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google/tapas-mini | google | tapas | 8 | 4 | transformers | 0 | feature-extraction | true | true | false | apache-2.0 | ['en'] | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['tapas', 'TapasModel'] | false | true | true | 4,607 | false |
# TAPAS mini model
This model has 2 versions which can be used. The latest version, which is the default one, corresponds to the `tapas_inter_masklm_mini_reset` checkpoint of the [original Github repository](https://github.com/google-research/tapas).
This model was pre-trained on MLM and an additional step which the authors call intermediate pre-training. It uses relative position embeddings by default (i.e. resetting the position index at every cell of the table).
The other (non-default) version which can be used is the one with absolute position embeddings:
- `revision="no_reset"`, which corresponds to `tapas_inter_masklm_mini`
Disclaimer: The team releasing TAPAS did not write a model card for this model so this model card has been written by
the Hugging Face team and contributors.
## Model description
TAPAS is a BERT-like transformers model pretrained on a large corpus of English data from Wikipedia in a self-supervised fashion.
This means it was pretrained on the raw tables and associated texts only, with no humans labelling them in any way (which is why it
can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely, it
was pretrained with two objectives:
- Masked language modeling (MLM): taking a (flattened) table and associated context, the model randomly masks 15% of the words in
the input, then runs the entire (partially masked) sequence through the model. The model then has to predict the masked words.
This is different from traditional recurrent neural networks (RNNs) that usually see the words one after the other,
or from autoregressive models like GPT which internally mask the future tokens. It allows the model to learn a bidirectional
representation of a table and associated text.
- Intermediate pre-training: to encourage numerical reasoning on tables, the authors additionally pre-trained the model by creating
a balanced dataset of millions of syntactically created training examples. Here, the model must predict (classify) whether a sentence
is supported or refuted by the contents of a table. The training examples are created based on synthetic as well as counterfactual statements.
This way, the model learns an inner representation of the English language used in tables and associated texts, which can then be used
to extract features useful for downstream tasks such as answering questions about a table, or determining whether a sentence is entailed
or refuted by the contents of a table. Fine-tuning is done by adding one or more classification heads on top of the pre-trained model, and then
jointly train these randomly initialized classification heads with the base model on a downstream task.
## Intended uses & limitations
You can use the raw model for getting hidden representatons about table-question pairs, but it's mostly intended to be fine-tuned on a downstream task such as question answering or sequence classification. See the [model hub](https://huggingface.co/models?filter=tapas) to look for fine-tuned versions on a task that interests you.
## Training procedure
### Preprocessing
The texts are lowercased and tokenized using WordPiece and a vocabulary size of 30,000. The inputs of the model are
then of the form:
```
[CLS] Sentence [SEP] Flattened table [SEP]
```
### Pre-training
The model was pre-trained on 32 Cloud TPU v3 cores for 1,000,000 steps with maximum sequence length 512 and batch size of 512.
In this setup, pre-training on MLM only takes around 3 days. Aditionally, the model has been further pre-trained on a second task (table entailment). See the original TAPAS [paper](https://www.aclweb.org/anthology/2020.acl-main.398/) and the [follow-up paper](https://www.aclweb.org/anthology/2020.findings-emnlp.27/) for more details.
The optimizer used is Adam with a learning rate of 5e-5, and a warmup
ratio of 0.01.
### BibTeX entry and citation info
```bibtex
@misc{herzig2020tapas,
title={TAPAS: Weakly Supervised Table Parsing via Pre-training},
author={Jonathan Herzig and Paweł Krzysztof Nowak and Thomas Müller and Francesco Piccinno and Julian Martin Eisenschlos},
year={2020},
eprint={2004.02349},
archivePrefix={arXiv},
primaryClass={cs.IR}
}
```
```bibtex
@misc{eisenschlos2020understanding,
title={Understanding tables with intermediate pre-training},
author={Julian Martin Eisenschlos and Syrine Krichene and Thomas Müller},
year={2020},
eprint={2010.00571},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
``` | bd4e8bca526ab71979d30b03e480df87 |
Helsinki-NLP/opus-mt-en-iir | Helsinki-NLP | marian | 11 | 8 | transformers | 0 | translation | true | true | false | apache-2.0 | ['en', 'bn', 'or', 'gu', 'mr', 'ur', 'hi', 'ps', 'os', 'as', 'si', 'iir'] | null | null | 1 | 1 | 0 | 0 | 0 | 0 | 0 | ['translation'] | false | true | true | 4,150 | false |
### eng-iir
* source group: English
* target group: Indo-Iranian languages
* OPUS readme: [eng-iir](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/eng-iir/README.md)
* model: transformer
* source language(s): eng
* target language(s): asm awa ben bho gom guj hif_Latn hin jdt_Cyrl kur_Arab kur_Latn mai mar npi ori oss pan_Guru pes pes_Latn pes_Thaa pnb pus rom san_Deva sin snd_Arab tgk_Cyrl tly_Latn urd zza
* model: transformer
* pre-processing: normalization + SentencePiece (spm32k,spm32k)
* a sentence initial language token is required in the form of `>>id<<` (id = valid target language ID)
* download original weights: [opus2m-2020-08-01.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-iir/opus2m-2020-08-01.zip)
* test set translations: [opus2m-2020-08-01.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-iir/opus2m-2020-08-01.test.txt)
* test set scores: [opus2m-2020-08-01.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-iir/opus2m-2020-08-01.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| newsdev2014-enghin.eng.hin | 6.7 | 0.326 |
| newsdev2019-engu-engguj.eng.guj | 6.0 | 0.283 |
| newstest2014-hien-enghin.eng.hin | 10.4 | 0.353 |
| newstest2019-engu-engguj.eng.guj | 6.6 | 0.282 |
| Tatoeba-test.eng-asm.eng.asm | 2.7 | 0.249 |
| Tatoeba-test.eng-awa.eng.awa | 0.4 | 0.122 |
| Tatoeba-test.eng-ben.eng.ben | 15.3 | 0.459 |
| Tatoeba-test.eng-bho.eng.bho | 3.7 | 0.161 |
| Tatoeba-test.eng-fas.eng.fas | 3.4 | 0.227 |
| Tatoeba-test.eng-guj.eng.guj | 18.5 | 0.365 |
| Tatoeba-test.eng-hif.eng.hif | 1.0 | 0.064 |
| Tatoeba-test.eng-hin.eng.hin | 17.0 | 0.461 |
| Tatoeba-test.eng-jdt.eng.jdt | 3.9 | 0.122 |
| Tatoeba-test.eng-kok.eng.kok | 5.5 | 0.059 |
| Tatoeba-test.eng-kur.eng.kur | 4.0 | 0.125 |
| Tatoeba-test.eng-lah.eng.lah | 0.3 | 0.008 |
| Tatoeba-test.eng-mai.eng.mai | 9.3 | 0.445 |
| Tatoeba-test.eng-mar.eng.mar | 20.7 | 0.473 |
| Tatoeba-test.eng.multi | 13.7 | 0.392 |
| Tatoeba-test.eng-nep.eng.nep | 0.6 | 0.060 |
| Tatoeba-test.eng-ori.eng.ori | 2.4 | 0.193 |
| Tatoeba-test.eng-oss.eng.oss | 2.1 | 0.174 |
| Tatoeba-test.eng-pan.eng.pan | 9.7 | 0.355 |
| Tatoeba-test.eng-pus.eng.pus | 1.0 | 0.126 |
| Tatoeba-test.eng-rom.eng.rom | 1.3 | 0.230 |
| Tatoeba-test.eng-san.eng.san | 1.3 | 0.101 |
| Tatoeba-test.eng-sin.eng.sin | 11.7 | 0.384 |
| Tatoeba-test.eng-snd.eng.snd | 2.8 | 0.180 |
| Tatoeba-test.eng-tgk.eng.tgk | 8.1 | 0.353 |
| Tatoeba-test.eng-tly.eng.tly | 0.5 | 0.015 |
| Tatoeba-test.eng-urd.eng.urd | 12.3 | 0.409 |
| Tatoeba-test.eng-zza.eng.zza | 0.5 | 0.025 |
### System Info:
- hf_name: eng-iir
- source_languages: eng
- target_languages: iir
- opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/eng-iir/README.md
- original_repo: Tatoeba-Challenge
- tags: ['translation']
- languages: ['en', 'bn', 'or', 'gu', 'mr', 'ur', 'hi', 'ps', 'os', 'as', 'si', 'iir']
- src_constituents: {'eng'}
- tgt_constituents: {'pnb', 'gom', 'ben', 'hif_Latn', 'ori', 'guj', 'pan_Guru', 'snd_Arab', 'npi', 'mar', 'urd', 'pes', 'bho', 'kur_Arab', 'tgk_Cyrl', 'hin', 'kur_Latn', 'pes_Thaa', 'pus', 'san_Deva', 'oss', 'tly_Latn', 'jdt_Cyrl', 'asm', 'zza', 'rom', 'mai', 'pes_Latn', 'awa', 'sin'}
- src_multilingual: False
- tgt_multilingual: True
- prepro: normalization + SentencePiece (spm32k,spm32k)
- url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/eng-iir/opus2m-2020-08-01.zip
- url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/eng-iir/opus2m-2020-08-01.test.txt
- src_alpha3: eng
- tgt_alpha3: iir
- short_pair: en-iir
- chrF2_score: 0.392
- bleu: 13.7
- brevity_penalty: 1.0
- ref_len: 63351.0
- src_name: English
- tgt_name: Indo-Iranian languages
- train_date: 2020-08-01
- src_alpha2: en
- tgt_alpha2: iir
- prefer_old: False
- long_pair: eng-iir
- helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535
- transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b
- port_machine: brutasse
- port_time: 2020-08-21-14:41 | d445c4cef1420bf2b922b2116901045b |
KoichiYasuoka/roberta-base-coptic-ud-goeswith | KoichiYasuoka | roberta | 10 | 5 | transformers | 0 | token-classification | true | false | false | cc-by-sa-4.0 | ['cop'] | ['universal_dependencies'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['coptic', 'token-classification', 'pos', 'dependency-parsing'] | false | true | true | 2,735 | false |
# roberta-base-coptic-ud-goeswith
## Model Description
This is a RoBERTa model pre-trained on Coptic Scriptorium Corpora for POS-tagging and dependency-parsing (using `goeswith` for subwords), derived from [roberta-base-coptic](https://huggingface.co/KoichiYasuoka/roberta-base-coptic).
