2604 | 47.0 | 6815 | 1.9097 | 0.5106 | 0.2928 | 0.4617 | 0.4621 | 0.8796 | 0.8761 | 8.7477 | 17 | 3 | 13.5135 | 9.009 |
| 0.2514 | 48.0 | 6960 | 1.9477 | 0.5156 | 0.2959 | 0.463 | 0.4633 | 0.8813 | 0.876 | 8.6006 | 17 | 3 | 13.3453 | 8.4084 |
| 0.2444 | 49.0 | 7105 | 1.9599 | 0.5107 | 0.2903 | 0.4581 | 0.4586 | 0.8796 | 0.875 | 8.6607 | 16 | 4 | 13.3994 | 8.4084 |
| 0.2428 | 50.0 | 7250 | 1.9775 | 0.5082 | 0.2903 | 0.4587 | 0.4587 | 0.88 | 0.8748 | 8.5435 | 16 | 3 | 13.2823 | 8.1081 |
| 0.2395 | 51.0 | 7395 | 1.9783 | 0.5154 | 0.2948 | 0.4647 | 0.4647 | 0.8809 | 0.8768 | 8.6817 | 17 | 3 | 13.3303 | 9.6096 |
| 0.2317 | 52.0 | 7540 | 1.9881 | 0.5092 | 0.2895 | 0.4545 | 0.4546 | 0.8807 | 0.8766 | 8.6126 | 17 | 3 | 13.3964 | 7.8078 |
| 0.224 | 53.0 | 7685 | 2.0001 | 0.5165 | 0.3017 | 0.4622 | 0.4627 | 0.8802 | 0.8777 | 8.7598 | 17 | 3 | 13.4895 | 9.3093 |
| 0.2161 | 54.0 | 7830 | 2.0140 | 0.5176 | 0.2974 | 0.465 | 0.4652 | 0.881 | 0.878 | 8.7327 | 17 | 3 | 13.4384 | 9.9099 |
| 0.2201 | 55.0 | 7975 | 2.0317 | 0.5102 | 0.2904 | 0.4554 | 0.4553 | 0.8802 | 0.8765 | 8.6306 | 16 | 3 | 13.3754 | 10.8108 |
| 0.2153 | 56.0 | 8120 | 2.0427 | 0.5172 | 0.2983 | 0.4632 | 0.4632 | 0.8808 | 0.8771 | 8.7297 | 17 | 3 | 13.4114 | 11.1111 |
| 0.211 | 57.0 | 8265 | 2.0432 | 0.5165 | 0.2983 | 0.4652 | 0.4652 | 0.8815 | 0.8765 | 8.5976 | 17 | 3 | 13.2432 | 9.9099 |
| 0.1995 | 58.0 | 8410 | 2.0720 | 0.5062 | 0.2913 | 0.4528 | 0.4528 | 0.8781 | 0.8739 | 8.6006 | 17 | 3 | 13.2763 | 8.7087 |
| 0.2072 | 59.0 | 8555 | 2.0574 | 0.5099 | 0.2902 | 0.4554 | 0.4563 | 0.8803 | 0.8751 | 8.5435 | 17 | 3 | 13.1411 | 9.009 |
| 0.1989 | 60.0 | 8700 | 2.0722 | 0.5127 | 0.2943 | 0.459 | 0.4585 | 0.8807 | 0.8767 | 8.6967 | 17 | 4 | 13.3213 | 11.1111 |
| 0.1911 | 61.0 | 8845 | 2.0669 | 0.5125 | 0.2922 | 0.459 | 0.4581 | 0.8806 | 0.875 | 8.5556 | 16 | 3 | 13.1622 | 9.009 |
| 0.1902 | 62.0 | 8990 | 2.0912 | 0.5063 | 0.2892 | 0.4498 | 0.45 | 0.8795 | 0.8739 | 8.5105 | 17 | 4 | 13.0751 | 9.9099 |
| 0.1905 | 63.0 | 9135 | 2.0875 | 0.5029 | 0.2845 | 0.4492 | 0.4492 | 0.878 | 0.8745 | 8.6727 | 16 | 4 | 13.3423 | 10.5105 |
| 0.1895 | 64.0 | 9280 | 2.0787 | 0.5094 | 0.2941 | 0.4551 | 0.4557 | 0.8791 | 0.8751 | 8.7117 | 17 | 4 | 13.2973 | 9.9099 |
| 0.1813 | 65.0 | 9425 | 2.0960 | 0.5168 | 0.2998 | 0.462 | 0.4619 | 0.8812 | 0.8773 | 8.7177 | 17 | 4 | 13.3634 | 10.8108 |
| 0.1856 | 66.0 | 9570 | 2.0888 | 0.5053 | 0.2921 | 0.4549 | 0.4552 | 0.8793 | 0.8746 | 8.5676 | 17 | 3 | 13.1772 | 8.7087 |
| 0.1669 | 67.0 | 9715 | 2.1158 | 0.5184 | 0.3018 | 0.4623 | 0.4624 | 0.8814 | 0.8772 | 8.6517 | 17 | 4 | 13.2462 | 12.012 |
| 0.1676 | 68.0 | 9860 | 2.1246 | 0.5195 | 0.2977 | 0.4642 | 0.4638 | 0.8814 | 0.8778 | 8.7207 | 17 | 4 | 13.3243 | 11.4114 |
| 0.1682 | 69.0 | 10005 | 2.1325 | 0.5112 | 0.2963 | 0.4572 | 0.4579 | 0.8805 | 0.8759 | 8.5916 | 17 | 4 | 13.1742 | 9.9099 |
| 0.1664 | 70.0 | 10150 | 2.1442 | 0.5048 | 0.2828 | 0.4505 | 0.4506 | 0.8786 | 0.8743 | 8.6366 | 17 | 4 | 13.2883 | 8.7087 |
| 0.1655 | 71.0 | 10295 | 2.1339 | 0.5132 | 0.295 | 0.4603 | 0.4603 | 0.8802 | 0.8754 | 8.7087 | 17 | 4 | 13.3273 | 10.8108 |
| 0.1621 | 72.0 | 10440 | 2.1391 | 0.5036 | 0.2858 | 0.4527 | 0.4526 | 0.8786 | 0.8722 | 8.4715 | 17 | 4 | 13.0901 | 9.009 |
| 0.1624 | 73.0 | 10585 | 2.1438 | 0.5055 | 0.2865 | 0.4558 | 0.4557 | 0.8786 | 0.8737 | 8.5255 | 17 | 4 | 13.1832 | 9.009 |
| 0.1486 | 74.0 | 10730 | 2.1623 | 0.5073 | 0.2871 | 0.4554 | 0.4551 | 0.8794 | 0.8745 | 8.5375 | 17 | 4 | 13.2372 | 8.4084 |
| 0.1593 | 75.0 | 10875 | 2.1699 | 0.5054 | 0.2873 | 0.4527 | 0.4526 | 0.8782 | 0.874 | 8.6126 | 17 | 4 | 13.2913 | 10.2102 |
| 0.16 | 76.0 | 11020 | 2.1652 | 0.5062 | 0.284 | 0.4557 | 0.4556 | 0.8788 | 0.8748 | 8.6937 | 17 | 4 | 13.2733 | 9.9099 |
| 0.1464 | 77.0 | 11165 | 2.1777 | 0.5073 | 0.2876 | 0.4556 | 0.4553 | 0.8786 | 0.8749 | 8.6787 | 17 | 4 | 13.3453 | 10.8108 |
| 0.1492 | 78.0 | 11310 | 2.1705 | 0.5027 | 0.2854 | 0.4498 | 0.45 | 0.8774 | 0.8738 | 8.6937 | 17 | 4 | 13.3724 | 10.5105 |
| 0.1565 | 79.0 | 11455 | 2.1738 | 0.4946 | 0.2768 | 0.4432 | 0.4431 | 0.8757 | 0.8718 | 8.5916 | 17 | 4 | 13.3303 | 10.2102 |
| 0.1429 | 80.0 | 11600 | 2.1968 | 0.5021 | 0.2878 | 0.4523 | 0.452 | 0.8781 | 0.8737 | 8.5375 | 17 | 4 | 13.2583 | 9.009 |
| 0.1424 | 81.0 | 11745 | 2.1810 | 0.509 | 0.2909 | 0.4562 | 0.4558 | 0.8785 | 0.8752 | 8.6186 | 17 | 3 | 13.2703 | 10.8108 |
| 0.1447 | 82.0 | 11890 | 2.1790 | 0.5042 | 0.283 | 0.4504 | 0.4507 | 0.8782 | 0.874 | 8.5616 | 15 | 4 | 13.2162 | 10.5105 |
| 0.1399 | 83.0 | 12035 | 2.1908 | 0.5018 | 0.2801 | 0.4489 | 0.4488 | 0.8772 | 0.8733 | 8.5796 | 17 | 3 | 13.2042 | 10.2102 |
| 0.1417 | 84.0 | 12180 | 2.1985 | 0.504 | 0.2812 | 0.4534 | 0.4527 | 0.8782 | 0.8739 | 8.5375 | 17 | 3 | 13.0751 | 9.6096 |
| 0.1375 | 85.0 | 12325 | 2.1914 | 0.5061 | 0.2844 | 0.4557 | 0.4549 | 0.8791 | 0.8749 | 8.5435 | 17 | 4 | 13.1441 | 9.9099 |
| 0.1354 | 86.0 | 12470 | 2.2087 | 0.5084 | 0.2889 | 0.4592 | 0.4589 | 0.8798 | 0.8755 | 8.5315 | 17 | 4 | 13.1321 | 10.2102 |
| 0.1381 | 87.0 | 12615 | 2.2014 | 0.5068 | 0.2857 | 0.4555 | 0.4551 | 0.8792 | 0.8754 | 8.5345 | 17 | 4 | 13.1802 | 10.2102 |
| 0.137 | 88.0 | 12760 | 2.2022 | 0.5077 | 0.2894 | 0.4561 | 0.4552 | 0.8793 | 0.8753 | 8.5495 | 17 | 4 | 13.1682 | 10.2102 |
| 0.1301 | 89.0 | 12905 | 2.2055 | 0.5096 | 0.2905 | 0.4581 | 0.4581 | 0.8795 | 0.8758 | 8.6186 | 17 | 4 | 13.1802 | 10.2102 |
| 0.1374 | 90.0 | 13050 | 2.2118 | 0.507 | 0.2865 | 0.4544 | 0.4544 | 0.8793 | 0.8751 | 8.5766 | 17 | 4 | 13.1532 | 9.9099 |
| 0.1338 | 91.0 | 13195 | 2.2074 | 0.5048 | 0.2863 | 0.453 | 0.4529 | 0.8791 | 0.8747 | 8.5135 | 17 | 4 | 13.0661 | 8.7087 |
| 0.1308 | 92.0 | 13340 | 2.2144 | 0.5053 | 0.2886 | 0.4542 | 0.4545 | 0.8789 | 0.8742 | 8.5195 | 17 | 3 | 13.0961 | 8.4084 |
| 0.1254 | 93.0 | 13485 | 2.2208 | 0.5118 | 0.294 | 0.4611 | 0.4612 | 0.8805 | 0.8763 | 8.5225 | 17 | 3 | 13.1141 | 8.4084 |
| 0.1311 | 94.0 | 13630 | 2.2254 | 0.5084 | 0.2909 | 0.4573 | 0.4573 | 0.8798 | 0.8752 | 8.5165 | 17 | 3 | 13.0751 | 7.8078 |
| 0.1272 | 95.0 | 13775 | 2.2274 | 0.5056 | 0.2872 | 0.454 | 0.4538 | 0.8792 | 0.8745 | 8.5766 | 17 | 3 | 13.1982 | 8.4084 |
| 0.1304 | 96.0 | 13920 | 2.2313 | 0.5053 | 0.2879 | 0.4526 | 0.4526 | 0.8794 | 0.8747 | 8.5435 | 17 | 3 | 13.1652 | 8.7087 |
| 0.1303 | 97.0 | 14065 | 2.2304 | 0.5061 | 0.2871 | 0.4532 | 0.4532 | 0.8793 | 0.8748 | 8.5586 | 17 | 3 | 13.2012 | 8.7087 |
| 0.1306 | 98.0 | 14210 | 2.2303 | 0.5081 | 0.2889 | 0.4556 | 0.4552 | 0.8796 | 0.8753 | 8.5766 | 17 | 3 | 13.2102 | 8.7087 |
| 0.1387 | 99.0 | 14355 | 2.2304 | 0.5088 | 0.2903 | 0.4563 | 0.4561 | 0.8799 | 0.8754 | 8.5766 | 17 | 3 | 13.2042 | 9.009 |
| 0.1339 | 100.0 | 14500 | 2.2306 | 0.5085 | 0.2908 | 0.4563 | 0.456 | 0.88 | 0.8755 | 8.5646 | 17 | 3 | 13.2012 | 8.7087 |
### Framework versions
- Transformers 4.33.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
|
mindchain/llama2-adapter_01
|
mindchain
| 2023-09-11T11:26:08Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-09-11T11:26:04Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.6.0.dev0
|
Shishir1807/cherry-gopher
|
Shishir1807
| 2023-09-11T11:24:12Z | 140 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt_neox",
"text-generation",
"gpt",
"llm",
"large language model",
"h2o-llmstudio",
"en",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-09-11T11:23:32Z |
---
language:
- en
library_name: transformers
tags:
- gpt
- llm
- large language model
- h2o-llmstudio
inference: false
thumbnail: https://h2o.ai/etc.clientlibs/h2o/clientlibs/clientlib-site/resources/images/favicon.ico
---
# Model Card
## Summary
This model was trained using [H2O LLM Studio](https://github.com/h2oai/h2o-llmstudio).
- Base model: [EleutherAI/pythia-2.8b-deduped](https://huggingface.co/EleutherAI/pythia-2.8b-deduped)
## Usage
To use the model with the `transformers` library on a machine with GPUs, first make sure you have the `transformers`, `accelerate` and `torch` libraries installed.
```bash
pip install transformers==4.29.2
pip install einops==0.6.1
pip install accelerate==0.19.0
pip install torch==2.0.0
```
```python
import torch
from transformers import pipeline
generate_text = pipeline(
model="Shishir1807/cherry-gopher",
torch_dtype="auto",
trust_remote_code=True,
use_fast=True,
device_map={"": "cuda:0"},
)
res = generate_text(
"Why is drinking water so healthy?",
min_new_tokens=2,
max_new_tokens=256,
do_sample=False,
num_beams=1,
temperature=float(0.3),
repetition_penalty=float(1.2),
renormalize_logits=True
)
print(res[0]["generated_text"])
```
You can print a sample prompt after the preprocessing step to see how it is feed to the tokenizer:
```python
print(generate_text.preprocess("Why is drinking water so healthy?")["prompt_text"])
```
```bash
<|prompt|>Why is drinking water so healthy?<|endoftext|><|answer|>
```
Alternatively, you can download [h2oai_pipeline.py](h2oai_pipeline.py), store it alongside your notebook, and construct the pipeline yourself from the loaded model and tokenizer. If the model and the tokenizer are fully supported in the `transformers` package, this will allow you to set `trust_remote_code=False`.
```python
import torch
from h2oai_pipeline import H2OTextGenerationPipeline
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained(
"Shishir1807/cherry-gopher",
use_fast=True,
padding_side="left",
trust_remote_code=True,
)
model = AutoModelForCausalLM.from_pretrained(
"Shishir1807/cherry-gopher",
torch_dtype="auto",
device_map={"": "cuda:0"},
trust_remote_code=True,
)
generate_text = H2OTextGenerationPipeline(model=model, tokenizer=tokenizer)
res = generate_text(
"Why is drinking water so healthy?",
min_new_tokens=2,
max_new_tokens=256,
do_sample=False,
num_beams=1,
temperature=float(0.3),
repetition_penalty=float(1.2),
renormalize_logits=True
)
print(res[0]["generated_text"])
```
You may also construct the pipeline from the loaded model and tokenizer yourself and consider the preprocessing steps:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "Shishir1807/cherry-gopher" # either local folder or huggingface model name
# Important: The prompt needs to be in the same format the model was trained with.
# You can find an example prompt in the experiment logs.
prompt = "<|prompt|>How are you?<|endoftext|><|answer|>"
tokenizer = AutoTokenizer.from_pretrained(
model_name,
use_fast=True,
trust_remote_code=True,
)
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype="auto",
device_map={"": "cuda:0"},
trust_remote_code=True,
)
model.cuda().eval()
inputs = tokenizer(prompt, return_tensors="pt", add_special_tokens=False).to("cuda")
# generate configuration can be modified to your needs
tokens = model.generate(
input_ids=inputs["input_ids"],
attention_mask=inputs["attention_mask"],
min_new_tokens=2,
max_new_tokens=256,
do_sample=False,
num_beams=1,
temperature=float(0.3),
repetition_penalty=float(1.2),
renormalize_logits=True
)[0]
tokens = tokens[inputs["input_ids"].shape[1]:]
answer = tokenizer.decode(tokens, skip_special_tokens=True)
print(answer)
```
## Quantization and sharding
You can load the models using quantization by specifying ```load_in_8bit=True``` or ```load_in_4bit=True```. Also, sharding on multiple GPUs is possible by setting ```device_map=auto```.
## Model Architecture
```
GPTNeoXForCausalLM(
(gpt_neox): GPTNeoXModel(
(embed_in): Embedding(50304, 2560)
(layers): ModuleList(
(0-31): 32 x GPTNeoXLayer(
(input_layernorm): LayerNorm((2560,), eps=1e-05, elementwise_affine=True)
(post_attention_layernorm): LayerNorm((2560,), eps=1e-05, elementwise_affine=True)
(attention): GPTNeoXAttention(
(rotary_emb): RotaryEmbedding()
(query_key_value): Linear(in_features=2560, out_features=7680, bias=True)
(dense): Linear(in_features=2560, out_features=2560, bias=True)
)
(mlp): GPTNeoXMLP(
(dense_h_to_4h): Linear(in_features=2560, out_features=10240, bias=True)
(dense_4h_to_h): Linear(in_features=10240, out_features=2560, bias=True)
(act): GELUActivation()
)
)
)
(final_layer_norm): LayerNorm((2560,), eps=1e-05, elementwise_affine=True)
)
(embed_out): Linear(in_features=2560, out_features=50304, bias=False)
)
```
## Model Configuration
This model was trained using H2O LLM Studio and with the configuration in [cfg.yaml](cfg.yaml). Visit [H2O LLM Studio](https://github.com/h2oai/h2o-llmstudio) to learn how to train your own large language models.
## Disclaimer
Please read this disclaimer carefully before using the large language model provided in this repository. Your use of the model signifies your agreement to the following terms and conditions.
- Biases and Offensiveness: The large language model is trained on a diverse range of internet text data, which may contain biased, racist, offensive, or otherwise inappropriate content. By using this model, you acknowledge and accept that the generated content may sometimes exhibit biases or produce content that is offensive or inappropriate. The developers of this repository do not endorse, support, or promote any such content or viewpoints.
- Limitations: The large language model is an AI-based tool and not a human. It may produce incorrect, nonsensical, or irrelevant responses. It is the user's responsibility to critically evaluate the generated content and use it at their discretion.
- Use at Your Own Risk: Users of this large language model must assume full responsibility for any consequences that may arise from their use of the tool. The developers and contributors of this repository shall not be held liable for any damages, losses, or harm resulting from the use or misuse of the provided model.
- Ethical Considerations: Users are encouraged to use the large language model responsibly and ethically. By using this model, you agree not to use it for purposes that promote hate speech, discrimination, harassment, or any form of illegal or harmful activities.
- Reporting Issues: If you encounter any biased, offensive, or otherwise inappropriate content generated by the large language model, please report it to the repository maintainers through the provided channels. Your feedback will help improve the model and mitigate potential issues.
- Changes to this Disclaimer: The developers of this repository reserve the right to modify or update this disclaimer at any time without prior notice. It is the user's responsibility to periodically review the disclaimer to stay informed about any changes.
By using the large language model provided in this repository, you agree to accept and comply with the terms and conditions outlined in this disclaimer. If you do not agree with any part of this disclaimer, you should refrain from using the model and any content generated by it.
|
DragosGorduza/FRPile_GPL_test_pipeline_BAAI-bge-large-en_1400
|
DragosGorduza
| 2023-09-11T11:20:32Z | 2 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2023-09-11T11:01:22Z |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 1024 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
def cls_pooling(model_output, attention_mask):
return model_output[0][:,0]
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
model = AutoModel.from_pretrained('{MODEL_NAME}')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, cls pooling.
