modelId stringlengths 4 111 | lastModified stringlengths 24 24 | tags list | pipeline_tag stringlengths 5 30 ⌀ | author stringlengths 2 34 ⌀ | config null | securityStatus null | id stringlengths 4 111 | likes int64 0 9.53k | downloads int64 2 73.6M | library_name stringlengths 2 84 ⌀ | created timestamp[us] | card stringlengths 101 901k | card_len int64 101 901k | embeddings list |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
seeoo/distilbert-base-uncased-finetuned-emotion | 2023-05-22T04:59:55.000Z | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | seeoo | null | null | seeoo/distilbert-base-uncased-finetuned-emotion | 0 | 2 | transformers | 2023-05-22T04:53:42 | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2138
- Accuracy: 0.926
- F1: 0.9261
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8297 | 1.0 | 250 | 0.3079 | 0.905 | 0.9018 |
| 0.2463 | 2.0 | 500 | 0.2138 | 0.926 | 0.9261 |
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu118
- Tokenizers 0.13.3
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fcuadra/distilbert_classifier_newsgroups | 2023-05-22T05:59:26.000Z | [
"transformers",
"tf",
"distilbert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | fcuadra | null | null | fcuadra/distilbert_classifier_newsgroups | 0 | 2 | transformers | 2023-05-22T05:32:19 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: distilbert_classifier_newsgroups
results: []
pipeline_tag: text-classification
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# distilbert_classifier_newsgroups
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on [20Newsgroups](http://qwone.com/~jason/20Newsgroups/) dataset.
It achieves the following results on the evaluation set:
## Model description
We have fine-tuned the distilbert-base-uncased to classify news in 20 main topics based on the labeled dataset [20Newsgroups](http://qwone.com/~jason/20Newsgroups/).
## Training and evaluation data
The 20 newsgroups dataset comprises around 18000 newsgroups posts on 20 topics split in two subsets: one for training (or development) and
the other one for testing (or for performance evaluation).
The split between the train and test set is based upon a messages posted before and after a specific date.
These are the 20 topics we fine-tuned the model on:
'alt.atheism',
'comp.graphics',
'comp.os.ms-windows.misc',
'comp.sys.ibm.pc.hardware',
'comp.sys.mac.hardware',
'comp.windows.x',
'misc.forsale',
'rec.autos',
'rec.motorcycles',
'rec.sport.baseball',
'rec.sport.hockey',
'sci.crypt',
'sci.electronics',
'sci.med',
'sci.space',
'soc.religion.christian',
'talk.politics.guns',
'talk.politics.mideast',
'talk.politics.misc',
'talk.religion.misc'
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 1908, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
Epoch 1/3
637/637 [==============================] - 110s 131ms/step - loss: 1.3480 - accuracy: 0.6633 - val_loss: 0.6122 - val_accuracy: 0.8304
Epoch 2/3
637/637 [==============================] - 44s 70ms/step - loss: 0.4498 - accuracy: 0.8812 - val_loss: 0.4342 - val_accuracy: 0.8799
Epoch 3/3
637/637 [==============================] - 40s 64ms/step - loss: 0.2685 - accuracy: 0.9355 - val_loss: 0.3756 - val_accuracy: 0.8993
CPU times: user 3min 4s, sys: 8.76 s, total: 3min 13s
Wall time: 3min 15s
<keras.callbacks.History at 0x7f481afbfbb0>
### Framework versions
- Transformers 4.28.0
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3 | 2,909 | [
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ehanJ/distilbert-base-uncased-finetuned-emotion | 2023-05-22T06:25:51.000Z | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | text-classification | ehanJ | null | null | ehanJ/distilbert-base-uncased-finetuned-emotion | 0 | 2 | transformers | 2023-05-22T06:20:44 | ---
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: 0.9205
- name: F1
type: f1
value: 0.9205899308588681
---
<!-- 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: 0.2240
- Accuracy: 0.9205
- F1: 0.9206
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8441 | 1.0 | 250 | 0.3201 | 0.904 | 0.9018 |
| 0.2551 | 2.0 | 500 | 0.2240 | 0.9205 | 0.9206 |
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
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Afsara/fb_bart_large_cnn | 2023-05-22T07:06:45.000Z | [
"transformers",
"pytorch",
"tf",
"jax",
"rust",
"bart",
"text2text-generation",
"summarization",
"en",
"dataset:cnn_dailymail",
"arxiv:1910.13461",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | summarization | Afsara | null | null | Afsara/fb_bart_large_cnn | 0 | 2 | transformers | 2023-05-22T06:53:18 | ---
language:
- en
tags:
- summarization
license: mit
thumbnail: https://huggingface.co/front/thumbnails/facebook.png
datasets:
- cnn_dailymail
model-index:
- name: facebook/bart-large-cnn
results:
- task:
type: summarization
name: Summarization
dataset:
name: cnn_dailymail
type: cnn_dailymail
config: 3.0.0
split: train
metrics:
- name: ROUGE-1
type: rouge
value: 42.9486
verified: true
- name: ROUGE-2
type: rouge
value: 20.8149
verified: true
- name: ROUGE-L
type: rouge
value: 30.6186
verified: true
- name: ROUGE-LSUM
type: rouge
value: 40.0376
verified: true
- name: loss
type: loss
value: 2.529000997543335
verified: true
- name: gen_len
type: gen_len
value: 78.5866
verified: true
---
# BART (large-sized model), fine-tuned on CNN Daily Mail
BART model pre-trained on English language, and fine-tuned on [CNN Daily Mail](https://huggingface.co/datasets/cnn_dailymail). It was introduced in the paper [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://arxiv.org/abs/1910.13461) by Lewis et al. and first released in [this repository (https://github.com/pytorch/fairseq/tree/master/examples/bart).
Disclaimer: The team releasing BART did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
BART is a transformer encoder-encoder (seq2seq) model with a bidirectional (BERT-like) encoder and an autoregressive (GPT-like) decoder. BART is pre-trained by (1) corrupting text with an arbitrary noising function, and (2) learning a model to reconstruct the original text.
BART is particularly effective when fine-tuned for text generation (e.g. summarization, translation) but also works well for comprehension tasks (e.g. text classification, question answering). This particular checkpoint has been fine-tuned on CNN Daily Mail, a large collection of text-summary pairs.
## Intended uses & limitations
You can use this model for text summarization.
### How to use
Here is how to use this model with the [pipeline API](https://huggingface.co/transformers/main_classes/pipelines.html):
```python
from transformers import pipeline
summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
ARTICLE = """ New York (CNN)When Liana Barrientos was 23 years old, she got married in Westchester County, New York.
A year later, she got married again in Westchester County, but to a different man and without divorcing her first husband.
Only 18 days after that marriage, she got hitched yet again. Then, Barrientos declared "I do" five more times, sometimes only within two weeks of each other.
In 2010, she married once more, this time in the Bronx. In an application for a marriage license, she stated it was her "first and only" marriage.
Barrientos, now 39, is facing two criminal counts of "offering a false instrument for filing in the first degree," referring to her false statements on the
2010 marriage license application, according to court documents.
Prosecutors said the marriages were part of an immigration scam.
On Friday, she pleaded not guilty at State Supreme Court in the Bronx, according to her attorney, Christopher Wright, who declined to comment further.
After leaving court, Barrientos was arrested and charged with theft of service and criminal trespass for allegedly sneaking into the New York subway through an emergency exit, said Detective
Annette Markowski, a police spokeswoman. In total, Barrientos has been married 10 times, with nine of her marriages occurring between 1999 and 2002.
All occurred either in Westchester County, Long Island, New Jersey or the Bronx. She is believed to still be married to four men, and at one time, she was married to eight men at once, prosecutors say.
Prosecutors said the immigration scam involved some of her husbands, who filed for permanent residence status shortly after the marriages.
Any divorces happened only after such filings were approved. It was unclear whether any of the men will be prosecuted.
The case was referred to the Bronx District Attorney\'s Office by Immigration and Customs Enforcement and the Department of Homeland Security\'s
Investigation Division. Seven of the men are from so-called "red-flagged" countries, including Egypt, Turkey, Georgia, Pakistan and Mali.
Her eighth husband, Rashid Rajput, was deported in 2006 to his native Pakistan after an investigation by the Joint Terrorism Task Force.
If convicted, Barrientos faces up to four years in prison. Her next court appearance is scheduled for May 18.
"""
print(summarizer(ARTICLE, max_length=130, min_length=30, do_sample=False))
>>> [{'summary_text': 'Liana Barrientos, 39, is charged with two counts of "offering a false instrument for filing in the first degree" In total, she has been married 10 times, with nine of her marriages occurring between 1999 and 2002. She is believed to still be married to four men.'}]
```
### BibTeX entry and citation info
```bibtex
@article{DBLP:journals/corr/abs-1910-13461,
author = {Mike Lewis and
Yinhan Liu and
Naman Goyal and
Marjan Ghazvininejad and
Abdelrahman Mohamed and
Omer Levy and
Veselin Stoyanov and
Luke Zettlemoyer},
title = {{BART:} Denoising Sequence-to-Sequence Pre-training for Natural Language
Generation, Translation, and Comprehension},
journal = {CoRR},
volume = {abs/1910.13461},
year = {2019},
url = {http://arxiv.org/abs/1910.13461},
eprinttype = {arXiv},
eprint = {1910.13461},
timestamp = {Thu, 31 Oct 2019 14:02:26 +0100},
biburl = {https://dblp.org/rec/journals/corr/abs-1910-13461.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
} | 5,999 | [
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Afsara/cse_buet_bangla_t5 | 2023-05-22T07:23:22.000Z | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"bn",
"arxiv:2205.11081",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | text2text-generation | Afsara | null | null | Afsara/cse_buet_bangla_t5 | 1 | 2 | transformers | 2023-05-22T07:14:46 | ---
language:
- bn
licenses:
- cc-by-nc-sa-4.0
---
# BanglaT5
This repository contains the pretrained checkpoint of the model **BanglaT5**. This is a sequence to sequence transformer model pretrained with the ["Span Corruption"]() objective. Finetuned models using this checkpoint achieve state-of-the-art results on many of the NLG tasks in bengali.
For finetuning on different downstream tasks such as `Machine Translation`, `Abstractive Text Summarization`, `Question Answering` etc., refer to the scripts in the official GitHub [repository](https://github.com/csebuetnlp/BanglaNLG).
**Note**: This model was pretrained using a specific normalization pipeline available [here](https://github.com/csebuetnlp/normalizer). All finetuning scripts in the official GitHub repository use this normalization by default. If you need to adapt the pretrained model for a different task make sure the text units are normalized using this pipeline before tokenizing to get best results. A basic example is given below:
## Using this model in `transformers` (tested on 4.11.0.dev0)
```python
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
from normalizer import normalize # pip install git+https://github.com/csebuetnlp/normalizer
model = AutoModelForSeq2SeqLM.from_pretrained("csebuetnlp/banglat5")
tokenizer = AutoTokenizer.from_pretrained("csebuetnlp/banglat5", use_fast=False)
input_sentence = ""
input_ids = tokenizer(normalize(input_sentence), return_tensors="pt").input_ids
generated_tokens = model.generate(input_ids)
decoded_tokens = tokenizer.batch_decode(generated_tokens)[0]
print(decoded_tokens)
```
## Benchmarks
* Supervised fine-tuning
| Model | Params | MT (SacreBLEU) | TS (ROUGE-2) | QA (EM/F1) | MD (SacreBLEU-1) | NHG (ROUGE-2) | XLS (ROUGE-2) | BNLG score |
|--------------------|------------|-----------------------|------------------------|-------------------|--------------------|----------------|----------------|---------------|
|[mT5 (base)](https://huggingface.co/google/mt5-base) | 582M | 36.6/22.5 | 10.3 | 59.0/65.3 | 17.5 | 9.6 | 2.7/0.7 | 24.9 |
|[XLM-ProphetNet](https://huggingface.co/microsoft/xprophetnet-large-wiki100-cased) | 616M | 23.3/16.4 | 7.8 | 53.0/57.3 | 20.0 | 9.5 | 6.2/2.7 | 21.8 |
|[mBART-50](https://huggingface.co/facebook/mbart-large-50) | 611M | 23.6/16.7 | 10.4 | 53.4/58.9 | 18.5 | 11.2 | 5.4/3.7 | 22.4 |
|[IndicBART](https://huggingface.co/ai4bharat/IndicBART) | 244M | 22.7/13.1 | 8.1 | 53.3/58.8 | 14.8 | 7.9 | 6.3/2.5 | 20.8 |
|[BanglaT5](https://huggingface.co/csebuetnlp/banglat5) | 247M | 38.8/25.2 | 13.7 | 68.5/74.8 | 19.0 | 13.8 | 6.4/4.0 | 29.4 |
The benchmarking datasets are as follows:
* **MT:** **[Machine Translation](https://github.com/csebuetnlp/banglanmt#datasets)**
* **TS:** **[Abstractive Text Summarization](https://huggingface.co/datasets/csebuetnlp/xlsum)**
* **QA:** **[Question Answering](https://huggingface.co/datasets/csebuetnlp/squad_bn)**
* **MD:** **[Multi Turn Dialogue Generation](https://drive.google.com/file/d/1qPmNN6qA4evbh4cD_BDDTCFOwMu4H2JS/view?usp=sharing)**
* **NHG:** **[News Headline Generation](https://huggingface.co/datasets/csebuetnlp/xlsum)**
* **XLS:** **[Cross-lingual Summarization](https://huggingface.co/datasets/csebuetnlp/CrossSum)**
## Citation
If you use this model, please cite the following paper:
```
@article{bhattacharjee2022banglanlg,
author = {Abhik Bhattacharjee and Tahmid Hasan and Wasi Uddin Ahmad and Rifat Shahriyar},
title = {BanglaNLG: Benchmarks and Resources for Evaluating Low-Resource Natural Language Generation in Bangla},
journal = {CoRR},
volume = {abs/2205.11081},
year = {2022},
url = {https://arxiv.org/abs/2205.11081},
eprinttype = {arXiv},
eprint = {2205.11081}
}
```
If you use the normalization module, please cite the following paper:
```
@inproceedings{hasan-etal-2020-low,
title = "Not Low-Resource Anymore: Aligner Ensembling, Batch Filtering, and New Datasets for {B}engali-{E}nglish Machine Translation",
author = "Hasan, Tahmid and
Bhattacharjee, Abhik and
Samin, Kazi and
Hasan, Masum and
Basak, Madhusudan and
Rahman, M. Sohel and
Shahriyar, Rifat",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.emnlp-main.207",
doi = "10.18653/v1/2020.emnlp-main.207",
pages = "2612--2623",
abstract = "Despite being the seventh most widely spoken language in the world, Bengali has received much less attention in machine translation literature due to being low in resources. Most publicly available parallel corpora for Bengali are not large enough; and have rather poor quality, mostly because of incorrect sentence alignments resulting from erroneous sentence segmentation, and also because of a high volume of noise present in them. In this work, we build a customized sentence segmenter for Bengali and propose two novel methods for parallel corpus creation on low-resource setups: aligner ensembling and batch filtering. With the segmenter and the two methods combined, we compile a high-quality Bengali-English parallel corpus comprising of 2.75 million sentence pairs, more than 2 million of which were not available before. Training on neural models, we achieve an improvement of more than 9 BLEU score over previous approaches to Bengali-English machine translation. We also evaluate on a new test set of 1000 pairs made with extensive quality control. We release the segmenter, parallel corpus, and the evaluation set, thus elevating Bengali from its low-resource status. To the best of our knowledge, this is the first ever large scale study on Bengali-English machine translation. We believe our study will pave the way for future research on Bengali-English machine translation as well as other low-resource languages. Our data and code are available at https://github.com/csebuetnlp/banglanmt.",
}
```
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KeiHeityuu/my_awesome_model | 2023-10-18T11:39:12.000Z | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | text-classification | KeiHeityuu | null | null | KeiHeityuu/my_awesome_model | 0 | 2 | transformers | 2023-05-22T09:05:08 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
metrics:
- accuracy
model-index:
- name: my_awesome_model
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: imdb
type: imdb
config: plain_text
split: test
args: plain_text
metrics:
- name: Accuracy
type: accuracy
value: 0.93104
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# my_awesome_model
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2349
- Accuracy: 0.9310
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.2301 | 1.0 | 1563 | 0.1888 | 0.9272 |
| 0.1512 | 2.0 | 3126 | 0.2349 | 0.9310 |
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,675 | [
[
-0.03936767578125,
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0.01485443115234375,
0.002330780029296875,
-0.0266571044921875,
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0.002964019775390625,
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0.01435089111328125,
0.0238494873046875,
-0.051788330078125,
-0.043975830078125,
-0.05902099609375... |
Ioanaaaaaaa/distilbert-base-uncased-finetuned-sexism-3 | 2023-05-22T10:54:56.000Z | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | Ioanaaaaaaa | null | null | Ioanaaaaaaa/distilbert-base-uncased-finetuned-sexism-3 | 0 | 2 | transformers | 2023-05-22T10:31:33 | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-sexism-3
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-sexism-3
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6098
- Accuracy: 0.8396
- F1: 0.8374
## 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: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.3246 | 1.0 | 1876 | 0.3858 | 0.8534 | 0.8490 |
| 0.2469 | 2.0 | 3752 | 0.6098 | 0.8396 | 0.8374 |
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,502 | [
[
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0.02093505859375,
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0.0181121826171875,
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Ioanaaaaaaa/distilbert-base-uncased-finetuned-sexism-4 | 2023-05-22T11:20:24.000Z | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | Ioanaaaaaaa | null | null | Ioanaaaaaaa/distilbert-base-uncased-finetuned-sexism-4 | 0 | 2 | transformers | 2023-05-22T10:59:37 | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-sexism-4
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-sexism-4
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7202
- Accuracy: 0.8406
- F1: 0.8399
## 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: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.14 | 1.0 | 1876 | 0.7202 | 0.8406 | 0.8399 |
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,431 | [
[
-0.02667236328125,
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0.0221405029296875,
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0.0016326904296875,
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Ztijn/bert-base-dutch-cased-finetuned-squad | 2023-05-22T14:22:35.000Z | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | question-answering | Ztijn | null | null | Ztijn/bert-base-dutch-cased-finetuned-squad | 0 | 2 | transformers | 2023-05-22T11:24:27 | ---
tags:
- generated_from_trainer
model-index:
- name: bert-base-dutch-cased-finetuned-squad
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-dutch-cased-finetuned-squad
This model is a fine-tuned version of [GroNLP/bert-base-dutch-cased](https://huggingface.co/GroNLP/bert-base-dutch-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4324
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 1.261 | 1.0 | 8350 | 1.1667 |
| 0.9583 | 2.0 | 16700 | 1.2665 |
| 0.6993 | 3.0 | 25050 | 1.4324 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,405 | [
[
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phnghiapro/distilbert-base-uncased-finetuned-cola | 2023-05-22T12:07:13.000Z | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | text-classification | phnghiapro | null | null | phnghiapro/distilbert-base-uncased-finetuned-cola | 0 | 2 | transformers | 2023-05-22T11:26:04 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- matthews_correlation
model-index:
- name: distilbert-base-uncased-finetuned-cola
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
config: cola
split: validation
args: cola
metrics:
- name: Matthews Correlation
type: matthews_correlation
value: 0.5282404248888111
---
<!-- 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-cola
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5703
- Matthews Correlation: 0.5282
## 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.5235 | 1.0 | 535 | 0.5332 | 0.4098 |
| 0.3452 | 2.0 | 1070 | 0.4980 | 0.4899 |
| 0.2301 | 3.0 | 1605 | 0.5703 | 0.5282 |