## How to Use
```py
class UDgoeswith(object):
def __init__(self,bert):
from transformers import AutoTokenizer,AutoModelForTokenClassification
self.tokenizer=AutoTokenizer.from_pretrained(bert)
self.model=AutoModelForTokenClassification.from_pretrained(bert)
def __call__(self,text):
import numpy,torch,ufal.chu_liu_edmonds
w=self.tokenizer(text,return_offsets_mapping=True)
v=w["input_ids"]
x=[v[0:i]+[self.tokenizer.mask_token_id]+v[i+1:]+[j] for i,j in enumerate(v[1:-1],1)]
with torch.no_grad():
e=self.model(input_ids=torch.tensor(x)).logits.numpy()[:,1:-2,:]
r=[1 if i==0 else -1 if j.endswith("|root") else 0 for i,j in sorted(self.model.config.id2label.items())]
e+=numpy.where(numpy.add.outer(numpy.identity(e.shape[0]),r)==0,0,numpy.nan)
g=self.model.config.label2id["X|_|goeswith"]
r=numpy.tri(e.shape[0])
for i in range(e.shape[0]):
for j in range(i+2,e.shape[1]):
r[i,j]=r[i,j-1] if numpy.nanargmax(e[i,j-1])==g else 1
e[:,:,g]+=numpy.where(r==0,0,numpy.nan)
m=numpy.full((e.shape[0]+1,e.shape[1]+1),numpy.nan)
m[1:,1:]=numpy.nanmax(e,axis=2).transpose()
p=numpy.zeros(m.shape)
p[1:,1:]=numpy.nanargmax(e,axis=2).transpose()
for i in range(1,m.shape[0]):
m[i,0],m[i,i],p[i,0]=m[i,i],numpy.nan,p[i,i]
h=ufal.chu_liu_edmonds.chu_liu_edmonds(m)[0]
if [0 for i in h if i==0]!=[0]:
m[:,0]+=numpy.where(m[:,0]==numpy.nanmax(m[[i for i,j in enumerate(h) if j==0],0]),0,numpy.nan)
m[[i for i,j in enumerate(h) if j==0]]+=[0 if i==0 or j==0 else numpy.nan for i,j in enumerate(h)]
h=ufal.chu_liu_edmonds.chu_liu_edmonds(m)[0]
u="# text = "+text+"\n"
v=[(s,e) for s,e in w["offset_mapping"] if s<e]
for i,(s,e) in enumerate(v,1):
q=self.model.config.id2label[p[i,h[i]]].split("|")
u+="\t".join([str(i),text[s:e],"_",q[0],"_","|".join(q[1:-1]),str(h[i]),q[-1],"_","_" if i<len(v) and e<v[i][0] else "SpaceAfter=No"])+"\n"
return u+"\n"
nlp=UDgoeswith("KoichiYasuoka/roberta-base-coptic-ud-goeswith")
print(nlp("ⲧⲉⲛⲟⲩⲇⲉⲛ̄ⲟⲩⲟⲉⲓⲛϩ︤ⲙ︥ⲡϫⲟⲉⲓⲥ·"))
```
with [ufal.chu-liu-edmonds](https://pypi.org/project/ufal.chu-liu-edmonds/).
Or without ufal.chu-liu-edmonds:
```
from transformers import pipeline
nlp=pipeline("universal-dependencies","KoichiYasuoka/roberta-base-coptic-ud-goeswith",trust_remote_code=True,aggregation_strategy="simple")
print(nlp("ⲧⲉⲛⲟⲩⲇⲉⲛ̄ⲟⲩⲟⲉⲓⲛϩ︤ⲙ︥ⲡϫⲟⲉⲓⲥ·"))
```
| b71d74a7c2465a49abdabba350e05911 |
edmz/distilbert-base-uncased-finetuned-ner | edmz | distilbert | 13 | 6 | transformers | 0 | token-classification | true | false | false | apache-2.0 | null | ['conll2003'] | null | 1 | 1 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 1,555 | false |
<!-- 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-ner
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0612
- Precision: 0.9247
- Recall: 0.9385
- F1: 0.9315
- Accuracy: 0.9837
## 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 | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2421 | 1.0 | 878 | 0.0701 | 0.9083 | 0.9217 | 0.9149 | 0.9801 |
| 0.0555 | 2.0 | 1756 | 0.0599 | 0.9204 | 0.9357 | 0.9280 | 0.9830 |
| 0.0311 | 3.0 | 2634 | 0.0612 | 0.9247 | 0.9385 | 0.9315 | 0.9837 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.11.6
| 6f5a3d47d721af92a7360e112167786b |
andreaparker/t5-small-finetuned-xsum | andreaparker | t5 | 9 | 4 | transformers | 0 | text2text-generation | true | false | false | apache-2.0 | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 1,255 | false |
<!-- 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-small-finetuned-xsum
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 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: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| No log | 1.0 | 244 | 2.6029 | 29.4956 | 13.5156 | 25.8306 | 25.842 | 18.2896 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.2
| ae028cfe1464b0f8732d8ec474719daa |
jonatasgrosman/exp_w2v2t_zh-cn_vp-es_s399 | jonatasgrosman | wav2vec2 | 10 | 5 | transformers | 0 | automatic-speech-recognition | true | false | false | apache-2.0 | ['zh-CN'] | ['mozilla-foundation/common_voice_7_0'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['automatic-speech-recognition', 'zh-CN'] | false | true | true | 475 | false | # exp_w2v2t_zh-cn_vp-es_s399
Fine-tuned [facebook/wav2vec2-large-es-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-es-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (zh-CN)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
| 52d135a63edd299c59fd39fe8baa5790 |
dbmdz/electra-base-turkish-mc4-cased-generator | dbmdz | electra | 7 | 2 | transformers | 0 | fill-mask | true | true | false | mit | ['tr'] | ['allenai/c4'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | [] | false | true | true | 2,532 | false |
# 🇹🇷 Turkish ELECTRA model
<p align="center">
<img alt="Logo provided by Merve Noyan" title="Awesome logo from Merve Noyan" src="https://raw.githubusercontent.com/stefan-it/turkish-bert/master/merve_logo.png">
</p>
[](https://zenodo.org/badge/latestdoi/237817454)
We present community-driven BERT, DistilBERT, ELECTRA and ConvBERT models for Turkish 🎉
Some datasets used for pretraining and evaluation are contributed from the
awesome Turkish NLP community, as well as the decision for the BERT model name: BERTurk.
Logo is provided by [Merve Noyan](https://twitter.com/mervenoyann).
# Stats
We've also trained an ELECTRA (cased) model on the recently released Turkish part of the
[multiligual C4 (mC4) corpus](https://github.com/allenai/allennlp/discussions/5265) from the AI2 team.
After filtering documents with a broken encoding, the training corpus has a size of 242GB resulting
in 31,240,963,926 tokens.
We used the original 32k vocab (instead of creating a new one).
# mC4 ELECTRA
In addition to the ELEC**TR**A base model, we also trained an ELECTRA model on the Turkish part of the mC4 corpus. We use a
sequence length of 512 over the full training time and train the model for 1M steps on a v3-32 TPU.
# Model usage
All trained models can be used from the [DBMDZ](https://github.com/dbmdz) Hugging Face [model hub page](https://huggingface.co/dbmdz)
using their model name.
Example usage with 🤗/Transformers:
```python
tokenizer = AutoTokenizer.from_pretrained("dbmdz/electra-base-turkish-mc4-cased-generator")
model = AutoModel.from_pretrained("dbmdz/electra-base-turkish-mc4-cased-generator")
```
# Citation
You can use the following BibTeX entry for citation:
```bibtex
@software{stefan_schweter_2020_3770924,
author = {Stefan Schweter},
title = {BERTurk - BERT models for Turkish},
month = apr,
year = 2020,
publisher = {Zenodo},
version = {1.0.0},
doi = {10.5281/zenodo.3770924},
url = {https://doi.org/10.5281/zenodo.3770924}
}
```
# Acknowledgments
Thanks to [Kemal Oflazer](http://www.andrew.cmu.edu/user/ko/) for providing us
additional large corpora for Turkish. Many thanks to Reyyan Yeniterzi for providing
us the Turkish NER dataset for evaluation.
We would like to thank [Merve Noyan](https://twitter.com/mervenoyann) for the
awesome logo!
Research supported with Cloud TPUs from Google's TensorFlow Research Cloud (TFRC).
Thanks for providing access to the TFRC ❤️
| 265f80c4dd2781f75977882d49737c55 |
xrverse/distilbert-base-uncased-distilled-clinc | xrverse | distilbert | 10 | 3 | transformers | 0 | text-classification | true | false | false | apache-2.0 | null | ['clinc_oos'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 1,786 | false |
<!-- 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: 0.0332
- Accuracy: 0.9303
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.4409 | 1.0 | 318 | 0.2288 | 0.6206 |
| 0.1898 | 2.0 | 636 | 0.1106 | 0.8461 |
| 0.116 | 3.0 | 954 | 0.0729 | 0.8994 |
| 0.0861 | 4.0 | 1272 | 0.0548 | 0.9097 |
| 0.0707 | 5.0 | 1590 | 0.0454 | 0.9184 |
| 0.0613 | 6.0 | 1908 | 0.0399 | 0.9239 |
| 0.0557 | 7.0 | 2226 | 0.0371 | 0.9294 |
| 0.0522 | 8.0 | 2544 | 0.0348 | 0.93 |
| 0.05 | 9.0 | 2862 | 0.0336 | 0.9297 |
| 0.0487 | 10.0 | 3180 | 0.0332 | 0.9303 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.11.0
- Datasets 2.4.0
- Tokenizers 0.10.3
| 9f20eb8cdf384db44961e3d464086334 |
ajitjadhav/t5-small-finetuned-summarization-app | ajitjadhav | t5 | 13 | 3 | transformers | 0 | text2text-generation | true | false | false | apache-2.0 | null | ['cnn_dailymail'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 1,545 | false |
<!-- 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-small-finetuned-summarization-app
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the cnn_dailymail dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6614
- Rouge1: 24.5589
- Rouge2: 11.8509
- Rougel: 20.3011
- Rougelsum: 23.1768
- Gen Len: 19.0
## 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: 4e-05
- train_batch_size: 12
- eval_batch_size: 12
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| 1.8267 | 1.0 | 23927 | 1.6689 | 24.4634 | 11.7413 | 20.2154 | 23.0875 | 18.9993 |
| 1.81 | 2.0 | 47854 | 1.6614 | 24.5589 | 11.8509 | 20.3011 | 23.1768 | 19.0 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.7.1
- Tokenizers 0.13.2
| 44c0871f6f0570980ddaf7edb166dca0 |
Imene/vit-base-patch16-224-in21k-wi2 | Imene | vit | 7 | 2 | transformers | 0 | image-classification | false | true | false | apache-2.0 | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_keras_callback'] | true | true | true | 2,443 | false |
<!-- 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. -->
# Imene/vit-base-patch16-224-in21k-wi2
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 2.9892
- Train Accuracy: 0.5568
- Train Top-3-accuracy: 0.8130
- Validation Loss: 3.0923
- Validation Accuracy: 0.4280
- Validation Top-3-accuracy: 0.7034
- 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: {'inner_optimizer': {'class_name': 'AdamWeightDecay', 'config': {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 3e-05, 'decay_steps': 500, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}}, 'dynamic': True, 'initial_scale': 32768.0, 'dynamic_growth_steps': 2000}
- training_precision: mixed_float16
### Training results
| Train Loss | Train Accuracy | Train Top-3-accuracy | Validation Loss | Validation Accuracy | Validation Top-3-accuracy | Epoch |
|:----------:|:--------------:|:--------------------:|:---------------:|:-------------------:|:-------------------------:|:-----:|
| 3.8488 | 0.0720 | 0.1713 | 3.7116 | 0.1564 | 0.3617 | 0 |
| 3.5246 | 0.2703 | 0.4898 | 3.4122 | 0.3217 | 0.5732 | 1 |
| 3.2493 | 0.4150 | 0.6827 | 3.2232 | 0.3880 | 0.6633 | 2 |
| 3.0840 | 0.5002 | 0.7670 | 3.1275 | 0.4255 | 0.6921 | 3 |
| 2.9892 | 0.5568 | 0.8130 | 3.0923 | 0.4280 | 0.7034 | 4 |
### Framework versions
- Transformers 4.21.3
- TensorFlow 2.8.2
- Datasets 2.4.0
- Tokenizers 0.12.1
| 2484e9a21bb2044e0823013cb23068c4 |
kpriyanshu256/whisper-large-v2-as-600-32-1e-05-pretrain-bn | kpriyanshu256 | whisper | 15 | 0 | transformers | 0 | automatic-speech-recognition | true | false | false | apache-2.0 | ['as'] | ['mozilla-foundation/common_voice_11_0'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['whisper-event', 'generated_from_trainer'] | true | true | true | 1,596 | false |
<!-- 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. -->
# kpriyanshu256/whisper-large-v2-as-600-32-1e-05-bn-Assamese
This model is a fine-tuned version of [kpriyanshu256/whisper-large-v2-as-600-32-1e-05-bn](https://huggingface.co/kpriyanshu256/whisper-large-v2-as-600-32-1e-05-bn) on the Common Voice 11.0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2637
- Wer: 21.6928
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 8
- 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: 50
- training_steps: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.1915 | 1.1 | 50 | 0.2129 | 26.3851 |
| 0.0639 | 3.06 | 100 | 0.2305 | 23.0825 |
| 0.0192 | 5.03 | 150 | 0.2391 | 22.0538 |
| 0.0041 | 6.13 | 200 | 0.2637 | 21.6928 |
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.0+cu117
- Datasets 2.7.1.dev0
- Tokenizers 0.13.2
| 075e489da2b351c3bc897f8e925e5d86 |
hsge/TESS_768_v1 | hsge | albert | 8 | 210 | transformers | 0 | null | true | false | false | mit | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | [] | false | true | true | 935 | false | <h1>Transformer Encoder for Social Science (TESS)</h1>
TESS is a deep neural network model intended for social science related NLP tasks. The model is developed by Haosen Ge, In Young Park, Xuancheng Qian, and Grace Zeng.
