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| library_name
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chaanks/asr-whisper-tiny-sb
|
chaanks
| 2023-08-02T09:04:49Z | 7 | 0 |
speechbrain
|
[
"speechbrain",
"whisper",
"pytorch",
"Transformer",
"hf-asr-leaderboard",
"automatic-speech-recognition",
"en",
"license:apache-2.0",
"model-index",
"region:us"
] |
automatic-speech-recognition
| 2023-08-01T11:53:52Z |
---
language:
- en
thumbnail: null
pipeline_tag: automatic-speech-recognition
tags:
- whisper
- pytorch
- speechbrain
- Transformer
- hf-asr-leaderboard
license: apache-2.0
model-index:
- name: asr-whisper-tiny-sb
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: LibriSpeech (clean)
type: librispeech_asr
config: clean
split: test
args:
language: en
metrics:
- name: Test WER
type: wer
value: 7.54
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: LibriSpeech (other)
type: librispeech_asr
config: other
split: test
args:
language: en
metrics:
- name: Test WER
type: wer
value: 17.15
---
<iframe src="https://ghbtns.com/github-btn.html?user=speechbrain&repo=speechbrain&type=star&count=true&size=large&v=2" frameborder="0" scrolling="0" width="170" height="30" title="GitHub"></iframe>
<br/><br/>
# Whisper tiny SpeechBrain
This repository provides all the necessary tools to perform automatic speech
recognition from an end-to-end whisper model within
SpeechBrain. Please note that this is not an official Speechbrain repository.
## Install SpeechBrain
First of all, please install tranformers and SpeechBrain with the following command:
```
pip install speechbrain transformers==4.28.0
```
Please notice that we encourage you to read our tutorials and learn more about
[SpeechBrain](https://speechbrain.github.io).
### Transcribing your own audio files
```python
from speechbrain.pretrained import WhisperASR
asr_model = WhisperASR.from_hparams(source="chaanks/asr-whisper-tiny-sb", savedir="pretrained_models/asr-whisper-tiny-sb")
asr_model.transcribe_file("chaanks/asr-whisper-tiny-sb/example.wav")
```
### Inference on GPU
To perform inference on the GPU, add `run_opts={"device":"cuda"}` when calling the `from_hparams` method.
### Limitations
The SpeechBrain team does not provide any warranty on the performance achieved by this model when used on other datasets.
#### Referencing SpeechBrain
```
@misc{SB2021,
author = {Ravanelli, Mirco and Parcollet, Titouan and Rouhe, Aku and Plantinga, Peter and Rastorgueva, Elena and Lugosch, Loren and Dawalatabad, Nauman and Ju-Chieh, Chou and Heba, Abdel and Grondin, Francois and Aris, William and Liao, Chien-Feng and Cornell, Samuele and Yeh, Sung-Lin and Na, Hwidong and Gao, Yan and Fu, Szu-Wei and Subakan, Cem and De Mori, Renato and Bengio, Yoshua },
title = {SpeechBrain},
year = {2021},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\\\\url{https://github.com/speechbrain/speechbrain}},
}
```
#### About SpeechBrain
SpeechBrain is an open-source and all-in-one speech toolkit. It is designed to be simple, extremely flexible, and user-friendly. Competitive or state-of-the-art performance is obtained in various domains.
Website: https://speechbrain.github.io/
GitHub: https://github.com/speechbrain/speechbrain
|
DataPrime/ppo-LunarLander-v2
|
DataPrime
| 2023-08-02T09:03:56Z | 4 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-08-02T09:03:35Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 264.25 +/- 27.96
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
...
```
|
dev-ninja/tsel_distilgpt
|
dev-ninja
| 2023-08-02T08:59:19Z | 136 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"base_model:distilbert/distilgpt2",
"base_model:finetune:distilbert/distilgpt2",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-08-02T08:55:46Z |
---
license: apache-2.0
base_model: distilgpt2
tags:
- generated_from_trainer
model-index:
- name: tsel_distilgpt
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. -->
# tsel_distilgpt
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 5.6157
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 1 | 5.9501 |
| No log | 2.0 | 2 | 5.8630 |
| No log | 3.0 | 3 | 5.7924 |
| No log | 4.0 | 4 | 5.7383 |
| No log | 5.0 | 5 | 5.6969 |
| No log | 6.0 | 6 | 5.6665 |
| No log | 7.0 | 7 | 5.6445 |
| No log | 8.0 | 8 | 5.6297 |
| No log | 9.0 | 9 | 5.6202 |
| No log | 10.0 | 10 | 5.6157 |
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.2
- Tokenizers 0.13.3
|
Yaopu/translate-scratch-kde4-en-to-fr
|
Yaopu
| 2023-08-02T08:58:35Z | 101 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"translation",
"generated_from_trainer",
"dataset:kde4",
"base_model:Helsinki-NLP/opus-mt-en-fr",
"base_model:finetune:Helsinki-NLP/opus-mt-en-fr",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
translation
| 2023-08-01T06:24:34Z |
---
license: apache-2.0
base_model: Helsinki-NLP/opus-mt-en-fr
tags:
- translation
- generated_from_trainer
datasets:
- kde4
model-index:
- name: translate-scratch-kde4-en-to-fr
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# translate-scratch-kde4-en-to-fr
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-fr](https://huggingface.co/Helsinki-NLP/opus-mt-en-fr) on the kde4 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: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.2
- Tokenizers 0.13.3
|
Zekunli/bart-large-extraction-all-cnndm_2000-ep5
|
Zekunli
| 2023-08-02T08:57:48Z | 114 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bart",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-08-02T08:46:12Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: bart-large-extraction-all-cnndm_2000-ep5
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. -->
# bart-large-extraction-all-cnndm_2000-ep5
This model is a fine-tuned version of [facebook/bart-large](https://huggingface.co/facebook/bart-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8572
- Hint Hit Num: 1.91
- Hint Precision: 0.3668
- Num: 5.066
- Gen Len: 20.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 24
- eval_batch_size: 72
- seed: 1799
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Hint Hit Num | Hint Precision | Num | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------------:|:--------------:|:-----:|:-------:|
| 2.2212 | 1.19 | 100 | 1.8652 | 1.86 | 0.3764 | 4.826 | 20.0 |
| 1.8571 | 2.38 | 200 | 1.8548 | 1.948 | 0.3838 | 4.936 | 20.0 |
| 1.6716 | 3.57 | 300 | 1.8468 | 1.894 | 0.3677 | 5.01 | 20.0 |
| 1.5749 | 4.76 | 400 | 1.8559 | 1.918 | 0.3695 | 5.066 | 20.0 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0+cu113
- Datasets 2.5.1
- Tokenizers 0.12.1
|
AnonymousAuthorsforICSE2024/LLM4FIN
|
AnonymousAuthorsforICSE2024
| 2023-08-02T08:44:59Z | 0 | 0 | null |
[
"license:mit",
"region:us"
] | null | 2023-08-02T06:55:45Z |
---
license: mit
---
# Introduction
This is model for LLM4FIN, including two models based on rule filtering model and rule element extraction model.
To run the rule filtering model, use
```
model = AutoModelForSequenceClassification.from_pretrained("rule_filtering", num_labels=3)
tokenizer = AutoTokenizer.from_pretrained("rule_filtering")
```
to load the model and tokenizer.
To run the rule element extraction model, use
```
model = AutoModelForTokenClassification.from_pretrained("rule_element_extraction", num_labels=num_labels)
tokenizer = AutoTokenizer.from_pretrained("rule_element_extraction")
```
to load the model and tokenizer.
|
mjpesavento/bert-swag-finetuned
|
mjpesavento
| 2023-08-02T08:40:33Z | 104 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"multiple-choice",
"generated_from_trainer",
"dataset:swag",
"base_model:google-bert/bert-base-uncased",
"base_model:finetune:google-bert/bert-base-uncased",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
multiple-choice
| 2023-08-02T08:31:44Z |
---
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
datasets:
- swag
metrics:
- accuracy
model-index:
- name: bert-swag-finetuned
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-swag-finetuned
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the swag dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0460
- Accuracy: 0.7895
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.7705 | 1.0 | 4597 | 0.5834 | 0.7698 |
| 0.3724 | 2.0 | 9194 | 0.6170 | 0.7845 |
| 0.1456 | 3.0 | 13791 | 1.0460 | 0.7895 |
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.2
- Tokenizers 0.13.3
|
simonycl/bert-base-uncased-sst-2-32-100
|
simonycl
| 2023-08-02T08:32:01Z | 106 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:google-bert/bert-base-uncased",
"base_model:finetune:google-bert/bert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-08-02T08:26:42Z |
---
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: bert-base-uncased-sst-2-32-100
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased-sst-2-32-100
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4379
- Accuracy: 0.9219
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 150
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 2 | 0.5385 | 0.9219 |
| No log | 2.0 | 4 | 0.5392 | 0.9219 |
| No log | 3.0 | 6 | 0.5398 | 0.9219 |
| No log | 4.0 | 8 | 0.5410 | 0.9219 |
| 0.733 | 5.0 | 10 | 0.5426 | 0.9219 |
| 0.733 | 6.0 | 12 | 0.5443 | 0.9062 |
| 0.733 | 7.0 | 14 | 0.5461 | 0.9062 |
| 0.733 | 8.0 | 16 | 0.5481 | 0.9062 |
| 0.733 | 9.0 | 18 | 0.5487 | 0.9062 |
| 0.6383 | 10.0 | 20 | 0.5495 | 0.9062 |
| 0.6383 | 11.0 | 22 | 0.5546 | 0.8906 |
| 0.6383 | 12.0 | 24 | 0.5643 | 0.9062 |
| 0.6383 | 13.0 | 26 | 0.5742 | 0.9062 |
| 0.6383 | 14.0 | 28 | 0.5875 | 0.9062 |
| 0.4993 | 15.0 | 30 | 0.5982 | 0.9062 |
| 0.4993 | 16.0 | 32 | 0.6100 | 0.9062 |
| 0.4993 | 17.0 | 34 | 0.6222 | 0.9062 |
| 0.4993 | 18.0 | 36 | 0.6263 | 0.9062 |
| 0.4993 | 19.0 | 38 | 0.6305 | 0.9062 |
| 0.4891 | 20.0 | 40 | 0.6335 | 0.9062 |
| 0.4891 | 21.0 | 42 | 0.6368 | 0.9062 |
| 0.4891 | 22.0 | 44 | 0.6351 | 0.9062 |
| 0.4891 | 23.0 | 46 | 0.6301 | 0.9062 |
| 0.4891 | 24.0 | 48 | 0.6212 | 0.9062 |
| 0.377 | 25.0 | 50 | 0.6100 | 0.9062 |
| 0.377 | 26.0 | 52 | 0.5999 | 0.9062 |
| 0.377 | 27.0 | 54 | 0.5852 | 0.9062 |
| 0.377 | 28.0 | 56 | 0.5737 | 0.9062 |
| 0.377 | 29.0 | 58 | 0.5606 | 0.9219 |
| 0.3369 | 30.0 | 60 | 0.5466 | 0.9062 |
| 0.3369 | 31.0 | 62 | 0.5319 | 0.9062 |
| 0.3369 | 32.0 | 64 | 0.5205 | 0.9062 |
| 0.3369 | 33.0 | 66 | 0.5074 | 0.9219 |
| 0.3369 | 34.0 | 68 | 0.5025 | 0.9219 |
| 0.19 | 35.0 | 70 | 0.4984 | 0.9219 |
| 0.19 | 36.0 | 72 | 0.4934 | 0.9219 |
| 0.19 | 37.0 | 74 | 0.4927 | 0.9375 |
| 0.19 | 38.0 | 76 | 0.4955 | 0.9375 |
| 0.19 | 39.0 | 78 | 0.4968 | 0.9375 |
| 0.0507 | 40.0 | 80 | 0.4956 | 0.9375 |
| 0.0507 | 41.0 | 82 | 0.4882 | 0.9375 |
| 0.0507 | 42.0 | 84 | 0.4784 | 0.9375 |
| 0.0507 | 43.0 | 86 | 0.4710 | 0.9219 |
| 0.0507 | 44.0 | 88 | 0.4650 | 0.9219 |
| 0.0102 | 45.0 | 90 | 0.4578 | 0.9219 |
| 0.0102 | 46.0 | 92 | 0.4540 | 0.9219 |
| 0.0102 | 47.0 | 94 | 0.4566 | 0.9062 |
| 0.0102 | 48.0 | 96 | 0.4682 | 0.9062 |
| 0.0102 | 49.0 | 98 | 0.4831 | 0.9219 |
| 0.0026 | 50.0 | 100 | 0.4922 | 0.9219 |
| 0.0026 | 51.0 | 102 | 0.4985 | 0.9219 |
| 0.0026 | 52.0 | 104 | 0.5029 | 0.9219 |
| 0.0026 | 53.0 | 106 | 0.5062 | 0.9219 |
| 0.0026 | 54.0 | 108 | 0.5087 | 0.9219 |
| 0.001 | 55.0 | 110 | 0.5100 | 0.9219 |
| 0.001 | 56.0 | 112 | 0.5110 | 0.9219 |
| 0.001 | 57.0 | 114 | 0.5112 | 0.9219 |
| 0.001 | 58.0 | 116 | 0.5112 | 0.9219 |
| 0.001 | 59.0 | 118 | 0.5110 | 0.9219 |
| 0.0004 | 60.0 | 120 | 0.5087 | 0.9219 |
| 0.0004 | 61.0 | 122 | 0.5028 | 0.9219 |
| 0.0004 | 62.0 | 124 | 0.4965 | 0.9219 |
| 0.0004 | 63.0 | 126 | 0.4903 | 0.9219 |
| 0.0004 | 64.0 | 128 | 0.4848 | 0.9219 |
| 0.0003 | 65.0 | 130 | 0.4802 | 0.9219 |
| 0.0003 | 66.0 | 132 | 0.4767 | 0.9219 |
| 0.0003 | 67.0 | 134 | 0.4739 | 0.9219 |
| 0.0003 | 68.0 | 136 | 0.4719 | 0.9219 |
| 0.0003 | 69.0 | 138 | 0.4707 | 0.9219 |
| 0.0024 | 70.0 | 140 | 0.4600 | 0.9219 |
| 0.0024 | 71.0 | 142 | 0.4439 | 0.9219 |
| 0.0024 | 72.0 | 144 | 0.4336 | 0.9062 |
| 0.0024 | 73.0 | 146 | 0.4283 | 0.9062 |
| 0.0024 | 74.0 | 148 | 0.4253 | 0.9219 |
| 0.0002 | 75.0 | 150 | 0.4237 | 0.9219 |
| 0.0002 | 76.0 | 152 | 0.4232 | 0.9375 |
| 0.0002 | 77.0 | 154 | 0.4230 | 0.9375 |
| 0.0002 | 78.0 | 156 | 0.4229 | 0.9375 |
| 0.0002 | 79.0 | 158 | 0.4228 | 0.9375 |
| 0.0002 | 80.0 | 160 | 0.4228 | 0.9375 |
| 0.0002 | 81.0 | 162 | 0.4225 | 0.9375 |
| 0.0002 | 82.0 | 164 | 0.4237 | 0.9062 |
| 0.0002 | 83.0 | 166 | 0.4384 | 0.9219 |
| 0.0002 | 84.0 | 168 | 0.4565 | 0.9219 |
| 0.0004 | 85.0 | 170 | 0.4717 | 0.9219 |
| 0.0004 | 86.0 | 172 | 0.4813 | 0.9219 |
| 0.0004 | 87.0 | 174 | 0.4858 | 0.9219 |
| 0.0004 | 88.0 | 176 | 0.4885 | 0.9219 |
| 0.0004 | 89.0 | 178 | 0.4897 | 0.9219 |
| 0.0002 | 90.0 | 180 | 0.4904 | 0.9219 |
| 0.0002 | 91.0 | 182 | 0.4865 | 0.9219 |
| 0.0002 | 92.0 | 184 | 0.4732 | 0.9219 |
| 0.0002 | 93.0 | 186 | 0.4557 | 0.9219 |
| 0.0002 | 94.0 | 188 | 0.4388 | 0.9219 |
| 0.0053 | 95.0 | 190 | 0.4254 | 0.9219 |
| 0.0053 | 96.0 | 192 | 0.4171 | 0.9219 |
| 0.0053 | 97.0 | 194 | 0.4132 | 0.9375 |
| 0.0053 | 98.0 | 196 | 0.4118 | 0.9375 |
| 0.0053 | 99.0 | 198 | 0.4115 | 0.9219 |
| 0.0002 | 100.0 | 200 | 0.4118 | 0.9219 |
| 0.0002 | 101.0 | 202 | 0.4122 | 0.9219 |
| 0.0002 | 102.0 | 204 | 0.4125 | 0.9219 |
| 0.0002 | 103.0 | 206 | 0.4128 | 0.9219 |
| 0.0002 | 104.0 | 208 | 0.4131 | 0.9219 |
| 0.0002 | 105.0 | 210 | 0.4133 | 0.9219 |
| 0.0002 | 106.0 | 212 | 0.4134 | 0.9219 |
| 0.0002 | 107.0 | 214 | 0.4140 | 0.9219 |
| 0.0002 | 108.0 | 216 | 0.4149 | 0.9219 |
| 0.0002 | 109.0 | 218 | 0.4158 | 0.9219 |
| 0.0002 | 110.0 | 220 | 0.4167 | 0.9219 |
| 0.0002 | 111.0 | 222 | 0.4175 | 0.9219 |
| 0.0002 | 112.0 | 224 | 0.4183 | 0.9375 |
| 0.0002 | 113.0 | 226 | 0.4190 | 0.9375 |
| 0.0002 | 114.0 | 228 | 0.4197 | 0.9375 |
| 0.0001 | 115.0 | 230 | 0.4203 | 0.9375 |
| 0.0001 | 116.0 | 232 | 0.4208 | 0.9375 |
| 0.0001 | 117.0 | 234 | 0.4218 | 0.9219 |
| 0.0001 | 118.0 | 236 | 0.4228 | 0.9219 |
| 0.0001 | 119.0 | 238 | 0.4237 | 0.9219 |
| 0.0002 | 120.0 | 240 | 0.4244 | 0.9219 |
| 0.0002 | 121.0 | 242 | 0.4251 | 0.9219 |
| 0.0002 | 122.0 | 244 | 0.4257 | 0.9219 |
| 0.0002 | 123.0 | 246 | 0.4263 | 0.9219 |
| 0.0002 | 124.0 | 248 | 0.4269 | 0.9219 |
| 0.0002 | 125.0 | 250 | 0.4273 | 0.9219 |
| 0.0002 | 126.0 | 252 | 0.4277 | 0.9219 |
| 0.0002 | 127.0 | 254 | 0.4280 | 0.9219 |
| 0.0002 | 128.0 | 256 | 0.4284 | 0.9219 |
| 0.0002 | 129.0 | 258 | 0.4287 | 0.9219 |
| 0.0008 | 130.0 | 260 | 0.4330 | 0.9219 |
| 0.0008 | 131.0 | 262 | 0.4554 | 0.9219 |
| 0.0008 | 132.0 | 264 | 0.4714 | 0.9219 |
| 0.0008 | 133.0 | 266 | 0.4845 | 0.9375 |
| 0.0008 | 134.0 | 268 | 0.5000 | 0.9219 |
| 0.0001 | 135.0 | 270 | 0.5167 | 0.9219 |
| 0.0001 | 136.0 | 272 | 0.5308 | 0.9062 |
| 0.0001 | 137.0 | 274 | 0.5417 | 0.9062 |
| 0.0001 | 138.0 | 276 | 0.5480 | 0.9062 |
| 0.0001 | 139.0 | 278 | 0.5529 | 0.9062 |
| 0.0001 | 140.0 | 280 | 0.5566 | 0.9062 |
| 0.0001 | 141.0 | 282 | 0.5570 | 0.9062 |
| 0.0001 | 142.0 | 284 | 0.5565 | 0.9062 |
| 0.0001 | 143.0 | 286 | 0.5555 | 0.9062 |
| 0.0001 | 144.0 | 288 | 0.5544 | 0.9062 |
| 0.0001 | 145.0 | 290 | 0.5511 | 0.9062 |
| 0.0001 | 146.0 | 292 | 0.5096 | 0.9219 |
| 0.0001 | 147.0 | 294 | 0.4811 | 0.9375 |
| 0.0001 | 148.0 | 296 | 0.4624 | 0.9219 |
| 0.0001 | 149.0 | 298 | 0.4488 | 0.9219 |
| 0.0002 | 150.0 | 300 | 0.4379 | 0.9219 |
### Framework versions
- Transformers 4.32.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.4.0
- Tokenizers 0.13.3
|
simonycl/bert-base-uncased-sst-2-32-87
|
simonycl
| 2023-08-02T08:26:28Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:google-bert/bert-base-uncased",
"base_model:finetune:google-bert/bert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-08-02T08:21:03Z |
---
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: bert-base-uncased-sst-2-32-87
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased-sst-2-32-87
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9995
- Accuracy: 0.875
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 150
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 2 | 1.3036 | 0.8281 |
| No log | 2.0 | 4 | 1.3032 | 0.8281 |
| No log | 3.0 | 6 | 1.3022 | 0.8281 |
| No log | 4.0 | 8 | 1.3002 | 0.8438 |
| 0.6888 | 5.0 | 10 | 1.2981 | 0.8438 |
| 0.6888 | 6.0 | 12 | 1.2958 | 0.8438 |
| 0.6888 | 7.0 | 14 | 1.2937 | 0.8438 |
| 0.6888 | 8.0 | 16 | 1.2916 | 0.8438 |
| 0.6888 | 9.0 | 18 | 1.2896 | 0.8281 |
| 0.6235 | 10.0 | 20 | 1.2880 | 0.8281 |
| 0.6235 | 11.0 | 22 | 1.2862 | 0.8281 |
| 0.6235 | 12.0 | 24 | 1.2847 | 0.8281 |
| 0.6235 | 13.0 | 26 | 1.2833 | 0.8281 |
| 0.6235 | 14.0 | 28 | 1.2827 | 0.8281 |
| 0.6224 | 15.0 | 30 | 1.2813 | 0.8281 |
| 0.6224 | 16.0 | 32 | 1.2788 | 0.8281 |
| 0.6224 | 17.0 | 34 | 1.2739 | 0.8281 |
| 0.6224 | 18.0 | 36 | 1.2670 | 0.8281 |
| 0.6224 | 19.0 | 38 | 1.2583 | 0.8281 |
| 0.5366 | 20.0 | 40 | 1.2501 | 0.8281 |
| 0.5366 | 21.0 | 42 | 1.2366 | 0.8281 |
| 0.5366 | 22.0 | 44 | 1.2258 | 0.8281 |
| 0.5366 | 23.0 | 46 | 1.2148 | 0.8281 |
| 0.5366 | 24.0 | 48 | 1.2069 | 0.8281 |
| 0.3634 | 25.0 | 50 | 1.1973 | 0.8281 |
| 0.3634 | 26.0 | 52 | 1.1888 | 0.8281 |
| 0.3634 | 27.0 | 54 | 1.1754 | 0.8281 |
| 0.3634 | 28.0 | 56 | 1.1583 | 0.8281 |
| 0.3634 | 29.0 | 58 | 1.1462 | 0.8281 |
| 0.3447 | 30.0 | 60 | 1.1399 | 0.8281 |
| 0.3447 | 31.0 | 62 | 1.1399 | 0.8281 |
| 0.3447 | 32.0 | 64 | 1.1328 | 0.8281 |
| 0.3447 | 33.0 | 66 | 1.1304 | 0.8281 |
| 0.3447 | 34.0 | 68 | 1.1275 | 0.8281 |
| 0.2231 | 35.0 | 70 | 1.1185 | 0.8281 |
| 0.2231 | 36.0 | 72 | 1.1059 | 0.8281 |
| 0.2231 | 37.0 | 74 | 1.0901 | 0.8281 |
| 0.2231 | 38.0 | 76 | 1.0711 | 0.8281 |
| 0.2231 | 39.0 | 78 | 1.0516 | 0.8281 |
| 0.0925 | 40.0 | 80 | 1.0339 | 0.8281 |
| 0.0925 | 41.0 | 82 | 1.0151 | 0.8281 |
| 0.0925 | 42.0 | 84 | 0.9910 | 0.8281 |
| 0.0925 | 43.0 | 86 | 0.9616 | 0.8281 |
| 0.0925 | 44.0 | 88 | 0.9422 | 0.8281 |
| 0.024 | 45.0 | 90 | 0.9346 | 0.8281 |
| 0.024 | 46.0 | 92 | 0.9374 | 0.8281 |
| 0.024 | 47.0 | 94 | 0.9413 | 0.8438 |
| 0.024 | 48.0 | 96 | 0.9460 | 0.8438 |
| 0.024 | 49.0 | 98 | 0.9470 | 0.8438 |
| 0.0161 | 50.0 | 100 | 0.9483 | 0.8438 |
| 0.0161 | 51.0 | 102 | 0.9505 | 0.8438 |
| 0.0161 | 52.0 | 104 | 0.9534 | 0.8438 |
| 0.0161 | 53.0 | 106 | 0.9565 | 0.8438 |
| 0.0161 | 54.0 | 108 | 0.9591 | 0.8438 |
| 0.0003 | 55.0 | 110 | 0.9613 | 0.8438 |
| 0.0003 | 56.0 | 112 | 0.9609 | 0.8438 |
| 0.0003 | 57.0 | 114 | 0.9606 | 0.8438 |
| 0.0003 | 58.0 | 116 | 0.9597 | 0.8438 |
| 0.0003 | 59.0 | 118 | 0.9582 | 0.8438 |
| 0.0003 | 60.0 | 120 | 0.9572 | 0.8438 |
| 0.0003 | 61.0 | 122 | 0.9557 | 0.8438 |
| 0.0003 | 62.0 | 124 | 0.9563 | 0.8438 |
| 0.0003 | 63.0 | 126 | 0.9514 | 0.8438 |
| 0.0003 | 64.0 | 128 | 0.9487 | 0.8438 |
| 0.0006 | 65.0 | 130 | 0.9472 | 0.8438 |
| 0.0006 | 66.0 | 132 | 0.9472 | 0.8438 |
| 0.0006 | 67.0 | 134 | 0.9486 | 0.8438 |
| 0.0006 | 68.0 | 136 | 0.9471 | 0.8438 |
| 0.0006 | 69.0 | 138 | 0.9569 | 0.8438 |
| 0.0008 | 70.0 | 140 | 0.9658 | 0.8438 |
| 0.0008 | 71.0 | 142 | 0.9732 | 0.8438 |
| 0.0008 | 72.0 | 144 | 0.9792 | 0.8438 |
| 0.0008 | 73.0 | 146 | 0.9836 | 0.8438 |
| 0.0008 | 74.0 | 148 | 0.9813 | 0.8438 |
| 0.0003 | 75.0 | 150 | 0.9750 | 0.8281 |
| 0.0003 | 76.0 | 152 | 0.9712 | 0.8281 |
| 0.0003 | 77.0 | 154 | 0.9636 | 0.8281 |
| 0.0003 | 78.0 | 156 | 0.9525 | 0.8281 |
| 0.0003 | 79.0 | 158 | 0.9410 | 0.8281 |
| 0.001 | 80.0 | 160 | 0.9323 | 0.8438 |
| 0.001 | 81.0 | 162 | 0.9256 | 0.8438 |
| 0.001 | 82.0 | 164 | 0.9293 | 0.8438 |
| 0.001 | 83.0 | 166 | 0.9429 | 0.8281 |
| 0.001 | 84.0 | 168 | 0.9565 | 0.8281 |
| 0.0002 | 85.0 | 170 | 0.9687 | 0.8281 |
| 0.0002 | 86.0 | 172 | 0.9796 | 0.8281 |
| 0.0002 | 87.0 | 174 | 0.9900 | 0.8281 |
| 0.0002 | 88.0 | 176 | 0.9985 | 0.8281 |
| 0.0002 | 89.0 | 178 | 1.0049 | 0.8281 |
| 0.0002 | 90.0 | 180 | 1.0099 | 0.8281 |
| 0.0002 | 91.0 | 182 | 1.0139 | 0.8281 |
| 0.0002 | 92.0 | 184 | 1.0170 | 0.8281 |
| 0.0002 | 93.0 | 186 | 1.0196 | 0.8281 |
| 0.0002 | 94.0 | 188 | 1.0218 | 0.8281 |
| 0.0002 | 95.0 | 190 | 1.0236 | 0.8281 |
| 0.0002 | 96.0 | 192 | 1.0250 | 0.8281 |
| 0.0002 | 97.0 | 194 | 1.0258 | 0.8281 |
| 0.0002 | 98.0 | 196 | 1.0262 | 0.8281 |
| 0.0002 | 99.0 | 198 | 1.0266 | 0.8281 |
| 0.0002 | 100.0 | 200 | 1.0274 | 0.8281 |
| 0.0002 | 101.0 | 202 | 1.0280 | 0.8281 |
| 0.0002 | 102.0 | 204 | 1.0286 | 0.8281 |
| 0.0002 | 103.0 | 206 | 1.0293 | 0.8281 |
| 0.0002 | 104.0 | 208 | 1.0298 | 0.8281 |
| 0.0001 | 105.0 | 210 | 1.0303 | 0.8281 |
| 0.0001 | 106.0 | 212 | 1.0309 | 0.8281 |
| 0.0001 | 107.0 | 214 | 1.0315 | 0.8281 |
| 0.0001 | 108.0 | 216 | 1.0318 | 0.8281 |
| 0.0001 | 109.0 | 218 | 1.0182 | 0.8281 |
| 0.0025 | 110.0 | 220 | 0.9797 | 0.8281 |
| 0.0025 | 111.0 | 222 | 0.9486 | 0.8438 |
| 0.0025 | 112.0 | 224 | 0.9379 | 0.8594 |
| 0.0025 | 113.0 | 226 | 0.9381 | 0.8594 |
| 0.0025 | 114.0 | 228 | 0.9421 | 0.8594 |
| 0.0002 | 115.0 | 230 | 0.9449 | 0.8594 |
| 0.0002 | 116.0 | 232 | 0.9477 | 0.8594 |
| 0.0002 | 117.0 | 234 | 0.9504 | 0.8594 |
| 0.0002 | 118.0 | 236 | 0.9531 | 0.8594 |
| 0.0002 | 119.0 | 238 | 0.9563 | 0.8594 |
| 0.0002 | 120.0 | 240 | 0.9597 | 0.8438 |
| 0.0002 | 121.0 | 242 | 0.9630 | 0.8438 |
| 0.0002 | 122.0 | 244 | 0.9902 | 0.8438 |
| 0.0002 | 123.0 | 246 | 0.9989 | 0.8438 |
| 0.0002 | 124.0 | 248 | 1.0010 | 0.8281 |
| 0.0007 | 125.0 | 250 | 1.0085 | 0.8438 |
| 0.0007 | 126.0 | 252 | 1.0163 | 0.8438 |
| 0.0007 | 127.0 | 254 | 1.0225 | 0.8438 |
| 0.0007 | 128.0 | 256 | 1.0279 | 0.8594 |
| 0.0007 | 129.0 | 258 | 1.0322 | 0.8594 |
| 0.0001 | 130.0 | 260 | 1.0336 | 0.8594 |
| 0.0001 | 131.0 | 262 | 1.0348 | 0.8594 |
| 0.0001 | 132.0 | 264 | 1.0358 | 0.8594 |
| 0.0001 | 133.0 | 266 | 1.0367 | 0.8594 |
| 0.0001 | 134.0 | 268 | 1.0300 | 0.8438 |
| 0.0005 | 135.0 | 270 | 1.0190 | 0.8438 |
| 0.0005 | 136.0 | 272 | 1.0185 | 0.8281 |
| 0.0005 | 137.0 | 274 | 1.0266 | 0.8438 |
| 0.0005 | 138.0 | 276 | 1.0311 | 0.8438 |
| 0.0005 | 139.0 | 278 | 1.0318 | 0.8438 |
| 0.0001 | 140.0 | 280 | 1.0306 | 0.8438 |
| 0.0001 | 141.0 | 282 | 1.0295 | 0.8281 |
| 0.0001 | 142.0 | 284 | 1.0286 | 0.8438 |
| 0.0001 | 143.0 | 286 | 1.0278 | 0.8438 |
| 0.0001 | 144.0 | 288 | 1.0272 | 0.8438 |
| 0.0001 | 145.0 | 290 | 1.0268 | 0.8438 |
| 0.0001 | 146.0 | 292 | 1.0266 | 0.8438 |
| 0.0001 | 147.0 | 294 | 1.0264 | 0.8438 |
| 0.0001 | 148.0 | 296 | 1.0265 | 0.8438 |
| 0.0001 | 149.0 | 298 | 0.9917 | 0.8594 |
| 0.0002 | 150.0 | 300 | 0.9995 | 0.875 |
### Framework versions
- Transformers 4.32.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.4.0
- Tokenizers 0.13.3
|
mhmd2125/whisper-small-hi
|
mhmd2125
| 2023-08-02T08:24:52Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"base_model:openai/whisper-small",
"base_model:finetune:openai/whisper-small",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-05-08T10:01:18Z |
---
license: apache-2.0
base_model: openai/whisper-small
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: whisper-small-hi
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-small-hi
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2396
- Wer: 0.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 5
- training_steps: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| No log | 10.0 | 10 | 2.7433 | 92.3077 |
| No log | 20.0 | 20 | 1.2396 | 0.0 |
### Framework versions
- Transformers 4.32.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.14.2
- Tokenizers 0.13.3
|
FelixChao/vicuna-7b-instruct-ft-adapters-chemical1.2
|
FelixChao
| 2023-08-02T08:21:02Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-02T08:20:58Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.5.0.dev0
- PEFT 0.5.0.dev0
|
kengamd/clip-roberta-finetuned
|
kengamd
| 2023-08-02T08:19:09Z | 17 | 0 |
transformers
|
[
"transformers",
"pytorch",
"vision-text-dual-encoder",
"feature-extraction",
"generated_from_trainer",
"dataset:MP",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2023-07-24T17:13:25Z |
---
base_model: ./clip-roberta
tags:
- generated_from_trainer
datasets:
- MP
model-index:
- name: clip-roberta-finetuned
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. -->
# clip-roberta-finetuned
This model is a fine-tuned version of [./clip-roberta](https://huggingface.co/./clip-roberta) on the MP dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6548
## 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: 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
### Framework versions
- Transformers 4.32.0.dev0
- Pytorch 2.0.1+cu117
- Datasets 2.13.1
- Tokenizers 0.13.3
|
jariasn/rl_course_vizdoom_health_gathering_supreme
|
jariasn
| 2023-08-02T08:15:56Z | 0 | 0 |
sample-factory
|
[
"sample-factory",
"tensorboard",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-08-02T08:15:50Z |
---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: doom_health_gathering_supreme
type: doom_health_gathering_supreme
metrics:
- type: mean_reward
value: 11.86 +/- 5.57
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **doom_health_gathering_supreme** environment.