sentence_embeddings = cls_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 700 with parameters:
```
{'batch_size': 4, 'sampler': 'torch.utils.data.sampler.SequentialSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`gpl.toolkit.loss.MarginDistillationLoss`
Parameters of the fit()-Method:
```
{
"epochs": 1,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": 1400,
"warmup_steps": 1000,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 350, 'do_lower_case': True}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information -->
|
bigmorning/whisper_4_with_init_sun_syl_wd_0__0040
|
bigmorning
| 2023-09-11T11:18:12Z | 59 | 0 |
transformers
|
[
"transformers",
"tf",
"whisper",
"automatic-speech-recognition",
"generated_from_keras_callback",
"base_model:openai/whisper-tiny",
"base_model:finetune:openai/whisper-tiny",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-09-11T11:18:04Z |
---
license: apache-2.0
base_model: openai/whisper-tiny
tags:
- generated_from_keras_callback
model-index:
- name: whisper_4_with_init_sun_syl_wd_0__0040
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# whisper_4_with_init_sun_syl_wd_0__0040
This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.9922
- Train Accuracy: 0.0275
- Train Wermet: 0.2580
- Train Wermet Syl: 0.2542
- Validation Loss: 1.2566
- Validation Accuracy: 0.0197
- Validation Wermet: 0.3801
- Validation Wermet Syl: 0.3444
- Epoch: 39
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 1e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Train Wermet | Train Wermet Syl | Validation Loss | Validation Accuracy | Validation Wermet | Validation Wermet Syl | Epoch |
|:----------:|:--------------:|:------------:|:----------------:|:---------------:|:-------------------:|:-----------------:|:---------------------:|:-----:|
| 5.3409 | 0.0111 | 1.3547 | 1.2898 | 3.9789 | 0.0114 | 0.9710 | 0.9563 | 0 |
| 4.7143 | 0.0116 | 0.8622 | 0.8228 | 3.9404 | 0.0113 | 0.9823 | 0.9735 | 1 |
| 4.6752 | 0.0117 | 0.8472 | 0.8057 | 3.9081 | 0.0114 | 0.9579 | 0.9359 | 2 |
| 4.6500 | 0.0117 | 0.8382 | 0.7945 | 3.8820 | 0.0115 | 0.9213 | 0.8856 | 3 |
| 4.6282 | 0.0118 | 0.8286 | 0.7805 | 3.8738 | 0.0114 | 0.9433 | 0.9119 | 4 |
| 4.6095 | 0.0118 | 0.8190 | 0.7696 | 3.8630 | 0.0115 | 0.9117 | 0.8698 | 5 |
| 4.5875 | 0.0119 | 0.7976 | 0.7465 | 3.8341 | 0.0116 | 0.8976 | 0.8552 | 6 |
| 4.5682 | 0.0120 | 0.7753 | 0.7227 | 3.8277 | 0.0116 | 0.9014 | 0.8653 | 7 |
| 4.5376 | 0.0121 | 0.7528 | 0.7005 | 3.7844 | 0.0118 | 0.8332 | 0.7815 | 8 |
| 4.5060 | 0.0122 | 0.7392 | 0.6844 | 3.7537 | 0.0118 | 0.8578 | 0.8152 | 9 |
| 4.4580 | 0.0124 | 0.7221 | 0.6694 | 3.7038 | 0.0120 | 0.8190 | 0.7679 | 10 |
| 4.3989 | 0.0125 | 0.7156 | 0.6636 | 3.6169 | 0.0122 | 0.7979 | 0.7429 | 11 |
| 4.3056 | 0.0128 | 0.7069 | 0.6557 | 3.5098 | 0.0125 | 0.7924 | 0.7396 | 12 |
| 4.1673 | 0.0132 | 0.7054 | 0.6584 | 3.3542 | 0.0128 | 0.7759 | 0.7240 | 13 |
| 3.9762 | 0.0138 | 0.6987 | 0.6559 | 3.1318 | 0.0133 | 0.7644 | 0.7231 | 14 |
| 3.7385 | 0.0145 | 0.6835 | 0.6448 | 2.9144 | 0.0138 | 0.7392 | 0.6955 | 15 |
| 3.5040 | 0.0152 | 0.6644 | 0.6298 | 2.7413 | 0.0142 | 0.7019 | 0.6548 | 16 |
| 3.2728 | 0.0160 | 0.6408 | 0.6101 | 2.5183 | 0.0149 | 0.6798 | 0.6363 | 17 |
| 3.0657 | 0.0167 | 0.6188 | 0.5912 | 2.3594 | 0.0153 | 0.6528 | 0.6103 | 18 |
| 2.8703 | 0.0174 | 0.5936 | 0.5685 | 2.2644 | 0.0156 | 0.6310 | 0.5925 | 19 |
| 2.6850 | 0.0181 | 0.5680 | 0.5453 | 2.1296 | 0.0160 | 0.6040 | 0.5652 | 20 |
| 2.5227 | 0.0188 | 0.5423 | 0.5215 | 2.0019 | 0.0165 | 0.5793 | 0.5403 | 21 |
| 2.3878 | 0.0194 | 0.5199 | 0.5015 | 1.8996 | 0.0169 | 0.5592 | 0.5229 | 22 |
| 2.2437 | 0.0201 | 0.4959 | 0.4788 | 1.8141 | 0.0172 | 0.5414 | 0.5045 | 23 |
| 2.1205 | 0.0207 | 0.4752 | 0.4607 | 1.7245 | 0.0175 | 0.5208 | 0.4838 | 24 |
| 1.9919 | 0.0213 | 0.4533 | 0.4390 | 1.6673 | 0.0178 | 0.5026 | 0.4659 | 25 |
| 1.9140 | 0.0217 | 0.4355 | 0.4216 | 1.6041 | 0.0181 | 0.4873 | 0.4512 | 26 |
| 1.8225 | 0.0222 | 0.4184 | 0.4052 | 1.6271 | 0.0179 | 0.4852 | 0.4511 | 27 |
| 1.7265 | 0.0227 | 0.4016 | 0.3895 | 1.5219 | 0.0184 | 0.4635 | 0.4275 | 28 |
| 1.6240 | 0.0233 | 0.3833 | 0.3729 | 1.4718 | 0.0186 | 0.4515 | 0.4170 | 29 |
| 1.5610 | 0.0236 | 0.3697 | 0.3588 | 1.4404 | 0.0188 | 0.4407 | 0.4056 | 30 |
| 1.4719 | 0.0242 | 0.3540 | 0.3449 | 1.4125 | 0.0189 | 0.4310 | 0.3961 | 31 |
| 1.4152 | 0.0245 | 0.3421 | 0.3339 | 1.3655 | 0.0191 | 0.4234 | 0.3881 | 32 |
| 1.3546 | 0.0249 | 0.3277 | 0.3195 | 1.3419 | 0.0192 | 0.4156 | 0.3816 | 33 |
| 1.2565 | 0.0256 | 0.3135 | 0.3060 | 1.3172 | 0.0194 | 0.4065 | 0.3722 | 34 |
| 1.2135 | 0.0258 | 0.3026 | 0.2958 | 1.3019 | 0.0194 | 0.4006 | 0.3662 | 35 |
| 1.1739 | 0.0261 | 0.2923 | 0.2861 | 1.3843 | 0.0190 | 0.3951 | 0.3587 | 36 |
| 1.0950 | 0.0267 | 0.2782 | 0.2733 | 1.2665 | 0.0197 | 0.3883 | 0.3541 | 37 |
| 1.0435 | 0.0271 | 0.2673 | 0.2631 | 1.2567 | 0.0197 | 0.3837 | 0.3497 | 38 |
| 0.9922 | 0.0275 | 0.2580 | 0.2542 | 1.2566 | 0.0197 | 0.3801 | 0.3444 | 39 |
### Framework versions
- Transformers 4.34.0.dev0
- TensorFlow 2.13.0
- Tokenizers 0.13.3
|
Yoshimitsujhi/11-09-falcon7b-code
|
Yoshimitsujhi
| 2023-09-11T11:14:24Z | 0 | 0 | null |
[
"generated_from_trainer",
"base_model:tiiuae/falcon-7b",
"base_model:finetune:tiiuae/falcon-7b",
"license:apache-2.0",
"region:us"
] | null | 2023-09-11T10:44:55Z |
---
license: apache-2.0
base_model: tiiuae/falcon-7b
tags:
- generated_from_trainer
model-index:
- name: 11-09-falcon7b-code
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. -->
# 11-09-falcon7b-code
This model is a fine-tuned version of [tiiuae/falcon-7b](https://huggingface.co/tiiuae/falcon-7b) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.03
- training_steps: 320
### Training results
### Framework versions
- Transformers 4.32.1
- Pytorch 2.0.1
- Datasets 2.14.4
- Tokenizers 0.13.3
|
ritheeshrl/ppo-LunarLander-v2
|
ritheeshrl
| 2023-09-11T11:11:01Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-09-11T11:09:33Z |
---
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: 268.03 +/- 15.19
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
...
```
|
bigmorning/whisper_4_with_init_sun_syl_wd_0__0035
|
bigmorning
| 2023-09-11T11:03:15Z | 59 | 0 |
transformers
|
[
"transformers",
"tf",
"whisper",
"automatic-speech-recognition",
"generated_from_keras_callback",
"base_model:openai/whisper-tiny",
"base_model:finetune:openai/whisper-tiny",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-09-11T11:03:07Z |
---
license: apache-2.0
base_model: openai/whisper-tiny
tags:
- generated_from_keras_callback
model-index:
- name: whisper_4_with_init_sun_syl_wd_0__0035
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# whisper_4_with_init_sun_syl_wd_0__0035
This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 1.2565
- Train Accuracy: 0.0256
- Train Wermet: 0.3135
- Train Wermet Syl: 0.3060
- Validation Loss: 1.3172
- Validation Accuracy: 0.0194
- Validation Wermet: 0.4065
- Validation Wermet Syl: 0.3722
- Epoch: 34
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 1e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Train Wermet | Train Wermet Syl | Validation Loss | Validation Accuracy | Validation Wermet | Validation Wermet Syl | Epoch |
|:----------:|:--------------:|:------------:|:----------------:|:---------------:|:-------------------:|:-----------------:|:---------------------:|:-----:|
| 5.3409 | 0.0111 | 1.3547 | 1.2898 | 3.9789 | 0.0114 | 0.9710 | 0.9563 | 0 |
| 4.7143 | 0.0116 | 0.8622 | 0.8228 | 3.9404 | 0.0113 | 0.9823 | 0.9735 | 1 |
| 4.6752 | 0.0117 | 0.8472 | 0.8057 | 3.9081 | 0.0114 | 0.9579 | 0.9359 | 2 |
| 4.6500 | 0.0117 | 0.8382 | 0.7945 | 3.8820 | 0.0115 | 0.9213 | 0.8856 | 3 |
| 4.6282 | 0.0118 | 0.8286 | 0.7805 | 3.8738 | 0.0114 | 0.9433 | 0.9119 | 4 |
| 4.6095 | 0.0118 | 0.8190 | 0.7696 | 3.8630 | 0.0115 | 0.9117 | 0.8698 | 5 |
| 4.5875 | 0.0119 | 0.7976 | 0.7465 | 3.8341 | 0.0116 | 0.8976 | 0.8552 | 6 |
| 4.5682 | 0.0120 | 0.7753 | 0.7227 | 3.8277 | 0.0116 | 0.9014 | 0.8653 | 7 |
| 4.5376 | 0.0121 | 0.7528 | 0.7005 | 3.7844 | 0.0118 | 0.8332 | 0.7815 | 8 |
| 4.5060 | 0.0122 | 0.7392 | 0.6844 | 3.7537 | 0.0118 | 0.8578 | 0.8152 | 9 |
| 4.4580 | 0.0124 | 0.7221 | 0.6694 | 3.7038 | 0.0120 | 0.8190 | 0.7679 | 10 |
| 4.3989 | 0.0125 | 0.7156 | 0.6636 | 3.6169 | 0.0122 | 0.7979 | 0.7429 | 11 |
| 4.3056 | 0.0128 | 0.7069 | 0.6557 | 3.5098 | 0.0125 | 0.7924 | 0.7396 | 12 |
| 4.1673 | 0.0132 | 0.7054 | 0.6584 | 3.3542 | 0.0128 | 0.7759 | 0.7240 | 13 |
| 3.9762 | 0.0138 | 0.6987 | 0.6559 | 3.1318 | 0.0133 | 0.7644 | 0.7231 | 14 |
| 3.7385 | 0.0145 | 0.6835 | 0.6448 | 2.9144 | 0.0138 | 0.7392 | 0.6955 | 15 |
| 3.5040 | 0.0152 | 0.6644 | 0.6298 | 2.7413 | 0.0142 | 0.7019 | 0.6548 | 16 |
| 3.2728 | 0.0160 | 0.6408 | 0.6101 | 2.5183 | 0.0149 | 0.6798 | 0.6363 | 17 |
| 3.0657 | 0.0167 | 0.6188 | 0.5912 | 2.3594 | 0.0153 | 0.6528 | 0.6103 | 18 |
| 2.8703 | 0.0174 | 0.5936 | 0.5685 | 2.2644 | 0.0156 | 0.6310 | 0.5925 | 19 |
| 2.6850 | 0.0181 | 0.5680 | 0.5453 | 2.1296 | 0.0160 | 0.6040 | 0.5652 | 20 |
| 2.5227 | 0.0188 | 0.5423 | 0.5215 | 2.0019 | 0.0165 | 0.5793 | 0.5403 | 21 |
| 2.3878 | 0.0194 | 0.5199 | 0.5015 | 1.8996 | 0.0169 | 0.5592 | 0.5229 | 22 |
| 2.2437 | 0.0201 | 0.4959 | 0.4788 | 1.8141 | 0.0172 | 0.5414 | 0.5045 | 23 |
| 2.1205 | 0.0207 | 0.4752 | 0.4607 | 1.7245 | 0.0175 | 0.5208 | 0.4838 | 24 |
| 1.9919 | 0.0213 | 0.4533 | 0.4390 | 1.6673 | 0.0178 | 0.5026 | 0.4659 | 25 |
| 1.9140 | 0.0217 | 0.4355 | 0.4216 | 1.6041 | 0.0181 | 0.4873 | 0.4512 | 26 |
| 1.8225 | 0.0222 | 0.4184 | 0.4052 | 1.6271 | 0.0179 | 0.4852 | 0.4511 | 27 |
| 1.7265 | 0.0227 | 0.4016 | 0.3895 | 1.5219 | 0.0184 | 0.4635 | 0.4275 | 28 |
| 1.6240 | 0.0233 | 0.3833 | 0.3729 | 1.4718 | 0.0186 | 0.4515 | 0.4170 | 29 |
| 1.5610 | 0.0236 | 0.3697 | 0.3588 | 1.4404 | 0.0188 | 0.4407 | 0.4056 | 30 |
| 1.4719 | 0.0242 | 0.3540 | 0.3449 | 1.4125 | 0.0189 | 0.4310 | 0.3961 | 31 |
| 1.4152 | 0.0245 | 0.3421 | 0.3339 | 1.3655 | 0.0191 | 0.4234 | 0.3881 | 32 |
| 1.3546 | 0.0249 | 0.3277 | 0.3195 | 1.3419 | 0.0192 | 0.4156 | 0.3816 | 33 |
| 1.2565 | 0.0256 | 0.3135 | 0.3060 | 1.3172 | 0.0194 | 0.4065 | 0.3722 | 34 |
### Framework versions
- Transformers 4.34.0.dev0
- TensorFlow 2.13.0
- Tokenizers 0.13.3
|
bigmorning/whisper_4_with_init_sun_syl_wd_0__0030
|
bigmorning
| 2023-09-11T10:48:13Z | 60 | 0 |
transformers
|
[
"transformers",
"tf",
"whisper",
"automatic-speech-recognition",
"generated_from_keras_callback",
"base_model:openai/whisper-tiny",
"base_model:finetune:openai/whisper-tiny",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-09-11T10:48:06Z |
---
license: apache-2.0
base_model: openai/whisper-tiny
tags:
- generated_from_keras_callback
model-index:
- name: whisper_4_with_init_sun_syl_wd_0__0030
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# whisper_4_with_init_sun_syl_wd_0__0030
This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 1.6240
- Train Accuracy: 0.0233
- Train Wermet: 0.3833
- Train Wermet Syl: 0.3729
- Validation Loss: 1.4718
- Validation Accuracy: 0.0186
- Validation Wermet: 0.4515
- Validation Wermet Syl: 0.4170
- Epoch: 29
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 1e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Train Wermet | Train Wermet Syl | Validation Loss | Validation Accuracy | Validation Wermet | Validation Wermet Syl | Epoch |
|:----------:|:--------------:|:------------:|:----------------:|:---------------:|:-------------------:|:-----------------:|:---------------------:|:-----:|
| 5.3409 | 0.0111 | 1.3547 | 1.2898 | 3.9789 | 0.0114 | 0.9710 | 0.9563 | 0 |
| 4.7143 | 0.0116 | 0.8622 | 0.8228 | 3.9404 | 0.0113 | 0.9823 | 0.9735 | 1 |
| 4.6752 | 0.0117 | 0.8472 | 0.8057 | 3.9081 | 0.0114 | 0.9579 | 0.9359 | 2 |
| 4.6500 | 0.0117 | 0.8382 | 0.7945 | 3.8820 | 0.0115 | 0.9213 | 0.8856 | 3 |
| 4.6282 | 0.0118 | 0.8286 | 0.7805 | 3.8738 | 0.0114 | 0.9433 | 0.9119 | 4 |
| 4.6095 | 0.0118 | 0.8190 | 0.7696 | 3.8630 | 0.0115 | 0.9117 | 0.8698 | 5 |
| 4.5875 | 0.0119 | 0.7976 | 0.7465 | 3.8341 | 0.0116 | 0.8976 | 0.8552 | 6 |
| 4.5682 | 0.0120 | 0.7753 | 0.7227 | 3.8277 | 0.0116 | 0.9014 | 0.8653 | 7 |
| 4.5376 | 0.0121 | 0.7528 | 0.7005 | 3.7844 | 0.0118 | 0.8332 | 0.7815 | 8 |
| 4.5060 | 0.0122 | 0.7392 | 0.6844 | 3.7537 | 0.0118 | 0.8578 | 0.8152 | 9 |
| 4.4580 | 0.0124 | 0.7221 | 0.6694 | 3.7038 | 0.0120 | 0.8190 | 0.7679 | 10 |
| 4.3989 | 0.0125 | 0.7156 | 0.6636 | 3.6169 | 0.0122 | 0.7979 | 0.7429 | 11 |
| 4.3056 | 0.0128 | 0.7069 | 0.6557 | 3.5098 | 0.0125 | 0.7924 | 0.7396 | 12 |
| 4.1673 | 0.0132 | 0.7054 | 0.6584 | 3.3542 | 0.0128 | 0.7759 | 0.7240 | 13 |
| 3.9762 | 0.0138 | 0.6987 | 0.6559 | 3.1318 | 0.0133 | 0.7644 | 0.7231 | 14 |
| 3.7385 | 0.0145 | 0.6835 | 0.6448 | 2.9144 | 0.0138 | 0.7392 | 0.6955 | 15 |
| 3.5040 | 0.0152 | 0.6644 | 0.6298 | 2.7413 | 0.0142 | 0.7019 | 0.6548 | 16 |
| 3.2728 | 0.0160 | 0.6408 | 0.6101 | 2.5183 | 0.0149 | 0.6798 | 0.6363 | 17 |
| 3.0657 | 0.0167 | 0.6188 | 0.5912 | 2.3594 | 0.0153 | 0.6528 | 0.6103 | 18 |
| 2.8703 | 0.0174 | 0.5936 | 0.5685 | 2.2644 | 0.0156 | 0.6310 | 0.5925 | 19 |
| 2.6850 | 0.0181 | 0.5680 | 0.5453 | 2.1296 | 0.0160 | 0.6040 | 0.5652 | 20 |
| 2.5227 | 0.0188 | 0.5423 | 0.5215 | 2.0019 | 0.0165 | 0.5793 | 0.5403 | 21 |
| 2.3878 | 0.0194 | 0.5199 | 0.5015 | 1.8996 | 0.0169 | 0.5592 | 0.5229 | 22 |
| 2.2437 | 0.0201 | 0.4959 | 0.4788 | 1.8141 | 0.0172 | 0.5414 | 0.5045 | 23 |
| 2.1205 | 0.0207 | 0.4752 | 0.4607 | 1.7245 | 0.0175 | 0.5208 | 0.4838 | 24 |
| 1.9919 | 0.0213 | 0.4533 | 0.4390 | 1.6673 | 0.0178 | 0.5026 | 0.4659 | 25 |
| 1.9140 | 0.0217 | 0.4355 | 0.4216 | 1.6041 | 0.0181 | 0.4873 | 0.4512 | 26 |
| 1.8225 | 0.0222 | 0.4184 | 0.4052 | 1.6271 | 0.0179 | 0.4852 | 0.4511 | 27 |
| 1.7265 | 0.0227 | 0.4016 | 0.3895 | 1.5219 | 0.0184 | 0.4635 | 0.4275 | 28 |
| 1.6240 | 0.0233 | 0.3833 | 0.3729 | 1.4718 | 0.0186 | 0.4515 | 0.4170 | 29 |
### Framework versions
- Transformers 4.34.0.dev0
- TensorFlow 2.13.0
- Tokenizers 0.13.3
|
dragocennova/chlbnskatr
|
dragocennova
| 2023-09-11T10:36:40Z | 29 | 0 |
diffusers
|
[
"diffusers",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-09-11T10:20:12Z |
---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
---
### CHLBNSKatr Dreambooth model trained by dragocennova with TheLastBen's fast-DreamBooth notebook
|
bigmorning/whisper_4_with_init_sun_syl_wd_0__0025
|
bigmorning
| 2023-09-11T10:33:14Z | 61 | 0 |
transformers
|
[
"transformers",
"tf",
"whisper",
"automatic-speech-recognition",
"generated_from_keras_callback",
"base_model:openai/whisper-tiny",
"base_model:finetune:openai/whisper-tiny",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-09-11T10:33:07Z |
---
license: apache-2.0
base_model: openai/whisper-tiny
tags:
- generated_from_keras_callback
model-index:
- name: whisper_4_with_init_sun_syl_wd_0__0025
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# whisper_4_with_init_sun_syl_wd_0__0025
This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 2.1205
- Train Accuracy: 0.0207
- Train Wermet: 0.4752
- Train Wermet Syl: 0.4607
- Validation Loss: 1.7245
- Validation Accuracy: 0.0175
- Validation Wermet: 0.5208
- Validation Wermet Syl: 0.4838
- Epoch: 24
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 1e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Train Wermet | Train Wermet Syl | Validation Loss | Validation Accuracy | Validation Wermet | Validation Wermet Syl | Epoch |
|:----------:|:--------------:|:------------:|:----------------:|:---------------:|:-------------------:|:-----------------:|:---------------------:|:-----:|
| 5.3409 | 0.0111 | 1.3547 | 1.2898 | 3.9789 | 0.0114 | 0.9710 | 0.9563 | 0 |
| 4.7143 | 0.0116 | 0.8622 | 0.8228 | 3.9404 | 0.0113 | 0.9823 | 0.9735 | 1 |
| 4.6752 | 0.0117 | 0.8472 | 0.8057 | 3.9081 | 0.0114 | 0.9579 | 0.9359 | 2 |
| 4.6500 | 0.0117 | 0.8382 | 0.7945 | 3.8820 | 0.0115 | 0.9213 | 0.8856 | 3 |
| 4.6282 | 0.0118 | 0.8286 | 0.7805 | 3.8738 | 0.0114 | 0.9433 | 0.9119 | 4 |
| 4.6095 | 0.0118 | 0.8190 | 0.7696 | 3.8630 | 0.0115 | 0.9117 | 0.8698 | 5 |
| 4.5875 | 0.0119 | 0.7976 | 0.7465 | 3.8341 | 0.0116 | 0.8976 | 0.8552 | 6 |
| 4.5682 | 0.0120 | 0.7753 | 0.7227 | 3.8277 | 0.0116 | 0.9014 | 0.8653 | 7 |
| 4.5376 | 0.0121 | 0.7528 | 0.7005 | 3.7844 | 0.0118 | 0.8332 | 0.7815 | 8 |
| 4.5060 | 0.0122 | 0.7392 | 0.6844 | 3.7537 | 0.0118 | 0.8578 | 0.8152 | 9 |
| 4.4580 | 0.0124 | 0.7221 | 0.6694 | 3.7038 | 0.0120 | 0.8190 | 0.7679 | 10 |
| 4.3989 | 0.0125 | 0.7156 | 0.6636 | 3.6169 | 0.0122 | 0.7979 | 0.7429 | 11 |
| 4.3056 | 0.0128 | 0.7069 | 0.6557 | 3.5098 | 0.0125 | 0.7924 | 0.7396 | 12 |
| 4.1673 | 0.0132 | 0.7054 | 0.6584 | 3.3542 | 0.0128 | 0.7759 | 0.7240 | 13 |
| 3.9762 | 0.0138 | 0.6987 | 0.6559 | 3.1318 | 0.0133 | 0.7644 | 0.7231 | 14 |
| 3.7385 | 0.0145 | 0.6835 | 0.6448 | 2.9144 | 0.0138 | 0.7392 | 0.6955 | 15 |
| 3.5040 | 0.0152 | 0.6644 | 0.6298 | 2.7413 | 0.0142 | 0.7019 | 0.6548 | 16 |
| 3.2728 | 0.0160 | 0.6408 | 0.6101 | 2.5183 | 0.0149 | 0.6798 | 0.6363 | 17 |
| 3.0657 | 0.0167 | 0.6188 | 0.5912 | 2.3594 | 0.0153 | 0.6528 | 0.6103 | 18 |
| 2.8703 | 0.0174 | 0.5936 | 0.5685 | 2.2644 | 0.0156 | 0.6310 | 0.5925 | 19 |
| 2.6850 | 0.0181 | 0.5680 | 0.5453 | 2.1296 | 0.0160 | 0.6040 | 0.5652 | 20 |
| 2.5227 | 0.0188 | 0.5423 | 0.5215 | 2.0019 | 0.0165 | 0.5793 | 0.5403 | 21 |
| 2.3878 | 0.0194 | 0.5199 | 0.5015 | 1.8996 | 0.0169 | 0.5592 | 0.5229 | 22 |
| 2.2437 | 0.0201 | 0.4959 | 0.4788 | 1.8141 | 0.0172 | 0.5414 | 0.5045 | 23 |
| 2.1205 | 0.0207 | 0.4752 | 0.4607 | 1.7245 | 0.0175 | 0.5208 | 0.4838 | 24 |
### Framework versions
- Transformers 4.34.0.dev0
- TensorFlow 2.13.0
- Tokenizers 0.13.3
|
Ionut-Marius/rare-dogs
|
Ionut-Marius
| 2023-09-11T10:12:29Z | 191 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-09-11T10:12:22Z |
---
tags:
- image-classification
- pytorch
- huggingpics
metrics:
- accuracy
model-index:
- name: rare-dogs
results:
- task:
name: Image Classification
type: image-classification
metrics:
- name: Accuracy
type: accuracy
value: 0.8089887499809265
---
# rare-dogs
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics).