We demonstrate in two validation tests that TESS outperforms BERT and RoBERTa by 16.7\% on average, especially when the number of training samples is limited (<1,000 training instances). The results display the superiority of TESS on social science text processing tasks.
GitHub: [TESS](https://github.com/haosenge/TESS).
<h2>Training Corpus</h2>
| TEXT | SOURCE |
| ------------- | ------------- |
| Preferential Trade Agreements | ToTA |
| Congressional Bills | Kornilova and Eidelman (2019) |
|UNGA Resolutions | UN |
|Firms' Annual Reports | Loughran and McDonald (2016)|
| U.S. Court Opinions | Caselaw Access Project|
The model is trained on 4 NVIDIA A100 GPUs for 120K steps.
| da58b1e3168cb7f1e11ab1961216e5e1 |
Kumicho/distilbert-base-uncased-finetuned-cola | Kumicho | distilbert | 15 | 3 | transformers | 0 | text-classification | true | false | false | apache-2.0 | null | ['glue'] | null | 1 | 1 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 1,276 | false |
<!-- 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: 0.7758
- Matthews Correlation: 0.5259
## 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: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.1926 | 1.0 | 535 | 0.7758 | 0.5259 |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.0
| 70bf2a2871e194a10620ccf3d3ff5135 |
Go2Heart/BERT_Mod_7_Squad | Go2Heart | bert | 10 | 5 | transformers | 0 | question-answering | true | false | false | apache-2.0 | null | ['squad_v2'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 1,247 | false |
<!-- 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_Mod_7_Squad
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the squad_v2 dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0928
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.001
- train_batch_size: 32
- eval_batch_size: 32
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|
| 1.189 | 1.0 | 4089 | 1.2196 |
| 1.0312 | 2.0 | 8178 | 1.0691 |
| 0.8954 | 3.0 | 12267 | 1.0928 |
### Framework versions
- Transformers 4.21.1
- Pytorch 1.12.1
- Datasets 1.17.0
- Tokenizers 0.12.1
| 706d90f981059e57b6eda9a8e5eabf16 |
jonatasgrosman/exp_w2v2t_es_unispeech-ml_s952 | jonatasgrosman | unispeech | 10 | 3 | transformers | 0 | automatic-speech-recognition | true | false | false | apache-2.0 | ['es'] | ['mozilla-foundation/common_voice_7_0'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['automatic-speech-recognition', 'es'] | false | true | true | 500 | false | # exp_w2v2t_es_unispeech-ml_s952
Fine-tuned [microsoft/unispeech-large-multi-lingual-1500h-cv](https://huggingface.co/microsoft/unispeech-large-multi-lingual-1500h-cv) for speech recognition using the train split of [Common Voice 7.0 (es)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
| 59e537cee6e9d87ad72f88aa56c25daf |
fathyshalab/all-roberta-large-v1-work-4-16-5-oos | fathyshalab | roberta | 11 | 3 | transformers | 0 | text-classification | true | false | false | apache-2.0 | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 1,513 | false |
<!-- 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. -->
# all-roberta-large-v1-work-4-16-5-oos
This model is a fine-tuned version of [sentence-transformers/all-roberta-large-v1](https://huggingface.co/sentence-transformers/all-roberta-large-v1) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.3586
- Accuracy: 0.3689
## 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: 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.8058 | 1.0 | 1 | 2.6169 | 0.2356 |
| 2.3524 | 2.0 | 2 | 2.5215 | 0.2978 |
| 1.9543 | 3.0 | 3 | 2.4427 | 0.3422 |
| 1.5539 | 4.0 | 4 | 2.3874 | 0.36 |
| 1.4133 | 5.0 | 5 | 2.3586 | 0.3689 |
### Framework versions
- Transformers 4.20.0
- Pytorch 1.11.0+cu102
- Datasets 2.3.2
- Tokenizers 0.12.1
| 9bb58e01dc001e4e7bac81d6dcdd8fd5 |
Theivaprakasham/wav2vec2-base-timit-demo-colab | Theivaprakasham | wav2vec2 | 12 | 9 | transformers | 0 | automatic-speech-recognition | true | false | false | apache-2.0 | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 1,641 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base-timit-demo-colab
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4475
- Wer: 0.3400
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 3.6929 | 4.0 | 500 | 2.4485 | 1.0009 |
| 0.9441 | 8.0 | 1000 | 0.4848 | 0.4758 |
| 0.3016 | 12.0 | 1500 | 0.4464 | 0.4016 |
| 0.1715 | 16.0 | 2000 | 0.4666 | 0.3765 |
| 0.1277 | 20.0 | 2500 | 0.4340 | 0.3515 |
| 0.1082 | 24.0 | 3000 | 0.4544 | 0.3495 |
| 0.0819 | 28.0 | 3500 | 0.4475 | 0.3400 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.13.3
- Tokenizers 0.10.3
| db4c9ce7101cea4cd8f9d8fe11107bbf |
nateraw/vit-base-beans-demo-v3 | nateraw | vit | 14 | 13 | transformers | 0 | image-classification | true | false | false | apache-2.0 | null | ['beans'] | null | 1 | 1 | 0 | 0 | 0 | 0 | 0 | ['image-classification', 'other-image-classification', 'generated_from_trainer'] | true | true | true | 1,276 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# vit-base-beans-demo-v3
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the beans dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0645
- Accuracy: 0.9850
## 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: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.0397 | 1.54 | 100 | 0.0645 | 0.9850 |
### Framework versions
- Transformers 4.10.0.dev0
- Pytorch 1.9.0+cu102
- Datasets 1.11.0
- Tokenizers 0.10.3
| 1fa52ca9527685386c01403ea314ceb5 |
paola-md/recipe-lr8e06-wd0.01-bs32 | paola-md | roberta | 6 | 1 | transformers | 0 | text-classification | true | false | false | apache-2.0 | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 1,701 | false |
<!-- 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. -->
# recipe-lr8e06-wd0.01-bs32
This model is a fine-tuned version of [paola-md/recipe-distilroberta-Is](https://huggingface.co/paola-md/recipe-distilroberta-Is) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2753
- Rmse: 0.5246
- Mse: 0.2753
- Mae: 0.4184
## 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: 8e-06
- 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rmse | Mse | Mae |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|
| 0.2769 | 1.0 | 623 | 0.2774 | 0.5266 | 0.2774 | 0.4296 |
| 0.2745 | 2.0 | 1246 | 0.2739 | 0.5233 | 0.2739 | 0.4145 |
| 0.2733 | 3.0 | 1869 | 0.2752 | 0.5246 | 0.2752 | 0.4215 |
| 0.2722 | 4.0 | 2492 | 0.2744 | 0.5238 | 0.2744 | 0.4058 |
| 0.2714 | 5.0 | 3115 | 0.2758 | 0.5251 | 0.2758 | 0.4232 |
| 0.2705 | 6.0 | 3738 | 0.2753 | 0.5246 | 0.2753 | 0.4184 |
### Framework versions
- Transformers 4.19.0.dev0
- Pytorch 1.9.0+cu111
- Datasets 2.4.0
- Tokenizers 0.12.1
| 545aa4d2645d6b1d1adc946cd5178a39 |
gchhablani/fnet-large-finetuned-cola-copy4 | gchhablani | fnet | 71 | 4 | transformers | 0 | text-classification | true | false | false | apache-2.0 | ['en'] | ['glue'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 1,409 | false |
<!-- 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. -->
# fnet-large-finetuned-cola-copy4
This model is a fine-tuned version of [google/fnet-large](https://huggingface.co/google/fnet-large) on the GLUE COLA dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6500
- Matthews Correlation: 0.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 4e-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: polynomial
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.6345 | 1.0 | 2138 | 0.6611 | 0.0 |
| 0.6359 | 2.0 | 4276 | 0.6840 | 0.0 |
| 0.6331 | 3.0 | 6414 | 0.6500 | 0.0 |
### Framework versions
- Transformers 4.11.0.dev0
- Pytorch 1.9.0
- Datasets 1.12.1
- Tokenizers 0.10.3
| 822664923da3664f52979a3c1e05900f |
nandysoham16/Canadian_Armed_Forces-clustered | nandysoham16 | distilbert | 8 | 10 | transformers | 0 | question-answering | false | true | false | mit | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_keras_callback'] | true | true | true | 1,874 | false |
<!-- 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. -->
# nandysoham16/Canadian_Armed_Forces-clustered
This model is a fine-tuned version of [nandysoham16/0-clustered_aug](https://huggingface.co/nandysoham16/0-clustered_aug) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.5493
- Train End Logits Accuracy: 0.8611
- Train Start Logits Accuracy: 0.7812
- Validation Loss: 0.3839
- Validation End Logits Accuracy: 1.0
- Validation Start Logits Accuracy: 0.8000
- Epoch: 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:
- optimizer: {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 18, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train End Logits Accuracy | Train Start Logits Accuracy | Validation Loss | Validation End Logits Accuracy | Validation Start Logits Accuracy | Epoch |
|:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:--------------------------------:|:-----:|
| 0.5493 | 0.8611 | 0.7812 | 0.3839 | 1.0 | 0.8000 | 0 |
### Framework versions
- Transformers 4.26.0
- TensorFlow 2.9.2
- Datasets 2.9.0
- Tokenizers 0.13.2
| 41b8da9d31c5ee09f78d33daf8fcb654 |
jonatasgrosman/exp_w2v2t_th_vp-100k_s403 | jonatasgrosman | wav2vec2 | 10 | 5 | transformers | 0 | automatic-speech-recognition | true | false | false | apache-2.0 | ['th'] | ['mozilla-foundation/common_voice_7_0'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['automatic-speech-recognition', 'th'] | false | true | true | 478 | false | # exp_w2v2t_th_vp-100k_s403
Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) for speech recognition on Thai using the train split of [Common Voice 7.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
| bd5a3994f721d2a4375bad3d8b4e07f4 |
svo2/roberta-finetuned-country-neg | svo2 | roberta | 13 | 14 | transformers | 0 | question-answering | true | false | false | cc-by-4.0 | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 986 | false |
<!-- 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-finetuned-country-neg
This model is a fine-tuned version of [deepset/roberta-base-squad2](https://huggingface.co/deepset/roberta-base-squad2) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
| 08131fbd2a07f50ba4ac8a04128924e3 |
mrm8488/ddpm-ema-anime-256 | mrm8488 | null | 9 | 8 | diffusers | 1 | null | false | false | false | apache-2.0 | ['en'] | ['huggan/selfie2anime'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | [] | false | true | true | 1,338 | false |
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# ddpm-ema-anime-256
## Model description
This diffusion model is trained with the [🤗 Diffusers](https://github.com/huggingface/diffusers) library
on the `huggan/selfie2anime` dataset.