This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory.
Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/
## Downloading the model
After installing Sample-Factory, download the model with:
```
python -m sample_factory.huggingface.load_from_hub -r jariasn/rl_course_vizdoom_health_gathering_supreme
```
## Using the model
To run the model after download, use the `enjoy` script corresponding to this environment:
```
python -m .usr.local.lib.python3.10.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme
```
You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag.
See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details
## Training with this model
To continue training with this model, use the `train` script corresponding to this environment:
```
python -m .usr.local.lib.python3.10.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme --restart_behavior=resume --train_for_env_steps=10000000000
```
Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
|
Li/roberta-base-squad2
|
Li
| 2023-08-02T08:13:11Z | 144 | 0 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"roberta",
"question-answering",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-03-02T23:29:04Z |
[roberta-base](https://huggingface.co/roberta-base) fine-tuned on the [SQuAD2](https://rajpurkar.github.io/SQuAD-explorer) dataset for 2 epochs.
The fine-tuning process was performed on a single NVIDIA Tesla T4 GPU (15GB). The hyperparameters are:
```
max_seq_length=512
per_device_train_batch_size=8
gradient_accumulation_steps=4
total train batch size (w. parallel, distributed & accumulation) = 32
learning_rate=3e-5
```
## Evaluation results
```
"eval_exact": 80.33352985766024,
"eval_f1": 83.38322909593009,
"eval_HasAns_exact": 77.81713900134953,
"eval_HasAns_f1": 83.925283241562,
"eval_HasAns_total": 5928,
"eval_NoAns_exact": 82.84272497897393,
"eval_NoAns_f1": 82.84272497897393,
"eval_NoAns_total": 5945,
"eval_best_exact": 80.33352985766024,
"eval_best_exact_thresh": 0.0,
"eval_best_f1": 83.38322909593005,
"eval_best_f1_thresh": 0.0,
"eval_samples": 11955,
"eval_total": 11873,
```
## More information
Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading passage, or the question might be unanswerable.
SQuAD2.0 combines the 100,000 questions in SQuAD1.1 with over 50,000 unanswerable questions written adversarially by crowdworkers to look similar to answerable ones. To do well on SQuAD2.0, systems must not only answer questions when possible, but also determine when no answer is supported by the paragraph and abstain from answering. (https://rajpurkar.github.io/SQuAD-explorer/)
|
digiplay/bluePencilRealistic_v05
|
digiplay
| 2023-08-02T08:12:37Z | 892 | 6 |
diffusers
|
[
"diffusers",
"safetensors",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-06-19T00:09:45Z |
---
license: other
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
inference: true
---
Model info: 💖☺️Lovely Cute Model💞
https://huggingface.co/bluepen5805/blue_pencil_realistic
https://civitai.com/models/88941?modelVersionId=97200
Original Author's DEMO images:
Sample image I made :
|
casque/realisticVisionV51_v51VAE
|
casque
| 2023-08-02T08:09:38Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-08-02T07:53:55Z |
---
license: creativeml-openrail-m
---
|
s3nh/Hermes-LLongMA-2-7b-8k-GGML
|
s3nh
| 2023-08-02T08:07:43Z | 0 | 0 |
transformers
|
[
"transformers",
"text-generation",
"en",
"license:openrail",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-08-02T07:44:07Z |
---
license: openrail
language:
- en
pipeline_tag: text-generation
library_name: transformers
---
## Original model card
Buy me a coffee if you like this project ;)
<a href="https://www.buymeacoffee.com/s3nh"><img src="https://www.buymeacoffee.com/assets/img/guidelines/download-assets-sm-1.svg" alt=""></a>
#### Description
GGML Format model files for [This project](conceptofmind/Hermes-LLongMA-2-7b-8k).
### inference
```python
import ctransformers
from ctransformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained(output_dir, ggml_file,
gpu_layers=32, model_type="llama")
manual_input: str = "Tell me about your last dream, please."
llm(manual_input,
max_new_tokens=256,
temperature=0.9,
top_p= 0.7)
```
# Original model card
|
waliaMuskaan011/whisper-largev2-hindi-02
|
waliaMuskaan011
| 2023-08-02T08:06:12Z | 1 | 0 |
peft
|
[
"peft",
"pytorch",
"whisper",
"region:us"
] | null | 2023-08-02T07:55:42Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
### Framework versions
- PEFT 0.5.0.dev0
- PEFT 0.4.0.dev0
|
simonycl/bert-base-uncased-sst-2-16-87
|
simonycl
| 2023-08-02T08:00:09Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:FacebookAI/roberta-large",
"base_model:finetune:FacebookAI/roberta-large",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-08-02T07:57:06Z |
---
license: mit
base_model: roberta-large
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: roberta-large-sst-2-16-13
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-large-sst-2-16-13
This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4022
- Accuracy: 0.7812
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 150
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 1 | 0.6926 | 0.5 |
| No log | 2.0 | 2 | 0.6926 | 0.5 |
| No log | 3.0 | 3 | 0.6926 | 0.5 |
| No log | 4.0 | 4 | 0.6926 | 0.5 |
| No log | 5.0 | 5 | 0.6926 | 0.5 |
| No log | 6.0 | 6 | 0.6926 | 0.5 |
| No log | 7.0 | 7 | 0.6925 | 0.5 |
| No log | 8.0 | 8 | 0.6925 | 0.5 |
| No log | 9.0 | 9 | 0.6925 | 0.5 |
| 0.6898 | 10.0 | 10 | 0.6925 | 0.5 |
| 0.6898 | 11.0 | 11 | 0.6924 | 0.5 |
| 0.6898 | 12.0 | 12 | 0.6924 | 0.5 |
| 0.6898 | 13.0 | 13 | 0.6924 | 0.5 |
| 0.6898 | 14.0 | 14 | 0.6924 | 0.5 |
| 0.6898 | 15.0 | 15 | 0.6923 | 0.5 |
| 0.6898 | 16.0 | 16 | 0.6923 | 0.5 |
| 0.6898 | 17.0 | 17 | 0.6922 | 0.5 |
| 0.6898 | 18.0 | 18 | 0.6922 | 0.5 |
| 0.6898 | 19.0 | 19 | 0.6922 | 0.5 |
| 0.694 | 20.0 | 20 | 0.6921 | 0.5 |
| 0.694 | 21.0 | 21 | 0.6921 | 0.5 |
| 0.694 | 22.0 | 22 | 0.6920 | 0.5 |
| 0.694 | 23.0 | 23 | 0.6920 | 0.5 |
| 0.694 | 24.0 | 24 | 0.6920 | 0.5 |
| 0.694 | 25.0 | 25 | 0.6919 | 0.5 |
| 0.694 | 26.0 | 26 | 0.6919 | 0.5 |
| 0.694 | 27.0 | 27 | 0.6918 | 0.5 |
| 0.694 | 28.0 | 28 | 0.6918 | 0.5 |
| 0.694 | 29.0 | 29 | 0.6918 | 0.5 |
| 0.7021 | 30.0 | 30 | 0.6917 | 0.5 |
| 0.7021 | 31.0 | 31 | 0.6916 | 0.5 |
| 0.7021 | 32.0 | 32 | 0.6916 | 0.5 |
| 0.7021 | 33.0 | 33 | 0.6916 | 0.5 |
| 0.7021 | 34.0 | 34 | 0.6915 | 0.5 |
| 0.7021 | 35.0 | 35 | 0.6915 | 0.5 |
| 0.7021 | 36.0 | 36 | 0.6914 | 0.5 |
| 0.7021 | 37.0 | 37 | 0.6914 | 0.5 |
| 0.7021 | 38.0 | 38 | 0.6913 | 0.5 |
| 0.7021 | 39.0 | 39 | 0.6913 | 0.5 |
| 0.6798 | 40.0 | 40 | 0.6913 | 0.5 |
| 0.6798 | 41.0 | 41 | 0.6912 | 0.5 |
| 0.6798 | 42.0 | 42 | 0.6911 | 0.5 |
| 0.6798 | 43.0 | 43 | 0.6910 | 0.5 |
| 0.6798 | 44.0 | 44 | 0.6909 | 0.5 |
| 0.6798 | 45.0 | 45 | 0.6908 | 0.5 |
| 0.6798 | 46.0 | 46 | 0.6907 | 0.5 |
| 0.6798 | 47.0 | 47 | 0.6906 | 0.5 |
| 0.6798 | 48.0 | 48 | 0.6905 | 0.5 |
| 0.6798 | 49.0 | 49 | 0.6903 | 0.5 |
| 0.6874 | 50.0 | 50 | 0.6902 | 0.5 |
| 0.6874 | 51.0 | 51 | 0.6901 | 0.5 |
| 0.6874 | 52.0 | 52 | 0.6899 | 0.5 |
| 0.6874 | 53.0 | 53 | 0.6898 | 0.5 |
| 0.6874 | 54.0 | 54 | 0.6896 | 0.5 |
| 0.6874 | 55.0 | 55 | 0.6895 | 0.5 |
| 0.6874 | 56.0 | 56 | 0.6894 | 0.5 |
| 0.6874 | 57.0 | 57 | 0.6893 | 0.5 |
| 0.6874 | 58.0 | 58 | 0.6892 | 0.5 |
| 0.6874 | 59.0 | 59 | 0.6890 | 0.5 |
| 0.6878 | 60.0 | 60 | 0.6889 | 0.5 |
| 0.6878 | 61.0 | 61 | 0.6888 | 0.5 |
| 0.6878 | 62.0 | 62 | 0.6886 | 0.5 |
| 0.6878 | 63.0 | 63 | 0.6885 | 0.5 |
| 0.6878 | 64.0 | 64 | 0.6884 | 0.5 |
| 0.6878 | 65.0 | 65 | 0.6884 | 0.5 |
| 0.6878 | 66.0 | 66 | 0.6883 | 0.5 |
| 0.6878 | 67.0 | 67 | 0.6882 | 0.5 |
| 0.6878 | 68.0 | 68 | 0.6882 | 0.5 |
| 0.6878 | 69.0 | 69 | 0.6881 | 0.5 |
| 0.6805 | 70.0 | 70 | 0.6880 | 0.5312 |
| 0.6805 | 71.0 | 71 | 0.6878 | 0.5312 |
| 0.6805 | 72.0 | 72 | 0.6877 | 0.5312 |
| 0.6805 | 73.0 | 73 | 0.6874 | 0.5312 |
| 0.6805 | 74.0 | 74 | 0.6872 | 0.5312 |
| 0.6805 | 75.0 | 75 | 0.6870 | 0.5312 |
| 0.6805 | 76.0 | 76 | 0.6868 | 0.5312 |
| 0.6805 | 77.0 | 77 | 0.6865 | 0.5312 |
| 0.6805 | 78.0 | 78 | 0.6862 | 0.5 |
| 0.6805 | 79.0 | 79 | 0.6860 | 0.5 |
| 0.6675 | 80.0 | 80 | 0.6857 | 0.5 |
| 0.6675 | 81.0 | 81 | 0.6853 | 0.5312 |
| 0.6675 | 82.0 | 82 | 0.6849 | 0.5312 |
| 0.6675 | 83.0 | 83 | 0.6845 | 0.5312 |
| 0.6675 | 84.0 | 84 | 0.6840 | 0.5312 |
| 0.6675 | 85.0 | 85 | 0.6834 | 0.5625 |
| 0.6675 | 86.0 | 86 | 0.6827 | 0.5625 |
| 0.6675 | 87.0 | 87 | 0.6818 | 0.5625 |
| 0.6675 | 88.0 | 88 | 0.6809 | 0.5625 |
| 0.6675 | 89.0 | 89 | 0.6798 | 0.5625 |
| 0.65 | 90.0 | 90 | 0.6786 | 0.5625 |
| 0.65 | 91.0 | 91 | 0.6772 | 0.5625 |
| 0.65 | 92.0 | 92 | 0.6758 | 0.5625 |
| 0.65 | 93.0 | 93 | 0.6741 | 0.5625 |
| 0.65 | 94.0 | 94 | 0.6718 | 0.5625 |
| 0.65 | 95.0 | 95 | 0.6687 | 0.5625 |
| 0.65 | 96.0 | 96 | 0.6649 | 0.5625 |
| 0.65 | 97.0 | 97 | 0.6615 | 0.5625 |
| 0.65 | 98.0 | 98 | 0.6596 | 0.5625 |
| 0.65 | 99.0 | 99 | 0.6605 | 0.5625 |
| 0.611 | 100.0 | 100 | 0.6642 | 0.5625 |
| 0.611 | 101.0 | 101 | 0.6683 | 0.5625 |
| 0.611 | 102.0 | 102 | 0.6689 | 0.5625 |
| 0.611 | 103.0 | 103 | 0.6670 | 0.5625 |
| 0.611 | 104.0 | 104 | 0.6627 | 0.5312 |
| 0.611 | 105.0 | 105 | 0.6595 | 0.5312 |
| 0.611 | 106.0 | 106 | 0.6577 | 0.5625 |
| 0.611 | 107.0 | 107 | 0.6575 | 0.5938 |
| 0.611 | 108.0 | 108 | 0.6552 | 0.5938 |
| 0.611 | 109.0 | 109 | 0.6555 | 0.625 |
| 0.5787 | 110.0 | 110 | 0.6560 | 0.625 |
| 0.5787 | 111.0 | 111 | 0.6566 | 0.625 |
| 0.5787 | 112.0 | 112 | 0.6560 | 0.625 |
| 0.5787 | 113.0 | 113 | 0.6543 | 0.6562 |
| 0.5787 | 114.0 | 114 | 0.6530 | 0.6562 |
| 0.5787 | 115.0 | 115 | 0.6518 | 0.6562 |
| 0.5787 | 116.0 | 116 | 0.6512 | 0.6562 |
| 0.5787 | 117.0 | 117 | 0.6506 | 0.6562 |
| 0.5787 | 118.0 | 118 | 0.6500 | 0.6562 |
| 0.5787 | 119.0 | 119 | 0.6499 | 0.6875 |
| 0.5279 | 120.0 | 120 | 0.6497 | 0.6875 |
| 0.5279 | 121.0 | 121 | 0.6496 | 0.6875 |
| 0.5279 | 122.0 | 122 | 0.6494 | 0.6875 |
| 0.5279 | 123.0 | 123 | 0.6486 | 0.6875 |
| 0.5279 | 124.0 | 124 | 0.6472 | 0.6875 |
| 0.5279 | 125.0 | 125 | 0.6443 | 0.6875 |
| 0.5279 | 126.0 | 126 | 0.6397 | 0.6562 |
| 0.5279 | 127.0 | 127 | 0.6328 | 0.6562 |
| 0.5279 | 128.0 | 128 | 0.6238 | 0.6875 |
| 0.5279 | 129.0 | 129 | 0.6173 | 0.6875 |
| 0.4721 | 130.0 | 130 | 0.6138 | 0.6875 |
| 0.4721 | 131.0 | 131 | 0.6175 | 0.625 |
| 0.4721 | 132.0 | 132 | 0.6137 | 0.6562 |
| 0.4721 | 133.0 | 133 | 0.6101 | 0.6562 |
| 0.4721 | 134.0 | 134 | 0.6062 | 0.6562 |
| 0.4721 | 135.0 | 135 | 0.6027 | 0.6562 |
| 0.4721 | 136.0 | 136 | 0.6015 | 0.625 |
| 0.4721 | 137.0 | 137 | 0.5982 | 0.625 |
| 0.4721 | 138.0 | 138 | 0.6102 | 0.625 |
| 0.4721 | 139.0 | 139 | 0.5983 | 0.625 |
| 0.378 | 140.0 | 140 | 0.6020 | 0.625 |
| 0.378 | 141.0 | 141 | 0.5921 | 0.625 |
| 0.378 | 142.0 | 142 | 0.5790 | 0.625 |
| 0.378 | 143.0 | 143 | 0.5654 | 0.6562 |
| 0.378 | 144.0 | 144 | 0.5493 | 0.6562 |
| 0.378 | 145.0 | 145 | 0.5279 | 0.6562 |
| 0.378 | 146.0 | 146 | 0.5064 | 0.6562 |
| 0.378 | 147.0 | 147 | 0.4834 | 0.6875 |
| 0.378 | 148.0 | 148 | 0.4557 | 0.7188 |
| 0.378 | 149.0 | 149 | 0.4318 | 0.75 |
| 0.2537 | 150.0 | 150 | 0.4022 | 0.7812 |
### Framework versions
- Transformers 4.32.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.4.0
- Tokenizers 0.13.3
|
omegaodin/llama2-qlora-finetunined-spanish
|
omegaodin
| 2023-08-02T07:57:27Z | 1 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-02T07:57:20Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.5.0.dev0
|
omegaodin/llama2-qlora-finetunined-french
|
omegaodin
| 2023-08-02T07:57:05Z | 1 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-02T07:56:58Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.5.0.dev0
|
s3nh/Hermes-LLongMA-2-13b-8k-GGML
|
s3nh
| 2023-08-02T07:52:05Z | 0 | 0 |
transformers
|
[
"transformers",
"text-generation",
"en",
"license:openrail",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-08-02T07:35:08Z |
---
license: openrail
language:
- en
pipeline_tag: text-generation
library_name: transformers
---
## Original model card
Buy me a coffee if you like this project ;)
<a href="https://www.buymeacoffee.com/s3nh"><img src="https://www.buymeacoffee.com/assets/img/guidelines/download-assets-sm-1.svg" alt=""></a>
#### Description
GGML Format model files for [This project](https://huggingface.co/conceptofmind/Hermes-LLongMA-2-13b-8k).
### inference
```python
import ctransformers
from ctransformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained(output_dir, ggml_file,
gpu_layers=32, model_type="llama")
manual_input: str = "Tell me about your last dream, please."
llm(manual_input,
max_new_tokens=256,
temperature=0.9,
top_p= 0.7)
```
# Original model card
You can find the Llama-2 usage policy here: https://ai.meta.com/llama/use-policy/
Llama 2 Community License Agreement
Llama 2 Version Release Date: July 18, 2023
“Agreement” means the terms and conditions for use, reproduction, distribution and modification of the Llama Materials set forth herein.
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“Llama 2” means the foundational large language models and software and algorithms, including machine-learning model code, trained model weights, inference-enabling code, training-enabling code, fine-tuning enabling code and other elements of the foregoing distributed by Meta at ai.meta.com/resources/models-and-libraries/llama-downloads/.
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a. No trademark licenses are granted under this Agreement, and in connection with the Llama Materials, neither Meta nor Licensee may use any name or mark owned by or associated with the other or any of its affiliates, except as required for reasonable and customary use in describing and redistributing the Llama Materials.
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c. If you institute litigation or other proceedings against Meta or any entity (including a cross-claim or counterclaim in a lawsuit) alleging that the Llama Materials or Llama 2 outputs or results, or any portion of any of the foregoing, constitutes infringement of intellectual property or other rights owned or licensable by you, then any licenses granted to you under this Agreement shall terminate as of the date such litigation or claim is filed or instituted. You will indemnify and hold harmless Meta from and against any claim by any third party arising out of or related to your use or distribution of the Llama Materials.
Term and Termination. The term of this Agreement will commence upon your acceptance of this Agreement or access to the Llama Materials and will continue in full force and effect until terminated in accordance with the terms and conditions herein. Meta may terminate this Agreement if you are in breach of any term or condition of this Agreement. Upon termination of this Agreement, you shall delete and cease use of the Llama Materials. Sections 3, 4 and 7 shall survive the termination of this Agreement.
Governing Law and Jurisdiction. This Agreement will be governed and construed under the laws of the State of California without regard to choice of law principles, and the UN Convention on Contracts for the International Sale of Goods does not apply to this Agreement. The courts of California shall have exclusive jurisdiction of any dispute arising out of this Agreement.
|
bioformers/bioformer-8L-mnli
|
bioformers
| 2023-08-02T07:51:11Z | 118 | 0 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
[bioformer-cased-v1.0](https://huggingface.co/bioformers/bioformer-cased-v1.0) fined-tuned on the [MNLI](https://cims.nyu.edu/~sbowman/multinli/) dataset for 2 epochs.
The fine-tuning process was performed on two NVIDIA GeForce GTX 1080 Ti GPUs (11GB). The parameters are:
```
max_seq_length=512
per_device_train_batch_size=16
total train batch size (w. parallel, distributed & accumulation) = 32
learning_rate=3e-5
```
## Evaluation results
eval_accuracy = 0.803973
## Speed
In our experiments, the inference speed of Bioformer is 3x as fast as BERT-base/BioBERT/PubMedBERT, and is 40% faster than DistilBERT.
## More information
The Multi-Genre Natural Language Inference Corpus is a crowdsourced collection of sentence pairs with textual entailment annotations. Given a premise sentence and a hypothesis sentence, the task is to predict whether the premise entails the hypothesis (entailment), contradicts the hypothesis (contradiction), or neither (neutral). The premise sentences are gathered from ten different sources, including transcribed speech, fiction, and government reports. The authors of the benchmark use the standard test set, for which they obtained private labels from the RTE authors, and evaluate on both the matched (in-domain) and mismatched (cross-domain) section. They also uses and recommend the SNLI corpus as 550k examples of auxiliary training data. (source: https://huggingface.co/datasets/glue)
|
RoundtTble/dog.pt
|
RoundtTble
| 2023-08-02T07:46:40Z | 31 | 0 |
diffusers
|
[
"diffusers",
"tensorboard",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"dreambooth",
"base_model:CompVis/stable-diffusion-v1-4",
"base_model:finetune:CompVis/stable-diffusion-v1-4",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-08-02T06:41:40Z |
---
license: creativeml-openrail-m
base_model: CompVis/stable-diffusion-v1-4
instance_prompt: a photo of sks dog
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- dreambooth
inference: true
---
# DreamBooth - RoundtTble/dog.pt
This is a dreambooth model derived from CompVis/stable-diffusion-v1-4. The weights were trained on a photo of sks dog using [DreamBooth](https://dreambooth.github.io/).
You can find some example images in the following.
DreamBooth for the text encoder was enabled: False.
|
bioformers/bioformer-8L
|
bioformers
| 2023-08-02T07:45:33Z | 193 | 7 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"safetensors",
"bert",
"fill-mask",
"en",
"arxiv:2302.01588",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:05Z |
---
language:
- en
license: apache-2.0
pipeline_tag: fill-mask
---
**_NOTE: `bioformer-cased-v1.0` has been renamed to `bioformer-8L`. All links to `bioformer-cased-v1.0` will automatically redirect to `bioformer-8L`, including git operations. However, to avoid confusion, we recommend updating any existing local clones to point to the new repository URL._**
Bioformer-8L is a lightweight BERT model for biomedical text mining. Bioformer-8L uses a biomedical vocabulary and is pre-trained from scratch only on biomedical domain corpora. Our experiments show that Bioformer-8L is 3x as fast as BERT-base, and achieves comparable or even better performance than BioBERT/PubMedBERT on downstream NLP tasks.
Bioformer-8L has 8 layers (transformer blocks) with a hidden embedding size of 512, and the number of self-attention heads is 8. Its total number of parameters is 42,820,610.
**The usage of Bioformer-8L is the same as a standard BERT model. The documentation of BERT can be found [here](https://huggingface.co/docs/transformers/model_doc/bert).**
## Vocabulary of Bioformer-8L
Bioformer-8L uses a cased WordPiece vocabulary trained from a biomedical corpus, which included all PubMed abstracts (33 million, as of Feb 1, 2021) and 1 million PMC full-text articles. PMC has 3.6 million articles but we down-sampled them to 1 million such that the total size of PubMed abstracts and PMC full-text articles are approximately equal. To mitigate the out-of-vocabulary issue and include special symbols (e.g. male and female symbols) in biomedical literature, we trained Bioformer’s vocabulary from the Unicode text of the two resources. The vocabulary size of Bioformer-8L is 32768 (2^15), which is similar to that of the original BERT.
## Pre-training of Bioformer-8L
Bioformer-8L was pre-trained from scratch on the same corpus as the vocabulary (33 million PubMed abstracts + 1 million PMC full-text articles). For the masked language modeling (MLM) objective, we used whole-word masking with a masking rate of 15%. There are debates on whether the next sentence prediction (NSP) objective could improve the performance on downstream tasks. We include it in our pre-training experiment in case the prediction of the next sentence is needed by end-users. Sentence segmentation of all training text was performed using [SciSpacy](https://allenai.github.io/scispacy/).
Pre-training of Bioformer-8L was performed on a single Cloud TPU device (TPUv2, 8 cores, 8GB memory per core). The maximum input sequence length was fixed to 512, and the batch size was set to 256. We pre-trained Bioformer-8L for 2 million steps, which took about 8.3 days.
## Usage
Prerequisites: python3, pytorch, transformers and datasets
We have tested the following commands on Python v3.9.16, PyTorch v1.13.1+cu117, Datasets v2.9.0 and Transformers v4.26.
To install pytorch, please refer to instructions [here](https://pytorch.org/get-started/locally).
To install the `transformers` and `datasets` library:
```
pip install transformers
pip install datasets
```
### Filling mask
```
from transformers import pipeline
unmasker8L = pipeline('fill-mask', model='bioformers/bioformer-8L')
unmasker8L("[MASK] refers to a group of diseases that affect how the body uses blood sugar (glucose)")
unmasker16L = pipeline('fill-mask', model='bioformers/bioformer-16L')
unmasker16L("[MASK] refers to a group of diseases that affect how the body uses blood sugar (glucose)")
```
Output of `bioformer-8L`:
```
[{'score': 0.3207533359527588,
'token': 13473,
'token_str': 'Diabetes',
'sequence': 'Diabetes refers to a group of diseases that affect how the body uses blood sugar ( glucose )'},
{'score': 0.19234347343444824,
'token': 17740,
'token_str': 'Obesity',
'sequence': 'Obesity refers to a group of diseases that affect how the body uses blood sugar ( glucose )'},
{'score': 0.09200277179479599,
'token': 10778,
'token_str': 'T2DM',
'sequence': 'T2DM refers to a group of diseases that affect how the body uses blood sugar ( glucose )'},
{'score': 0.08494312316179276,
'token': 2228,
'token_str': 'It',
'sequence': 'It refers to a group of diseases that affect how the body uses blood sugar ( glucose )'},
{'score': 0.0412776917219162,
'token': 22263,
'token_str':
'Hypertension',
'sequence': 'Hypertension refers to a group of diseases that affect how the body uses blood sugar ( glucose )'}]
```
Output of `bioformer-16L`:
```
[{'score': 0.7262957692146301,
'token': 13473,
'token_str': 'Diabetes',
'sequence': 'Diabetes refers to a group of diseases that affect how the body uses blood sugar ( glucose )'},
{'score': 0.124954953789711,
'token': 10778,
'token_str': 'T2DM',
'sequence': 'T2DM refers to a group of diseases that affect how the body uses blood sugar ( glucose )'},
{'score': 0.04062706232070923,
'token': 2228,
'token_str': 'It',
'sequence': 'It refers to a group of diseases that affect how the body uses blood sugar ( glucose )'},
{'score': 0.022694870829582214,
'token': 17740,
'token_str': 'Obesity',
'sequence': 'Obesity refers to a group of diseases that affect how the body uses blood sugar ( glucose )'},
{'score': 0.009743048809468746,
'token': 13960,
'token_str': 'T2D',
'sequence': 'T2D refers to a group of diseases that affect how the body uses blood sugar ( glucose )'}]
```
## Awards
Bioformer-8L achieved top performance (highest micro-F1 score) in the BioCreative VII COVID-19 multi-label topic classification challenge (https://doi.org/10.1093/database/baac069)
## Links
[Bioformer-16L](https://huggingface.co/bioformers/bioformer-16L)
## Acknowledgment
Training and evaluation of Bioformer-8L is supported by the Google TPU Research Cloud (TRC) program, the Intramural Research Program of the National Library of Medicine (NLM), National Institutes of Health (NIH), and NIH/NLM grants LM012895 and 1K99LM014024-01.
## Questions
If you have any questions, please submit an issue here: https://github.com/WGLab/bioformer/issues
You can also send an email to Li Fang (fangli9@mail.sysu.edu.cn, https://fangli80.github.io/).
## Citation
You can cite our preprint on arXiv:
Fang L, Chen Q, Wei C-H, Lu Z, Wang K: Bioformer: an efficient transformer language model for biomedical text mining. arXiv preprint arXiv:2302.01588 (2023). DOI: https://doi.org/10.48550/arXiv.2302.01588
BibTeX format:
```
@ARTICLE{fangli2023bioformer,
author = {{Fang}, Li and {Chen}, Qingyu and {Wei}, Chih-Hsuan and {Lu}, Zhiyong and {Wang}, Kai},
title = "{Bioformer: an efficient transformer language model for biomedical text mining}",
journal = {arXiv preprint arXiv:2302.01588},
year = {2023}
}
```
|
breakjl/distilbert-base-food_review
|
breakjl
| 2023-08-02T07:42:13Z | 127 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"fill-mask",
"exbert",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1910.01108",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2023-08-01T08:35:00Z |
---
language: en
tags:
- exbert
license: apache-2.0
datasets:
- bookcorpus
- wikipedia
---
# DistilBERT base model (uncased)
This model is a distilled version of the [BERT base model](https://huggingface.co/bert-base-uncased). It was
introduced in [this paper](https://arxiv.org/abs/1910.01108). The code for the distillation process can be found
[here](https://github.com/huggingface/transformers/tree/main/examples/research_projects/distillation). This model is uncased: it does
not make a difference between english and English.
## Model description
DistilBERT is a transformers model, smaller and faster than BERT, which was pretrained on the same corpus in a
self-supervised fashion, using the BERT base model as a teacher. This means it was pretrained on the raw texts only,
with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic
process to generate inputs and labels from those texts using the BERT base model. More precisely, it was pretrained
with three objectives:
- Distillation loss: the model was trained to return the same probabilities as the BERT base model.
- Masked language modeling (MLM): this is part of the original training loss of the BERT base model. When taking a
sentence, the model randomly masks 15% of the words in the input then run the entire masked sentence through the
model and has to predict the masked words. This is different from traditional recurrent neural networks (RNNs) that
usually see the words one after the other, or from autoregressive models like GPT which internally mask the future
tokens. It allows the model to learn a bidirectional representation of the sentence.
- Cosine embedding loss: the model was also trained to generate hidden states as close as possible as the BERT base
model.
This way, the model learns the same inner representation of the English language than its teacher model, while being
faster for inference or downstream tasks.
## Intended uses & limitations
You can use the raw model for either masked language modeling or next sentence prediction, but it's mostly intended to
be fine-tuned on a downstream task. See the [model hub](https://huggingface.co/models?filter=distilbert) to look for
fine-tuned versions on a task that interests you.
Note that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked)
to make decisions, such as sequence classification, token classification or question answering. For tasks such as text
generation you should look at model like GPT2.
### How to use
You can use this model directly with a pipeline for masked language modeling:
```python
>>> from transformers import pipeline
>>> unmasker = pipeline('fill-mask', model='distilbert-base-uncased')
>>> unmasker("Hello I'm a [MASK] model.")
[{'sequence': "[CLS] hello i'm a role model. [SEP]",
'score': 0.05292855575680733,
'token': 2535,
'token_str': 'role'},
{'sequence': "[CLS] hello i'm a fashion model. [SEP]",
'score': 0.03968575969338417,
'token': 4827,
'token_str': 'fashion'},
{'sequence': "[CLS] hello i'm a business model. [SEP]",
'score': 0.034743521362543106,
'token': 2449,
'token_str': 'business'},
{'sequence': "[CLS] hello i'm a model model. [SEP]",
'score': 0.03462274372577667,
'token': 2944,
'token_str': 'model'},
{'sequence': "[CLS] hello i'm a modeling model. [SEP]",
'score': 0.018145186826586723,
'token': 11643,
'token_str': 'modeling'}]
```
Here is how to use this model to get the features of a given text in PyTorch:
```python
from transformers import DistilBertTokenizer, DistilBertModel
tokenizer = DistilBertTokenizer.from_pretrained('distilbert-base-uncased')
model = DistilBertModel.from_pretrained("distilbert-base-uncased")
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
```
and in TensorFlow:
```python
from transformers import DistilBertTokenizer, TFDistilBertModel
tokenizer = DistilBertTokenizer.from_pretrained('distilbert-base-uncased')
model = TFDistilBertModel.from_pretrained("distilbert-base-uncased")
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='tf')
output = model(encoded_input)
```
### Limitations and bias
Even if the training data used for this model could be characterized as fairly neutral, this model can have biased
predictions. It also inherits some of
[the bias of its teacher model](https://huggingface.co/bert-base-uncased#limitations-and-bias).
```python
>>> from transformers import pipeline
>>> unmasker = pipeline('fill-mask', model='distilbert-base-uncased')
>>> unmasker("The White man worked as a [MASK].")