## Example Images
#### corgi
#### husky
#### samoyed
#### shiba inu
|
yogeshchandrasekharuni/ludwig-llama-2-7b-skil-internal-wiki-no-lm-manual-v1
|
yogeshchandrasekharuni
| 2023-09-11T10:11:18Z | 1 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-09-11T10:11:16Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: float16
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.5.0
- PEFT 0.5.0
|
bigmorning/whisper_4_with_init_sun_syl_wd_0__0015
|
bigmorning
| 2023-09-11T10:03:19Z | 59 | 0 |
transformers
|
[
"transformers",
"tf",
"whisper",
"automatic-speech-recognition",
"generated_from_keras_callback",
"base_model:openai/whisper-tiny",
"base_model:finetune:openai/whisper-tiny",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-09-11T10:03:11Z |
---
license: apache-2.0
base_model: openai/whisper-tiny
tags:
- generated_from_keras_callback
model-index:
- name: whisper_4_with_init_sun_syl_wd_0__0015
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# whisper_4_with_init_sun_syl_wd_0__0015
This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 3.9762
- Train Accuracy: 0.0138
- Train Wermet: 0.6987
- Train Wermet Syl: 0.6559
- Validation Loss: 3.1318
- Validation Accuracy: 0.0133
- Validation Wermet: 0.7644
- Validation Wermet Syl: 0.7231
- Epoch: 14
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 1e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Train Wermet | Train Wermet Syl | Validation Loss | Validation Accuracy | Validation Wermet | Validation Wermet Syl | Epoch |
|:----------:|:--------------:|:------------:|:----------------:|:---------------:|:-------------------:|:-----------------:|:---------------------:|:-----:|
| 5.3409 | 0.0111 | 1.3547 | 1.2898 | 3.9789 | 0.0114 | 0.9710 | 0.9563 | 0 |
| 4.7143 | 0.0116 | 0.8622 | 0.8228 | 3.9404 | 0.0113 | 0.9823 | 0.9735 | 1 |
| 4.6752 | 0.0117 | 0.8472 | 0.8057 | 3.9081 | 0.0114 | 0.9579 | 0.9359 | 2 |
| 4.6500 | 0.0117 | 0.8382 | 0.7945 | 3.8820 | 0.0115 | 0.9213 | 0.8856 | 3 |
| 4.6282 | 0.0118 | 0.8286 | 0.7805 | 3.8738 | 0.0114 | 0.9433 | 0.9119 | 4 |
| 4.6095 | 0.0118 | 0.8190 | 0.7696 | 3.8630 | 0.0115 | 0.9117 | 0.8698 | 5 |
| 4.5875 | 0.0119 | 0.7976 | 0.7465 | 3.8341 | 0.0116 | 0.8976 | 0.8552 | 6 |
| 4.5682 | 0.0120 | 0.7753 | 0.7227 | 3.8277 | 0.0116 | 0.9014 | 0.8653 | 7 |
| 4.5376 | 0.0121 | 0.7528 | 0.7005 | 3.7844 | 0.0118 | 0.8332 | 0.7815 | 8 |
| 4.5060 | 0.0122 | 0.7392 | 0.6844 | 3.7537 | 0.0118 | 0.8578 | 0.8152 | 9 |
| 4.4580 | 0.0124 | 0.7221 | 0.6694 | 3.7038 | 0.0120 | 0.8190 | 0.7679 | 10 |
| 4.3989 | 0.0125 | 0.7156 | 0.6636 | 3.6169 | 0.0122 | 0.7979 | 0.7429 | 11 |
| 4.3056 | 0.0128 | 0.7069 | 0.6557 | 3.5098 | 0.0125 | 0.7924 | 0.7396 | 12 |
| 4.1673 | 0.0132 | 0.7054 | 0.6584 | 3.3542 | 0.0128 | 0.7759 | 0.7240 | 13 |
| 3.9762 | 0.0138 | 0.6987 | 0.6559 | 3.1318 | 0.0133 | 0.7644 | 0.7231 | 14 |
### Framework versions
- Transformers 4.34.0.dev0
- TensorFlow 2.13.0
- Tokenizers 0.13.3
|
fedbor/13bllama2_lora16_modello2
|
fedbor
| 2023-09-11T10:01:55Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-09-11T10:01:52Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.6.0.dev0
|
thangvip/distilbert-base-uncased-finetuned-imdb
|
thangvip
| 2023-09-11T09:51:51Z | 121 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"fill-mask",
"generated_from_trainer",
"dataset:imdb",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2023-09-11T09:48:03Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- imdb
model-index:
- name: distilbert-base-uncased-finetuned-imdb
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-imdb
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4119
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.7024 | 1.0 | 157 | 2.4968 |
| 2.5794 | 2.0 | 314 | 2.4281 |
| 2.5354 | 3.0 | 471 | 2.4509 |
### Framework versions
- Transformers 4.33.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
|
bigmorning/whisper_4_with_init_sun_syl_wd_0__0010
|
bigmorning
| 2023-09-11T09:48:24Z | 59 | 0 |
transformers
|
[
"transformers",
"tf",
"whisper",
"automatic-speech-recognition",
"generated_from_keras_callback",
"base_model:openai/whisper-tiny",
"base_model:finetune:openai/whisper-tiny",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-09-11T09:48:16Z |
---
license: apache-2.0
base_model: openai/whisper-tiny
tags:
- generated_from_keras_callback
model-index:
- name: whisper_4_with_init_sun_syl_wd_0__0010
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# whisper_4_with_init_sun_syl_wd_0__0010
This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 4.5060
- Train Accuracy: 0.0122
- Train Wermet: 0.7392
- Train Wermet Syl: 0.6844
- Validation Loss: 3.7537
- Validation Accuracy: 0.0118
- Validation Wermet: 0.8578
- Validation Wermet Syl: 0.8152
- Epoch: 9
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 1e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Train Wermet | Train Wermet Syl | Validation Loss | Validation Accuracy | Validation Wermet | Validation Wermet Syl | Epoch |
|:----------:|:--------------:|:------------:|:----------------:|:---------------:|:-------------------:|:-----------------:|:---------------------:|:-----:|
| 5.3409 | 0.0111 | 1.3547 | 1.2898 | 3.9789 | 0.0114 | 0.9710 | 0.9563 | 0 |
| 4.7143 | 0.0116 | 0.8622 | 0.8228 | 3.9404 | 0.0113 | 0.9823 | 0.9735 | 1 |
| 4.6752 | 0.0117 | 0.8472 | 0.8057 | 3.9081 | 0.0114 | 0.9579 | 0.9359 | 2 |
| 4.6500 | 0.0117 | 0.8382 | 0.7945 | 3.8820 | 0.0115 | 0.9213 | 0.8856 | 3 |
| 4.6282 | 0.0118 | 0.8286 | 0.7805 | 3.8738 | 0.0114 | 0.9433 | 0.9119 | 4 |
| 4.6095 | 0.0118 | 0.8190 | 0.7696 | 3.8630 | 0.0115 | 0.9117 | 0.8698 | 5 |
| 4.5875 | 0.0119 | 0.7976 | 0.7465 | 3.8341 | 0.0116 | 0.8976 | 0.8552 | 6 |
| 4.5682 | 0.0120 | 0.7753 | 0.7227 | 3.8277 | 0.0116 | 0.9014 | 0.8653 | 7 |
| 4.5376 | 0.0121 | 0.7528 | 0.7005 | 3.7844 | 0.0118 | 0.8332 | 0.7815 | 8 |
| 4.5060 | 0.0122 | 0.7392 | 0.6844 | 3.7537 | 0.0118 | 0.8578 | 0.8152 | 9 |
### Framework versions
- Transformers 4.34.0.dev0
- TensorFlow 2.13.0
- Tokenizers 0.13.3
|
Shishir1807/pistachio-tapir
|
Shishir1807
| 2023-09-11T09:39:48Z | 141 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt_neox",
"text-generation",
"gpt",
"llm",
"large language model",
"h2o-llmstudio",
"en",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-09-11T09:39:12Z |
---
language:
- en
library_name: transformers
tags:
- gpt
- llm
- large language model
- h2o-llmstudio
inference: false
thumbnail: https://h2o.ai/etc.clientlibs/h2o/clientlibs/clientlib-site/resources/images/favicon.ico
---
# Model Card
## Summary
This model was trained using [H2O LLM Studio](https://github.com/h2oai/h2o-llmstudio).
- Base model: [EleutherAI/pythia-2.8b-deduped](https://huggingface.co/EleutherAI/pythia-2.8b-deduped)
## Usage
To use the model with the `transformers` library on a machine with GPUs, first make sure you have the `transformers`, `accelerate` and `torch` libraries installed.
```bash
pip install transformers==4.29.2
pip install einops==0.6.1
pip install accelerate==0.19.0
pip install torch==2.0.0
```
```python
import torch
from transformers import pipeline
generate_text = pipeline(
model="Shishir1807/pistachio-tapir",
torch_dtype="auto",
trust_remote_code=True,
use_fast=True,
device_map={"": "cuda:0"},
)
res = generate_text(
"Why is drinking water so healthy?",
min_new_tokens=2,
max_new_tokens=512,
do_sample=False,
num_beams=1,
temperature=float(0.3),
repetition_penalty=float(1.2),
renormalize_logits=True
)
print(res[0]["generated_text"])
```
You can print a sample prompt after the preprocessing step to see how it is feed to the tokenizer:
```python
print(generate_text.preprocess("Why is drinking water so healthy?")["prompt_text"])
```
```bash
<|prompt|>Why is drinking water so healthy?<|endoftext|><|answer|>
```
Alternatively, you can download [h2oai_pipeline.py](h2oai_pipeline.py), store it alongside your notebook, and construct the pipeline yourself from the loaded model and tokenizer. If the model and the tokenizer are fully supported in the `transformers` package, this will allow you to set `trust_remote_code=False`.
```python
import torch
from h2oai_pipeline import H2OTextGenerationPipeline
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained(
"Shishir1807/pistachio-tapir",
use_fast=True,
padding_side="left",
trust_remote_code=True,
)
model = AutoModelForCausalLM.from_pretrained(
"Shishir1807/pistachio-tapir",
torch_dtype="auto",
device_map={"": "cuda:0"},
trust_remote_code=True,
)
generate_text = H2OTextGenerationPipeline(model=model, tokenizer=tokenizer)
res = generate_text(
"Why is drinking water so healthy?",
min_new_tokens=2,
max_new_tokens=512,
do_sample=False,
num_beams=1,
temperature=float(0.3),
repetition_penalty=float(1.2),
renormalize_logits=True
)
print(res[0]["generated_text"])
```
You may also construct the pipeline from the loaded model and tokenizer yourself and consider the preprocessing steps:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "Shishir1807/pistachio-tapir" # either local folder or huggingface model name
# Important: The prompt needs to be in the same format the model was trained with.
# You can find an example prompt in the experiment logs.
prompt = "<|prompt|>How are you?<|endoftext|><|answer|>"
tokenizer = AutoTokenizer.from_pretrained(
model_name,
use_fast=True,
trust_remote_code=True,
)
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype="auto",
device_map={"": "cuda:0"},
trust_remote_code=True,
)
model.cuda().eval()
inputs = tokenizer(prompt, return_tensors="pt", add_special_tokens=False).to("cuda")
# generate configuration can be modified to your needs
tokens = model.generate(
input_ids=inputs["input_ids"],
attention_mask=inputs["attention_mask"],
min_new_tokens=2,
max_new_tokens=512,
do_sample=False,
num_beams=1,
temperature=float(0.3),
repetition_penalty=float(1.2),
renormalize_logits=True
)[0]
tokens = tokens[inputs["input_ids"].shape[1]:]
answer = tokenizer.decode(tokens, skip_special_tokens=True)
print(answer)
```
## Quantization and sharding
You can load the models using quantization by specifying ```load_in_8bit=True``` or ```load_in_4bit=True```. Also, sharding on multiple GPUs is possible by setting ```device_map=auto```.
## Model Architecture
```
GPTNeoXForCausalLM(
(gpt_neox): GPTNeoXModel(
(embed_in): Embedding(50304, 2560)
(layers): ModuleList(
(0-31): 32 x GPTNeoXLayer(
(input_layernorm): LayerNorm((2560,), eps=1e-05, elementwise_affine=True)
(post_attention_layernorm): LayerNorm((2560,), eps=1e-05, elementwise_affine=True)
(attention): GPTNeoXAttention(
(rotary_emb): RotaryEmbedding()
(query_key_value): Linear(in_features=2560, out_features=7680, bias=True)
(dense): Linear(in_features=2560, out_features=2560, bias=True)
)
(mlp): GPTNeoXMLP(
(dense_h_to_4h): Linear(in_features=2560, out_features=10240, bias=True)
(dense_4h_to_h): Linear(in_features=10240, out_features=2560, bias=True)
(act): GELUActivation()
)
)
)
(final_layer_norm): LayerNorm((2560,), eps=1e-05, elementwise_affine=True)
)
(embed_out): Linear(in_features=2560, out_features=50304, bias=False)
)
```
## Model Configuration
This model was trained using H2O LLM Studio and with the configuration in [cfg.yaml](cfg.yaml). Visit [H2O LLM Studio](https://github.com/h2oai/h2o-llmstudio) to learn how to train your own large language models.
## Disclaimer
Please read this disclaimer carefully before using the large language model provided in this repository. Your use of the model signifies your agreement to the following terms and conditions.
- Biases and Offensiveness: The large language model is trained on a diverse range of internet text data, which may contain biased, racist, offensive, or otherwise inappropriate content. By using this model, you acknowledge and accept that the generated content may sometimes exhibit biases or produce content that is offensive or inappropriate. The developers of this repository do not endorse, support, or promote any such content or viewpoints.
- Limitations: The large language model is an AI-based tool and not a human. It may produce incorrect, nonsensical, or irrelevant responses. It is the user's responsibility to critically evaluate the generated content and use it at their discretion.
- Use at Your Own Risk: Users of this large language model must assume full responsibility for any consequences that may arise from their use of the tool. The developers and contributors of this repository shall not be held liable for any damages, losses, or harm resulting from the use or misuse of the provided model.
- Ethical Considerations: Users are encouraged to use the large language model responsibly and ethically. By using this model, you agree not to use it for purposes that promote hate speech, discrimination, harassment, or any form of illegal or harmful activities.
- Reporting Issues: If you encounter any biased, offensive, or otherwise inappropriate content generated by the large language model, please report it to the repository maintainers through the provided channels. Your feedback will help improve the model and mitigate potential issues.
- Changes to this Disclaimer: The developers of this repository reserve the right to modify or update this disclaimer at any time without prior notice. It is the user's responsibility to periodically review the disclaimer to stay informed about any changes.
By using the large language model provided in this repository, you agree to accept and comply with the terms and conditions outlined in this disclaimer. If you do not agree with any part of this disclaimer, you should refrain from using the model and any content generated by it.
|
bigmorning/whisper_4_with_init_sun_syl_wd_0__0005
|
bigmorning
| 2023-09-11T09:33:31Z | 58 | 0 |
transformers
|
[
"transformers",
"tf",
"whisper",
"automatic-speech-recognition",
"generated_from_keras_callback",
"base_model:openai/whisper-tiny",
"base_model:finetune:openai/whisper-tiny",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-09-11T09:33:21Z |
---
license: apache-2.0
base_model: openai/whisper-tiny
tags:
- generated_from_keras_callback
model-index:
- name: whisper_4_with_init_sun_syl_wd_0__0005
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# whisper_4_with_init_sun_syl_wd_0__0005
This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 4.6282
- Train Accuracy: 0.0118
- Train Wermet: 0.8286
- Train Wermet Syl: 0.7805
- Validation Loss: 3.8738
- Validation Accuracy: 0.0114
- Validation Wermet: 0.9433
- Validation Wermet Syl: 0.9119
- Epoch: 4
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 1e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Train Wermet | Train Wermet Syl | Validation Loss | Validation Accuracy | Validation Wermet | Validation Wermet Syl | Epoch |
|:----------:|:--------------:|:------------:|:----------------:|:---------------:|:-------------------:|:-----------------:|:---------------------:|:-----:|
| 5.3409 | 0.0111 | 1.3547 | 1.2898 | 3.9789 | 0.0114 | 0.9710 | 0.9563 | 0 |
| 4.7143 | 0.0116 | 0.8622 | 0.8228 | 3.9404 | 0.0113 | 0.9823 | 0.9735 | 1 |
| 4.6752 | 0.0117 | 0.8472 | 0.8057 | 3.9081 | 0.0114 | 0.9579 | 0.9359 | 2 |
| 4.6500 | 0.0117 | 0.8382 | 0.7945 | 3.8820 | 0.0115 | 0.9213 | 0.8856 | 3 |
| 4.6282 | 0.0118 | 0.8286 | 0.7805 | 3.8738 | 0.0114 | 0.9433 | 0.9119 | 4 |
### Framework versions
- Transformers 4.34.0.dev0
- TensorFlow 2.13.0
- Tokenizers 0.13.3
|
EuropeanParliament/eurovoc_fr
|
EuropeanParliament
| 2023-09-11T09:30:25Z | 0 | 0 | null |
[
"pytorch",
"license:eupl-1.1",
"endpoints_compatible",
"region:us"