## Intended uses & limitations
#### How to use
```python
# TODO: add an example code snippet for running this diffusion pipeline
```
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training data
[TODO: describe the data used to train the model]
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 8
- eval_batch_size: 16
- gradient_accumulation_steps: 1
- optimizer: AdamW with betas=(0.95, 0.999), weight_decay=1e-06 and epsilon=1e-08
- lr_scheduler: cosine
- lr_warmup_steps: 500
- ema_inv_gamma: 1.0
- ema_inv_gamma: 0.75
- ema_inv_gamma: 0.9999
- mixed_precision: fp16
### Training results
📈 [TensorBoard logs](https://huggingface.co/mrm8488/ddpm-ema-anime-256/tensorboard?#scalars)
> Created by [Manuel Romero/@mrm8488](https://twitter.com/mrm8488) with the support of [Q Blocks](https://www.qblocks.cloud/)
| 484015960c20caaeb5d6c22329b1df78 |
edugp/wav2vec2-xls-r-300m-cv8-es | edugp | wav2vec2 | 11 | 12 | transformers | 0 | automatic-speech-recognition | true | false | false | apache-2.0 | null | ['common_voice'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 1,286 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-xls-r-300m-cv8-es
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.2115
- eval_wer: 0.1931
- eval_runtime: 859.964
- eval_samples_per_second: 17.954
- eval_steps_per_second: 2.244
- epoch: 6.97
- step: 50000
## 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: 0.0003
- 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
- num_epochs: 30
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.1+cu102
- Datasets 1.18.3
- Tokenizers 0.11.0
| 558aeea427b868ecbbb1cd306db18f1c |
Ankit15nov/xlm-roberta-base-finetuned-panx-de | Ankit15nov | xlm-roberta | 12 | 7 | transformers | 0 | token-classification | true | false | false | mit | null | ['xtreme'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 1,313 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-de
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1368
- F1: 0.8599
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2618 | 1.0 | 525 | 0.1748 | 0.8134 |
| 0.1274 | 2.0 | 1050 | 0.1398 | 0.8461 |
| 0.0817 | 3.0 | 1575 | 0.1368 | 0.8599 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.5.1
- Datasets 1.16.1
- Tokenizers 0.10.3
| 94dbfd8aeefb96db6c9ca2a44dbcca8a |
caffsean/gpt2-dzongkha-text | caffsean | gpt2 | 17 | 3 | transformers | 0 | text-generation | true | false | false | mit | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 1,219 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# gpt2-dzongkha-text
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.5939
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 249 | 3.1538 |
| No log | 2.0 | 498 | 2.6796 |
| 4.0415 | 3.0 | 747 | 2.5939 |
### Framework versions
- Transformers 4.21.2
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
| 1fd7b84344b6557e53378da941470099 |
jonatasgrosman/exp_w2v2r_es_vp-100k_age_teens-8_sixties-2_s284 | jonatasgrosman | wav2vec2 | 10 | 0 | transformers | 0 | automatic-speech-recognition | true | false | false | apache-2.0 | ['es'] | ['mozilla-foundation/common_voice_7_0'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['automatic-speech-recognition', 'es'] | false | true | true | 497 | false | # exp_w2v2r_es_vp-100k_age_teens-8_sixties-2_s284
Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (es)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
| 5264bedb86505039612e0d9d89301899 |
AlekseyKorshuk/dalio-all-io-125m-3-epoch | AlekseyKorshuk | opt | 13 | 5 | transformers | 0 | text-generation | true | false | false | other | null | ['AlekseyKorshuk/dalio-all-io'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 6,666 | false |
<!-- 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. -->
# dalio-all-io-125m-3-epoch
This model is a fine-tuned version of [facebook/opt-125m](https://huggingface.co/facebook/opt-125m) on the AlekseyKorshuk/dalio-all-io dataset.
It achieves the following results on the evaluation set:
- Loss: 2.7656
- Accuracy: 0.0497
## 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: 2
- eval_batch_size: 2
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- total_train_batch_size: 16
- total_eval_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 3.1406 | 0.03 | 1 | 3.0762 | 0.0451 |
| 3.074 | 0.07 | 2 | 3.0762 | 0.0451 |
| 3.0557 | 0.1 | 3 | 3.0762 | 0.0451 |
| 3.2166 | 0.14 | 4 | 3.0176 | 0.0457 |
| 3.0989 | 0.17 | 5 | 2.9922 | 0.0460 |
| 3.0732 | 0.21 | 6 | 2.9746 | 0.0464 |
| 3.0867 | 0.24 | 7 | 2.9629 | 0.0463 |
| 2.979 | 0.28 | 8 | 2.9512 | 0.0467 |
| 3.1838 | 0.31 | 9 | 2.9414 | 0.0467 |
| 2.9399 | 0.34 | 10 | 2.9336 | 0.0467 |
| 2.926 | 0.38 | 11 | 2.9258 | 0.0471 |
| 3.2144 | 0.41 | 12 | 2.9199 | 0.0473 |
| 2.978 | 0.45 | 13 | 2.9141 | 0.0474 |
| 3.0076 | 0.48 | 14 | 2.9082 | 0.0476 |
| 2.9897 | 0.52 | 15 | 2.9023 | 0.0477 |
| 2.8831 | 0.55 | 16 | 2.8945 | 0.0479 |
| 2.9749 | 0.59 | 17 | 2.8867 | 0.0479 |
| 2.9431 | 0.62 | 18 | 2.8828 | 0.0478 |
| 3.0498 | 0.66 | 19 | 2.8770 | 0.0479 |
| 2.9409 | 0.69 | 20 | 2.8711 | 0.0479 |
| 2.96 | 0.72 | 21 | 2.8672 | 0.0480 |
| 3.0767 | 0.76 | 22 | 2.8633 | 0.0478 |
| 2.772 | 0.79 | 23 | 2.8594 | 0.0479 |
| 3.0574 | 0.83 | 24 | 2.8535 | 0.0480 |
| 2.8137 | 0.86 | 25 | 2.8496 | 0.0480 |
| 2.8872 | 0.9 | 26 | 2.8438 | 0.0483 |
| 3.0085 | 0.93 | 27 | 2.8398 | 0.0484 |
| 2.9165 | 0.97 | 28 | 2.8359 | 0.0485 |
| 2.8525 | 1.0 | 29 | 2.8340 | 0.0486 |
| 2.7759 | 1.03 | 30 | 2.8301 | 0.0485 |
| 2.7312 | 1.07 | 31 | 2.8281 | 0.0485 |
| 2.6641 | 1.1 | 32 | 2.8262 | 0.0487 |
| 2.7896 | 1.14 | 33 | 2.8242 | 0.0486 |
| 2.7878 | 1.17 | 34 | 2.8223 | 0.0487 |
| 2.4028 | 1.21 | 35 | 2.8203 | 0.0487 |
| 2.5618 | 1.24 | 36 | 2.8184 | 0.0488 |
| 2.6697 | 1.28 | 37 | 2.8164 | 0.0488 |
| 2.6333 | 1.31 | 38 | 2.8145 | 0.0487 |
| 2.4897 | 1.34 | 39 | 2.8125 | 0.0486 |
| 2.4908 | 1.38 | 40 | 2.8105 | 0.0487 |
| 2.6926 | 1.41 | 41 | 2.8086 | 0.0488 |
| 2.6602 | 1.45 | 42 | 2.8066 | 0.0489 |
| 2.8054 | 1.48 | 43 | 2.8047 | 0.0489 |
| 2.5532 | 1.52 | 44 | 2.8047 | 0.0490 |
| 2.4756 | 1.55 | 45 | 2.8027 | 0.0491 |
| 2.6123 | 1.59 | 46 | 2.8008 | 0.0491 |
| 2.5117 | 1.62 | 47 | 2.7988 | 0.0490 |
| 2.5552 | 1.66 | 48 | 2.7969 | 0.0490 |
| 2.5122 | 1.69 | 49 | 2.7949 | 0.0490 |
| 2.5593 | 1.72 | 50 | 2.7930 | 0.0491 |
| 2.5759 | 1.76 | 51 | 2.7910 | 0.0491 |
| 2.5535 | 1.79 | 52 | 2.7891 | 0.0493 |
| 2.6531 | 1.83 | 53 | 2.7871 | 0.0494 |
| 2.5701 | 1.86 | 54 | 2.7852 | 0.0495 |
| 2.6621 | 1.9 | 55 | 2.7832 | 0.0497 |
| 2.532 | 1.93 | 56 | 2.7812 | 0.0496 |
| 2.5928 | 1.97 | 57 | 2.7793 | 0.0497 |
| 2.5486 | 2.0 | 58 | 2.7754 | 0.0497 |
| 2.5009 | 2.03 | 59 | 2.7734 | 0.0497 |
| 2.4346 | 2.07 | 60 | 2.7734 | 0.0498 |
| 2.3259 | 2.1 | 61 | 2.7715 | 0.0497 |
| 2.3569 | 2.14 | 62 | 2.7695 | 0.0498 |
| 2.5898 | 2.17 | 63 | 2.7695 | 0.0498 |
| 2.3657 | 2.21 | 64 | 2.7676 | 0.0498 |
| 2.4875 | 2.24 | 65 | 2.7676 | 0.0498 |
| 2.4392 | 2.28 | 66 | 2.7676 | 0.0497 |