[{'sequence': '[CLS] the white man worked as a blacksmith. [SEP]',
'score': 0.1235365942120552,
'token': 20987,
'token_str': 'blacksmith'},
{'sequence': '[CLS] the white man worked as a carpenter. [SEP]',
'score': 0.10142576694488525,
'token': 10533,
'token_str': 'carpenter'},
{'sequence': '[CLS] the white man worked as a farmer. [SEP]',
'score': 0.04985016956925392,
'token': 7500,
'token_str': 'farmer'},
{'sequence': '[CLS] the white man worked as a miner. [SEP]',
'score': 0.03932540491223335,
'token': 18594,
'token_str': 'miner'},
{'sequence': '[CLS] the white man worked as a butcher. [SEP]',
'score': 0.03351764753460884,
'token': 14998,
'token_str': 'butcher'}]
>>> unmasker("The Black woman worked as a [MASK].")
[{'sequence': '[CLS] the black woman worked as a waitress. [SEP]',
'score': 0.13283951580524445,
'token': 13877,
'token_str': 'waitress'},
{'sequence': '[CLS] the black woman worked as a nurse. [SEP]',
'score': 0.12586183845996857,
'token': 6821,
'token_str': 'nurse'},
{'sequence': '[CLS] the black woman worked as a maid. [SEP]',
'score': 0.11708822101354599,
'token': 10850,
'token_str': 'maid'},
{'sequence': '[CLS] the black woman worked as a prostitute. [SEP]',
'score': 0.11499975621700287,
'token': 19215,
'token_str': 'prostitute'},
{'sequence': '[CLS] the black woman worked as a housekeeper. [SEP]',
'score': 0.04722772538661957,
'token': 22583,
'token_str': 'housekeeper'}]
```
This bias will also affect all fine-tuned versions of this model.
## Training data
DistilBERT pretrained on the same data as BERT, which is [BookCorpus](https://yknzhu.wixsite.com/mbweb), a dataset
consisting of 11,038 unpublished books and [English Wikipedia](https://en.wikipedia.org/wiki/English_Wikipedia)
(excluding lists, tables and headers).
## Training procedure
### Preprocessing
The texts are lowercased and tokenized using WordPiece and a vocabulary size of 30,000. The inputs of the model are
then of the form:
```
[CLS] Sentence A [SEP] Sentence B [SEP]
```
With probability 0.5, sentence A and sentence B correspond to two consecutive sentences in the original corpus and in
the other cases, it's another random sentence in the corpus. Note that what is considered a sentence here is a
consecutive span of text usually longer than a single sentence. The only constrain is that the result with the two
"sentences" has a combined length of less than 512 tokens.
The details of the masking procedure for each sentence are the following:
- 15% of the tokens are masked.
- In 80% of the cases, the masked tokens are replaced by `[MASK]`.
- In 10% of the cases, the masked tokens are replaced by a random token (different) from the one they replace.
- In the 10% remaining cases, the masked tokens are left as is.
### Pretraining
The model was trained on 8 16 GB V100 for 90 hours. See the
[training code](https://github.com/huggingface/transformers/tree/master/examples/distillation) for all hyperparameters
details.
## Evaluation results
When fine-tuned on downstream tasks, this model achieves the following results:
Glue test results:
| Task | MNLI | QQP | QNLI | SST-2 | CoLA | STS-B | MRPC | RTE |
|:----:|:----:|:----:|:----:|:-----:|:----:|:-----:|:----:|:----:|
| | 82.2 | 88.5 | 89.2 | 91.3 | 51.3 | 85.8 | 87.5 | 59.9 |
### BibTeX entry and citation info
```bibtex
@article{Sanh2019DistilBERTAD,
title={DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter},
author={Victor Sanh and Lysandre Debut and Julien Chaumond and Thomas Wolf},
journal={ArXiv},
year={2019},
volume={abs/1910.01108}
}
```
<a href="https://huggingface.co/exbert/?model=distilbert-base-uncased">
<img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png">
</a>
|
FelixChao/vicuna-7b-instruct-ft-adapters-chemical1.1
|
FelixChao
| 2023-08-02T07:38:59Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-02T07:38:56Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.5.0.dev0
- PEFT 0.5.0.dev0
|
Lajonbot/vicuna-13b-v1.3-PL-lora_GGML
|
Lajonbot
| 2023-08-02T07:22:18Z | 0 | 0 | null |
[
"facebook",
"meta",
"pytorch",
"llama",
"llama-2",
"text-generation",
"pl",
"dataset:Lajonbot/alpaca-dolly-chrisociepa-instruction-only-polish",
"license:other",
"region:us"
] |
text-generation
| 2023-08-02T07:10:19Z |
---
language:
- pl
datasets:
- Lajonbot/alpaca-dolly-chrisociepa-instruction-only-polish
license: other
model_type: llama-2
pipeline_tag: text-generation
tags:
- facebook
- meta
- pytorch
- llama
- llama-2
---
|
shylee2021/llm-tolkien
|
shylee2021
| 2023-08-02T07:11:49Z | 1 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-02T06:01:28Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.4.0
|
marco-bordessoule/falcon-qlora-finetunined-guanaco
|
marco-bordessoule
| 2023-08-02T07:10:40Z | 1 | 0 |
peft
|
[
"peft",
"text-generation",
"region:us"
] |
text-generation
| 2023-08-02T07:08:09Z |
---
library_name: peft
pipeline_tag: text-generation
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.5.0.dev0
|
jkhan447/HateXplain-DS-labeled-1
|
jkhan447
| 2023-08-02T06:58:42Z | 106 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:google-bert/bert-base-uncased",
"base_model:finetune:google-bert/bert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-08-02T06:06:01Z |
---
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: HateXplain-DS-labeled-1
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# HateXplain-DS-labeled-1
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.0581
- Accuracy: 0.6271
## 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: 10
### Training results
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.2
- Tokenizers 0.13.3
|
Sookeyy/Reinforce-Pixelcopter-PLE-v0
|
Sookeyy
| 2023-08-02T06:58:04Z | 0 | 0 | null |
[
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-08-02T03:41:22Z |
---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-Pixelcopter-PLE-v0
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 41.72 +/- 33.43
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
NebulaByte/hindi_gpt2
|
NebulaByte
| 2023-08-02T06:45:14Z | 296 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"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
| 2023-07-24T10:13:27Z |
---
license: mit
base_model: gpt2
tags:
- generated_from_trainer
model-index:
- name: hindi_gpt2
results: []
widget:
- text: "अपने अनुप्रयोग को पहुंचनीयता व्यायाम"
- text: "जनतंत्र की सफलता केवल इस बात से नहीं हो सकती है कि हर"
- text: "अगर इसके बाद भी वे फैसले पर कायम रहते हैं और"
- text: "मामले का खुलासा होने के बाद"
- text: "My name is Julien and I like to"
- text: "My name is Thomas and my main"
inference:
parameters:
max_length: 200
---
<!-- 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. -->
# hindi_gpt2
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9187
## 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: 40
- eval_batch_size: 40
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 160
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 400
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 4.694 | 0.18 | 400 | 2.7361 |
| 2.3952 | 0.35 | 800 | 2.1608 |
| 2.1311 | 0.53 | 1200 | 2.0237 |
| 2.0282 | 0.71 | 1600 | 1.9518 |
| 1.9731 | 0.89 | 2000 | 1.9187 |
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.2
- Tokenizers 0.13.3
|
osman2001/test_model
|
osman2001
| 2023-08-02T06:42:00Z | 0 | 0 |
adapter-transformers
|
[
"adapter-transformers",
"en",
"dataset:openchat/openchat_sharegpt4_dataset",
"license:afl-3.0",
"region:us"
] | null | 2023-08-02T06:39:17Z |
---
license: afl-3.0
datasets:
- openchat/openchat_sharegpt4_dataset
language:
- en
metrics:
- code_eval
- accuracy
library_name: adapter-transformers
---
|
simonycl/best_model-yelp_polarity-64-42
|
simonycl
| 2023-08-02T06:29:03Z | 107 | 0 |
transformers
|
[
"transformers",
"pytorch",
"albert",
"text-classification",
"generated_from_trainer",
"base_model:albert/albert-base-v2",
"base_model:finetune:albert/albert-base-v2",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-08-02T03:24:14Z |
---
license: apache-2.0
base_model: albert-base-v2
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: best_model-yelp_polarity-64-42
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. -->
# best_model-yelp_polarity-64-42
This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6069
- Accuracy: 0.9375
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 150
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 4 | 0.7342 | 0.9219 |
| No log | 2.0 | 8 | 0.7290 | 0.9219 |
| 0.5102 | 3.0 | 12 | 0.7270 | 0.9219 |
| 0.5102 | 4.0 | 16 | 0.7253 | 0.9219 |
| 0.4089 | 5.0 | 20 | 0.7208 | 0.9219 |
| 0.4089 | 6.0 | 24 | 0.7191 | 0.9219 |
| 0.4089 | 7.0 | 28 | 0.7271 | 0.9297 |
| 0.3981 | 8.0 | 32 | 0.7192 | 0.9297 |
| 0.3981 | 9.0 | 36 | 0.7009 | 0.9219 |
| 0.1982 | 10.0 | 40 | 0.6963 | 0.9141 |
| 0.1982 | 11.0 | 44 | 0.6904 | 0.9219 |
| 0.1982 | 12.0 | 48 | 0.6924 | 0.9219 |
| 0.2128 | 13.0 | 52 | 0.6921 | 0.9297 |
| 0.2128 | 14.0 | 56 | 0.6866 | 0.9219 |
| 0.0935 | 15.0 | 60 | 0.6841 | 0.9219 |
| 0.0935 | 16.0 | 64 | 0.6494 | 0.9219 |
| 0.0935 | 17.0 | 68 | 0.6201 | 0.9219 |
| 0.0365 | 18.0 | 72 | 0.6122 | 0.9219 |
| 0.0365 | 19.0 | 76 | 0.6047 | 0.9219 |
| 0.026 | 20.0 | 80 | 0.5870 | 0.9219 |
| 0.026 | 21.0 | 84 | 0.5739 | 0.9219 |
| 0.026 | 22.0 | 88 | 0.5737 | 0.9219 |
| 0.0139 | 23.0 | 92 | 0.5677 | 0.9219 |
| 0.0139 | 24.0 | 96 | 0.5579 | 0.9219 |
| 0.0149 | 25.0 | 100 | 0.5468 | 0.9219 |
| 0.0149 | 26.0 | 104 | 0.5277 | 0.9219 |
| 0.0149 | 27.0 | 108 | 0.5168 | 0.9219 |
| 0.0085 | 28.0 | 112 | 0.5036 | 0.9141 |
| 0.0085 | 29.0 | 116 | 0.4960 | 0.9141 |
| 0.0 | 30.0 | 120 | 0.4941 | 0.9219 |
| 0.0 | 31.0 | 124 | 0.4956 | 0.9297 |
| 0.0 | 32.0 | 128 | 0.4987 | 0.9297 |
| 0.0 | 33.0 | 132 | 0.5018 | 0.9297 |
| 0.0 | 34.0 | 136 | 0.5053 | 0.9297 |
| 0.0 | 35.0 | 140 | 0.5081 | 0.9297 |
| 0.0 | 36.0 | 144 | 0.5107 | 0.9297 |
| 0.0 | 37.0 | 148 | 0.5125 | 0.9297 |
| 0.0 | 38.0 | 152 | 0.5135 | 0.9297 |
| 0.0 | 39.0 | 156 | 0.5146 | 0.9297 |
| 0.0 | 40.0 | 160 | 0.5157 | 0.9297 |
| 0.0 | 41.0 | 164 | 0.5168 | 0.9297 |
| 0.0 | 42.0 | 168 | 0.5182 | 0.9297 |
| 0.0 | 43.0 | 172 | 0.5197 | 0.9297 |
| 0.0 | 44.0 | 176 | 0.5209 | 0.9297 |
| 0.0 | 45.0 | 180 | 0.5224 | 0.9297 |
| 0.0 | 46.0 | 184 | 0.5240 | 0.9297 |
| 0.0 | 47.0 | 188 | 0.5257 | 0.9297 |
| 0.0 | 48.0 | 192 | 0.5272 | 0.9297 |
| 0.0 | 49.0 | 196 | 0.5286 | 0.9297 |
| 0.0 | 50.0 | 200 | 0.5300 | 0.9297 |
| 0.0 | 51.0 | 204 | 0.5313 | 0.9297 |
| 0.0 | 52.0 | 208 | 0.5329 | 0.9297 |
| 0.0 | 53.0 | 212 | 0.5343 | 0.9297 |
| 0.0 | 54.0 | 216 | 0.5355 | 0.9297 |
| 0.0 | 55.0 | 220 | 0.5369 | 0.9297 |
| 0.0 | 56.0 | 224 | 0.5382 | 0.9297 |
| 0.0 | 57.0 | 228 | 0.5395 | 0.9297 |
| 0.0 | 58.0 | 232 | 0.5407 | 0.9297 |
| 0.0 | 59.0 | 236 | 0.5419 | 0.9297 |
| 0.0 | 60.0 | 240 | 0.5431 | 0.9297 |
| 0.0 | 61.0 | 244 | 0.5444 | 0.9297 |
| 0.0 | 62.0 | 248 | 0.5455 | 0.9297 |
| 0.0 | 63.0 | 252 | 0.5466 | 0.9297 |
| 0.0 | 64.0 | 256 | 0.5478 | 0.9297 |
| 0.0 | 65.0 | 260 | 0.5489 | 0.9297 |
| 0.0 | 66.0 | 264 | 0.5501 | 0.9297 |
| 0.0 | 67.0 | 268 | 0.5513 | 0.9297 |
| 0.0 | 68.0 | 272 | 0.5524 | 0.9297 |
| 0.0 | 69.0 | 276 | 0.5535 | 0.9297 |
| 0.0 | 70.0 | 280 | 0.5548 | 0.9297 |
| 0.0 | 71.0 | 284 | 0.5559 | 0.9297 |
| 0.0 | 72.0 | 288 | 0.5570 | 0.9297 |
| 0.0 | 73.0 | 292 | 0.5581 | 0.9297 |
| 0.0 | 74.0 | 296 | 0.5592 | 0.9297 |
| 0.0 | 75.0 | 300 | 0.5601 | 0.9297 |
| 0.0 | 76.0 | 304 | 0.5610 | 0.9297 |
| 0.0 | 77.0 | 308 | 0.5620 | 0.9297 |
| 0.0 | 78.0 | 312 | 0.5630 | 0.9297 |
| 0.0 | 79.0 | 316 | 0.5640 | 0.9297 |
| 0.0 | 80.0 | 320 | 0.5648 | 0.9297 |
| 0.0 | 81.0 | 324 | 0.5658 | 0.9297 |
| 0.0 | 82.0 | 328 | 0.5667 | 0.9297 |
| 0.0 | 83.0 | 332 | 0.5675 | 0.9297 |
| 0.0 | 84.0 | 336 | 0.5684 | 0.9297 |
| 0.0 | 85.0 | 340 | 0.5693 | 0.9297 |
| 0.0 | 86.0 | 344 | 0.5701 | 0.9297 |
| 0.0 | 87.0 | 348 | 0.5710 | 0.9297 |
| 0.0 | 88.0 | 352 | 0.5719 | 0.9297 |
| 0.0 | 89.0 | 356 | 0.5728 | 0.9297 |
| 0.0 | 90.0 | 360 | 0.5736 | 0.9297 |
| 0.0 | 91.0 | 364 | 0.5745 | 0.9297 |
| 0.0 | 92.0 | 368 | 0.5754 | 0.9297 |
| 0.0 | 93.0 | 372 | 0.5762 | 0.9297 |
| 0.0 | 94.0 | 376 | 0.5771 | 0.9297 |
| 0.0 | 95.0 | 380 | 0.5779 | 0.9297 |
| 0.0 | 96.0 | 384 | 0.5788 | 0.9297 |
| 0.0 | 97.0 | 388 | 0.5796 | 0.9297 |
| 0.0 | 98.0 | 392 | 0.5804 | 0.9297 |
| 0.0 | 99.0 | 396 | 0.5812 | 0.9297 |
| 0.0 | 100.0 | 400 | 0.5820 | 0.9297 |
| 0.0 | 101.0 | 404 | 0.5828 | 0.9297 |
| 0.0 | 102.0 | 408 | 0.5836 | 0.9297 |
| 0.0 | 103.0 | 412 | 0.5843 | 0.9297 |
| 0.0 | 104.0 | 416 | 0.5851 | 0.9297 |
| 0.0 | 105.0 | 420 | 0.5859 | 0.9297 |
| 0.0 | 106.0 | 424 | 0.5866 | 0.9297 |
| 0.0 | 107.0 | 428 | 0.5874 | 0.9297 |
| 0.0 | 108.0 | 432 | 0.5881 | 0.9297 |
| 0.0 | 109.0 | 436 | 0.5889 | 0.9297 |
| 0.0 | 110.0 | 440 | 0.5896 | 0.9297 |
| 0.0 | 111.0 | 444 | 0.5902 | 0.9297 |
| 0.0 | 112.0 | 448 | 0.5910 | 0.9375 |
| 0.0 | 113.0 | 452 | 0.5916 | 0.9375 |
| 0.0 | 114.0 | 456 | 0.5924 | 0.9375 |
| 0.0 | 115.0 | 460 | 0.5931 | 0.9375 |
| 0.0 | 116.0 | 464 | 0.5938 | 0.9375 |
| 0.0 | 117.0 | 468 | 0.5945 | 0.9375 |
| 0.0 | 118.0 | 472 | 0.5952 | 0.9375 |
| 0.0 | 119.0 | 476 | 0.5958 | 0.9375 |
| 0.0 | 120.0 | 480 | 0.5964 | 0.9375 |
| 0.0 | 121.0 | 484 | 0.5971 | 0.9375 |
| 0.0 | 122.0 | 488 | 0.5978 | 0.9375 |
| 0.0 | 123.0 | 492 | 0.5985 | 0.9375 |
| 0.0 | 124.0 | 496 | 0.5991 | 0.9375 |
| 0.0 | 125.0 | 500 | 0.5997 | 0.9375 |
| 0.0 | 126.0 | 504 | 0.6004 | 0.9375 |
| 0.0 | 127.0 | 508 | 0.6009 | 0.9375 |
| 0.0 | 128.0 | 512 | 0.6015 | 0.9375 |
| 0.0 | 129.0 | 516 | 0.6020 | 0.9375 |
| 0.0 | 130.0 | 520 | 0.6025 | 0.9375 |
| 0.0 | 131.0 | 524 | 0.6029 | 0.9375 |
| 0.0 | 132.0 | 528 | 0.6034 | 0.9375 |
| 0.0 | 133.0 | 532 | 0.6038 | 0.9375 |
| 0.0 | 134.0 | 536 | 0.6042 | 0.9375 |
| 0.0 | 135.0 | 540 | 0.6045 | 0.9375 |
| 0.0 | 136.0 | 544 | 0.6048 | 0.9375 |
| 0.0 | 137.0 | 548 | 0.6051 | 0.9375 |
| 0.0 | 138.0 | 552 | 0.6054 | 0.9375 |
| 0.0 | 139.0 | 556 | 0.6056 | 0.9375 |
| 0.0 | 140.0 | 560 | 0.6058 | 0.9375 |
| 0.0 | 141.0 | 564 | 0.6061 | 0.9375 |
| 0.0 | 142.0 | 568 | 0.6062 | 0.9375 |
| 0.0 | 143.0 | 572 | 0.6064 | 0.9375 |
| 0.0 | 144.0 | 576 | 0.6065 | 0.9375 |
| 0.0 | 145.0 | 580 | 0.6066 | 0.9375 |
| 0.0 | 146.0 | 584 | 0.6067 | 0.9375 |
| 0.0 | 147.0 | 588 | 0.6068 | 0.9375 |
| 0.0 | 148.0 | 592 | 0.6068 | 0.9375 |
| 0.0 | 149.0 | 596 | 0.6069 | 0.9375 |
| 0.0 | 150.0 | 600 | 0.6069 | 0.9375 |
### Framework versions
- Transformers 4.32.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.4.0
- Tokenizers 0.13.3
|
Lajonbot/WizardLM-13B-V1.2-PL-lora_adapter_model
|
Lajonbot
| 2023-08-02T06:27:21Z | 0 | 0 | null |
[
"facebook",
"meta",
"pytorch",
"llama",
"llama-2",
"text-generation",
"pl",
"dataset:Lajonbot/alpaca-dolly-chrisociepa-instruction-only-polish",
"license:other",
"region:us"
] |
text-generation
| 2023-08-02T06:27:19Z |
---
language:
- pl
datasets:
- Lajonbot/alpaca-dolly-chrisociepa-instruction-only-polish
license: other
model_type: llama-2
pipeline_tag: text-generation
tags:
- facebook
- meta
- pytorch
- llama
- llama-2
---
|
zangyuchen2008/llama2-lora-test
|
zangyuchen2008
| 2023-08-02T06:26:49Z | 1 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-02T06:26:43Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.4.0
|
waliaMuskaan011/whisper-largev2-hindi
|
waliaMuskaan011
| 2023-08-02T06:20:10Z | 2 | 0 |
peft
|
[
"peft",
"pytorch",
"tensorboard",
"whisper",
"region:us"
] | null | 2023-07-12T19:01:48Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
### Framework versions
- PEFT 0.5.0.dev0
- PEFT 0.4.0.dev0
|
simonycl/best_model-yelp_polarity-64-21
|
simonycl
| 2023-08-02T06:13:07Z | 108 | 0 |
transformers
|
[
"transformers",
"pytorch",
"albert",
"text-classification",
"generated_from_trainer",
"base_model:albert/albert-base-v2",
"base_model:finetune:albert/albert-base-v2",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-08-02T03:09:26Z |
---
license: apache-2.0
base_model: albert-base-v2
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: best_model-yelp_polarity-64-21
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. -->
# best_model-yelp_polarity-64-21
This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6300
- Accuracy: 0.9219
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 150
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 4 | 0.6653 | 0.9297 |
| No log | 2.0 | 8 | 0.6599 | 0.9375 |
| 0.3506 | 3.0 | 12 | 0.6517 | 0.9375 |
| 0.3506 | 4.0 | 16 | 0.6448 | 0.9375 |
| 0.4992 | 5.0 | 20 | 0.6507 | 0.9375 |
| 0.4992 | 6.0 | 24 | 0.6967 | 0.9219 |
| 0.4992 | 7.0 | 28 | 0.7602 | 0.9141 |
| 0.3039 | 8.0 | 32 | 0.9351 | 0.8984 |
| 0.3039 | 9.0 | 36 | 0.9244 | 0.8984 |
| 0.2241 | 10.0 | 40 | 0.7974 | 0.9062 |
| 0.2241 | 11.0 | 44 | 0.7229 | 0.9219 |
| 0.2241 | 12.0 | 48 | 0.6981 | 0.9219 |
| 0.1025 | 13.0 | 52 | 0.6961 | 0.9219 |
| 0.1025 | 14.0 | 56 | 0.6819 | 0.9219 |
| 0.1057 | 15.0 | 60 | 0.6655 | 0.9219 |
| 0.1057 | 16.0 | 64 | 0.6463 | 0.9219 |
| 0.1057 | 17.0 | 68 | 0.6240 | 0.9219 |
| 0.0733 | 18.0 | 72 | 0.6086 | 0.9141 |
| 0.0733 | 19.0 | 76 | 0.6109 | 0.9141 |
| 0.0366 | 20.0 | 80 | 0.6219 | 0.9141 |
| 0.0366 | 21.0 | 84 | 0.6291 | 0.9141 |
| 0.0366 | 22.0 | 88 | 0.6227 | 0.9219 |
| 0.0449 | 23.0 | 92 | 0.6182 | 0.9219 |
| 0.0449 | 24.0 | 96 | 0.6148 | 0.9219 |
| 0.0188 | 25.0 | 100 | 0.5999 | 0.9219 |
| 0.0188 | 26.0 | 104 | 0.5537 | 0.9297 |
| 0.0188 | 27.0 | 108 | 0.5538 | 0.9297 |
| 0.0146 | 28.0 | 112 | 0.5492 | 0.9297 |
| 0.0146 | 29.0 | 116 | 0.5275 | 0.9297 |
| 0.0131 | 30.0 | 120 | 0.5212 | 0.9219 |
| 0.0131 | 31.0 | 124 | 0.5486 | 0.9219 |
| 0.0131 | 32.0 | 128 | 0.5641 | 0.9141 |
| 0.0074 | 33.0 | 132 | 0.5489 | 0.9219 |
| 0.0074 | 34.0 | 136 | 0.5426 | 0.9219 |
| 0.0042 | 35.0 | 140 | 0.5468 | 0.9141 |
| 0.0042 | 36.0 | 144 | 0.5411 | 0.9141 |
| 0.0042 | 37.0 | 148 | 0.5366 | 0.9219 |
| 0.0027 | 38.0 | 152 | 0.5306 | 0.9219 |
| 0.0027 | 39.0 | 156 | 0.5182 | 0.9219 |
| 0.0011 | 40.0 | 160 | 0.5096 | 0.9219 |
| 0.0011 | 41.0 | 164 | 0.5059 | 0.9219 |
| 0.0011 | 42.0 | 168 | 0.5130 | 0.9219 |
| 0.0007 | 43.0 | 172 | 0.5198 | 0.9219 |
| 0.0007 | 44.0 | 176 | 0.5172 | 0.9219 |
| 0.0007 | 45.0 | 180 | 0.5129 | 0.9219 |
| 0.0007 | 46.0 | 184 | 0.5337 | 0.9062 |
| 0.0007 | 47.0 | 188 | 0.5600 | 0.9141 |
| 0.0003 | 48.0 | 192 | 0.5687 | 0.9141 |
| 0.0003 | 49.0 | 196 | 0.5413 | 0.9141 |
| 0.0003 | 50.0 | 200 | 0.5270 | 0.9062 |
| 0.0003 | 51.0 | 204 | 0.5249 | 0.9141 |
| 0.0003 | 52.0 | 208 | 0.5315 | 0.9141 |
| 0.0002 | 53.0 | 212 | 0.5528 | 0.9141 |
| 0.0002 | 54.0 | 216 | 0.5732 | 0.9141 |
| 0.0001 | 55.0 | 220 | 0.5812 | 0.9141 |
| 0.0001 | 56.0 | 224 | 0.5871 | 0.9141 |
| 0.0001 | 57.0 | 228 | 0.5854 | 0.9141 |
| 0.0001 | 58.0 | 232 | 0.5846 | 0.9141 |
| 0.0001 | 59.0 | 236 | 0.5842 | 0.9141 |
| 0.0 | 60.0 | 240 | 0.5865 | 0.9141 |
| 0.0 | 61.0 | 244 | 0.5895 | 0.9141 |
| 0.0 | 62.0 | 248 | 0.5908 | 0.9141 |
| 0.0001 | 63.0 | 252 | 0.5911 | 0.9141 |
| 0.0001 | 64.0 | 256 | 0.5905 | 0.9141 |
| 0.0 | 65.0 | 260 | 0.5870 | 0.9141 |
| 0.0 | 66.0 | 264 | 0.5859 | 0.9141 |
| 0.0 | 67.0 | 268 | 0.5863 | 0.9141 |
| 0.0 | 68.0 | 272 | 0.5881 | 0.9141 |
| 0.0 | 69.0 | 276 | 0.5888 | 0.9141 |
| 0.0 | 70.0 | 280 | 0.5902 | 0.9141 |
| 0.0 | 71.0 | 284 | 0.5926 | 0.9141 |
| 0.0 | 72.0 | 288 | 0.5945 | 0.9141 |
| 0.0 | 73.0 | 292 | 0.5949 | 0.9141 |
| 0.0 | 74.0 | 296 | 0.5962 | 0.9141 |
| 0.0 | 75.0 | 300 | 0.5982 | 0.9141 |
| 0.0 | 76.0 | 304 | 0.6003 | 0.9141 |
| 0.0 | 77.0 | 308 | 0.6014 | 0.9141 |
| 0.0 | 78.0 | 312 | 0.6018 | 0.9219 |
| 0.0 | 79.0 | 316 | 0.6024 | 0.9219 |
| 0.0 | 80.0 | 320 | 0.6037 | 0.9219 |
| 0.0 | 81.0 | 324 | 0.6041 | 0.9219 |
| 0.0 | 82.0 | 328 | 0.6052 | 0.9219 |
| 0.0 | 83.0 | 332 | 0.6064 | 0.9219 |
| 0.0 | 84.0 | 336 | 0.6069 | 0.9219 |
| 0.0 | 85.0 | 340 | 0.6069 | 0.9219 |
| 0.0 | 86.0 | 344 | 0.6074 | 0.9219 |
| 0.0 | 87.0 | 348 | 0.6089 | 0.9219 |
| 0.0 | 88.0 | 352 | 0.6098 | 0.9219 |
| 0.0 | 89.0 | 356 | 0.6098 | 0.9219 |
| 0.0 | 90.0 | 360 | 0.6100 | 0.9219 |
| 0.0 | 91.0 | 364 | 0.6098 | 0.9219 |
| 0.0 | 92.0 | 368 | 0.6098 | 0.9219 |
| 0.0 | 93.0 | 372 | 0.6101 | 0.9219 |
| 0.0 | 94.0 | 376 | 0.6111 | 0.9219 |
| 0.0 | 95.0 | 380 | 0.6122 | 0.9219 |
| 0.0 | 96.0 | 384 | 0.6131 | 0.9219 |
| 0.0 | 97.0 | 388 | 0.6122 | 0.9219 |
| 0.0 | 98.0 | 392 | 0.6127 | 0.9219 |
| 0.0 | 99.0 | 396 | 0.6124 | 0.9219 |
| 0.0 | 100.0 | 400 | 0.6120 | 0.9219 |
| 0.0 | 101.0 | 404 | 0.6127 | 0.9219 |
| 0.0 | 102.0 | 408 | 0.6132 | 0.9219 |
| 0.0 | 103.0 | 412 | 0.6140 | 0.9219 |
| 0.0 | 104.0 | 416 | 0.6150 | 0.9219 |
| 0.0 | 105.0 | 420 | 0.6158 | 0.9219 |
| 0.0 | 106.0 | 424 | 0.6160 | 0.9219 |
| 0.0 | 107.0 | 428 | 0.6161 | 0.9219 |
| 0.0 | 108.0 | 432 | 0.6166 | 0.9219 |
| 0.0 | 109.0 | 436 | 0.6168 | 0.9219 |
| 0.0 | 110.0 | 440 | 0.6170 | 0.9219 |
| 0.0 | 111.0 | 444 | 0.6178 | 0.9219 |
| 0.0 | 112.0 | 448 | 0.6184 | 0.9219 |
| 0.0 | 113.0 | 452 | 0.6189 | 0.9219 |
| 0.0 | 114.0 | 456 | 0.6197 | 0.9219 |
| 0.0 | 115.0 | 460 | 0.6213 | 0.9219 |
| 0.0 | 116.0 | 464 | 0.6220 | 0.9219 |
| 0.0 | 117.0 | 468 | 0.6226 | 0.9219 |
| 0.0 | 118.0 | 472 | 0.6229 | 0.9219 |
| 0.0 | 119.0 | 476 | 0.6235 | 0.9219 |
| 0.0 | 120.0 | 480 | 0.6219 | 0.9219 |
| 0.0 | 121.0 | 484 | 0.6219 | 0.9219 |
| 0.0 | 122.0 | 488 | 0.6223 | 0.9219 |
| 0.0 | 123.0 | 492 | 0.6236 | 0.9219 |
| 0.0 | 124.0 | 496 | 0.6246 | 0.9219 |
| 0.0 | 125.0 | 500 | 0.6259 | 0.9219 |
| 0.0 | 126.0 | 504 | 0.6265 | 0.9219 |
| 0.0 | 127.0 | 508 | 0.6270 | 0.9219 |
| 0.0 | 128.0 | 512 | 0.6272 | 0.9219 |
| 0.0 | 129.0 | 516 | 0.6271 | 0.9219 |
| 0.0 | 130.0 | 520 | 0.6262 | 0.9219 |
| 0.0 | 131.0 | 524 | 0.6257 | 0.9219 |
| 0.0 | 132.0 | 528 | 0.6255 | 0.9219 |
| 0.0 | 133.0 | 532 | 0.6258 | 0.9219 |
| 0.0 | 134.0 | 536 | 0.6262 | 0.9219 |
| 0.0 | 135.0 | 540 | 0.6272 | 0.9219 |
| 0.0 | 136.0 | 544 | 0.6277 | 0.9219 |
| 0.0 | 137.0 | 548 | 0.6286 | 0.9219 |
| 0.0 | 138.0 | 552 | 0.6288 | 0.9219 |
| 0.0 | 139.0 | 556 | 0.6292 | 0.9219 |
| 0.0 | 140.0 | 560 | 0.6295 | 0.9219 |
| 0.0 | 141.0 | 564 | 0.6293 | 0.9219 |
| 0.0 | 142.0 | 568 | 0.6294 | 0.9219 |
| 0.0 | 143.0 | 572 | 0.6296 | 0.9219 |
| 0.0 | 144.0 | 576 | 0.6299 | 0.9219 |
| 0.0 | 145.0 | 580 | 0.6297 | 0.9219 |
| 0.0 | 146.0 | 584 | 0.6299 | 0.9219 |
| 0.0 | 147.0 | 588 | 0.6300 | 0.9219 |
| 0.0 | 148.0 | 592 | 0.6300 | 0.9219 |
| 0.0 | 149.0 | 596 | 0.6300 | 0.9219 |
| 0.0 | 150.0 | 600 | 0.6300 | 0.9219 |
### Framework versions
- Transformers 4.32.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.4.0
- Tokenizers 0.13.3
|
lchiang/layoutlmv3-finetuned-cne_100
|
lchiang
| 2023-08-02T06:07:59Z | 75 | 0 |
transformers
|
[
"transformers",
"pytorch",
"layoutlmv3",
"token-classification",
"generated_from_trainer",
"dataset:cne-layoutlmv3-data",
"license:cc-by-nc-sa-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2023-08-02T04:41:32Z |
---
license: cc-by-nc-sa-4.0
tags:
- generated_from_trainer
datasets:
- cne-layoutlmv3-data
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: layoutlmv3-finetuned-cne_100
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: cne-layoutlmv3-data
type: cne-layoutlmv3-data
config: cne-dataset
split: test
args: cne-dataset
metrics:
- name: Precision
type: precision
value: 0.9950738916256158
- name: Recall
type: recall
value: 0.9950738916256158
- name: F1
type: f1
value: 0.9950738916256159
- name: Accuracy
type: accuracy
value: 0.9992716678805535
---
<!-- 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. -->
# layoutlmv3-finetuned-cne_100
This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the cne-layoutlmv3-data dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0008
- Precision: 0.9951
- Recall: 0.9951
- F1: 0.9951
- Accuracy: 0.9993
## 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: 3
- eval_batch_size: 3
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 2500
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 7.81 | 250 | 0.0028 | 0.9951 | 0.9951 | 0.9951 | 0.9993 |
| 0.0229 | 15.62 | 500 | 0.0015 | 0.9951 | 0.9951 | 0.9951 | 0.9993 |
| 0.0229 | 23.44 | 750 | 0.0011 | 0.9951 | 0.9951 | 0.9951 | 0.9993 |
| 0.0031 | 31.25 | 1000 | 0.0009 | 0.9951 | 0.9951 | 0.9951 | 0.9993 |
| 0.0031 | 39.06 | 1250 | 0.0009 | 0.9951 | 0.9951 | 0.9951 | 0.9993 |
| 0.0019 | 46.88 | 1500 | 0.0008 | 0.9951 | 0.9951 | 0.9951 | 0.9993 |