| 0.1786 | 4.0 | 2140 | 0.7849 | 0.5126 |
| 0.134 | 5.0 | 2675 | 0.8406 | 0.5185 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
| 2,042 | [
[
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0.0233917236328125,
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AustinCarthy/Onlyphish_100KP_BFall_fromB_40KGen_topP_0.75 | 2023-05-22T20:54:05.000Z | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | AustinCarthy | null | null | AustinCarthy/Onlyphish_100KP_BFall_fromB_40KGen_topP_0.75 | 0 | 2 | transformers | 2023-05-22T11:28:12 | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: Onlyphish_100KP_BFall_fromB_40KGen_topP_0.75
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. -->
# Onlyphish_100KP_BFall_fromB_40KGen_topP_0.75
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: 0.0202
- Accuracy: 0.9974
- F1: 0.9722
- Precision: 0.9989
- Recall: 0.9468
- Roc Auc Score: 0.9734
- Tpr At Fpr 0.01: 0.9538
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | Roc Auc Score | Tpr At Fpr 0.01 |
|:-------------:|:-----:|:------:|:---------------:|:--------:|:------:|:---------:|:------:|:-------------:|:---------------:|
| 0.0187 | 1.0 | 91875 | 0.0418 | 0.9936 | 0.9283 | 0.9968 | 0.8686 | 0.9342 | 0.8558 |
| 0.0035 | 2.0 | 183750 | 0.0279 | 0.9954 | 0.9488 | 0.9991 | 0.9034 | 0.9517 | 0.9336 |
| 0.0021 | 3.0 | 275625 | 0.0237 | 0.9971 | 0.9688 | 0.9979 | 0.9414 | 0.9707 | 0.9384 |
| 0.0021 | 4.0 | 367500 | 0.0202 | 0.9973 | 0.9713 | 0.9985 | 0.9456 | 0.9728 | 0.9532 |
| 0.0003 | 5.0 | 459375 | 0.0202 | 0.9974 | 0.9722 | 0.9989 | 0.9468 | 0.9734 | 0.9538 |
### Framework versions
- Transformers 4.29.1
- Pytorch 1.9.0+cu111
- Datasets 2.10.1
- Tokenizers 0.13.2
| 2,257 | [
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0.030181884765625,
0.028564453125,
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Ioanaaaaaaa/distilbert-base-uncased-finetuned-sexism-7 | 2023-05-22T12:26:43.000Z | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | Ioanaaaaaaa | null | null | Ioanaaaaaaa/distilbert-base-uncased-finetuned-sexism-7 | 0 | 2 | transformers | 2023-05-22T12:07:18 | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-sexism-7
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-sexism-7
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2813
- Accuracy: 0.8406
- F1: 0.8399
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.0586 | 1.0 | 938 | 1.1347 | 0.8316 | 0.8316 |
| 0.0219 | 2.0 | 1876 | 1.2813 | 0.8406 | 0.8399 |
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,504 | [
[
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0.0002... |
igh197/distilbert-base-uncased-finetuned-emotion | 2023-05-22T13:01:20.000Z | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | text-classification | igh197 | null | null | igh197/distilbert-base-uncased-finetuned-emotion | 0 | 2 | transformers | 2023-05-22T12:55:24 | ---
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: 0.9245
- name: F1
type: f1
value: 0.9242747341236085
---
<!-- 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: 0.2153
- Accuracy: 0.9245
- F1: 0.9243
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.7987 | 1.0 | 250 | 0.3092 | 0.907 | 0.9034 |
| 0.2449 | 2.0 | 500 | 0.2153 | 0.9245 | 0.9243 |
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,848 | [
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Stern5497/sbert-legal-xlm-roberta-base | 2023-05-22T14:01:53.000Z | [
"sentence-transformers",
"pytorch",
"roberta",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | sentence-similarity | Stern5497 | null | null | Stern5497/sbert-legal-xlm-roberta-base | 1 | 2 | sentence-transformers | 2023-05-22T13:59:42 | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
model = AutoModel.from_pretrained('{MODEL_NAME}')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 8301 with parameters:
```
{'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters:
```
{'scale': 20.0, 'similarity_fct': 'cos_sim'}
```
Parameters of the fit()-Method:
```
{
"epochs": 1,
"evaluation_steps": 5000,
"evaluator": "sentence_transformers.evaluation.SequentialEvaluator.SequentialEvaluator",
"max_grad_norm": 1,
"optimizer_class": "<class 'transformers.optimization.AdamW'>",
"optimizer_params": {
"correct_bias": false,
"eps": 1e-06,
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 830,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: RobertaModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information --> | 3,921 | [
[
-0.0200958251953125,
-0.06353759765625,
0.022552490234375,
0.023162841796875,
-0.01837158203125,
-0.031219482421875,
-0.020355224609375,
0.004352569580078125,
0.0151824951171875,
0.0279998779296875,
-0.051361083984375,
-0.04779052734375,
-0.052276611328125,
... |
Backdrive/distilbert-base-uncased-finetuned-emotion | 2023-05-22T14:52:30.000Z | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | text-classification | Backdrive | null | null | Backdrive/distilbert-base-uncased-finetuned-emotion | 0 | 2 | transformers | 2023-05-22T14:39:04 | ---
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: 0.9285
- name: F1
type: f1
value: 0.9285478749765623
---
<!-- 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: 0.2158
- Accuracy: 0.9285
- F1: 0.9285
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8112 | 1.0 | 250 | 0.3104 | 0.9005 | 0.8968 |
| 0.2447 | 2.0 | 500 | 0.2158 | 0.9285 | 0.9285 |
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,848 | [
[
-0.037994384765625,
-0.04156494140625,
0.01468658447265625,
0.021820068359375,
-0.0258636474609375,
-0.0192413330078125,
-0.013458251953125,
-0.0085601806640625,
0.0106353759765625,
0.008636474609375,
-0.056396484375,
-0.051483154296875,
-0.05963134765625,
-... |
AlexC98/BertGoodCommitPreprocessed | 2023-05-22T14:49:18.000Z | [
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"license:mit",
"endpoints_compatible",
"region:us"
] | text-classification | AlexC98 | null | null | AlexC98/BertGoodCommitPreprocessed | 0 | 2 | transformers | 2023-05-22T14:45:20 | ---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: BertGoodCommitPreprocessed
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. -->
# BertGoodCommitPreprocessed
This model is a fine-tuned version of [prajjwal1/bert-small](https://huggingface.co/prajjwal1/bert-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5242
- Accuracy: 0.8424
## 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: 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: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 24 | 0.5858 | 0.6788 |
| No log | 2.0 | 48 | 0.5640 | 0.7273 |
| No log | 3.0 | 72 | 0.5381 | 0.7394 |
| No log | 4.0 | 96 | 0.5246 | 0.7394 |
| No log | 5.0 | 120 | 0.5214 | 0.7394 |
| No log | 6.0 | 144 | 0.5093 | 0.7394 |
| No log | 7.0 | 168 | 0.4986 | 0.7515 |
| No log | 8.0 | 192 | 0.5131 | 0.7455 |
| No log | 9.0 | 216 | 0.5093 | 0.7697 |
| No log | 10.0 | 240 | 0.5064 | 0.7758 |
| No log | 11.0 | 264 | 0.5069 | 0.7697 |
| No log | 12.0 | 288 | 0.4774 | 0.7818 |
| No log | 13.0 | 312 | 0.5096 | 0.7879 |
| No log | 14.0 | 336 | 0.4933 | 0.7939 |
| No log | 15.0 | 360 | 0.4740 | 0.7939 |
| No log | 16.0 | 384 | 0.4787 | 0.7939 |
| No log | 17.0 | 408 | 0.4675 | 0.8 |
| No log | 18.0 | 432 | 0.4971 | 0.8121 |
| No log | 19.0 | 456 | 0.4935 | 0.8303 |
| No log | 20.0 | 480 | 0.4947 | 0.8121 |
| 0.3574 | 21.0 | 504 | 0.4968 | 0.8242 |
| 0.3574 | 22.0 | 528 | 0.5158 | 0.8303 |
| 0.3574 | 23.0 | 552 | 0.5146 | 0.8061 |
| 0.3574 | 24.0 | 576 | 0.4963 | 0.8303 |
| 0.3574 | 25.0 | 600 | 0.5024 | 0.8182 |
| 0.3574 | 26.0 | 624 | 0.5069 | 0.8242 |
| 0.3574 | 27.0 | 648 | 0.5242 | 0.8424 |
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
| 2,959 | [
[
-0.043426513671875,
-0.044281005859375,
0.009185791015625,
-0.004001617431640625,
-0.005611419677734375,
-0.01122283935546875,
-0.004909515380859375,
-0.0096893310546875,
0.038330078125,
0.0201873779296875,
-0.052978515625,
-0.04638671875,
-0.052398681640625,
... |
AlexC98/BertGoodCommitOriginal | 2023-05-22T14:57:09.000Z | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:mit",
"endpoints_compatible",
"region:us"
] | text-classification | AlexC98 | null | null | AlexC98/BertGoodCommitOriginal | 0 | 2 | transformers | 2023-05-22T14:47:30 | ---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: BertGoodCommitOriginal
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. -->
# BertGoodCommitOriginal
This model is a fine-tuned version of [prajjwal1/bert-small](https://huggingface.co/prajjwal1/bert-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5639
- Accuracy: 0.8242
## 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: 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: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 24 | 0.5844 | 0.7030 |
| No log | 2.0 | 48 | 0.5566 | 0.7212 |
| No log | 3.0 | 72 | 0.5375 | 0.7333 |
| No log | 4.0 | 96 | 0.5321 | 0.7212 |
| No log | 5.0 | 120 | 0.5221 | 0.7333 |
| No log | 6.0 | 144 | 0.5112 | 0.7394 |
| No log | 7.0 | 168 | 0.4828 | 0.7515 |
| No log | 8.0 | 192 | 0.4857 | 0.7818 |
| No log | 9.0 | 216 | 0.4672 | 0.7879 |
| No log | 10.0 | 240 | 0.4740 | 0.7879 |
| No log | 11.0 | 264 | 0.4758 | 0.7818 |
| No log | 12.0 | 288 | 0.4554 | 0.8061 |
| No log | 13.0 | 312 | 0.4697 | 0.8182 |
| No log | 14.0 | 336 | 0.4810 | 0.8242 |
| No log | 15.0 | 360 | 0.4612 | 0.8182 |
| No log | 16.0 | 384 | 0.4663 | 0.8242 |
| No log | 17.0 | 408 | 0.4757 | 0.8182 |
| No log | 18.0 | 432 | 0.4928 | 0.8182 |
| No log | 19.0 | 456 | 0.5371 | 0.8242 |
| No log | 20.0 | 480 | 0.5345 | 0.8182 |
| 0.3387 | 21.0 | 504 | 0.5341 | 0.8182 |
| 0.3387 | 22.0 | 528 | 0.5639 | 0.8242 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
| 2,641 | [
[
-0.04180908203125,
-0.0439453125,
0.0084228515625,
-0.002185821533203125,
-0.0117340087890625,
-0.0175323486328125,
-0.00673675537109375,
-0.012542724609375,
0.0325927734375,
0.017425537109375,
-0.053741455078125,
-0.048065185546875,
-0.0513916015625,
-0.018... |
AlexC98/BertWhyCommitPreprocessed | 2023-05-22T15:29:54.000Z | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:mit",
"endpoints_compatible",
"region:us"
] | text-classification | AlexC98 | null | null | AlexC98/BertWhyCommitPreprocessed | 0 | 2 | transformers | 2023-05-22T15:20:33 | ---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: BertWhyCommitPreprocessed
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. -->
# BertWhyCommitPreprocessed
This model is a fine-tuned version of [prajjwal1/bert-small](https://huggingface.co/prajjwal1/bert-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4699
- Accuracy: 0.8848
## 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: 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: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 31 | 0.5237 | 0.7333 |
| No log | 2.0 | 62 | 0.4632 | 0.7636 |
| No log | 3.0 | 93 | 0.4243 | 0.8 |
| No log | 4.0 | 124 | 0.3896 | 0.8182 |
| No log | 5.0 | 155 | 0.3824 | 0.8242 |
| No log | 6.0 | 186 | 0.3661 | 0.8182 |
| No log | 7.0 | 217 | 0.3597 | 0.8242 |
| No log | 8.0 | 248 | 0.3569 | 0.8364 |
| No log | 9.0 | 279 | 0.3518 | 0.8606 |
| No log | 10.0 | 310 | 0.3618 | 0.8485 |
| No log | 11.0 | 341 | 0.3462 | 0.8545 |
| No log | 12.0 | 372 | 0.3636 | 0.8485 |
| No log | 13.0 | 403 | 0.3759 | 0.8485 |
| No log | 14.0 | 434 | 0.3771 | 0.8727 |
| No log | 15.0 | 465 | 0.3957 | 0.8727 |
| No log | 16.0 | 496 | 0.4154 | 0.8788 |
| 0.2682 | 17.0 | 527 | 0.3980 | 0.8606 |
| 0.2682 | 18.0 | 558 | 0.4442 | 0.8667 |
| 0.2682 | 19.0 | 589 | 0.4028 | 0.8788 |
| 0.2682 | 20.0 | 620 | 0.4653 | 0.8606 |
| 0.2682 | 21.0 | 651 | 0.4699 | 0.8848 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
| 2,585 | [
[
-0.04193115234375,
-0.040618896484375,
0.012237548828125,
-0.001842498779296875,
-0.0087432861328125,
-0.0188140869140625,
-0.00969696044921875,
-0.0132293701171875,
0.0305328369140625,
0.01995849609375,
-0.057830810546875,
-0.043670654296875,
-0.050811767578125... |
AlexC98/BertWhatCommitPreprocessed | 2023-05-22T15:38:18.000Z | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:mit",
"endpoints_compatible",
"region:us"
] | text-classification | AlexC98 | null | null | AlexC98/BertWhatCommitPreprocessed | 0 | 2 | transformers | 2023-05-22T15:31:15 | ---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: BertWhatCommitPreprocessed
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. -->
# BertWhatCommitPreprocessed
This model is a fine-tuned version of [prajjwal1/bert-small](https://huggingface.co/prajjwal1/bert-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3631
- Accuracy: 0.9152
## 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: 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: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 38 | 0.5383 | 0.7333 |
| No log | 2.0 | 76 | 0.4130 | 0.8485 |
| No log | 3.0 | 114 | 0.3096 | 0.8727 |
| No log | 4.0 | 152 | 0.3140 | 0.8788 |
| No log | 5.0 | 190 | 0.2983 | 0.8970 |
| No log | 6.0 | 228 | 0.3019 | 0.8848 |
| No log | 7.0 | 266 | 0.3235 | 0.9030 |
| No log | 8.0 | 304 | 0.3571 | 0.8970 |
| No log | 9.0 | 342 | 0.3457 | 0.8970 |
| No log | 10.0 | 380 | 0.3340 | 0.8909 |
| No log | 11.0 | 418 | 0.3378 | 0.9091 |
| No log | 12.0 | 456 | 0.3389 | 0.9091 |
| No log | 13.0 | 494 | 0.3753 | 0.9030 |
| 0.2144 | 14.0 | 532 | 0.3492 | 0.9152 |
| 0.2144 | 15.0 | 570 | 0.3631 | 0.9152 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
| 2,215 | [
[
-0.039154052734375,
-0.03955078125,
0.01153564453125,
0.0018901824951171875,
-0.0160369873046875,
-0.0301361083984375,
-0.01465606689453125,
-0.018280029296875,
0.01678466796875,
0.0187835693359375,
-0.062042236328125,
-0.039947509765625,
-0.04901123046875,
... |
AlexC98/BertWhatCommitOriginal | 2023-05-22T15:59:51.000Z | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:mit",
"endpoints_compatible",
"region:us"
] | text-classification | AlexC98 | null | null | AlexC98/BertWhatCommitOriginal | 0 | 2 | transformers | 2023-05-22T15:51:45 | ---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: BertWhatCommitOriginal
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. -->
# BertWhatCommitOriginal
This model is a fine-tuned version of [prajjwal1/bert-small](https://huggingface.co/prajjwal1/bert-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3530
- Accuracy: 0.9091
## 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: 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: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 38 | 0.5485 | 0.7152 |
| No log | 2.0 | 76 | 0.4204 | 0.8364 |
| No log | 3.0 | 114 | 0.2951 | 0.8788 |
| No log | 4.0 | 152 | 0.2811 | 0.8848 |
| No log | 5.0 | 190 | 0.2628 | 0.8909 |
| No log | 6.0 | 228 | 0.2605 | 0.8970 |
| No log | 7.0 | 266 | 0.2790 | 0.8970 |
| No log | 8.0 | 304 | 0.2821 | 0.9030 |
| No log | 9.0 | 342 | 0.2724 | 0.9212 |
| No log | 10.0 | 380 | 0.2871 | 0.9091 |
| No log | 11.0 | 418 | 0.3067 | 0.9273 |
| No log | 12.0 | 456 | 0.3404 | 0.9273 |
| No log | 13.0 | 494 | 0.3645 | 0.9212 |
| 0.2027 | 14.0 | 532 | 0.3422 | 0.9152 |
| 0.2027 | 15.0 | 570 | 0.4038 | 0.9212 |
| 0.2027 | 16.0 | 608 | 0.3530 | 0.9091 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
| 2,269 | [
[
-0.039642333984375,
-0.041046142578125,
0.0099334716796875,
0.00234222412109375,
-0.0157470703125,
-0.0257110595703125,
-0.01242828369140625,
-0.0152740478515625,
0.02178955078125,
0.0147705078125,
-0.058349609375,
-0.0439453125,
-0.048583984375,
-0.01843261... |
AlexC98/BertWhyCommitOriginal | 2023-05-22T16:09:27.000Z | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:mit",
"endpoints_compatible",
"region:us"
] | text-classification | AlexC98 | null | null | AlexC98/BertWhyCommitOriginal | 0 | 2 | transformers | 2023-05-22T16:00:23 | ---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: BertWhyCommitOriginal
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. -->
# BertWhyCommitOriginal
This model is a fine-tuned version of [prajjwal1/bert-small](https://huggingface.co/prajjwal1/bert-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4881
- Accuracy: 0.8788
## 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: 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: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 31 | 0.5058 | 0.7394 |
| No log | 2.0 | 62 | 0.4463 | 0.7758 |
| No log | 3.0 | 93 | 0.4260 | 0.7758 |
| No log | 4.0 | 124 | 0.3954 | 0.8061 |
| No log | 5.0 | 155 | 0.3745 | 0.8061 |
| No log | 6.0 | 186 | 0.3653 | 0.8303 |
| No log | 7.0 | 217 | 0.3533 | 0.8424 |
| No log | 8.0 | 248 | 0.3500 | 0.8364 |
| No log | 9.0 | 279 | 0.3416 | 0.8606 |
| No log | 10.0 | 310 | 0.3546 | 0.8424 |
| No log | 11.0 | 341 | 0.3469 | 0.8485 |
| No log | 12.0 | 372 | 0.3511 | 0.8606 |
| No log | 13.0 | 403 | 0.3883 | 0.8545 |
| No log | 14.0 | 434 | 0.4090 | 0.8485 |
| No log | 15.0 | 465 | 0.4301 | 0.8485 |
| No log | 16.0 | 496 | 0.4415 | 0.8606 |
| 0.2667 | 17.0 | 527 | 0.4732 | 0.8545 |
| 0.2667 | 18.0 | 558 | 0.4849 | 0.8727 |
| 0.2667 | 19.0 | 589 | 0.4881 | 0.8788 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
| 2,453 | [
[
-0.041351318359375,
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antonkurylo/t5-base-samsum | 2023-05-22T18:06:51.000Z | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"summarization",
"generated_from_trainer",
"dataset:samsum",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | summarization | antonkurylo | null | null | antonkurylo/t5-base-samsum | 0 | 2 | transformers | 2023-05-22T16:18:30 | ---
license: apache-2.0
tags:
- summarization
- generated_from_trainer
datasets:
- samsum
metrics:
- rouge
model-index:
- name: t5-base-samsum
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: samsum
type: samsum
config: samsum
split: validation
args: samsum
metrics:
- name: Rouge1
type: rouge
value: 48.9131
---
<!-- 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. -->
# t5-base-samsum
This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the samsum dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6172
- Rouge1: 48.9131
- Rouge2: 25.4942
- Rougel: 41.2363
- Rougelsum: 45.3434
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|
| 0.7606 | 1.0 | 3683 | 0.6254 | 46.9778 | 23.8245 | 39.8294 | 43.4639 |
| 0.6273 | 2.0 | 7366 | 0.6119 | 48.2515 | 24.7534 | 40.4415 | 44.5567 |
| 0.5769 | 3.0 | 11049 | 0.6116 | 48.228 | 24.7865 | 40.7537 | 44.4026 |
| 0.5412 | 4.0 | 14732 | 0.6145 | 48.8563 | 25.356 | 41.1913 | 45.186 |
| 0.5199 | 5.0 | 18415 | 0.6172 | 48.9131 | 25.4942 | 41.2363 | 45.3434 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
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cyrildever/distilbert-base-uncased-finetuned-emotion | 2023-05-22T17:44:05.000Z | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | text-classification | cyrildever | null | null | cyrildever/distilbert-base-uncased-finetuned-emotion | 0 | 2 | transformers | 2023-05-22T16:25:44 | ---
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: 0.925
- name: F1
type: f1
value: 0.9247451469405729
---
<!-- 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: 0.2111
- Accuracy: 0.925
- F1: 0.9247
## Model description
More information needed
## Intended uses & limitations
This is a simple test from the O'Reilly's book "Natural Language Processing with Transformers". Not to use for anything but testing purposes.
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.7938 | 1.0 | 250 | 0.3038 | 0.9075 | 0.9054 |
| 0.2377 | 2.0 | 500 | 0.2111 | 0.925 | 0.9247 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.13.3
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soteroshanthi/distilbert_classifier_newsgroups | 2023-05-22T19:45:49.000Z | [
"transformers",
"tf",
"distilbert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | soteroshanthi | null | null | soteroshanthi/distilbert_classifier_newsgroups | 0 | 2 | transformers | 2023-05-22T19:45:39 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: distilbert_classifier_newsgroups
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# distilbert_classifier_newsgroups
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 1908, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
### Framework versions
- Transformers 4.28.0
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
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michaelfeil/codegen2-1B-gptj | 2023-06-22T13:13:46.000Z | [
"transformers",
"pytorch",
"safetensors",
"gptj",
"text-generation",
"fauxpilot",
"gpt-j",
"float16",
"arxiv:2305.02309",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-generation | michaelfeil | null | null | michaelfeil/codegen2-1B-gptj | 1 | 2 | transformers | 2023-05-22T20:41:37 |
---
tags:
- fauxpilot
- gpt-j
- float16
license: apache-2.0
---
# Conversion for FauxPilot, Codegen-2 as GPT-J
It feels like GPT-J, acts like any other GPT-J, but its Codegen-2 weights under the hood.
Converted on 2023-05-22 using
```
python /home/michael/fauxpilot/converter/codegen_gptj_convert.py --code_model Salesforce/codegen2-1B /home/michael/tmp-codegen2-1B-gptj
```
# Licence and other remarks:
Licence conditions are intended to be idential to original huggingface repo.