| 2.3595 | 2.31 | 67 | 2.7656 | 0.0497 |
| 2.4757 | 2.34 | 68 | 2.7656 | 0.0498 |
| 2.4617 | 2.38 | 69 | 2.7656 | 0.0498 |
| 2.3376 | 2.41 | 70 | 2.7656 | 0.0499 |
| 2.3129 | 2.45 | 71 | 2.7656 | 0.0498 |
| 2.5703 | 2.48 | 72 | 2.7656 | 0.0498 |
| 2.3491 | 2.52 | 73 | 2.7656 | 0.0498 |
| 2.3484 | 2.55 | 74 | 2.7656 | 0.0498 |
| 2.3782 | 2.59 | 75 | 2.7656 | 0.0497 |
| 2.4033 | 2.62 | 76 | 2.7656 | 0.0498 |
| 2.3821 | 2.66 | 77 | 2.7656 | 0.0498 |
| 2.39 | 2.69 | 78 | 2.7656 | 0.0498 |
| 2.3984 | 2.72 | 79 | 2.7656 | 0.0497 |
| 2.3936 | 2.76 | 80 | 2.7656 | 0.0498 |
| 2.4414 | 2.79 | 81 | 2.7656 | 0.0497 |
| 2.4727 | 2.83 | 82 | 2.7656 | 0.0497 |
| 2.3192 | 2.86 | 83 | 2.7656 | 0.0497 |
| 2.4365 | 2.9 | 84 | 2.7656 | 0.0497 |
| 2.5042 | 2.93 | 85 | 2.7656 | 0.0497 |
| 2.4746 | 2.97 | 86 | 2.7656 | 0.0497 |
| 2.5383 | 3.0 | 87 | 2.7656 | 0.0497 |
### Framework versions
- Transformers 4.25.0.dev0
- Pytorch 1.12.1+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
| 5da301c5fd8b621838900407730b2b73 |
ViktorDo/DistilBERT-POWO_MGH_Growth_Form_Finetuned | ViktorDo | distilbert | 12 | 5 | transformers | 0 | text-classification | true | false | false | apache-2.0 | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 1,319 | false |
<!-- 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-POWO_MGH_Growth_Form_Finetuned
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: 0.2182
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.2379 | 1.0 | 2054 | 0.2241 |
| 0.2098 | 2.0 | 4108 | 0.2173 |
| 0.2168 | 3.0 | 6162 | 0.2182 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.7.1
- Tokenizers 0.13.2
| 074d3fc9008afa8dce4bb7fa9002bb0e |
gokuls/distilbert_add_GLUE_Experiment_logit_kd_rte_96 | gokuls | distilbert | 17 | 2 | transformers | 0 | text-classification | true | false | false | apache-2.0 | ['en'] | ['glue'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 2,121 | false |
<!-- 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_add_GLUE_Experiment_logit_kd_rte_96
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE RTE dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4234
- Accuracy: 0.4729
## 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: 256
- eval_batch_size: 256
- seed: 10
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.4604 | 1.0 | 10 | 0.4429 | 0.4729 |
| 0.4358 | 2.0 | 20 | 0.4328 | 0.4729 |
| 0.4282 | 3.0 | 30 | 0.4290 | 0.4729 |
| 0.4246 | 4.0 | 40 | 0.4269 | 0.4729 |
| 0.4227 | 5.0 | 50 | 0.4252 | 0.4729 |
| 0.4204 | 6.0 | 60 | 0.4243 | 0.4729 |
| 0.4191 | 7.0 | 70 | 0.4238 | 0.4729 |
| 0.4185 | 8.0 | 80 | 0.4235 | 0.4729 |
| 0.4175 | 9.0 | 90 | 0.4234 | 0.4729 |
| 0.4164 | 10.0 | 100 | 0.4235 | 0.4729 |
| 0.418 | 11.0 | 110 | 0.4236 | 0.4729 |
| 0.4169 | 12.0 | 120 | 0.4236 | 0.4729 |
| 0.4173 | 13.0 | 130 | 0.4238 | 0.4729 |
| 0.4168 | 14.0 | 140 | 0.4239 | 0.4729 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.14.0a0+410ce96
- Datasets 2.9.0
- Tokenizers 0.13.2
| 4b7d904c42b406940a2a0e2d4f60bc83 |
kasrahabib/20_propogated | kasrahabib | bert | 10 | 0 | transformers | 0 | text-classification | false | true | false | apache-2.0 | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_keras_callback'] | true | true | true | 1,915 | false |
<!-- 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. -->
# kasrahabib/20_propogated
This model is a fine-tuned version of [kasrahabib/XXX08_02_23__-bucket-finetunned](https://huggingface.co/kasrahabib/XXX08_02_23__-bucket-finetunned) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0504
- Validation Loss: 0.1528
- Epoch: 9
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': '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': 7660, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 0.2492 | 0.1740 | 0 |
| 0.1527 | 0.1501 | 1 |
| 0.1092 | 0.1582 | 2 |
| 0.0879 | 0.1568 | 3 |
| 0.0774 | 0.1577 | 4 |
| 0.0689 | 0.1513 | 5 |
| 0.0597 | 0.1598 | 6 |
| 0.0600 | 0.1536 | 7 |
| 0.0526 | 0.1519 | 8 |
| 0.0504 | 0.1528 | 9 |
### Framework versions
- Transformers 4.26.1
- TensorFlow 2.11.0
- Datasets 2.9.0
- Tokenizers 0.13.2
| e73adce7c14477b566a3b199a71e9eaf |
alk/t5-small-finetuned-cnn_dailymail-en-es | alk | t5 | 8 | 1 | transformers | 0 | text2text-generation | false | true | false | apache-2.0 | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_keras_callback'] | true | true | true | 1,465 | false |
<!-- 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. -->
# alk/t5-small-finetuned-cnn_dailymail-en-es
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 1.9163
- Validation Loss: 1.7610
- 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': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5.6e-05, 'decay_steps': 71776, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 1.9945 | 1.7837 | 0 |
| 1.9478 | 1.7694 | 1 |
| 1.9278 | 1.7646 | 2 |
| 1.9163 | 1.7610 | 3 |
### Framework versions
- Transformers 4.19.0
- TensorFlow 2.8.0
- Datasets 2.2.1
- Tokenizers 0.12.1
| b0c6ca3b8b5b236aebce9f7971e33c9c |
muhtasham/tiny-mlm-glue-mnli-target-glue-qnli | muhtasham | bert | 10 | 1 | transformers | 0 | text-classification | true | false | false | apache-2.0 | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 1,806 | false |
<!-- 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. -->
# tiny-mlm-glue-mnli-target-glue-qnli
This model is a fine-tuned version of [muhtasham/tiny-mlm-glue-mnli](https://huggingface.co/muhtasham/tiny-mlm-glue-mnli) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4695
- Accuracy: 0.7814
## 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: constant
- num_epochs: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6034 | 0.15 | 500 | 0.5431 | 0.7335 |
| 0.5403 | 0.31 | 1000 | 0.5253 | 0.7459 |
| 0.5174 | 0.46 | 1500 | 0.4953 | 0.7659 |
| 0.5137 | 0.61 | 2000 | 0.5259 | 0.7483 |
| 0.511 | 0.76 | 2500 | 0.4814 | 0.7750 |
| 0.5032 | 0.92 | 3000 | 0.4670 | 0.7847 |
| 0.4901 | 1.07 | 3500 | 0.4525 | 0.7904 |
| 0.4798 | 1.22 | 4000 | 0.4679 | 0.7836 |
| 0.4667 | 1.37 | 4500 | 0.4752 | 0.7798 |
| 0.4736 | 1.53 | 5000 | 0.4695 | 0.7814 |
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.0+cu116
- Datasets 2.8.1.dev0
- Tokenizers 0.13.2
| ecd412a5e409636c916fbbe9ec5cd199 |
ZabonZooY/BasilticAbyssDream | ZabonZooY | null | 56 | 0 | null | 2 | null | false | false | false | unlicense | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | [] | false | true | true | 666 | false |
#BasilticAbyssDream
>BAD에는 종류가 3가지 있습니다. 원하는대로 다운받으세요. 참고 사진은 샘플 파일에 올려두었습니다.
>BAD model has 3 types. Choose any model what you like. Sample pics included.
#권장사항 (Recommend)
* 가장 추천되는 모델은 BAD 0.3입니다.
* BA 0.1이 가장 반실사에 가깝고, BAD 0.5는 매우 실사스럽고 드림 특유의 뭉개짐이 많습니다.
* 권장 프롬프트 : detailed face, restore face
* 권장 네거티브 : (worst quality, low quality:1.4), (loli, child, infant, baby:1.3), accessories
* Most recommended model is BAD 0.3.
* BA 0.1 likes semi-realistic, BAD 0.5 is very realistic but it has many errors.