| 0.0019 | 54.69 | 1750 | 0.0008 | 0.9951 | 0.9951 | 0.9951 | 0.9993 |
| 0.0014 | 62.5 | 2000 | 0.0008 | 0.9951 | 0.9951 | 0.9951 | 0.9993 |
| 0.0014 | 70.31 | 2250 | 0.0008 | 0.9951 | 0.9951 | 0.9951 | 0.9993 |
| 0.001 | 78.12 | 2500 | 0.0008 | 0.9951 | 0.9951 | 0.9951 | 0.9993 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1
- Datasets 2.13.1
- Tokenizers 0.13.3
|
simonycl/best_model-yelp_polarity-64-13
|
simonycl
| 2023-08-02T05:57:11Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"albert",
"text-classification",
"generated_from_trainer",
"base_model:albert/albert-base-v2",
"base_model:finetune:albert/albert-base-v2",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-08-02T02:54:38Z |
---
license: apache-2.0
base_model: albert-base-v2
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: best_model-yelp_polarity-64-13
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. -->
# best_model-yelp_polarity-64-13
This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9118
- Accuracy: 0.9062
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 150
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 4 | 0.9825 | 0.8828 |
| No log | 2.0 | 8 | 0.9391 | 0.8906 |
| 0.0791 | 3.0 | 12 | 0.8979 | 0.8984 |
| 0.0791 | 4.0 | 16 | 0.8416 | 0.875 |
| 0.0238 | 5.0 | 20 | 0.8260 | 0.8906 |
| 0.0238 | 6.0 | 24 | 0.8079 | 0.8984 |
| 0.0238 | 7.0 | 28 | 0.7782 | 0.8906 |
| 0.0015 | 8.0 | 32 | 0.7635 | 0.8984 |
| 0.0015 | 9.0 | 36 | 0.7694 | 0.9062 |
| 0.0001 | 10.0 | 40 | 0.7757 | 0.9062 |
| 0.0001 | 11.0 | 44 | 0.7786 | 0.9141 |
| 0.0001 | 12.0 | 48 | 0.7749 | 0.9141 |
| 0.0 | 13.0 | 52 | 0.7730 | 0.9141 |
| 0.0 | 14.0 | 56 | 0.7692 | 0.9141 |
| 0.0 | 15.0 | 60 | 0.7662 | 0.9141 |
| 0.0 | 16.0 | 64 | 0.7640 | 0.9141 |
| 0.0 | 17.0 | 68 | 0.7616 | 0.9141 |
| 0.0 | 18.0 | 72 | 0.7600 | 0.9141 |
| 0.0 | 19.0 | 76 | 0.7608 | 0.9141 |
| 0.0 | 20.0 | 80 | 0.7625 | 0.9141 |
| 0.0 | 21.0 | 84 | 0.7641 | 0.9141 |
| 0.0 | 22.0 | 88 | 0.7656 | 0.9141 |
| 0.0 | 23.0 | 92 | 0.7670 | 0.9141 |
| 0.0 | 24.0 | 96 | 0.7692 | 0.9141 |
| 0.0 | 25.0 | 100 | 0.7709 | 0.9141 |
| 0.0 | 26.0 | 104 | 0.7737 | 0.9141 |
| 0.0 | 27.0 | 108 | 0.7763 | 0.9141 |
| 0.0 | 28.0 | 112 | 0.7774 | 0.9141 |
| 0.0 | 29.0 | 116 | 0.7802 | 0.9141 |
| 0.0 | 30.0 | 120 | 0.7819 | 0.9141 |
| 0.0 | 31.0 | 124 | 0.7846 | 0.9141 |
| 0.0 | 32.0 | 128 | 0.7864 | 0.9141 |
| 0.0 | 33.0 | 132 | 0.7891 | 0.9141 |
| 0.0 | 34.0 | 136 | 0.7923 | 0.9141 |
| 0.0 | 35.0 | 140 | 0.7953 | 0.9141 |
| 0.0 | 36.0 | 144 | 0.7967 | 0.9141 |
| 0.0 | 37.0 | 148 | 0.7973 | 0.9141 |
| 0.0 | 38.0 | 152 | 0.7987 | 0.9141 |
| 0.0 | 39.0 | 156 | 0.8002 | 0.9141 |
| 0.0 | 40.0 | 160 | 0.8022 | 0.9141 |
| 0.0 | 41.0 | 164 | 0.8030 | 0.9141 |
| 0.0 | 42.0 | 168 | 0.8043 | 0.9141 |
| 0.0 | 43.0 | 172 | 0.8048 | 0.9141 |
| 0.0 | 44.0 | 176 | 0.8057 | 0.9141 |
| 0.0 | 45.0 | 180 | 0.8068 | 0.9141 |
| 0.0 | 46.0 | 184 | 0.8080 | 0.9141 |
| 0.0 | 47.0 | 188 | 0.8104 | 0.9141 |
| 0.0 | 48.0 | 192 | 0.8121 | 0.9141 |
| 0.0 | 49.0 | 196 | 0.8122 | 0.9141 |
| 0.0 | 50.0 | 200 | 0.8133 | 0.9141 |
| 0.0 | 51.0 | 204 | 0.8146 | 0.9141 |
| 0.0 | 52.0 | 208 | 0.8154 | 0.9141 |
| 0.0 | 53.0 | 212 | 0.8160 | 0.9141 |
| 0.0 | 54.0 | 216 | 0.8182 | 0.9141 |
| 0.0 | 55.0 | 220 | 0.8204 | 0.9141 |
| 0.0 | 56.0 | 224 | 0.8226 | 0.9141 |
| 0.0 | 57.0 | 228 | 0.8228 | 0.9141 |
| 0.0 | 58.0 | 232 | 0.8241 | 0.9141 |
| 0.0 | 59.0 | 236 | 0.8263 | 0.9141 |
| 0.0 | 60.0 | 240 | 0.8284 | 0.9062 |
| 0.0 | 61.0 | 244 | 0.8287 | 0.9062 |
| 0.0 | 62.0 | 248 | 0.8300 | 0.9062 |
| 0.0 | 63.0 | 252 | 0.8317 | 0.9062 |
| 0.0 | 64.0 | 256 | 0.8327 | 0.9062 |
| 0.0 | 65.0 | 260 | 0.8342 | 0.9062 |
| 0.0 | 66.0 | 264 | 0.8353 | 0.9062 |
| 0.0 | 67.0 | 268 | 0.8369 | 0.9062 |
| 0.0 | 68.0 | 272 | 0.8378 | 0.9062 |
| 0.0 | 69.0 | 276 | 0.8386 | 0.9062 |
| 0.0 | 70.0 | 280 | 0.8394 | 0.9062 |
| 0.0 | 71.0 | 284 | 0.8403 | 0.9062 |
| 0.0 | 72.0 | 288 | 0.8413 | 0.9062 |
| 0.0 | 73.0 | 292 | 0.8414 | 0.9062 |
| 0.0 | 74.0 | 296 | 0.8430 | 0.9062 |
| 0.0 | 75.0 | 300 | 0.8439 | 0.9062 |
| 0.0 | 76.0 | 304 | 0.8452 | 0.9062 |
| 0.0 | 77.0 | 308 | 0.8469 | 0.9062 |
| 0.0 | 78.0 | 312 | 0.8484 | 0.9062 |
| 0.0 | 79.0 | 316 | 0.8499 | 0.9062 |
| 0.0 | 80.0 | 320 | 0.8517 | 0.9062 |
| 0.0 | 81.0 | 324 | 0.8533 | 0.9062 |
| 0.0 | 82.0 | 328 | 0.8538 | 0.9062 |
| 0.0 | 83.0 | 332 | 0.8549 | 0.9062 |
| 0.0 | 84.0 | 336 | 0.8565 | 0.9062 |
| 0.0 | 85.0 | 340 | 0.8575 | 0.9062 |
| 0.0 | 86.0 | 344 | 0.8585 | 0.9062 |
| 0.0 | 87.0 | 348 | 0.8596 | 0.9062 |
| 0.0 | 88.0 | 352 | 0.8609 | 0.9062 |
| 0.0 | 89.0 | 356 | 0.8623 | 0.9062 |
| 0.0 | 90.0 | 360 | 0.8641 | 0.9062 |
| 0.0 | 91.0 | 364 | 0.8653 | 0.9062 |
| 0.0 | 92.0 | 368 | 0.8664 | 0.9062 |
| 0.0 | 93.0 | 372 | 0.8674 | 0.9062 |
| 0.0 | 94.0 | 376 | 0.8695 | 0.9062 |
| 0.0 | 95.0 | 380 | 0.8711 | 0.9062 |
| 0.0 | 96.0 | 384 | 0.8715 | 0.9062 |
| 0.0 | 97.0 | 388 | 0.8713 | 0.9062 |
| 0.0 | 98.0 | 392 | 0.8725 | 0.9062 |
| 0.0 | 99.0 | 396 | 0.8725 | 0.9062 |
| 0.0 | 100.0 | 400 | 0.8730 | 0.9062 |
| 0.0 | 101.0 | 404 | 0.8730 | 0.9062 |
| 0.0 | 102.0 | 408 | 0.8738 | 0.9062 |
| 0.0 | 103.0 | 412 | 0.8750 | 0.9062 |
| 0.0 | 104.0 | 416 | 0.8756 | 0.9062 |
| 0.0 | 105.0 | 420 | 0.8757 | 0.9062 |
| 0.0 | 106.0 | 424 | 0.8772 | 0.9062 |
| 0.0 | 107.0 | 428 | 0.8785 | 0.9062 |
| 0.0 | 108.0 | 432 | 0.8795 | 0.9062 |
| 0.0 | 109.0 | 436 | 0.8806 | 0.9062 |
| 0.0 | 110.0 | 440 | 0.8815 | 0.9062 |
| 0.0 | 111.0 | 444 | 0.8826 | 0.9062 |
| 0.0 | 112.0 | 448 | 0.8837 | 0.9062 |
| 0.0 | 113.0 | 452 | 0.8846 | 0.9062 |
| 0.0 | 114.0 | 456 | 0.8859 | 0.9062 |
| 0.0 | 115.0 | 460 | 0.8877 | 0.9062 |
| 0.0 | 116.0 | 464 | 0.8891 | 0.9062 |
| 0.0 | 117.0 | 468 | 0.8913 | 0.9062 |
| 0.0 | 118.0 | 472 | 0.8926 | 0.9062 |
| 0.0 | 119.0 | 476 | 0.8940 | 0.9062 |
| 0.0 | 120.0 | 480 | 0.8959 | 0.9062 |
| 0.0 | 121.0 | 484 | 0.8978 | 0.9062 |
| 0.0 | 122.0 | 488 | 0.8987 | 0.9062 |
| 0.0 | 123.0 | 492 | 0.8999 | 0.9062 |
| 0.0 | 124.0 | 496 | 0.8998 | 0.9062 |
| 0.0 | 125.0 | 500 | 0.9010 | 0.9062 |
| 0.0 | 126.0 | 504 | 0.9019 | 0.9062 |
| 0.0 | 127.0 | 508 | 0.9031 | 0.9062 |
| 0.0 | 128.0 | 512 | 0.9036 | 0.9062 |
| 0.0 | 129.0 | 516 | 0.9039 | 0.9062 |
| 0.0 | 130.0 | 520 | 0.9043 | 0.9062 |
| 0.0 | 131.0 | 524 | 0.9043 | 0.9062 |
| 0.0 | 132.0 | 528 | 0.9052 | 0.9062 |
| 0.0 | 133.0 | 532 | 0.9052 | 0.9062 |
| 0.0 | 134.0 | 536 | 0.9060 | 0.9062 |
| 0.0 | 135.0 | 540 | 0.9071 | 0.9062 |
| 0.0 | 136.0 | 544 | 0.9078 | 0.9062 |
| 0.0 | 137.0 | 548 | 0.9085 | 0.9062 |
| 0.0 | 138.0 | 552 | 0.9087 | 0.9062 |
| 0.0 | 139.0 | 556 | 0.9094 | 0.9062 |
| 0.0 | 140.0 | 560 | 0.9097 | 0.9062 |
| 0.0 | 141.0 | 564 | 0.9101 | 0.9062 |
| 0.0 | 142.0 | 568 | 0.9105 | 0.9062 |
| 0.0 | 143.0 | 572 | 0.9108 | 0.9062 |
| 0.0 | 144.0 | 576 | 0.9110 | 0.9062 |
| 0.0 | 145.0 | 580 | 0.9112 | 0.9062 |
| 0.0 | 146.0 | 584 | 0.9115 | 0.9062 |
| 0.0 | 147.0 | 588 | 0.9116 | 0.9062 |
| 0.0 | 148.0 | 592 | 0.9117 | 0.9062 |
| 0.0 | 149.0 | 596 | 0.9118 | 0.9062 |
| 0.0 | 150.0 | 600 | 0.9118 | 0.9062 |
### Framework versions
- Transformers 4.32.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.4.0
- Tokenizers 0.13.3
|
NasimB/aochildes-gutenberg_fixed-notm-log-rarity-seed
|
NasimB
| 2023-08-02T05:56:10Z | 133 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"generated_from_trainer",
"dataset:generator",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-08-02T00:34:55Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- generator
model-index:
- name: aochildes-gutenberg_fixed-notm-log-rarity-seed
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. -->
# aochildes-gutenberg_fixed-notm-log-rarity-seed
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the generator dataset.
It achieves the following results on the evaluation set:
- Loss: 4.1452
## 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: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 1000
- num_epochs: 6
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 6.2287 | 0.59 | 500 | 5.1296 |
| 4.7894 | 1.17 | 1000 | 4.6882 |
| 4.4055 | 1.76 | 1500 | 4.4303 |
| 4.1038 | 2.34 | 2000 | 4.2799 |
| 3.9529 | 2.93 | 2500 | 4.1705 |
| 3.7134 | 3.52 | 3000 | 4.1288 |
| 3.6138 | 4.1 | 3500 | 4.0921 |
| 3.4188 | 4.69 | 4000 | 4.0665 |
| 3.3146 | 5.28 | 4500 | 4.0712 |
| 3.2243 | 5.86 | 5000 | 4.0652 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.11.0+cu113
- Datasets 2.13.0
- Tokenizers 0.13.3
|
jaswant50/distilbert-base-uncased-finetuned-emotion
|
jaswant50
| 2023-08-02T05:27:11Z | 110 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-08-01T09:32:19Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
config: split
split: validation
args: split
metrics:
- name: Accuracy
type: accuracy
value: 0.9335
- name: F1
type: f1
value: 0.9336214774727247
library_name: transformers
pipeline_tag: text-classification
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2144
- Accuracy: 0.9335
- F1: 0.9336
## Model description
label0 = sadness
label1 = joy
label2 = love
label3 = anger
label4 = fear
label5 = surprise
eg:
model("I am extremely mesmerised")
output : [{'label': 'LABEL_5', 'score': 0.857551097869873}]
label5 = surprise
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.0275 | 1.0 | 250 | 0.2920 | 0.9355 | 0.9359 |
| 0.072 | 2.0 | 500 | 0.2144 | 0.9335 | 0.9336 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.13.3
|
simonycl/best_model-yelp_polarity-32-21
|
simonycl
| 2023-08-02T05:08:14Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"albert",
"text-classification",
"generated_from_trainer",
"base_model:albert/albert-base-v2",
"base_model:finetune:albert/albert-base-v2",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-08-02T02:14:19Z |
---
license: apache-2.0
base_model: albert-base-v2
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: best_model-yelp_polarity-32-21
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. -->
# best_model-yelp_polarity-32-21
This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8940
- Accuracy: 0.875
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 150
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 2 | 0.8845 | 0.875 |
| No log | 2.0 | 4 | 0.8817 | 0.875 |
| No log | 3.0 | 6 | 0.8770 | 0.875 |
| No log | 4.0 | 8 | 0.8735 | 0.875 |
| 0.4208 | 5.0 | 10 | 0.8676 | 0.875 |
| 0.4208 | 6.0 | 12 | 0.8661 | 0.875 |
| 0.4208 | 7.0 | 14 | 0.8671 | 0.875 |
| 0.4208 | 8.0 | 16 | 0.8603 | 0.875 |
| 0.4208 | 9.0 | 18 | 0.8539 | 0.875 |
| 0.3008 | 10.0 | 20 | 0.8486 | 0.875 |
| 0.3008 | 11.0 | 22 | 0.8322 | 0.875 |
| 0.3008 | 12.0 | 24 | 0.8044 | 0.875 |
| 0.3008 | 13.0 | 26 | 0.7829 | 0.875 |
| 0.3008 | 14.0 | 28 | 0.7727 | 0.875 |
| 0.1225 | 15.0 | 30 | 0.7704 | 0.875 |
| 0.1225 | 16.0 | 32 | 0.7792 | 0.8594 |
| 0.1225 | 17.0 | 34 | 0.7959 | 0.8594 |
| 0.1225 | 18.0 | 36 | 0.8441 | 0.8594 |
| 0.1225 | 19.0 | 38 | 0.8519 | 0.8594 |
| 0.0141 | 20.0 | 40 | 0.8216 | 0.8594 |
| 0.0141 | 21.0 | 42 | 0.7810 | 0.875 |
| 0.0141 | 22.0 | 44 | 0.7611 | 0.875 |
| 0.0141 | 23.0 | 46 | 0.7566 | 0.875 |
| 0.0141 | 24.0 | 48 | 0.7634 | 0.875 |
| 0.0011 | 25.0 | 50 | 0.7747 | 0.875 |
| 0.0011 | 26.0 | 52 | 0.7894 | 0.8594 |
| 0.0011 | 27.0 | 54 | 0.8063 | 0.8594 |
| 0.0011 | 28.0 | 56 | 0.8136 | 0.8594 |
| 0.0011 | 29.0 | 58 | 0.8142 | 0.8594 |
| 0.0003 | 30.0 | 60 | 0.8096 | 0.8594 |
| 0.0003 | 31.0 | 62 | 0.8001 | 0.8594 |
| 0.0003 | 32.0 | 64 | 0.7901 | 0.8594 |
| 0.0003 | 33.0 | 66 | 0.7819 | 0.875 |
| 0.0003 | 34.0 | 68 | 0.7763 | 0.875 |
| 0.0002 | 35.0 | 70 | 0.7729 | 0.875 |
| 0.0002 | 36.0 | 72 | 0.7707 | 0.875 |
| 0.0002 | 37.0 | 74 | 0.7693 | 0.875 |
| 0.0002 | 38.0 | 76 | 0.7684 | 0.875 |
| 0.0002 | 39.0 | 78 | 0.7684 | 0.875 |
| 0.0002 | 40.0 | 80 | 0.7686 | 0.875 |
| 0.0002 | 41.0 | 82 | 0.7692 | 0.875 |
| 0.0002 | 42.0 | 84 | 0.7701 | 0.875 |
| 0.0002 | 43.0 | 86 | 0.7712 | 0.875 |
| 0.0002 | 44.0 | 88 | 0.7726 | 0.875 |
| 0.0002 | 45.0 | 90 | 0.7741 | 0.875 |
| 0.0002 | 46.0 | 92 | 0.7758 | 0.875 |
| 0.0002 | 47.0 | 94 | 0.7778 | 0.875 |
| 0.0002 | 48.0 | 96 | 0.7796 | 0.875 |
| 0.0002 | 49.0 | 98 | 0.7815 | 0.875 |
| 0.0001 | 50.0 | 100 | 0.7835 | 0.875 |
| 0.0001 | 51.0 | 102 | 0.7855 | 0.875 |
| 0.0001 | 52.0 | 104 | 0.7872 | 0.875 |
| 0.0001 | 53.0 | 106 | 0.7888 | 0.875 |
| 0.0001 | 54.0 | 108 | 0.7905 | 0.875 |
| 0.0001 | 55.0 | 110 | 0.7922 | 0.875 |
| 0.0001 | 56.0 | 112 | 0.7938 | 0.875 |
| 0.0001 | 57.0 | 114 | 0.7954 | 0.875 |
| 0.0001 | 58.0 | 116 | 0.7969 | 0.875 |
| 0.0001 | 59.0 | 118 | 0.7982 | 0.875 |
| 0.0001 | 60.0 | 120 | 0.7995 | 0.875 |
| 0.0001 | 61.0 | 122 | 0.8007 | 0.875 |
| 0.0001 | 62.0 | 124 | 0.8020 | 0.875 |
| 0.0001 | 63.0 | 126 | 0.8031 | 0.875 |
| 0.0001 | 64.0 | 128 | 0.8041 | 0.875 |
| 0.0001 | 65.0 | 130 | 0.8052 | 0.875 |
| 0.0001 | 66.0 | 132 | 0.8063 | 0.875 |
| 0.0001 | 67.0 | 134 | 0.8073 | 0.875 |
| 0.0001 | 68.0 | 136 | 0.8084 | 0.875 |
| 0.0001 | 69.0 | 138 | 0.8095 | 0.875 |
| 0.0001 | 70.0 | 140 | 0.8104 | 0.875 |
| 0.0001 | 71.0 | 142 | 0.8115 | 0.875 |
| 0.0001 | 72.0 | 144 | 0.8125 | 0.875 |
| 0.0001 | 73.0 | 146 | 0.8135 | 0.875 |
| 0.0001 | 74.0 | 148 | 0.8143 | 0.875 |
| 0.0001 | 75.0 | 150 | 0.8151 | 0.875 |
| 0.0001 | 76.0 | 152 | 0.8159 | 0.875 |
| 0.0001 | 77.0 | 154 | 0.8167 | 0.875 |
| 0.0001 | 78.0 | 156 | 0.8176 | 0.875 |
| 0.0001 | 79.0 | 158 | 0.8187 | 0.875 |
| 0.0001 | 80.0 | 160 | 0.8198 | 0.875 |
| 0.0001 | 81.0 | 162 | 0.8210 | 0.875 |
| 0.0001 | 82.0 | 164 | 0.8222 | 0.875 |
| 0.0001 | 83.0 | 166 | 0.8232 | 0.875 |
| 0.0001 | 84.0 | 168 | 0.8243 | 0.875 |
| 0.0001 | 85.0 | 170 | 0.8254 | 0.875 |
| 0.0001 | 86.0 | 172 | 0.8266 | 0.875 |
| 0.0001 | 87.0 | 174 | 0.8278 | 0.875 |
| 0.0001 | 88.0 | 176 | 0.8290 | 0.875 |
| 0.0001 | 89.0 | 178 | 0.8302 | 0.875 |
| 0.0001 | 90.0 | 180 | 0.8314 | 0.875 |
| 0.0001 | 91.0 | 182 | 0.8326 | 0.875 |
| 0.0001 | 92.0 | 184 | 0.8337 | 0.875 |
| 0.0001 | 93.0 | 186 | 0.8347 | 0.875 |
| 0.0001 | 94.0 | 188 | 0.8358 | 0.875 |
| 0.0001 | 95.0 | 190 | 0.8369 | 0.875 |
| 0.0001 | 96.0 | 192 | 0.8379 | 0.875 |
| 0.0001 | 97.0 | 194 | 0.8390 | 0.875 |
| 0.0001 | 98.0 | 196 | 0.8401 | 0.875 |
| 0.0001 | 99.0 | 198 | 0.8411 | 0.875 |
| 0.0001 | 100.0 | 200 | 0.8421 | 0.875 |
| 0.0001 | 101.0 | 202 | 0.8431 | 0.875 |
| 0.0001 | 102.0 | 204 | 0.8442 | 0.875 |
| 0.0001 | 103.0 | 206 | 0.8454 | 0.875 |
| 0.0001 | 104.0 | 208 | 0.8464 | 0.875 |
| 0.0001 | 105.0 | 210 | 0.8475 | 0.875 |
| 0.0001 | 106.0 | 212 | 0.8486 | 0.875 |
| 0.0001 | 107.0 | 214 | 0.8498 | 0.875 |
| 0.0001 | 108.0 | 216 | 0.8510 | 0.875 |
| 0.0001 | 109.0 | 218 | 0.8520 | 0.875 |
| 0.0001 | 110.0 | 220 | 0.8532 | 0.875 |
| 0.0001 | 111.0 | 222 | 0.8544 | 0.875 |
| 0.0001 | 112.0 | 224 | 0.8556 | 0.875 |
| 0.0001 | 113.0 | 226 | 0.8568 | 0.875 |
| 0.0001 | 114.0 | 228 | 0.8580 | 0.875 |
| 0.0 | 115.0 | 230 | 0.8591 | 0.875 |
| 0.0 | 116.0 | 232 | 0.8601 | 0.875 |
| 0.0 | 117.0 | 234 | 0.8612 | 0.875 |
| 0.0 | 118.0 | 236 | 0.8623 | 0.875 |
| 0.0 | 119.0 | 238 | 0.8633 | 0.875 |
| 0.0 | 120.0 | 240 | 0.8643 | 0.875 |
| 0.0 | 121.0 | 242 | 0.8652 | 0.875 |
| 0.0 | 122.0 | 244 | 0.8662 | 0.875 |
| 0.0 | 123.0 | 246 | 0.8671 | 0.875 |
| 0.0 | 124.0 | 248 | 0.8680 | 0.875 |
| 0.0 | 125.0 | 250 | 0.8689 | 0.875 |
| 0.0 | 126.0 | 252 | 0.8699 | 0.875 |
| 0.0 | 127.0 | 254 | 0.8708 | 0.875 |
| 0.0 | 128.0 | 256 | 0.8717 | 0.875 |
| 0.0 | 129.0 | 258 | 0.8727 | 0.875 |
| 0.0 | 130.0 | 260 | 0.8736 | 0.875 |
| 0.0 | 131.0 | 262 | 0.8746 | 0.875 |
| 0.0 | 132.0 | 264 | 0.8755 | 0.875 |
| 0.0 | 133.0 | 266 | 0.8764 | 0.875 |
| 0.0 | 134.0 | 268 | 0.8774 | 0.875 |
| 0.0 | 135.0 | 270 | 0.8784 | 0.875 |
| 0.0 | 136.0 | 272 | 0.8794 | 0.875 |
| 0.0 | 137.0 | 274 | 0.8803 | 0.875 |
| 0.0 | 138.0 | 276 | 0.8814 | 0.875 |
| 0.0 | 139.0 | 278 | 0.8825 | 0.875 |
| 0.0 | 140.0 | 280 | 0.8835 | 0.875 |
| 0.0 | 141.0 | 282 | 0.8846 | 0.875 |
| 0.0 | 142.0 | 284 | 0.8857 | 0.875 |
| 0.0 | 143.0 | 286 | 0.8869 | 0.875 |
| 0.0 | 144.0 | 288 | 0.8880 | 0.875 |
| 0.0 | 145.0 | 290 | 0.8890 | 0.875 |
| 0.0 | 146.0 | 292 | 0.8900 | 0.875 |
| 0.0 | 147.0 | 294 | 0.8911 | 0.875 |
| 0.0 | 148.0 | 296 | 0.8921 | 0.875 |
| 0.0 | 149.0 | 298 | 0.8931 | 0.875 |
| 0.0 | 150.0 | 300 | 0.8940 | 0.875 |
### Framework versions
- Transformers 4.32.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.4.0
- Tokenizers 0.13.3
|
reecursion/falcon-7b-HR-performance-reviewer
|
reecursion
| 2023-08-02T04:55:51Z | 33 | 0 |
peft
|
[
"peft",
"text-generation",
"region:us"
] |
text-generation
| 2023-08-01T15:16:19Z |
---
library_name: peft
pipeline_tag: text-generation
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float16
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.5.0.dev0
- PEFT 0.5.0.dev0
|
simonycl/best_model-yelp_polarity-16-100
|
simonycl
| 2023-08-02T04:46:16Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"albert",
"text-classification",
"generated_from_trainer",
"base_model:albert/albert-base-v2",
"base_model:finetune:albert/albert-base-v2",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-08-02T01:15:17Z |
---
license: apache-2.0
base_model: albert-base-v2
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: best_model-yelp_polarity-16-100
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. -->
# best_model-yelp_polarity-16-100
This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1862
- Accuracy: 0.8125
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 150
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 1 | 1.0619 | 0.8438 |
| No log | 2.0 | 2 | 1.0610 | 0.8438 |
| No log | 3.0 | 3 | 1.0591 | 0.8438 |
| No log | 4.0 | 4 | 1.0563 | 0.8438 |
| No log | 5.0 | 5 | 1.0524 | 0.8438 |
| No log | 6.0 | 6 | 1.0473 | 0.8438 |
| No log | 7.0 | 7 | 1.0408 | 0.8438 |
| No log | 8.0 | 8 | 1.0325 | 0.8438 |
| No log | 9.0 | 9 | 1.0221 | 0.8438 |
| 0.5215 | 10.0 | 10 | 1.0093 | 0.8438 |
| 0.5215 | 11.0 | 11 | 0.9939 | 0.8438 |
| 0.5215 | 12.0 | 12 | 0.9775 | 0.8438 |
| 0.5215 | 13.0 | 13 | 0.9630 | 0.8438 |
| 0.5215 | 14.0 | 14 | 0.9517 | 0.8438 |
| 0.5215 | 15.0 | 15 | 0.9431 | 0.8125 |
| 0.5215 | 16.0 | 16 | 0.9352 | 0.7812 |
| 0.5215 | 17.0 | 17 | 0.9263 | 0.7812 |
| 0.5215 | 18.0 | 18 | 0.9195 | 0.7812 |
| 0.5215 | 19.0 | 19 | 0.9178 | 0.7812 |
| 0.3945 | 20.0 | 20 | 0.9230 | 0.8125 |
| 0.3945 | 21.0 | 21 | 0.9374 | 0.8125 |
| 0.3945 | 22.0 | 22 | 0.9628 | 0.8125 |
| 0.3945 | 23.0 | 23 | 1.0035 | 0.8438 |
| 0.3945 | 24.0 | 24 | 1.0608 | 0.8125 |
| 0.3945 | 25.0 | 25 | 1.1258 | 0.8125 |
| 0.3945 | 26.0 | 26 | 1.1859 | 0.8125 |
| 0.3945 | 27.0 | 27 | 1.2311 | 0.8125 |
| 0.3945 | 28.0 | 28 | 1.2580 | 0.8125 |
| 0.3945 | 29.0 | 29 | 1.2702 | 0.8125 |
| 0.2334 | 30.0 | 30 | 1.2750 | 0.8125 |
| 0.2334 | 31.0 | 31 | 1.2763 | 0.8125 |
| 0.2334 | 32.0 | 32 | 1.2763 | 0.8125 |
| 0.2334 | 33.0 | 33 | 1.2757 | 0.8125 |
| 0.2334 | 34.0 | 34 | 1.2733 | 0.8125 |
| 0.2334 | 35.0 | 35 | 1.2687 | 0.8125 |
| 0.2334 | 36.0 | 36 | 1.2612 | 0.8125 |
| 0.2334 | 37.0 | 37 | 1.2508 | 0.8125 |
| 0.2334 | 38.0 | 38 | 1.2376 | 0.8125 |
| 0.2334 | 39.0 | 39 | 1.2213 | 0.8125 |
| 0.024 | 40.0 | 40 | 1.2024 | 0.8125 |
| 0.024 | 41.0 | 41 | 1.1803 | 0.8125 |
| 0.024 | 42.0 | 42 | 1.1548 | 0.8125 |
| 0.024 | 43.0 | 43 | 1.1254 | 0.8125 |
| 0.024 | 44.0 | 44 | 1.0929 | 0.8125 |
| 0.024 | 45.0 | 45 | 1.0591 | 0.8125 |
| 0.024 | 46.0 | 46 | 1.0257 | 0.8125 |
| 0.024 | 47.0 | 47 | 0.9942 | 0.8125 |
| 0.024 | 48.0 | 48 | 0.9662 | 0.8125 |
| 0.024 | 49.0 | 49 | 0.9436 | 0.8125 |
| 0.0008 | 50.0 | 50 | 0.9266 | 0.8125 |
| 0.0008 | 51.0 | 51 | 0.9148 | 0.8125 |
| 0.0008 | 52.0 | 52 | 0.9073 | 0.8125 |
| 0.0008 | 53.0 | 53 | 0.9039 | 0.8125 |
| 0.0008 | 54.0 | 54 | 0.9049 | 0.8125 |
| 0.0008 | 55.0 | 55 | 0.9087 | 0.8125 |
| 0.0008 | 56.0 | 56 | 0.9152 | 0.8125 |
| 0.0008 | 57.0 | 57 | 0.9238 | 0.8125 |
| 0.0008 | 58.0 | 58 | 0.9340 | 0.8125 |
| 0.0008 | 59.0 | 59 | 0.9450 | 0.8125 |
| 0.0006 | 60.0 | 60 | 0.9566 | 0.8438 |
| 0.0006 | 61.0 | 61 | 0.9682 | 0.8438 |
| 0.0006 | 62.0 | 62 | 0.9797 | 0.8438 |
| 0.0006 | 63.0 | 63 | 0.9912 | 0.8438 |
| 0.0006 | 64.0 | 64 | 1.0028 | 0.8438 |
| 0.0006 | 65.0 | 65 | 1.0141 | 0.8438 |
| 0.0006 | 66.0 | 66 | 1.0251 | 0.8438 |
| 0.0006 | 67.0 | 67 | 1.0358 | 0.8438 |
| 0.0006 | 68.0 | 68 | 1.0460 | 0.8438 |
| 0.0006 | 69.0 | 69 | 1.0558 | 0.8438 |
| 0.0005 | 70.0 | 70 | 1.0646 | 0.8438 |
| 0.0005 | 71.0 | 71 | 1.0730 | 0.8438 |
| 0.0005 | 72.0 | 72 | 1.0808 | 0.8438 |
| 0.0005 | 73.0 | 73 | 1.0882 | 0.8438 |
| 0.0005 | 74.0 | 74 | 1.0951 | 0.8438 |
| 0.0005 | 75.0 | 75 | 1.1013 | 0.8125 |
| 0.0005 | 76.0 | 76 | 1.1070 | 0.8125 |
| 0.0005 | 77.0 | 77 | 1.1122 | 0.8125 |
| 0.0005 | 78.0 | 78 | 1.1170 | 0.8125 |
| 0.0005 | 79.0 | 79 | 1.1214 | 0.8125 |
| 0.0004 | 80.0 | 80 | 1.1255 | 0.8125 |
| 0.0004 | 81.0 | 81 | 1.1292 | 0.8125 |
| 0.0004 | 82.0 | 82 | 1.1324 | 0.8125 |
| 0.0004 | 83.0 | 83 | 1.1354 | 0.8125 |
| 0.0004 | 84.0 | 84 | 1.1383 | 0.8125 |
| 0.0004 | 85.0 | 85 | 1.1411 | 0.8125 |
| 0.0004 | 86.0 | 86 | 1.1437 | 0.8125 |
| 0.0004 | 87.0 | 87 | 1.1462 | 0.8125 |
| 0.0004 | 88.0 | 88 | 1.1484 | 0.8125 |
| 0.0004 | 89.0 | 89 | 1.1506 | 0.8125 |
| 0.0004 | 90.0 | 90 | 1.1527 | 0.8125 |
| 0.0004 | 91.0 | 91 | 1.1546 | 0.8125 |
| 0.0004 | 92.0 | 92 | 1.1563 | 0.8125 |
| 0.0004 | 93.0 | 93 | 1.1579 | 0.8125 |
| 0.0004 | 94.0 | 94 | 1.1596 | 0.8125 |
| 0.0004 | 95.0 | 95 | 1.1611 | 0.8125 |
| 0.0004 | 96.0 | 96 | 1.1624 | 0.8125 |
| 0.0004 | 97.0 | 97 | 1.1636 | 0.8125 |
| 0.0004 | 98.0 | 98 | 1.1648 | 0.8125 |
| 0.0004 | 99.0 | 99 | 1.1658 | 0.8125 |
| 0.0003 | 100.0 | 100 | 1.1668 | 0.8125 |
| 0.0003 | 101.0 | 101 | 1.1678 | 0.8125 |
| 0.0003 | 102.0 | 102 | 1.1689 | 0.8125 |
| 0.0003 | 103.0 | 103 | 1.1697 | 0.8125 |
| 0.0003 | 104.0 | 104 | 1.1706 | 0.8125 |
| 0.0003 | 105.0 | 105 | 1.1715 | 0.8125 |
| 0.0003 | 106.0 | 106 | 1.1722 | 0.8125 |
| 0.0003 | 107.0 | 107 | 1.1728 | 0.8125 |
| 0.0003 | 108.0 | 108 | 1.1734 | 0.8125 |
| 0.0003 | 109.0 | 109 | 1.1739 | 0.8125 |
| 0.0003 | 110.0 | 110 | 1.1745 | 0.8125 |
| 0.0003 | 111.0 | 111 | 1.1749 | 0.8125 |
| 0.0003 | 112.0 | 112 | 1.1754 | 0.8125 |
| 0.0003 | 113.0 | 113 | 1.1759 | 0.8125 |
| 0.0003 | 114.0 | 114 | 1.1764 | 0.8125 |
| 0.0003 | 115.0 | 115 | 1.1768 | 0.8125 |
| 0.0003 | 116.0 | 116 | 1.1772 | 0.8125 |
| 0.0003 | 117.0 | 117 | 1.1774 | 0.8125 |
| 0.0003 | 118.0 | 118 | 1.1776 | 0.8125 |
| 0.0003 | 119.0 | 119 | 1.1776 | 0.8125 |
| 0.0003 | 120.0 | 120 | 1.1778 | 0.8125 |
| 0.0003 | 121.0 | 121 | 1.1780 | 0.8125 |
| 0.0003 | 122.0 | 122 | 1.1781 | 0.8125 |
| 0.0003 | 123.0 | 123 | 1.1783 | 0.8125 |
| 0.0003 | 124.0 | 124 | 1.1784 | 0.8125 |
| 0.0003 | 125.0 | 125 | 1.1787 | 0.8125 |
| 0.0003 | 126.0 | 126 | 1.1790 | 0.8125 |
| 0.0003 | 127.0 | 127 | 1.1794 | 0.8125 |
| 0.0003 | 128.0 | 128 | 1.1797 | 0.8125 |
| 0.0003 | 129.0 | 129 | 1.1800 | 0.8125 |
| 0.0003 | 130.0 | 130 | 1.1803 | 0.8125 |
| 0.0003 | 131.0 | 131 | 1.1807 | 0.8125 |
| 0.0003 | 132.0 | 132 | 1.1809 | 0.8125 |
| 0.0003 | 133.0 | 133 | 1.1812 | 0.8125 |
| 0.0003 | 134.0 | 134 | 1.1815 | 0.8125 |
| 0.0003 | 135.0 | 135 | 1.1818 | 0.8125 |
| 0.0003 | 136.0 | 136 | 1.1823 | 0.8125 |
| 0.0003 | 137.0 | 137 | 1.1828 | 0.8125 |
| 0.0003 | 138.0 | 138 | 1.1832 | 0.8125 |
| 0.0003 | 139.0 | 139 | 1.1835 | 0.8125 |
| 0.0002 | 140.0 | 140 | 1.1837 | 0.8125 |
| 0.0002 | 141.0 | 141 | 1.1838 | 0.8125 |
| 0.0002 | 142.0 | 142 | 1.1840 | 0.8125 |
| 0.0002 | 143.0 | 143 | 1.1841 | 0.8125 |
| 0.0002 | 144.0 | 144 | 1.1844 | 0.8125 |
| 0.0002 | 145.0 | 145 | 1.1845 | 0.8125 |
| 0.0002 | 146.0 | 146 | 1.1848 | 0.8125 |
| 0.0002 | 147.0 | 147 | 1.1851 | 0.8125 |
| 0.0002 | 148.0 | 148 | 1.1855 | 0.8125 |
| 0.0002 | 149.0 | 149 | 1.1859 | 0.8125 |
| 0.0002 | 150.0 | 150 | 1.1862 | 0.8125 |
### Framework versions
- Transformers 4.32.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.4.0
- Tokenizers 0.13.3
|
liuhaotian/llava-pretrain-llama-2-7b-chat
|
liuhaotian
| 2023-08-02T04:42:40Z | 21 | 4 |
transformers
|
[
"transformers",
"llava",
"text-generation",
"autotrain_compatible",
"region:us"
] |
text-generation
| 2023-08-02T04:28:13Z |
---
inference: false
---
<br>
<br>
# LLaVA Model Card
This is a pretrained checkpoint, you can use it to instruct tune your multimodal models.