] | null | 2023-09-08T14:08:03Z |
---
license: eupl-1.1
---
French version of https://huggingface.co/EuropeanParliament/eurovoc_en
Mean F1 score threshold 0.21 : value 0.4744
Example:
> Journal officiel de l'Union européenne L 60/40 DÉCISION D'EXÉCUTION (UE) 2018/313 DE LA COMMISSION du 28 février 2018 modifiant la décision 2009/821/CE en ce qui concerne la liste des postes d'inspection frontaliers et celle des unités vétérinaires du système TRACES [notifiée sous le numéro C(2018) 1149] (Texte présentant de l'intérêt pour l'EEE) LA COMMISSION EUROPÉENNE, vu le traité sur le fonctionnement de l'Union européenne, vu la directive 90/425/CEE du Conseil du 26 juin 1990 relative aux contrôles vétérinaires et zootechniques applicables dans les échanges intracommunautaires de certains animaux vivants et produits dans la perspective de la réalisation du marché intérieur (1), et notamment son article 20, paragraphes 1 et 3, vu la directive 91/496/CEE du Conseil du 15 juillet 1991 fixant les principes relatifs à l'organisation des contrôles vétérinaires pour les animaux en provenance des pays tiers
Predictions:
- customs
- import (EU)
- veterinary inspection
|
Youssef320/Public100_1L_BERT_20epoch_notweettokenizer
|
Youssef320
| 2023-09-11T09:29:05Z | 112 | 1 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-09-06T22:33:38Z |
---
tags:
- generated_from_trainer
model-index:
- name: Public100_1L_BERT_20epoch_notweettokenizer
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. -->
# Public100_1L_BERT_20epoch_notweettokenizer
This model is a fine-tuned version of [Youssef320/LSTM-finetuned-50label-15epoch](https://huggingface.co/Youssef320/LSTM-finetuned-50label-15epoch) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.5924
- Top 1 Macro F1 Score: 0.1416
- Top 1 Weighted F1score: 0.1916
- Top 3 Macro F1 Score: 0.2701
- Top3 3 Weighted F1 Score : 0.3431
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 32
- total_train_batch_size: 2048
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- num_epochs: 20.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Top 1 Macro F1 Score | Top 1 Weighted F1score | Top 3 Macro F1 Score | Top3 3 Weighted F1 Score |
|:-------------:|:-----:|:-----:|:---------------:|:--------------------:|:----------------------:|:--------------------:|:-------------------------:|
| 3.9476 | 0.12 | 64 | 3.8956 | 0.0154 | 0.0435 | 0.0778 | 0.1518 |
| 3.7671 | 0.25 | 128 | 3.7204 | 0.0321 | 0.0730 | 0.1125 | 0.1998 |
| 3.6435 | 0.38 | 192 | 3.6285 | 0.0470 | 0.0933 | 0.1347 | 0.2295 |
| 3.6113 | 0.5 | 256 | 3.5790 | 0.0511 | 0.1013 | 0.1452 | 0.2450 |
| 3.5837 | 0.62 | 320 | 3.5457 | 0.0557 | 0.1080 | 0.1501 | 0.2515 |
| 3.5886 | 0.75 | 384 | 3.5189 | 0.0630 | 0.1166 | 0.1633 | 0.2631 |
| 3.5612 | 0.88 | 448 | 3.5018 | 0.0665 | 0.1207 | 0.1666 | 0.2677 |
| 3.5261 | 1.0 | 512 | 3.4790 | 0.0710 | 0.1259 | 0.1736 | 0.2763 |
| 3.4758 | 1.12 | 576 | 3.4725 | 0.0734 | 0.1287 | 0.1763 | 0.2780 |
| 3.4484 | 1.25 | 640 | 3.4608 | 0.0761 | 0.1318 | 0.1776 | 0.2808 |
| 3.4559 | 1.38 | 704 | 3.4483 | 0.0800 | 0.1357 | 0.1827 | 0.2846 |
| 3.4483 | 1.5 | 768 | 3.4403 | 0.0790 | 0.1340 | 0.1831 | 0.2839 |
| 3.4488 | 1.62 | 832 | 3.4306 | 0.0833 | 0.1394 | 0.1888 | 0.2917 |
| 3.4398 | 1.75 | 896 | 3.4241 | 0.0825 | 0.1383 | 0.1880 | 0.2908 |
| 3.4068 | 1.88 | 960 | 3.4159 | 0.0807 | 0.1364 | 0.1890 | 0.2898 |
| 3.4246 | 2.0 | 1024 | 3.4081 | 0.0864 | 0.1426 | 0.1965 | 0.2976 |
| 3.3525 | 2.12 | 1088 | 3.4119 | 0.0897 | 0.1473 | 0.1990 | 0.3016 |
| 3.3207 | 2.25 | 1152 | 3.4120 | 0.0882 | 0.1445 | 0.1986 | 0.2991 |
| 3.3495 | 2.38 | 1216 | 3.4062 | 0.0896 | 0.1460 | 0.1993 | 0.2999 |
| 3.3679 | 2.5 | 1280 | 3.3947 | 0.0922 | 0.1489 | 0.2028 | 0.3023 |
| 3.3537 | 2.62 | 1344 | 3.3908 | 0.0919 | 0.1484 | 0.2050 | 0.3043 |
| 3.3593 | 2.75 | 1408 | 3.3848 | 0.0938 | 0.1514 | 0.2056 | 0.3066 |
| 3.3545 | 2.88 | 1472 | 3.3797 | 0.0931 | 0.1506 | 0.2057 | 0.3042 |
| 3.3591 | 3.0 | 1536 | 3.3719 | 0.0960 | 0.1534 | 0.2087 | 0.3105 |
| 3.2401 | 3.12 | 1600 | 3.3882 | 0.0976 | 0.1548 | 0.2111 | 0.3093 |
| 3.2436 | 3.25 | 1664 | 3.3915 | 0.0966 | 0.1532 | 0.2081 | 0.3081 |
| 3.2566 | 3.38 | 1728 | 3.3859 | 0.0966 | 0.1529 | 0.2111 | 0.3076 |
| 3.284 | 3.5 | 1792 | 3.3851 | 0.0979 | 0.1543 | 0.2144 | 0.3104 |
| 3.2874 | 3.62 | 1856 | 3.3747 | 0.0997 | 0.1577 | 0.2164 | 0.3130 |
| 3.2583 | 3.75 | 1920 | 3.3705 | 0.0975 | 0.1543 | 0.2135 | 0.3101 |
| 3.2894 | 3.88 | 1984 | 3.3630 | 0.0993 | 0.1558 | 0.2168 | 0.3125 |
| 3.2938 | 4.0 | 2048 | 3.3581 | 0.1002 | 0.1579 | 0.2194 | 0.3163 |
| 3.1876 | 4.12 | 2112 | 3.3837 | 0.1020 | 0.1606 | 0.2183 | 0.3148 |
| 3.1862 | 4.25 | 2176 | 3.3821 | 0.1006 | 0.1578 | 0.2167 | 0.3124 |
| 3.2146 | 4.38 | 2240 | 3.3766 | 0.0999 | 0.1571 | 0.2203 | 0.3142 |
| 3.2184 | 4.5 | 2304 | 3.3691 | 0.1039 | 0.1603 | 0.2236 | 0.3181 |
| 3.1851 | 4.62 | 2368 | 3.3677 | 0.1007 | 0.1584 | 0.2207 | 0.3144 |
| 3.2276 | 4.75 | 2432 | 3.3640 | 0.1044 | 0.1631 | 0.2242 | 0.3185 |
| 3.2099 | 4.88 | 2496 | 3.3576 | 0.1057 | 0.1615 | 0.2293 | 0.3186 |
| 3.2162 | 5.0 | 2560 | 3.3523 | 0.1071 | 0.1635 | 0.2296 | 0.3223 |
| 3.1196 | 5.12 | 2624 | 3.3784 | 0.1082 | 0.1649 | 0.2268 | 0.3204 |
| 3.1171 | 5.25 | 2688 | 3.3842 | 0.1061 | 0.1629 | 0.2270 | 0.3189 |
| 3.1548 | 5.38 | 2752 | 3.3796 | 0.1066 | 0.1630 | 0.2293 | 0.3198 |
| 3.1555 | 5.5 | 2816 | 3.3660 | 0.1093 | 0.1659 | 0.2295 | 0.3223 |
| 3.151 | 5.62 | 2880 | 3.3707 | 0.1078 | 0.1646 | 0.2242 | 0.3195 |
| 3.1547 | 5.75 | 2944 | 3.3622 | 0.1079 | 0.1639 | 0.2310 | 0.3212 |
| 3.1672 | 5.88 | 3008 | 3.3592 | 0.1090 | 0.1658 | 0.2316 | 0.3224 |
| 3.1675 | 6.0 | 3072 | 3.3489 | 0.1110 | 0.1668 | 0.2320 | 0.3243 |
| 3.0247 | 6.12 | 3136 | 3.3832 | 0.1111 | 0.1656 | 0.2320 | 0.3214 |
| 3.0598 | 6.25 | 3200 | 3.3871 | 0.1089 | 0.1672 | 0.2330 | 0.3224 |
| 3.0756 | 6.38 | 3264 | 3.3814 | 0.1070 | 0.1648 | 0.2309 | 0.3214 |
| 3.0705 | 6.5 | 3328 | 3.3747 | 0.1123 | 0.1675 | 0.2343 | 0.3235 |
| 3.1081 | 6.62 | 3392 | 3.3693 | 0.1138 | 0.1686 | 0.2328 | 0.3239 |
| 3.1071 | 6.75 | 3456 | 3.3693 | 0.1144 | 0.1688 | 0.2373 | 0.3253 |
| 3.0873 | 6.88 | 3520 | 3.3653 | 0.1134 | 0.1686 | 0.2391 | 0.3255 |
| 3.0756 | 7.0 | 3584 | 3.3604 | 0.1135 | 0.1691 | 0.2368 | 0.3260 |
| 2.9902 | 7.12 | 3648 | 3.3951 | 0.1159 | 0.1685 | 0.2380 | 0.3237 |
| 2.9916 | 7.25 | 3712 | 3.3906 | 0.1171 | 0.1705 | 0.2385 | 0.3263 |
| 2.9935 | 7.38 | 3776 | 3.3849 | 0.1159 | 0.1700 | 0.2390 | 0.3258 |
| 3.0519 | 7.5 | 3840 | 3.3888 | 0.1149 | 0.1694 | 0.2372 | 0.3249 |
| 3.0453 | 7.62 | 3904 | 3.3777 | 0.1156 | 0.1697 | 0.2378 | 0.3256 |
| 3.0489 | 7.75 | 3968 | 3.3689 | 0.1180 | 0.1725 | 0.2381 | 0.3281 |
| 3.0915 | 7.88 | 4032 | 3.3688 | 0.1176 | 0.1710 | 0.2422 | 0.3284 |
| 3.068 | 8.0 | 4096 | 3.3630 | 0.1196 | 0.1737 | 0.2440 | 0.3311 |
| 2.936 | 8.12 | 4160 | 3.4107 | 0.1174 | 0.1718 | 0.2414 | 0.3276 |
| 2.9538 | 8.25 | 4224 | 3.4067 | 0.1194 | 0.1725 | 0.2418 | 0.3271 |
| 2.9462 | 8.38 | 4288 | 3.4039 | 0.1177 | 0.1736 | 0.2406 | 0.3275 |
| 2.9749 | 8.5 | 4352 | 3.3934 | 0.1187 | 0.1739 | 0.2426 | 0.3286 |
| 2.9765 | 8.62 | 4416 | 3.3843 | 0.1197 | 0.1739 | 0.2432 | 0.3296 |
| 3.0085 | 8.75 | 4480 | 3.3747 | 0.1191 | 0.1741 | 0.2425 | 0.3304 |
| 3.011 | 8.88 | 4544 | 3.3753 | 0.1197 | 0.1742 | 0.2421 | 0.3306 |
| 3.0206 | 9.0 | 4608 | 3.3720 | 0.1207 | 0.1744 | 0.2445 | 0.3307 |
| 2.8646 | 9.12 | 4672 | 3.4271 | 0.1209 | 0.1736 | 0.2459 | 0.3280 |
| 2.901 | 9.25 | 4736 | 3.4206 | 0.1208 | 0.1743 | 0.2458 | 0.3288 |
| 2.9068 | 9.38 | 4800 | 3.4083 | 0.1210 | 0.1756 | 0.2453 | 0.3305 |
| 2.9034 | 9.5 | 4864 | 3.4071 | 0.1226 | 0.1753 | 0.2471 | 0.3307 |
| 2.9234 | 9.62 | 4928 | 3.4125 | 0.1232 | 0.1754 | 0.2483 | 0.3307 |
| 2.9476 | 9.75 | 4992 | 3.3911 | 0.1232 | 0.1759 | 0.2499 | 0.3324 |
| 2.9721 | 9.88 | 5056 | 3.3894 | 0.1224 | 0.1750 | 0.2500 | 0.3323 |
| 2.9635 | 10.0 | 5120 | 3.3853 | 0.1232 | 0.1757 | 0.2473 | 0.3319 |
| 2.8175 | 10.12 | 5184 | 3.4355 | 0.1234 | 0.1752 | 0.2482 | 0.3302 |
| 2.8413 | 10.25 | 5248 | 3.4343 | 0.1252 | 0.1776 | 0.2516 | 0.3321 |
| 2.8334 | 10.38 | 5312 | 3.4430 | 0.1260 | 0.1769 | 0.2509 | 0.3312 |
| 2.8687 | 10.5 | 5376 | 3.4307 | 0.1240 | 0.1763 | 0.2467 | 0.3298 |
| 2.8712 | 10.62 | 5440 | 3.4227 | 0.1254 | 0.1777 | 0.2519 | 0.3330 |
| 2.932 | 10.75 | 5504 | 3.4023 | 0.1244 | 0.1791 | 0.2523 | 0.3353 |
| 2.9086 | 10.88 | 5568 | 3.4013 | 0.1260 | 0.1791 | 0.2505 | 0.3344 |
| 2.9175 | 11.0 | 5632 | 3.4031 | 0.1241 | 0.1773 | 0.2509 | 0.3337 |
| 2.7496 | 11.12 | 5696 | 3.4659 | 0.1259 | 0.1777 | 0.2528 | 0.3319 |
| 2.789 | 11.25 | 5760 | 3.4639 | 0.1271 | 0.1779 | 0.2540 | 0.3319 |
| 2.8063 | 11.38 | 5824 | 3.4506 | 0.1241 | 0.1773 | 0.2534 | 0.3320 |
| 2.8347 | 11.5 | 5888 | 3.4467 | 0.1257 | 0.1783 | 0.2548 | 0.3333 |
| 2.86 | 11.62 | 5952 | 3.4319 | 0.1261 | 0.1784 | 0.2533 | 0.3341 |
| 2.8587 | 11.75 | 6016 | 3.4353 | 0.1292 | 0.1805 | 0.2573 | 0.3355 |
| 2.8614 | 11.88 | 6080 | 3.4346 | 0.1271 | 0.1804 | 0.2548 | 0.3361 |
| 2.8685 | 12.0 | 6144 | 3.4163 | 0.1284 | 0.1810 | 0.2558 | 0.3361 |
| 2.7086 | 12.12 | 6208 | 3.4749 | 0.1299 | 0.1812 | 0.2554 | 0.3351 |
| 2.7243 | 12.25 | 6272 | 3.4758 | 0.1309 | 0.1802 | 0.2575 | 0.3341 |
| 2.7587 | 12.38 | 6336 | 3.4686 | 0.1269 | 0.1793 | 0.2553 | 0.3340 |
| 2.7425 | 12.5 | 6400 | 3.4612 | 0.1297 | 0.1800 | 0.2565 | 0.3341 |
| 2.8271 | 12.62 | 6464 | 3.4618 | 0.1310 | 0.1806 | 0.2578 | 0.3343 |
| 2.8029 | 12.75 | 6528 | 3.4484 | 0.1318 | 0.1821 | 0.2596 | 0.3362 |
| 2.7919 | 12.88 | 6592 | 3.4504 | 0.1277 | 0.1797 | 0.2540 | 0.3347 |
| 2.8563 | 13.0 | 6656 | 3.4359 | 0.1302 | 0.1827 | 0.2576 | 0.3373 |
| 2.64 | 13.12 | 6720 | 3.5101 | 0.1317 | 0.1816 | 0.2598 | 0.3350 |
| 2.6662 | 13.25 | 6784 | 3.5056 | 0.1319 | 0.1822 | 0.2585 | 0.3347 |
| 2.6846 | 13.38 | 6848 | 3.5022 | 0.1319 | 0.1824 | 0.2586 | 0.3355 |
| 2.7247 | 13.5 | 6912 | 3.4979 | 0.1308 | 0.1819 | 0.2575 | 0.3342 |
| 2.765 | 13.62 | 6976 | 3.4800 | 0.1308 | 0.1820 | 0.2592 | 0.3362 |
| 2.7755 | 13.75 | 7040 | 3.4690 | 0.1319 | 0.1835 | 0.2614 | 0.3385 |
| 2.7942 | 13.88 | 7104 | 3.4701 | 0.1307 | 0.1834 | 0.2596 | 0.3382 |
| 2.7924 | 14.0 | 7168 | 3.4530 | 0.1315 | 0.1833 | 0.2612 | 0.3384 |
| 2.629 | 14.12 | 7232 | 3.5201 | 0.1325 | 0.1843 | 0.2576 | 0.3369 |
| 2.6385 | 14.25 | 7296 | 3.5230 | 0.1319 | 0.1830 | 0.2605 | 0.3366 |
| 2.6686 | 14.38 | 7360 | 3.5289 | 0.1350 | 0.1833 | 0.2611 | 0.3360 |
| 2.6554 | 14.5 | 7424 | 3.5077 | 0.1330 | 0.1834 | 0.2615 | 0.3359 |
| 2.6983 | 14.62 | 7488 | 3.5098 | 0.1341 | 0.1838 | 0.2624 | 0.3372 |
| 2.7053 | 14.75 | 7552 | 3.4997 | 0.1337 | 0.1850 | 0.2643 | 0.3391 |
| 2.7072 | 14.88 | 7616 | 3.4865 | 0.1326 | 0.1831 | 0.2622 | 0.3385 |
| 2.7289 | 15.0 | 7680 | 3.4797 | 0.1315 | 0.1832 | 0.2607 | 0.3382 |
| 2.5489 | 15.12 | 7744 | 3.5449 | 0.1321 | 0.1831 | 0.2605 | 0.3354 |
| 2.5588 | 15.25 | 7808 | 3.5659 | 0.1327 | 0.1844 | 0.2621 | 0.3368 |
| 2.6278 | 15.38 | 7872 | 3.5455 | 0.1345 | 0.1856 | 0.2609 | 0.3372 |
| 2.6577 | 15.5 | 7936 | 3.5211 | 0.1356 | 0.1858 | 0.2627 | 0.3392 |
| 2.6756 | 15.62 | 8000 | 3.5211 | 0.1345 | 0.1849 | 0.2624 | 0.3386 |
| 2.6792 | 15.75 | 8064 | 3.5099 | 0.1349 | 0.1860 | 0.2636 | 0.3387 |
| 2.7076 | 15.88 | 8128 | 3.5140 | 0.1364 | 0.1864 | 0.2663 | 0.3394 |
| 2.6966 | 16.0 | 8192 | 3.5095 | 0.1363 | 0.1872 | 0.2660 | 0.3412 |
| 2.5254 | 16.12 | 8256 | 3.5661 | 0.1326 | 0.1850 | 0.2626 | 0.3374 |
| 2.5661 | 16.25 | 8320 | 3.5637 | 0.1341 | 0.1862 | 0.2618 | 0.3377 |
| 2.6016 | 16.38 | 8384 | 3.5735 | 0.1349 | 0.1860 | 0.2646 | 0.3384 |
| 2.599 | 16.5 | 8448 | 3.5743 | 0.1361 | 0.1870 | 0.2660 | 0.3396 |
| 2.5939 | 16.62 | 8512 | 3.5511 | 0.1354 | 0.1849 | 0.2665 | 0.3389 |
| 2.6532 | 16.75 | 8576 | 3.5462 | 0.1362 | 0.1868 | 0.2660 | 0.3401 |
| 2.6507 | 16.88 | 8640 | 3.5305 | 0.1354 | 0.1867 | 0.2661 | 0.3405 |
| 2.6816 | 17.0 | 8704 | 3.5186 | 0.1371 | 0.1883 | 0.2682 | 0.3427 |
| 2.5029 | 17.12 | 8768 | 3.5992 | 0.1370 | 0.1865 | 0.2662 | 0.3386 |
| 2.5321 | 17.25 | 8832 | 3.5813 | 0.1359 | 0.1873 | 0.2647 | 0.3390 |
| 2.5431 | 17.38 | 8896 | 3.6024 | 0.1378 | 0.1869 | 0.2675 | 0.3396 |
| 2.5516 | 17.5 | 8960 | 3.5993 | 0.1362 | 0.1863 | 0.2676 | 0.3386 |
| 2.5854 | 17.62 | 9024 | 3.5717 | 0.1359 | 0.1875 | 0.2644 | 0.3398 |
| 2.6053 | 17.75 | 9088 | 3.5717 | 0.1361 | 0.1875 | 0.2660 | 0.3402 |
| 2.5922 | 17.88 | 9152 | 3.5571 | 0.1374 | 0.1889 | 0.2671 | 0.3417 |
| 2.6054 | 18.0 | 9216 | 3.5500 | 0.1385 | 0.1893 | 0.2680 | 0.3419 |
| 2.4719 | 18.12 | 9280 | 3.6322 | 0.1388 | 0.1885 | 0.2681 | 0.3392 |
| 2.5108 | 18.25 | 9344 | 3.6259 | 0.1382 | 0.1876 | 0.2689 | 0.3384 |
| 2.5403 | 18.38 | 9408 | 3.6152 | 0.1387 | 0.1889 | 0.2684 | 0.3408 |
| 2.5282 | 18.5 | 9472 | 3.6076 | 0.1384 | 0.1905 | 0.2686 | 0.3425 |
| 2.5471 | 18.62 | 9536 | 3.5930 | 0.1388 | 0.1895 | 0.2693 | 0.3417 |
| 2.5404 | 18.75 | 9600 | 3.6039 | 0.1386 | 0.1905 | 0.2686 | 0.3426 |
| 2.5889 | 18.88 | 9664 | 3.5814 | 0.1393 | 0.1890 | 0.2681 | 0.3420 |
| 2.6072 | 19.0 | 9728 | 3.5757 | 0.1405 | 0.1915 | 0.2694 | 0.3436 |
| 2.4302 | 19.12 | 9792 | 3.6515 | 0.1404 | 0.1893 | 0.2675 | 0.3399 |
| 2.4458 | 19.25 | 9856 | 3.6381 | 0.1398 | 0.1892 | 0.2679 | 0.3407 |
| 2.4839 | 19.38 | 9920 | 3.6380 | 0.1407 | 0.1903 | 0.2698 | 0.3413 |
| 2.4615 | 19.5 | 9984 | 3.6431 | 0.1416 | 0.1909 | 0.2699 | 0.3422 |
| 2.5243 | 19.62 | 10048 | 3.6180 | 0.1400 | 0.1891 | 0.2709 | 0.3408 |
| 2.4949 | 19.75 | 10112 | 3.6116 | 0.1387 | 0.1899 | 0.2685 | 0.3421 |
| 2.5115 | 19.88 | 10176 | 3.6154 | 0.1404 | 0.1900 | 0.2711 | 0.3428 |
| 2.5604 | 20.0 | 10240 | 3.5924 | 0.1416 | 0.1916 | 0.2701 | 0.3431 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.12.1+cu102
- Datasets 2.0.0
- Tokenizers 0.11.0
|
GroNLP/gpt2-small-italian
|
GroNLP
| 2023-09-11T08:57:44Z | 884 | 10 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"jax",
"safetensors",
"gpt2",
"text-generation",
"adaption",
"recycled",
"gpt2-small",
"it",
"arxiv:2012.05628",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:04Z |
---
language: it
tags:
- adaption
- recycled
- gpt2-small
pipeline_tag: text-generation
---
# GPT-2 recycled for Italian (small)
[Wietse de Vries](https://www.semanticscholar.org/author/Wietse-de-Vries/144611157) •
[Malvina Nissim](https://www.semanticscholar.org/author/M.-Nissim/2742475)
## Model description
This model is based on the small OpenAI GPT-2 ([`gpt2`](https://huggingface.co/gpt2)) model.
For details, check out our paper on [arXiv](https://arxiv.org/abs/2012.05628) and the code on [Github](https://github.com/wietsedv/gpt2-recycle).
## Related models
### Dutch
- [`gpt2-small-dutch-embeddings`](https://huggingface.co/GroNLP/gpt2-small-dutch-embeddings): Small model size with only retrained lexical embeddings.
- [`gpt2-small-dutch`](https://huggingface.co/GroNLP/gpt2-small-dutch): Small model size with retrained lexical embeddings and additional fine-tuning of the full model. (**Recommended**)
- [`gpt2-medium-dutch-embeddings`](https://huggingface.co/GroNLP/gpt2-medium-dutch-embeddings): Medium model size with only retrained lexical embeddings.
### Italian
- [`gpt2-small-italian-embeddings`](https://huggingface.co/GroNLP/gpt2-small-italian-embeddings): Small model size with only retrained lexical embeddings.
- [`gpt2-small-italian`](https://huggingface.co/GroNLP/gpt2-small-italian): Small model size with retrained lexical embeddings and additional fine-tuning of the full model. (**Recommended**)
- [`gpt2-medium-italian-embeddings`](https://huggingface.co/GroNLP/gpt2-medium-italian-embeddings): Medium model size with only retrained lexical embeddings.