* Prompts recommended : detailed face, restore face
* Negative recommended : (worst quality, low quality:1.4), (loli, child, infant, baby:1.3), accessories | 2f62d35bcd16732232844275fa734db1 |
RawMean/model_dir | RawMean | deberta-v2 | 11 | 3 | transformers | 0 | text-classification | true | false | false | mit | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 1,824 | false |
<!-- 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. -->
# model_dir
This model is a fine-tuned version of [microsoft/deberta-v3-small](https://huggingface.co/microsoft/deberta-v3-small) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0380
- Pearson: 0.9399
## 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: 8e-05
- train_batch_size: 128
- eval_batch_size: 256
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Pearson |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| No log | 1.0 | 12 | 0.2773 | 0.7230 |
| No log | 2.0 | 24 | 0.1120 | 0.7812 |
| No log | 3.0 | 36 | 0.1090 | 0.8638 |
| No log | 4.0 | 48 | 0.0613 | 0.9163 |
| No log | 5.0 | 60 | 0.0447 | 0.9409 |
| No log | 6.0 | 72 | 0.0356 | 0.9402 |
| No log | 7.0 | 84 | 0.0368 | 0.9359 |
| No log | 8.0 | 96 | 0.0408 | 0.9295 |
| No log | 9.0 | 108 | 0.0397 | 0.9382 |
| No log | 10.0 | 120 | 0.0380 | 0.9399 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.2
| 55698d38152f764b0d345f2c541c9056 |
semindan/xnli_xlm_r_base_broken | semindan | xlm-roberta | 10 | 1 | transformers | 0 | text-classification | true | false | false | mit | null | ['xnli'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['text-classification', 'generated_from_trainer'] | true | true | true | 5,754 | false |
<!-- 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. -->
# xnli_xlm_r_base_only_en_automodel_single_gpu
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xnli dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0986
- Accuracy: 0.3333
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.1064 | 0.04 | 1000 | 1.1003 | 0.3333 |
| 1.1042 | 0.08 | 2000 | 1.1006 | 0.3333 |
| 1.1049 | 0.12 | 3000 | 1.0992 | 0.3333 |
| 1.1037 | 0.16 | 4000 | 1.1019 | 0.3333 |
| 1.1037 | 0.2 | 5000 | 1.0986 | 0.3333 |
| 1.1028 | 0.24 | 6000 | 1.1014 | 0.3333 |
| 1.1044 | 0.29 | 7000 | 1.1059 | 0.3333 |
| 1.102 | 0.33 | 8000 | 1.1000 | 0.3333 |
| 1.1022 | 0.37 | 9000 | 1.1012 | 0.3333 |
| 1.1019 | 0.41 | 10000 | 1.0995 | 0.3333 |
| 1.1018 | 0.45 | 11000 | 1.0990 | 0.3333 |
| 1.103 | 0.49 | 12000 | 1.1018 | 0.3333 |
| 1.1016 | 0.53 | 13000 | 1.0989 | 0.3333 |
| 1.1021 | 0.57 | 14000 | 1.0995 | 0.3333 |
| 1.1012 | 0.61 | 15000 | 1.1026 | 0.3333 |
| 1.1012 | 0.65 | 16000 | 1.1000 | 0.3333 |
| 1.1018 | 0.69 | 17000 | 1.0992 | 0.3333 |
| 1.1004 | 0.73 | 18000 | 1.0996 | 0.3333 |
| 1.101 | 0.77 | 19000 | 1.0987 | 0.3333 |
| 1.1011 | 0.81 | 20000 | 1.1001 | 0.3333 |
| 1.1006 | 0.86 | 21000 | 1.0991 | 0.3333 |
| 1.1006 | 0.9 | 22000 | 1.1028 | 0.3333 |
| 1.1003 | 0.94 | 23000 | 1.0988 | 0.3333 |
| 1.1006 | 0.98 | 24000 | 1.0987 | 0.3333 |
| 1.1008 | 1.02 | 25000 | 1.0995 | 0.3333 |
| 1.1011 | 1.06 | 26000 | 1.0987 | 0.3333 |
| 1.1003 | 1.1 | 27000 | 1.0987 | 0.3333 |
| 1.1002 | 1.14 | 28000 | 1.1020 | 0.3333 |
| 1.1 | 1.18 | 29000 | 1.0988 | 0.3333 |
| 1.1002 | 1.22 | 30000 | 1.0995 | 0.3333 |
| 1.1001 | 1.26 | 31000 | 1.0989 | 0.3333 |
| 1.1001 | 1.3 | 32000 | 1.0986 | 0.3333 |
| 1.0999 | 1.34 | 33000 | 1.0989 | 0.3333 |
| 1.1004 | 1.39 | 34000 | 1.0987 | 0.3333 |
| 1.0993 | 1.43 | 35000 | 1.0989 | 0.3333 |
| 1.1003 | 1.47 | 36000 | 1.0989 | 0.3333 |
| 1.0999 | 1.51 | 37000 | 1.0991 | 0.3333 |
| 1.0999 | 1.55 | 38000 | 1.0993 | 0.3333 |
| 1.0994 | 1.59 | 39000 | 1.0993 | 0.3333 |
| 1.0994 | 1.63 | 40000 | 1.0989 | 0.3333 |
| 1.0999 | 1.67 | 41000 | 1.0988 | 0.3333 |
| 1.0995 | 1.71 | 42000 | 1.0996 | 0.3333 |
| 1.1003 | 1.75 | 43000 | 1.0987 | 0.3333 |
| 1.0996 | 1.79 | 44000 | 1.0987 | 0.3333 |
| 1.0996 | 1.83 | 45000 | 1.0990 | 0.3333 |
| 1.0994 | 1.87 | 46000 | 1.0990 | 0.3333 |
| 1.0992 | 1.91 | 47000 | 1.1000 | 0.3333 |
| 1.0992 | 1.96 | 48000 | 1.0989 | 0.3333 |
| 1.0991 | 2.0 | 49000 | 1.0991 | 0.3333 |
| 1.099 | 2.04 | 50000 | 1.0987 | 0.3333 |
| 1.0992 | 2.08 | 51000 | 1.0987 | 0.3333 |
| 1.0995 | 2.12 | 52000 | 1.0988 | 0.3333 |
| 1.0994 | 2.16 | 53000 | 1.0989 | 0.3333 |
| 1.0994 | 2.2 | 54000 | 1.0989 | 0.3333 |
| 1.0993 | 2.24 | 55000 | 1.0988 | 0.3333 |
| 1.0988 | 2.28 | 56000 | 1.0986 | 0.3333 |
| 1.0995 | 2.32 | 57000 | 1.0986 | 0.3333 |
| 1.0991 | 2.36 | 58000 | 1.0988 | 0.3333 |
| 1.0989 | 2.4 | 59000 | 1.0987 | 0.3333 |
| 1.0991 | 2.44 | 60000 | 1.0990 | 0.3333 |
| 1.0992 | 2.49 | 61000 | 1.0989 | 0.3333 |
| 1.0992 | 2.53 | 62000 | 1.0987 | 0.3333 |
| 1.0989 | 2.57 | 63000 | 1.0986 | 0.3333 |
| 1.099 | 2.61 | 64000 | 1.0987 | 0.3333 |
| 1.0991 | 2.65 | 65000 | 1.0986 | 0.3333 |
| 1.0991 | 2.69 | 66000 | 1.0986 | 0.3333 |
| 1.0991 | 2.73 | 67000 | 1.0987 | 0.3333 |
| 1.0986 | 2.77 | 68000 | 1.0987 | 0.3333 |
| 1.0992 | 2.81 | 69000 | 1.0986 | 0.3333 |
| 1.0989 | 2.85 | 70000 | 1.0986 | 0.3333 |
| 1.099 | 2.89 | 71000 | 1.0987 | 0.3333 |
| 1.0989 | 2.93 | 72000 | 1.0986 | 0.3333 |
| 1.0989 | 2.97 | 73000 | 1.0986 | 0.3333 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.13.0
- Datasets 2.6.1
- Tokenizers 0.13.1
| 92d75fdfaf617936b39ed96bb62a470c |
WillHeld/t5-base-pointer-mtop | WillHeld | mt5 | 17 | 3 | transformers | 0 | text2text-generation | true | false | false | apache-2.0 | ['en'] | ['mtop'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 2,184 | false |
<!-- 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-pointer-mtop
This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) on the mtop dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1131
- Exact Match: 0.7199
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 64
- total_train_batch_size: 512
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 3000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Exact Match |
|:-------------:|:-----:|:----:|:---------------:|:-----------:|
| 1.7749 | 6.65 | 200 | 0.5892 | 0.0031 |
| 0.6021 | 13.33 | 400 | 0.5160 | 0.0139 |
| 0.6044 | 19.98 | 600 | 0.4080 | 0.0532 |
| 0.3302 | 26.65 | 800 | 0.1865 | 0.3620 |
| 0.1483 | 33.33 | 1000 | 0.1267 | 0.5105 |
| 0.0768 | 39.98 | 1200 | 0.1131 | 0.5298 |
| 0.0525 | 46.65 | 1400 | 0.1219 | 0.5414 |
| 0.0801 | 53.33 | 1600 | 0.1186 | 0.5275 |
| 0.0331 | 59.98 | 1800 | 0.1306 | 0.5423 |
| 0.0254 | 66.65 | 2000 | 0.1396 | 0.5396 |
| 0.0168 | 73.33 | 2200 | 0.1560 | 0.5436 |
| 0.0129 | 79.98 | 2400 | 0.1659 | 0.5494 |
| 0.0105 | 86.65 | 2600 | 0.1699 | 0.5423 |
| 0.0088 | 93.33 | 2800 | 0.1742 | 0.5472 |
| 0.0077 | 99.98 | 3000 | 0.1775 | 0.5468 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu117
- Datasets 2.7.1
- Tokenizers 0.13.2
| a74cd0dd0d4f6c398ffc8f07901ac0cf |
surajjoshi/swin-tiny-patch4-window7-224-finetuned-brainTumorData | surajjoshi | swin | 45 | 7 | transformers | 1 | image-classification | true | false | false | apache-2.0 | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 1,063 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# swin-tiny-patch4-window7-224-finetuned-brainTumorData
This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) 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: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 4
### Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1
- Datasets 2.6.1
- Tokenizers 0.13.1
| 8dd1bdbc466d45ca185d2522be9a3942 |
comodoro/wav2vec2-xls-r-300m-west-slavic-cv8 | comodoro | wav2vec2 | 12 | 30 | transformers | 0 | automatic-speech-recognition | true | false | false | apache-2.0 | ['cs', 'hsb', 'pl', 'sk', 'sl'] | ['mozilla-foundation/common_voice_8_0'] | null | 1 | 1 | 0 | 0 | 0 | 0 | 0 | ['automatic-speech-recognition', 'generated_from_trainer', 'hf-asr-leaderboard', 'mozilla-foundation/common_voice_8_0', 'robust-speech-event', 'xlsr-fine-tuning-week'] | true | true | true | 1,241 | false |
# wav2vec2-xls-r-300m-west-slavic-cv8
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the Common Voice 8 dataset of five similar languages with similar scripts: Czech, Slovak, Polish, Slovenian and Upper Sorbian. Training and validation sets were concatenated and shuffled.
Evaluation set used for training was concatenated from the respective test sets and shuffled while limiting each language to at most 2000 samples. During training, cca WER 70 was achieved on this set.
### Evaluation script
```
python eval.py --model_id comodoro/wav2vec2-xls-r-300m-west-slavic-cv8 --dataset mozilla-foundation/common_voice_8_0 --split test --config {lang}
```
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 32
- 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: 500
- num_epochs: 50
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.1+cu102
- Datasets 1.18.3
- Tokenizers 0.11.0
| 69ee2b325c0350105c461ac4978ff6b1 |
mrm8488/santacoder-finetuned-the-stack-bash-3 | mrm8488 | gpt2 | 11 | 1 | transformers | 0 | text-generation | true | false | false | openrail | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 1,760 | false |
<!-- 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. -->
# santacoder-finetuned-the-stack-bash-3
This model is a fine-tuned version of [bigcode/santacoder](https://huggingface.co/bigcode/santacoder) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: nan
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 100
- training_steps: 5000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.0 | 0.1 | 500 | nan |
| 0.0 | 0.2 | 1000 | nan |
| 0.0 | 0.3 | 1500 | nan |
| 0.0 | 0.4 | 2000 | nan |
| 0.0 | 0.5 | 2500 | nan |
| 0.0 | 0.6 | 3000 | nan |
| 0.0 | 0.7 | 3500 | nan |
| 0.0 | 0.8 | 4000 | nan |
| 0.0 | 0.9 | 4500 | nan |
| 0.0 | 1.0 | 5000 | nan |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.1+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
| 86abdd6f3d8543089fe1f9b2b130645e |
deprem-ml/deprem-roberta-intent | deprem-ml | null | 11 | 0 | transformers | 0 | text-classification | false | false | false | apache-2.0 | ['tr'] | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | [] | false | true | true | 1,202 | false |
# Türkçe Multi-label Intent Classification RoBERTa
Depremzedelerin ihtiyaçlarını karşılamak için etiketlenmiş eğitilmiş multi-label RoBERTa modeli. Aşağıda değerlendirme sonuçları var.