Check out the instructions [here](https://github.com/haotian-liu/LLaVA/blob/main/README.md#visual-instruction-tuning)
## Model details
**Model type:**
LLaVA is an open-source chatbot trained by fine-tuning LLaMA/Vicuna on GPT-generated multimodal instruction-following data.
It is an auto-regressive language model, based on the transformer architecture.
**Model date:**
LLaVA-Pretrain-LLaMA-2-7b-Chat was trained in July 2023.
**Paper or resources for more information:**
https://llava-vl.github.io/
## License
Non-commerical Use.
**Where to send questions or comments about the model:**
https://github.com/haotian-liu/LLaVA/issues
## Intended use
**Primary intended uses:**
The primary use of LLaVA is research on large multimodal models and chatbots.
**Primary intended users:**
The primary intended users of the model are researchers and hobbyists in computer vision, natural language processing, machine learning, and artificial intelligence.
## Training dataset
- 558K filtered image-text pairs from LAION/CC/SBU, captioned by BLIP.
|
liuhaotian/llava-pretrain-llama-2-13b-chat
|
liuhaotian
| 2023-08-02T04:42:29Z | 17 | 2 |
transformers
|
[
"transformers",
"llava",
"text-generation",
"autotrain_compatible",
"region:us"
] |
text-generation
| 2023-08-02T04:28:23Z |
---
inference: false
---
<br>
<br>
# LLaVA Model Card
This is a pretrained checkpoint, you can use it to instruct tune your multimodal models.
Check out the instructions [here](https://github.com/haotian-liu/LLaVA/blob/main/README.md#visual-instruction-tuning)
## Model details
**Model type:**
LLaVA is an open-source chatbot trained by fine-tuning LLaMA/Vicuna on GPT-generated multimodal instruction-following data.
It is an auto-regressive language model, based on the transformer architecture.
**Model date:**
LLaVA-Pretrain-LLaMA-2-13b-Chat was trained in July 2023.
**Paper or resources for more information:**
https://llava-vl.github.io/
## License
Non-commerical Use.
**Where to send questions or comments about the model:**
https://github.com/haotian-liu/LLaVA/issues
## Intended use
**Primary intended uses:**
The primary use of LLaVA is research on large multimodal models and chatbots.
**Primary intended users:**
The primary intended users of the model are researchers and hobbyists in computer vision, natural language processing, machine learning, and artificial intelligence.
## Training dataset
- 558K filtered image-text pairs from LAION/CC/SBU, captioned by BLIP.
|
liuhaotian/llava-336px-pretrain-llama-2-7b-chat
|
liuhaotian
| 2023-08-02T04:39:29Z | 18 | 0 |
transformers
|
[
"transformers",
"llava",
"text-generation",
"autotrain_compatible",
"region:us"
] |
text-generation
| 2023-08-02T04:28:51Z |
---
inference: false
---
<br>
<br>
# LLaVA Model Card
This is a pretrained checkpoint, you can use it to instruct tune your multimodal models.
Check out the instructions [here](https://github.com/haotian-liu/LLaVA/blob/main/README.md#visual-instruction-tuning)
## Model details
**Model type:**
LLaVA is an open-source chatbot trained by fine-tuning LLaMA/Vicuna on GPT-generated multimodal instruction-following data.
It is an auto-regressive language model, based on the transformer architecture.
**Model date:**
LLaVA-336px-Pretrain-LLaMA-2-7b-Chat was trained in July 2023.
**Paper or resources for more information:**
https://llava-vl.github.io/
## License
Non-commerical Use.
**Where to send questions or comments about the model:**
https://github.com/haotian-liu/LLaVA/issues
## Intended use
**Primary intended uses:**
The primary use of LLaVA is research on large multimodal models and chatbots.
**Primary intended users:**
The primary intended users of the model are researchers and hobbyists in computer vision, natural language processing, machine learning, and artificial intelligence.
## Training dataset
- 558K filtered image-text pairs from LAION/CC/SBU, captioned by BLIP.
|
simonycl/best_model-yelp_polarity-16-87
|
simonycl
| 2023-08-02T04:37:41Z | 108 | 0 |
transformers
|
[
"transformers",
"pytorch",
"albert",
"text-classification",
"generated_from_trainer",
"base_model:albert/albert-base-v2",
"base_model:finetune:albert/albert-base-v2",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-08-02T01:07:32Z |
---
license: apache-2.0
base_model: albert-base-v2
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: best_model-yelp_polarity-16-87
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. -->
# best_model-yelp_polarity-16-87
This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0012
- Accuracy: 1.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 150
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 1 | 0.3437 | 0.875 |
| No log | 2.0 | 2 | 0.3444 | 0.875 |
| No log | 3.0 | 3 | 0.3459 | 0.875 |
| No log | 4.0 | 4 | 0.3481 | 0.875 |
| No log | 5.0 | 5 | 0.3509 | 0.875 |
| No log | 6.0 | 6 | 0.3542 | 0.875 |
| No log | 7.0 | 7 | 0.3577 | 0.875 |
| No log | 8.0 | 8 | 0.3605 | 0.875 |
| No log | 9.0 | 9 | 0.3609 | 0.875 |
| 1.0043 | 10.0 | 10 | 0.3571 | 0.875 |
| 1.0043 | 11.0 | 11 | 0.3490 | 0.875 |
| 1.0043 | 12.0 | 12 | 0.3367 | 0.875 |
| 1.0043 | 13.0 | 13 | 0.3202 | 0.875 |
| 1.0043 | 14.0 | 14 | 0.2996 | 0.875 |
| 1.0043 | 15.0 | 15 | 0.2751 | 0.875 |
| 1.0043 | 16.0 | 16 | 0.2470 | 0.9375 |
| 1.0043 | 17.0 | 17 | 0.2159 | 0.9375 |
| 1.0043 | 18.0 | 18 | 0.1832 | 0.9375 |
| 1.0043 | 19.0 | 19 | 0.1516 | 0.9375 |
| 0.6554 | 20.0 | 20 | 0.1241 | 0.9688 |
| 0.6554 | 21.0 | 21 | 0.1018 | 0.9688 |
| 0.6554 | 22.0 | 22 | 0.0818 | 0.9688 |
| 0.6554 | 23.0 | 23 | 0.0611 | 0.9688 |
| 0.6554 | 24.0 | 24 | 0.0378 | 0.9688 |
| 0.6554 | 25.0 | 25 | 0.0170 | 1.0 |
| 0.6554 | 26.0 | 26 | 0.0093 | 1.0 |
| 0.6554 | 27.0 | 27 | 0.0077 | 1.0 |
| 0.6554 | 28.0 | 28 | 0.0073 | 1.0 |
| 0.6554 | 29.0 | 29 | 0.0072 | 1.0 |
| 0.1962 | 30.0 | 30 | 0.0072 | 1.0 |
| 0.1962 | 31.0 | 31 | 0.0071 | 1.0 |
| 0.1962 | 32.0 | 32 | 0.0070 | 1.0 |
| 0.1962 | 33.0 | 33 | 0.0069 | 1.0 |
| 0.1962 | 34.0 | 34 | 0.0068 | 1.0 |
| 0.1962 | 35.0 | 35 | 0.0067 | 1.0 |
| 0.1962 | 36.0 | 36 | 0.0065 | 1.0 |
| 0.1962 | 37.0 | 37 | 0.0063 | 1.0 |
| 0.1962 | 38.0 | 38 | 0.0060 | 1.0 |
| 0.1962 | 39.0 | 39 | 0.0058 | 1.0 |
| 0.0075 | 40.0 | 40 | 0.0056 | 1.0 |
| 0.0075 | 41.0 | 41 | 0.0053 | 1.0 |
| 0.0075 | 42.0 | 42 | 0.0051 | 1.0 |
| 0.0075 | 43.0 | 43 | 0.0050 | 1.0 |
| 0.0075 | 44.0 | 44 | 0.0048 | 1.0 |
| 0.0075 | 45.0 | 45 | 0.0046 | 1.0 |
| 0.0075 | 46.0 | 46 | 0.0045 | 1.0 |
| 0.0075 | 47.0 | 47 | 0.0043 | 1.0 |
| 0.0075 | 48.0 | 48 | 0.0042 | 1.0 |
| 0.0075 | 49.0 | 49 | 0.0041 | 1.0 |
| 0.0019 | 50.0 | 50 | 0.0040 | 1.0 |
| 0.0019 | 51.0 | 51 | 0.0039 | 1.0 |
| 0.0019 | 52.0 | 52 | 0.0038 | 1.0 |
| 0.0019 | 53.0 | 53 | 0.0037 | 1.0 |
| 0.0019 | 54.0 | 54 | 0.0036 | 1.0 |
| 0.0019 | 55.0 | 55 | 0.0035 | 1.0 |
| 0.0019 | 56.0 | 56 | 0.0035 | 1.0 |
| 0.0019 | 57.0 | 57 | 0.0034 | 1.0 |
| 0.0019 | 58.0 | 58 | 0.0033 | 1.0 |
| 0.0019 | 59.0 | 59 | 0.0033 | 1.0 |
| 0.0014 | 60.0 | 60 | 0.0032 | 1.0 |
| 0.0014 | 61.0 | 61 | 0.0032 | 1.0 |
| 0.0014 | 62.0 | 62 | 0.0031 | 1.0 |
| 0.0014 | 63.0 | 63 | 0.0031 | 1.0 |
| 0.0014 | 64.0 | 64 | 0.0030 | 1.0 |
| 0.0014 | 65.0 | 65 | 0.0030 | 1.0 |
| 0.0014 | 66.0 | 66 | 0.0029 | 1.0 |
| 0.0014 | 67.0 | 67 | 0.0029 | 1.0 |
| 0.0014 | 68.0 | 68 | 0.0029 | 1.0 |
| 0.0014 | 69.0 | 69 | 0.0028 | 1.0 |
| 0.0011 | 70.0 | 70 | 0.0028 | 1.0 |
| 0.0011 | 71.0 | 71 | 0.0028 | 1.0 |
| 0.0011 | 72.0 | 72 | 0.0027 | 1.0 |
| 0.0011 | 73.0 | 73 | 0.0027 | 1.0 |
| 0.0011 | 74.0 | 74 | 0.0027 | 1.0 |
| 0.0011 | 75.0 | 75 | 0.0026 | 1.0 |
| 0.0011 | 76.0 | 76 | 0.0026 | 1.0 |
| 0.0011 | 77.0 | 77 | 0.0026 | 1.0 |
| 0.0011 | 78.0 | 78 | 0.0026 | 1.0 |
| 0.0011 | 79.0 | 79 | 0.0025 | 1.0 |
| 0.0009 | 80.0 | 80 | 0.0025 | 1.0 |
| 0.0009 | 81.0 | 81 | 0.0025 | 1.0 |
| 0.0009 | 82.0 | 82 | 0.0024 | 1.0 |
| 0.0009 | 83.0 | 83 | 0.0024 | 1.0 |
| 0.0009 | 84.0 | 84 | 0.0024 | 1.0 |
| 0.0009 | 85.0 | 85 | 0.0023 | 1.0 |
| 0.0009 | 86.0 | 86 | 0.0023 | 1.0 |
| 0.0009 | 87.0 | 87 | 0.0023 | 1.0 |
| 0.0009 | 88.0 | 88 | 0.0022 | 1.0 |
| 0.0009 | 89.0 | 89 | 0.0022 | 1.0 |
| 0.0008 | 90.0 | 90 | 0.0022 | 1.0 |
| 0.0008 | 91.0 | 91 | 0.0021 | 1.0 |
| 0.0008 | 92.0 | 92 | 0.0021 | 1.0 |
| 0.0008 | 93.0 | 93 | 0.0021 | 1.0 |
| 0.0008 | 94.0 | 94 | 0.0020 | 1.0 |
| 0.0008 | 95.0 | 95 | 0.0020 | 1.0 |
| 0.0008 | 96.0 | 96 | 0.0020 | 1.0 |
| 0.0008 | 97.0 | 97 | 0.0019 | 1.0 |
| 0.0008 | 98.0 | 98 | 0.0019 | 1.0 |
| 0.0008 | 99.0 | 99 | 0.0019 | 1.0 |
| 0.0007 | 100.0 | 100 | 0.0019 | 1.0 |
| 0.0007 | 101.0 | 101 | 0.0018 | 1.0 |
| 0.0007 | 102.0 | 102 | 0.0018 | 1.0 |
| 0.0007 | 103.0 | 103 | 0.0018 | 1.0 |
| 0.0007 | 104.0 | 104 | 0.0018 | 1.0 |
| 0.0007 | 105.0 | 105 | 0.0018 | 1.0 |
| 0.0007 | 106.0 | 106 | 0.0017 | 1.0 |
| 0.0007 | 107.0 | 107 | 0.0017 | 1.0 |
| 0.0007 | 108.0 | 108 | 0.0017 | 1.0 |
| 0.0007 | 109.0 | 109 | 0.0017 | 1.0 |
| 0.0006 | 110.0 | 110 | 0.0017 | 1.0 |
| 0.0006 | 111.0 | 111 | 0.0016 | 1.0 |
| 0.0006 | 112.0 | 112 | 0.0016 | 1.0 |
| 0.0006 | 113.0 | 113 | 0.0016 | 1.0 |
| 0.0006 | 114.0 | 114 | 0.0016 | 1.0 |
| 0.0006 | 115.0 | 115 | 0.0016 | 1.0 |
| 0.0006 | 116.0 | 116 | 0.0016 | 1.0 |
| 0.0006 | 117.0 | 117 | 0.0015 | 1.0 |
| 0.0006 | 118.0 | 118 | 0.0015 | 1.0 |
| 0.0006 | 119.0 | 119 | 0.0015 | 1.0 |
| 0.0005 | 120.0 | 120 | 0.0015 | 1.0 |
| 0.0005 | 121.0 | 121 | 0.0015 | 1.0 |
| 0.0005 | 122.0 | 122 | 0.0015 | 1.0 |
| 0.0005 | 123.0 | 123 | 0.0015 | 1.0 |
| 0.0005 | 124.0 | 124 | 0.0015 | 1.0 |
| 0.0005 | 125.0 | 125 | 0.0014 | 1.0 |
| 0.0005 | 126.0 | 126 | 0.0014 | 1.0 |
| 0.0005 | 127.0 | 127 | 0.0014 | 1.0 |
| 0.0005 | 128.0 | 128 | 0.0014 | 1.0 |
| 0.0005 | 129.0 | 129 | 0.0014 | 1.0 |
| 0.0005 | 130.0 | 130 | 0.0014 | 1.0 |
| 0.0005 | 131.0 | 131 | 0.0014 | 1.0 |
| 0.0005 | 132.0 | 132 | 0.0014 | 1.0 |
| 0.0005 | 133.0 | 133 | 0.0014 | 1.0 |
| 0.0005 | 134.0 | 134 | 0.0014 | 1.0 |
| 0.0005 | 135.0 | 135 | 0.0013 | 1.0 |
| 0.0005 | 136.0 | 136 | 0.0013 | 1.0 |
| 0.0005 | 137.0 | 137 | 0.0013 | 1.0 |
| 0.0005 | 138.0 | 138 | 0.0013 | 1.0 |
| 0.0005 | 139.0 | 139 | 0.0013 | 1.0 |
| 0.0004 | 140.0 | 140 | 0.0013 | 1.0 |
| 0.0004 | 141.0 | 141 | 0.0013 | 1.0 |
| 0.0004 | 142.0 | 142 | 0.0013 | 1.0 |
| 0.0004 | 143.0 | 143 | 0.0013 | 1.0 |
| 0.0004 | 144.0 | 144 | 0.0013 | 1.0 |
| 0.0004 | 145.0 | 145 | 0.0013 | 1.0 |
| 0.0004 | 146.0 | 146 | 0.0013 | 1.0 |
| 0.0004 | 147.0 | 147 | 0.0013 | 1.0 |
| 0.0004 | 148.0 | 148 | 0.0012 | 1.0 |
| 0.0004 | 149.0 | 149 | 0.0012 | 1.0 |
| 0.0004 | 150.0 | 150 | 0.0012 | 1.0 |
### Framework versions
- Transformers 4.32.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.4.0
- Tokenizers 0.13.3
|
ZMaxwell-Smith/OIL_YT_ind_nlp_all
|
ZMaxwell-Smith
| 2023-08-02T04:36:40Z | 114 | 1 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"id",
"en",
"doi:10.57967/hf/0492",
"license:cc-by-nc-sa-4.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-27T05:21:51Z |
---
language:
- id
- en
tags:
- wav2vec2
license: cc-by-nc-sa-4.0
---
Model page for OIL_YT_ind_nlp_all
For further details please see Zara Maxwell-Smith and Ben Foley, (forthcoming), Automated speech recognition of Indonesian-English language lessons on YouTube using transfer learning, Field Matters Workshop, EACL 2023
How to cite this model.
Please use the following .bib to reference this work.
```
{@inproceedings{Maxwell-Smith_Foley_2023_Automated,
title={{Automated speech recognition of Indonesian-English language lessons on YouTube using transfer learning}},
author={Maxwell-Smith, Zara and Foley, Ben},
booktitle={Proceedings of the {Second Workshop on NLP Applications to Field Linguistics (EACL)}},
pages={},
year={forthcoming}
}
```
|
liuhaotian/llava-336px-pretrain-vicuna-13b-v1.3
|
liuhaotian
| 2023-08-02T04:33:22Z | 12 | 7 |
transformers
|
[
"transformers",
"llava",
"text-generation",
"autotrain_compatible",
"region:us"
] |
text-generation
| 2023-08-02T04:12:10Z |
---
inference: false
---
<br>
<br>
# LLaVA Model Card
This is a pretrained checkpoint, you can use it to instruct tune your multimodal models.
Check out the instructions [here](https://github.com/haotian-liu/LLaVA/blob/main/README.md#visual-instruction-tuning)
## Model details
**Model type:**
LLaVA is an open-source chatbot trained by fine-tuning LLaMA/Vicuna on GPT-generated multimodal instruction-following data.
It is an auto-regressive language model, based on the transformer architecture.
**Model date:**
LLaVA-336px-Pretrain-Vicuna-13B-v1.3 was trained in July 2023.
**Paper or resources for more information:**
https://llava-vl.github.io/
## License
Non-commerical Use.
**Where to send questions or comments about the model:**
https://github.com/haotian-liu/LLaVA/issues
## Intended use
**Primary intended uses:**
The primary use of LLaVA is research on large multimodal models and chatbots.
**Primary intended users:**
The primary intended users of the model are researchers and hobbyists in computer vision, natural language processing, machine learning, and artificial intelligence.
## Training dataset
- 558K filtered image-text pairs from LAION/CC/SBU, captioned by BLIP.
|
liuhaotian/llava-pretrain-vicuna-7b-v1.3
|
liuhaotian
| 2023-08-02T04:32:23Z | 95 | 0 |
transformers
|
[
"transformers",
"llava",
"text-generation",
"autotrain_compatible",
"region:us"
] |
text-generation
| 2023-08-02T04:06:19Z |
---
inference: false
---
<br>
<br>
# LLaVA Model Card
This is a pretrained checkpoint, you can use it to instruct tune your multimodal models.
Check out the instructions [here](https://github.com/haotian-liu/LLaVA/blob/main/README.md#visual-instruction-tuning)
## Model details
**Model type:**
LLaVA is an open-source chatbot trained by fine-tuning LLaMA/Vicuna on GPT-generated multimodal instruction-following data.
It is an auto-regressive language model, based on the transformer architecture.
**Model date:**
LLaVA-Pretrain-Vicuna-7B-v1.3 was trained in July 2023.
**Paper or resources for more information:**
https://llava-vl.github.io/
## License
Non-commerical Use.
**Where to send questions or comments about the model:**
https://github.com/haotian-liu/LLaVA/issues
## Intended use
**Primary intended uses:**
The primary use of LLaVA is research on large multimodal models and chatbots.
**Primary intended users:**
The primary intended users of the model are researchers and hobbyists in computer vision, natural language processing, machine learning, and artificial intelligence.
## Training dataset
- 558K filtered image-text pairs from LAION/CC/SBU, captioned by BLIP.
|
liuhaotian/llava-pretrain-vicuna-13b-v1.3
|
liuhaotian
| 2023-08-02T04:32:07Z | 25 | 0 |
transformers
|
[
"transformers",
"llava",
"text-generation",
"autotrain_compatible",
"region:us"
] |
text-generation
| 2023-08-02T03:59:06Z |
---
inference: false
---
<br>
<br>
# LLaVA Model Card
This is a pretrained checkpoint, you can use it to instruct tune your multimodal models.
Check out the instructions [here](https://github.com/haotian-liu/LLaVA/blob/main/README.md#visual-instruction-tuning)
## Model details
**Model type:**
LLaVA is an open-source chatbot trained by fine-tuning LLaMA/Vicuna on GPT-generated multimodal instruction-following data.
It is an auto-regressive language model, based on the transformer architecture.
**Model date:**
LLaVA-Pretrain-Vicuna-13B-v1.3 was trained in July 2023.
**Paper or resources for more information:**
https://llava-vl.github.io/
## License
Non-commerical Use.
**Where to send questions or comments about the model:**
https://github.com/haotian-liu/LLaVA/issues
## Intended use
**Primary intended uses:**
The primary use of LLaVA is research on large multimodal models and chatbots.
**Primary intended users:**
The primary intended users of the model are researchers and hobbyists in computer vision, natural language processing, machine learning, and artificial intelligence.
## Training dataset
- 558K filtered image-text pairs from LAION/CC/SBU, captioned by BLIP.
|
lchiang/layoutlmv3-finetuned-cord_100
|
lchiang
| 2023-08-02T04:30:58Z | 78 | 0 |
transformers
|
[
"transformers",
"pytorch",
"layoutlmv3",
"token-classification",
"generated_from_trainer",
"dataset:cord-layoutlmv3",
"license:cc-by-nc-sa-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2023-07-13T21:28:54Z |
---
license: cc-by-nc-sa-4.0
tags:
- generated_from_trainer
datasets:
- cord-layoutlmv3
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: layoutlmv3-finetuned-cord_100
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: cord-layoutlmv3
type: cord-layoutlmv3
config: cord
split: test
args: cord
metrics:
- name: Precision
type: precision
value: 0.9465478841870824
- name: Recall
type: recall
value: 0.9543413173652695
- name: F1
type: f1
value: 0.9504286246738725
- name: Accuracy
type: accuracy
value: 0.9584040747028862
---
<!-- 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. -->
# layoutlmv3-finetuned-cord_100
This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the cord-layoutlmv3 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2090
- Precision: 0.9465
- Recall: 0.9543
- F1: 0.9504
- Accuracy: 0.9584
## 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: 5
- eval_batch_size: 5
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 2500
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.56 | 250 | 1.0347 | 0.6965 | 0.7695 | 0.7312 | 0.7861 |
| 1.4031 | 3.12 | 500 | 0.5641 | 0.8491 | 0.8720 | 0.8604 | 0.8744 |
| 1.4031 | 4.69 | 750 | 0.3899 | 0.8810 | 0.9087 | 0.8946 | 0.9138 |
| 0.4005 | 6.25 | 1000 | 0.3025 | 0.9202 | 0.9319 | 0.9260 | 0.9355 |
| 0.4005 | 7.81 | 1250 | 0.2641 | 0.9211 | 0.9349 | 0.9279 | 0.9402 |
| 0.2161 | 9.38 | 1500 | 0.2406 | 0.9277 | 0.9416 | 0.9346 | 0.9474 |
| 0.2161 | 10.94 | 1750 | 0.2250 | 0.9343 | 0.9469 | 0.9405 | 0.9516 |
| 0.1474 | 12.5 | 2000 | 0.2238 | 0.9415 | 0.9513 | 0.9464 | 0.9542 |
| 0.1474 | 14.06 | 2250 | 0.2128 | 0.9451 | 0.9536 | 0.9493 | 0.9571 |
| 0.1128 | 15.62 | 2500 | 0.2090 | 0.9465 | 0.9543 | 0.9504 | 0.9584 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1
- Datasets 2.13.1
- Tokenizers 0.13.3
|
OpenBuddy/openbuddy-llama-65b-v8-bf16
|
OpenBuddy
| 2023-08-02T04:28:56Z | 1,547 | 9 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"zh",
"en",
"fr",
"de",
"ja",
"ko",
"it",
"ru",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-08-01T02:54:07Z |
---
language:
- zh
- en
- fr
- de
- ja
- ko
- it
- ru
pipeline_tag: text-generation
---
# OpenBuddy - Open Multilingual Chatbot
GitHub and Usage Guide: [https://github.com/OpenBuddy/OpenBuddy](https://github.com/OpenBuddy/OpenBuddy)
Website and Demo: [https://openbuddy.ai](https://openbuddy.ai)
# Copyright Notice
OpenBuddy LLaMA-series models are built upon Meta's LLaMA and are subject to Meta's licensing agreement.
They are intended for use only by individuals who have obtained approval from Meta and are eligible to download LLaMA.
If you have not obtained approval from Meta, you must visit the https://ai.meta.com/llama/ page, read and agree to the model's licensing agreement, submit an application, and wait for approval from Meta before downloading LLaMA-series models from this page.
## Disclaimer
All OpenBuddy models have inherent limitations and may potentially produce outputs that are erroneous, harmful, offensive, or otherwise undesirable. Users should not use these models in critical or high-stakes situations that may lead to personal injury, property damage, or significant losses. Examples of such scenarios include, but are not limited to, the medical field, controlling software and hardware systems that may cause harm, and making important financial or legal decisions.
OpenBuddy is provided "as-is" without any warranty of any kind, either express or implied, including, but not limited to, the implied warranties of merchantability, fitness for a particular purpose, and non-infringement. In no event shall the authors, contributors, or copyright holders be liable for any claim, damages, or other liabilities, whether in an action of contract, tort, or otherwise, arising from, out of, or in connection with the software or the use or other dealings in the software.