## How to use
```python
from transformers import pipeline
pipe = pipeline("text-generation", model="GroNLP/gpt2-small-italian")
```
```python
from transformers import AutoTokenizer, AutoModel, TFAutoModel
tokenizer = AutoTokenizer.from_pretrained("GroNLP/gpt2-small-italian")
model = AutoModel.from_pretrained("GroNLP/gpt2-small-italian") # PyTorch
model = TFAutoModel.from_pretrained("GroNLP/gpt2-small-italian") # Tensorflow
```
## BibTeX entry
```bibtex
@misc{devries2020good,
title={As good as new. How to successfully recycle English GPT-2 to make models for other languages},
author={Wietse de Vries and Malvina Nissim},
year={2020},
eprint={2012.05628},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
wietsedv/xlm-roberta-base-ft-udpos28-ko
|
wietsedv
| 2023-09-11T08:57:03Z | 109 | 0 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"xlm-roberta",
"token-classification",
"part-of-speech",
"ko",
"dataset:universal_dependencies",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-03-02T23:29:05Z |
---
language:
- ko
license: apache-2.0
library_name: transformers
tags:
- part-of-speech
- token-classification
datasets:
- universal_dependencies
metrics:
- accuracy
model-index:
- name: xlm-roberta-base-ft-udpos28-ko
results:
- task:
type: token-classification
name: Part-of-Speech Tagging
dataset:
type: universal_dependencies
name: Universal Dependencies v2.8
metrics:
- type: accuracy
name: English Test accuracy
value: 61.7
- type: accuracy
name: Dutch Test accuracy
value: 55.9
- type: accuracy
name: German Test accuracy
value: 58.9
- type: accuracy
name: Italian Test accuracy
value: 58.7
- type: accuracy
name: French Test accuracy
value: 53.6
- type: accuracy
name: Spanish Test accuracy
value: 52.6
- type: accuracy
name: Russian Test accuracy
value: 66.4
- type: accuracy
name: Swedish Test accuracy
value: 64.0
- type: accuracy
name: Norwegian Test accuracy
value: 58.9
- type: accuracy
name: Danish Test accuracy
value: 63.7
- type: accuracy
name: Low Saxon Test accuracy
value: 41.1
- type: accuracy
name: Akkadian Test accuracy
value: 37.5
- type: accuracy
name: Armenian Test accuracy
value: 66.2
- type: accuracy
name: Welsh Test accuracy
value: 51.1
- type: accuracy
name: Old East Slavic Test accuracy
value: 56.3
- type: accuracy
name: Albanian Test accuracy
value: 54.7
- type: accuracy
name: Slovenian Test accuracy
value: 51.3
- type: accuracy
name: Guajajara Test accuracy
value: 36.4
- type: accuracy
name: Kurmanji Test accuracy
value: 62.4
- type: accuracy
name: Turkish Test accuracy
value: 65.7
- type: accuracy
name: Finnish Test accuracy
value: 67.6
- type: accuracy
name: Indonesian Test accuracy
value: 62.3
- type: accuracy
name: Ukrainian Test accuracy
value: 64.7
- type: accuracy
name: Polish Test accuracy
value: 61.7
- type: accuracy
name: Portuguese Test accuracy
value: 63.1
- type: accuracy
name: Kazakh Test accuracy
value: 70.6
- type: accuracy
name: Latin Test accuracy
value: 59.9
- type: accuracy
name: Old French Test accuracy
value: 41.5
- type: accuracy
name: Buryat Test accuracy
value: 56.4
- type: accuracy
name: Kaapor Test accuracy
value: 30.0
- type: accuracy
name: Korean Test accuracy
value: 82.5
- type: accuracy
name: Estonian Test accuracy
value: 67.8
- type: accuracy
name: Croatian Test accuracy
value: 57.1
- type: accuracy
name: Gothic Test accuracy
value: 20.5
- type: accuracy
name: Swiss German Test accuracy
value: 41.9
- type: accuracy
name: Assyrian Test accuracy
value: 17.7
- type: accuracy
name: North Sami Test accuracy
value: 41.7
- type: accuracy
name: Naija Test accuracy
value: 33.0
- type: accuracy
name: Latvian Test accuracy
value: 69.6
- type: accuracy
name: Chinese Test accuracy
value: 61.9
- type: accuracy
name: Tagalog Test accuracy
value: 58.9
- type: accuracy
name: Bambara Test accuracy
value: 29.2
- type: accuracy
name: Lithuanian Test accuracy
value: 72.7
- type: accuracy
name: Galician Test accuracy
value: 60.5
- type: accuracy
name: Vietnamese Test accuracy
value: 57.2
- type: accuracy
name: Greek Test accuracy
value: 57.6
- type: accuracy
name: Catalan Test accuracy
value: 50.6
- type: accuracy
name: Czech Test accuracy
value: 60.8
- type: accuracy
name: Erzya Test accuracy
value: 49.1
- type: accuracy
name: Bhojpuri Test accuracy
value: 45.6
- type: accuracy
name: Thai Test accuracy
value: 52.1
- type: accuracy
name: Marathi Test accuracy
value: 79.8
- type: accuracy
name: Basque Test accuracy
value: 65.7
- type: accuracy
name: Slovak Test accuracy
value: 61.4
- type: accuracy
name: Kiche Test accuracy
value: 39.0
- type: accuracy
name: Yoruba Test accuracy
value: 31.7
- type: accuracy
name: Warlpiri Test accuracy
value: 38.5
- type: accuracy
name: Tamil Test accuracy
value: 76.4
- type: accuracy
name: Maltese Test accuracy
value: 29.4
- type: accuracy
name: Ancient Greek Test accuracy
value: 54.7
- type: accuracy
name: Icelandic Test accuracy
value: 61.4
- type: accuracy
name: Mbya Guarani Test accuracy
value: 31.2
- type: accuracy
name: Urdu Test accuracy
value: 49.6
- type: accuracy
name: Romanian Test accuracy
value: 60.5
- type: accuracy
name: Persian Test accuracy
value: 60.0
- type: accuracy
name: Apurina Test accuracy
value: 39.1
- type: accuracy
name: Japanese Test accuracy
value: 55.5
- type: accuracy
name: Hungarian Test accuracy
value: 55.7
- type: accuracy
name: Hindi Test accuracy
value: 52.6
- type: accuracy
name: Classical Chinese Test accuracy
value: 38.9
- type: accuracy
name: Komi Permyak Test accuracy
value: 48.5
- type: accuracy
name: Faroese Test accuracy
value: 55.1
- type: accuracy
name: Sanskrit Test accuracy
value: 41.2
- type: accuracy
name: Livvi Test accuracy
value: 55.5
- type: accuracy
name: Arabic Test accuracy
value: 57.3
- type: accuracy
name: Wolof Test accuracy
value: 31.9
- type: accuracy
name: Bulgarian Test accuracy
value: 60.8
- type: accuracy
name: Akuntsu Test accuracy
value: 44.5
- type: accuracy
name: Makurap Test accuracy
value: 24.7
- type: accuracy
name: Kangri Test accuracy
value: 46.9
- type: accuracy
name: Breton Test accuracy
value: 54.6
- type: accuracy
name: Telugu Test accuracy
value: 76.3
- type: accuracy
name: Cantonese Test accuracy
value: 58.3
- type: accuracy
name: Old Church Slavonic Test accuracy
value: 45.2
- type: accuracy
name: Karelian Test accuracy
value: 57.5
- type: accuracy
name: Upper Sorbian Test accuracy
value: 54.2
- type: accuracy
name: South Levantine Arabic Test accuracy
value: 57.7
- type: accuracy
name: Komi Zyrian Test accuracy
value: 45.1
- type: accuracy
name: Irish Test accuracy
value: 46.3
- type: accuracy
name: Nayini Test accuracy
value: 50.0
- type: accuracy
name: Munduruku Test accuracy
value: 43.3
- type: accuracy
name: Manx Test accuracy
value: 35.6
- type: accuracy
name: Skolt Sami Test accuracy
value: 40.5
- type: accuracy
name: Afrikaans Test accuracy
value: 57.3
- type: accuracy
name: Old Turkish Test accuracy
value: 37.1
- type: accuracy
name: Tupinamba Test accuracy
value: 38.7
- type: accuracy
name: Belarusian Test accuracy
value: 63.7
- type: accuracy
name: Serbian Test accuracy
value: 56.9
- type: accuracy
name: Moksha Test accuracy
value: 48.0
- type: accuracy
name: Western Armenian Test accuracy
value: 61.9
- type: accuracy
name: Scottish Gaelic Test accuracy
value: 46.2
- type: accuracy
name: Khunsari Test accuracy
value: 50.0
- type: accuracy
name: Hebrew Test accuracy
value: 85.4
- type: accuracy
name: Uyghur Test accuracy
value: 60.2
- type: accuracy
name: Chukchi Test accuracy
value: 37.4
---
# XLM-RoBERTa base Universal Dependencies v2.8 POS tagging: Korean
This model is part of our paper called:
- Make the Best of Cross-lingual Transfer: Evidence from POS Tagging with over 100 Languages
Check the [Space](https://huggingface.co/spaces/wietsedv/xpos) for more details.
## Usage
```python
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-ko")
model = AutoModelForTokenClassification.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-ko")
```
|
wietsedv/xlm-roberta-base-ft-udpos28-ja
|
wietsedv
| 2023-09-11T08:57:00Z | 133 | 0 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"xlm-roberta",
"token-classification",
"part-of-speech",
"ja",
"dataset:universal_dependencies",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-03-02T23:29:05Z |
---
language:
- ja
license: apache-2.0
library_name: transformers
tags:
- part-of-speech
- token-classification
datasets:
- universal_dependencies
metrics:
- accuracy
model-index:
- name: xlm-roberta-base-ft-udpos28-ja
results:
- task:
type: token-classification
name: Part-of-Speech Tagging
dataset:
type: universal_dependencies
name: Universal Dependencies v2.8
metrics:
- type: accuracy
name: English Test accuracy
value: 47.7
- type: accuracy
name: Dutch Test accuracy
value: 49.8
- type: accuracy
name: German Test accuracy
value: 55.7
- type: accuracy
name: Italian Test accuracy
value: 52.0
- type: accuracy
name: French Test accuracy
value: 47.2
- type: accuracy
name: Spanish Test accuracy
value: 48.2
- type: accuracy
name: Russian Test accuracy
value: 62.7
- type: accuracy
name: Swedish Test accuracy
value: 52.6
- type: accuracy
name: Norwegian Test accuracy
value: 48.6
- type: accuracy
name: Danish Test accuracy
value: 54.3
- type: accuracy
name: Low Saxon Test accuracy
value: 34.7
- type: accuracy
name: Akkadian Test accuracy
value: 38.6
- type: accuracy
name: Armenian Test accuracy
value: 67.0
- type: accuracy
name: Welsh Test accuracy
value: 48.4
- type: accuracy
name: Old East Slavic Test accuracy
value: 55.2
- type: accuracy
name: Albanian Test accuracy
value: 51.8
- type: accuracy
name: Slovenian Test accuracy
value: 46.6
- type: accuracy
name: Guajajara Test accuracy
value: 39.3
- type: accuracy
name: Kurmanji Test accuracy
value: 54.6
- type: accuracy
name: Turkish Test accuracy
value: 65.4
- type: accuracy
name: Finnish Test accuracy
value: 69.1
- type: accuracy
name: Indonesian Test accuracy
value: 59.1
- type: accuracy
name: Ukrainian Test accuracy
value: 63.2
- type: accuracy
name: Polish Test accuracy
value: 60.5
- type: accuracy
name: Portuguese Test accuracy
value: 53.3
- type: accuracy
name: Kazakh Test accuracy
value: 71.9
- type: accuracy
name: Latin Test accuracy
value: 53.5
- type: accuracy
name: Old French Test accuracy
value: 30.0
- type: accuracy
name: Buryat Test accuracy
value: 58.2
- type: accuracy
name: Kaapor Test accuracy
value: 21.7
- type: accuracy
name: Korean Test accuracy
value: 64.5
- type: accuracy
name: Estonian Test accuracy
value: 67.0
- type: accuracy
name: Croatian Test accuracy
value: 57.5
- type: accuracy
name: Gothic Test accuracy
value: 15.4
- type: accuracy
name: Swiss German Test accuracy
value: 34.5
- type: accuracy
name: Assyrian Test accuracy
value: 28.3
- type: accuracy
name: North Sami Test accuracy
value: 35.1
- type: accuracy
name: Naija Test accuracy
value: 16.8
- type: accuracy
name: Latvian Test accuracy
value: 69.6
- type: accuracy
name: Chinese Test accuracy
value: 66.2
- type: accuracy
name: Tagalog Test accuracy
value: 50.4
- type: accuracy
name: Bambara Test accuracy
value: 27.5
- type: accuracy
name: Lithuanian Test accuracy
value: 69.7
- type: accuracy
name: Galician Test accuracy
value: 51.6
- type: accuracy
name: Vietnamese Test accuracy
value: 50.6
- type: accuracy
name: Greek Test accuracy
value: 54.9
- type: accuracy
name: Catalan Test accuracy
value: 46.1
- type: accuracy
name: Czech Test accuracy
value: 61.1
- type: accuracy
name: Erzya Test accuracy
value: 41.3
- type: accuracy
name: Bhojpuri Test accuracy
value: 41.9
- type: accuracy
name: Thai Test accuracy
value: 52.3
- type: accuracy
name: Marathi Test accuracy
value: 77.3
- type: accuracy
name: Basque Test accuracy
value: 68.4
- type: accuracy
name: Slovak Test accuracy
value: 62.3
- type: accuracy
name: Kiche Test accuracy
value: 41.0
- type: accuracy
name: Yoruba Test accuracy
value: 28.8
- type: accuracy
name: Warlpiri Test accuracy
value: 30.4
- type: accuracy
name: Tamil Test accuracy
value: 75.9
- type: accuracy
name: Maltese Test accuracy
value: 29.8
- type: accuracy
name: Ancient Greek Test accuracy
value: 50.2
- type: accuracy
name: Icelandic Test accuracy
value: 54.4
- type: accuracy
name: Mbya Guarani Test accuracy
value: 28.1
- type: accuracy
name: Urdu Test accuracy
value: 46.4
- type: accuracy
name: Romanian Test accuracy
value: 55.4
- type: accuracy
name: Persian Test accuracy
value: 51.8
- type: accuracy
name: Apurina Test accuracy
value: 34.5
- type: accuracy
name: Japanese Test accuracy
value: 92.6
- type: accuracy
name: Hungarian Test accuracy
value: 61.2
- type: accuracy
name: Hindi Test accuracy
value: 48.2
- type: accuracy
name: Classical Chinese Test accuracy
value: 46.1
- type: accuracy
name: Komi Permyak Test accuracy
value: 42.8
- type: accuracy
name: Faroese Test accuracy
value: 51.1
- type: accuracy
name: Sanskrit Test accuracy
value: 33.0
- type: accuracy
name: Livvi Test accuracy
value: 57.2
- type: accuracy
name: Arabic Test accuracy
value: 52.7
- type: accuracy
name: Wolof Test accuracy
value: 32.1
- type: accuracy
name: Bulgarian Test accuracy
value: 55.1
- type: accuracy
name: Akuntsu Test accuracy
value: 41.4
- type: accuracy
name: Makurap Test accuracy
value: 19.9
- type: accuracy
name: Kangri Test accuracy
value: 41.0
- type: accuracy
name: Breton Test accuracy
value: 46.4
- type: accuracy
name: Telugu Test accuracy
value: 71.8
- type: accuracy
name: Cantonese Test accuracy
value: 60.4
- type: accuracy
name: Old Church Slavonic Test accuracy
value: 39.5
- type: accuracy
name: Karelian Test accuracy
value: 60.7
- type: accuracy
name: Upper Sorbian Test accuracy
value: 54.6
- type: accuracy
name: South Levantine Arabic Test accuracy
value: 49.4
- type: accuracy
name: Komi Zyrian Test accuracy
value: 39.8
- type: accuracy
name: Irish Test accuracy
value: 46.8
- type: accuracy
name: Nayini Test accuracy
value: 37.2
- type: accuracy
name: Munduruku Test accuracy
value: 39.3
- type: accuracy
name: Manx Test accuracy
value: 33.9
- type: accuracy
name: Skolt Sami Test accuracy
value: 36.4
- type: accuracy
name: Afrikaans Test accuracy
value: 45.7
- type: accuracy
name: Old Turkish Test accuracy
value: 18.1
- type: accuracy
name: Tupinamba Test accuracy
value: 32.0
- type: accuracy
name: Belarusian Test accuracy
value: 62.6
- type: accuracy
name: Serbian Test accuracy
value: 58.0
- type: accuracy
name: Moksha Test accuracy
value: 42.2
- type: accuracy
name: Western Armenian Test accuracy
value: 62.3
- type: accuracy
name: Scottish Gaelic Test accuracy
value: 38.6
- type: accuracy
name: Khunsari Test accuracy
value: 44.6
- type: accuracy
name: Hebrew Test accuracy
value: 69.8
- type: accuracy
name: Uyghur Test accuracy
value: 65.4
- type: accuracy
name: Chukchi Test accuracy
value: 33.7
---
# XLM-RoBERTa base Universal Dependencies v2.8 POS tagging: Japanese
This model is part of our paper called:
- Make the Best of Cross-lingual Transfer: Evidence from POS Tagging with over 100 Languages
Check the [Space](https://huggingface.co/spaces/wietsedv/xpos) for more details.
## Usage
```python
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-ja")
model = AutoModelForTokenClassification.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-ja")
```
|
GroNLP/bert_dutch_base_abusive_language
|
GroNLP
| 2023-09-11T08:56:56Z | 114 | 0 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"bert",
"text-classification",
"nl",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-06-07T07:29:11Z |
---
license: apache-2.0
language:
- nl
pipeline_tag: text-classification
---
Fine-tuned model for detecting instances of abusive language in Ducth tweets. The model has been trained with [DALC v2.0 ](https://github.com/tommasoc80/DALC).
Abusive language is defined as "Impolite, harsh, or hurtful language (that may contain profanities or vulgar language) that result in a debasement, harassment,
threat, or aggression of an individual or a (social) group, but not necessarily of an entity, an institution, an organisations, or a concept." ([Ruitenbeek et al., 2022](https://aclanthology.org/2022.woah-1.5/))
The model achieve the following results on multiple test data:
- DALC held-out test set: macro F1: 72.23; F1 Abusive: 51.60
- HateCheck-NL (functional benchmark for hate speech): Accuracy: 60.19; Accuracy non-hateful tests: 57.38 ; Accuracy hateful tests: 59.58
- OP-NL (dynamyc benchmark for offensive language): macro F1: 57.57
More details on the training settings and pre-processind are available [here](https://github.com/tommasoc80/DALC)
|
SHENMU007/neunit_BASE_V9.5.11
|
SHENMU007
| 2023-09-11T08:56:39Z | 76 | 0 |
transformers
|
[
"transformers",
"pytorch",
"speecht5",
"text-to-audio",
"1.1.0",
"generated_from_trainer",
"zh",
"dataset:facebook/voxpopuli",
"base_model:microsoft/speecht5_tts",
"base_model:finetune:microsoft/speecht5_tts",
"license:mit",
"endpoints_compatible",
"region:us"
] |
text-to-audio
| 2023-09-11T07:51:35Z |
---
language:
- zh
license: mit
base_model: microsoft/speecht5_tts
tags:
- 1.1.0
- generated_from_trainer
datasets:
- facebook/voxpopuli
model-index:
- name: SpeechT5 TTS Dutch neunit
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# SpeechT5 TTS Dutch neunit
This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the VoxPopuli 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: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 4000
### Training results
### Framework versions
- Transformers 4.33.1
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.13.3
|
wietsedv/xlm-roberta-base-ft-udpos28-el
|
wietsedv
| 2023-09-11T08:56:35Z | 109 | 0 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"xlm-roberta",
"token-classification",
"part-of-speech",
"el",
"dataset:universal_dependencies",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-03-02T23:29:05Z |
---
language:
- el
license: apache-2.0
library_name: transformers
tags:
- part-of-speech
- token-classification
datasets:
- universal_dependencies
metrics:
- accuracy
model-index:
- name: xlm-roberta-base-ft-udpos28-el
results:
- task:
type: token-classification
name: Part-of-Speech Tagging
dataset:
type: universal_dependencies
name: Universal Dependencies v2.8
metrics:
- type: accuracy
name: English Test accuracy
value: 83.6
- type: accuracy
name: Dutch Test accuracy
value: 82.2
- type: accuracy
name: German Test accuracy
value: 82.6
- type: accuracy
name: Italian Test accuracy
value: 82.0
- type: accuracy
name: French Test accuracy
value: 78.7
- type: accuracy
name: Spanish Test accuracy
value: 82.2
- type: accuracy
name: Russian Test accuracy
value: 88.4
- type: accuracy
name: Swedish Test accuracy
value: 87.4
- type: accuracy
name: Norwegian Test accuracy
value: 82.1
- type: accuracy
name: Danish Test accuracy
value: 85.9
- type: accuracy
name: Low Saxon Test accuracy
value: 49.8
- type: accuracy
name: Akkadian Test accuracy
value: 24.4
- type: accuracy
name: Armenian Test accuracy
value: 84.0
- type: accuracy
name: Welsh Test accuracy
value: 68.9
- type: accuracy
name: Old East Slavic Test accuracy
value: 75.0
- type: accuracy
name: Albanian Test accuracy
value: 87.7
- type: accuracy
name: Slovenian Test accuracy
value: 77.2
- type: accuracy
name: Guajajara Test accuracy
value: 25.8
- type: accuracy
name: Kurmanji Test accuracy
value: 74.3
- type: accuracy
name: Turkish Test accuracy
value: 75.3
- type: accuracy
name: Finnish Test accuracy
value: 83.4
- type: accuracy
name: Indonesian Test accuracy
value: 75.4
- type: accuracy
name: Ukrainian Test accuracy
value: 88.6
- type: accuracy
name: Polish Test accuracy
value: 84.0
- type: accuracy
name: Portuguese Test accuracy
value: 82.4
- type: accuracy
name: Kazakh Test accuracy
value: 80.5
- type: accuracy
name: Latin Test accuracy
value: 77.3
- type: accuracy
name: Old French Test accuracy
value: 52.5
- type: accuracy
name: Buryat Test accuracy
value: 56.0
- type: accuracy
name: Kaapor Test accuracy
value: 11.2
- type: accuracy
name: Korean Test accuracy
value: 59.9
- type: accuracy
name: Estonian Test accuracy
value: 83.6
- type: accuracy
name: Croatian Test accuracy
value: 84.9
- type: accuracy
name: Gothic Test accuracy
value: 20.2
- type: accuracy
name: Swiss German Test accuracy
value: 43.6
- type: accuracy
name: Assyrian Test accuracy
value: 14.6
- type: accuracy
name: North Sami Test accuracy
value: 33.5
- type: accuracy
name: Naija Test accuracy
value: 42.7
- type: accuracy
name: Latvian Test accuracy
value: 84.9
- type: accuracy
name: Chinese Test accuracy
value: 42.1
- type: accuracy
name: Tagalog Test accuracy
value: 66.7
- type: accuracy
name: Bambara Test accuracy
value: 28.2
- type: accuracy
name: Lithuanian Test accuracy
value: 85.3
- type: accuracy
name: Galician Test accuracy
value: 82.1
- type: accuracy
name: Vietnamese Test accuracy
value: 62.8
- type: accuracy
name: Greek Test accuracy
value: 98.0
- type: accuracy
name: Catalan Test accuracy
value: 80.4
- type: accuracy
name: Czech Test accuracy
value: 85.0
- type: accuracy
name: Erzya Test accuracy
value: 43.9
- type: accuracy
name: Bhojpuri Test accuracy
value: 45.0
- type: accuracy
name: Thai Test accuracy
value: 58.6
- type: accuracy
name: Marathi Test accuracy
value: 85.3
- type: accuracy
name: Basque Test accuracy
value: 72.4
- type: accuracy
name: Slovak Test accuracy
value: 82.8
- type: accuracy
name: Kiche Test accuracy
value: 36.2
- type: accuracy
name: Yoruba Test accuracy
value: 28.9
- type: accuracy
name: Warlpiri Test accuracy
value: 38.9
- type: accuracy
name: Tamil Test accuracy
value: 83.0
- type: accuracy
name: Maltese Test accuracy
value: 22.3
- type: accuracy
name: Ancient Greek Test accuracy
value: 64.2
- type: accuracy
name: Icelandic Test accuracy
value: 80.7
- type: accuracy
name: Mbya Guarani Test accuracy
value: 32.4
- type: accuracy
name: Urdu Test accuracy
value: 53.0
- type: accuracy
name: Romanian Test accuracy
value: 83.7
- type: accuracy
name: Persian Test accuracy
value: 74.4
- type: accuracy
name: Apurina Test accuracy
value: 41.3
- type: accuracy
name: Japanese Test accuracy
value: 30.0
- type: accuracy
name: Hungarian Test accuracy
value: 80.2
- type: accuracy
name: Hindi Test accuracy
value: 60.0
- type: accuracy
name: Classical Chinese Test accuracy
value: 30.1
- type: accuracy
name: Komi Permyak Test accuracy
value: 44.2
- type: accuracy
name: Faroese Test accuracy
value: 72.9
- type: accuracy
name: Sanskrit Test accuracy
value: 40.4
- type: accuracy
name: Livvi Test accuracy
value: 65.2
- type: accuracy
name: Arabic Test accuracy
value: 76.6
- type: accuracy
name: Wolof Test accuracy
value: 28.0
- type: accuracy
name: Bulgarian Test accuracy
value: 89.6
- type: accuracy
name: Akuntsu Test accuracy
value: 26.7
- type: accuracy
name: Makurap Test accuracy
value: 18.5
- type: accuracy
name: Kangri Test accuracy
value: 43.1
- type: accuracy
name: Breton Test accuracy
value: 63.5
- type: accuracy
name: Telugu Test accuracy
value: 85.3
- type: accuracy
name: Cantonese Test accuracy
value: 48.3
- type: accuracy
name: Old Church Slavonic Test accuracy
value: 51.6
- type: accuracy
name: Karelian Test accuracy
value: 71.0
- type: accuracy
name: Upper Sorbian Test accuracy
value: 69.5
- type: accuracy
name: South Levantine Arabic Test accuracy
value: 69.2
- type: accuracy
name: Komi Zyrian Test accuracy
value: 36.5
- type: accuracy
name: Irish Test accuracy
value: 61.3
- type: accuracy
name: Nayini Test accuracy
value: 43.6
- type: accuracy
name: Munduruku Test accuracy
value: 29.4
- type: accuracy
name: Manx Test accuracy
value: 33.8
- type: accuracy
name: Skolt Sami Test accuracy
value: 31.5
- type: accuracy
name: Afrikaans Test accuracy
value: 85.0
- type: accuracy
name: Old Turkish Test accuracy
value: 37.1
- type: accuracy
name: Tupinamba Test accuracy
value: 29.2
- type: accuracy
name: Belarusian Test accuracy
value: 89.1
- type: accuracy
name: Serbian Test accuracy
value: 85.2
- type: accuracy
name: Moksha Test accuracy
value: 43.8
- type: accuracy
name: Western Armenian Test accuracy
value: 76.9
- type: accuracy
name: Scottish Gaelic Test accuracy
value: 54.8
- type: accuracy
name: Khunsari Test accuracy
value: 45.9
- type: accuracy
name: Hebrew Test accuracy
value: 88.5
- type: accuracy
name: Uyghur Test accuracy
value: 75.7
- type: accuracy
name: Chukchi Test accuracy
value: 34.8
---
# XLM-RoBERTa base Universal Dependencies v2.8 POS tagging: Greek
This model is part of our paper called:
- Make the Best of Cross-lingual Transfer: Evidence from POS Tagging with over 100 Languages
Check the [Space](https://huggingface.co/spaces/wietsedv/xpos) for more details.