# Original description
see https://huggingface.co/'Salesforce/codegen2-1B'
# CodeGen2 (CodeGen2-16B)
## Model description
[CodeGen2](https://github.com/salesforce/CodeGen2) is a family of autoregressive language models for **program synthesis**, introduced in the paper:
[CodeGen2: Lessons for Training LLMs on Programming and Natural Languages](https://arxiv.org/abs/2305.02309) by Erik Nijkamp\*, Hiroaki Hayashi\*, Caiming Xiong, Silvio Savarese, Yingbo Zhou.
Unlike the original CodeGen model family (i.e., CodeGen1), CodeGen2 is capable of infilling, and supports more programming languages.
Four model sizes are released: `1B`, `3.7B`, `7B`, `16B`.
## How to use
This model can be easily loaded using the `AutoModelForCausalLM` functionality.
### Causal sampling
For regular causal sampling, simply generate completions given the context:
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("Salesforce/codegen2-16B")
model = AutoModelForCausalLM.from_pretrained("Salesforce/codegen2-16B", trust_remote_code=True, revision="main")
text = "def hello_world():"
input_ids = tokenizer(text, return_tensors="pt").input_ids
generated_ids = model.generate(input_ids, max_length=128)
print(tokenizer.decode(generated_ids[0], skip_special_tokens=True))
```
### Infill sampling
For **infill** sampling, we introduce three new special token types:
* `<mask_N>`: N-th span to be masked. In practice, use `<mask_1>` to where you want to sample infill.
* `<sep>`: Seperator token between the suffix and the infilled sample. See below.
* `<eom>`: "End-Of-Mask" token that model will output at the end of infilling. You may use this token to truncate the output.
For example, if we want to generate infill for the following cursor position of a function:
```python
def hello_world():
|
return name
```
we construct an input to the model by
1. Inserting `<mask_1>` token in place of cursor position
2. Append `<sep>` token to indicate the boundary
3. Insert another `<mask_1>` to indicate which mask we want to infill.
The final snippet looks as follows:
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("Salesforce/codegen2-16B")
model = AutoModelForCausalLM.from_pretrained("Salesforce/codegen2-16B", trust_remote_code=True, revision="main")
def format(prefix, suffix):
return prefix + "<mask_1>" + suffix + "<|endoftext|>" + "<sep>" + "<mask_1>"
prefix = "def hello_world():
"
suffix = " return name"
text = format(prefix, suffix)
input_ids = tokenizer(text, return_tensors="pt").input_ids
generated_ids = model.generate(input_ids, max_length=128)
print(tokenizer.decode(generated_ids[0], skip_special_tokens=False)[len(text):])
```
You might want to truncate the model output with `<eom>`.
## Training data
This checkpoint is trained on the stricter permissive subset of [the deduplicated version of the Stack dataset (v1.1)](https://huggingface.co/datasets/bigcode/the-stack-dedup). Supported languages (and frameworks) are as follows:
`c`, `c++`, `c-sharp`, `dart`, `go`, `java`, `javascript`, `kotlin`, `lua`, `php`, `python`, `ruby`, `rust`, `scala`, `shell`, `sql`, `swift`, `typescript`, `vue`.
## Training procedure
CodeGen2 was trained using cross-entropy loss to maximize the likelihood of sequential inputs.
The input sequences are formatted in two ways: (1) causal language modeling and (2) file-level span corruption.
Please refer to the paper for more details.
## Evaluation results
We evaluate our models on HumanEval and HumanEval-Infill. Please refer to the [paper](https://arxiv.org/abs/2305.02309) for more details.
## Intended use and limitations
As an autoregressive language model, CodeGen2 is capable of extracting features from given natural language and programming language texts, and calculating the likelihood of them.
However, the model is intended for and best at **program synthesis**, that is, generating executable code given English prompts, where the prompts should be in the form of a comment string. The model can complete partially-generated code as well.
## BibTeX entry and citation info
```bibtex
@article{Nijkamp2023codegen2,
title={CodeGen2: Lessons for Training LLMs on Programming and Natural Languages},
author={Nijkamp, Erik and Hayashi, Hiroaki and Xiong, Caiming and Savarese, Silvio and Zhou, Yingbo},
journal={arXiv preprint},
year={2023}
}
```
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filoux/course_distilbert_classifier_newsgroups | 2023-05-22T20:59:14.000Z | [
"transformers",
"tf",
"distilbert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | filoux | null | null | filoux/course_distilbert_classifier_newsgroups | 0 | 2 | transformers | 2023-05-22T20:58:56 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: course_distilbert_classifier_newsgroups
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# course_distilbert_classifier_newsgroups
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 1908, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
### Framework versions
- Transformers 4.28.0
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,485 | [
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shinta0615/distilbert-base-uncased-distilled-clinc | 2023-05-24T21:58:05.000Z | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:clinc_oos",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | text-classification | shinta0615 | null | null | shinta0615/distilbert-base-uncased-distilled-clinc | 0 | 2 | transformers | 2023-05-22T22:14:31 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- clinc_oos
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased-distilled-clinc
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: clinc_oos
type: clinc_oos
config: plus
split: validation
args: plus
metrics:
- name: Accuracy
type: accuracy
value: 0.9483870967741935
---
<!-- 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-distilled-clinc
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the clinc_oos dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3469
- Accuracy: 0.9484
## 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: 48
- eval_batch_size: 48
- 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 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 318 | 2.4602 | 0.7535 |
| 2.8954 | 2.0 | 636 | 1.2412 | 0.8558 |
| 2.8954 | 3.0 | 954 | 0.6810 | 0.9126 |
| 1.0885 | 4.0 | 1272 | 0.4728 | 0.9335 |
| 0.455 | 5.0 | 1590 | 0.4025 | 0.9439 |
| 0.455 | 6.0 | 1908 | 0.3754 | 0.9439 |
| 0.2936 | 7.0 | 2226 | 0.3600 | 0.9471 |
| 0.2422 | 8.0 | 2544 | 0.3522 | 0.9468 |
| 0.2422 | 9.0 | 2862 | 0.3493 | 0.9481 |
| 0.2251 | 10.0 | 3180 | 0.3469 | 0.9484 |
### Framework versions
- Transformers 4.29.1
- Pytorch 2.0.1
- Datasets 2.12.0
- Tokenizers 0.13.3
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dan21cg/distilbert-base-uncased-finetuned-clinc | 2023-05-22T23:41:47.000Z | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:clinc_oos",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | text-classification | dan21cg | null | null | dan21cg/distilbert-base-uncased-finetuned-clinc | 0 | 2 | transformers | 2023-05-22T23:11:50 | Temporary Redirect. Redirecting to /jupitercoder/distilbert-base-uncased-finetuned-clinc/resolve/main/README.md | 111 | [
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wiorz/legal_bert_small_summarized_defined | 2023-05-23T23:21:46.000Z | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:cc-by-sa-4.0",
"endpoints_compatible",
"region:us"
] | text-classification | wiorz | null | null | wiorz/legal_bert_small_summarized_defined | 0 | 2 | transformers | 2023-05-22T23:57:17 | ---
license: cc-by-sa-4.0
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: legal_bert_small_summarized_defined
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. -->
# legal_bert_small_summarized_defined
This model is a fine-tuned version of [nlpaueb/legal-bert-base-uncased](https://huggingface.co/nlpaueb/legal-bert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8897
- Accuracy: 0.835
- Precision: 0.5
- Recall: 0.1515
- F1: 0.2326
- D-index: 1.5181
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1600
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | D-index |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:-------:|
| No log | 1.0 | 200 | 0.4467 | 0.835 | 0.0 | 0.0 | 0.0 | 1.4607 |
| No log | 2.0 | 400 | 0.4909 | 0.835 | 0.0 | 0.0 | 0.0 | 1.4607 |
| 0.5409 | 3.0 | 600 | 0.4941 | 0.83 | 0.4545 | 0.1515 | 0.2273 | 1.5113 |
| 0.5409 | 4.0 | 800 | 0.5612 | 0.84 | 0.6 | 0.0909 | 0.1579 | 1.5021 |
| 0.4849 | 5.0 | 1000 | 0.6301 | 0.84 | 0.5714 | 0.1212 | 0.2 | 1.5135 |
| 0.4849 | 6.0 | 1200 | 0.8969 | 0.84 | 0.6 | 0.0909 | 0.1579 | 1.5021 |
| 0.4849 | 7.0 | 1400 | 1.3171 | 0.82 | 0.3636 | 0.1212 | 0.1818 | 1.4865 |
| 0.2104 | 8.0 | 1600 | 1.6653 | 0.775 | 0.2692 | 0.2121 | 0.2373 | 1.4593 |
| 0.2104 | 9.0 | 1800 | 1.7041 | 0.795 | 0.3182 | 0.2121 | 0.2545 | 1.4866 |
| 0.0314 | 10.0 | 2000 | 1.7495 | 0.815 | 0.3571 | 0.1515 | 0.2128 | 1.4911 |
| 0.0314 | 11.0 | 2200 | 1.7627 | 0.815 | 0.3571 | 0.1515 | 0.2128 | 1.4911 |
| 0.0314 | 12.0 | 2400 | 1.7892 | 0.825 | 0.375 | 0.0909 | 0.1463 | 1.4819 |
| 0.0067 | 13.0 | 2600 | 1.8211 | 0.83 | 0.4444 | 0.1212 | 0.1905 | 1.5000 |
| 0.0067 | 14.0 | 2800 | 1.8567 | 0.83 | 0.4444 | 0.1212 | 0.1905 | 1.5000 |
| 0.0 | 15.0 | 3000 | 1.8817 | 0.83 | 0.4444 | 0.1212 | 0.1905 | 1.5000 |
| 0.0 | 16.0 | 3200 | 1.8590 | 0.825 | 0.4167 | 0.1515 | 0.2222 | 1.5046 |
| 0.0 | 17.0 | 3400 | 1.8619 | 0.835 | 0.5 | 0.1515 | 0.2326 | 1.5181 |
| 0.0014 | 18.0 | 3600 | 1.8744 | 0.835 | 0.5 | 0.1515 | 0.2326 | 1.5181 |
| 0.0014 | 19.0 | 3800 | 1.8849 | 0.835 | 0.5 | 0.1515 | 0.2326 | 1.5181 |
| 0.0 | 20.0 | 4000 | 1.8897 | 0.835 | 0.5 | 0.1515 | 0.2326 | 1.5181 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
| 3,612 | [
[
-0.0438232421875,
-0.041900634765625,
0.01499176025390625,
0.0081787109375,
-0.00832366943359375,
-0.011688232421875,
0.00048661231994628906,
-0.01264190673828125,
0.044677734375,
0.0271453857421875,
-0.043548583984375,
-0.0526123046875,
-0.046539306640625,
... |
kdeeaz/distilbert_classifier_newsgroups | 2023-05-23T00:59:39.000Z | [
"transformers",
"tf",
"distilbert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | kdeeaz | null | null | kdeeaz/distilbert_classifier_newsgroups | 0 | 2 | transformers | 2023-05-23T00:59:24 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: distilbert_classifier_newsgroups
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# distilbert_classifier_newsgroups
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 1908, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
### Framework versions
- Transformers 4.28.0
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,471 | [
[
-0.0386962890625,
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0.021240234375,
0.0084228515625,
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-0.00620269775390625,
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mauhcs/distilbert-base-uncased-finetuned-emotion | 2023-05-24T01:35:18.000Z | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | text-classification | mauhcs | null | null | mauhcs/distilbert-base-uncased-finetuned-emotion | 0 | 2 | transformers | 2023-05-23T01:56:39 | ---
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: 0.925
- name: F1
type: f1
value: 0.9249666408719047
---
<!-- 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: 0.2147
- Accuracy: 0.925
- F1: 0.9250
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8493 | 1.0 | 250 | 0.3120 | 0.9115 | 0.9084 |
| 0.2513 | 2.0 | 500 | 0.2147 | 0.925 | 0.9250 |
### Framework versions
- Transformers 4.29.0
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,846 | [
[
-0.037567138671875,
-0.041290283203125,
0.0137176513671875,
0.021820068359375,
-0.02581787109375,
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0.01076507568359375,
0.007770538330078125,
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AustinCarthy/MixGPT2_100KP_BFall_fromB_20KGen_topP_0.75 | 2023-05-23T10:17:41.000Z | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | AustinCarthy | null | null | AustinCarthy/MixGPT2_100KP_BFall_fromB_20KGen_topP_0.75 | 0 | 2 | transformers | 2023-05-23T02:02:04 | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: MixGPT2_100KP_BFall_fromB_20KGen_topP_0.75
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. -->
# MixGPT2_100KP_BFall_fromB_20KGen_topP_0.75
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: 0.0189
- Accuracy: 0.9972
- F1: 0.9700
- Precision: 0.9994
- Recall: 0.9424
- Roc Auc Score: 0.9712
- Tpr At Fpr 0.01: 0.9544
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | Roc Auc Score | Tpr At Fpr 0.01 |
|:-------------:|:-----:|:------:|:---------------:|:--------:|:------:|:---------:|:------:|:-------------:|:---------------:|
| 0.0051 | 1.0 | 78750 | 0.0225 | 0.9964 | 0.9605 | 0.9961 | 0.9274 | 0.9636 | 0.9226 |
| 0.0044 | 2.0 | 157500 | 0.0219 | 0.9963 | 0.9593 | 0.9985 | 0.923 | 0.9615 | 0.933 |
| 0.0018 | 3.0 | 236250 | 0.0216 | 0.9969 | 0.9669 | 0.9991 | 0.9366 | 0.9683 | 0.9496 |
| 0.0012 | 4.0 | 315000 | 0.0233 | 0.9967 | 0.9646 | 0.9994 | 0.9322 | 0.9661 | 0.9448 |
| 0.0011 | 5.0 | 393750 | 0.0189 | 0.9972 | 0.9700 | 0.9994 | 0.9424 | 0.9712 | 0.9544 |
### Framework versions
- Transformers 4.29.1
- Pytorch 1.9.0+cu111
- Datasets 2.10.1
- Tokenizers 0.13.2
| 2,253 | [
[
-0.044830322265625,
-0.04193115234375,
0.00714111328125,
0.0156707763671875,
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0.0273284912109375,
0.024993896484375,
-0.05181884765625,
-0.0477294921875,
-0.0543212890625,
-0.01... |
KINGeorge2000/sentiment_roberta_yu | 2023-07-07T09:31:20.000Z | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | text-classification | KINGeorge2000 | null | null | KINGeorge2000/sentiment_roberta_yu | 0 | 2 | transformers | 2023-05-23T05:49:16 | ---
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: sentiment_roberta_yu
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. -->
# sentiment_roberta_yu
This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base](https://huggingface.co/cardiffnlp/twitter-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.2580
- Accuracy: 0.6668
- F1: 0.6668
## 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 4.29.2
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,173 | [
[
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satyamverma/distilbert-base-uncased-finetuned-mrpc | 2023-05-23T09:05:28.000Z | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | text-classification | satyamverma | null | null | satyamverma/distilbert-base-uncased-finetuned-mrpc | 0 | 2 | transformers | 2023-05-23T06:19:04 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-mrpc
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
config: mrpc
split: validation
args: mrpc
metrics:
- name: Accuracy
type: accuracy
value: 0.8480392156862745
- name: F1
type: f1
value: 0.8945578231292517
---
<!-- 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-mrpc
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4304
- Accuracy: 0.8480
- F1: 0.8946
## 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log | 1.0 | 230 | 0.3851 | 0.8137 | 0.8652 |
| No log | 2.0 | 460 | 0.3614 | 0.8456 | 0.8948 |
| 0.4318 | 3.0 | 690 | 0.4304 | 0.8480 | 0.8946 |
| 0.4318 | 4.0 | 920 | 0.5555 | 0.8407 | 0.8900 |
| 0.1697 | 5.0 | 1150 | 0.5883 | 0.8456 | 0.8927 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
| 2,053 | [
[
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0.006938934326171875,
0.0115509033203125,
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0.0121307373046875,
0.01490020751953125,
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songys/distilbert-base-uncased-finetuned-clinc | 2023-06-05T06:59:46.000Z | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:clinc_oos",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | text-classification | songys | null | null | songys/distilbert-base-uncased-finetuned-clinc | 0 | 2 | transformers | 2023-05-23T06:52:33 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- clinc_oos
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased-finetuned-clinc
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: clinc_oos
type: clinc_oos
config: plus
split: validation
args: plus
metrics:
- name: Accuracy
type: accuracy
value: 0.9180645161290323
---
<!-- 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-clinc
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the clinc_oos dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7720
- Accuracy: 0.9181
## 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: 48
- eval_batch_size: 48
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 4.2896 | 1.0 | 318 | 3.2887 | 0.7419 |
| 2.6282 | 2.0 | 636 | 1.8753 | 0.8371 |
| 1.548 | 3.0 | 954 | 1.1570 | 0.8961 |
| 1.0148 | 4.0 | 1272 | 0.8573 | 0.9129 |
| 0.7952 | 5.0 | 1590 | 0.7720 | 0.9181 |
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,932 | [
[
-0.034637451171875,
-0.041107177734375,
0.01275634765625,
0.007160186767578125,
-0.0271453857421875,
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0.0028514862060546875,
0.021942138671875,
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songys/distilbert-base-uncased-distilled-clinc | 2023-06-05T09:03:08.000Z | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:clinc_oos",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | text-classification | songys | null | null | songys/distilbert-base-uncased-distilled-clinc | 0 | 2 | transformers | 2023-05-23T07:23:37 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- clinc_oos
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased-distilled-clinc
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: clinc_oos
type: clinc_oos
config: plus
split: validation
args: plus
metrics:
- name: Accuracy
type: accuracy
value: 0.9474193548387096
---
<!-- 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-distilled-clinc
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the clinc_oos dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2676
- Accuracy: 0.9474
## 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: 48
- eval_batch_size: 48
- 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 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 4.1402 | 1.0 | 318 | 3.0979 | 0.7503 |
| 2.3572 | 2.0 | 636 | 1.5361 | 0.8577 |
| 1.1469 | 3.0 | 954 | 0.7670 | 0.9168 |
| 0.5652 | 4.0 | 1272 | 0.4659 | 0.9345 |
| 0.308 | 5.0 | 1590 | 0.3458 | 0.9448 |
| 0.1934 | 6.0 | 1908 | 0.3009 | 0.9448 |
| 0.1368 | 7.0 | 2226 | 0.2781 | 0.9471 |
| 0.1088 | 8.0 | 2544 | 0.2724 | 0.9484 |
| 0.0949 | 9.0 | 2862 | 0.2704 | 0.9468 |
| 0.0897 | 10.0 | 3180 | 0.2676 | 0.9474 |
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
| 2,243 | [
[
-0.0330810546875,
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0.01433563232421875,
0.00614166259765625,
-0.023773193359375,
-0.019378662109375,
-0.01038360595703125,
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0.007251739501953125,
0.02099609375,
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... |
YakovElm/test2 | 2023-05-23T08:38:43.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/test2 | 0 | 2 | transformers | 2023-05-23T08:37:33 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: test2
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# test2
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:
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: None
- training_precision: float32
### Training results
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 930 | [
[
-0.03985595703125,
-0.050201416015625,
0.0169830322265625,
0.006561279296875,
-0.0462646484375,
-0.0287017822265625,
-0.01451873779296875,
-0.0306396484375,
-0.001171112060546875,
0.03192138671875,
-0.048065185546875,
-0.033782958984375,
-0.057220458984375,
... |
elftsdmr/5000 | 2023-05-23T09:11:57.000Z | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | elftsdmr | null | null | elftsdmr/5000 | 0 | 2 | transformers | 2023-05-23T08:58:53 | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: '5000'
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. -->
# 5000
This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1912
- Accuracy: 0.952
- Precision: 0.9751
- Recall: 0.9287
- F1: 0.9513
## 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
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| No log | 1.0 | 63 | 0.1936 | 0.939 | 0.9890 | 0.8891 | 0.9364 |
| No log | 2.0 | 126 | 0.2011 | 0.946 | 0.9747 | 0.9168 | 0.9449 |
| No log | 3.0 | 189 | 0.1912 | 0.952 | 0.9751 | 0.9287 | 0.9513 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.1+cu117
- Datasets 2.1.0
- Tokenizers 0.13.3
| 1,674 | [
[
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darrel999/distilbert-base-uncased_emotion_ft_0523 | 2023-05-23T09:30:38.000Z | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | text-classification | darrel999 | null | null | darrel999/distilbert-base-uncased_emotion_ft_0523 | 0 | 2 | transformers | 2023-05-23T09:11:52 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
- precision
model-index:
- name: distilbert-base-uncased_emotion_ft_0523
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: 0.917
- name: F1
type: f1
value: 0.9167815299071149
- name: Precision
type: precision
value: 0.8882036697297124
---
<!-- 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_emotion_ft_0523
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: 0.2694
- Accuracy: 0.917
- F1: 0.9168
- Precision: 0.8882
## 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: 256
- eval_batch_size: 256
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|
| No log | 1.0 | 63 | 0.9564 | 0.641 | 0.5522 | 0.5005 |
| No log | 2.0 | 126 | 0.4544 | 0.8635 | 0.8507 | 0.8714 |
| No log | 3.0 | 189 | 0.2987 | 0.91 | 0.9093 | 0.8805 |
| 0.67 | 4.0 | 252 | 0.2694 | 0.917 | 0.9168 | 0.8882 |
### Framework versions
- Transformers 4.29.1
- Pytorch 2.0.1
- Datasets 2.12.0
- Tokenizers 0.13.3
| 2,160 | [
[
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atrytone/scibert_claim_id_2e-05 | 2023-05-23T10:44:58.000Z | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | text-classification | atrytone | null | null | atrytone/scibert_claim_id_2e-05 | 0 | 2 | transformers | 2023-05-23T10:04:04 | ---
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: scibert_claim_id_2e-05
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. -->
# scibert_claim_id_2e-05
This model is a fine-tuned version of [allenai/scibert_scivocab_uncased](https://huggingface.co/allenai/scibert_scivocab_uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0162
- Accuracy: 0.9962
- F1: 0.9880
- Precision: 0.9889
- Recall: 0.9870
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 6
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.3131 | 1.0 | 666 | 0.2551 | 0.8880 | 0.5518 | 0.7419 | 0.4392 |
| 0.267 | 2.0 | 1332 | 0.1821 | 0.9280 | 0.7636 | 0.7875 | 0.7410 |
| 0.2245 | 3.0 | 1998 | 0.0942 | 0.9695 | 0.9034 | 0.8968 | 0.9101 |
| 0.1135 | 4.0 | 2664 | 0.0514 | 0.9845 | 0.9517 | 0.9339 | 0.9702 |
| 0.0821 | 5.0 | 3330 | 0.0223 | 0.9944 | 0.9822 | 0.9808 | 0.9837 |
| 0.0618 | 6.0 | 3996 | 0.0162 | 0.9962 | 0.9880 | 0.9889 | 0.9870 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,972 | [
[
-0.0293426513671875,
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0.01313018798828125,
0.0110321044921875,
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0.0291748046875,
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-0... |
Karlpy/LunarLander-v2 | 2023-05-23T10:05:17.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | Karlpy | null | null | Karlpy/LunarLander-v2 | 0 | 2 | stable-baselines3 | 2023-05-23T10:04:17 | ---
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: 296.84 +/- 13.13
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
...