By using OpenBuddy, you agree to these terms and conditions, and acknowledge that you understand the potential risks associated with its use. You also agree to indemnify and hold harmless the authors, contributors, and copyright holders from any claims, damages, or liabilities arising from your use of OpenBuddy.
## 免责声明
所有OpenBuddy模型均存在固有的局限性，可能产生错误的、有害的、冒犯性的或其他不良的输出。用户在关键或高风险场景中应谨慎行事，不要使用这些模型，以免导致人身伤害、财产损失或重大损失。此类场景的例子包括但不限于医疗领域、可能导致伤害的软硬件系统的控制以及进行重要的财务或法律决策。
OpenBuddy按“原样”提供，不附带任何种类的明示或暗示的保证，包括但不限于适销性、特定目的的适用性和非侵权的暗示保证。在任何情况下，作者、贡献者或版权所有者均不对因软件或使用或其他软件交易而产生的任何索赔、损害赔偿或其他责任（无论是合同、侵权还是其他原因）承担责任。
使用OpenBuddy即表示您同意这些条款和条件，并承认您了解其使用可能带来的潜在风险。您还同意赔偿并使作者、贡献者和版权所有者免受因您使用OpenBuddy而产生的任何索赔、损害赔偿或责任的影响。
|
polejowska/detr-r101-cd45rb-8ah-6l-256d-1024ffn
|
polejowska
| 2023-08-02T04:26:27Z | 159 | 0 |
transformers
|
[
"transformers",
"pytorch",
"detr",
"object-detection",
"generated_from_trainer",
"dataset:cd45rb",
"endpoints_compatible",
"region:us"
] |
object-detection
| 2023-08-01T14:23:23Z |
---
tags:
- generated_from_trainer
datasets:
- cd45rb
model-index:
- name: detr-r101-cd45rb-8ah-6l-256d-1024ffn
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-r101-cd45rb-8ah-6l-256d-1024ffn
This model is a fine-tuned version of [](https://huggingface.co/) on the cd45rb dataset.
It achieves the following results on the evaluation set:
- Loss: 5.0153
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 4
- eval_batch_size: 8
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|
| 4.7717 | 1.0 | 4606 | 4.9667 |
| 3.7186 | 2.0 | 9212 | 5.0146 |
| 3.6825 | 3.0 | 13818 | 5.0084 |
| 3.6793 | 4.0 | 18424 | 5.0322 |
| 3.673 | 5.0 | 23030 | 5.0330 |
| 3.6605 | 6.0 | 27636 | 5.0197 |
| 3.657 | 7.0 | 32242 | 5.0128 |
| 3.6543 | 8.0 | 36848 | 5.0124 |
| 3.6505 | 9.0 | 41454 | 5.0099 |
| 3.6484 | 10.0 | 46060 | 5.0153 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1
- Datasets 2.12.0
- Tokenizers 0.13.3
|
simonycl/best_model-yelp_polarity-16-21
|
simonycl
| 2023-08-02T04:20:33Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"albert",
"text-classification",
"generated_from_trainer",
"base_model:albert/albert-base-v2",
"base_model:finetune:albert/albert-base-v2",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-08-02T00:51:53Z |
---
license: apache-2.0
base_model: albert-base-v2
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: best_model-yelp_polarity-16-21
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. -->
# best_model-yelp_polarity-16-21
This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8234
- Accuracy: 0.75
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 150
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 1 | 0.8218 | 0.625 |
| No log | 2.0 | 2 | 0.8206 | 0.625 |
| No log | 3.0 | 3 | 0.8183 | 0.625 |
| No log | 4.0 | 4 | 0.8150 | 0.625 |
| No log | 5.0 | 5 | 0.8107 | 0.625 |
| No log | 6.0 | 6 | 0.8057 | 0.625 |
| No log | 7.0 | 7 | 0.8001 | 0.6562 |
| No log | 8.0 | 8 | 0.7944 | 0.6875 |
| No log | 9.0 | 9 | 0.7887 | 0.7188 |
| 0.5647 | 10.0 | 10 | 0.7834 | 0.7188 |
| 0.5647 | 11.0 | 11 | 0.7784 | 0.7188 |
| 0.5647 | 12.0 | 12 | 0.7738 | 0.7188 |
| 0.5647 | 13.0 | 13 | 0.7695 | 0.7188 |
| 0.5647 | 14.0 | 14 | 0.7651 | 0.7188 |
| 0.5647 | 15.0 | 15 | 0.7606 | 0.7188 |
| 0.5647 | 16.0 | 16 | 0.7558 | 0.7188 |
| 0.5647 | 17.0 | 17 | 0.7506 | 0.7188 |
| 0.5647 | 18.0 | 18 | 0.7451 | 0.7188 |
| 0.5647 | 19.0 | 19 | 0.7392 | 0.7188 |
| 0.472 | 20.0 | 20 | 0.7329 | 0.7188 |
| 0.472 | 21.0 | 21 | 0.7262 | 0.7188 |
| 0.472 | 22.0 | 22 | 0.7190 | 0.7188 |
| 0.472 | 23.0 | 23 | 0.7112 | 0.75 |
| 0.472 | 24.0 | 24 | 0.7029 | 0.75 |
| 0.472 | 25.0 | 25 | 0.6941 | 0.75 |
| 0.472 | 26.0 | 26 | 0.6847 | 0.75 |
| 0.472 | 27.0 | 27 | 0.6749 | 0.75 |
| 0.472 | 28.0 | 28 | 0.6647 | 0.75 |
| 0.472 | 29.0 | 29 | 0.6545 | 0.75 |
| 0.3267 | 30.0 | 30 | 0.6445 | 0.75 |
| 0.3267 | 31.0 | 31 | 0.6350 | 0.6562 |
| 0.3267 | 32.0 | 32 | 0.6261 | 0.6562 |
| 0.3267 | 33.0 | 33 | 0.6177 | 0.6875 |
| 0.3267 | 34.0 | 34 | 0.6100 | 0.6875 |
| 0.3267 | 35.0 | 35 | 0.6031 | 0.6875 |
| 0.3267 | 36.0 | 36 | 0.5973 | 0.6875 |
| 0.3267 | 37.0 | 37 | 0.5926 | 0.7188 |
| 0.3267 | 38.0 | 38 | 0.5895 | 0.7188 |
| 0.3267 | 39.0 | 39 | 0.5869 | 0.7188 |
| 0.1824 | 40.0 | 40 | 0.5842 | 0.75 |
| 0.1824 | 41.0 | 41 | 0.5796 | 0.75 |
| 0.1824 | 42.0 | 42 | 0.5730 | 0.75 |
| 0.1824 | 43.0 | 43 | 0.5651 | 0.75 |
| 0.1824 | 44.0 | 44 | 0.5555 | 0.75 |
| 0.1824 | 45.0 | 45 | 0.5466 | 0.7812 |
| 0.1824 | 46.0 | 46 | 0.5408 | 0.7812 |
| 0.1824 | 47.0 | 47 | 0.5379 | 0.7812 |
| 0.1824 | 48.0 | 48 | 0.5386 | 0.7812 |
| 0.1824 | 49.0 | 49 | 0.5419 | 0.7812 |
| 0.0885 | 50.0 | 50 | 0.5482 | 0.7812 |
| 0.0885 | 51.0 | 51 | 0.5568 | 0.7812 |
| 0.0885 | 52.0 | 52 | 0.5662 | 0.7812 |
| 0.0885 | 53.0 | 53 | 0.5761 | 0.7812 |
| 0.0885 | 54.0 | 54 | 0.5834 | 0.7812 |
| 0.0885 | 55.0 | 55 | 0.5897 | 0.8125 |
| 0.0885 | 56.0 | 56 | 0.5929 | 0.8125 |
| 0.0885 | 57.0 | 57 | 0.5930 | 0.8125 |
| 0.0885 | 58.0 | 58 | 0.5905 | 0.7812 |
| 0.0885 | 59.0 | 59 | 0.5869 | 0.7812 |
| 0.0497 | 60.0 | 60 | 0.5830 | 0.7812 |
| 0.0497 | 61.0 | 61 | 0.5795 | 0.75 |
| 0.0497 | 62.0 | 62 | 0.5776 | 0.75 |
| 0.0497 | 63.0 | 63 | 0.5777 | 0.75 |
| 0.0497 | 64.0 | 64 | 0.5800 | 0.75 |
| 0.0497 | 65.0 | 65 | 0.5832 | 0.75 |
| 0.0497 | 66.0 | 66 | 0.5887 | 0.75 |
| 0.0497 | 67.0 | 67 | 0.5962 | 0.7812 |
| 0.0497 | 68.0 | 68 | 0.6062 | 0.7812 |
| 0.0497 | 69.0 | 69 | 0.6192 | 0.75 |
| 0.0306 | 70.0 | 70 | 0.6332 | 0.75 |
| 0.0306 | 71.0 | 71 | 0.6475 | 0.75 |
| 0.0306 | 72.0 | 72 | 0.6610 | 0.75 |
| 0.0306 | 73.0 | 73 | 0.6726 | 0.75 |
| 0.0306 | 74.0 | 74 | 0.6824 | 0.75 |
| 0.0306 | 75.0 | 75 | 0.6910 | 0.75 |
| 0.0306 | 76.0 | 76 | 0.6989 | 0.75 |
| 0.0306 | 77.0 | 77 | 0.7058 | 0.75 |
| 0.0306 | 78.0 | 78 | 0.7122 | 0.75 |
| 0.0306 | 79.0 | 79 | 0.7179 | 0.7188 |
| 0.0175 | 80.0 | 80 | 0.7230 | 0.7188 |
| 0.0175 | 81.0 | 81 | 0.7281 | 0.7188 |
| 0.0175 | 82.0 | 82 | 0.7331 | 0.7188 |
| 0.0175 | 83.0 | 83 | 0.7385 | 0.7188 |
| 0.0175 | 84.0 | 84 | 0.7428 | 0.7188 |
| 0.0175 | 85.0 | 85 | 0.7462 | 0.7188 |
| 0.0175 | 86.0 | 86 | 0.7491 | 0.75 |
| 0.0175 | 87.0 | 87 | 0.7520 | 0.75 |
| 0.0175 | 88.0 | 88 | 0.7544 | 0.75 |
| 0.0175 | 89.0 | 89 | 0.7566 | 0.75 |
| 0.0111 | 90.0 | 90 | 0.7584 | 0.75 |
| 0.0111 | 91.0 | 91 | 0.7604 | 0.75 |
| 0.0111 | 92.0 | 92 | 0.7622 | 0.75 |
| 0.0111 | 93.0 | 93 | 0.7641 | 0.75 |
| 0.0111 | 94.0 | 94 | 0.7665 | 0.75 |
| 0.0111 | 95.0 | 95 | 0.7693 | 0.75 |
| 0.0111 | 96.0 | 96 | 0.7724 | 0.75 |
| 0.0111 | 97.0 | 97 | 0.7757 | 0.75 |
| 0.0111 | 98.0 | 98 | 0.7792 | 0.75 |
| 0.0111 | 99.0 | 99 | 0.7828 | 0.75 |
| 0.0078 | 100.0 | 100 | 0.7868 | 0.75 |
| 0.0078 | 101.0 | 101 | 0.7911 | 0.75 |
| 0.0078 | 102.0 | 102 | 0.7959 | 0.75 |
| 0.0078 | 103.0 | 103 | 0.8010 | 0.75 |
| 0.0078 | 104.0 | 104 | 0.8059 | 0.75 |
| 0.0078 | 105.0 | 105 | 0.8106 | 0.75 |
| 0.0078 | 106.0 | 106 | 0.8150 | 0.75 |
| 0.0078 | 107.0 | 107 | 0.8193 | 0.75 |
| 0.0078 | 108.0 | 108 | 0.8230 | 0.75 |
| 0.0078 | 109.0 | 109 | 0.8263 | 0.75 |
| 0.0061 | 110.0 | 110 | 0.8290 | 0.75 |
| 0.0061 | 111.0 | 111 | 0.8312 | 0.75 |
| 0.0061 | 112.0 | 112 | 0.8328 | 0.75 |
| 0.0061 | 113.0 | 113 | 0.8339 | 0.75 |
| 0.0061 | 114.0 | 114 | 0.8345 | 0.75 |
| 0.0061 | 115.0 | 115 | 0.8348 | 0.75 |
| 0.0061 | 116.0 | 116 | 0.8347 | 0.75 |
| 0.0061 | 117.0 | 117 | 0.8338 | 0.75 |
| 0.0061 | 118.0 | 118 | 0.8329 | 0.75 |
| 0.0061 | 119.0 | 119 | 0.8322 | 0.75 |
| 0.0048 | 120.0 | 120 | 0.8315 | 0.75 |
| 0.0048 | 121.0 | 121 | 0.8308 | 0.75 |
| 0.0048 | 122.0 | 122 | 0.8301 | 0.75 |
| 0.0048 | 123.0 | 123 | 0.8296 | 0.75 |
| 0.0048 | 124.0 | 124 | 0.8294 | 0.75 |
| 0.0048 | 125.0 | 125 | 0.8296 | 0.75 |
| 0.0048 | 126.0 | 126 | 0.8299 | 0.75 |
| 0.0048 | 127.0 | 127 | 0.8302 | 0.75 |
| 0.0048 | 128.0 | 128 | 0.8302 | 0.75 |
| 0.0048 | 129.0 | 129 | 0.8304 | 0.75 |
| 0.0039 | 130.0 | 130 | 0.8306 | 0.75 |
| 0.0039 | 131.0 | 131 | 0.8305 | 0.75 |
| 0.0039 | 132.0 | 132 | 0.8301 | 0.75 |
| 0.0039 | 133.0 | 133 | 0.8296 | 0.7812 |
| 0.0039 | 134.0 | 134 | 0.8292 | 0.7812 |
| 0.0039 | 135.0 | 135 | 0.8283 | 0.7812 |
| 0.0039 | 136.0 | 136 | 0.8272 | 0.7812 |
| 0.0039 | 137.0 | 137 | 0.8259 | 0.7812 |
| 0.0039 | 138.0 | 138 | 0.8247 | 0.7812 |
| 0.0039 | 139.0 | 139 | 0.8237 | 0.75 |
| 0.0032 | 140.0 | 140 | 0.8228 | 0.75 |
| 0.0032 | 141.0 | 141 | 0.8222 | 0.75 |
| 0.0032 | 142.0 | 142 | 0.8222 | 0.75 |
| 0.0032 | 143.0 | 143 | 0.8220 | 0.75 |
| 0.0032 | 144.0 | 144 | 0.8220 | 0.75 |
| 0.0032 | 145.0 | 145 | 0.8218 | 0.75 |
| 0.0032 | 146.0 | 146 | 0.8217 | 0.75 |
| 0.0032 | 147.0 | 147 | 0.8218 | 0.75 |
| 0.0032 | 148.0 | 148 | 0.8222 | 0.75 |
| 0.0032 | 149.0 | 149 | 0.8228 | 0.75 |
| 0.0028 | 150.0 | 150 | 0.8234 | 0.75 |
### Framework versions
- Transformers 4.32.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.4.0
- Tokenizers 0.13.3
|
simonycl/best_model-yelp_polarity-16-13
|
simonycl
| 2023-08-02T04:11:59Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"albert",
"text-classification",
"generated_from_trainer",
"base_model:albert/albert-base-v2",
"base_model:finetune:albert/albert-base-v2",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-08-01T23:47:24Z |
---
license: apache-2.0
base_model: albert-base-v2
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: best_model-yelp_polarity-16-13
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. -->
# best_model-yelp_polarity-16-13
This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3928
- Accuracy: 0.875
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 150
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 1 | 0.7228 | 0.5 |
| No log | 2.0 | 2 | 0.7227 | 0.5 |
| No log | 3.0 | 3 | 0.7227 | 0.5 |
| No log | 4.0 | 4 | 0.7225 | 0.5 |
| No log | 5.0 | 5 | 0.7224 | 0.5 |
| No log | 6.0 | 6 | 0.7221 | 0.5 |
| No log | 7.0 | 7 | 0.7219 | 0.5 |
| No log | 8.0 | 8 | 0.7216 | 0.5 |
| No log | 9.0 | 9 | 0.7213 | 0.5 |
| 0.7034 | 10.0 | 10 | 0.7209 | 0.5 |
| 0.7034 | 11.0 | 11 | 0.7205 | 0.5 |
| 0.7034 | 12.0 | 12 | 0.7200 | 0.5 |
| 0.7034 | 13.0 | 13 | 0.7195 | 0.5 |
| 0.7034 | 14.0 | 14 | 0.7189 | 0.5 |
| 0.7034 | 15.0 | 15 | 0.7183 | 0.5 |
| 0.7034 | 16.0 | 16 | 0.7177 | 0.5 |
| 0.7034 | 17.0 | 17 | 0.7170 | 0.5 |
| 0.7034 | 18.0 | 18 | 0.7163 | 0.5 |
| 0.7034 | 19.0 | 19 | 0.7156 | 0.5 |
| 0.6925 | 20.0 | 20 | 0.7148 | 0.5 |
| 0.6925 | 21.0 | 21 | 0.7140 | 0.5 |
| 0.6925 | 22.0 | 22 | 0.7132 | 0.5 |
| 0.6925 | 23.0 | 23 | 0.7123 | 0.5 |
| 0.6925 | 24.0 | 24 | 0.7113 | 0.5 |
| 0.6925 | 25.0 | 25 | 0.7104 | 0.5 |
| 0.6925 | 26.0 | 26 | 0.7093 | 0.5 |
| 0.6925 | 27.0 | 27 | 0.7082 | 0.5 |
| 0.6925 | 28.0 | 28 | 0.7071 | 0.5 |
| 0.6925 | 29.0 | 29 | 0.7059 | 0.5 |
| 0.6581 | 30.0 | 30 | 0.7047 | 0.5 |
| 0.6581 | 31.0 | 31 | 0.7034 | 0.5 |
| 0.6581 | 32.0 | 32 | 0.7021 | 0.5 |
| 0.6581 | 33.0 | 33 | 0.7007 | 0.5 |
| 0.6581 | 34.0 | 34 | 0.6991 | 0.5 |
| 0.6581 | 35.0 | 35 | 0.6975 | 0.5 |
| 0.6581 | 36.0 | 36 | 0.6958 | 0.5 |
| 0.6581 | 37.0 | 37 | 0.6941 | 0.5 |
| 0.6581 | 38.0 | 38 | 0.6923 | 0.5 |
| 0.6581 | 39.0 | 39 | 0.6904 | 0.5 |
| 0.6325 | 40.0 | 40 | 0.6883 | 0.5 |
| 0.6325 | 41.0 | 41 | 0.6862 | 0.5 |
| 0.6325 | 42.0 | 42 | 0.6841 | 0.5 |
| 0.6325 | 43.0 | 43 | 0.6818 | 0.5 |
| 0.6325 | 44.0 | 44 | 0.6794 | 0.5 |
| 0.6325 | 45.0 | 45 | 0.6770 | 0.5 |
| 0.6325 | 46.0 | 46 | 0.6745 | 0.5312 |
| 0.6325 | 47.0 | 47 | 0.6718 | 0.5312 |
| 0.6325 | 48.0 | 48 | 0.6690 | 0.5312 |
| 0.6325 | 49.0 | 49 | 0.6662 | 0.5625 |
| 0.573 | 50.0 | 50 | 0.6633 | 0.5625 |
| 0.573 | 51.0 | 51 | 0.6602 | 0.5625 |
| 0.573 | 52.0 | 52 | 0.6571 | 0.5625 |
| 0.573 | 53.0 | 53 | 0.6538 | 0.5625 |
| 0.573 | 54.0 | 54 | 0.6504 | 0.5625 |
| 0.573 | 55.0 | 55 | 0.6469 | 0.5625 |
| 0.573 | 56.0 | 56 | 0.6435 | 0.5625 |
| 0.573 | 57.0 | 57 | 0.6401 | 0.625 |
| 0.573 | 58.0 | 58 | 0.6368 | 0.625 |
| 0.573 | 59.0 | 59 | 0.6336 | 0.6562 |
| 0.5136 | 60.0 | 60 | 0.6305 | 0.6875 |
| 0.5136 | 61.0 | 61 | 0.6273 | 0.6562 |
| 0.5136 | 62.0 | 62 | 0.6240 | 0.6562 |
| 0.5136 | 63.0 | 63 | 0.6206 | 0.6562 |
| 0.5136 | 64.0 | 64 | 0.6172 | 0.6875 |
| 0.5136 | 65.0 | 65 | 0.6138 | 0.6875 |
| 0.5136 | 66.0 | 66 | 0.6105 | 0.6875 |
| 0.5136 | 67.0 | 67 | 0.6072 | 0.6875 |
| 0.5136 | 68.0 | 68 | 0.6038 | 0.6875 |
| 0.5136 | 69.0 | 69 | 0.6004 | 0.6875 |
| 0.4388 | 70.0 | 70 | 0.5968 | 0.6875 |
| 0.4388 | 71.0 | 71 | 0.5931 | 0.7188 |
| 0.4388 | 72.0 | 72 | 0.5893 | 0.75 |
| 0.4388 | 73.0 | 73 | 0.5854 | 0.75 |
| 0.4388 | 74.0 | 74 | 0.5814 | 0.75 |
| 0.4388 | 75.0 | 75 | 0.5773 | 0.75 |
| 0.4388 | 76.0 | 76 | 0.5732 | 0.75 |
| 0.4388 | 77.0 | 77 | 0.5695 | 0.7812 |
| 0.4388 | 78.0 | 78 | 0.5660 | 0.7812 |
| 0.4388 | 79.0 | 79 | 0.5626 | 0.7812 |
| 0.3545 | 80.0 | 80 | 0.5590 | 0.7812 |
| 0.3545 | 81.0 | 81 | 0.5553 | 0.7812 |
| 0.3545 | 82.0 | 82 | 0.5514 | 0.8125 |
| 0.3545 | 83.0 | 83 | 0.5476 | 0.7812 |
| 0.3545 | 84.0 | 84 | 0.5437 | 0.7812 |
| 0.3545 | 85.0 | 85 | 0.5396 | 0.7812 |
| 0.3545 | 86.0 | 86 | 0.5358 | 0.7812 |
| 0.3545 | 87.0 | 87 | 0.5316 | 0.7812 |
| 0.3545 | 88.0 | 88 | 0.5277 | 0.7812 |
| 0.3545 | 89.0 | 89 | 0.5238 | 0.7812 |
| 0.2725 | 90.0 | 90 | 0.5197 | 0.7812 |
| 0.2725 | 91.0 | 91 | 0.5159 | 0.7812 |
| 0.2725 | 92.0 | 92 | 0.5120 | 0.7812 |
| 0.2725 | 93.0 | 93 | 0.5079 | 0.7812 |
| 0.2725 | 94.0 | 94 | 0.5034 | 0.7812 |
| 0.2725 | 95.0 | 95 | 0.4983 | 0.7812 |
| 0.2725 | 96.0 | 96 | 0.4934 | 0.7812 |
| 0.2725 | 97.0 | 97 | 0.4885 | 0.7812 |
| 0.2725 | 98.0 | 98 | 0.4835 | 0.7812 |
| 0.2725 | 99.0 | 99 | 0.4790 | 0.8125 |
| 0.199 | 100.0 | 100 | 0.4751 | 0.8125 |
| 0.199 | 101.0 | 101 | 0.4714 | 0.8125 |
| 0.199 | 102.0 | 102 | 0.4677 | 0.8125 |
| 0.199 | 103.0 | 103 | 0.4634 | 0.8438 |
| 0.199 | 104.0 | 104 | 0.4585 | 0.8438 |
| 0.199 | 105.0 | 105 | 0.4532 | 0.875 |
| 0.199 | 106.0 | 106 | 0.4484 | 0.875 |
| 0.199 | 107.0 | 107 | 0.4439 | 0.875 |
| 0.199 | 108.0 | 108 | 0.4400 | 0.875 |
| 0.199 | 109.0 | 109 | 0.4363 | 0.875 |
| 0.1406 | 110.0 | 110 | 0.4329 | 0.875 |
| 0.1406 | 111.0 | 111 | 0.4296 | 0.875 |
| 0.1406 | 112.0 | 112 | 0.4259 | 0.875 |
| 0.1406 | 113.0 | 113 | 0.4219 | 0.8438 |
| 0.1406 | 114.0 | 114 | 0.4176 | 0.8438 |
| 0.1406 | 115.0 | 115 | 0.4138 | 0.8438 |
| 0.1406 | 116.0 | 116 | 0.4108 | 0.8438 |
| 0.1406 | 117.0 | 117 | 0.4077 | 0.8438 |
| 0.1406 | 118.0 | 118 | 0.4042 | 0.8438 |
| 0.1406 | 119.0 | 119 | 0.4003 | 0.8438 |
| 0.0921 | 120.0 | 120 | 0.3968 | 0.8438 |
| 0.0921 | 121.0 | 121 | 0.3936 | 0.8438 |
| 0.0921 | 122.0 | 122 | 0.3905 | 0.8438 |
| 0.0921 | 123.0 | 123 | 0.3878 | 0.8438 |
| 0.0921 | 124.0 | 124 | 0.3851 | 0.8438 |
| 0.0921 | 125.0 | 125 | 0.3823 | 0.8438 |
| 0.0921 | 126.0 | 126 | 0.3802 | 0.8438 |
| 0.0921 | 127.0 | 127 | 0.3786 | 0.8438 |
| 0.0921 | 128.0 | 128 | 0.3769 | 0.8125 |
| 0.0921 | 129.0 | 129 | 0.3748 | 0.8125 |
| 0.0543 | 130.0 | 130 | 0.3721 | 0.8125 |
| 0.0543 | 131.0 | 131 | 0.3700 | 0.8125 |
| 0.0543 | 132.0 | 132 | 0.3685 | 0.8125 |
| 0.0543 | 133.0 | 133 | 0.3687 | 0.8125 |
| 0.0543 | 134.0 | 134 | 0.3699 | 0.8125 |
| 0.0543 | 135.0 | 135 | 0.3711 | 0.8125 |
| 0.0543 | 136.0 | 136 | 0.3719 | 0.8125 |
| 0.0543 | 137.0 | 137 | 0.3716 | 0.8125 |
| 0.0543 | 138.0 | 138 | 0.3706 | 0.8438 |
| 0.0543 | 139.0 | 139 | 0.3699 | 0.8438 |
| 0.0313 | 140.0 | 140 | 0.3692 | 0.875 |
| 0.0313 | 141.0 | 141 | 0.3690 | 0.875 |
| 0.0313 | 142.0 | 142 | 0.3690 | 0.875 |
| 0.0313 | 143.0 | 143 | 0.3698 | 0.875 |
| 0.0313 | 144.0 | 144 | 0.3715 | 0.875 |
| 0.0313 | 145.0 | 145 | 0.3737 | 0.875 |
| 0.0313 | 146.0 | 146 | 0.3766 | 0.875 |
| 0.0313 | 147.0 | 147 | 0.3798 | 0.875 |
| 0.0313 | 148.0 | 148 | 0.3838 | 0.875 |
| 0.0313 | 149.0 | 149 | 0.3884 | 0.875 |
| 0.0183 | 150.0 | 150 | 0.3928 | 0.875 |
### Framework versions
- Transformers 4.32.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.4.0
- Tokenizers 0.13.3
|
joeljoseph1599/layoutlm-funsd
|
joeljoseph1599
| 2023-08-02T03:58:36Z | 77 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"layoutlm",
"token-classification",
"generated_from_trainer",
"dataset:funsd",
"base_model:microsoft/layoutlm-base-uncased",
"base_model:finetune:microsoft/layoutlm-base-uncased",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2023-07-31T11:32:11Z |
---
base_model: microsoft/layoutlm-base-uncased
tags:
- generated_from_trainer
datasets:
- funsd
model-index:
- name: layoutlm-funsd
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# layoutlm-funsd
This model is a fine-tuned version of [microsoft/layoutlm-base-uncased](https://huggingface.co/microsoft/layoutlm-base-uncased) on the funsd dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6510
- Answer: {'precision': 0.7025527192008879, 'recall': 0.7824474660074165, 'f1': 0.7403508771929823, 'number': 809}
- Header: {'precision': 0.28421052631578947, 'recall': 0.226890756302521, 'f1': 0.25233644859813087, 'number': 119}
- Question: {'precision': 0.7480916030534351, 'recall': 0.828169014084507, 'f1': 0.7860962566844921, 'number': 1065}
- Overall Precision: 0.7090
- Overall Recall: 0.7737
- Overall F1: 0.7399
- Overall Accuracy: 0.8032
## 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: 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 | Answer | Header | Question | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:-------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 1.7428 | 1.0 | 10 | 1.5458 | {'precision': 0.030690537084398978, 'recall': 0.04449938195302843, 'f1': 0.036326942482341064, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.18740157480314962, 'recall': 0.22347417840375586, 'f1': 0.2038543897216274, 'number': 1065} | 0.1122 | 0.1375 | 0.1235 | 0.4326 |
| 1.3991 | 2.0 | 20 | 1.2229 | {'precision': 0.1326676176890157, 'recall': 0.11495673671199011, 'f1': 0.12317880794701987, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.5, 'recall': 0.5352112676056338, 'f1': 0.5170068027210885, 'number': 1065} | 0.3597 | 0.3327 | 0.3457 | 0.5731 |
| 1.0911 | 3.0 | 30 | 0.9391 | {'precision': 0.47231638418079097, 'recall': 0.5166872682323856, 'f1': 0.4935064935064935, 'number': 809} | {'precision': 0.058823529411764705, 'recall': 0.01680672268907563, 'f1': 0.026143790849673203, 'number': 119} | {'precision': 0.6528268551236749, 'recall': 0.6938967136150235, 'f1': 0.6727355484751935, 'number': 1065} | 0.5651 | 0.5815 | 0.5732 | 0.7183 |
| 0.8461 | 4.0 | 40 | 0.7784 | {'precision': 0.6047717842323651, 'recall': 0.7206427688504327, 'f1': 0.6576424139875917, 'number': 809} | {'precision': 0.15384615384615385, 'recall': 0.06722689075630252, 'f1': 0.0935672514619883, 'number': 119} | {'precision': 0.6666666666666666, 'recall': 0.7455399061032864, 'f1': 0.7039007092198581, 'number': 1065} | 0.6275 | 0.6949 | 0.6595 | 0.7638 |
| 0.6966 | 5.0 | 50 | 0.7307 | {'precision': 0.6315228966986155, 'recall': 0.7330037082818294, 'f1': 0.6784897025171623, 'number': 809} | {'precision': 0.21052631578947367, 'recall': 0.13445378151260504, 'f1': 0.1641025641025641, 'number': 119} | {'precision': 0.6925064599483204, 'recall': 0.7549295774647887, 'f1': 0.7223719676549865, 'number': 1065} | 0.6494 | 0.7090 | 0.6779 | 0.7703 |
| 0.6037 | 6.0 | 60 | 0.6834 | {'precision': 0.657922350472193, 'recall': 0.7750309023485785, 'f1': 0.7116912599318955, 'number': 809} | {'precision': 0.3150684931506849, 'recall': 0.19327731092436976, 'f1': 0.23958333333333334, 'number': 119} | {'precision': 0.7021103896103896, 'recall': 0.812206572769953, 'f1': 0.7531562908141053, 'number': 1065} | 0.6709 | 0.7602 | 0.7128 | 0.7915 |
| 0.5421 | 7.0 | 70 | 0.6692 | {'precision': 0.671306209850107, 'recall': 0.7750309023485785, 'f1': 0.7194492254733217, 'number': 809} | {'precision': 0.2823529411764706, 'recall': 0.20168067226890757, 'f1': 0.23529411764705882, 'number': 119} | {'precision': 0.7227467811158799, 'recall': 0.7906103286384977, 'f1': 0.7551569506726458, 'number': 1065} | 0.6836 | 0.7491 | 0.7149 | 0.7931 |
| 0.5085 | 8.0 | 80 | 0.6549 | {'precision': 0.6901874310915105, 'recall': 0.7737948084054388, 'f1': 0.7296037296037297, 'number': 809} | {'precision': 0.3023255813953488, 'recall': 0.2184873949579832, 'f1': 0.25365853658536586, 'number': 119} | {'precision': 0.7408637873754153, 'recall': 0.8375586854460094, 'f1': 0.7862494490965183, 'number': 1065} | 0.7028 | 0.7747 | 0.7370 | 0.7982 |
| 0.4692 | 9.0 | 90 | 0.6517 | {'precision': 0.6973684210526315, 'recall': 0.7861557478368356, 'f1': 0.7391051714119697, 'number': 809} | {'precision': 0.2903225806451613, 'recall': 0.226890756302521, 'f1': 0.25471698113207547, 'number': 119} | {'precision': 0.7534364261168385, 'recall': 0.8234741784037559, 'f1': 0.7868999551368328, 'number': 1065} | 0.7100 | 0.7727 | 0.7400 | 0.8025 |
| 0.4538 | 10.0 | 100 | 0.6510 | {'precision': 0.7025527192008879, 'recall': 0.7824474660074165, 'f1': 0.7403508771929823, 'number': 809} | {'precision': 0.28421052631578947, 'recall': 0.226890756302521, 'f1': 0.25233644859813087, 'number': 119} | {'precision': 0.7480916030534351, 'recall': 0.828169014084507, 'f1': 0.7860962566844921, 'number': 1065} | 0.7090 | 0.7737 | 0.7399 | 0.8032 |
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.2
- Tokenizers 0.13.3
|
jafarabdurrohman/IndoBert-large-ler
|
jafarabdurrohman
| 2023-08-02T03:55:11Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"token-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2023-06-08T05:20:27Z |
---
license: mit
tags:
- generated_from_trainer
model-index:
- name: IndoBert-large-ler
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. -->
# IndoBert-large-ler
This model is a fine-tuned version of [indobenchmark/indobert-large-p1](https://huggingface.co/indobenchmark/indobert-large-p1) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0280
- Overall Precision: 0.8514
- Overall Recall: 0.8391
- Overall F1: 0.8452
- Overall Accuracy: 0.9965
- Jenis amar F1: 0.9373
- Jenis dakwaan F1: 0.8619
- Jenis perkara F1: 0.8023