## Usage
```python
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-el")
model = AutoModelForTokenClassification.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-el")
```
|
wietsedv/xlm-roberta-base-ft-udpos28-de
|
wietsedv
| 2023-09-11T08:56:20Z | 110 | 2 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"xlm-roberta",
"token-classification",
"part-of-speech",
"de",
"dataset:universal_dependencies",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-03-02T23:29:05Z |
---
language:
- de
license: apache-2.0
library_name: transformers
tags:
- part-of-speech
- token-classification
datasets:
- universal_dependencies
metrics:
- accuracy
model-index:
- name: xlm-roberta-base-ft-udpos28-de
results:
- task:
type: token-classification
name: Part-of-Speech Tagging
dataset:
type: universal_dependencies
name: Universal Dependencies v2.8
metrics:
- type: accuracy
name: English Test accuracy
value: 87.0
- type: accuracy
name: Dutch Test accuracy
value: 89.6
- type: accuracy
name: German Test accuracy
value: 97.2
- type: accuracy
name: Italian Test accuracy
value: 85.6
- type: accuracy
name: French Test accuracy
value: 84.8
- type: accuracy
name: Spanish Test accuracy
value: 88.4
- type: accuracy
name: Russian Test accuracy
value: 89.4
- type: accuracy
name: Swedish Test accuracy
value: 92.3
- type: accuracy
name: Norwegian Test accuracy
value: 87.7
- type: accuracy
name: Danish Test accuracy
value: 88.9
- type: accuracy
name: Low Saxon Test accuracy
value: 44.3
- type: accuracy
name: Akkadian Test accuracy
value: 21.4
- type: accuracy
name: Armenian Test accuracy
value: 85.6
- type: accuracy
name: Welsh Test accuracy
value: 69.0
- type: accuracy
name: Old East Slavic Test accuracy
value: 67.7
- type: accuracy
name: Albanian Test accuracy
value: 84.6
- type: accuracy
name: Slovenian Test accuracy
value: 76.5
- type: accuracy
name: Guajajara Test accuracy
value: 18.1
- type: accuracy
name: Kurmanji Test accuracy
value: 74.1
- type: accuracy
name: Turkish Test accuracy
value: 75.6
- type: accuracy
name: Finnish Test accuracy
value: 83.8
- type: accuracy
name: Indonesian Test accuracy
value: 82.2
- type: accuracy
name: Ukrainian Test accuracy
value: 89.0
- type: accuracy
name: Polish Test accuracy
value: 86.6
- type: accuracy
name: Portuguese Test accuracy
value: 87.8
- type: accuracy
name: Kazakh Test accuracy
value: 80.6
- type: accuracy
name: Latin Test accuracy
value: 75.8
- type: accuracy
name: Old French Test accuracy
value: 36.3
- type: accuracy
name: Buryat Test accuracy
value: 49.8
- type: accuracy
name: Kaapor Test accuracy
value: 11.7
- type: accuracy
name: Korean Test accuracy
value: 61.4
- type: accuracy
name: Estonian Test accuracy
value: 86.6
- type: accuracy
name: Croatian Test accuracy
value: 88.8
- type: accuracy
name: Gothic Test accuracy
value: 8.1
- type: accuracy
name: Swiss German Test accuracy
value: 54.4
- type: accuracy
name: Assyrian Test accuracy
value: 17.2
- type: accuracy
name: North Sami Test accuracy
value: 25.0
- type: accuracy
name: Naija Test accuracy
value: 28.2
- type: accuracy
name: Latvian Test accuracy
value: 83.9
- type: accuracy
name: Chinese Test accuracy
value: 52.6
- type: accuracy
name: Tagalog Test accuracy
value: 72.1
- type: accuracy
name: Bambara Test accuracy
value: 17.5
- type: accuracy
name: Lithuanian Test accuracy
value: 82.6
- type: accuracy
name: Galician Test accuracy
value: 85.2
- type: accuracy
name: Vietnamese Test accuracy
value: 60.8
- type: accuracy
name: Greek Test accuracy
value: 88.7
- type: accuracy
name: Catalan Test accuracy
value: 86.8
- type: accuracy
name: Czech Test accuracy
value: 87.4
- type: accuracy
name: Erzya Test accuracy
value: 33.6
- type: accuracy
name: Bhojpuri Test accuracy
value: 46.5
- type: accuracy
name: Thai Test accuracy
value: 62.4
- type: accuracy
name: Marathi Test accuracy
value: 86.5
- type: accuracy
name: Basque Test accuracy
value: 77.3
- type: accuracy
name: Slovak Test accuracy
value: 87.6
- type: accuracy
name: Kiche Test accuracy
value: 21.6
- type: accuracy
name: Yoruba Test accuracy
value: 16.6
- type: accuracy
name: Warlpiri Test accuracy
value: 21.5
- type: accuracy
name: Tamil Test accuracy
value: 84.2
- type: accuracy
name: Maltese Test accuracy
value: 15.3
- type: accuracy
name: Ancient Greek Test accuracy
value: 62.0
- type: accuracy
name: Icelandic Test accuracy
value: 84.1
- type: accuracy
name: Mbya Guarani Test accuracy
value: 20.5
- type: accuracy
name: Urdu Test accuracy
value: 68.0
- type: accuracy
name: Romanian Test accuracy
value: 83.5
- type: accuracy
name: Persian Test accuracy
value: 76.0
- type: accuracy
name: Apurina Test accuracy
value: 22.2
- type: accuracy
name: Japanese Test accuracy
value: 36.2
- type: accuracy
name: Hungarian Test accuracy
value: 86.7
- type: accuracy
name: Hindi Test accuracy
value: 73.0
- type: accuracy
name: Classical Chinese Test accuracy
value: 28.6
- type: accuracy
name: Komi Permyak Test accuracy
value: 34.9
- type: accuracy
name: Faroese Test accuracy
value: 76.6
- type: accuracy
name: Sanskrit Test accuracy
value: 9.4
- type: accuracy
name: Livvi Test accuracy
value: 50.9
- type: accuracy
name: Arabic Test accuracy
value: 79.4
- type: accuracy
name: Wolof Test accuracy
value: 21.1
- type: accuracy
name: Bulgarian Test accuracy
value: 91.1
- type: accuracy
name: Akuntsu Test accuracy
value: 14.4
- type: accuracy
name: Makurap Test accuracy
value: 1.4
- type: accuracy
name: Kangri Test accuracy
value: 40.5
- type: accuracy
name: Breton Test accuracy
value: 60.0
- type: accuracy
name: Telugu Test accuracy
value: 83.2
- type: accuracy
name: Cantonese Test accuracy
value: 48.9
- type: accuracy
name: Old Church Slavonic Test accuracy
value: 38.7
- type: accuracy
name: Karelian Test accuracy
value: 64.4
- type: accuracy
name: Upper Sorbian Test accuracy
value: 65.5
- type: accuracy
name: South Levantine Arabic Test accuracy
value: 66.8
- type: accuracy
name: Komi Zyrian Test accuracy
value: 28.4
- type: accuracy
name: Irish Test accuracy
value: 66.3
- type: accuracy
name: Nayini Test accuracy
value: 44.9
- type: accuracy
name: Munduruku Test accuracy
value: 8.0
- type: accuracy
name: Manx Test accuracy
value: 20.6
- type: accuracy
name: Skolt Sami Test accuracy
value: 25.8
- type: accuracy
name: Afrikaans Test accuracy
value: 88.9
- type: accuracy
name: Old Turkish Test accuracy
value: 31.7
- type: accuracy
name: Tupinamba Test accuracy
value: 20.9
- type: accuracy
name: Belarusian Test accuracy
value: 89.5
- type: accuracy
name: Serbian Test accuracy
value: 89.8
- type: accuracy
name: Moksha Test accuracy
value: 31.3
- type: accuracy
name: Western Armenian Test accuracy
value: 77.6
- type: accuracy
name: Scottish Gaelic Test accuracy
value: 56.5
- type: accuracy
name: Khunsari Test accuracy
value: 35.1
- type: accuracy
name: Hebrew Test accuracy
value: 91.7
- type: accuracy
name: Uyghur Test accuracy
value: 71.5
- type: accuracy
name: Chukchi Test accuracy
value: 29.0
---
# XLM-RoBERTa base Universal Dependencies v2.8 POS tagging: German
This model is part of our paper called:
- Make the Best of Cross-lingual Transfer: Evidence from POS Tagging with over 100 Languages
Check the [Space](https://huggingface.co/spaces/wietsedv/xpos) for more details.
## Usage
```python
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-de")
model = AutoModelForTokenClassification.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-de")
```
|
wietsedv/xlm-roberta-base-ft-udpos28-fa
|
wietsedv
| 2023-09-11T08:56:13Z | 120 | 2 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"xlm-roberta",
"token-classification",
"part-of-speech",
"fa",
"dataset:universal_dependencies",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-03-02T23:29:05Z |
---
language:
- fa
license: apache-2.0
library_name: transformers
tags:
- part-of-speech
- token-classification
datasets:
- universal_dependencies
metrics:
- accuracy
model-index:
- name: xlm-roberta-base-ft-udpos28-fa
results:
- task:
type: token-classification
name: Part-of-Speech Tagging
dataset:
type: universal_dependencies
name: Universal Dependencies v2.8
metrics:
- type: accuracy
name: English Test accuracy
value: 77.1
- type: accuracy
name: Dutch Test accuracy
value: 75.7
- type: accuracy
name: German Test accuracy
value: 75.4
- type: accuracy
name: Italian Test accuracy
value: 76.0
- type: accuracy
name: French Test accuracy
value: 73.7
- type: accuracy
name: Spanish Test accuracy
value: 76.7
- type: accuracy
name: Russian Test accuracy
value: 81.5
- type: accuracy
name: Swedish Test accuracy
value: 80.5
- type: accuracy
name: Norwegian Test accuracy
value: 79.6
- type: accuracy
name: Danish Test accuracy
value: 81.1
- type: accuracy
name: Low Saxon Test accuracy
value: 51.1
- type: accuracy
name: Akkadian Test accuracy
value: 40.8
- type: accuracy
name: Armenian Test accuracy
value: 79.5
- type: accuracy
name: Welsh Test accuracy
value: 72.1
- type: accuracy
name: Old East Slavic Test accuracy
value: 71.6
- type: accuracy
name: Albanian Test accuracy
value: 73.6
- type: accuracy
name: Slovenian Test accuracy
value: 74.0
- type: accuracy
name: Guajajara Test accuracy
value: 32.5
- type: accuracy
name: Kurmanji Test accuracy
value: 78.9
- type: accuracy
name: Turkish Test accuracy
value: 76.9
- type: accuracy
name: Finnish Test accuracy
value: 79.8
- type: accuracy
name: Indonesian Test accuracy
value: 76.1
- type: accuracy
name: Ukrainian Test accuracy
value: 80.0
- type: accuracy
name: Polish Test accuracy
value: 82.7
- type: accuracy
name: Portuguese Test accuracy
value: 77.8
- type: accuracy
name: Kazakh Test accuracy
value: 78.5
- type: accuracy
name: Latin Test accuracy
value: 73.5
- type: accuracy
name: Old French Test accuracy
value: 53.0
- type: accuracy
name: Buryat Test accuracy
value: 62.4
- type: accuracy
name: Kaapor Test accuracy
value: 19.6
- type: accuracy
name: Korean Test accuracy
value: 62.6
- type: accuracy
name: Estonian Test accuracy
value: 83.5
- type: accuracy
name: Croatian Test accuracy
value: 81.5
- type: accuracy
name: Gothic Test accuracy
value: 22.6
- type: accuracy
name: Swiss German Test accuracy
value: 50.2
- type: accuracy
name: Assyrian Test accuracy
value: 20.1
- type: accuracy
name: North Sami Test accuracy
value: 39.4
- type: accuracy
name: Naija Test accuracy
value: 39.6
- type: accuracy
name: Latvian Test accuracy
value: 80.0
- type: accuracy
name: Chinese Test accuracy
value: 41.2
- type: accuracy
name: Tagalog Test accuracy
value: 79.0
- type: accuracy
name: Bambara Test accuracy
value: 34.4
- type: accuracy
name: Lithuanian Test accuracy
value: 78.5
- type: accuracy
name: Galician Test accuracy
value: 74.9
- type: accuracy
name: Vietnamese Test accuracy
value: 57.0
- type: accuracy
name: Greek Test accuracy
value: 66.7
- type: accuracy
name: Catalan Test accuracy
value: 75.0
- type: accuracy
name: Czech Test accuracy
value: 80.3
- type: accuracy
name: Erzya Test accuracy
value: 47.8
- type: accuracy
name: Bhojpuri Test accuracy
value: 61.1
- type: accuracy
name: Thai Test accuracy
value: 53.6
- type: accuracy
name: Marathi Test accuracy
value: 84.0
- type: accuracy
name: Basque Test accuracy
value: 72.2
- type: accuracy
name: Slovak Test accuracy
value: 77.2
- type: accuracy
name: Kiche Test accuracy
value: 37.9
- type: accuracy
name: Yoruba Test accuracy
value: 30.8
- type: accuracy
name: Warlpiri Test accuracy
value: 35.2
- type: accuracy
name: Tamil Test accuracy
value: 82.0
- type: accuracy
name: Maltese Test accuracy
value: 30.5
- type: accuracy
name: Ancient Greek Test accuracy
value: 62.9
- type: accuracy
name: Icelandic Test accuracy
value: 82.3
- type: accuracy
name: Mbya Guarani Test accuracy
value: 31.0
- type: accuracy
name: Urdu Test accuracy
value: 74.4
- type: accuracy
name: Romanian Test accuracy
value: 79.2
- type: accuracy
name: Persian Test accuracy
value: 91.4
- type: accuracy
name: Apurina Test accuracy
value: 41.8
- type: accuracy
name: Japanese Test accuracy
value: 31.8
- type: accuracy
name: Hungarian Test accuracy
value: 71.0
- type: accuracy
name: Hindi Test accuracy
value: 79.2
- type: accuracy
name: Classical Chinese Test accuracy
value: 30.2
- type: accuracy
name: Komi Permyak Test accuracy
value: 46.7
- type: accuracy
name: Faroese Test accuracy
value: 73.4
- type: accuracy
name: Sanskrit Test accuracy
value: 35.1
- type: accuracy
name: Livvi Test accuracy
value: 63.9
- type: accuracy
name: Arabic Test accuracy
value: 81.7
- type: accuracy
name: Wolof Test accuracy
value: 35.7
- type: accuracy
name: Bulgarian Test accuracy
value: 83.5
- type: accuracy
name: Akuntsu Test accuracy
value: 37.5
- type: accuracy
name: Makurap Test accuracy
value: 15.1
- type: accuracy
name: Kangri Test accuracy
value: 52.3
- type: accuracy
name: Breton Test accuracy
value: 57.4
- type: accuracy
name: Telugu Test accuracy
value: 82.1
- type: accuracy
name: Cantonese Test accuracy
value: 45.4
- type: accuracy
name: Old Church Slavonic Test accuracy
value: 51.2
- type: accuracy
name: Karelian Test accuracy
value: 65.1
- type: accuracy
name: Upper Sorbian Test accuracy
value: 68.9
- type: accuracy
name: South Levantine Arabic Test accuracy
value: 69.7
- type: accuracy
name: Komi Zyrian Test accuracy
value: 41.6
- type: accuracy
name: Irish Test accuracy
value: 66.5
- type: accuracy
name: Nayini Test accuracy
value: 48.7
- type: accuracy
name: Munduruku Test accuracy
value: 24.9
- type: accuracy
name: Manx Test accuracy
value: 33.3
- type: accuracy
name: Skolt Sami Test accuracy
value: 38.3
- type: accuracy
name: Afrikaans Test accuracy
value: 75.4
- type: accuracy
name: Old Turkish Test accuracy
value: 37.1
- type: accuracy
name: Tupinamba Test accuracy
value: 39.2
- type: accuracy
name: Belarusian Test accuracy
value: 78.9
- type: accuracy
name: Serbian Test accuracy
value: 82.5
- type: accuracy
name: Moksha Test accuracy
value: 45.4
- type: accuracy
name: Western Armenian Test accuracy
value: 76.4
- type: accuracy
name: Scottish Gaelic Test accuracy
value: 54.7
- type: accuracy
name: Khunsari Test accuracy
value: 44.6
- type: accuracy
name: Hebrew Test accuracy
value: 89.6
- type: accuracy
name: Uyghur Test accuracy
value: 77.0
- type: accuracy
name: Chukchi Test accuracy
value: 33.6
---
# XLM-RoBERTa base Universal Dependencies v2.8 POS tagging: Persian
This model is part of our paper called:
- Make the Best of Cross-lingual Transfer: Evidence from POS Tagging with over 100 Languages
Check the [Space](https://huggingface.co/spaces/wietsedv/xpos) for more details.
## Usage
```python
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-fa")
model = AutoModelForTokenClassification.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-fa")
```
|
GroNLP/gpt2-medium-dutch-embeddings
|
GroNLP
| 2023-09-11T08:56:08Z | 325 | 3 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"jax",
"safetensors",
"gpt2",
"text-generation",
"adaption",
"recycled",
"gpt2-medium",
"nl",
"arxiv:2012.05628",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:04Z |
---
language: nl
tags:
- adaption
- recycled
- gpt2-medium
pipeline_tag: text-generation
---
# GPT-2 recycled for Dutch (medium, adapted lexical embeddings)
[Wietse de Vries](https://www.semanticscholar.org/author/Wietse-de-Vries/144611157) •
[Malvina Nissim](https://www.semanticscholar.org/author/M.-Nissim/2742475)
## Model description
This model is based on the medium OpenAI GPT-2 ([`gpt2-medium`](https://huggingface.co/gpt2-medium)) model.
The Transformer layer weights in this model are identical to the original English, model but the lexical layer has been retrained for a Dutch vocabulary.
For details, check out our paper on [arXiv](https://arxiv.org/abs/2012.05628) and the code on [Github](https://github.com/wietsedv/gpt2-recycle).
## Related models
### Dutch
- [`gpt2-small-dutch-embeddings`](https://huggingface.co/GroNLP/gpt2-small-dutch-embeddings): Small model size with only retrained lexical embeddings.
- [`gpt2-small-dutch`](https://huggingface.co/GroNLP/gpt2-small-dutch): Small model size with retrained lexical embeddings and additional fine-tuning of the full model. (**Recommended**)
- [`gpt2-medium-dutch-embeddings`](https://huggingface.co/GroNLP/gpt2-medium-dutch-embeddings): Medium model size with only retrained lexical embeddings.
### Italian
- [`gpt2-small-italian-embeddings`](https://huggingface.co/GroNLP/gpt2-small-italian-embeddings): Small model size with only retrained lexical embeddings.
- [`gpt2-small-italian`](https://huggingface.co/GroNLP/gpt2-small-italian): Small model size with retrained lexical embeddings and additional fine-tuning of the full model. (**Recommended**)
- [`gpt2-medium-italian-embeddings`](https://huggingface.co/GroNLP/gpt2-medium-italian-embeddings): Medium model size with only retrained lexical embeddings.