```
| 784 | [
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AustinCarthy/MixGPT2_100KP_BFall_fromB_30KGen_topP_0.75 | 2023-05-24T14:23:52.000Z | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | AustinCarthy | null | null | AustinCarthy/MixGPT2_100KP_BFall_fromB_30KGen_topP_0.75 | 0 | 2 | transformers | 2023-05-23T10:18:04 | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: MixGPT2_100KP_BFall_fromB_30KGen_topP_0.75
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. -->
# MixGPT2_100KP_BFall_fromB_30KGen_topP_0.75
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: 0.0196
- Accuracy: 0.9968
- F1: 0.9656
- Precision: 0.9994
- Recall: 0.934
- Roc Auc Score: 0.9670
- Tpr At Fpr 0.01: 0.9596
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | Roc Auc Score | Tpr At Fpr 0.01 |
|:-------------:|:-----:|:------:|:---------------:|:--------:|:------:|:---------:|:------:|:-------------:|:---------------:|
| 0.0021 | 1.0 | 85313 | 0.0313 | 0.9952 | 0.9469 | 0.9984 | 0.9004 | 0.9502 | 0.9028 |
| 0.0031 | 2.0 | 170626 | 0.0236 | 0.9970 | 0.9671 | 0.9987 | 0.9374 | 0.9687 | 0.9466 |
| 0.0039 | 3.0 | 255939 | 0.0182 | 0.9971 | 0.9688 | 0.9981 | 0.9412 | 0.9706 | 0.9394 |
| 0.002 | 4.0 | 341252 | 0.0199 | 0.9973 | 0.9709 | 0.9987 | 0.9446 | 0.9723 | 0.9508 |
| 0.001 | 5.0 | 426565 | 0.0196 | 0.9968 | 0.9656 | 0.9994 | 0.934 | 0.9670 | 0.9596 |
### Framework versions
- Transformers 4.29.1
- Pytorch 1.9.0+cu111
- Datasets 2.10.1
- Tokenizers 0.13.2
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[
-0.045013427734375,
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0.015899658203125,
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atrytone/scibert_claim_id_3e-05 | 2023-05-23T11:22:38.000Z | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | text-classification | atrytone | null | null | atrytone/scibert_claim_id_3e-05 | 0 | 2 | transformers | 2023-05-23T10:45:10 | ---
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: scibert_claim_id_3e-05
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. -->
# scibert_claim_id_3e-05
This model is a fine-tuned version of [allenai/scibert_scivocab_uncased](https://huggingface.co/allenai/scibert_scivocab_uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0071
- Accuracy: 0.9980
- F1: 0.9935
- Precision: 0.9957
- Recall: 0.9914
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 6
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.3163 | 1.0 | 666 | 0.2554 | 0.8884 | 0.5534 | 0.7437 | 0.4407 |
| 0.2673 | 2.0 | 1332 | 0.1671 | 0.9361 | 0.7850 | 0.8309 | 0.7439 |
| 0.2188 | 3.0 | 1998 | 0.0689 | 0.9769 | 0.9268 | 0.9232 | 0.9303 |
| 0.0925 | 4.0 | 2664 | 0.0369 | 0.9879 | 0.9624 | 0.9428 | 0.9827 |
| 0.0635 | 5.0 | 3330 | 0.0109 | 0.9971 | 0.9909 | 0.9928 | 0.9889 |
| 0.038 | 6.0 | 3996 | 0.0071 | 0.9980 | 0.9935 | 0.9957 | 0.9914 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
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0.019744873046875,
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kapilchauhan/fintuned-bert-free-speech-structure | 2023-05-25T01:32:35.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | kapilchauhan | null | null | kapilchauhan/fintuned-bert-free-speech-structure | 0 | 2 | transformers | 2023-05-23T10:47:05 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: fintuned-bert-free-speech-structure
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# fintuned-bert-free-speech-structure
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.6916
- Train Sparse Categorical Accuracy: 0.5276
- Validation Loss: 0.6917
- Validation Sparse Categorical Accuracy: 0.5280
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': 5e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Sparse Categorical Accuracy | Validation Loss | Validation Sparse Categorical Accuracy | Epoch |
|:----------:|:---------------------------------:|:---------------:|:--------------------------------------:|:-----:|
| 0.6709 | 0.5396 | 0.6917 | 0.5280 | 0 |
| 0.6916 | 0.5275 | 0.6916 | 0.5280 | 1 |
| 0.6916 | 0.5276 | 0.6917 | 0.5280 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
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GhifSmile/distilbert-base-uncased-PINA-dfnew-tuning | 2023-05-23T14:44:53.000Z | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | GhifSmile | null | null | GhifSmile/distilbert-base-uncased-PINA-dfnew-tuning | 0 | 2 | transformers | 2023-05-23T11:22:58 | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
model-index:
- name: distilbert-base-uncased-PINA-dfnew-tuning
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-PINA-dfnew-tuning
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3403
- Accuracy: 0.9438
- Precision: 0.8528
- Recall: 0.8454
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:---------:|:------:|
| 0.8484 | 1.0 | 1436 | 0.4963 | 0.8896 | 0.7918 | 0.7575 |
| 0.3783 | 2.0 | 2872 | 0.4298 | 0.9114 | 0.8288 | 0.7918 |
| 0.2649 | 3.0 | 4308 | 0.3808 | 0.9302 | 0.8484 | 0.8148 |
| 0.1951 | 4.0 | 5744 | 0.3627 | 0.9363 | 0.8631 | 0.8205 |
| 0.149 | 5.0 | 7180 | 0.3403 | 0.9438 | 0.8528 | 0.8454 |
| 0.1061 | 6.0 | 8616 | 0.3415 | 0.9455 | 0.8571 | 0.8366 |
| 0.0745 | 7.0 | 10052 | 0.3441 | 0.9467 | 0.8554 | 0.8418 |
| 0.0452 | 8.0 | 11488 | 0.3850 | 0.9500 | 0.8697 | 0.8711 |
| 0.0273 | 9.0 | 12924 | 0.3941 | 0.9506 | 0.8546 | 0.8469 |
| 0.0166 | 10.0 | 14360 | 0.4046 | 0.9525 | 0.8621 | 0.8492 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
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Sejan/bert-finetuned-mrpc | 2023-05-23T12:25:07.000Z | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | Sejan | null | null | Sejan/bert-finetuned-mrpc | 0 | 2 | transformers | 2023-05-23T12:20:01 | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: bert-finetuned-mrpc
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-finetuned-mrpc
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Tokenizers 0.13.3
| 1,017 | [
[
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0.01096343994140625,
0.02960205078125,
-0.06121826171875,
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bhattronak14/distilbert-base-uncased-finetuned-Pre_requisite_finder | 2023-05-24T13:41:32.000Z | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | bhattronak14 | null | null | bhattronak14/distilbert-base-uncased-finetuned-Pre_requisite_finder | 0 | 2 | transformers | 2023-05-23T12:22:06 | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased-finetuned-Pre_requisite_finder
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-Pre_requisite_finder
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0000
- 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: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.0416 | 1.0 | 810 | 0.0008 | 0.9997 |
| 0.0013 | 2.0 | 1620 | 0.0000 | 1.0 |
| 0.0 | 3.0 | 2430 | 0.0000 | 1.0 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,535 | [
[
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VinsmokeMir/FineTuning_Method_2_SC | 2023-05-23T14:49:02.000Z | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | text-classification | VinsmokeMir | null | null | VinsmokeMir/FineTuning_Method_2_SC | 0 | 2 | transformers | 2023-05-23T13:55:32 | ---
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: FineTuning_Method_2_SC
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_Method_2_SC
This model is a fine-tuned version of [rafsankabir/Pretrained_E13_Method2](https://huggingface.co/rafsankabir/Pretrained_E13_Method2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.3223
- Accuracy: 0.6790
- F1 Macro: 0.6487
## 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_steps: 500
- num_epochs: 40
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Macro |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:|
| No log | 0.32 | 500 | 1.0745 | 0.3976 | 0.1896 |
| 1.0543 | 0.64 | 1000 | 0.9059 | 0.5967 | 0.4614 |
| 1.0543 | 0.95 | 1500 | 0.8259 | 0.6414 | 0.5633 |
| 0.8389 | 1.27 | 2000 | 0.8177 | 0.6394 | 0.5715 |
| 0.8389 | 1.59 | 2500 | 0.8269 | 0.6356 | 0.5724 |
| 0.7713 | 1.91 | 3000 | 0.7916 | 0.6631 | 0.6238 |
| 0.7713 | 2.23 | 3500 | 0.7996 | 0.6745 | 0.6155 |
| 0.6734 | 2.54 | 4000 | 0.7921 | 0.6624 | 0.6307 |
| 0.6734 | 2.86 | 4500 | 0.7743 | 0.6726 | 0.6459 |
| 0.6309 | 3.18 | 5000 | 0.8343 | 0.6803 | 0.6382 |
| 0.6309 | 3.5 | 5500 | 0.8233 | 0.6784 | 0.6390 |
| 0.5582 | 3.82 | 6000 | 0.8678 | 0.6631 | 0.6273 |
| 0.5582 | 4.13 | 6500 | 0.8621 | 0.6758 | 0.6368 |
| 0.4988 | 4.45 | 7000 | 0.9389 | 0.6720 | 0.6386 |
| 0.4988 | 4.77 | 7500 | 0.9067 | 0.6918 | 0.6505 |
| 0.4885 | 5.09 | 8000 | 0.9116 | 0.6937 | 0.6583 |
| 0.4885 | 5.41 | 8500 | 1.0357 | 0.6822 | 0.6459 |
| 0.427 | 5.73 | 9000 | 0.9428 | 0.6847 | 0.6479 |
| 0.427 | 6.04 | 9500 | 1.0233 | 0.6752 | 0.6531 |
| 0.4034 | 6.36 | 10000 | 1.1578 | 0.6835 | 0.6515 |
| 0.4034 | 6.68 | 10500 | 1.1870 | 0.6790 | 0.6545 |
| 0.4053 | 7.0 | 11000 | 1.0370 | 0.7007 | 0.6651 |
| 0.4053 | 7.32 | 11500 | 1.2087 | 0.6822 | 0.6497 |
| 0.3545 | 7.63 | 12000 | 1.2255 | 0.6847 | 0.6605 |
| 0.3545 | 7.95 | 12500 | 1.2710 | 0.6905 | 0.6609 |
| 0.3437 | 8.27 | 13000 | 1.3646 | 0.6918 | 0.6618 |
| 0.3437 | 8.59 | 13500 | 1.3767 | 0.6879 | 0.6563 |
| 0.3407 | 8.91 | 14000 | 1.2705 | 0.6796 | 0.6506 |
| 0.3407 | 9.22 | 14500 | 1.4605 | 0.6803 | 0.6496 |
| 0.2876 | 9.54 | 15000 | 1.4202 | 0.6860 | 0.6555 |
| 0.2876 | 9.86 | 15500 | 1.4151 | 0.6847 | 0.6517 |
| 0.3035 | 10.18 | 16000 | 1.4536 | 0.6713 | 0.6514 |
| 0.3035 | 10.5 | 16500 | 1.4806 | 0.6828 | 0.6469 |
| 0.2733 | 10.81 | 17000 | 1.4596 | 0.6899 | 0.6552 |
| 0.2733 | 11.13 | 17500 | 1.6183 | 0.6886 | 0.6557 |
| 0.2562 | 11.45 | 18000 | 1.6054 | 0.6771 | 0.6591 |
| 0.2562 | 11.77 | 18500 | 1.5966 | 0.6701 | 0.6503 |
| 0.2582 | 12.09 | 19000 | 1.5659 | 0.6822 | 0.6531 |
| 0.2582 | 12.4 | 19500 | 1.6146 | 0.6867 | 0.6575 |
| 0.2368 | 12.72 | 20000 | 1.6207 | 0.6899 | 0.6629 |
| 0.2368 | 13.04 | 20500 | 1.5220 | 0.6918 | 0.6640 |
| 0.245 | 13.36 | 21000 | 1.6572 | 0.6720 | 0.6489 |
| 0.245 | 13.68 | 21500 | 1.6443 | 0.6860 | 0.6590 |
| 0.2226 | 13.99 | 22000 | 1.6238 | 0.6847 | 0.6589 |
| 0.2226 | 14.31 | 22500 | 1.7241 | 0.6777 | 0.6521 |
| 0.2117 | 14.63 | 23000 | 1.6134 | 0.6867 | 0.6580 |
| 0.2117 | 14.95 | 23500 | 1.6723 | 0.6911 | 0.6618 |
| 0.2056 | 15.27 | 24000 | 1.6257 | 0.6892 | 0.6529 |
| 0.2056 | 15.59 | 24500 | 1.7072 | 0.6796 | 0.6531 |
| 0.1859 | 15.9 | 25000 | 1.7174 | 0.6771 | 0.6554 |
| 0.1859 | 16.22 | 25500 | 1.6951 | 0.6879 | 0.6555 |
| 0.1725 | 16.54 | 26000 | 1.7240 | 0.6905 | 0.6632 |
| 0.1725 | 16.86 | 26500 | 1.7126 | 0.6879 | 0.6608 |
| 0.1817 | 17.18 | 27000 | 1.7949 | 0.6847 | 0.6520 |
| 0.1817 | 17.49 | 27500 | 1.7694 | 0.6911 | 0.6622 |
| 0.1617 | 17.81 | 28000 | 1.7891 | 0.6828 | 0.6527 |
| 0.1617 | 18.13 | 28500 | 1.7860 | 0.6790 | 0.6526 |
| 0.1628 | 18.45 | 29000 | 1.8127 | 0.6867 | 0.6605 |
| 0.1628 | 18.77 | 29500 | 1.7317 | 0.6892 | 0.6610 |
| 0.1736 | 19.08 | 30000 | 1.7273 | 0.6899 | 0.6569 |
| 0.1736 | 19.4 | 30500 | 1.7853 | 0.6854 | 0.6584 |
| 0.1441 | 19.72 | 31000 | 1.7866 | 0.6918 | 0.6624 |
| 0.1441 | 20.04 | 31500 | 1.7842 | 0.6873 | 0.6580 |
| 0.1392 | 20.36 | 32000 | 1.8669 | 0.6860 | 0.6597 |
| 0.1392 | 20.67 | 32500 | 1.8392 | 0.6899 | 0.6639 |
| 0.159 | 20.99 | 33000 | 1.8412 | 0.6784 | 0.6552 |
| 0.159 | 21.31 | 33500 | 1.8673 | 0.6854 | 0.6584 |
| 0.1275 | 21.63 | 34000 | 1.8622 | 0.6854 | 0.6571 |
| 0.1275 | 21.95 | 34500 | 1.8622 | 0.6796 | 0.6583 |
| 0.1216 | 22.26 | 35000 | 1.9509 | 0.6854 | 0.6604 |
| 0.1216 | 22.58 | 35500 | 1.9425 | 0.6809 | 0.6550 |
| 0.1351 | 22.9 | 36000 | 1.9496 | 0.6784 | 0.6559 |
| 0.1351 | 23.22 | 36500 | 1.9685 | 0.6847 | 0.6582 |
| 0.1221 | 23.54 | 37000 | 1.9112 | 0.6911 | 0.6642 |
| 0.1221 | 23.85 | 37500 | 1.9341 | 0.6726 | 0.6526 |
| 0.1155 | 24.17 | 38000 | 1.9573 | 0.6899 | 0.6614 |
| 0.1155 | 24.49 | 38500 | 1.9853 | 0.6873 | 0.6580 |
| 0.1139 | 24.81 | 39000 | 1.9915 | 0.6790 | 0.6533 |
| 0.1139 | 25.13 | 39500 | 1.9997 | 0.6796 | 0.6539 |
| 0.1166 | 25.45 | 40000 | 1.9994 | 0.6847 | 0.6592 |
| 0.1166 | 25.76 | 40500 | 1.9848 | 0.6745 | 0.6513 |
| 0.1128 | 26.08 | 41000 | 2.0095 | 0.6867 | 0.6578 |
| 0.1128 | 26.4 | 41500 | 2.0585 | 0.6822 | 0.6547 |
| 0.1048 | 26.72 | 42000 | 2.0293 | 0.6777 | 0.6510 |
| 0.1048 | 27.04 | 42500 | 2.0797 | 0.6758 | 0.6512 |
| 0.1 | 27.35 | 43000 | 2.1162 | 0.6822 | 0.6544 |
| 0.1 | 27.67 | 43500 | 2.0569 | 0.6835 | 0.6538 |
| 0.1106 | 27.99 | 44000 | 2.0991 | 0.6828 | 0.6565 |
| 0.1106 | 28.31 | 44500 | 2.0976 | 0.6841 | 0.6563 |
| 0.0886 | 28.63 | 45000 | 2.1305 | 0.6854 | 0.6532 |
| 0.0886 | 28.94 | 45500 | 2.1015 | 0.6867 | 0.6564 |
| 0.1027 | 29.26 | 46000 | 2.1105 | 0.6867 | 0.6559 |
| 0.1027 | 29.58 | 46500 | 2.1396 | 0.6765 | 0.6499 |
| 0.1057 | 29.9 | 47000 | 2.1237 | 0.6790 | 0.6501 |
| 0.1057 | 30.22 | 47500 | 2.1849 | 0.6790 | 0.6518 |
| 0.0876 | 30.53 | 48000 | 2.1346 | 0.6841 | 0.6533 |
| 0.0876 | 30.85 | 48500 | 2.1441 | 0.6828 | 0.6540 |
| 0.0856 | 31.17 | 49000 | 2.1528 | 0.6911 | 0.6600 |
| 0.0856 | 31.49 | 49500 | 2.1725 | 0.6847 | 0.6509 |
| 0.0869 | 31.81 | 50000 | 2.2085 | 0.6771 | 0.6503 |
| 0.0869 | 32.12 | 50500 | 2.2606 | 0.6688 | 0.6434 |
| 0.0848 | 32.44 | 51000 | 2.2510 | 0.6745 | 0.6451 |
| 0.0848 | 32.76 | 51500 | 2.2528 | 0.6739 | 0.6496 |
| 0.0816 | 33.08 | 52000 | 2.2532 | 0.6758 | 0.6503 |
| 0.0816 | 33.4 | 52500 | 2.2356 | 0.6803 | 0.6500 |
| 0.0793 | 33.72 | 53000 | 2.2579 | 0.6745 | 0.6483 |
| 0.0793 | 34.03 | 53500 | 2.2126 | 0.6816 | 0.6520 |
| 0.0767 | 34.35 | 54000 | 2.2504 | 0.6803 | 0.6497 |
| 0.0767 | 34.67 | 54500 | 2.2601 | 0.6803 | 0.6524 |
| 0.0844 | 34.99 | 55000 | 2.2785 | 0.6733 | 0.6470 |
| 0.0844 | 35.31 | 55500 | 2.2756 | 0.6784 | 0.6520 |
| 0.0755 | 35.62 | 56000 | 2.2813 | 0.6816 | 0.6542 |
| 0.0755 | 35.94 | 56500 | 2.2752 | 0.6803 | 0.6518 |
| 0.077 | 36.26 | 57000 | 2.2815 | 0.6796 | 0.6518 |
| 0.077 | 36.58 | 57500 | 2.2861 | 0.6803 | 0.6514 |
| 0.0752 | 36.9 | 58000 | 2.2929 | 0.6771 | 0.6505 |
| 0.0752 | 37.21 | 58500 | 2.2859 | 0.6816 | 0.6537 |
| 0.0698 | 37.53 | 59000 | 2.3117 | 0.6796 | 0.6525 |
| 0.0698 | 37.85 | 59500 | 2.3038 | 0.6816 | 0.6511 |
| 0.0613 | 38.17 | 60000 | 2.3176 | 0.6765 | 0.6477 |
| 0.0613 | 38.49 | 60500 | 2.3131 | 0.6796 | 0.6493 |
| 0.0706 | 38.8 | 61000 | 2.3161 | 0.6777 | 0.6477 |
| 0.0706 | 39.12 | 61500 | 2.3127 | 0.6784 | 0.6484 |
| 0.0678 | 39.44 | 62000 | 2.3174 | 0.6765 | 0.6467 |
| 0.0678 | 39.76 | 62500 | 2.3223 | 0.6790 | 0.6487 |
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
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Xenova/all-roberta-large-v1 | 2023-09-01T21:43:11.000Z | [
"transformers.js",
"onnx",
"roberta",
"fill-mask",
"feature-extraction",
"region:us"
] | feature-extraction | Xenova | null | null | Xenova/all-roberta-large-v1 | 0 | 2 | transformers.js | 2023-05-23T14:27:43 | ---
library_name: transformers.js
pipeline_tag: feature-extraction
---
https://huggingface.co/sentence-transformers/all-roberta-large-v1 with ONNX weights to be compatible with Transformers.js.