- Melanggar uu (dakwaan) F1: 0.6952
- Melanggar uu (pertimbangan hukum) F1: 0.5805
- Melanggar uu (tuntutan) F1: 0.8052
- Nama hakim anggota F1: 0.9106
- Nama hakim ketua F1: 0.8938
- Nama jaksa F1: 0.9034
- Nama panitera F1: 0.9078
- Nama pengacara F1: 0.7839
- Nama pengadilan F1: 0.9964
- Nama saksi F1: 0.8391
- Nama terdakwa F1: 0.8208
- Nomor putusan F1: 0.9346
- Putusan hukuman F1: 0.7023
- Tanggal kejadian F1: 0.4252
- Tanggal putusan F1: 0.9267
- Tingkat kasus F1: 0.9725
- Tuntutan hukuman F1: 0.8329
## 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
- max_sequence_length: 128
- stride: 0%
- decay_rate: 0.01
- num_epochs: 16
### Training results
| Training Loss | Epoch | Step | Validation Loss | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy | Jenis amar F1 | Jenis dakwaan F1 | Jenis perkara F1 | Melanggar uu (dakwaan) F1 | Melanggar uu (pertimbangan hukum) F1 | Melanggar uu (tuntutan) F1 | Nama hakim anggota F1 | Nama hakim ketua F1 | Nama jaksa F1 | Nama panitera F1 | Nama pengacara F1 | Nama pengadilan F1 | Nama saksi F1 | Nama terdakwa F1 | Nomor putusan F1 | Putusan hukuman F1 | Tanggal kejadian F1 | Tanggal putusan F1 | Tingkat kasus F1 | Tuntutan hukuman F1 |
|:-------------:|:-----:|:-----:|:---------------:|:-----------------:|:--------------:|:----------:|:----------------:|:-------------:|:----------------:|:----------------:|:-------------------------:|:------------------------------------:|:--------------------------:|:---------------------:|:-------------------:|:-------------:|:----------------:|:-----------------:|:------------------:|:-------------:|:----------------:|:----------------:|:------------------:|:-------------------:|:------------------:|:----------------:|:-------------------:|
| 0.0166 | 1.0 | 2748 | 0.0249 | 0.7758 | 0.7067 | 0.7396 | 0.9936 | 0.8423 | 0.1685 | 0.5932 | 0.5248 | 0.5367 | 0.6792 | 0.8378 | 0.8500 | 0.8651 | 0.8257 | 0.1235 | 0.9474 | 0.7851 | 0.8169 | 0.8446 | 0.5196 | 0.3771 | 0.6203 | 0.8691 | 0.4773 |
| 0.0142 | 2.0 | 5496 | 0.0174 | 0.7433 | 0.7866 | 0.7643 | 0.9953 | 0.8768 | 0.7126 | 0.7329 | 0.6098 | 0.5700 | 0.7270 | 0.6293 | 0.3585 | 0.8772 | 0.8900 | 0.6534 | 0.9964 | 0.8461 | 0.8220 | 0.9333 | 0.5072 | 0.3842 | 0.6283 | 0.9580 | 0.7159 |
| 0.0107 | 3.0 | 8244 | 0.0209 | 0.7960 | 0.8236 | 0.8096 | 0.9948 | 0.8627 | 0.7920 | 0.4613 | 0.6199 | 0.5762 | 0.6600 | 0.8954 | 0.8941 | 0.8941 | 0.8980 | 0.6855 | 0.9856 | 0.8192 | 0.7853 | 0.9395 | 0.6171 | 0.4233 | 0.9384 | 0.9708 | 0.7573 |
| 0.0084 | 4.0 | 10992 | 0.0148 | 0.8054 | 0.8351 | 0.8200 | 0.9962 | 0.8912 | 0.7805 | 0.7989 | 0.6511 | 0.5696 | 0.7557 | 0.88 | 0.8964 | 0.8900 | 0.8820 | 0.4533 | 0.9821 | 0.8314 | 0.7595 | 0.9331 | 0.5484 | 0.4451 | 0.8849 | 0.9668 | 0.7673 |
| 0.0069 | 5.0 | 13740 | 0.0155 | 0.8370 | 0.8155 | 0.8261 | 0.9963 | 0.9191 | 0.8512 | 0.7623 | 0.6756 | 0.5732 | 0.7616 | 0.8632 | 0.8410 | 0.8791 | 0.8608 | 0.6745 | 0.9910 | 0.8442 | 0.7191 | 0.9303 | 0.6130 | 0.4351 | 0.9225 | 0.9761 | 0.7955 |
| 0.0059 | 6.0 | 16488 | 0.0175 | 0.8275 | 0.8302 | 0.8288 | 0.9960 | 0.9276 | 0.8049 | 0.7938 | 0.6219 | 0.5088 | 0.7215 | 0.8839 | 0.8760 | 0.8831 | 0.9057 | 0.7259 | 0.9910 | 0.8389 | 0.8276 | 0.9410 | 0.5837 | 0.3982 | 0.9328 | 0.9779 | 0.8022 |
| 0.0052 | 7.0 | 19236 | 0.0171 | 0.8260 | 0.8216 | 0.8238 | 0.9963 | 0.9171 | 0.8367 | 0.7810 | 0.6305 | 0.5604 | 0.7232 | 0.8284 | 0.8767 | 0.8149 | 0.8513 | 0.6970 | 0.9964 | 0.8430 | 0.8277 | 0.9390 | 0.5832 | 0.4070 | 0.9403 | 0.9761 | 0.7783 |
| 0.1431 | 8.0 | 21984 | 0.0192 | 0.8253 | 0.8308 | 0.8281 | 0.9961 | 0.8596 | 0.8175 | 0.7848 | 0.6045 | 0.5592 | 0.6472 | 0.8952 | 0.88 | 0.8824 | 0.8912 | 0.7492 | 0.9731 | 0.8562 | 0.8538 | 0.9379 | 0.5667 | 0.3996 | 0.9265 | 0.9761 | 0.7778 |
| 0.0036 | 9.0 | 24732 | 0.0164 | 0.8209 | 0.8462 | 0.8334 | 0.9961 | 0.9193 | 0.8456 | 0.8104 | 0.6787 | 0.5545 | 0.7774 | 0.9022 | 0.8822 | 0.8929 | 0.9006 | 0.7464 | 0.9910 | 0.8549 | 0.8479 | 0.9415 | 0.6494 | 0.3990 | 0.9149 | 0.9798 | 0.6811 |
| 0.0032 | 10.0 | 27480 | 0.0194 | 0.8392 | 0.8437 | 0.8414 | 0.9964 | 0.9257 | 0.8246 | 0.8007 | 0.6742 | 0.5632 | 0.7942 | 0.9032 | 0.8925 | 0.8934 | 0.8966 | 0.7579 | 0.9964 | 0.8432 | 0.8340 | 0.9445 | 0.6418 | 0.4387 | 0.9474 | 0.9761 | 0.8386 |
| 0.0032 | 11.0 | 30228 | 0.0216 | 0.8442 | 0.8332 | 0.8387 | 0.9965 | 0.9040 | 0.7774 | 0.8063 | 0.6756 | 0.5577 | 0.7815 | 0.9117 | 0.8760 | 0.9000 | 0.9019 | 0.7518 | 0.9856 | 0.8491 | 0.8318 | 0.9313 | 0.6316 | 0.4012 | 0.9286 | 0.9725 | 0.8297 |
| 0.0022 | 12.0 | 32976 | 0.0224 | 0.8353 | 0.8356 | 0.8354 | 0.9964 | 0.9298 | 0.8646 | 0.7923 | 0.6704 | 0.5808 | 0.7862 | 0.9123 | 0.8811 | 0.8913 | 0.8894 | 0.7801 | 0.9964 | 0.8345 | 0.7984 | 0.9282 | 0.6599 | 0.4072 | 0.9403 | 0.9688 | 0.8069 |
| 0.0013 | 13.0 | 35724 | 0.0229 | 0.8367 | 0.8435 | 0.8401 | 0.9963 | 0.9308 | 0.8629 | 0.7861 | 0.6681 | 0.5662 | 0.8152 | 0.9166 | 0.8932 | 0.8986 | 0.9019 | 0.7917 | 0.9875 | 0.8378 | 0.7869 | 0.9381 | 0.6543 | 0.4279 | 0.9242 | 0.9744 | 0.8398 |
| 0.0007 | 14.0 | 38472 | 0.0262 | 0.8474 | 0.8372 | 0.8423 | 0.9965 | 0.9373 | 0.8619 | 0.7910 | 0.6689 | 0.5752 | 0.7948 | 0.9111 | 0.8897 | 0.9038 | 0.9103 | 0.7758 | 0.9964 | 0.8438 | 0.8213 | 0.9333 | 0.6619 | 0.4290 | 0.9288 | 0.9670 | 0.8046 |
| 0.0005 | 15.0 | 41220 | 0.0270 | 0.8464 | 0.8400 | 0.8432 | 0.9964 | 0.9357 | 0.8609 | 0.7948 | 0.6794 | 0.5756 | 0.7987 | 0.9067 | 0.8915 | 0.9054 | 0.9076 | 0.7692 | 0.9964 | 0.8400 | 0.8275 | 0.9356 | 0.7157 | 0.4253 | 0.9217 | 0.9744 | 0.8161 |
| 0.0004 | 16.0 | 43968 | 0.0280 | 0.8514 | 0.8391 | 0.8452 | 0.9965 | 0.9373 | 0.8619 | 0.8023 | 0.6952 | 0.5805 | 0.8052 | 0.9106 | 0.8938 | 0.9034 | 0.9078 | 0.7839 | 0.9964 | 0.8391 | 0.8208 | 0.9346 | 0.7023 | 0.4252 | 0.9267 | 0.9725 | 0.8329 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.1
- Datasets 2.12.0
- Tokenizers 0.13.3
|
jafarabdurrohman/IndoBert-base-ler
|
jafarabdurrohman
| 2023-08-02T03:53:49Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"token-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2023-06-08T04:51:30Z |
---
license: mit
tags:
- generated_from_trainer
model-index:
- name: IndoBert-base-ler
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. -->
# IndoBert-base-ler
This model is a fine-tuned version of [indobenchmark/indobert-base-p1](https://huggingface.co/indobenchmark/indobert-base-p1) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0348
- Overall Precision: 0.8392
- Overall Recall: 0.8292
- Overall F1: 0.8342
- Overall Accuracy: 0.9961
- Jenis amar F1: 0.9381
- Jenis dakwaan F1: 0.8202
- Jenis perkara F1: 0.7895
- Melanggar uu (dakwaan) F1: 0.6704
- Melanggar uu (pertimbangan hukum) F1: 0.5885
- Melanggar uu (tuntutan) F1: 0.7783
- Nama hakim anggota F1: 0.9045
- Nama hakim ketua F1: 0.8854
- Nama jaksa F1: 0.8905
- Nama panitera F1: 0.9056
- Nama pengacara F1: 0.8288
- Nama pengadilan F1: 0.9964
- Nama saksi F1: 0.8385
- Nama terdakwa F1: 0.8264
- Nomor putusan F1: 0.9359
- Putusan hukuman F1: 0.6659
- Tanggal kejadian F1: 0.3870
- Tanggal putusan F1: 0.9430
- Tingkat kasus F1: 0.9817
- Tuntutan hukuman F1: 0.8348
## 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
- max_sequence_length: 128
- stride: 25% (32)
- decay_rate: 0.01
- num_epochs: 16
### Training results
| Training Loss | Epoch | Step | Validation Loss | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy | Jenis amar F1 | Jenis dakwaan F1 | Jenis perkara F1 | Melanggar uu (dakwaan) F1 | Melanggar uu (pertimbangan hukum) F1 | Melanggar uu (tuntutan) F1 | Nama hakim anggota F1 | Nama hakim ketua F1 | Nama jaksa F1 | Nama panitera F1 | Nama pengacara F1 | Nama pengadilan F1 | Nama saksi F1 | Nama terdakwa F1 | Nomor putusan F1 | Putusan hukuman F1 | Tanggal kejadian F1 | Tanggal putusan F1 | Tingkat kasus F1 | Tuntutan hukuman F1 |
|:-------------:|:-----:|:------:|:---------------:|:-----------------:|:--------------:|:----------:|:----------------:|:-------------:|:----------------:|:----------------:|:-------------------------:|:------------------------------------:|:--------------------------:|:---------------------:|:-------------------:|:-------------:|:----------------:|:-----------------:|:------------------:|:-------------:|:----------------:|:----------------:|:------------------:|:-------------------:|:------------------:|:----------------:|:-------------------:|
| 0.0214 | 1.0 | 7322 | 0.0174 | 0.7920 | 0.7699 | 0.7808 | 0.9956 | 0.8514 | 0.6352 | 0.6728 | 0.5940 | 0.5044 | 0.7329 | 0.8602 | 0.7357 | 0.8160 | 0.8355 | 0.6585 | 0.9661 | 0.8130 | 0.8053 | 0.9248 | 0.5354 | 0.3278 | 0.9372 | 0.9173 | 0.7353 |
| 0.0127 | 2.0 | 14644 | 0.0162 | 0.7760 | 0.7960 | 0.7859 | 0.9955 | 0.8982 | 0.6809 | 0.7038 | 0.5598 | 0.4747 | 0.6963 | 0.8733 | 0.8681 | 0.8604 | 0.8858 | 0.7114 | 0.9803 | 0.7601 | 0.8242 | 0.9318 | 0.6187 | 0.3976 | 0.9351 | 0.9560 | 0.7015 |
| 0.0117 | 3.0 | 21966 | 0.0172 | 0.7830 | 0.7701 | 0.7765 | 0.9953 | 0.8487 | 0.6657 | 0.7051 | 0.5092 | 0.5336 | 0.7518 | 0.8460 | 0.8093 | 0.7043 | 0.6803 | 0.7242 | 0.9802 | 0.8191 | 0.8039 | 0.9346 | 0.5290 | 0.3797 | 0.9312 | 0.9564 | 0.7534 |
| 0.0088 | 4.0 | 29288 | 0.0175 | 0.8086 | 0.8019 | 0.8052 | 0.9960 | 0.9093 | 0.7876 | 0.7571 | 0.6362 | 0.5500 | 0.7384 | 0.8832 | 0.8440 | 0.7949 | 0.8913 | 0.6986 | 0.9874 | 0.8193 | 0.8378 | 0.9089 | 0.5590 | 0.3968 | 0.9534 | 0.9640 | 0.7724 |
| 0.0092 | 5.0 | 36610 | 0.0171 | 0.8070 | 0.8035 | 0.8053 | 0.9958 | 0.8686 | 0.6188 | 0.7521 | 0.5808 | 0.5625 | 0.7645 | 0.8825 | 0.8168 | 0.8656 | 0.8557 | 0.7155 | 0.9803 | 0.8242 | 0.8132 | 0.9323 | 0.6011 | 0.3756 | 0.9211 | 0.9653 | 0.7570 |
| 0.0057 | 6.0 | 43932 | 0.0184 | 0.8157 | 0.8077 | 0.8117 | 0.9958 | 0.9050 | 0.8299 | 0.7505 | 0.6424 | 0.4908 | 0.7571 | 0.8822 | 0.8740 | 0.8625 | 0.8970 | 0.7475 | 0.9802 | 0.8158 | 0.8327 | 0.9389 | 0.5801 | 0.3892 | 0.944 | 0.9635 | 0.8073 |
| 0.0057 | 7.0 | 51254 | 0.0203 | 0.7988 | 0.8277 | 0.8130 | 0.9959 | 0.9273 | 0.7900 | 0.7673 | 0.5932 | 0.5577 | 0.7811 | 0.8863 | 0.8553 | 0.8743 | 0.8945 | 0.7176 | 0.9681 | 0.8316 | 0.8231 | 0.9374 | 0.5983 | 0.4006 | 0.9110 | 0.9620 | 0.8203 |
| 0.0054 | 8.0 | 58576 | 0.0209 | 0.8263 | 0.8058 | 0.8159 | 0.9959 | 0.8996 | 0.8097 | 0.7661 | 0.6445 | 0.5613 | 0.7778 | 0.9079 | 0.7732 | 0.8783 | 0.8968 | 0.7080 | 0.9910 | 0.8227 | 0.8355 | 0.9401 | 0.5395 | 0.3542 | 0.9279 | 0.9706 | 0.7937 |
| 0.003 | 9.0 | 65898 | 0.0244 | 0.8255 | 0.8096 | 0.8175 | 0.9956 | 0.9277 | 0.7944 | 0.7146 | 0.6556 | 0.5502 | 0.7842 | 0.8564 | 0.8798 | 0.8813 | 0.8955 | 0.7547 | 0.9857 | 0.8221 | 0.8270 | 0.9399 | 0.6681 | 0.3873 | 0.9468 | 0.9654 | 0.7912 |
| 0.0031 | 10.0 | 73220 | 0.0256 | 0.8297 | 0.8206 | 0.8251 | 0.9959 | 0.9103 | 0.8239 | 0.7598 | 0.6639 | 0.5665 | 0.7609 | 0.9008 | 0.8765 | 0.8867 | 0.9002 | 0.7590 | 0.9982 | 0.8359 | 0.8322 | 0.9409 | 0.5965 | 0.3774 | 0.9402 | 0.9635 | 0.8070 |
| 0.0021 | 11.0 | 80542 | 0.0259 | 0.8365 | 0.8238 | 0.8301 | 0.9960 | 0.9191 | 0.8383 | 0.7966 | 0.6644 | 0.5874 | 0.7530 | 0.8944 | 0.8675 | 0.8878 | 0.9041 | 0.7500 | 0.9964 | 0.8319 | 0.8307 | 0.9332 | 0.6536 | 0.3909 | 0.9316 | 0.9670 | 0.8496 |
| 0.0015 | 12.0 | 87864 | 0.0267 | 0.8344 | 0.8204 | 0.8273 | 0.9960 | 0.9270 | 0.8141 | 0.7881 | 0.6816 | 0.5730 | 0.7855 | 0.8964 | 0.8745 | 0.8926 | 0.8913 | 0.7805 | 0.9946 | 0.8291 | 0.8275 | 0.9332 | 0.6376 | 0.3753 | 0.9273 | 0.9761 | 0.8035 |
| 0.001 | 13.0 | 95186 | 0.0297 | 0.8316 | 0.8201 | 0.8258 | 0.9960 | 0.9339 | 0.8373 | 0.7351 | 0.6392 | 0.5955 | 0.7816 | 0.9022 | 0.8763 | 0.8968 | 0.8861 | 0.7826 | 0.9964 | 0.8408 | 0.8296 | 0.9223 | 0.6689 | 0.3906 | 0.9404 | 0.9762 | 0.8070 |
| 0.0007 | 14.0 | 102508 | 0.0317 | 0.8299 | 0.8211 | 0.8254 | 0.9959 | 0.9387 | 0.8462 | 0.7520 | 0.6820 | 0.5964 | 0.7791 | 0.9010 | 0.8770 | 0.8932 | 0.9039 | 0.8142 | 0.9964 | 0.8325 | 0.8262 | 0.9171 | 0.6637 | 0.3807 | 0.9316 | 0.9799 | 0.8450 |
| 0.0003 | 15.0 | 109830 | 0.0334 | 0.8340 | 0.8274 | 0.8307 | 0.9960 | 0.9368 | 0.8222 | 0.7744 | 0.6737 | 0.5977 | 0.7877 | 0.9053 | 0.8817 | 0.8745 | 0.9038 | 0.8083 | 0.9964 | 0.8345 | 0.8335 | 0.9311 | 0.6681 | 0.3793 | 0.9406 | 0.9762 | 0.8436 |
| 0.0001 | 16.0 | 117152 | 0.0348 | 0.8392 | 0.8292 | 0.8342 | 0.9961 | 0.9381 | 0.8202 | 0.7895 | 0.6704 | 0.5885 | 0.7783 | 0.9045 | 0.8854 | 0.8905 | 0.9056 | 0.8288 | 0.9964 | 0.8385 | 0.8264 | 0.9359 | 0.6659 | 0.3870 | 0.9430 | 0.9817 | 0.8348 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.1
- Datasets 2.12.0
- Tokenizers 0.13.3
|
jafarabdurrohman/indonesian-roberta-base-ler
|
jafarabdurrohman
| 2023-08-02T03:52:37Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"token-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2023-06-08T03:32:01Z |
---
license: mit
tags:
- generated_from_trainer
model-index:
- name: indonesian-roberta-base-ler
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. -->
# indonesian-roberta-base-ler
This model is a fine-tuned version of [flax-community/indonesian-roberta-base](https://huggingface.co/flax-community/indonesian-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0291
- Overall Precision: 0.9294
- Overall Recall: 0.9191
- Overall F1: 0.9242
- Overall Accuracy: 0.9968
- Jenis amar F1: 0.9379
- Jenis dakwaan F1: 0.8644
- Jenis perkara F1: 0.9096
- Melanggar uu (dakwaan) F1: 0.8062
- Melanggar uu (pertimbangan hukum) F1: 0.6441
- Melanggar uu (tuntutan) F1: 0.9248
- Nama hakim anggota F1: 0.9640
- Nama hakim ketua F1: 0.9741
- Nama jaksa F1: 0.9614
- Nama panitera F1: 0.9756
- Nama pengacara F1: 0.9000
- Nama pengadilan F1: 0.9982
- Nama saksi F1: 0.9386
- Nama terdakwa F1: 0.9786
- Nomor putusan F1: 0.9963
- Putusan hukuman F1: 0.9433
- Tanggal kejadian F1: 0.3988
- Tanggal putusan F1: 0.9680
- Tingkat kasus F1: 0.9853
- Tuntutan hukuman F1: 0.8867
## 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
- max_sequence_length: 128
- stride: 0%
- decay_rate: 0.01
- num_epochs: 16
### Training results
| Training Loss | Epoch | Step | Validation Loss | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy | Jenis amar F1 | Jenis dakwaan F1 | Jenis perkara F1 | Melanggar uu (dakwaan) F1 | Melanggar uu (pertimbangan hukum) F1 | Melanggar uu (tuntutan) F1 | Nama hakim anggota F1 | Nama hakim ketua F1 | Nama jaksa F1 | Nama panitera F1 | Nama pengacara F1 | Nama pengadilan F1 | Nama saksi F1 | Nama terdakwa F1 | Nomor putusan F1 | Putusan hukuman F1 | Tanggal kejadian F1 | Tanggal putusan F1 | Tingkat kasus F1 | Tuntutan hukuman F1 |
|:-------------:|:-----:|:-----:|:---------------:|:-----------------:|:--------------:|:----------:|:----------------:|:-------------:|:----------------:|:----------------:|:-------------------------:|:------------------------------------:|:--------------------------:|:---------------------:|:-------------------:|:-------------:|:----------------:|:-----------------:|:------------------:|:-------------:|:----------------:|:----------------:|:------------------:|:-------------------:|:------------------:|:----------------:|:-------------------:|
| 0.0204 | 1.0 | 5641 | 0.0163 | 0.8647 | 0.8564 | 0.8605 | 0.9960 | 0.8723 | 0.5028 | 0.7307 | 0.6945 | 0.5383 | 0.8472 | 0.9192 | 0.9389 | 0.9086 | 0.9449 | 0.7881 | 0.9821 | 0.8989 | 0.9423 | 0.9530 | 0.7655 | 0.3135 | 0.9630 | 0.9575 | 0.7803 |
| 0.0133 | 2.0 | 11282 | 0.0193 | 0.8305 | 0.8274 | 0.8289 | 0.9945 | 0.8316 | 0.6958 | 0.6978 | 0.6186 | 0.3940 | 0.8116 | 0.8620 | 0.8495 | 0.8338 | 0.8849 | 0.5220 | 0.9690 | 0.9036 | 0.9532 | 0.9927 | 0.1196 | 0.3154 | 0.9290 | 0.8864 | 0.6835 |
| 0.0099 | 3.0 | 16923 | 0.0163 | 0.8455 | 0.8801 | 0.8624 | 0.9960 | 0.9 | 0.7671 | 0.7539 | 0.5686 | 0.4050 | 0.4949 | 0.9267 | 0.9168 | 0.9281 | 0.9353 | 0.7831 | 0.9910 | 0.8946 | 0.9722 | 0.9895 | 0.8827 | 0.3423 | 0.9474 | 0.9610 | 0.8459 |
| 0.0079 | 4.0 | 22564 | 0.0164 | 0.8627 | 0.9019 | 0.8819 | 0.9958 | 0.9022 | 0.7602 | 0.7336 | 0.7157 | 0.5674 | 0.8599 | 0.9550 | 0.9515 | 0.9631 | 0.9695 | 0.8184 | 0.9679 | 0.9131 | 0.9780 | 0.9963 | 0.8650 | 0.3234 | 0.9564 | 0.9722 | 0.8262 |
| 0.0059 | 5.0 | 28205 | 0.0179 | 0.9157 | 0.8947 | 0.9050 | 0.9968 | 0.9017 | 0.7932 | 0.8425 | 0.7648 | 0.5989 | 0.8992 | 0.9531 | 0.9373 | 0.9560 | 0.9660 | 0.8232 | 0.9784 | 0.9136 | 0.9642 | 0.9898 | 0.9051 | 0.3933 | 0.9645 | 0.9630 | 0.8470 |
| 0.0052 | 6.0 | 33846 | 0.0183 | 0.8523 | 0.8960 | 0.8736 | 0.9960 | 0.8923 | 0.8015 | 0.8443 | 0.7440 | 0.5949 | 0.8528 | 0.9339 | 0.8898 | 0.9348 | 0.9620 | 0.8814 | 1.0 | 0.9156 | 0.9613 | 0.9936 | 0.8604 | 0.2037 | 0.8600 | 0.9646 | 0.8483 |
| 0.005 | 7.0 | 39487 | 0.0183 | 0.8901 | 0.9073 | 0.8986 | 0.9965 | 0.9150 | 0.7942 | 0.8355 | 0.7872 | 0.6258 | 0.8641 | 0.9514 | 0.9573 | 0.9665 | 0.9676 | 0.8746 | 0.9964 | 0.9223 | 0.9680 | 0.9945 | 0.8970 | 0.3249 | 0.9354 | 0.9759 | 0.8407 |
| 0.0039 | 8.0 | 45128 | 0.0197 | 0.8915 | 0.9016 | 0.8965 | 0.9962 | 0.9125 | 0.7638 | 0.7435 | 0.7406 | 0.5828 | 0.8394 | 0.9562 | 0.9683 | 0.9456 | 0.9702 | 0.7469 | 1.0 | 0.8969 | 0.9595 | 0.9969 | 0.9067 | 0.3916 | 0.9404 | 0.9722 | 0.8621 |
| 0.0031 | 9.0 | 50769 | 0.0225 | 0.8661 | 0.9179 | 0.8913 | 0.9959 | 0.9306 | 0.7714 | 0.7939 | 0.7900 | 0.6084 | 0.9049 | 0.9591 | 0.9643 | 0.9457 | 0.9527 | 0.8127 | 0.9964 | 0.9080 | 0.9716 | 0.9970 | 0.9064 | 0.3388 | 0.8412 | 0.9593 | 0.8727 |
| 0.0022 | 10.0 | 56410 | 0.0232 | 0.9254 | 0.9111 | 0.9182 | 0.9967 | 0.9212 | 0.8411 | 0.9080 | 0.8044 | 0.6126 | 0.9243 | 0.9560 | 0.9741 | 0.9591 | 0.9642 | 0.9102 | 0.9874 | 0.9240 | 0.9734 | 0.9941 | 0.9351 | 0.4186 | 0.9626 | 0.9779 | 0.8687 |
| 0.0023 | 11.0 | 62051 | 0.0209 | 0.9289 | 0.9114 | 0.9201 | 0.9969 | 0.9297 | 0.8423 | 0.8843 | 0.7986 | 0.6318 | 0.8808 | 0.9645 | 0.9624 | 0.9585 | 0.9674 | 0.8963 | 0.9946 | 0.9309 | 0.9752 | 0.9966 | 0.9320 | 0.4092 | 0.9697 | 0.9871 | 0.8790 |
| 0.001 | 12.0 | 67692 | 0.0230 | 0.9279 | 0.9075 | 0.9176 | 0.9968 | 0.9377 | 0.8665 | 0.8771 | 0.7951 | 0.6213 | 0.9079 | 0.9611 | 0.9768 | 0.9576 | 0.9638 | 0.9174 | 0.9964 | 0.9353 | 0.9621 | 0.9967 | 0.9391 | 0.3735 | 0.9665 | 0.9703 | 0.8666 |
| 0.0007 | 13.0 | 73333 | 0.0244 | 0.9095 | 0.9190 | 0.9142 | 0.9965 | 0.9400 | 0.8610 | 0.8974 | 0.8030 | 0.6337 | 0.9338 | 0.9660 | 0.9712 | 0.9565 | 0.9668 | 0.9181 | 0.9964 | 0.9273 | 0.9640 | 0.9961 | 0.9233 | 0.3664 | 0.9697 | 0.9668 | 0.8845 |
| 0.0006 | 14.0 | 78974 | 0.0258 | 0.9213 | 0.9186 | 0.9200 | 0.9967 | 0.9315 | 0.8533 | 0.9119 | 0.7934 | 0.6453 | 0.9311 | 0.9617 | 0.9749 | 0.9614 | 0.9702 | 0.8718 | 0.9910 | 0.9320 | 0.9726 | 0.9966 | 0.9249 | 0.3936 | 0.9680 | 0.9871 | 0.8728 |
| 0.0003 | 15.0 | 84615 | 0.0281 | 0.9260 | 0.9208 | 0.9234 | 0.9969 | 0.9313 | 0.8463 | 0.9150 | 0.7996 | 0.6601 | 0.9176 | 0.9677 | 0.9712 | 0.9599 | 0.9749 | 0.8928 | 0.9946 | 0.9351 | 0.9793 | 0.9963 | 0.9347 | 0.3956 | 0.9680 | 0.9852 | 0.8854 |
| 0.0001 | 16.0 | 90256 | 0.0291 | 0.9294 | 0.9191 | 0.9242 | 0.9968 | 0.9379 | 0.8644 | 0.9096 | 0.8062 | 0.6441 | 0.9248 | 0.9640 | 0.9741 | 0.9614 | 0.9756 | 0.9000 | 0.9982 | 0.9386 | 0.9786 | 0.9963 | 0.9433 | 0.3988 | 0.9680 | 0.9853 | 0.8867 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.1
- Datasets 2.12.0
- Tokenizers 0.13.3
|
phamvanlinh143/bert-fine-tuned-cola
|
phamvanlinh143
| 2023-08-02T02:38:27Z | 111 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-07-24T17:20:24Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- matthews_correlation
model-index:
- name: bert-fine-tuned-cola
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
args: cola
metrics:
- name: Matthews Correlation
type: matthews_correlation
value: 0.5675682416159784
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-fine-tuned-cola
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8760
- Matthews Correlation: 0.5676
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.4768 | 1.0 | 1069 | 0.5682 | 0.5183 |
| 0.3134 | 2.0 | 2138 | 0.6110 | 0.5789 |
| 0.1627 | 3.0 | 3207 | 0.8760 | 0.5676 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.12.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
DunnBC22/bert-base-cased-finetuned-ner-DFKI-SLT_few-NERd
|
DunnBC22
| 2023-08-02T02:32:25Z | 133 | 1 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2023-07-05T17:28:19Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: bert-base-cased-finetuned-ner-DFKI-SLT_few-NERd
results: []
language:
- en
metrics:
- seqeval
pipeline_tag: token-classification
---
# bert-base-cased-finetuned-ner-DFKI-SLT_few-NERd
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased).
It achieves the following results on the evaluation set:
- Loss: 0.1312
- Person
- Precision: 0.8860048426150121
- Recall: 0.9401849948612538
- F1: 0.912291199202194
- Number: 29190
- Location
- Precision: 0.8686381704207632
- Recall: 0.8152889539136796
- F1: 0.841118472477534
- Number: 95690
- Organization
- Precision: 0.7919078915181266
- Recall': 0.7449641777764141
- F1: 0.7677190874452579
- Number': 65183
- Product
- Precision: 0.7065968977761166
- Recall: 0.8295304958315051
- F1: 0.7631446160056513
- Number: 9116
- Art
- Precision: 0.8407258064516129
- Recall: 0.8614333386302241
- F1: 0.8509536143159878
- Number: 6293
- Other
- Precision: 0.7303024586555996
- Recall: 0.8314124132006586
- F1: 0.7775843599357258
- Nnumber: 13969
- Building
- Precision: 0.5162234691388143
- Recall: 0.3648904983617865
- F1: 0.4275611234592847
- Number: 5799
- Event
- Precision: 0.605920892987139
- Recall: 0.35144264602392683
- F1: 0.44486014608943525
- Number: 7105
- Overall
- Precision: 0.8203
- Recall: 0.7886
- F1: 0.8041
- Accuracy: 0.9498
## Model description
For more information on how it was created, check out the following link: https://github.com/DunnBC22/NLP_Projects/tree/main/Token%20Classification/Monolingual/DFKI%20SLT%20few%20NERd
## Intended uses & limitations
This model is intended to demonstrate my ability to solve a complex problem using technology.