**Evaluation**
- 'eval_loss': 0.18568251545368838,
- 'eval_runtime': 2.7693,
- 'eval_samples_per_second': 254.935,
- 'eval_steps_per_second': 8.305,
- 'epoch': 3.0
**Classification Report**
```
precision recall f1-score support
Alakasiz 0.95 0.87 0.91 781
Barinma 0.86 0.52 0.65 234
Elektronik 0.00 0.00 0.00 171
Giysi 0.89 0.25 0.39 122
Kurtarma 0.86 0.78 0.82 472
Lojistik 0.00 0.00 0.00 123
Saglik 0.78 0.05 0.09 148
Su 0.92 0.11 0.20 96
Yagma 0.00 0.00 0.00 19
Yemek 0.94 0.42 0.58 158
micro avg 0.91 0.55 0.69 2324
macro avg 0.62 0.30 0.36 2324
weighted avg 0.78 0.55 0.61 2324
samples avg 0.69 0.63 0.65 2324
``` | 61ef829b9d8de415a15edd5498a97107 |
austinmw/distilbert-base-uncased-finetuned-health_facts | austinmw | distilbert | 50 | 4 | transformers | 0 | text-classification | true | false | false | apache-2.0 | null | ['health_fact'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 1,562 | false |
<!-- 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-health_facts
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the health_fact dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1227
- Accuracy: 0.6285
- F1: 0.6545
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 1.1367 | 1.0 | 154 | 0.9423 | 0.5560 | 0.6060 |
| 0.9444 | 2.0 | 308 | 0.9267 | 0.5733 | 0.6170 |
| 0.8248 | 3.0 | 462 | 0.9483 | 0.5832 | 0.6256 |
| 0.7213 | 4.0 | 616 | 1.0119 | 0.5815 | 0.6219 |
| 0.608 | 5.0 | 770 | 1.1227 | 0.6285 | 0.6545 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0
- Datasets 1.16.1
- Tokenizers 0.10.3
| 497e9cd499ed7bfa2ba135eda52092d3 |
classla/roberta-base-frenk-hate | classla | roberta | 11 | 3 | transformers | 0 | text-classification | true | false | false | cc-by-sa-4.0 | ['en'] | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['text-classification', 'hate-speech'] | false | true | true | 4,216 | false |
# roberta-base-frenk-hate
Text classification model based on [`roberta-base`](https://huggingface.co/roberta-base) and fine-tuned on the [FRENK dataset](https://www.clarin.si/repository/xmlui/handle/11356/1433) comprising of LGBT and migrant hatespeech. Only the English subset of the data was used for fine-tuning and the dataset has been relabeled for binary classification (offensive or acceptable).
## Fine-tuning hyperparameters
Fine-tuning was performed with `simpletransformers`. Beforehand a brief hyperparameter optimisation was performed and the presumed optimal hyperparameters are:
```python
model_args = {
"num_train_epochs": 6,
"learning_rate": 3e-6,
"train_batch_size": 69}
```
## Performance
The same pipeline was run with two other transformer models and `fasttext` for comparison. Accuracy and macro F1 score were recorded for each of the 6 fine-tuning sessions and post festum analyzed.
| model | average accuracy | average macro F1|
|---|---|---|
|roberta-base-frenk-hate|0.7915|0.7785|
|xlm-roberta-large |0.7904|0.77876|
|xlm-roberta-base |0.7577|0.7402|
|fasttext|0.725 |0.707 |
From recorded accuracies and macro F1 scores p-values were also calculated:
Comparison with `xlm-roberta-base`:
| test | accuracy p-value | macro F1 p-value|
| --- | --- | --- |
|Wilcoxon|0.00781|0.00781|
|Mann Whithney U-test|0.00108|0.00108|
|Student t-test | 1.35e-08 | 1.05e-07|
Comparison with `xlm-roberta-large` yielded inconclusive results. `roberta-base` has average accuracy 0.7915, while `xlm-roberta-large` has average accuracy of 0.7904. If macro F1 scores were to be compared, `roberta-base` actually has lower average than `xlm-roberta-large`: 0.77852 vs 0.77876 respectively. The same statistical tests were performed with the premise that `roberta-base` has greater metrics, and the results are given below.
| test | accuracy p-value | macro F1 p-value|
| --- | --- | --- |
|Wilcoxon|0.188|0.406|
|Mann Whithey|0.375|0.649|
|Student t-test | 0.681| 0.934|
With reversed premise (i.e., that `xlm-roberta-large` has greater statistics) the Wilcoxon p-value for macro F1 scores for this case reaches 0.656, Mann-Whithey p-value is 0.399, and of course the Student p-value stays the same. It was therefore concluded that performance of the two models are not statistically significantly different from one another.
## Use examples
```python
from simpletransformers.classification import ClassificationModel
model_args = {
"num_train_epochs": 6,
"learning_rate": 3e-6,
"train_batch_size": 69}
model = ClassificationModel(
"roberta", "5roop/roberta-base-frenk-hate", use_cuda=True,
args=model_args
)
predictions, logit_output = model.predict(["Build the wall",
"Build the wall of trust"]
)
predictions
### Output:
### array([1, 0])
```
## Citation
If you use the model, please cite the following paper on which the original model is based:
```
@article{DBLP:journals/corr/abs-1907-11692,
author = {Yinhan Liu and
Myle Ott and
Naman Goyal and
Jingfei Du and
Mandar Joshi and
Danqi Chen and
Omer Levy and
Mike Lewis and
Luke Zettlemoyer and
Veselin Stoyanov},
title = {RoBERTa: {A} Robustly Optimized {BERT} Pretraining Approach},
journal = {CoRR},
volume = {abs/1907.11692},
year = {2019},
url = {http://arxiv.org/abs/1907.11692},
archivePrefix = {arXiv},
eprint = {1907.11692},
timestamp = {Thu, 01 Aug 2019 08:59:33 +0200},
biburl = {https://dblp.org/rec/journals/corr/abs-1907-11692.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
```
and the dataset used for fine-tuning:
```
@misc{ljubešić2019frenk,
title={The FRENK Datasets of Socially Unacceptable Discourse in Slovene and English},
author={Nikola Ljubešić and Darja Fišer and Tomaž Erjavec},
year={2019},
eprint={1906.02045},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/1906.02045}
}
```
| 21422e77c86c200b9a213cebd39c6183 |
cj-mills/bert-base-uncased-issues-128 | cj-mills | bert | 10 | 2 | transformers | 0 | fill-mask | true | false | false | apache-2.0 | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 1,951 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased-issues-128
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: 1.2526
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 16
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.1071 | 1.0 | 291 | 1.6964 |
| 1.6421 | 2.0 | 582 | 1.4279 |
| 1.4853 | 3.0 | 873 | 1.3924 |
| 1.4014 | 4.0 | 1164 | 1.3701 |
| 1.3388 | 5.0 | 1455 | 1.1944 |
| 1.283 | 6.0 | 1746 | 1.2795 |
| 1.2394 | 7.0 | 2037 | 1.2671 |
| 1.2014 | 8.0 | 2328 | 1.2084 |
| 1.1668 | 9.0 | 2619 | 1.1783 |
| 1.14 | 10.0 | 2910 | 1.2076 |
| 1.1277 | 11.0 | 3201 | 1.2081 |
| 1.1053 | 12.0 | 3492 | 1.1628 |
| 1.0819 | 13.0 | 3783 | 1.2544 |
| 1.0763 | 14.0 | 4074 | 1.1695 |
| 1.0634 | 15.0 | 4365 | 1.1157 |
| 1.0637 | 16.0 | 4656 | 1.2526 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1
| c47f5cd9227a8bb44ba8811a475a1813 |
huggingnft/boredapeyachtclub__2__mutant-ape-yacht-club | huggingnft | null | 3 | 0 | null | 1 | image-to-image | true | false | false | mit | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['huggan', 'gan', 'image-to-image', 'huggingnft', 'nft', 'image', 'images'] | false | true | true | 8,484 | false |
# CycleGAN for unpaired image-to-image translation.
## Model description
CycleGAN for unpaired image-to-image translation.
Given two image domains A and B, the following components are trained end2end to translate between such domains:
- A generator A to B, named G_AB conditioned on an image from A
- A generator B to A, named G_BA conditioned on an image from B
- A domain classifier D_A, associated with G_AB
- A domain classifier D_B, associated with G_BA
At inference time, G_AB or G_BA are relevant to translate images, respectively A to B or B to A.
In the general setting, this technique provides style transfer functionalities between the selected image domains A and B.
This allows to obtain a generated translation by G_AB, of an image from domain A that resembles the distribution of the images from domain B, and viceversa for the generator G_BA.
Under these framework, these aspects have been used to perform style transfer between NFT collections.
A collection is selected as domain A, another one as domain B and the CycleGAN provides forward and backward translation between A and B.
This has showed to allows high quality translation even in absence of paired sample-ground-truth data.
In particular, the model performs well with stationary backgrounds (no drastic texture changes in the appearance of backgrounds) as it is capable of recognizing the attributes of each of the elements of an NFT collections.
An attribute can be a variation in type of dressed fashion items such as sunglasses, earrings, clothes and also face or body attributes with respect to a common template model of the given NFT collection).
## Intended uses & limitations
#### How to use
```python
import torch
from PIL import Image
from huggan.pytorch.cyclegan.modeling_cyclegan import GeneratorResNet
from torchvision import transforms as T
from torchvision.transforms import Compose, Resize, ToTensor, Normalize
from torchvision.utils import make_grid
from huggingface_hub import hf_hub_download, file_download
from accelerate import Accelerator
import json
def load_lightweight_model(model_name):
file_path = file_download.hf_hub_download(
repo_id=model_name,
filename="config.json"
)
config = json.loads(open(file_path).read())
organization_name, name = model_name.split("/")
model = Trainer(**config, organization_name=organization_name, name=name)
model.load(use_cpu=True)
model.accelerator = Accelerator()
return model
def get_concat_h(im1, im2):
dst = Image.new('RGB', (im1.width + im2.width, im1.height))
dst.paste(im1, (0, 0))
dst.paste(im2, (im1.width, 0))
return dst
n_channels = 3
image_size = 256
input_shape = (image_size, image_size)
transform = Compose([
T.ToPILImage(),
T.Resize(input_shape),
ToTensor(),
Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
])
# load the translation model from source to target images: source will be generated by a separate Lightweight GAN, w
# while the target images are the result of the translation applied by the GeneratorResnet to the generated source images.