Note: Having a separate repo for ONNX weights is intended to be a temporary solution until WebML gains more traction. If you would like to make your models web-ready, we recommend converting to ONNX using [🤗 Optimum](https://huggingface.co/docs/optimum/index) and structuring your repo like this one (with ONNX weights located in a subfolder named `onnx`). | 552 | [
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Xenova/paraphrase-multilingual-mpnet-base-v2 | 2023-05-30T22:29:55.000Z | [
"transformers.js",
"onnx",
"xlm-roberta",
"feature-extraction",
"region:us"
] | feature-extraction | Xenova | null | null | Xenova/paraphrase-multilingual-mpnet-base-v2 | 1 | 2 | transformers.js | 2023-05-23T14:31:51 | ---
library_name: "transformers.js"
---
https://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-v2 with ONNX weights to be compatible with Transformers.js.
Note: Having a separate repo for ONNX weights is intended to be a temporary solution until WebML gains more traction. If you would like to make your models web-ready, we recommend converting to ONNX using [🤗 Optimum](https://huggingface.co/docs/optimum/index) and structuring your repo like this one (with ONNX weights located in a subfolder named `onnx`). | 538 | [
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P3ps/bert-finetuned-cross-ner | 2023-05-24T11:36:32.000Z | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | P3ps | null | null | P3ps/bert-finetuned-cross-ner | 0 | 2 | transformers | 2023-05-23T15:00:41 | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-finetuned-cross-ner
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-finetuned-cross-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1761
- Precision: 0.8267
- Recall: 0.8619
- F1: 0.8439
- Accuracy: 0.9561
## 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
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2037 | 1.0 | 2607 | 0.1973 | 0.7633 | 0.8122 | 0.7870 | 0.9449 |
| 0.1264 | 2.0 | 5214 | 0.1709 | 0.8102 | 0.8484 | 0.8289 | 0.9542 |
| 0.0817 | 3.0 | 7821 | 0.1761 | 0.8267 | 0.8619 | 0.8439 | 0.9561 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
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VinsmokeMir/Fine_Tuning_SC_Method_2_Epoch_13B | 2023-05-23T15:44:19.000Z | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | text-classification | VinsmokeMir | null | null | VinsmokeMir/Fine_Tuning_SC_Method_2_Epoch_13B | 0 | 2 | transformers | 2023-05-23T15:28:29 | ---
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: Fine_Tuning_SC_Method_2_Epoch_13B
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. -->
# Fine_Tuning_SC_Method_2_Epoch_13B
This model is a fine-tuned version of [rafsankabir/Pretrained_E13B_Method2](https://huggingface.co/rafsankabir/Pretrained_E13B_Method2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4244
- Accuracy: 0.6873
- F1 Macro: 0.6544
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 40
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Macro |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:|
| No log | 1.27 | 500 | 1.0673 | 0.3976 | 0.1896 |
| 1.0138 | 2.54 | 1000 | 0.8217 | 0.6331 | 0.5569 |
| 1.0138 | 3.82 | 1500 | 0.7889 | 0.6662 | 0.6049 |
| 0.7305 | 5.09 | 2000 | 0.7821 | 0.6765 | 0.6382 |
| 0.7305 | 6.36 | 2500 | 0.7867 | 0.6918 | 0.6457 |
| 0.5856 | 7.63 | 3000 | 0.8236 | 0.6892 | 0.6623 |
| 0.5856 | 8.91 | 3500 | 0.8490 | 0.6835 | 0.6551 |
| 0.4723 | 10.18 | 4000 | 0.9057 | 0.6854 | 0.6533 |
| 0.4723 | 11.45 | 4500 | 0.9237 | 0.6796 | 0.6455 |
| 0.3896 | 12.72 | 5000 | 0.9814 | 0.6879 | 0.6499 |
| 0.3896 | 13.99 | 5500 | 0.9984 | 0.6745 | 0.6487 |
| 0.3299 | 15.27 | 6000 | 1.0226 | 0.6822 | 0.6545 |
| 0.3299 | 16.54 | 6500 | 1.0579 | 0.6758 | 0.6485 |
| 0.2783 | 17.81 | 7000 | 1.0932 | 0.6796 | 0.6487 |
| 0.2783 | 19.08 | 7500 | 1.1047 | 0.6950 | 0.6609 |
| 0.2455 | 20.36 | 8000 | 1.1643 | 0.6860 | 0.6559 |
| 0.2455 | 21.63 | 8500 | 1.1953 | 0.6841 | 0.6548 |
| 0.2181 | 22.9 | 9000 | 1.2043 | 0.6835 | 0.6516 |
| 0.2181 | 24.17 | 9500 | 1.2603 | 0.6867 | 0.6502 |
| 0.1894 | 25.45 | 10000 | 1.2652 | 0.6860 | 0.6552 |
| 0.1894 | 26.72 | 10500 | 1.2860 | 0.6790 | 0.6474 |
| 0.1757 | 27.99 | 11000 | 1.2892 | 0.6854 | 0.6541 |
| 0.1757 | 29.26 | 11500 | 1.3400 | 0.6803 | 0.6496 |
| 0.1599 | 30.53 | 12000 | 1.3630 | 0.6828 | 0.6493 |
| 0.1599 | 31.81 | 12500 | 1.3688 | 0.6854 | 0.6538 |
| 0.1531 | 33.08 | 13000 | 1.3962 | 0.6854 | 0.6534 |
| 0.1531 | 34.35 | 13500 | 1.4021 | 0.6841 | 0.6523 |
| 0.1452 | 35.62 | 14000 | 1.4029 | 0.6847 | 0.6524 |
| 0.1452 | 36.9 | 14500 | 1.4130 | 0.6886 | 0.6562 |
| 0.1391 | 38.17 | 15000 | 1.4203 | 0.6879 | 0.6553 |
| 0.1391 | 39.44 | 15500 | 1.4244 | 0.6873 | 0.6544 |
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
| 3,724 | [
[
-0.048248291015625,
-0.03875732421875,
0.0099945068359375,
0.00829315185546875,
-0.01190948486328125,
-0.008697509765625,
-0.00424957275390625,
-0.0106353759765625,
0.0330810546875,
0.0262603759765625,
-0.05267333984375,
-0.046966552734375,
-0.046844482421875,
... |
YakovElm/Apache5Classic_with_cleaning | 2023-05-23T15:56:00.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/Apache5Classic_with_cleaning | 0 | 2 | transformers | 2023-05-23T15:55:23 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Apache5Classic_with_cleaning
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Apache5Classic_with_cleaning
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.2193
- Train Accuracy: 0.9235
- Validation Loss: 0.6107
- Validation Accuracy: 0.8194
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.3142 | 0.9001 | 0.4816 | 0.8233 | 0 |
| 0.2820 | 0.9099 | 0.4622 | 0.8233 | 1 |
| 0.2193 | 0.9235 | 0.6107 | 0.8194 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,800 | [
[
-0.047943115234375,
-0.045257568359375,
0.0209503173828125,
-0.0019140243530273438,
-0.035858154296875,
-0.03179931640625,
-0.0160064697265625,
-0.0297393798828125,
0.006927490234375,
0.018096923828125,
-0.053314208984375,
-0.0498046875,
-0.053070068359375,
... |
VinsmokeMir/Hinton_SC_BS32_LR3e5 | 2023-05-23T16:22:55.000Z | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | text-classification | VinsmokeMir | null | null | VinsmokeMir/Hinton_SC_BS32_LR3e5 | 0 | 2 | transformers | 2023-05-23T16:07:35 | ---
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: Hinton_SC_BS32_LR3e5
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. -->
# Hinton_SC_BS32_LR3e5
This model is a fine-tuned version of [rafsankabir/Pretrained_Final_E6](https://huggingface.co/rafsankabir/Pretrained_Final_E6) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4069
- Accuracy: 0.6790
- F1 Macro: 0.6473
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 40
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Macro |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:|
| No log | 1.27 | 500 | 1.0674 | 0.3976 | 0.1896 |
| 1.0108 | 2.54 | 1000 | 0.8270 | 0.6426 | 0.5565 |
| 1.0108 | 3.82 | 1500 | 0.8016 | 0.6522 | 0.5753 |
| 0.7423 | 5.09 | 2000 | 0.7922 | 0.6611 | 0.6099 |
| 0.7423 | 6.36 | 2500 | 0.8057 | 0.6726 | 0.6155 |
| 0.6098 | 7.63 | 3000 | 0.8303 | 0.6860 | 0.6456 |
| 0.6098 | 8.91 | 3500 | 0.8322 | 0.6847 | 0.6481 |
| 0.5049 | 10.18 | 4000 | 0.8775 | 0.6994 | 0.6603 |
| 0.5049 | 11.45 | 4500 | 0.9122 | 0.6956 | 0.6510 |
| 0.4132 | 12.72 | 5000 | 0.9451 | 0.6879 | 0.6564 |
| 0.4132 | 13.99 | 5500 | 0.9600 | 0.6809 | 0.6433 |
| 0.3571 | 15.27 | 6000 | 1.0050 | 0.6854 | 0.6515 |
| 0.3571 | 16.54 | 6500 | 1.0671 | 0.6847 | 0.6496 |
| 0.2952 | 17.81 | 7000 | 1.0836 | 0.6873 | 0.6525 |
| 0.2952 | 19.08 | 7500 | 1.0993 | 0.6873 | 0.6558 |
| 0.2577 | 20.36 | 8000 | 1.1465 | 0.6924 | 0.6613 |
| 0.2577 | 21.63 | 8500 | 1.2137 | 0.6828 | 0.6541 |
| 0.2314 | 22.9 | 9000 | 1.1916 | 0.6924 | 0.6610 |
| 0.2314 | 24.17 | 9500 | 1.2445 | 0.6860 | 0.6525 |
| 0.2044 | 25.45 | 10000 | 1.2564 | 0.6867 | 0.6554 |
| 0.2044 | 26.72 | 10500 | 1.2770 | 0.6828 | 0.6509 |
| 0.1899 | 27.99 | 11000 | 1.3005 | 0.6854 | 0.6553 |
| 0.1899 | 29.26 | 11500 | 1.3149 | 0.6816 | 0.6519 |
| 0.1777 | 30.53 | 12000 | 1.3320 | 0.6835 | 0.6512 |
| 0.1777 | 31.81 | 12500 | 1.3456 | 0.6847 | 0.6538 |
| 0.1652 | 33.08 | 13000 | 1.3620 | 0.6796 | 0.6486 |
| 0.1652 | 34.35 | 13500 | 1.3808 | 0.6796 | 0.6500 |
| 0.1544 | 35.62 | 14000 | 1.3878 | 0.6841 | 0.6533 |
| 0.1544 | 36.9 | 14500 | 1.3989 | 0.6790 | 0.6490 |
| 0.1521 | 38.17 | 15000 | 1.4031 | 0.6822 | 0.6501 |
| 0.1521 | 39.44 | 15500 | 1.4069 | 0.6790 | 0.6473 |
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
| 3,690 | [
[
-0.04571533203125,
-0.03302001953125,
0.0079498291015625,
0.00583648681640625,
-0.0081024169921875,
-0.00862884521484375,
-0.0003750324249267578,
-0.007701873779296875,
0.036285400390625,
0.0247650146484375,
-0.0491943359375,
-0.046905517578125,
-0.0469055175781... |
VinsmokeMir/Method2_E13B_SC_BS4_LR3e5 | 2023-05-23T18:18:39.000Z | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | text-classification | VinsmokeMir | null | null | VinsmokeMir/Method2_E13B_SC_BS4_LR3e5 | 0 | 2 | transformers | 2023-05-23T16:27:50 | ---
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: Method2_E13B_SC_BS4_LR3e5
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. -->
# Method2_E13B_SC_BS4_LR3e5
This model is a fine-tuned version of [rafsankabir/Pretrained_E13B_Method2](https://huggingface.co/rafsankabir/Pretrained_E13B_Method2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.5641
- Accuracy: 0.6803
- F1 Macro: 0.6446
## 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: 4
- eval_batch_size: 4
- 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: 40
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Macro |
|:-------------:|:-----:|:------:|:---------------:|:--------:|:--------:|
| No log | 0.16 | 500 | 1.0767 | 0.3976 | 0.1896 |
| 1.075 | 0.32 | 1000 | 1.0769 | 0.3976 | 0.1896 |
| 1.075 | 0.48 | 1500 | 1.0183 | 0.5539 | 0.4151 |
| 1.0246 | 0.64 | 2000 | 0.8956 | 0.5916 | 0.4745 |
| 1.0246 | 0.8 | 2500 | 0.8743 | 0.6082 | 0.5120 |
| 0.8948 | 0.95 | 3000 | 0.8365 | 0.6216 | 0.5546 |
| 0.8948 | 1.11 | 3500 | 0.8635 | 0.6311 | 0.5752 |
| 0.8069 | 1.27 | 4000 | 0.9060 | 0.6158 | 0.5398 |
| 0.8069 | 1.43 | 4500 | 0.8231 | 0.6388 | 0.5924 |
| 0.7969 | 1.59 | 5000 | 0.8368 | 0.6331 | 0.5935 |
| 0.7969 | 1.75 | 5500 | 0.8262 | 0.6477 | 0.5981 |
| 0.7804 | 1.91 | 6000 | 0.8299 | 0.6579 | 0.6208 |
| 0.7804 | 2.07 | 6500 | 0.8197 | 0.6579 | 0.6364 |
| 0.715 | 2.23 | 7000 | 0.8498 | 0.6624 | 0.5955 |
| 0.715 | 2.39 | 7500 | 0.8357 | 0.6669 | 0.6218 |
| 0.6953 | 2.54 | 8000 | 0.8438 | 0.6560 | 0.6269 |
| 0.6953 | 2.7 | 8500 | 0.8528 | 0.6669 | 0.6022 |
| 0.7074 | 2.86 | 9000 | 0.8009 | 0.6745 | 0.6457 |
| 0.7074 | 3.02 | 9500 | 0.8222 | 0.6720 | 0.6402 |
| 0.6598 | 3.18 | 10000 | 0.9347 | 0.6650 | 0.6062 |
| 0.6598 | 3.34 | 10500 | 0.9053 | 0.6803 | 0.6510 |
| 0.6634 | 3.5 | 11000 | 0.8902 | 0.6720 | 0.6434 |
| 0.6634 | 3.66 | 11500 | 0.9370 | 0.6733 | 0.6415 |
| 0.6182 | 3.82 | 12000 | 0.8914 | 0.6745 | 0.6519 |
| 0.6182 | 3.98 | 12500 | 0.8938 | 0.6752 | 0.6389 |
| 0.6043 | 4.13 | 13000 | 1.0143 | 0.6745 | 0.6413 |
| 0.6043 | 4.29 | 13500 | 1.0768 | 0.6765 | 0.6543 |
| 0.587 | 4.45 | 14000 | 1.1154 | 0.6790 | 0.6421 |
| 0.587 | 4.61 | 14500 | 1.1295 | 0.6828 | 0.6525 |
| 0.6345 | 4.77 | 15000 | 1.1210 | 0.6822 | 0.6390 |
| 0.6345 | 4.93 | 15500 | 1.0062 | 0.6726 | 0.6380 |
| 0.6 | 5.09 | 16000 | 1.1504 | 0.6739 | 0.6369 |
| 0.6 | 5.25 | 16500 | 1.3298 | 0.6733 | 0.6280 |
| 0.5667 | 5.41 | 17000 | 1.2751 | 0.6662 | 0.6308 |
| 0.5667 | 5.57 | 17500 | 1.4070 | 0.6567 | 0.6069 |
| 0.614 | 5.73 | 18000 | 1.2956 | 0.6694 | 0.6284 |
| 0.614 | 5.88 | 18500 | 1.2795 | 0.6822 | 0.6382 |
| 0.5651 | 6.04 | 19000 | 1.3021 | 0.6739 | 0.6478 |
| 0.5651 | 6.2 | 19500 | 1.4076 | 0.6682 | 0.6333 |
| 0.5347 | 6.36 | 20000 | 1.3917 | 0.6733 | 0.6344 |
| 0.5347 | 6.52 | 20500 | 1.4203 | 0.6790 | 0.6285 |
| 0.5278 | 6.68 | 21000 | 1.3340 | 0.6860 | 0.6628 |
| 0.5278 | 6.84 | 21500 | 1.3521 | 0.6873 | 0.6489 |
| 0.5796 | 7.0 | 22000 | 1.2835 | 0.6847 | 0.6567 |
| 0.5796 | 7.16 | 22500 | 1.4437 | 0.6879 | 0.6563 |
| 0.4627 | 7.32 | 23000 | 1.5052 | 0.6835 | 0.6435 |
| 0.4627 | 7.47 | 23500 | 1.4991 | 0.6707 | 0.6434 |
| 0.518 | 7.63 | 24000 | 1.5436 | 0.6656 | 0.6403 |
| 0.518 | 7.79 | 24500 | 1.5247 | 0.6784 | 0.6433 |
| 0.5373 | 7.95 | 25000 | 1.4743 | 0.6835 | 0.6537 |
| 0.5373 | 8.11 | 25500 | 1.5379 | 0.6777 | 0.6385 |
| 0.4539 | 8.27 | 26000 | 1.5548 | 0.6739 | 0.6393 |
| 0.4539 | 8.43 | 26500 | 1.6174 | 0.6669 | 0.6378 |
| 0.4519 | 8.59 | 27000 | 1.5949 | 0.6816 | 0.6504 |
| 0.4519 | 8.75 | 27500 | 1.5558 | 0.6816 | 0.6357 |
| 0.4813 | 8.91 | 28000 | 1.5826 | 0.6739 | 0.6553 |
| 0.4813 | 9.06 | 28500 | 1.5929 | 0.6867 | 0.6540 |
| 0.4121 | 9.22 | 29000 | 1.6260 | 0.6886 | 0.6545 |
| 0.4121 | 9.38 | 29500 | 1.5950 | 0.6841 | 0.6500 |
| 0.4451 | 9.54 | 30000 | 1.6146 | 0.6854 | 0.6481 |
| 0.4451 | 9.7 | 30500 | 1.6587 | 0.6796 | 0.6493 |
| 0.4039 | 9.86 | 31000 | 1.6173 | 0.6758 | 0.6400 |
| 0.4039 | 10.02 | 31500 | 1.5952 | 0.6803 | 0.6517 |
| 0.3921 | 10.18 | 32000 | 1.7298 | 0.6694 | 0.6413 |
| 0.3921 | 10.34 | 32500 | 1.7106 | 0.6796 | 0.6467 |
| 0.3799 | 10.5 | 33000 | 1.6695 | 0.6867 | 0.6505 |
| 0.3799 | 10.66 | 33500 | 1.6907 | 0.6803 | 0.6550 |
| 0.4003 | 10.81 | 34000 | 1.6811 | 0.6809 | 0.6413 |
| 0.4003 | 10.97 | 34500 | 1.6644 | 0.6771 | 0.6352 |
| 0.3812 | 11.13 | 35000 | 1.7371 | 0.6822 | 0.6386 |
| 0.3812 | 11.29 | 35500 | 1.7405 | 0.6841 | 0.6516 |
| 0.3399 | 11.45 | 36000 | 1.6981 | 0.6822 | 0.6503 |
| 0.3399 | 11.61 | 36500 | 1.6536 | 0.6847 | 0.6483 |
| 0.3653 | 11.77 | 37000 | 1.7461 | 0.6790 | 0.6475 |
| 0.3653 | 11.93 | 37500 | 1.7247 | 0.6790 | 0.6485 |
| 0.338 | 12.09 | 38000 | 1.7433 | 0.6905 | 0.6532 |
| 0.338 | 12.25 | 38500 | 1.7331 | 0.6765 | 0.6558 |
| 0.3302 | 12.4 | 39000 | 1.7603 | 0.6796 | 0.6456 |
| 0.3302 | 12.56 | 39500 | 1.7784 | 0.6726 | 0.6505 |
| 0.3195 | 12.72 | 40000 | 1.8032 | 0.6784 | 0.6469 |
| 0.3195 | 12.88 | 40500 | 1.7869 | 0.6822 | 0.6553 |
| 0.3508 | 13.04 | 41000 | 1.7761 | 0.6752 | 0.6506 |
| 0.3508 | 13.2 | 41500 | 1.7806 | 0.6847 | 0.6454 |
| 0.2915 | 13.36 | 42000 | 1.8542 | 0.6707 | 0.6528 |
| 0.2915 | 13.52 | 42500 | 1.8365 | 0.6796 | 0.6520 |
| 0.3023 | 13.68 | 43000 | 1.8563 | 0.6828 | 0.6524 |
| 0.3023 | 13.84 | 43500 | 1.7947 | 0.6752 | 0.6495 |
| 0.3213 | 13.99 | 44000 | 1.8130 | 0.6796 | 0.6546 |
| 0.3213 | 14.15 | 44500 | 1.8288 | 0.6841 | 0.6502 |
| 0.2644 | 14.31 | 45000 | 1.8140 | 0.6726 | 0.6453 |
| 0.2644 | 14.47 | 45500 | 1.8711 | 0.6809 | 0.6552 |
| 0.2739 | 14.63 | 46000 | 1.8439 | 0.6873 | 0.6534 |
| 0.2739 | 14.79 | 46500 | 1.8302 | 0.6828 | 0.6460 |
| 0.3012 | 14.95 | 47000 | 1.8708 | 0.6752 | 0.6454 |
| 0.3012 | 15.11 | 47500 | 1.8498 | 0.6822 | 0.6487 |
| 0.2805 | 15.27 | 48000 | 1.8908 | 0.6803 | 0.6453 |
| 0.2805 | 15.43 | 48500 | 1.9480 | 0.6790 | 0.6406 |
| 0.2895 | 15.59 | 49000 | 1.8994 | 0.6675 | 0.6392 |
| 0.2895 | 15.74 | 49500 | 1.9135 | 0.6790 | 0.6461 |
| 0.2444 | 15.9 | 50000 | 1.9387 | 0.6841 | 0.6480 |
| 0.2444 | 16.06 | 50500 | 1.9175 | 0.6745 | 0.6463 |
| 0.2569 | 16.22 | 51000 | 1.9332 | 0.6745 | 0.6472 |
| 0.2569 | 16.38 | 51500 | 1.9400 | 0.6771 | 0.6445 |
| 0.2251 | 16.54 | 52000 | 1.9596 | 0.6745 | 0.6441 |
| 0.2251 | 16.7 | 52500 | 1.9959 | 0.6835 | 0.6464 |
| 0.2686 | 16.86 | 53000 | 1.9879 | 0.6777 | 0.6456 |
| 0.2686 | 17.02 | 53500 | 1.9882 | 0.6828 | 0.6471 |
| 0.2168 | 17.18 | 54000 | 2.0254 | 0.6886 | 0.6520 |
| 0.2168 | 17.33 | 54500 | 2.0432 | 0.6777 | 0.6442 |
| 0.2735 | 17.49 | 55000 | 1.9843 | 0.6745 | 0.6443 |
| 0.2735 | 17.65 | 55500 | 2.0330 | 0.6828 | 0.6451 |
| 0.2159 | 17.81 | 56000 | 2.0698 | 0.6682 | 0.6423 |
| 0.2159 | 17.97 | 56500 | 1.9797 | 0.6771 | 0.6426 |
| 0.245 | 18.13 | 57000 | 2.0008 | 0.6726 | 0.6383 |
| 0.245 | 18.29 | 57500 | 2.0425 | 0.6816 | 0.6473 |
| 0.2036 | 18.45 | 58000 | 2.0482 | 0.6720 | 0.6356 |
| 0.2036 | 18.61 | 58500 | 2.0950 | 0.6675 | 0.6384 |
| 0.2336 | 18.77 | 59000 | 2.0167 | 0.6854 | 0.6458 |
| 0.2336 | 18.92 | 59500 | 1.9984 | 0.6809 | 0.6406 |
| 0.2332 | 19.08 | 60000 | 2.0552 | 0.6739 | 0.6441 |
| 0.2332 | 19.24 | 60500 | 2.0450 | 0.6784 | 0.6459 |
| 0.1984 | 19.4 | 61000 | 2.0599 | 0.6752 | 0.6434 |
| 0.1984 | 19.56 | 61500 | 2.0704 | 0.6784 | 0.6417 |
| 0.1945 | 19.72 | 62000 | 2.0755 | 0.6758 | 0.6445 |
| 0.1945 | 19.88 | 62500 | 2.0660 | 0.6809 | 0.6428 |
| 0.2143 | 20.04 | 63000 | 2.0670 | 0.6739 | 0.6448 |
| 0.2143 | 20.2 | 63500 | 2.0581 | 0.6873 | 0.6509 |
| 0.1878 | 20.36 | 64000 | 2.1272 | 0.6752 | 0.6452 |
| 0.1878 | 20.52 | 64500 | 2.1002 | 0.6803 | 0.6511 |
| 0.2144 | 20.67 | 65000 | 2.1383 | 0.6713 | 0.6438 |
| 0.2144 | 20.83 | 65500 | 2.1070 | 0.6809 | 0.6419 |
| 0.2121 | 20.99 | 66000 | 2.1273 | 0.6726 | 0.6412 |
| 0.2121 | 21.15 | 66500 | 2.1605 | 0.6707 | 0.6395 |
| 0.1835 | 21.31 | 67000 | 2.2891 | 0.6567 | 0.6331 |
| 0.1835 | 21.47 | 67500 | 2.2472 | 0.6765 | 0.6402 |
| 0.1991 | 21.63 | 68000 | 2.2238 | 0.6752 | 0.6412 |
| 0.1991 | 21.79 | 68500 | 2.1965 | 0.6669 | 0.6372 |
| 0.2018 | 21.95 | 69000 | 2.2050 | 0.6669 | 0.6395 |
| 0.2018 | 22.11 | 69500 | 2.1795 | 0.6803 | 0.6467 |
| 0.151 | 22.26 | 70000 | 2.2214 | 0.6777 | 0.6430 |
| 0.151 | 22.42 | 70500 | 2.1754 | 0.6867 | 0.6513 |
| 0.2078 | 22.58 | 71000 | 2.1959 | 0.6822 | 0.6488 |
| 0.2078 | 22.74 | 71500 | 2.1933 | 0.6860 | 0.6481 |
| 0.2004 | 22.9 | 72000 | 2.2001 | 0.6816 | 0.6500 |
| 0.2004 | 23.06 | 72500 | 2.2159 | 0.6784 | 0.6490 |
| 0.1773 | 23.22 | 73000 | 2.2603 | 0.6790 | 0.6462 |
| 0.1773 | 23.38 | 73500 | 2.2331 | 0.6777 | 0.6470 |
| 0.174 | 23.54 | 74000 | 2.2554 | 0.6765 | 0.6471 |
| 0.174 | 23.7 | 74500 | 2.2000 | 0.6854 | 0.6517 |
| 0.2071 | 23.85 | 75000 | 2.1896 | 0.6790 | 0.6500 |
| 0.2071 | 24.01 | 75500 | 2.2270 | 0.6828 | 0.6479 |
| 0.1419 | 24.17 | 76000 | 2.2776 | 0.6765 | 0.6426 |
| 0.1419 | 24.33 | 76500 | 2.2895 | 0.6809 | 0.6437 |
| 0.1564 | 24.49 | 77000 | 2.2746 | 0.6828 | 0.6515 |
| 0.1564 | 24.65 | 77500 | 2.3156 | 0.6765 | 0.6356 |
| 0.1802 | 24.81 | 78000 | 2.2891 | 0.6726 | 0.6426 |
| 0.1802 | 24.97 | 78500 | 2.2610 | 0.6835 | 0.6502 |
| 0.1795 | 25.13 | 79000 | 2.2856 | 0.6777 | 0.6478 |
| 0.1795 | 25.29 | 79500 | 2.2410 | 0.6828 | 0.6478 |
| 0.1753 | 25.45 | 80000 | 2.2738 | 0.6701 | 0.6451 |
| 0.1753 | 25.6 | 80500 | 2.2679 | 0.6847 | 0.6440 |
| 0.1517 | 25.76 | 81000 | 2.2667 | 0.6796 | 0.6525 |
| 0.1517 | 25.92 | 81500 | 2.3471 | 0.6682 | 0.6455 |
| 0.1593 | 26.08 | 82000 | 2.2945 | 0.6816 | 0.6504 |