## How to use
```python
from transformers import pipeline
pipe = pipeline("text-generation", model="GroNLP/gpt2-medium-dutch-embeddings")
```
```python
from transformers import AutoTokenizer, AutoModel, TFAutoModel
tokenizer = AutoTokenizer.from_pretrained("GroNLP/gpt2-medium-dutch-embeddings")
model = AutoModel.from_pretrained("GroNLP/gpt2-medium-dutch-embeddings") # PyTorch
model = TFAutoModel.from_pretrained("GroNLP/gpt2-medium-dutch-embeddings") # Tensorflow
```
## BibTeX entry
```bibtex
@misc{devries2020good,
title={As good as new. How to successfully recycle English GPT-2 to make models for other languages},
author={Wietse de Vries and Malvina Nissim},
year={2020},
eprint={2012.05628},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
wietsedv/xlm-roberta-base-ft-udpos28-en
|
wietsedv
| 2023-09-11T08:56:04Z | 110 | 2 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"xlm-roberta",
"token-classification",
"part-of-speech",
"en",
"dataset:universal_dependencies",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-03-02T23:29:05Z |
---
language:
- en
license: apache-2.0
library_name: transformers
tags:
- part-of-speech
- token-classification
datasets:
- universal_dependencies
metrics:
- accuracy
model-index:
- name: xlm-roberta-base-ft-udpos28-en
results:
- task:
type: token-classification
name: Part-of-Speech Tagging
dataset:
type: universal_dependencies
name: Universal Dependencies v2.8
metrics:
- type: accuracy
name: English Test accuracy
value: 96.0
- type: accuracy
name: Dutch Test accuracy
value: 90.4
- type: accuracy
name: German Test accuracy
value: 88.6
- type: accuracy
name: Italian Test accuracy
value: 87.8
- type: accuracy
name: French Test accuracy
value: 87.4
- type: accuracy
name: Spanish Test accuracy
value: 90.3
- type: accuracy
name: Russian Test accuracy
value: 91.0
- type: accuracy
name: Swedish Test accuracy
value: 94.0
- type: accuracy
name: Norwegian Test accuracy
value: 89.6
- type: accuracy
name: Danish Test accuracy
value: 91.6
- type: accuracy
name: Low Saxon Test accuracy
value: 57.4
- type: accuracy
name: Akkadian Test accuracy
value: 26.4
- type: accuracy
name: Armenian Test accuracy
value: 88.5
- type: accuracy
name: Welsh Test accuracy
value: 70.6
- type: accuracy
name: Old East Slavic Test accuracy
value: 76.5
- type: accuracy
name: Albanian Test accuracy
value: 82.3
- type: accuracy
name: Slovenian Test accuracy
value: 79.0
- type: accuracy
name: Guajajara Test accuracy
value: 17.2
- type: accuracy
name: Kurmanji Test accuracy
value: 76.9
- type: accuracy
name: Turkish Test accuracy
value: 79.1
- type: accuracy
name: Finnish Test accuracy
value: 87.2
- type: accuracy
name: Indonesian Test accuracy
value: 86.9
- type: accuracy
name: Ukrainian Test accuracy
value: 87.6
- type: accuracy
name: Polish Test accuracy
value: 87.2
- type: accuracy
name: Portuguese Test accuracy
value: 90.0
- type: accuracy
name: Kazakh Test accuracy
value: 82.5
- type: accuracy
name: Latin Test accuracy
value: 79.6
- type: accuracy
name: Old French Test accuracy
value: 53.4
- type: accuracy
name: Buryat Test accuracy
value: 58.8
- type: accuracy
name: Kaapor Test accuracy
value: 9.2
- type: accuracy
name: Korean Test accuracy
value: 64.0
- type: accuracy
name: Estonian Test accuracy
value: 88.4
- type: accuracy
name: Croatian Test accuracy
value: 87.9
- type: accuracy
name: Gothic Test accuracy
value: 20.5
- type: accuracy
name: Swiss German Test accuracy
value: 47.6
- type: accuracy
name: Assyrian Test accuracy
value: 14.6
- type: accuracy
name: North Sami Test accuracy
value: 32.0
- type: accuracy
name: Naija Test accuracy
value: 47.5
- type: accuracy
name: Latvian Test accuracy
value: 87.5
- type: accuracy
name: Chinese Test accuracy
value: 47.5
- type: accuracy
name: Tagalog Test accuracy
value: 73.5
- type: accuracy
name: Bambara Test accuracy
value: 27.7
- type: accuracy
name: Lithuanian Test accuracy
value: 87.3
- type: accuracy
name: Galician Test accuracy
value: 87.1
- type: accuracy
name: Vietnamese Test accuracy
value: 66.4
- type: accuracy
name: Greek Test accuracy
value: 87.6
- type: accuracy
name: Catalan Test accuracy
value: 89.7
- type: accuracy
name: Czech Test accuracy
value: 88.1
- type: accuracy
name: Erzya Test accuracy
value: 47.6
- type: accuracy
name: Bhojpuri Test accuracy
value: 50.7
- type: accuracy
name: Thai Test accuracy
value: 59.5
- type: accuracy
name: Marathi Test accuracy
value: 82.2
- type: accuracy
name: Basque Test accuracy
value: 76.0
- type: accuracy
name: Slovak Test accuracy
value: 88.5
- type: accuracy
name: Kiche Test accuracy
value: 25.4
- type: accuracy
name: Yoruba Test accuracy
value: 18.5
- type: accuracy
name: Warlpiri Test accuracy
value: 29.1
- type: accuracy
name: Tamil Test accuracy
value: 83.4
- type: accuracy
name: Maltese Test accuracy
value: 21.1
- type: accuracy
name: Ancient Greek Test accuracy
value: 66.8
- type: accuracy
name: Icelandic Test accuracy
value: 84.8
- type: accuracy
name: Mbya Guarani Test accuracy
value: 24.1
- type: accuracy
name: Urdu Test accuracy
value: 67.0
- type: accuracy
name: Romanian Test accuracy
value: 85.7
- type: accuracy
name: Persian Test accuracy
value: 76.7
- type: accuracy
name: Apurina Test accuracy
value: 28.6
- type: accuracy
name: Japanese Test accuracy
value: 34.1
- type: accuracy
name: Hungarian Test accuracy
value: 86.0
- type: accuracy
name: Hindi Test accuracy
value: 74.1
- type: accuracy
name: Classical Chinese Test accuracy
value: 29.4
- type: accuracy
name: Komi Permyak Test accuracy
value: 47.4
- type: accuracy
name: Faroese Test accuracy
value: 77.0
- type: accuracy
name: Sanskrit Test accuracy
value: 25.6
- type: accuracy
name: Livvi Test accuracy
value: 63.2
- type: accuracy
name: Arabic Test accuracy
value: 80.7
- type: accuracy
name: Wolof Test accuracy
value: 26.1
- type: accuracy
name: Bulgarian Test accuracy
value: 90.8
- type: accuracy
name: Akuntsu Test accuracy
value: 18.3
- type: accuracy
name: Makurap Test accuracy
value: 5.5
- type: accuracy
name: Kangri Test accuracy
value: 43.0
- type: accuracy
name: Breton Test accuracy
value: 64.1
- type: accuracy
name: Telugu Test accuracy
value: 84.7
- type: accuracy
name: Cantonese Test accuracy
value: 54.0
- type: accuracy
name: Old Church Slavonic Test accuracy
value: 53.7
- type: accuracy
name: Karelian Test accuracy
value: 69.7
- type: accuracy
name: Upper Sorbian Test accuracy
value: 75.6
- type: accuracy
name: South Levantine Arabic Test accuracy
value: 66.3
- type: accuracy
name: Komi Zyrian Test accuracy
value: 39.9
- type: accuracy
name: Irish Test accuracy
value: 67.0
- type: accuracy
name: Nayini Test accuracy
value: 44.9
- type: accuracy
name: Munduruku Test accuracy
value: 12.3
- type: accuracy
name: Manx Test accuracy
value: 25.4
- type: accuracy
name: Skolt Sami Test accuracy
value: 29.9
- type: accuracy
name: Afrikaans Test accuracy
value: 89.3
- type: accuracy
name: Old Turkish Test accuracy
value: 37.1
- type: accuracy
name: Tupinamba Test accuracy
value: 23.1
- type: accuracy
name: Belarusian Test accuracy
value: 89.1
- type: accuracy
name: Serbian Test accuracy
value: 88.4
- type: accuracy
name: Moksha Test accuracy
value: 44.1
- type: accuracy
name: Western Armenian Test accuracy
value: 80.1
- type: accuracy
name: Scottish Gaelic Test accuracy
value: 59.0
- type: accuracy
name: Khunsari Test accuracy
value: 43.2
- type: accuracy
name: Hebrew Test accuracy
value: 90.6
- type: accuracy
name: Uyghur Test accuracy
value: 75.8
- type: accuracy
name: Chukchi Test accuracy
value: 32.6
---
# XLM-RoBERTa base Universal Dependencies v2.8 POS tagging: English
This model is part of our paper called:
- Make the Best of Cross-lingual Transfer: Evidence from POS Tagging with over 100 Languages
Check the [Space](https://huggingface.co/spaces/wietsedv/xpos) for more details.
## Usage
```python
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-en")
model = AutoModelForTokenClassification.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-en")
```
|
wietsedv/xlm-roberta-base-ft-udpos28-zh
|
wietsedv
| 2023-09-11T08:55:57Z | 108 | 0 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"xlm-roberta",
"token-classification",
"part-of-speech",
"zh",
"dataset:universal_dependencies",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-03-02T23:29:05Z |
---
language:
- zh
license: apache-2.0
library_name: transformers
tags:
- part-of-speech
- token-classification
datasets:
- universal_dependencies
metrics:
- accuracy
model-index:
- name: xlm-roberta-base-ft-udpos28-zh
results:
- task:
type: token-classification
name: Part-of-Speech Tagging
dataset:
type: universal_dependencies
name: Universal Dependencies v2.8
metrics:
- type: accuracy
name: English Test accuracy
value: 60.2
- type: accuracy
name: Dutch Test accuracy
value: 56.9
- type: accuracy
name: German Test accuracy
value: 57.5
- type: accuracy
name: Italian Test accuracy
value: 57.3
- type: accuracy
name: French Test accuracy
value: 54.1
- type: accuracy
name: Spanish Test accuracy
value: 54.4
- type: accuracy
name: Russian Test accuracy
value: 69.6
- type: accuracy
name: Swedish Test accuracy
value: 61.8
- type: accuracy
name: Norwegian Test accuracy
value: 60.3
- type: accuracy
name: Danish Test accuracy
value: 62.6
- type: accuracy
name: Low Saxon Test accuracy
value: 29.6
- type: accuracy
name: Akkadian Test accuracy
value: 16.3
- type: accuracy
name: Armenian Test accuracy
value: 70.7
- type: accuracy
name: Welsh Test accuracy
value: 52.3
- type: accuracy
name: Old East Slavic Test accuracy
value: 50.1
- type: accuracy
name: Albanian Test accuracy
value: 59.0
- type: accuracy
name: Slovenian Test accuracy
value: 52.9
- type: accuracy
name: Guajajara Test accuracy
value: 20.3
- type: accuracy
name: Kurmanji Test accuracy
value: 66.5
- type: accuracy
name: Turkish Test accuracy
value: 69.6
- type: accuracy
name: Finnish Test accuracy
value: 70.3
- type: accuracy
name: Indonesian Test accuracy
value: 65.8
- type: accuracy
name: Ukrainian Test accuracy
value: 69.4
- type: accuracy
name: Polish Test accuracy
value: 65.3
- type: accuracy
name: Portuguese Test accuracy
value: 60.6
- type: accuracy
name: Kazakh Test accuracy
value: 76.2
- type: accuracy
name: Latin Test accuracy
value: 60.5
- type: accuracy
name: Old French Test accuracy
value: 19.5
- type: accuracy
name: Buryat Test accuracy
value: 56.2
- type: accuracy
name: Kaapor Test accuracy
value: 10.4
- type: accuracy
name: Korean Test accuracy
value: 63.2
- type: accuracy
name: Estonian Test accuracy
value: 70.4
- type: accuracy
name: Croatian Test accuracy
value: 61.2
- type: accuracy
name: Gothic Test accuracy
value: 5.4
- type: accuracy
name: Swiss German Test accuracy
value: 36.2
- type: accuracy
name: Assyrian Test accuracy
value: 17.0
- type: accuracy
name: North Sami Test accuracy
value: 22.9
- type: accuracy
name: Naija Test accuracy
value: 21.5
- type: accuracy
name: Latvian Test accuracy
value: 74.1
- type: accuracy
name: Chinese Test accuracy
value: 93.4
- type: accuracy
name: Tagalog Test accuracy
value: 59.1
- type: accuracy
name: Bambara Test accuracy
value: 21.0
- type: accuracy
name: Lithuanian Test accuracy
value: 73.8
- type: accuracy
name: Galician Test accuracy
value: 56.7
- type: accuracy
name: Vietnamese Test accuracy
value: 59.6
- type: accuracy
name: Greek Test accuracy
value: 58.4
- type: accuracy
name: Catalan Test accuracy
value: 52.2
- type: accuracy
name: Czech Test accuracy
value: 64.6
- type: accuracy
name: Erzya Test accuracy
value: 39.4
- type: accuracy
name: Bhojpuri Test accuracy
value: 42.7
- type: accuracy
name: Thai Test accuracy
value: 65.6
- type: accuracy
name: Marathi Test accuracy
value: 74.2
- type: accuracy
name: Basque Test accuracy
value: 66.0
- type: accuracy
name: Slovak Test accuracy
value: 66.0
- type: accuracy
name: Kiche Test accuracy
value: 23.1
- type: accuracy
name: Yoruba Test accuracy
value: 16.4
- type: accuracy
name: Warlpiri Test accuracy
value: 29.6
- type: accuracy
name: Tamil Test accuracy
value: 82.6
- type: accuracy
name: Maltese Test accuracy
value: 13.7
- type: accuracy
name: Ancient Greek Test accuracy
value: 65.2
- type: accuracy
name: Icelandic Test accuracy
value: 63.4
- type: accuracy
name: Mbya Guarani Test accuracy
value: 23.2
- type: accuracy
name: Urdu Test accuracy
value: 53.8
- type: accuracy
name: Romanian Test accuracy
value: 61.2
- type: accuracy
name: Persian Test accuracy
value: 59.6
- type: accuracy
name: Apurina Test accuracy
value: 24.7
- type: accuracy
name: Japanese Test accuracy
value: 56.4
- type: accuracy
name: Hungarian Test accuracy
value: 59.9
- type: accuracy
name: Hindi Test accuracy
value: 59.4
- type: accuracy
name: Classical Chinese Test accuracy
value: 58.2
- type: accuracy
name: Komi Permyak Test accuracy
value: 34.7
- type: accuracy
name: Faroese Test accuracy
value: 55.9
- type: accuracy
name: Sanskrit Test accuracy
value: 19.0
- type: accuracy
name: Livvi Test accuracy
value: 52.8
- type: accuracy
name: Arabic Test accuracy
value: 64.2
- type: accuracy
name: Wolof Test accuracy
value: 17.6
- type: accuracy
name: Bulgarian Test accuracy
value: 64.2
- type: accuracy
name: Akuntsu Test accuracy
value: 16.5
- type: accuracy
name: Makurap Test accuracy
value: 6.8
- type: accuracy
name: Kangri Test accuracy
value: 38.9
- type: accuracy
name: Breton Test accuracy
value: 49.9
- type: accuracy
name: Telugu Test accuracy
value: 82.8
- type: accuracy
name: Cantonese Test accuracy
value: 80.6
- type: accuracy
name: Old Church Slavonic Test accuracy
value: 41.0
- type: accuracy
name: Karelian Test accuracy
value: 60.5
- type: accuracy
name: Upper Sorbian Test accuracy
value: 47.0
- type: accuracy
name: South Levantine Arabic Test accuracy
value: 59.7
- type: accuracy
name: Komi Zyrian Test accuracy
value: 29.4
- type: accuracy
name: Irish Test accuracy
value: 49.7
- type: accuracy
name: Nayini Test accuracy
value: 50.0
- type: accuracy
name: Munduruku Test accuracy
value: 10.6
- type: accuracy
name: Manx Test accuracy
value: 22.3
- type: accuracy
name: Skolt Sami Test accuracy
value: 24.9
- type: accuracy
name: Afrikaans Test accuracy
value: 58.6
- type: accuracy
name: Old Turkish Test accuracy
value: 45.7
- type: accuracy
name: Tupinamba Test accuracy
value: 20.7
- type: accuracy
name: Belarusian Test accuracy
value: 69.7
- type: accuracy
name: Serbian Test accuracy
value: 61.9
- type: accuracy
name: Moksha Test accuracy
value: 35.1
- type: accuracy
name: Western Armenian Test accuracy
value: 67.2
- type: accuracy
name: Scottish Gaelic Test accuracy
value: 44.6
- type: accuracy
name: Khunsari Test accuracy
value: 44.6
- type: accuracy
name: Hebrew Test accuracy
value: 82.3
- type: accuracy
name: Uyghur Test accuracy
value: 71.6
- type: accuracy
name: Chukchi Test accuracy
value: 32.1
---
# XLM-RoBERTa base Universal Dependencies v2.8 POS tagging: Chinese
This model is part of our paper called:
- Make the Best of Cross-lingual Transfer: Evidence from POS Tagging with over 100 Languages
Check the [Space](https://huggingface.co/spaces/wietsedv/xpos) for more details.
## Usage
```python
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-zh")
model = AutoModelForTokenClassification.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-zh")
```
|
wietsedv/xlm-roberta-base-ft-udpos28-cs
|
wietsedv
| 2023-09-11T08:55:51Z | 114 | 0 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"xlm-roberta",
"token-classification",
"part-of-speech",
"cs",
"dataset:universal_dependencies",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-03-02T23:29:05Z |
---
language:
- cs
license: apache-2.0
library_name: transformers
tags:
- part-of-speech
- token-classification
datasets:
- universal_dependencies
metrics:
- accuracy
model-index:
- name: xlm-roberta-base-ft-udpos28-cs
results:
- task:
type: token-classification
name: Part-of-Speech Tagging
dataset:
type: universal_dependencies
name: Universal Dependencies v2.8
metrics:
- type: accuracy
name: English Test accuracy
value: 83.4
- type: accuracy
name: Dutch Test accuracy
value: 83.9
- type: accuracy
name: German Test accuracy
value: 83.2
- type: accuracy
name: Italian Test accuracy
value: 81.5
- type: accuracy
name: French Test accuracy
value: 83.5
- type: accuracy
name: Spanish Test accuracy
value: 85.9
- type: accuracy
name: Russian Test accuracy
value: 91.2
- type: accuracy
name: Swedish Test accuracy
value: 88.3
- type: accuracy
name: Norwegian Test accuracy
value: 79.6
- type: accuracy
name: Danish Test accuracy
value: 85.4
- type: accuracy
name: Low Saxon Test accuracy
value: 55.4
- type: accuracy
name: Akkadian Test accuracy
value: 40.3
- type: accuracy
name: Armenian Test accuracy
value: 84.2
- type: accuracy
name: Welsh Test accuracy
value: 66.4
- type: accuracy
name: Old East Slavic Test accuracy
value: 76.6
- type: accuracy
name: Albanian Test accuracy
value: 76.8
- type: accuracy
name: Slovenian Test accuracy
value: 87.4
- type: accuracy
name: Guajajara Test accuracy
value: 37.3
- type: accuracy
name: Kurmanji Test accuracy
value: 79.3
- type: accuracy
name: Turkish Test accuracy
value: 77.5
- type: accuracy
name: Finnish Test accuracy
value: 83.3
- type: accuracy
name: Indonesian Test accuracy
value: 83.0
- type: accuracy
name: Ukrainian Test accuracy
value: 92.8
- type: accuracy
name: Polish Test accuracy
value: 92.1
- type: accuracy
name: Portuguese Test accuracy
value: 86.3
- type: accuracy
name: Kazakh Test accuracy
value: 80.2
- type: accuracy
name: Latin Test accuracy
value: 79.7
- type: accuracy
name: Old French Test accuracy
value: 59.4
- type: accuracy
name: Buryat Test accuracy
value: 60.3
- type: accuracy
name: Kaapor Test accuracy
value: 22.5
- type: accuracy
name: Korean Test accuracy
value: 58.9
- type: accuracy
name: Estonian Test accuracy
value: 85.7
- type: accuracy
name: Croatian Test accuracy
value: 94.9
- type: accuracy
name: Gothic Test accuracy
value: 27.2
- type: accuracy
name: Swiss German Test accuracy
value: 48.8
- type: accuracy
name: Assyrian Test accuracy
value: 15.0
- type: accuracy
name: North Sami Test accuracy
value: 43.4
- type: accuracy
name: Naija Test accuracy
value: 41.6
- type: accuracy
name: Latvian Test accuracy
value: 85.9
- type: accuracy
name: Chinese Test accuracy
value: 31.9
- type: accuracy
name: Tagalog Test accuracy
value: 72.0
- type: accuracy
name: Bambara Test accuracy
value: 27.8
- type: accuracy
name: Lithuanian Test accuracy
value: 86.5
- type: accuracy
name: Galician Test accuracy
value: 85.1
- type: accuracy
name: Vietnamese Test accuracy
value: 67.4
- type: accuracy
name: Greek Test accuracy
value: 84.2
- type: accuracy
name: Catalan Test accuracy
value: 84.6
- type: accuracy
name: Czech Test accuracy
value: 98.4
- type: accuracy
name: Erzya Test accuracy
value: 51.1
- type: accuracy
name: Bhojpuri Test accuracy
value: 48.7
- type: accuracy
name: Thai Test accuracy
value: 52.4
- type: accuracy
name: Marathi Test accuracy
value: 87.7
- type: accuracy
name: Basque Test accuracy
value: 74.0
- type: accuracy
name: Slovak Test accuracy
value: 95.8
- type: accuracy
name: Kiche Test accuracy
value: 36.8
- type: accuracy
name: Yoruba Test accuracy
value: 28.3
- type: accuracy
name: Warlpiri Test accuracy
value: 43.3
- type: accuracy
name: Tamil Test accuracy
value: 84.3
- type: accuracy
name: Maltese Test accuracy
value: 33.1
- type: accuracy
name: Ancient Greek Test accuracy
value: 59.5
- type: accuracy
name: Icelandic Test accuracy
value: 79.1
- type: accuracy
name: Mbya Guarani Test accuracy
value: 34.1
- type: accuracy
name: Urdu Test accuracy
value: 61.9
- type: accuracy
name: Romanian Test accuracy
value: 83.8
- type: accuracy
name: Persian Test accuracy
value: 80.1
- type: accuracy
name: Apurina Test accuracy
value: 48.4
- type: accuracy
name: Japanese Test accuracy
value: 19.4
- type: accuracy
name: Hungarian Test accuracy
value: 79.1
- type: accuracy
name: Hindi Test accuracy
value: 65.8
- type: accuracy
name: Classical Chinese Test accuracy
value: 15.7
- type: accuracy
name: Komi Permyak Test accuracy
value: 49.2
- type: accuracy
name: Faroese Test accuracy
value: 76.1
- type: accuracy
name: Sanskrit Test accuracy
value: 35.8
- type: accuracy
name: Livvi Test accuracy
value: 65.9
- type: accuracy
name: Arabic Test accuracy
value: 80.4
- type: accuracy
name: Wolof Test accuracy
value: 38.2
- type: accuracy
name: Bulgarian Test accuracy
value: 91.9
- type: accuracy
name: Akuntsu Test accuracy
value: 38.0
- type: accuracy
name: Makurap Test accuracy
value: 21.2
- type: accuracy
name: Kangri Test accuracy
value: 48.4
- type: accuracy
name: Breton Test accuracy
value: 58.2
- type: accuracy
name: Telugu Test accuracy
value: 82.1
- type: accuracy
name: Cantonese Test accuracy
value: 37.2
- type: accuracy
name: Old Church Slavonic Test accuracy
value: 48.4
- type: accuracy
name: Karelian Test accuracy
value: 69.5
- type: accuracy
name: Upper Sorbian Test accuracy
value: 81.0
- type: accuracy
name: South Levantine Arabic Test accuracy
value: 68.7
- type: accuracy
name: Komi Zyrian Test accuracy
value: 44.4
- type: accuracy
name: Irish Test accuracy
value: 67.8
- type: accuracy
name: Nayini Test accuracy
value: 47.4
- type: accuracy
name: Munduruku Test accuracy
value: 28.1
- type: accuracy
name: Manx Test accuracy
value: 37.6
- type: accuracy
name: Skolt Sami Test accuracy
value: 40.2
- type: accuracy
name: Afrikaans Test accuracy
value: 78.2
- type: accuracy
name: Old Turkish Test accuracy
value: 37.1
- type: accuracy
name: Tupinamba Test accuracy
value: 43.5
- type: accuracy
name: Belarusian Test accuracy
value: 90.1
- type: accuracy
name: Serbian Test accuracy
value: 96.0
- type: accuracy
name: Moksha Test accuracy
value: 48.5
- type: accuracy
name: Western Armenian Test accuracy
value: 74.9
- type: accuracy
name: Scottish Gaelic Test accuracy
value: 58.0
- type: accuracy
name: Khunsari Test accuracy
value: 39.2
- type: accuracy
name: Hebrew Test accuracy
value: 87.5
- type: accuracy
name: Uyghur Test accuracy
value: 72.1
- type: accuracy
name: Chukchi Test accuracy
value: 35.4
---
# XLM-RoBERTa base Universal Dependencies v2.8 POS tagging: Czech
This model is part of our paper called:
- Make the Best of Cross-lingual Transfer: Evidence from POS Tagging with over 100 Languages
Check the [Space](https://huggingface.co/spaces/wietsedv/xpos) for more details.