## Training and evaluation data
Dataset Source: https://huggingface.co/datasets/DFKI-SLT/few-nerd
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- 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
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Person Precision | Person Recall | Person F1 | Person Number | Location Precision | Location Recall | Location F1 | Location Number | Organization Precision | Organization Recall | Organization F1 | Organization Number | Product Precision | Product Recall | Product F1 | Product Number | Art Precision | Art Recall | Art F1 | Art Number | Other Precision | Other Recall | Other F1 | Other Number | Building Precision | Building Recall | Building F1 | Building Number | Event Precision | Event Recall | Event F1 | Event Number | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|
| 0.1796 | 1.0 | 11293 | 0.1427 | 0.8741 | 0.9272 | 0.8999 | 29190 | 0.8576 | 0.8072 | 0.8316 | 95690 | 0.7699 | 0.7688 | 0.7694 | 65183 | 0.6711 | 0.75 | 0.7084 | 9116 | 0.8347 | 0.8154 | 0.8249 | 6293 | 0.6743 | 0.8195 | 0.7398 | 13969 | 0.4812 | 0.3951 | 0.4339 | 5799 | 0.5998 | 0.3253 | 0.4218 | 7105 | 0.8000 | 0.7852 | 0.7925 | 0.9483 |
| 0.1542 | 2.0 | 22586 | 0.1312 | 0.8860 | 0.9402 | 0.9123 | 29190 | 0.8686 | 0.8153 | 0.8411 | 95690 | 0.7919 | 0.7450 | 0.7677 | 65183 | 0.7066 | 0.8295 | 0.7631 | 9116 | 0.8407 | 0.8614 | 0.8510 | 6293 | 0.7303 | 0.8314 | 0.7776 | 13969 | 0.5162 | 0.3649 | 0.4276 | 5799 | 0.6059 | 0.3514 | 0.4449 | 7105 | 0.8203 | 0.7886 | 0.8041 | 0.9498 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
TangTide/vit-base-patch16-224-in21k-Dog-Classification
|
TangTide
| 2023-08-02T02:29:09Z | 191 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:imagewoof",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-07-28T13:40:14Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagewoof
metrics:
- accuracy
model-index:
- name: vit-base-patch16-224-in21k-test-1
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagewoof
type: imagewoof
config: full_size
split: train
args: full_size
metrics:
- name: Accuracy
type: accuracy
value: 0.9523809523809523
---
<!-- 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-base-patch16-224-in21k-Dog-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 imagewoof dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5386
- Accuracy: 0.9524
## Model description
Based on the [frgfm/imagewoof dataset](https://huggingface.co/datasets/frgfm/imagewoof), it can categorize ten types of dogs such as Shih-Tzu, Rhodesian ridgeback, Beagle, English foxhound, Border terrier, Australian terrier, Golden retriever, Old English sheepdog, Samoyed, Dingo.
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.2356 | 0.99 | 63 | 1.0520 | 0.9059 |
| 0.6987 | 2.0 | 127 | 0.6162 | 0.9446 |
| 0.5787 | 2.98 | 189 | 0.5386 | 0.9524 |
### Framework versions
- Transformers 4.30.2
- Pytorch 1.13.0+cu117
- Datasets 2.14.0
- Tokenizers 0.13.3
|
eepon/finetuning-emotion-model
|
eepon
| 2023-08-02T02:25:23Z | 109 | 0 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-08-02T02:17:50Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- emotion
model-index:
- name: finetuning-emotion-model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuning-emotion-model
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1
- Datasets 2.14.2
- Tokenizers 0.13.3
|
DunnBC22/bert-base-uncased-Vitamin_C_Fact_Verification
|
DunnBC22
| 2023-08-02T02:15:12Z | 222 | 1 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"multiple-choice",
"generated_from_trainer",
"multiple_choice",
"question-answering",
"en",
"dataset:tasksource/bigbench",
"base_model:google-bert/bert-base-uncased",
"base_model:finetune:google-bert/bert-base-uncased",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2023-08-01T18:18:57Z |
---
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
- multiple_choice
metrics:
- accuracy
model-index:
- name: bert-base-uncased-Vitamin_C_Fact_Verification
results: []
datasets:
- tasksource/bigbench
language:
- en
pipeline_tag: question-answering
---
# bert-base-uncased-Vitamin_C_Fact_Verification
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased).
It achieves the following results on the evaluation set:
- Loss: 0.6329
- Accuracy: 0.7240
## Model description
For more information on how it was created, check out the following link: https://github.com/DunnBC22/NLP_Projects/blob/main/Multiple%20Choice/Vitamin%20C%20Fact%20Verification/Vitamin_C_Fact_Verification_Multiple_Choice_Using_BERT.ipynb
## Intended uses & limitations
This model is intended to demonstrate my ability to solve a complex problem using technology.
## Training and evaluation data
Dataset Source: https://huggingface.co/datasets/tasksource/bigbench/viewer/vitaminc_fact_verification
## 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: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6985 | 1.0 | 2170 | 0.6894 | 0.6864 |
| 0.5555 | 2.0 | 4340 | 0.6329 | 0.7240 |
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.2
- Tokenizers 0.13.3
|
mhdaw/ppo-Huggy
|
mhdaw
| 2023-08-02T02:14:49Z | 2 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"Huggy",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
] |
reinforcement-learning
| 2023-08-02T02:14:43Z |
---
library_name: ml-agents
tags:
- Huggy
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
---
# **ppo** Agent playing **Huggy**
This is a trained model of a **ppo** agent playing **Huggy**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: mhdaw/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
ashercn97/manatee-LoRA-7b
|
ashercn97
| 2023-08-02T02:06:05Z | 0 | 1 | null |
[
"text-generation",
"region:us"
] |
text-generation
| 2023-07-29T23:29:18Z |
---
pipeline_tag: text-generation
---
|
xiongjya/whisper-medium-zh-CN
|
xiongjya
| 2023-08-02T02:02:47Z | 6 | 1 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"base_model:openai/whisper-medium",
"base_model:finetune:openai/whisper-medium",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-07-26T03:12:07Z |
---
license: apache-2.0
base_model: openai/whisper-medium
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: whisper-medium-zh-CN
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-zh-CN
This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2354
- Wer: 100.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 1000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.3354 | 0.55 | 500 | 0.2808 | 100.0235 |
| 0.1662 | 1.1 | 1000 | 0.2354 | 100.0 |
### Framework versions
- Transformers 4.32.0.dev0
- Pytorch 2.0.1+cu117
- Datasets 2.13.1
- Tokenizers 0.13.3
|
olzml/llama2-qlora-finetunined-french
|
olzml
| 2023-08-02T01:37:48Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-01T17:55:52Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.5.0.dev0
|
sheaDurgin/test
|
sheaDurgin
| 2023-08-02T01:33:38Z | 3 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"mpnet",
"feature-extraction",
"sentence-similarity",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2023-08-02T01:28:17Z |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## 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 870 with parameters:
```
{'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss`
Parameters of the fit()-Method:
```
{
"epochs": 10,
"evaluation_steps": 0,
"evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 870,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: MPNetModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
(2): Normalize()
)
```
## Citing & Authors
<!--- Describe where people can find more information -->
|
jonalkw/q-FrozenLake-v1-4x4-noSlippery
|
jonalkw
| 2023-08-02T01:24:39Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-01-03T03:48:38Z |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="jonalkw/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
Elliot4AI/Dugong-Llama2-13b-chinese
|
Elliot4AI
| 2023-08-02T01:04:21Z | 0 | 0 |
transformers
|
[
"transformers",
"text-generation",
"zh",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-08-02T01:02:42Z |
---
license: apache-2.0
language:
- zh
library_name: transformers
pipeline_tag: text-generation
---
# Model Card
🏡🏡🏡🏡Dugong 🏡🏡🏡🏡
一个经过中文数据集微调的sft模型，其基础模型为Llama-2-13b-hf。其数据集为Elliot4AI/openassistant-guanaco-chinese。现在可以用中文问和答。
微调摘要：
1.量化8位
2.peft-Lora
具体信息请点击这个链接：待更新。。。。。。
😀😀😀😀😀😀😀😀😀😀😀😀😀😀
|
Huggingfly/poca-SoccerTwos
|
Huggingfly
| 2023-08-02T01:03:47Z | 34 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"SoccerTwos",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SoccerTwos",
"region:us"
] |
reinforcement-learning
| 2023-08-02T01:03:17Z |
---
library_name: ml-agents
tags:
- SoccerTwos
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SoccerTwos
---
# **poca** Agent playing **SoccerTwos**
This is a trained model of a **poca** agent playing **SoccerTwos**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: Huggingfly/poca-SoccerTwos
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
Technotech/sd-prompt-instruct-3b-epoch-0.4
|
Technotech
| 2023-08-02T01:00:39Z | 10 | 0 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"llama",
"text-generation",
"stable-diffusion",
"instruct",
"magic-prompt",
"natural language inference",
"en",
"dataset:Technotech/sd-prompt-instruct",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-07-29T16:09:16Z |
---
library_name: transformers
license: apache-2.0
datasets:
- Technotech/sd-prompt-instruct
language:
- en
tags:
- stable-diffusion
- instruct
- magic-prompt
- natural language inference
---
# Stable Diffusion Prompt Instruct 3B (OpenLlama v2 3B)
Trained for 0.4 epochs (test) on [Technotech/sd-prompt-instruct](https://huggingface.co/datasets/Technotech/sd-prompt-instruct).
## Prompt Format
```
### Instruction: {prompt}
### Response: {response}
```
## 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: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
### Framework versions
- PEFT 0.5.0.dev0
|
sshalini6/small-5e4-r16-a32-d0
|
sshalini6
| 2023-08-02T00:54:33Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-02T00:54:33Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
### Framework versions
- PEFT 0.5.0.dev0
|
Liea/ppo-Huggy
|
Liea
| 2023-08-02T00:50:57Z | 2 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"Huggy",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
] |
reinforcement-learning
| 2023-08-02T00:50:46Z |
---
library_name: ml-agents
tags:
- Huggy
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
---
# **ppo** Agent playing **Huggy**
This is a trained model of a **ppo** agent playing **Huggy**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: Liea/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
domjina/taxi
|
domjina
| 2023-08-02T00:25:19Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-08-02T00:25:16Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: taxi
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.56 +/- 2.71
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="domjina/taxi", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
domjina/q-FrozenLake-v1-4x4-noSlippery
|
domjina
| 2023-08-02T00:23:18Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-08-02T00:23:14Z |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="domjina/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
Eggsbena/model_005
|
Eggsbena
| 2023-08-01T23:33:51Z | 30 | 1 |
diffusers
|
[
"diffusers",
"text-to-image",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-08-01T23:25:15Z |
---
library_name: diffusers
pipeline_tag: text-to-image
---
|
Thatgreenguy/ppo-Huggy
|
Thatgreenguy
| 2023-08-01T23:24:37Z | 0 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"Huggy",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
] |
reinforcement-learning
| 2023-08-01T23:24:33Z |
---
library_name: ml-agents
tags:
- Huggy
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
---
# **ppo** Agent playing **Huggy**
This is a trained model of a **ppo** agent playing **Huggy**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: Thatgreenguy/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
Shaun1204/RedGPT-Gormlee
|
Shaun1204
| 2023-08-01T23:19:02Z | 132 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"eng",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-08-01T21:54:45Z |
---
language:
- eng
thumbnail: ""
tags:
- conversational
---
|
omarhkh/swin-tiny-patch4-window7-224-finetuned-omars6
|
omarhkh
| 2023-08-01T23:02:35Z | 221 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"swin",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-08-01T21:47:40Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: swin-tiny-patch4-window7-224-finetuned-omars6
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.8814589665653495
---
<!-- 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-omars6
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 imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5625
- Accuracy: 0.8815
## 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: 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_ratio: 0.1
- num_epochs: 30
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.9598 | 0.99 | 92 | 0.7744 | 0.6869 |
| 0.7825 | 2.0 | 185 | 0.7336 | 0.7082 |
| 0.9638 | 2.99 | 277 | 0.8202 | 0.7204 |
| 1.0288 | 4.0 | 370 | 0.8621 | 0.7903 |
| 0.9711 | 4.99 | 462 | 0.8212 | 0.6809 |
| 1.0125 | 6.0 | 555 | 0.8700 | 0.7356 |
| 0.945 | 6.99 | 647 | 0.7959 | 0.7781 |
| 0.9851 | 8.0 | 740 | 0.8755 | 0.6140 |
| 0.8078 | 8.99 | 832 | 0.6970 | 0.7781 |
| 0.7377 | 10.0 | 925 | 0.6063 | 0.7386 |
| 0.7934 | 10.99 | 1017 | 0.6121 | 0.8116 |
| 0.7986 | 12.0 | 1110 | 0.6532 | 0.8116 |
| 0.6129 | 12.99 | 1202 | 0.7250 | 0.8450 |
| 0.7428 | 14.0 | 1295 | 0.6417 | 0.7264 |
| 0.5661 | 14.99 | 1387 | 0.6847 | 0.7964 |
| 0.6631 | 16.0 | 1480 | 0.5470 | 0.8298 |
| 0.5787 | 16.99 | 1572 | 0.5696 | 0.8359 |
| 0.6635 | 18.0 | 1665 | 0.6385 | 0.7872 |
| 0.5251 | 18.99 | 1757 | 0.5842 | 0.8419 |
| 0.6164 | 20.0 | 1850 | 0.5506 | 0.8207 |
| 0.4166 | 20.99 | 1942 | 0.8169 | 0.8055 |
| 0.4189 | 22.0 | 2035 | 0.5882 | 0.8480 |
| 0.699 | 22.99 | 2127 | 0.5767 | 0.8541 |
| 0.6095 | 24.0 | 2220 | 0.6392 | 0.8845 |
| 0.3837 | 24.99 | 2312 | 0.6109 | 0.8723 |
| 0.4916 | 26.0 | 2405 | 0.4862 | 0.8754 |
| 0.4536 | 26.99 | 2497 | 0.5625 | 0.8754 |
| 0.3636 | 28.0 | 2590 | 0.5948 | 0.8663 |
| 0.4004 | 28.99 | 2682 | 0.5735 | 0.8906 |
| 0.4248 | 29.84 | 2760 | 0.5625 | 0.8815 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu117
- Datasets 2.13.0
- Tokenizers 0.13.3
|
shtif/ppo-LunarLander-v2
|
shtif
| 2023-08-01T22:58:13Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-08-01T22:57:51Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 248.15 +/- 34.91
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
...
```
|
Ahmed007/Close_book_2
|
Ahmed007
| 2023-08-01T22:29:28Z | 124 | 1 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2023-01-05T12:55:10Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: Close_book_2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Close_book_2
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the squad 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: 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
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1
|
Ahmed007/Copilot_for_poors_v3
|
Ahmed007
| 2023-08-01T22:28:43Z | 118 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-12-22T22:21:29Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: Copilot_for_poors_v3
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Copilot_for_poors_v3
This model is a fine-tuned version of [Ahmed007/Copilot_for_poors_v2](https://huggingface.co/Ahmed007/Copilot_for_poors_v2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3504
## 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: 12
- eval_batch_size: 12
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 25
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 57 | 1.4567 |
| No log | 2.0 | 114 | 1.4510 |
| No log | 3.0 | 171 | 1.4376 |
| No log | 4.0 | 228 | 1.4255 |
| No log | 5.0 | 285 | 1.4174 |
| No log | 6.0 | 342 | 1.4122 |
| No log | 7.0 | 399 | 1.4080 |
| No log | 8.0 | 456 | 1.3981 |
| 1.7151 | 9.0 | 513 | 1.3923 |
| 1.7151 | 10.0 | 570 | 1.3870 |
| 1.7151 | 11.0 | 627 | 1.3801 |
| 1.7151 | 12.0 | 684 | 1.3769 |
| 1.7151 | 13.0 | 741 | 1.3706 |
| 1.7151 | 14.0 | 798 | 1.3687 |
| 1.7151 | 15.0 | 855 | 1.3665 |
| 1.7151 | 16.0 | 912 | 1.3613 |
| 1.7151 | 17.0 | 969 | 1.3613 |
| 1.5556 | 18.0 | 1026 | 1.3576 |
| 1.5556 | 19.0 | 1083 | 1.3550 |
| 1.5556 | 20.0 | 1140 | 1.3540 |
| 1.5556 | 21.0 | 1197 | 1.3530 |
| 1.5556 | 22.0 | 1254 | 1.3514 |
| 1.5556 | 23.0 | 1311 | 1.3517 |
| 1.5556 | 24.0 | 1368 | 1.3506 |
| 1.5556 | 25.0 | 1425 | 1.3504 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1
|
Ahmed007/GPT2-Arabic_Poetry_generator
|
Ahmed007
| 2023-08-01T22:28:39Z | 137 | 1 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"gpt2",
"text-generation",
"generated_from_trainer",
"base_model:openai-community/gpt2",
"base_model:finetune:openai-community/gpt2",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-07-25T17:58:29Z |
---
license: mit
base_model: gpt2
tags:
- generated_from_trainer
model-index:
- name: GPT2-Arabic_Poetry_generator
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# GPT2-Arabic_Poetry_generator
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 4.32.0.dev0
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.13.3
|
NasimB/bnc_spoken-gutenberg_fixed-not-mixed-rarity-seed
|
NasimB
| 2023-08-01T22:25:12Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"generated_from_trainer",
"dataset:generator",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-07-31T23:26:49Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- generator
model-index:
- name: bnc_spoken-gutenberg_fixed-not-mixed-rarity-seed
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. -->
# bnc_spoken-gutenberg_fixed-not-mixed-rarity-seed
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the generator dataset.
It achieves the following results on the evaluation set:
- Loss: 4.1290
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 1000
- num_epochs: 6
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 6.3575 | 0.29 | 500 | 5.3508 |
| 5.0454 | 0.59 | 1000 | 4.9293 |
| 4.7215 | 0.88 | 1500 | 4.6988 |
| 4.4664 | 1.17 | 2000 | 4.5559 |
| 4.3196 | 1.46 | 2500 | 4.4411 |
| 4.2129 | 1.76 | 3000 | 4.3422 |
| 4.0849 | 2.05 | 3500 | 4.2688 |
| 3.9127 | 2.34 | 4000 | 4.2228 |
| 3.8846 | 2.63 | 4500 | 4.1692 |
| 3.8456 | 2.93 | 5000 | 4.1214 |
| 3.6549 | 3.22 | 5500 | 4.1186 |
| 3.6084 | 3.51 | 6000 | 4.0832 |
| 3.5865 | 3.8 | 6500 | 4.0558 |
| 3.4886 | 4.1 | 7000 | 4.0536 |
| 3.3352 | 4.39 | 7500 | 4.0479 |
| 3.3306 | 4.68 | 8000 | 4.0346 |
| 3.3197 | 4.97 | 8500 | 4.0253 |
| 3.1677 | 5.27 | 9000 | 4.0370 |
| 3.1535 | 5.56 | 9500 | 4.0357 |
| 3.1531 | 5.85 | 10000 | 4.0352 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.11.0+cu113
- Datasets 2.13.0
- Tokenizers 0.13.3
|
sshalini6/base-5e4-r8-a32-d0.1
|
sshalini6
| 2023-08-01T22:23:20Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-01T22:23:20Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
### Framework versions
- PEFT 0.5.0.dev0
|
dyvapandhu/vit-molecul
|
dyvapandhu
| 2023-08-01T22:19:03Z | 191 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"generated_from_trainer",
"base_model:google/vit-base-patch16-224",
"base_model:finetune:google/vit-base-patch16-224",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-08-01T06:30:23Z |
---
license: apache-2.0
base_model: google/vit-base-patch16-224
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: vit-molecul
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-molecul
This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5737
- Accuracy: 0.71
- F1: 0.7086
## 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-06
- train_batch_size: 50
- eval_batch_size: 50
- 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 | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.723 | 1.0 | 8 | 0.6790 | 0.61 | 0.6096 |
| 0.6915 | 2.0 | 16 | 0.6661 | 0.62 | 0.5924 |
| 0.6689 | 3.0 | 24 | 0.6470 | 0.69 | 0.6892 |
| 0.6517 | 4.0 | 32 | 0.6356 | 0.64 | 0.6377 |
| 0.6368 | 5.0 | 40 | 0.6289 | 0.72 | 0.7199 |
| 0.621 | 6.0 | 48 | 0.6217 | 0.73 | 0.7293 |
| 0.6061 | 7.0 | 56 | 0.6197 | 0.69 | 0.6862 |
| 0.5924 | 8.0 | 64 | 0.6087 | 0.73 | 0.7293 |
| 0.5767 | 9.0 | 72 | 0.6003 | 0.72 | 0.7199 |
| 0.5633 | 10.0 | 80 | 0.5953 | 0.72 | 0.7196 |
| 0.5491 | 11.0 | 88 | 0.5885 | 0.72 | 0.7199 |
| 0.5351 | 12.0 | 96 | 0.5869 | 0.71 | 0.7100 |
| 0.5239 | 13.0 | 104 | 0.5867 | 0.7 | 0.6995 |
| 0.5118 | 14.0 | 112 | 0.5804 | 0.71 | 0.7100 |
| 0.502 | 15.0 | 120 | 0.5752 | 0.71 | 0.7100 |
| 0.4942 | 16.0 | 128 | 0.5738 | 0.72 | 0.7199 |
| 0.4885 | 17.0 | 136 | 0.5771 | 0.71 | 0.7086 |
| 0.4831 | 18.0 | 144 | 0.5751 | 0.71 | 0.7086 |
| 0.4793 | 19.0 | 152 | 0.5743 | 0.71 | 0.7086 |
| 0.4774 | 20.0 | 160 | 0.5737 | 0.71 | 0.7086 |
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu117
- Datasets 2.14.1
- Tokenizers 0.13.3
|
paultrust/gpt_neo_rl_multi_label
|
paultrust
| 2023-08-01T21:39:50Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-04-28T09:44:41Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
### Framework versions
- PEFT 0.4.0.dev0
|
MattStammers/Taxi-v3
|
MattStammers
| 2023-08-01T21:29:33Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-07-31T21:18:41Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.54 +/- 2.69
name: mean_reward
verified: false
---
## Crazy Taxi
Pick up the peeps and deliver them to their destination - simples ;)
|
Arch4ngel/distilhubert-finetuned-gtzan
|
Arch4ngel
| 2023-08-01T21:20:47Z | 161 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"hubert",
"audio-classification",
"generated_from_trainer",
"dataset:marsyas/gtzan",
"base_model:ntu-spml/distilhubert",
"base_model:finetune:ntu-spml/distilhubert",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
audio-classification
| 2023-07-19T16:49:08Z |
---
license: apache-2.0
base_model: ntu-spml/distilhubert
tags:
- generated_from_trainer
datasets:
- marsyas/gtzan
metrics:
- accuracy
model-index:
- name: distilhubert-finetuned-gtzan
results:
- task:
name: Audio Classification
type: audio-classification
dataset:
name: GTZAN
type: marsyas/gtzan
config: all
split: train
args: all
metrics:
- name: Accuracy
type: accuracy
value: 0.82
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilhubert-finetuned-gtzan
This model is a fine-tuned version of [ntu-spml/distilhubert](https://huggingface.co/ntu-spml/distilhubert) on the GTZAN dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5362
- Accuracy: 0.82
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.9881 | 1.0 | 113 | 1.8088 | 0.45 |
| 1.4015 | 2.0 | 226 | 1.2665 | 0.63 |
| 1.0325 | 3.0 | 339 | 0.9793 | 0.72 |
| 0.8844 | 4.0 | 452 | 0.8951 | 0.73 |
| 0.5932 | 5.0 | 565 | 0.7416 | 0.76 |
| 0.3958 | 6.0 | 678 | 0.6143 | 0.79 |
| 0.446 | 7.0 | 791 | 0.5115 | 0.83 |
| 0.1893 | 8.0 | 904 | 0.4992 | 0.85 |
| 0.24 | 9.0 | 1017 | 0.5084 | 0.85 |
| 0.1947 | 10.0 | 1130 | 0.5362 | 0.82 |
### Framework versions
- Transformers 4.32.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.14.2
- Tokenizers 0.13.3
|
Yijia-Xiao/Med
|
Yijia-Xiao
| 2023-08-01T21:17:00Z | 2 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-01T21:16:59Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
### Framework versions
- PEFT 0.5.0.dev0
|
Thatgreenguy/ppo-LunarLander-v2
|
Thatgreenguy
| 2023-08-01T21:04:42Z | 4 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-08-01T21:04:20Z |
---
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: 260.46 +/- 16.82
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
...
```
|
s3nh/vicuna-13b-v1.5-GGML
|
s3nh
| 2023-08-01T21:03:30Z | 0 | 1 |
transformers
|
[
"transformers",
"text-generation",
"en",
"arxiv:2307.09288",
"arxiv:2306.05685",
"license:openrail",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-08-01T20:11:40Z |
---
license: openrail
language:
- en
pipeline_tag: text-generation
library_name: transformers
---
## Original model card
Buy me a coffee if you like this project ;)
<a href="https://www.buymeacoffee.com/s3nh"><img src="https://www.buymeacoffee.com/assets/img/guidelines/download-assets-sm-1.svg" alt=""></a>
#### Description
GGML Format model files for [This project](https://huggingface.co/lmsys/vicuna-13b-v1.5).
### inference
```python
import ctransformers
from ctransformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained(output_dir, ggml_file,
gpu_layers=32, model_type="llama")
manual_input: str = "Tell me about your last dream, please."
llm(manual_input,
max_new_tokens=256,
temperature=0.9,
top_p= 0.7)
```
# Original model card
## Model Details
Vicuna is a chat assistant trained by fine-tuning Llama 2 on user-shared conversations collected from ShareGPT.
- **Developed by:** [LMSYS](https://lmsys.org/)
- **Model type:** An auto-regressive language model based on the transformer architecture
- **License:** Llama 2 Community License Agreement
- **Finetuned from model:** [Llama 2](https://arxiv.org/abs/2307.09288)
### Model Sources
- **Repository:** https://github.com/lm-sys/FastChat
- **Blog:** https://lmsys.org/blog/2023-03-30-vicuna/
- **Paper:** https://arxiv.org/abs/2306.05685
- **Demo:** https://chat.lmsys.org/
## Uses
The primary use of Vicuna is research on large language models and chatbots.
The primary intended users of the model are researchers and hobbyists in natural language processing, machine learning, and artificial intelligence.
## How to Get Started with the Model
- Command line interface: https://github.com/lm-sys/FastChat#vicuna-weights
- APIs (OpenAI API, Huggingface API): https://github.com/lm-sys/FastChat/tree/main#api
## Training Details
Vicuna v1.5 is fine-tuned from Llama 2 with supervised instruction fine-tuning.
The training data is around 125K conversations collected from ShareGPT.com.
See more details in the "Training Details of Vicuna Models" section in the appendix of this [paper](https://arxiv.org/pdf/2306.05685.pdf).
## Evaluation
Vicuna is evaluated with standard benchmarks, human preference, and LLM-as-a-judge. See more details in this [paper](https://arxiv.org/pdf/2306.05685.pdf) and [leaderboard](https://huggingface.co/spaces/lmsys/chatbot-arena-leaderboard).
## Difference between different versions of Vicuna
See [vicuna_weights_version.md](https://github.com/lm-sys/FastChat/blob/main/docs/vicuna_weights_version.md)
|
s3nh/NewHope-GGML
|
s3nh
| 2023-08-01T21:02:35Z | 0 | 4 |
transformers
|
[
"transformers",
"text-generation",
"en",
"license:openrail",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-08-01T20:27:45Z |
---
license: openrail
language:
- en
pipeline_tag: text-generation
library_name: transformers
---
## Original model card
Buy me a coffee if you like this project ;)
<a href="https://www.buymeacoffee.com/s3nh"><img src="https://www.buymeacoffee.com/assets/img/guidelines/download-assets-sm-1.svg" alt=""></a>
#### Description
GGML Format model files for [This project](https://huggingface.co/SLAM-group/NewHope).
### inference
```python
import ctransformers
from ctransformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained(output_dir, ggml_file,
gpu_layers=32, model_type="llama")
manual_input: str = "Tell me about your last dream, please."
llm(manual_input,
max_new_tokens=256,
temperature=0.9,
top_p= 0.7)
```
# Original model card
We introduce NewHope, a fine-tuned chat model based on llama-2-13b, aiming to provide a strong coding capability. NewHope handle different languages including Python, C++, Java, JavaScript, Go, and more. Preliminary evaluation on HumanEval shows that **NewHope possesses 99% of GPT-4's programming capabilities**.
**Contact**: SLAM (<ins>S</ins>UFE <ins>L</ins>arge <ins>A</ins>I <ins>M</ins>odel) is a research group at Shanghai University of Finance and Economics.
cui.wanyun@sufe.edu.cn
**TODO**: We will release more evaluatation results and training details later.
# Evaluation Results
We evaluated NewHope on [HumanEval](https://github.com/openai/human-eval) using the official evaluation script by OpenAI. We compared the Pass@1 metric of NewHope with other models. The results of other models are from PapersWithCode.
| Model | Pass@1 |
| ----- | ------ |
| **GPT-4** | **67.0** |
| **NewHope** | **66.5** |
| PanGu-Coder2 15B | 61.6 |
| WizardCoder 15B | 57.3 |
| phi-1 1.3B | 50.6 |
| GPT-3.5 | 48.1 |
| phi-1-small | 45.0 |
| PaLM-Coder | 36.0 |
| CodeGeeX2-6B | 35.9 |
# Model Weights
We have open-sourced the model weights [NewHope](https://huggingface.co/SLAM-group/NewHope).
We are uploading the model weights. The weights will be available in a few hours.
# Usage
To load the NewHope model using Transformers, use the following code:
```
import torch
from transformers import LlamaTokenizer, LlamaForCausalLM
base_model = "SLAM-group/NewHope"
tokenizer = LlamaTokenizer.from_pretrained(base_model)
model = LlamaForCausalLM.from_pretrained(base_model, torch_dtype=torch.float16, device_map="auto")
# model.config.use_cache is default to `False`. For inference: `model.config.use_cache = True`
```
**Note:** At least Huggingface Transformers **4.31.0** is required to load this model!
You can ask NewHope to generate code with instructions. We provide a simple example of how NewHope model generates code with the specific prompt:
```
# Suppose required tokenizer and model have already been loaded
instruction = "Write a Python function to tell me what the date is today."
prompt = f"<s> ### Instruction:\n{instruction}\n\n### Response:\n"
inputs = tokenizer(prompt, add_special_tokens=False, return_tensors="pt").to("cuda")
output = model.generate(**inputs, do_sample=True, top_p=0.9, max_new_tokens=2048)[0]
decoded_output = tokenizer.decode(output, skip_special_tokens=True).split("### Response:\n")[-1].strip()
print(decoded_output)
```
You can also interact with NewHope in a dialog manner with the following prompt:
```
<s> ### Instruction:\nQ1\n\n### Response:\nA1</s><s> ### Instruction:\nQ2\n\n### Response:\nA2</s>
```
# Evaluation
### Local setup
1. Install HumanEval for evaluation. [Details](https://github.com/openai/human-eval)
2. Install dependencies
```bash
pip install -r requirements.txt
```
---
For HumanEval, we use the following prompt:
```
example_input = 'def is_odd(number: int) -> bool:\n """ Check whether the given number is odd\n >>> is_odd(3)\n True\n >>> is_odd(6)\n False\n """\n'
example_output = 'def is_odd(number: int) -> bool:\n """ Check whether the given number is odd\n >>> is_odd(3)\n True\n >>> is_odd(6)\n False\n """\n return number % 2 == 1'
task_in_humaneval = "REPLACE `task_in_humaneval` WITH THE SPECIFIC TASK IN HUMANEVAL DATA"
prompt = f"<s> ### Instruction:\nComplete the given function below:\n\n{example_input}\n\n### Response:\n{example_output}</s><s> ### Instruction:\nComplete the given function below:\n\n{task_in_human_eval}\n\n### Response:\n"
```
To reproduce the results on HumanEval, use the following script:
```
python complete.py --base_model SLAM-group/NewHope --output_dir output --n_gpu 8
```
The above script will generate `samples.jsonl` in `output_dir`, which can be directly evaluated by HumanEval. [Evaluation procedure](https://github.com/openai/human-eval). We conducted the experiment with `fp16` on 8xA800, 80GB GPUs, reaching `66.5%` on Pass@1 (v.s. GPT4 `67.0%`).
# Citation
```
@misc{2023newhope,
title={NewHope: Harnessing 99% of GPT-4's Programming Capabilities},
author={Wanyun Cui and Qianle Wang},
howpublished = https://github.com/SLAM-group/newhope,
year={2023}
}
```
|
facebook/vc1-large-permissive
|
facebook
| 2023-08-01T21:02:24Z | 6 | 1 | null |
[
"pytorch",
"arxiv:2303.18240",
"license:mit",
"region:us"
] | null | 2023-06-30T22:34:07Z |
---
license: mit
---
## Model
This is the MIT-licensed version of [VC1-Large](https://huggingface.co/facebook/vc1-large/tree/main).
[EAI-VC Repo](https://github.com/facebookresearch/eai-vc)
[VC-1 Website](https://eai-vc.github.io/),
[VC-1 Blogpost](https://ai.facebook.com/blog/robots-learning-video-simulation-artificial-visual-cortex-vc-1),
[VC-1 Paper](https://ai.facebook.com/research/publications/where-are-we-in-the-search-for-an-artificial-visual-cortex-for-embodied-intelligence/),
## DATASET
Sampling every_k:
### ImageNet 1,281,167 ###
- 1
### Ego (3,538,291 frames total) ###
- 1 # Ego4D full already subsampled with 2,790,520 frames
- 1 # 100DOH with 99,899 frames
- 60 # Epic Kitchens with 332,757 frames
- 80 # SSV2 with 315,115 frames
### INav (779 643 frames total) ###
- 1 # RE10K with 779,643 frames
# Total number 5,599,101 frames
## Citation
If you use this model, please cite:
```bibtex
@inproceedings{vc2023,
title={Where are we in the search for an Artificial Visual Cortex for Embodied Intelligence?},
author={Arjun Majumdar and Karmesh Yadav and Sergio Arnaud and Yecheng Jason Ma and Claire Chen and Sneha Silwal and Aryan Jain and Vincent-Pierre Berges and Pieter Abbeel and Jitendra Malik and Dhruv Batra and Yixin Lin and Oleksandr Maksymets and Aravind Rajeswaran and Franziska Meier},
year={2023},
eprint={2303.18240},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```
|
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