| 0.1593 | 26.24 | 82500 | 2.3202 | 0.6841 | 0.6456 |
| 0.1332 | 26.4 | 83000 | 2.3667 | 0.6733 | 0.6405 |
| 0.1332 | 26.56 | 83500 | 2.3295 | 0.6771 | 0.6377 |
| 0.1765 | 26.72 | 84000 | 2.3680 | 0.6720 | 0.6394 |
| 0.1765 | 26.88 | 84500 | 2.3246 | 0.6828 | 0.6456 |
| 0.1578 | 27.04 | 85000 | 2.3192 | 0.6745 | 0.6453 |
| 0.1578 | 27.19 | 85500 | 2.3216 | 0.6822 | 0.6471 |
| 0.1355 | 27.35 | 86000 | 2.3730 | 0.6796 | 0.6490 |
| 0.1355 | 27.51 | 86500 | 2.3650 | 0.6758 | 0.6415 |
| 0.1308 | 27.67 | 87000 | 2.4015 | 0.6784 | 0.6471 |
| 0.1308 | 27.83 | 87500 | 2.3700 | 0.6809 | 0.6403 |
| 0.1446 | 27.99 | 88000 | 2.3748 | 0.6796 | 0.6483 |
| 0.1446 | 28.15 | 88500 | 2.3575 | 0.6809 | 0.6497 |
| 0.1135 | 28.31 | 89000 | 2.3663 | 0.6835 | 0.6438 |
| 0.1135 | 28.47 | 89500 | 2.3817 | 0.6809 | 0.6490 |
| 0.1354 | 28.63 | 90000 | 2.4026 | 0.6739 | 0.6436 |
| 0.1354 | 28.78 | 90500 | 2.3825 | 0.6745 | 0.6392 |
| 0.1661 | 28.94 | 91000 | 2.3461 | 0.6771 | 0.6482 |
| 0.1661 | 29.1 | 91500 | 2.3496 | 0.6771 | 0.6422 |
| 0.1188 | 29.26 | 92000 | 2.3568 | 0.6790 | 0.6488 |
| 0.1188 | 29.42 | 92500 | 2.3496 | 0.6828 | 0.6430 |
| 0.1433 | 29.58 | 93000 | 2.4252 | 0.6707 | 0.6378 |
| 0.1433 | 29.74 | 93500 | 2.3805 | 0.6847 | 0.6459 |
| 0.1328 | 29.9 | 94000 | 2.3918 | 0.6860 | 0.6495 |
| 0.1328 | 30.06 | 94500 | 2.4026 | 0.6828 | 0.6495 |
| 0.1317 | 30.22 | 95000 | 2.4319 | 0.6841 | 0.6483 |
| 0.1317 | 30.38 | 95500 | 2.4375 | 0.6828 | 0.6492 |
| 0.122 | 30.53 | 96000 | 2.4401 | 0.6822 | 0.6475 |
| 0.122 | 30.69 | 96500 | 2.4397 | 0.6860 | 0.6473 |
| 0.1266 | 30.85 | 97000 | 2.4572 | 0.6847 | 0.6504 |
| 0.1266 | 31.01 | 97500 | 2.4506 | 0.6847 | 0.6513 |
| 0.1437 | 31.17 | 98000 | 2.4251 | 0.6822 | 0.6496 |
| 0.1437 | 31.33 | 98500 | 2.4420 | 0.6822 | 0.6521 |
| 0.1205 | 31.49 | 99000 | 2.4446 | 0.6816 | 0.6464 |
| 0.1205 | 31.65 | 99500 | 2.4408 | 0.6790 | 0.6450 |
| 0.1188 | 31.81 | 100000 | 2.4522 | 0.6765 | 0.6487 |
| 0.1188 | 31.97 | 100500 | 2.4313 | 0.6828 | 0.6495 |
| 0.1326 | 32.12 | 101000 | 2.4577 | 0.6784 | 0.6466 |
| 0.1326 | 32.28 | 101500 | 2.4524 | 0.6822 | 0.6479 |
| 0.1103 | 32.44 | 102000 | 2.4665 | 0.6765 | 0.6426 |
| 0.1103 | 32.6 | 102500 | 2.4642 | 0.6777 | 0.6431 |
| 0.118 | 32.76 | 103000 | 2.4628 | 0.6771 | 0.6451 |
| 0.118 | 32.92 | 103500 | 2.4671 | 0.6835 | 0.6474 |
| 0.1214 | 33.08 | 104000 | 2.4613 | 0.6771 | 0.6503 |
| 0.1214 | 33.24 | 104500 | 2.4833 | 0.6771 | 0.6475 |
| 0.0965 | 33.4 | 105000 | 2.4888 | 0.6803 | 0.6450 |
| 0.0965 | 33.56 | 105500 | 2.4910 | 0.6816 | 0.6476 |
| 0.1207 | 33.72 | 106000 | 2.4806 | 0.6860 | 0.6482 |
| 0.1207 | 33.87 | 106500 | 2.4741 | 0.6771 | 0.6445 |
| 0.1277 | 34.03 | 107000 | 2.5050 | 0.6790 | 0.6409 |
| 0.1277 | 34.19 | 107500 | 2.4809 | 0.6777 | 0.6402 |
| 0.1164 | 34.35 | 108000 | 2.5006 | 0.6777 | 0.6428 |
| 0.1164 | 34.51 | 108500 | 2.4889 | 0.6822 | 0.6474 |
| 0.1103 | 34.67 | 109000 | 2.4852 | 0.6822 | 0.6457 |
| 0.1103 | 34.83 | 109500 | 2.4923 | 0.6771 | 0.6418 |
| 0.1013 | 34.99 | 110000 | 2.4662 | 0.6784 | 0.6437 |
| 0.1013 | 35.15 | 110500 | 2.4755 | 0.6822 | 0.6483 |
| 0.0922 | 35.31 | 111000 | 2.4908 | 0.6816 | 0.6465 |
| 0.0922 | 35.46 | 111500 | 2.4922 | 0.6809 | 0.6502 |
| 0.0856 | 35.62 | 112000 | 2.5096 | 0.6828 | 0.6422 |
| 0.0856 | 35.78 | 112500 | 2.5035 | 0.6828 | 0.6463 |
| 0.1005 | 35.94 | 113000 | 2.5231 | 0.6828 | 0.6452 |
| 0.1005 | 36.1 | 113500 | 2.5196 | 0.6796 | 0.6469 |
| 0.0884 | 36.26 | 114000 | 2.5187 | 0.6796 | 0.6444 |
| 0.0884 | 36.42 | 114500 | 2.5180 | 0.6790 | 0.6454 |
| 0.0891 | 36.58 | 115000 | 2.5407 | 0.6771 | 0.6442 |
| 0.0891 | 36.74 | 115500 | 2.5349 | 0.6765 | 0.6417 |
| 0.1082 | 36.9 | 116000 | 2.5451 | 0.6777 | 0.6427 |
| 0.1082 | 37.05 | 116500 | 2.5349 | 0.6803 | 0.6469 |
| 0.1072 | 37.21 | 117000 | 2.5507 | 0.6816 | 0.6457 |
| 0.1072 | 37.37 | 117500 | 2.5485 | 0.6790 | 0.6459 |
| 0.0882 | 37.53 | 118000 | 2.5477 | 0.6809 | 0.6448 |
| 0.0882 | 37.69 | 118500 | 2.5620 | 0.6790 | 0.6401 |
| 0.0852 | 37.85 | 119000 | 2.5597 | 0.6790 | 0.6447 |
| 0.0852 | 38.01 | 119500 | 2.5545 | 0.6796 | 0.6436 |
| 0.1029 | 38.17 | 120000 | 2.5519 | 0.6796 | 0.6436 |
| 0.1029 | 38.33 | 120500 | 2.5539 | 0.6822 | 0.6463 |
| 0.0903 | 38.49 | 121000 | 2.5590 | 0.6822 | 0.6490 |
| 0.0903 | 38.65 | 121500 | 2.5658 | 0.6803 | 0.6457 |
| 0.092 | 38.8 | 122000 | 2.5590 | 0.6803 | 0.6433 |
| 0.092 | 38.96 | 122500 | 2.5620 | 0.6803 | 0.6449 |
| 0.094 | 39.12 | 123000 | 2.5634 | 0.6796 | 0.6436 |
| 0.094 | 39.28 | 123500 | 2.5677 | 0.6790 | 0.6435 |
| 0.0801 | 39.44 | 124000 | 2.5662 | 0.6803 | 0.6445 |
| 0.0801 | 39.6 | 124500 | 2.5648 | 0.6796 | 0.6440 |
| 0.103 | 39.76 | 125000 | 2.5641 | 0.6809 | 0.6451 |
| 0.103 | 39.92 | 125500 | 2.5641 | 0.6803 | 0.6446 |
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
| 20,239 | [
[
-0.049652099609375,
-0.03302001953125,
0.0169677734375,
0.00710296630859375,
-0.001514434814453125,
0.00897979736328125,
0.004123687744140625,
0.0033054351806640625,
0.04986572265625,
0.0263519287109375,
-0.042449951171875,
-0.03961181640625,
-0.037445068359375,... |
YakovElm/Apache10Classic_with_cleaning | 2023-05-23T17:00:42.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/Apache10Classic_with_cleaning | 0 | 2 | transformers | 2023-05-23T17:00:05 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Apache10Classic_with_cleaning
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Apache10Classic_with_cleaning
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.1824
- Train Accuracy: 0.9385
- Validation Loss: 0.5452
- Validation Accuracy: 0.8644
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.2431 | 0.9340 | 0.4461 | 0.8644 | 0 |
| 0.2183 | 0.9383 | 0.4053 | 0.8644 | 1 |
| 0.1824 | 0.9385 | 0.5452 | 0.8644 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,802 | [
[
-0.0482177734375,
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-0.... |
YakovElm/Qt10Classic | 2023-05-23T17:48:15.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/Qt10Classic | 0 | 2 | transformers | 2023-05-23T17:47:40 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Qt10Classic
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Qt10Classic
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.2261
- Train Accuracy: 0.9202
- Validation Loss: 0.2375
- Validation Accuracy: 0.9408
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.2779 | 0.9208 | 0.2090 | 0.9416 | 0 |
| 0.2558 | 0.9210 | 0.2075 | 0.9416 | 1 |
| 0.2261 | 0.9202 | 0.2375 | 0.9408 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,766 | [
[
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-... |
YakovElm/Apache15Classic_with_cleaning | 2023-05-23T18:05:44.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/Apache15Classic_with_cleaning | 0 | 2 | transformers | 2023-05-23T18:04:54 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Apache15Classic_with_cleaning
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Apache15Classic_with_cleaning
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.1583
- Train Accuracy: 0.9535
- Validation Loss: 0.3355
- Validation Accuracy: 0.8924
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.1921 | 0.9542 | 0.3429 | 0.8924 | 0 |
| 0.1792 | 0.9542 | 0.3336 | 0.8924 | 1 |
| 0.1583 | 0.9535 | 0.3355 | 0.8924 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,802 | [
[
-0.047576904296875,
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0.020416259765625,
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0.008941650390625,
0.0185546875,
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bahmanreza/keras-dummy-sequential-demo | 2023-05-23T18:17:12.000Z | [
"keras",
"region:us"
] | null | bahmanreza | null | null | bahmanreza/keras-dummy-sequential-demo | 0 | 2 | keras | 2023-05-23T18:17:09 | ---
library_name: keras
---
## 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:
| Hyperparameters | Value |
| :-- | :-- |
| name | Adam |
| weight_decay | None |
| clipnorm | None |
| global_clipnorm | None |
| clipvalue | None |
| use_ema | False |
| ema_momentum | 0.99 |
| ema_overwrite_frequency | None |
| jit_compile | True |
| is_legacy_optimizer | False |
| learning_rate | 0.0010000000474974513 |
| beta_1 | 0.9 |
| beta_2 | 0.999 |
| epsilon | 1e-07 |
| amsgrad | False |
| training_precision | float32 |
## Model Plot
<details>
<summary>View Model Plot</summary>
</details> | 840 | [
[
-0.03759765625,
-0.0401611328125,
0.0321044921875,
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0.020172119140625,
0.0307464599609375,
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-0.051025390625,
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0.0002460... |
bahmanreza/keras-dummy-functional-demo | 2023-05-23T18:19:23.000Z | [
"keras",
"region:us"
] | null | bahmanreza | null | null | bahmanreza/keras-dummy-functional-demo | 0 | 2 | keras | 2023-05-23T18:19:20 | ---
library_name: keras
---
## 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:
| Hyperparameters | Value |
| :-- | :-- |
| name | Adam |
| weight_decay | None |
| clipnorm | None |
| global_clipnorm | None |
| clipvalue | None |
| use_ema | False |
| ema_momentum | 0.99 |
| ema_overwrite_frequency | None |
| jit_compile | True |
| is_legacy_optimizer | False |
| learning_rate | 0.0010000000474974513 |
| beta_1 | 0.9 |
| beta_2 | 0.999 |
| epsilon | 1e-07 |
| amsgrad | False |
| training_precision | float32 |
## Model Plot
<details>
<summary>View Model Plot</summary>
</details> | 840 | [
[
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-0.0401611328125,
0.0321044921875,
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0.030731201171875,
-0.043670654296875,
-0.051025390625,
-0.039306640625,
0.000247001... |
YakovElm/Qt15Classic | 2023-05-23T18:39:04.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/Qt15Classic | 0 | 2 | transformers | 2023-05-23T18:38:29 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Qt15Classic
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Qt15Classic
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.2046
- Train Accuracy: 0.9367
- Validation Loss: 0.2038
- Validation Accuracy: 0.9505
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.2400 | 0.9354 | 0.1896 | 0.9505 | 0 |
| 0.2235 | 0.9367 | 0.1826 | 0.9505 | 1 |
| 0.2046 | 0.9367 | 0.2038 | 0.9505 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,766 | [
[
-0.040802001953125,
-0.036529541015625,
0.0227813720703125,
0.0045928955078125,
-0.03509521484375,
-0.022796630859375,
-0.0131072998046875,
-0.021636962890625,
0.005573272705078125,
0.01251983642578125,
-0.054656982421875,
-0.049591064453125,
-0.04852294921875,
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AustinCarthy/MixGPT2_100KP_BFall_fromB_40KGen_topP_0.75 | 2023-05-24T04:28:24.000Z | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | AustinCarthy | null | null | AustinCarthy/MixGPT2_100KP_BFall_fromB_40KGen_topP_0.75 | 0 | 2 | transformers | 2023-05-23T18:55:41 | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: MixGPT2_100KP_BFall_fromB_40KGen_topP_0.75
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. -->
# MixGPT2_100KP_BFall_fromB_40KGen_topP_0.75
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: 0.0185
- Accuracy: 0.9973
- F1: 0.9712
- Precision: 0.9996
- Recall: 0.9444
- Roc Auc Score: 0.9722
- Tpr At Fpr 0.01: 0.9588
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | Roc Auc Score | Tpr At Fpr 0.01 |
|:-------------:|:-----:|:------:|:---------------:|:--------:|:------:|:---------:|:------:|:-------------:|:---------------:|
| 0.0052 | 1.0 | 91875 | 0.0700 | 0.9904 | 0.8876 | 0.9995 | 0.7982 | 0.8991 | 0.7842 |
| 0.0055 | 2.0 | 183750 | 0.0208 | 0.9968 | 0.9652 | 0.9985 | 0.934 | 0.9670 | 0.9362 |
| 0.0029 | 3.0 | 275625 | 0.0209 | 0.9970 | 0.9674 | 0.9991 | 0.9376 | 0.9688 | 0.9544 |
| 0.0006 | 4.0 | 367500 | 0.0290 | 0.9962 | 0.9579 | 0.9996 | 0.9196 | 0.9598 | 0.9528 |
| 0.001 | 5.0 | 459375 | 0.0185 | 0.9973 | 0.9712 | 0.9996 | 0.9444 | 0.9722 | 0.9588 |
### Framework versions
- Transformers 4.29.1
- Pytorch 1.9.0+cu111
- Datasets 2.10.1
- Tokenizers 0.13.2
| 2,253 | [
[
-0.04461669921875,
-0.041961669921875,
0.0078125,
0.015380859375,
-0.021484375,
-0.01885986328125,
-0.006397247314453125,
-0.0202789306640625,
0.0281829833984375,
0.023681640625,
-0.0521240234375,
-0.04656982421875,
-0.0538330078125,
-0.0179595947265625,
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YakovElm/Apache20Classic_with_cleaning | 2023-05-23T19:11:07.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/Apache20Classic_with_cleaning | 0 | 2 | transformers | 2023-05-23T19:10:31 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Apache20Classic_with_cleaning
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Apache20Classic_with_cleaning
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.1300
- Train Accuracy: 0.9622
- Validation Loss: 0.4258
- Validation Accuracy: 0.9055
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.1764 | 0.9548 | 0.3066 | 0.9055 | 0 |
| 0.1518 | 0.9624 | 0.3933 | 0.9055 | 1 |
| 0.1300 | 0.9622 | 0.4258 | 0.9055 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,802 | [
[
-0.047943115234375,
-0.047576904296875,
0.0211944580078125,
-0.0004603862762451172,
-0.035400390625,
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-0.017303466796875,
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0.00812530517578125,
0.0199737548828125,
-0.054718017578125,
-0.0487060546875,
-0.053466796875,
-0... |
oransom48/pretrained_bert_fordiseaseclassif_1 | 2023-05-23T19:34:04.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | oransom48 | null | null | oransom48/pretrained_bert_fordiseaseclassif_1 | 0 | 2 | transformers | 2023-05-23T19:12:22 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: pretrained_bert_fordiseaseclassif_1
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# pretrained_bert_fordiseaseclassif_1
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
- training_precision: float32
### Training results
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Tokenizers 0.13.3
| 1,280 | [
[
-0.040679931640625,
-0.05010986328125,
0.0290679931640625,
0.01085662841796875,
-0.043060302734375,
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-0.0144805908203125,
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0.0162200927734375,
0.01328277587890625,
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... |
damapika/roberta-base_mod_quoref | 2023-05-23T21:20:20.000Z | [
"transformers",
"pytorch",
"roberta",
"question-answering",
"generated_from_trainer",
"dataset:quoref",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | question-answering | damapika | null | null | damapika/roberta-base_mod_quoref | 0 | 2 | transformers | 2023-05-23T19:19:39 | ---
license: mit
tags:
- generated_from_trainer
datasets:
- quoref
model-index:
- name: roberta-base_mod_quoref
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-base_mod_quoref
This model is a fine-tuned version of [damapika/roberta-base_mod_squad](https://huggingface.co/damapika/roberta-base_mod_squad) on the quoref dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5566
## 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: 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 |
|:-------------:|:-----:|:----:|:---------------:|
| 1.1263 | 1.0 | 1213 | 1.2665 |
| 0.7404 | 2.0 | 2426 | 1.3567 |
| 0.5172 | 3.0 | 3639 | 1.5566 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
| 1,410 | [
[
-0.031524658203125,
-0.0506591796875,
0.01222991943359375,
0.01263427734375,
-0.0304718017578125,
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-0.008270263671875,
-0.01247406005859375,
-0.0020503997802734375,
0.025115966796875,
-0.0660400390625,
-0.036712646484375,
-0.049896240234375,
... |
YakovElm/Qt20Classic | 2023-05-23T19:29:42.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/Qt20Classic | 0 | 2 | transformers | 2023-05-23T19:29:07 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Qt20Classic
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Qt20Classic
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.1836
- Train Accuracy: 0.9462
- Validation Loss: 0.1813
- Validation Accuracy: 0.9594
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.2163 | 0.9454 | 0.1596 | 0.9586 | 0 |
| 0.2044 | 0.9462 | 0.1554 | 0.9586 | 1 |
| 0.1836 | 0.9462 | 0.1813 | 0.9594 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,766 | [
[
-0.038909912109375,
-0.0330810546875,
0.024505615234375,
0.0041656494140625,
-0.03521728515625,
-0.0181121826171875,
-0.00885772705078125,
-0.020416259765625,
0.0029697418212890625,
0.0134124755859375,
-0.05413818359375,
-0.04815673828125,
-0.04742431640625,
... |
YakovElm/Qt5Classic_with_cleaning | 2023-05-23T19:40:41.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/Qt5Classic_with_cleaning | 0 | 2 | transformers | 2023-05-23T19:39:30 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Qt5Classic_with_cleaning
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Qt5Classic_with_cleaning
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.2601
- Train Accuracy: 0.8948
- Validation Loss: 0.2534
- Validation Accuracy: 0.9262
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.3465 | 0.8910 | 0.2510 | 0.9294 | 0 |
| 0.3091 | 0.8943 | 0.2427 | 0.9294 | 1 |
| 0.2601 | 0.8948 | 0.2534 | 0.9262 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,792 | [
[
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-0.0300445556640625,
0.025909423828125,
-0.00838470458984375,
-0.036773681640625,
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-0.006999969482421875,
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0.0004801750183105469,
0.0156097412109375,
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DraiP/NELA-GT_Classifier | 2023-05-30T15:41:50.000Z | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | text-classification | DraiP | null | null | DraiP/NELA-GT_Classifier | 0 | 2 | transformers | 2023-05-23T19:52:04 | ---
tags:
- generated_from_trainer
model-index:
- name: NELA-GT_Classifier
results: []
metrics:
- f1
- accuracy
- roc_auc
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. -->
# NELA-GT_Classifier
This model was Fine-Tuned on the NELA-GT dataset.
## Model description
This is a pretrained distilbert-uncased model finetuned for Fake News classification.
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1500
- num_epochs: 5
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3 | 1,080 | [
[
-0.0269775390625,
-0.057342529296875,
0.00739288330078125,