## Usage
```python
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-cs")
model = AutoModelForTokenClassification.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-cs")
```
|
wietsedv/xlm-roberta-base-ft-udpos28-grc
|
wietsedv
| 2023-09-11T08:55:42Z | 112 | 0 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"xlm-roberta",
"token-classification",
"part-of-speech",
"grc",
"dataset:universal_dependencies",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-03-02T23:29:05Z |
---
language:
- grc
license: apache-2.0
library_name: transformers
tags:
- part-of-speech
- token-classification
datasets:
- universal_dependencies
metrics:
- accuracy
model-index:
- name: xlm-roberta-base-ft-udpos28-grc
results:
- task:
type: token-classification
name: Part-of-Speech Tagging
dataset:
type: universal_dependencies
name: Universal Dependencies v2.8
metrics:
- type: accuracy
name: English Test accuracy
value: 58.3
- type: accuracy
name: Dutch Test accuracy
value: 57.1
- type: accuracy
name: German Test accuracy
value: 61.3
- type: accuracy
name: Italian Test accuracy
value: 56.6
- type: accuracy
name: French Test accuracy
value: 57.3
- type: accuracy
name: Spanish Test accuracy
value: 54.5
- type: accuracy
name: Russian Test accuracy
value: 71.1
- type: accuracy
name: Swedish Test accuracy
value: 62.9
- type: accuracy
name: Norwegian Test accuracy
value: 59.9
- type: accuracy
name: Danish Test accuracy
value: 61.6
- type: accuracy
name: Low Saxon Test accuracy
value: 45.3
- type: accuracy
name: Akkadian Test accuracy
value: 38.9
- type: accuracy
name: Armenian Test accuracy
value: 69.4
- type: accuracy
name: Welsh Test accuracy
value: 57.9
- type: accuracy
name: Old East Slavic Test accuracy
value: 68.0
- type: accuracy
name: Albanian Test accuracy
value: 63.3
- type: accuracy
name: Slovenian Test accuracy
value: 58.2
- type: accuracy
name: Guajajara Test accuracy
value: 26.5
- type: accuracy
name: Kurmanji Test accuracy
value: 62.0
- type: accuracy
name: Turkish Test accuracy
value: 66.5
- type: accuracy
name: Finnish Test accuracy
value: 70.3
- type: accuracy
name: Indonesian Test accuracy
value: 59.7
- type: accuracy
name: Ukrainian Test accuracy
value: 72.6
- type: accuracy
name: Polish Test accuracy
value: 70.3
- type: accuracy
name: Portuguese Test accuracy
value: 59.7
- type: accuracy
name: Kazakh Test accuracy
value: 71.0
- type: accuracy
name: Latin Test accuracy
value: 68.8
- type: accuracy
name: Old French Test accuracy
value: 49.4
- type: accuracy
name: Buryat Test accuracy
value: 56.4
- type: accuracy
name: Kaapor Test accuracy
value: 27.9
- type: accuracy
name: Korean Test accuracy
value: 55.5
- type: accuracy
name: Estonian Test accuracy
value: 70.0
- type: accuracy
name: Croatian Test accuracy
value: 64.8
- type: accuracy
name: Gothic Test accuracy
value: 33.9
- type: accuracy
name: Swiss German Test accuracy
value: 47.2
- type: accuracy
name: Assyrian Test accuracy
value: 29.1
- type: accuracy
name: North Sami Test accuracy
value: 37.4
- type: accuracy
name: Naija Test accuracy
value: 37.2
- type: accuracy
name: Latvian Test accuracy
value: 74.5
- type: accuracy
name: Chinese Test accuracy
value: 56.6
- type: accuracy
name: Tagalog Test accuracy
value: 57.6
- type: accuracy
name: Bambara Test accuracy
value: 28.6
- type: accuracy
name: Lithuanian Test accuracy
value: 77.4
- type: accuracy
name: Galician Test accuracy
value: 61.6
- type: accuracy
name: Vietnamese Test accuracy
value: 63.7
- type: accuracy
name: Greek Test accuracy
value: 63.3
- type: accuracy
name: Catalan Test accuracy
value: 54.2
- type: accuracy
name: Czech Test accuracy
value: 70.1
- type: accuracy
name: Erzya Test accuracy
value: 46.7
- type: accuracy
name: Bhojpuri Test accuracy
value: 43.7
- type: accuracy
name: Thai Test accuracy
value: 61.1
- type: accuracy
name: Marathi Test accuracy
value: 75.5
- type: accuracy
name: Basque Test accuracy
value: 63.3
- type: accuracy
name: Slovak Test accuracy
value: 67.3
- type: accuracy
name: Kiche Test accuracy
value: 29.7
- type: accuracy
name: Yoruba Test accuracy
value: 30.4
- type: accuracy
name: Warlpiri Test accuracy
value: 49.4
- type: accuracy
name: Tamil Test accuracy
value: 68.7
- type: accuracy
name: Maltese Test accuracy
value: 29.6
- type: accuracy
name: Ancient Greek Test accuracy
value: 89.6
- type: accuracy
name: Icelandic Test accuracy
value: 63.6
- type: accuracy
name: Mbya Guarani Test accuracy
value: 36.4
- type: accuracy
name: Urdu Test accuracy
value: 44.8
- type: accuracy
name: Romanian Test accuracy
value: 66.3
- type: accuracy
name: Persian Test accuracy
value: 64.4
- type: accuracy
name: Apurina Test accuracy
value: 41.7
- type: accuracy
name: Japanese Test accuracy
value: 44.3
- type: accuracy
name: Hungarian Test accuracy
value: 61.4
- type: accuracy
name: Hindi Test accuracy
value: 47.8
- type: accuracy
name: Classical Chinese Test accuracy
value: 48.0
- type: accuracy
name: Komi Permyak Test accuracy
value: 45.9
- type: accuracy
name: Faroese Test accuracy
value: 59.2
- type: accuracy
name: Sanskrit Test accuracy
value: 42.9
- type: accuracy
name: Livvi Test accuracy
value: 61.8
- type: accuracy
name: Arabic Test accuracy
value: 65.3
- type: accuracy
name: Wolof Test accuracy
value: 27.8
- type: accuracy
name: Bulgarian Test accuracy
value: 64.9
- type: accuracy
name: Akuntsu Test accuracy
value: 30.8
- type: accuracy
name: Makurap Test accuracy
value: 18.5
- type: accuracy
name: Kangri Test accuracy
value: 45.9
- type: accuracy
name: Breton Test accuracy
value: 47.1
- type: accuracy
name: Telugu Test accuracy
value: 75.3
- type: accuracy
name: Cantonese Test accuracy
value: 60.2
- type: accuracy
name: Old Church Slavonic Test accuracy
value: 58.8
- type: accuracy
name: Karelian Test accuracy
value: 64.5
- type: accuracy
name: Upper Sorbian Test accuracy
value: 62.9
- type: accuracy
name: South Levantine Arabic Test accuracy
value: 61.7
- type: accuracy
name: Komi Zyrian Test accuracy
value: 45.4
- type: accuracy
name: Irish Test accuracy
value: 52.4
- type: accuracy
name: Nayini Test accuracy
value: 51.3
- type: accuracy
name: Munduruku Test accuracy
value: 21.6
- type: accuracy
name: Manx Test accuracy
value: 27.1
- type: accuracy
name: Skolt Sami Test accuracy
value: 44.7
- type: accuracy
name: Afrikaans Test accuracy
value: 58.4
- type: accuracy
name: Old Turkish Test accuracy
value: 37.1
- type: accuracy
name: Tupinamba Test accuracy
value: 44.4
- type: accuracy
name: Belarusian Test accuracy
value: 75.3
- type: accuracy
name: Serbian Test accuracy
value: 63.3
- type: accuracy
name: Moksha Test accuracy
value: 46.1
- type: accuracy
name: Western Armenian Test accuracy
value: 67.1
- type: accuracy
name: Scottish Gaelic Test accuracy
value: 49.2
- type: accuracy
name: Khunsari Test accuracy
value: 45.9
- type: accuracy
name: Hebrew Test accuracy
value: 72.9
- type: accuracy
name: Uyghur Test accuracy
value: 72.7
- type: accuracy
name: Chukchi Test accuracy
value: 40.2
---
# XLM-RoBERTa base Universal Dependencies v2.8 POS tagging: Ancient Greek
This model is part of our paper called:
- Make the Best of Cross-lingual Transfer: Evidence from POS Tagging with over 100 Languages
Check the [Space](https://huggingface.co/spaces/wietsedv/xpos) for more details.
## Usage
```python
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-grc")
model = AutoModelForTokenClassification.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-grc")
```
|
GroNLP/mdebertav3-subjectivity-multilingual
|
GroNLP
| 2023-09-11T08:55:38Z | 915 | 2 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"deberta-v2",
"text-classification",
"subjectivity",
"newspapers",
"CLEF2023",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-07-12T09:22:51Z |
---
tags:
- subjectivity
- newspapers
- CLEF2023
---
Fine-tuned [mDeBERTa V3](https://huggingface.co/microsoft/mdeberta-v3-base) model for subjectivity detection in newspaper sentences.
This model was developed as part of the CLEF 2023 CheckThat! Lab [Task 2: Subjectivity in News Articles](https://checkthat.gitlab.io/clef2023/task2/).
The goal in this task is to detect whether a sentence is objective (OBJ) or subjective (SUBJ). A sentence is subjective if its content is based on or influenced by personal feelings, tastes, or
opinions. Otherwise, the sentence is objective. [(Antici et al., 2023)](https://ceur-ws.org/Vol-3370/paper10.pdf).
The model was fine-tuned using a multilingual training and development dataset, for which the following (hyper)parameters were utilized:
```
Batch Size = 64
Max Epochs = 8
Learning Rate = 3e-5
Warmup Steps = 500
Weight Decay = 0.3
```
The model ranked second in the CheckThat! Lab and obtained a macro F1 of 0.81 and a SUBJ F1 of 0.81.
|
GroNLP/mdebertav3-subjectivity-dutch
|
GroNLP
| 2023-09-11T08:55:02Z | 106 | 0 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"deberta-v2",
"text-classification",
"subjectivity",
"newspapers",
"CLEF2023",
"nl",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-07-12T09:56:48Z |
---
language:
- nl
tags:
- subjectivity
- newspapers
- CLEF2023
---
Fine-tuned [mDeBERTa V3](https://huggingface.co/microsoft/mdeberta-v3-base) model for subjectivity detection in newspaper sentences.
This model was developed as part of the CLEF 2023 CheckThat! Lab [Task 2: Subjectivity in News Articles](https://checkthat.gitlab.io/clef2023/task2/).
The goal in this task is to detect whether a sentence is objective (OBJ) or subjective (SUBJ). A sentence is subjective if its content is based on or influenced by personal feelings, tastes, or
opinions. Otherwise, the sentence is objective. [(Antici et al., 2023)](https://ceur-ws.org/Vol-3370/paper10.pdf).
The model was fine-tuned using a multilingual training and Dutch development dataset, for which the following (hyper)parameters were utilized:
```
Batch Size = 64
Max Epochs = 6
Learning Rate = 4e-5
Warmup Steps = 100
Weight Decay = 0.2
```
The model ranked first in the CheckThat! Lab and obtained a macro F1 of 0.81 and a SUBJ F1 of 0.80.
|
wietsedv/wav2vec2-large-xlsr-53-dutch
|
wietsedv
| 2023-09-11T08:54:57Z | 1,989 | 0 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"nl",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language: nl
datasets:
- common_voice
metrics:
- wer
tags:
- audio
- automatic-speech-recognition
- speech
- xlsr-fine-tuning-week
license: apache-2.0
model-index:
- name: Dutch XLSR Wav2Vec2 Large 53 by Wietse de Vries
results:
- task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice nl
type: common_voice
args: nl
metrics:
- name: Test WER
type: wer
value: 17.09
---
# Wav2Vec2-Large-XLSR-53-Dutch
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Dutch using the [Common Voice](https://huggingface.co/datasets/common_voice) dataset.
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (without a language model) as follows:
```python
import torch
import torchaudio
from datasets import load_dataset
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
test_dataset = load_dataset("common_voice", "nl", split="test[:2%]")
processor = Wav2Vec2Processor.from_pretrained("wietsedv/wav2vec2-large-xlsr-53-dutch")
model = Wav2Vec2ForCTC.from_pretrained("wietsedv/wav2vec2-large-xlsr-53-dutch")
resampler = torchaudio.transforms.Resample(48_000, 16_000)
# Preprocessing the datasets.
# We need to read the aduio files as arrays
def speech_file_to_array_fn(batch):
speech_array, sampling_rate = torchaudio.load(batch["path"])
batch["speech"] = resampler(speech_array).squeeze().numpy()
return batch
test_dataset = test_dataset.map(speech_file_to_array_fn)
inputs = processor(test_dataset["speech"][:2], sampling_rate=16_000, return_tensors="pt", padding=True)
with torch.no_grad():
logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits
predicted_ids = torch.argmax(logits, dim=-1)
print("Prediction:", processor.batch_decode(predicted_ids))
print("Reference:", test_dataset["sentence"][:2])
```
## Evaluation
The model can be evaluated as follows on the Dutch test data of Common Voice.
```python
import torch
import torchaudio
from datasets import load_dataset, load_metric
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import re
test_dataset = load_dataset("common_voice", "nl", split="test")
wer = load_metric("wer")
processor = Wav2Vec2Processor.from_pretrained("wietsedv/wav2vec2-large-xlsr-53-dutch")
model = Wav2Vec2ForCTC.from_pretrained("wietsedv/wav2vec2-large-xlsr-53-dutch")
model.to("cuda")
chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\'\“\%\‘\”]'
resampler = torchaudio.transforms.Resample(48_000, 16_000)
# Preprocessing the datasets.
# We need to read the aduio files as arrays
def speech_file_to_array_fn(batch):
batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower()
speech_array, sampling_rate = torchaudio.load(batch["path"])
batch["speech"] = resampler(speech_array).squeeze().numpy()
return batch
test_dataset = test_dataset.map(speech_file_to_array_fn)
# Preprocessing the datasets.
# We need to read the aduio files as arrays
def evaluate(batch):
inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
with torch.no_grad():
logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits
pred_ids = torch.argmax(logits, dim=-1)
batch["pred_strings"] = processor.batch_decode(pred_ids)
return batch
result = test_dataset.map(evaluate, batched=True, batch_size=8)
print("WER: {:.2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"])))
```
**Test Result**: 17.09 %
## Training
The Common Voice `train` and `validation` datasets were used for training.
|
wietsedv/xlm-roberta-base-ft-udpos28-af
|
wietsedv
| 2023-09-11T08:54:52Z | 111 | 2 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"xlm-roberta",
"token-classification",
"part-of-speech",
"af",
"dataset:universal_dependencies",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-03-02T23:29:05Z |
---
language:
- af
license: apache-2.0
library_name: transformers
tags:
- part-of-speech
- token-classification
datasets:
- universal_dependencies
metrics:
- accuracy
model-index:
- name: xlm-roberta-base-ft-udpos28-af
results:
- task:
type: token-classification
name: Part-of-Speech Tagging
dataset:
type: universal_dependencies
name: Universal Dependencies v2.8
metrics:
- type: accuracy
name: English Test accuracy
value: 85.8
- type: accuracy
name: Dutch Test accuracy
value: 83.7
- type: accuracy
name: German Test accuracy
value: 83.6
- type: accuracy
name: Italian Test accuracy
value: 84.4
- type: accuracy
name: French Test accuracy
value: 83.1
- type: accuracy
name: Spanish Test accuracy
value: 86.7
- type: accuracy
name: Russian Test accuracy
value: 86.4
- type: accuracy
name: Swedish Test accuracy
value: 87.7
- type: accuracy
name: Norwegian Test accuracy
value: 81.3
- type: accuracy
name: Danish Test accuracy
value: 86.8
- type: accuracy
name: Low Saxon Test accuracy
value: 62.5
- type: accuracy
name: Akkadian Test accuracy
value: 28.6
- type: accuracy
name: Armenian Test accuracy
value: 82.7
- type: accuracy
name: Welsh Test accuracy
value: 70.3
- type: accuracy
name: Old East Slavic Test accuracy
value: 72.5
- type: accuracy
name: Albanian Test accuracy
value: 79.4
- type: accuracy
name: Slovenian Test accuracy
value: 76.6
- type: accuracy
name: Guajajara Test accuracy
value: 23.2
- type: accuracy
name: Kurmanji Test accuracy
value: 74.7
- type: accuracy
name: Turkish Test accuracy
value: 72.8
- type: accuracy
name: Finnish Test accuracy
value: 83.9
- type: accuracy
name: Indonesian Test accuracy
value: 79.5
- type: accuracy
name: Ukrainian Test accuracy
value: 84.0
- type: accuracy
name: Polish Test accuracy
value: 85.6
- type: accuracy
name: Portuguese Test accuracy
value: 85.5
- type: accuracy
name: Kazakh Test accuracy
value: 77.5
- type: accuracy
name: Latin Test accuracy
value: 76.2
- type: accuracy
name: Old French Test accuracy
value: 58.4
- type: accuracy
name: Buryat Test accuracy
value: 59.7
- type: accuracy
name: Kaapor Test accuracy
value: 23.8
- type: accuracy
name: Korean Test accuracy
value: 59.4
- type: accuracy
name: Estonian Test accuracy
value: 86.7
- type: accuracy
name: Croatian Test accuracy
value: 86.4
- type: accuracy
name: Gothic Test accuracy
value: 20.7
- type: accuracy
name: Swiss German Test accuracy
value: 55.5
- type: accuracy
name: Assyrian Test accuracy
value: 17.2
- type: accuracy
name: North Sami Test accuracy
value: 38.8
- type: accuracy
name: Naija Test accuracy
value: 39.3
- type: accuracy
name: Latvian Test accuracy
value: 83.0
- type: accuracy
name: Chinese Test accuracy
value: 49.8
- type: accuracy
name: Tagalog Test accuracy
value: 71.7
- type: accuracy
name: Bambara Test accuracy
value: 29.9
- type: accuracy
name: Lithuanian Test accuracy
value: 82.8
- type: accuracy
name: Galician Test accuracy
value: 83.6
- type: accuracy
name: Vietnamese Test accuracy
value: 60.3
- type: accuracy
name: Greek Test accuracy
value: 83.3
- type: accuracy
name: Catalan Test accuracy
value: 86.1
- type: accuracy
name: Czech Test accuracy
value: 85.1
- type: accuracy
name: Erzya Test accuracy
value: 43.6
- type: accuracy
name: Bhojpuri Test accuracy
value: 50.1
- type: accuracy
name: Thai Test accuracy
value: 62.5
- type: accuracy
name: Marathi Test accuracy
value: 87.1
- type: accuracy
name: Basque Test accuracy
value: 76.2
- type: accuracy
name: Slovak Test accuracy
value: 84.8
- type: accuracy
name: Kiche Test accuracy
value: 34.1
- type: accuracy
name: Yoruba Test accuracy
value: 26.4
- type: accuracy
name: Warlpiri Test accuracy
value: 39.7
- type: accuracy
name: Tamil Test accuracy
value: 81.0
- type: accuracy
name: Maltese Test accuracy
value: 24.2
- type: accuracy
name: Ancient Greek Test accuracy
value: 59.3
- type: accuracy
name: Icelandic Test accuracy
value: 82.6
- type: accuracy
name: Mbya Guarani Test accuracy
value: 31.3
- type: accuracy
name: Urdu Test accuracy
value: 63.2
- type: accuracy
name: Romanian Test accuracy
value: 81.4
- type: accuracy
name: Persian Test accuracy
value: 75.4
- type: accuracy
name: Apurina Test accuracy
value: 32.2
- type: accuracy
name: Japanese Test accuracy
value: 35.9
- type: accuracy
name: Hungarian Test accuracy
value: 84.9
- type: accuracy
name: Hindi Test accuracy
value: 70.2
- type: accuracy
name: Classical Chinese Test accuracy
value: 30.5
- type: accuracy
name: Komi Permyak Test accuracy
value: 46.0
- type: accuracy
name: Faroese Test accuracy
value: 76.5
- type: accuracy
name: Sanskrit Test accuracy
value: 32.4
- type: accuracy
name: Livvi Test accuracy
value: 66.5
- type: accuracy
name: Arabic Test accuracy
value: 79.7
- type: accuracy
name: Wolof Test accuracy
value: 31.8
- type: accuracy
name: Bulgarian Test accuracy
value: 87.0
- type: accuracy
name: Akuntsu Test accuracy
value: 24.4
- type: accuracy
name: Makurap Test accuracy
value: 15.1
- type: accuracy
name: Kangri Test accuracy
value: 49.6
- type: accuracy
name: Breton Test accuracy
value: 62.0
- type: accuracy
name: Telugu Test accuracy
value: 82.2
- type: accuracy
name: Cantonese Test accuracy
value: 52.4
- type: accuracy
name: Old Church Slavonic Test accuracy
value: 51.0
- type: accuracy
name: Karelian Test accuracy
value: 73.1
- type: accuracy
name: Upper Sorbian Test accuracy
value: 74.2
- type: accuracy
name: South Levantine Arabic Test accuracy
value: 69.3
- type: accuracy
name: Komi Zyrian Test accuracy
value: 37.3
- type: accuracy
name: Irish Test accuracy
value: 66.3
- type: accuracy
name: Nayini Test accuracy
value: 47.4
- type: accuracy
name: Munduruku Test accuracy
value: 19.0
- type: accuracy
name: Manx Test accuracy
value: 39.6
- type: accuracy
name: Skolt Sami Test accuracy
value: 33.0
- type: accuracy
name: Afrikaans Test accuracy
value: 98.9
- type: accuracy
name: Old Turkish Test accuracy
value: 37.1
- type: accuracy
name: Tupinamba Test accuracy
value: 25.9
- type: accuracy
name: Belarusian Test accuracy
value: 86.4
- type: accuracy
name: Serbian Test accuracy
value: 87.0
- type: accuracy
name: Moksha Test accuracy
value: 42.9
- type: accuracy
name: Western Armenian Test accuracy
value: 80.0
- type: accuracy
name: Scottish Gaelic Test accuracy
value: 59.4
- type: accuracy
name: Khunsari Test accuracy
value: 37.8
- type: accuracy
name: Hebrew Test accuracy
value: 84.4
- type: accuracy
name: Uyghur Test accuracy
value: 73.3
- type: accuracy
name: Chukchi Test accuracy
value: 33.3
---
# XLM-RoBERTa base Universal Dependencies v2.8 POS tagging: Afrikaans
This model is part of our paper called:
- Make the Best of Cross-lingual Transfer: Evidence from POS Tagging with over 100 Languages
Check the [Space](https://huggingface.co/spaces/wietsedv/xpos) for more details.
## Usage
```python
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-af")
model = AutoModelForTokenClassification.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-af")
```
|
GroNLP/mdebertav3-subjectivity-italian
|
GroNLP
| 2023-09-11T08:54:22Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"deberta-v2",
"text-classification",
"subjectivity",
"newspapers",
"CLEF2023",
"it",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-07-12T09:30:48Z |
---
language:
- it
tags:
- subjectivity
- newspapers
- CLEF2023
---
Fine-tuned [mDeBERTa V3](https://huggingface.co/microsoft/mdeberta-v3-base) model for subjectivity detection in newspaper sentences.
This model was developed as part of the CLEF 2023 CheckThat! Lab [Task 2: Subjectivity in News Articles](https://checkthat.gitlab.io/clef2023/task2/).
The goal in this task is to detect whether a sentence is objective (OBJ) or subjective (SUBJ). A sentence is subjective if its content is based on or influenced by personal feelings, tastes, or
opinions. Otherwise, the sentence is objective. [(Antici et al., 2023)](https://ceur-ws.org/Vol-3370/paper10.pdf).
The model was fine-tuned using a multilingual training and Italian development dataset, for which the following (hyper)parameters were utilized:
```
Batch Size = 32
Max Epochs = 2
Learning Rate = 5e-5
Warmup Steps = 300
Weight Decay = 0
```
The model ranked first in the CheckThat! Lab and obtained a macro F1 of 0.76 and a SUBJ F1 of 0.65.
|
Subsets and Splits
Filtered Qwen2.5 Distill Models
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