0.006839752197265625,
-0.0226287841796875,
-0.00942230224609375,
0.0014657974243164062,
-0.020751953125,
0.0220489501953125,
0.01140594482421875,
-0.036865234375,
-0.039886474609375,
-0.05670166015625,
... |
YakovElm/Qt10Classic_with_cleaning | 2023-05-23T20:30:37.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/Qt10Classic_with_cleaning | 0 | 2 | transformers | 2023-05-23T20:29:00 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Qt10Classic_with_cleaning
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Qt10Classic_with_cleaning
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.2156
- Train Accuracy: 0.9208
- Validation Loss: 0.2238
- Validation Accuracy: 0.9416
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.2830 | 0.9159 | 0.2121 | 0.9416 | 0 |
| 0.2515 | 0.9210 | 0.2015 | 0.9416 | 1 |
| 0.2156 | 0.9208 | 0.2238 | 0.9416 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,794 | [
[
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-0.03466796875,
0.02471923828125,
-0.005828857421875,
-0.037933349609375,
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0.00634002685546875,
0.01519012451171875,
-0.05230712890625,
-0.049163818359375,
-0.04901123046875,
-... |
YakovElm/Qt15Classic_with_cleaning | 2023-05-23T21:20:39.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/Qt15Classic_with_cleaning | 0 | 2 | transformers | 2023-05-23T21:19:37 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Qt15Classic_with_cleaning
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Qt15Classic_with_cleaning
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.2075
- Train Accuracy: 0.9367
- Validation Loss: 0.1841
- Validation Accuracy: 0.9505
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.2493 | 0.9319 | 0.1890 | 0.9505 | 0 |
| 0.2289 | 0.9367 | 0.1823 | 0.9505 | 1 |
| 0.2075 | 0.9367 | 0.1841 | 0.9505 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,794 | [
[
-0.041168212890625,
-0.0361328125,
0.0245513916015625,
-0.0038776397705078125,
-0.036834716796875,
-0.0240478515625,
-0.010223388671875,
-0.0207061767578125,
0.005218505859375,
0.0168914794921875,
-0.053192138671875,
-0.05023193359375,
-0.049072265625,
-0.02... |
YakovElm/Hyperledger5Classic_with_cleaning | 2023-05-23T21:26:48.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/Hyperledger5Classic_with_cleaning | 0 | 2 | transformers | 2023-05-23T21:25:29 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Hyperledger5Classic_with_cleaning
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Hyperledger5Classic_with_cleaning
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.2460
- Train Accuracy: 0.8983
- Validation Loss: 0.4738
- Validation Accuracy: 0.8102
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.4027 | 0.8537 | 0.4117 | 0.8361 | 0 |
| 0.3585 | 0.8571 | 0.4243 | 0.8330 | 1 |
| 0.2460 | 0.8983 | 0.4738 | 0.8102 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,810 | [
[
-0.049591064453125,
-0.0400390625,
0.022735595703125,
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0.0081939697265625,
0.0179901123046875,
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nakker/bert-base-banking77-pt2 | 2023-05-23T22:00:22.000Z | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:banking77",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | text-classification | nakker | null | null | nakker/bert-base-banking77-pt2 | 0 | 2 | transformers | 2023-05-23T21:45:48 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- banking77
metrics:
- f1
model-index:
- name: bert-base-banking77-pt2
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: banking77
type: banking77
config: default
split: test
args: default
metrics:
- name: F1
type: f1
value: 0.9287229411281823
---
<!-- 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-banking77-pt2
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the banking77 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3041
- F1: 0.9287
## 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: 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 | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 1.0427 | 1.0 | 626 | 0.7423 | 0.8439 |
| 0.3703 | 2.0 | 1252 | 0.3573 | 0.9200 |
| 0.174 | 3.0 | 1878 | 0.3041 | 0.9287 |
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1
- Datasets 2.12.0
- Tokenizers 0.11.0
| 1,723 | [
[
-0.0293731689453125,
-0.039154052734375,
0.01153564453125,
0.0139007568359375,
-0.04315185546875,
-0.0280303955078125,
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0.040771484375,
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futuredatascience/strat_call_followup_prod | 2023-05-23T21:55:37.000Z | [
"transformers",
"pytorch",
"deberta-v2",
"text-classification",
"autotrain",
"en",
"dataset:futuredatascience/autotrain-data-strat_call_follow_up",
"co2_eq_emissions",
"endpoints_compatible",
"region:us"
] | text-classification | futuredatascience | null | null | futuredatascience/strat_call_followup_prod | 0 | 2 | transformers | 2023-05-23T21:54:48 | ---
tags:
- autotrain
- text-classification
language:
- en
widget:
- text: "I love AutoTrain 🤗"
datasets:
- futuredatascience/autotrain-data-strat_call_follow_up
co2_eq_emissions:
emissions: 0.1979200898207588
---
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 61102134664
- CO2 Emissions (in grams): 0.1979
## Validation Metrics
- Loss: 0.266
- Accuracy: 0.939
- Precision: 0.955
- Recall: 0.913
- AUC: 0.952
- F1: 0.933
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/futuredatascience/autotrain-strat_call_follow_up-61102134664
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("futuredatascience/autotrain-strat_call_follow_up-61102134664", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("futuredatascience/autotrain-strat_call_follow_up-61102134664", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
``` | 1,214 | [
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YakovElm/Hyperledger10Classic_with_cleaning | 2023-05-23T22:05:19.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/Hyperledger10Classic_with_cleaning | 0 | 2 | transformers | 2023-05-23T22:04:44 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Hyperledger10Classic_with_cleaning
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Hyperledger10Classic_with_cleaning
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.2350
- Train Accuracy: 0.9004
- Validation Loss: 0.4827
- Validation Accuracy: 0.7552
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.3534 | 0.8831 | 0.3701 | 0.8600 | 0 |
| 0.3162 | 0.8841 | 0.3594 | 0.8600 | 1 |
| 0.2350 | 0.9004 | 0.4827 | 0.7552 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,812 | [
[
-0.048797607421875,
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... |
YakovElm/Qt20Classic_with_cleaning | 2023-05-23T22:11:51.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/Qt20Classic_with_cleaning | 0 | 2 | transformers | 2023-05-23T22:10:56 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Qt20Classic_with_cleaning
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Qt20Classic_with_cleaning
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.1619
- Train Accuracy: 0.9500
- Validation Loss: 0.1838
- Validation Accuracy: 0.9554
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.2142 | 0.9462 | 0.1640 | 0.9586 | 0 |
| 0.1934 | 0.9462 | 0.1576 | 0.9586 | 1 |
| 0.1619 | 0.9500 | 0.1838 | 0.9554 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,794 | [
[
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0.0169830322265625,
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wiorz/legal_bert_small | 2023-05-23T22:37:57.000Z | [
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"license:cc-by-sa-4.0",
"endpoints_compatible",
"region:us"
] | text-classification | wiorz | null | null | wiorz/legal_bert_small | 0 | 2 | transformers | 2023-05-23T22:34:52 | ---
license: cc-by-sa-4.0
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: legal_bert_small
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. -->
# legal_bert_small
This model is a fine-tuned version of [nlpaueb/legal-bert-base-uncased](https://huggingface.co/nlpaueb/legal-bert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.0455
- Accuracy: 0.815
- Precision: 0.5
- Recall: 0.3784
- F1: 0.4308
- D-index: 1.5791
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1600
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | D-index |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:-------:|
| No log | 1.0 | 200 | 0.4205 | 0.84 | 0.7778 | 0.1892 | 0.3043 | 1.5473 |
| No log | 2.0 | 400 | 0.5287 | 0.785 | 0.425 | 0.4595 | 0.4416 | 1.5664 |
| 0.4788 | 3.0 | 600 | 0.8663 | 0.78 | 0.4146 | 0.4595 | 0.4359 | 1.5597 |
| 0.4788 | 4.0 | 800 | 1.0432 | 0.8 | 0.4681 | 0.5946 | 0.5238 | 1.6309 |
| 0.2168 | 5.0 | 1000 | 1.2325 | 0.795 | 0.375 | 0.1622 | 0.2264 | 1.4766 |
| 0.2168 | 6.0 | 1200 | 1.3369 | 0.815 | 0.5 | 0.2432 | 0.3273 | 1.5326 |
| 0.2168 | 7.0 | 1400 | 1.4949 | 0.785 | 0.4286 | 0.4865 | 0.4557 | 1.5754 |
| 0.0682 | 8.0 | 1600 | 1.4499 | 0.815 | 0.5 | 0.3514 | 0.4127 | 1.5700 |
| 0.0682 | 9.0 | 1800 | 1.7761 | 0.8 | 0.4348 | 0.2703 | 0.3333 | 1.5218 |
| 0.0154 | 10.0 | 2000 | 1.8939 | 0.805 | 0.4375 | 0.1892 | 0.2642 | 1.5000 |
| 0.0154 | 11.0 | 2200 | 1.9630 | 0.8 | 0.4211 | 0.2162 | 0.2857 | 1.5028 |
| 0.0154 | 12.0 | 2400 | 1.9712 | 0.805 | 0.4545 | 0.2703 | 0.3390 | 1.5286 |
| 0.0132 | 13.0 | 2600 | 1.9184 | 0.805 | 0.4737 | 0.4865 | 0.4800 | 1.6021 |
| 0.0132 | 14.0 | 2800 | 1.9261 | 0.805 | 0.4706 | 0.4324 | 0.4507 | 1.5841 |
| 0.0 | 15.0 | 3000 | 1.9619 | 0.815 | 0.5 | 0.4054 | 0.4478 | 1.5883 |
| 0.0 | 16.0 | 3200 | 1.9798 | 0.82 | 0.5172 | 0.4054 | 0.4545 | 1.5949 |
| 0.0 | 17.0 | 3400 | 2.0126 | 0.815 | 0.5 | 0.3784 | 0.4308 | 1.5791 |
| 0.0 | 18.0 | 3600 | 2.0203 | 0.82 | 0.5185 | 0.3784 | 0.4375 | 1.5858 |
| 0.0 | 19.0 | 3800 | 2.0286 | 0.82 | 0.5185 | 0.3784 | 0.4375 | 1.5858 |
| 0.0 | 20.0 | 4000 | 2.0455 | 0.815 | 0.5 | 0.3784 | 0.4308 | 1.5791 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
| 3,574 | [
[
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YakovElm/Hyperledger15Classic_with_cleaning | 2023-05-23T22:44:21.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/Hyperledger15Classic_with_cleaning | 0 | 2 | transformers | 2023-05-23T22:43:21 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Hyperledger15Classic_with_cleaning
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Hyperledger15Classic_with_cleaning
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.2347
- Train Accuracy: 0.9045
- Validation Loss: 0.3515
- Validation Accuracy: 0.8651
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.3126 | 0.9031 | 0.3318 | 0.8807 | 0 |
| 0.2844 | 0.9028 | 0.3275 | 0.8807 | 1 |
| 0.2347 | 0.9045 | 0.3515 | 0.8651 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,812 | [
[
-0.050750732421875,
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0.0212249755859375,
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Gaivoronsky/ppo-Worm | 2023-05-23T22:58:10.000Z | [
"ml-agents",
"tensorboard",
"onnx",
"Worm",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Worm",
"region:us"
] | reinforcement-learning | Gaivoronsky | null | null | Gaivoronsky/ppo-Worm | 0 | 2 | ml-agents | 2023-05-23T22:58:04 | ---
library_name: ml-agents
tags:
- Worm
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Worm
---
# **ppo** Agent playing **Worm**
This is a trained model of a **ppo** agent playing **Worm** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
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. Go to https://huggingface.co/spaces/unity/ML-Agents-Worm
2. Step 1: Find your model_id: Gaivoronsky/ppo-Worm
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
| 930 | [
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wiorz/legal_bert_small_defined_summarized | 2023-05-23T23:21:17.000Z | [
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"license:cc-by-sa-4.0",
"endpoints_compatible",
"region:us"
] | text-classification | wiorz | null | null | wiorz/legal_bert_small_defined_summarized | 0 | 2 | transformers | 2023-05-23T23:19:50 | ---
license: cc-by-sa-4.0
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: legal_bert_small_defined_summarized
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. -->
# legal_bert_small_defined_summarized
This model is a fine-tuned version of [nlpaueb/legal-bert-base-uncased](https://huggingface.co/nlpaueb/legal-bert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4178
- Accuracy: 0.87
- Precision: 0.6
- Recall: 0.2143
- F1: 0.3158
- D-index: 1.5771
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1600
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | D-index |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:-------:|
| No log | 1.0 | 200 | 0.4008 | 0.86 | 0.0 | 0.0 | 0.0 | 1.4803 |
| No log | 2.0 | 400 | 0.3871 | 0.86 | 0.0 | 0.0 | 0.0 | 1.4803 |
| 0.5179 | 3.0 | 600 | 0.4293 | 0.87 | 0.625 | 0.1786 | 0.2778 | 1.5635 |
| 0.5179 | 4.0 | 800 | 0.6702 | 0.87 | 0.625 | 0.1786 | 0.2778 | 1.5635 |
| 0.3816 | 5.0 | 1000 | 0.7388 | 0.865 | 0.5455 | 0.2143 | 0.3077 | 1.5706 |
| 0.3816 | 6.0 | 1200 | 1.0422 | 0.86 | 0.5 | 0.1786 | 0.2632 | 1.5504 |
| 0.3816 | 7.0 | 1400 | 1.0804 | 0.875 | 0.7143 | 0.1786 | 0.2857 | 1.5700 |
| 0.0567 | 8.0 | 1600 | 1.1490 | 0.875 | 0.6364 | 0.25 | 0.3590 | 1.5970 |
| 0.0567 | 9.0 | 1800 | 1.3190 | 0.865 | 0.5556 | 0.1786 | 0.2703 | 1.5570 |
| 0.0125 | 10.0 | 2000 | 1.4220 | 0.835 | 0.3913 | 0.3214 | 0.3529 | 1.5718 |
| 0.0125 | 11.0 | 2200 | 1.3567 | 0.855 | 0.4706 | 0.2857 | 0.3556 | 1.5845 |
| 0.0125 | 12.0 | 2400 | 1.3349 | 0.875 | 0.7143 | 0.1786 | 0.2857 | 1.5700 |
| 0.0021 | 13.0 | 2600 | 1.3494 | 0.87 | 0.5714 | 0.2857 | 0.3810 | 1.6038 |
| 0.0021 | 14.0 | 2800 | 1.3747 | 0.87 | 0.6 | 0.2143 | 0.3158 | 1.5771 |
| 0.0 | 15.0 | 3000 | 1.3890 | 0.87 | 0.6 | 0.2143 | 0.3158 | 1.5771 |
| 0.0 | 16.0 | 3200 | 1.4069 | 0.875 | 0.6667 | 0.2143 | 0.3243 | 1.5835 |
| 0.0 | 17.0 | 3400 | 1.4185 | 0.875 | 0.6667 | 0.2143 | 0.3243 | 1.5835 |
| 0.0 | 18.0 | 3600 | 1.3945 | 0.865 | 0.5385 | 0.25 | 0.3415 | 1.5840 |
| 0.0 | 19.0 | 3800 | 1.3921 | 0.87 | 0.6 | 0.2143 | 0.3158 | 1.5771 |
| 0.0037 | 20.0 | 4000 | 1.4178 | 0.87 | 0.6 | 0.2143 | 0.3158 | 1.5771 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
| 3,611 | [
[
-0.041351318359375,
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0.017974853515625,
0.0082855224609375,
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0.03826904296875,
0.02703857421875,
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-0.... |
YakovElm/Hyperledger20Classic_with_cleaning | 2023-05-23T23:22:59.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/Hyperledger20Classic_with_cleaning | 0 | 2 | transformers | 2023-05-23T23:22:20 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Hyperledger20Classic_with_cleaning
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Hyperledger20Classic_with_cleaning
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.2203
- Train Accuracy: 0.9253
- Validation Loss: 0.3795
- Validation Accuracy: 0.8102
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.2855 | 0.9139 | 0.2936 | 0.8983 | 0 |
| 0.2684 | 0.9132 | 0.2944 | 0.8983 | 1 |
| 0.2203 | 0.9253 | 0.3795 | 0.8102 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,812 | [
[
-0.049530029296875,
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0.0203399658203125,
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YakovElm/Apache5Classic_Unbalance | 2023-05-24T00:53:55.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/Apache5Classic_Unbalance | 0 | 2 | transformers | 2023-05-24T00:52:53 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Apache5Classic_Unbalance
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Apache5Classic_Unbalance
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.2052
- Train Accuracy: 0.9296
- Validation Loss: 0.6112
- Validation Accuracy: 0.7634
- Epoch: 3
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': 0.001, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.3100 | 0.9086 | 0.4565 | 0.8233 | 0 |
| 0.2939 | 0.9094 | 0.4991 | 0.8233 | 1 |
| 0.2656 | 0.9096 | 0.5105 | 0.8214 | 2 |
| 0.2052 | 0.9296 | 0.6112 | 0.7634 | 3 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,873 | [
[
-0.045806884765625,
-0.03741455078125,
0.00998687744140625,
0.0162200927734375,
-0.035919189453125,
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0.0186767578125,
-0.055389404296875,
-0.045562744140625,
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YakovElm/IntelDAOS5Classic_with_cleaning | 2023-05-24T01:55:07.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/IntelDAOS5Classic_with_cleaning | 0 | 2 | transformers | 2023-05-24T01:54:32 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: IntelDAOS5Classic_with_cleaning
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# IntelDAOS5Classic_with_cleaning
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.3107
- Train Accuracy: 0.8770
- Validation Loss: 0.4998
- Validation Accuracy: 0.8168
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.4073 | 0.8740 | 0.4326 | 0.8438 | 0 |
| 0.3668 | 0.8740 | 0.4437 | 0.8438 | 1 |
| 0.3107 | 0.8770 | 0.4998 | 0.8168 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,806 | [
[
-0.045745849609375,
-0.03778076171875,
0.022003173828125,
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YakovElm/IntelDAOS10Classic_with_cleaning | 2023-05-24T02:08:53.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/IntelDAOS10Classic_with_cleaning | 0 | 2 | transformers | 2023-05-24T02:08:18 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: IntelDAOS10Classic_with_cleaning
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# IntelDAOS10Classic_with_cleaning
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.2283
- Train Accuracy: 0.9210
- Validation Loss: 0.4310
- Validation Accuracy: 0.8739
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.3141 | 0.9200 | 0.3738 | 0.8739 | 0 |
| 0.2612 | 0.9200 | 0.4105 | 0.8739 | 1 |
| 0.2283 | 0.9210 | 0.4310 | 0.8739 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,808 | [
[
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YakovElm/Apache10Classic_Unbalance | 2023-05-24T02:16:00.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/Apache10Classic_Unbalance | 0 | 2 | transformers | 2023-05-24T02:15:25 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Apache10Classic_Unbalance
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Apache10Classic_Unbalance
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.1445
- Train Accuracy: 0.9474
- Validation Loss: 0.5445
- Validation Accuracy: 0.8449
- Epoch: 3
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': 0.001, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.2424 | 0.9374 | 0.3809 | 0.8644 | 0 |
| 0.2210 | 0.9383 | 0.4042 | 0.8644 | 1 |
| 0.2036 | 0.9387 | 0.4134 | 0.8611 | 2 |
| 0.1445 | 0.9474 | 0.5445 | 0.8449 | 3 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,875 | [
[
-0.0455322265625,
-0.040618896484375,
0.0099945068359375,
0.018585205078125,
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0.01898193359375,
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-0.041259765625,
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-0.0252... |
YakovElm/IntelDAOS15Classic_with_cleaning | 2023-05-24T02:22:39.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/IntelDAOS15Classic_with_cleaning | 0 | 2 | transformers | 2023-05-24T02:22:05 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: IntelDAOS15Classic_with_cleaning
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# IntelDAOS15Classic_with_cleaning
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.1497
- Train Accuracy: 0.9460
- Validation Loss: 0.4865
- Validation Accuracy: 0.8859
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.2546 | 0.9420 | 0.3716 | 0.8859 | 0 |
| 0.1920 | 0.9460 | 0.3766 | 0.8859 | 1 |
| 0.1497 | 0.9460 | 0.4865 | 0.8859 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,808 | [
[
-0.045074462890625,
-0.0418701171875,
0.02056884765625,
-0.00637054443359375,
-0.03570556640625,
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-0.01922607421875,
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0.0137939453125,
0.01363372802734375,
-0.05413818359375,
-0.0498046875,
-0.05145263671875,
-0.0250091... |
YakovElm/IntelDAOS20Classic_with_cleaning | 2023-05-24T02:36:25.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/IntelDAOS20Classic_with_cleaning | 0 | 2 | transformers | 2023-05-24T02:35:50 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: IntelDAOS20Classic_with_cleaning
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# IntelDAOS20Classic_with_cleaning
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.1286
- Train Accuracy: 0.9610
- Validation Loss: 0.3677
- Validation Accuracy: 0.9099
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.2178 | 0.9600 | 0.3604 | 0.9099 | 0 |
| 0.1502 | 0.9610 | 0.3197 | 0.9099 | 1 |
| 0.1286 | 0.9610 | 0.3677 | 0.9099 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,808 | [
[
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0.0223236083984375,
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0.01458740234375,
-0.054779052734375,
-0.04962158203125,
-0.051971435546875,
-0.024... |
YakovElm/Jira5Classic_with_cleaning | 2023-05-24T03:37:03.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/Jira5Classic_with_cleaning | 0 | 2 | transformers | 2023-05-24T03:36:17 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Jira5Classic_with_cleaning
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Jira5Classic_with_cleaning
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.2646
- Train Accuracy: 0.8919
- Validation Loss: 1.1625
- Validation Accuracy: 0.5584
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.5257 | 0.7639 | 0.6646 | 0.5931 | 0 |
| 0.4200 | 0.7901 | 1.2433 | 0.4890 | 1 |
| 0.2646 | 0.8919 | 1.1625 | 0.5584 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,796 | [
[
-0.0386962890625,
-0.03851318359375,
0.022186279296875,
-0.0092926025390625,
-0.03546142578125,
-0.022430419921875,
-0.01493072509765625,
-0.0262908935546875,
0.0141448974609375,
0.0158538818359375,
-0.05096435546875,
-0.0513916015625,
-0.050079345703125,
-0... |
YakovElm/Jira10Classic_with_cleaning | 2023-05-24T03:49:14.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/Jira10Classic_with_cleaning | 0 | 2 | transformers | 2023-05-24T03:48:37 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Jira10Classic_with_cleaning
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Jira10Classic_with_cleaning
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.2784
- Train Accuracy: 0.8961
- Validation Loss: 1.2932
- Validation Accuracy: 0.5773
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.5051 | 0.7807 | 0.6951 | 0.4921 | 0 |
| 0.4049 | 0.8048 | 1.1332 | 0.5079 | 1 |
| 0.2784 | 0.8961 | 1.2932 | 0.5773 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,798 | [
[
-0.0390625,
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0.0220947265625,
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-0.0167999267578125,
-0.0243988037109375,
0.0175628662109375,
0.0159759521484375,
-0.048614501953125,
-0.04779052734375,
-0.050750732421875,
-0.026229... |
YakovElm/Jira15Classic_with_cleaning | 2023-05-24T04:01:31.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/Jira15Classic_with_cleaning | 0 | 2 | transformers | 2023-05-24T04:00:56 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Jira15Classic_with_cleaning
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Jira15Classic_with_cleaning
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.3366
- Train Accuracy: 0.8384
- Validation Loss: 0.8679
- Validation Accuracy: 0.5868
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.5010 | 0.7891 | 0.7283 | 0.5205 | 0 |
| 0.4284 | 0.8006 | 0.9625 | 0.5205 | 1 |
| 0.3366 | 0.8384 | 0.8679 | 0.5868 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,798 | [
[
-0.040771484375,
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0.021392822265625,
-0.007022857666015625,
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-0.0166778564453125,
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0.01526641845703125,
0.016815185546875,
-0.050811767578125,
-0.0499267578125,
-0.05029296875,
-0.026... |
YakovElm/Apache15Classic_Unbalance | 2023-05-24T04:09:32.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/Apache15Classic_Unbalance | 0 | 2 | transformers | 2023-05-24T04:08:55 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Apache15Classic_Unbalance
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Apache15Classic_Unbalance
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0303
- Train Accuracy: 0.9896
- Validation Loss: 0.7388
- Validation Accuracy: 0.8625
- Epoch: 5
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': 0.001, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.1954 | 0.9537 | 0.3336 | 0.8924 | 0 |
| 0.1814 | 0.9542 | 0.3277 | 0.8924 | 1 |
| 0.1669 | 0.9542 | 0.3218 | 0.8924 | 2 |
| 0.1210 | 0.9555 | 0.4820 | 0.8716 | 3 |
| 0.0538 | 0.9828 | 0.5766 | 0.8716 | 4 |
| 0.0303 | 0.9896 | 0.7388 | 0.8625 | 5 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 2,035 | [
[
-0.0455322265625,
-0.039154052734375,
0.00925445556640625,
0.01352691650390625,
-0.03424072265625,
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0.0167999267578125,
0.0197601318359375,
-0.055267333984375,
-0.0447998046875,
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-0.... |
YakovElm/Jira20Classic_with_cleaning | 2023-05-24T04:13:50.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/Jira20Classic_with_cleaning | 0 | 2 | transformers | 2023-05-24T04:13:15 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Jira20Classic_with_cleaning
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Jira20Classic_with_cleaning
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.1768
- Train Accuracy: 0.9339
- Validation Loss: 0.2889
- Validation Accuracy: 0.9085
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.3741 | 0.8720 | 0.2881 | 0.9306 | 0 |
| 0.2767 | 0.8793 | 0.2442 | 0.9338 | 1 |
| 0.1768 | 0.9339 | 0.2889 | 0.9085 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,798 | [
[
-0.03790283203125,
-0.041961669921875,
0.0215606689453125,
-0.0084228515625,
-0.034088134765625,
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0.017120361328125,
0.0178375244140625,
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-0.0... |
srglnjmb/mongolian-xlm-roberta-large-ner | 2023-05-24T10:04:55.000Z | [
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"mn",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | srglnjmb | null | null | srglnjmb/mongolian-xlm-roberta-large-ner | 1 | 2 | transformers | 2023-05-24T05:38:53 | ---
language:
- mn
license: mit
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: mongolian-xlm-roberta-large-ner
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mongolian-xlm-roberta-large-ner
This model is a fine-tuned version of [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1256
- Precision: 0.9361
- Recall: 0.9423
- F1: 0.9392
- Accuracy: 0.9824
## 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: 32
- 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 | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1837 | 1.0 | 477 | 0.0939 | 0.8524 | 0.8895 | 0.8705 | 0.9745 |
| 0.0736 | 2.0 | 954 | 0.0731 | 0.9318 | 0.9370 | 0.9344 | 0.9809 |
| 0.0525 | 3.0 | 1431 | 0.0724 | 0.9244 | 0.9311 | 0.9278 | 0.9795 |
| 0.036 | 4.0 | 1908 | 0.0807 | 0.9312 | 0.9409 | 0.9361 | 0.9819 |
| 0.0248 | 5.0 | 2385 | 0.0855 | 0.9314 | 0.9407 | 0.9360 | 0.9814 |
| 0.0163 | 6.0 | 2862 | 0.1014 | 0.9327 | 0.9397 | 0.9362 | 0.9815 |
| 0.0112 | 7.0 | 3339 | 0.0997 | 0.9354 | 0.9433 | 0.9393 | 0.9822 |
| 0.0064 | 8.0 | 3816 | 0.1171 | 0.9384 | 0.9432 | 0.9408 | 0.9824 |
| 0.0049 | 9.0 | 4293 | 0.1237 | 0.9355 | 0.9418 | 0.9387 | 0.9822 |
| 0.0024 | 10.0 | 4770 | 0.1256 | 0.9361 | 0.9423 | 0.9392 | 0.9824 |
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
| 2,357 | [
[
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0.01490020751953125,
0.0023593902587890625,
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0.0281982421875,
0.031036376953125,
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YakovElm/Apache20Classic_Unbalance | 2023-05-24T05:40:05.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/Apache20Classic_Unbalance | 0 | 2 | transformers | 2023-05-24T05:39:29 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Apache20Classic_Unbalance
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Apache20Classic_Unbalance
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0512
- Train Accuracy: 0.9824
- Validation Loss: 0.4866
- Validation Accuracy: 0.8748
- Epoch: 4
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': 0.001, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.1714 | 0.9611 | 0.3033 | 0.9055 | 0 |
| 0.1558 | 0.9624 | 0.2976 | 0.9055 | 1 |
| 0.1447 | 0.9624 | 0.3133 | 0.9055 | 2 |
| 0.1024 | 0.9666 | 0.4150 | 0.8598 | 3 |
| 0.0512 | 0.9824 | 0.4866 | 0.8748 | 4 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,955 | [
[
-0.0458984375,
-0.0401611328125,
0.00894927978515625,
0.0178985595703125,
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0.0207977294921875,
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YakovElm/MariaDB5Classic_with_cleaning | 2023-05-24T05:49:05.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/MariaDB5Classic_with_cleaning | 0 | 2 | transformers | 2023-05-24T05:48:30 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: MariaDB5Classic_with_cleaning
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# MariaDB5Classic_with_cleaning
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.2274
- Train Accuracy: 0.9079
- Validation Loss: 0.2936
- Validation Accuracy: 0.9271
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.3439 | 0.8962 | 0.2474 | 0.9322 | 0 |
| 0.2799 | 0.8979 | 0.2671 | 0.9322 | 1 |
| 0.2274 | 0.9079 | 0.2936 | 0.9271 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,802 | [
[
-0.0435791015625,
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0.022216796875,
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-0.053863525390625,
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YakovElm/MariaDB10Classic_with_cleaning | 2023-05-24T06:04:11.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/MariaDB10Classic_with_cleaning | 0 | 2 | transformers | 2023-05-24T06:03:32 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: MariaDB10Classic_with_cleaning
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# MariaDB10Classic_with_cleaning
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.1906
- Train Accuracy: 0.9163
- Validation Loss: 0.2498
- Validation Accuracy: 0.9523
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.3088 | 0.9138 | 0.1970 | 0.9523 | 0 |
| 0.2364 | 0.9163 | 0.2051 | 0.9523 | 1 |
| 0.1906 | 0.9163 | 0.2498 | 0.9523 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,804 | [
[
-0.042877197265625,
-0.04388427734375,
0.021209716796875,
-0.0034732818603515625,
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-0.029815673828125,
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YakovElm/MariaDB15Classic_with_cleaning | 2023-05-24T06:18:59.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/MariaDB15Classic_with_cleaning | 0 | 2 | transformers | 2023-05-24T06:18:23 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: MariaDB15Classic_with_cleaning
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# MariaDB15Classic_with_cleaning
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.1825
- Train Accuracy: 0.9381
- Validation Loss: 0.1702
- Validation Accuracy: 0.9598
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.2807 | 0.9264 | 0.1655 | 0.9598 | 0 |
| 0.2227 | 0.9339 | 0.1533 | 0.9598 | 1 |
| 0.1825 | 0.9381 | 0.1702 | 0.9598 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,804 | [
[
-0.04443359375,
-0.043914794921875,
0.0213775634765625,
-0.002410888671875,
-0.0343017578125,
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-0.01457977294921875,
-0.025390625,
0.01438140869140625,
0.01904296875,
-0.055755615234375,
-0.050628662109375,
-0.051239013671875,
-0.02590942... |
YakovElm/MariaDB20Classic_with_cleaning | 2023-05-24T06:34:03.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/MariaDB20Classic_with_cleaning | 0 | 2 | transformers | 2023-05-24T06:33:08 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: MariaDB20Classic_with_cleaning
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# MariaDB20Classic_with_cleaning
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.1994
- Train Accuracy: 0.9356
- Validation Loss: 0.1398
- Validation Accuracy: 0.9698
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.2732 | 0.9331 | 0.1507 | 0.9698 | 0 |
| 0.2300 | 0.9356 | 0.1264 | 0.9698 | 1 |
| 0.1994 | 0.9356 | 0.1398 | 0.9698 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,804 | [
[
-0.043121337890625,
-0.04595947265625,
0.0218658447265625,
-0.0036411285400390625,
-0.03582763671875,
-0.0298004150390625,
-0.01293182373046875,
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0.0167388916015625,
0.0198822021484375,
-0.0584716796875,
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... |
MJ03/distilbert-base-uncased-finetuned-emotion | 2023-05-24T07:13:05.000Z | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emo",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | text-classification | MJ03 | null | null | MJ03/distilbert-base-uncased-finetuned-emotion | 0 | 2 | transformers | 2023-05-24T06:55:05 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- emo
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emo
type: emo
config: emo2019
split: test
args: emo2019
metrics:
- name: Accuracy
type: accuracy
value: 0.8718460700671629
- name: F1
type: f1
value: 0.8831861224754917
---
<!-- 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 emo dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3598
- Accuracy: 0.8718
- F1: 0.8832
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.5285 | 1.0 | 472 | 0.3616 | 0.8673 | 0.8792 |
| 0.2833 | 2.0 | 944 | 0.3598 | 0.8718 | 0.8832 |
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu118
- Datasets 1.16.1
- Tokenizers 0.13.3
| 1,842 | [
[
-0.035125732421875,
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0.0233917236328125,
0.01415252685546875,
-0.024261474609375,
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sadra-barikbin/ppo-UnityPyramids | 2023-05-24T07:17:04.000Z | [
"ml-agents",
"tensorboard",
"onnx",
"Pyramids",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
] | reinforcement-learning | sadra-barikbin | null | null | sadra-barikbin/ppo-UnityPyramids | 0 | 2 | ml-agents | 2023-05-24T07:16:32 | ---
library_name: ml-agents
tags:
- Pyramids
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Pyramids
---
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
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. Go to https://huggingface.co/spaces/unity/ML-Agents-Pyramids
2. Step 1: Find your model_id: sadra-barikbin/ppo-UnityPyramids
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
| 962 | [
[
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0.035125732421875,
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Avitas8485/speecht5_tts_commonvoice_en | 2023-09-12T22:34:44.000Z | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"speecht5",
"text-to-audio",
"text-to-speech",
"generated_from_trainer",
"en",
"dataset:mozilla/commonvoice",
"license:mit",
"endpoints_compatible",
"region:us"
] | text-to-speech | Avitas8485 | null | null | Avitas8485/speecht5_tts_commonvoice_en | 1 | 2 | transformers | 2023-05-24T07:31:08 | ---
language:
- en
license: mit
tags:
- text-to-speech
- generated_from_trainer
datasets:
- mozilla/commonvoice
base_model: microsoft/speecht5_tts
model-index:
- name: SpeechT5 TTS English
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# SpeechT5 TTS English
This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the commonvoice dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4261
## 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
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 4000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.4543 | 13.61 | 1000 | 0.4225 |
| 0.4525 | 27.21 | 2000 | 0.4203 |
| 0.4359 | 40.82 | 3000 | 0.4228 |
| 0.4324 | 54.42 | 4000 | 0.4261 |
### Framework versions
- Transformers 4.30.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,667 | [
[
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-0.0004382133483886719,
0.0226287841796875,
-0.049163818359375,
-0.05462646484375,
-0.05215454101... |
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