# Hence, given the source domain A and target domain B,
# B = Translator(GAN(A))
translator = GeneratorResNet.from_pretrained(f'huggingnft/{model_name}',
input_shape=(n_channels, image_size, image_size),
num_residual_blocks=9)
# sample noise that is used to generate source images by the
z = torch.randn(nrows, 100, 1, 1)
# load the GAN generator of source images that will be translated by the translation model
model = load_lightweight_model(f"huggingnft/{model_name.split('__2__')[0]}")
collectionA = model.generate_app(
num=timestamped_filename(),
nrow=nrows,
checkpoint=-1,
types="default"
)[1]
# resize to translator model input shape
resize = T.Resize((256, 256))
input = resize(collectionA)
# translate the resized collectionA to collectionB
collectionB = translator(input)
out_transform = T.ToPILImage()
results = []
for collA_image, collB_image in zip(input, collectionB):
results.append(
get_concat_h(out_transform(make_grid(collA_image, nrow=1, normalize=True)), out_transform(make_grid(collB_image, nrow=1, normalize=True)))
)
```
#### Limitations and bias
Translation between collections provides exceptional output images in the case of NFT collections that portray subjects in the same way.
If the backgrounds vary too much within either of the collections, performance degrades or many more training iterations re required to achieve acceptable results.
## Training data
The CycleGAN model is trained on an unpaired dataset of samples from two selected NFT collections: colle tionA and collectionB.
To this end, two collections are loaded by means of the function load_dataset in the huggingface library, as follows.
A list of all available collections is available at [huggingNFT](https://huggingface.co/huggingnft)
```python
from datasets import load_dataset
collectionA = load_dataset("huggingnft/COLLECTION_A")
collectionB = load_dataset("huggingnft/COLLECTION_B")
```
## Training procedure
#### Preprocessing
The following transformations are applied to each input sample of collectionA and collectionB.
The input size is fixed to RGB images of height, width = 256, 256
```python
n_channels = 3
image_size = 256
input_shape = (image_size, image_size)
transform = Compose([
T.ToPILImage(),
T.Resize(input_shape),
ToTensor(),
Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
])
```
#### Hardware
The configuration has been tested on single GPU setup on a RTX5000 and A5000, as well as multi-gpu single-rank distributed setups composed of 2 of the mentioned GPUs.
#### Hyperparameters
The following configuration has been kept fixed for all translation models:
- learning rate 0.0002
- number of epochs 200
- learning rate decay activation at epoch 80
- number of residual blocks of the cyclegan 9
- cycle loss weight 10.0
- identity loss weight 5.0
- optimizer ADAM with beta1 0.5 and beta2 0.999
- batch size 8
- NO mixed precision training
## Eval results
#### Training reports
[Cryptopunks to boreapeyachtclub](https://wandb.ai/chris1nexus/experiments--experiments_cyclegan_punk_to_apes_HQ--0/reports/CycleGAN-training-report--VmlldzoxODUxNzQz?accessToken=vueurpbhd2i8n347j880yakggs0sqdf7u0hpz3bpfsbrxcmk1jk4obg18f6wfk9w)
[Boreapeyachtclub to mutant-ape-yacht-club](https://wandb.ai/chris1nexus/experiments--my_paperspace_boredapeyachtclub__2__mutant-ape-yacht-club--11/reports/CycleGAN-training-report--VmlldzoxODUxNzg4?accessToken=jpyviwn7kdf5216ycrthwp6l8t3heb0lt8djt7dz12guu64qnpdh3ekecfcnoahu)
#### Generated Images
In the provided images, row0 and row2 represent real images from the respective collections.
Row1 is the translation of the immediate above images in row0 by means of the G_AB translation model.
Row3 is the translation of the immediate above images in row2 by means of the G_BA translation model.
Visualization over the training iterations for [boreapeyachtclub to mutant-ape-yacht-club](https://wandb.ai/chris1nexus/experiments--my_paperspace_boredapeyachtclub__2__mutant-ape-yacht-club--11/reports/Shared-panel-22-04-15-08-04-99--VmlldzoxODQ0MDI3?accessToken=45m3kxex5m3rpev3s6vmrv69k3u9p9uxcsp2k90wvbxwxzlqbqjqlnmgpl9265c0)
Visualization over the training iterations for [Cryptopunks to boreapeyachtclub](https://wandb.ai/chris1nexus/experiments--experiments_cyclegan_punk_to_apes_HQ--0/reports/Shared-panel-22-04-17-11-04-83--VmlldzoxODUxNjk5?accessToken=o25si6nflp2xst649vt6ayt56bnb95mxmngt1ieso091j2oazmqnwaf4h78vc2tu)
### References
```bibtex
@misc{https://doi.org/10.48550/arxiv.1703.10593,
doi = {10.48550/ARXIV.1703.10593},
url = {https://arxiv.org/abs/1703.10593},
author = {Zhu, Jun-Yan and Park, Taesung and Isola, Phillip and Efros, Alexei A.},
keywords = {Computer Vision and Pattern Recognition (cs.CV), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks},
publisher = {arXiv},
year = {2017},
copyright = {arXiv.org perpetual, non-exclusive license}
}
```
### BibTeX entry and citation info
```bibtex
@InProceedings{huggingnft,
author={Aleksey Korshuk, Christian Cancedda}
year=2022
}
```
| 6083008688ef4ad09ff4d56977803903 |
sd-dreambooth-library/drag-queen-shangela | sd-dreambooth-library | null | 19 | 8 | diffusers | 0 | text-to-image | false | false | false | creativeml-openrail-m | null | null | null | 2 | 2 | 0 | 0 | 0 | 0 | 0 | ['text-to-image'] | false | true | true | 1,216 | false | ### drag_queen_Shangela on Stable Diffusion via Dreambooth trained on the [fast-DreamBooth.ipynb by TheLastBen](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook
#### Model by chrisin2d
This your the Stable Diffusion model fine-tuned the drag_queen_Shangela concept taught to Stable Diffusion with Dreambooth.
It can be used by modifying the `instance_prompt(s)`: ****
You can also train your own concepts and upload them to the library by using [the fast-DremaBooth.ipynb by TheLastBen](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb).
You can run your new concept via A1111 Colab :[Fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb)
Or you can run your new concept via `diffusers`: [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb), [Spaces with the Public Concepts loaded](https://huggingface.co/spaces/sd-dreambooth-library/stable-diffusion-dreambooth-concepts)
Sample pictures of this concept:
| 6f0fe8379702de3829b7657c18fbc47b |
google/multiberts-seed_1-step_700k | google | bert | 8 | 13 | transformers | 0 | null | true | true | false | apache-2.0 | ['en'] | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['multiberts', 'multiberts-seed_1', 'multiberts-seed_1-step_700k'] | false | true | true | 3,521 | false |
# MultiBERTs, Intermediate Checkpoint - Seed 1, Step 700k
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained with
similar hyper-parameters as
[the original BERT model](https://github.com/google-research/bert) but
with different random seeds, which causes variations in the initial weights and order of
training instances. The aim is to distinguish findings that apply to a specific
artifact (i.e., a particular instance of the model) from those that apply to the
more general procedure.
We also provide 140 intermediate checkpoints captured
during the course of pre-training (we saved 28 checkpoints for the first 5 runs).
The models were originally released through
[http://goo.gle/multiberts](http://goo.gle/multiberts). We describe them in our
paper
[The MultiBERTs: BERT Reproductions for Robustness Analysis](https://arxiv.org/abs/2106.16163).
This is model #1, captured at step 700k (max: 2000k, i.e., 2M steps).
## Model Description
This model was captured during a reproduction of
[BERT-base uncased](https://github.com/google-research/bert), for English: it
is a Transformers model pretrained on a large corpus of English data, using the
Masked Language Modelling (MLM) and the Next Sentence Prediction (NSP)
objectives.
The intended uses, limitations, training data and training procedure for the fully trained model are similar
to [BERT-base uncased](https://github.com/google-research/bert). Two major
differences with the original model:
* We pre-trained the MultiBERTs models for 2 million steps using sequence
length 512 (instead of 1 million steps using sequence length 128 then 512).
* We used an alternative version of Wikipedia and Books Corpus, initially
collected for [Turc et al., 2019](https://arxiv.org/abs/1908.08962).
This is a best-effort reproduction, and so it is probable that differences with
the original model have gone unnoticed. The performance of MultiBERTs on GLUE after full training is oftentimes comparable to that of original
BERT, but we found significant differences on the dev set of SQuAD (MultiBERTs outperforms original BERT).
See our [technical report](https://arxiv.org/abs/2106.16163) for more details.
### How to use
Using code from
[BERT-base uncased](https://huggingface.co/bert-base-uncased), here is an example based on
Tensorflow:
```
from transformers import BertTokenizer, TFBertModel
tokenizer = BertTokenizer.from_pretrained('google/multiberts-seed_1-step_700k')
model = TFBertModel.from_pretrained("google/multiberts-seed_1-step_700k")
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='tf')
output = model(encoded_input)
```
PyTorch version:
```
from transformers import BertTokenizer, BertModel
tokenizer = BertTokenizer.from_pretrained('google/multiberts-seed_1-step_700k')
model = BertModel.from_pretrained("google/multiberts-seed_1-step_700k")
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
```
## Citation info
```bibtex
@article{sellam2021multiberts,
title={The MultiBERTs: BERT Reproductions for Robustness Analysis},
author={Thibault Sellam and Steve Yadlowsky and Jason Wei and Naomi Saphra and Alexander D'Amour and Tal Linzen and Jasmijn Bastings and Iulia Turc and Jacob Eisenstein and Dipanjan Das and Ian Tenney and Ellie Pavlick},
journal={arXiv preprint arXiv:2106.16163},
year={2021}
}
```
| 314da41f03ac88c14ce1beeda14c72f4 |
doc2query/msmarco-german-mt5-base-v1 | doc2query | mt5 | 10 | 455 | transformers | 2 | text2text-generation | true | false | false | apache-2.0 | ['de'] | ['unicamp-dl/mmarco'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | [] | false | true | true | 3,823 | false |
# doc2query/msmarco-german-mt5-base-v1
This is a [doc2query](https://arxiv.org/abs/1904.08375) model based on mT5 (also known as [docT5query](https://cs.uwaterloo.ca/~jimmylin/publications/Nogueira_Lin_2019_docTTTTTquery-v2.pdf)).
It can be used for:
- **Document expansion**: You generate for your paragraphs 20-40 queries and index the paragraphs and the generates queries in a standard BM25 index like Elasticsearch, OpenSearch, or Lucene. The generated queries help to close the lexical gap of lexical search, as the generate queries contain synonyms. Further, it re-weights words giving important words a higher weight even if they appear seldomn in a paragraph. In our [BEIR](https://arxiv.org/abs/2104.08663) paper we showed that BM25+docT5query is a powerful search engine. In the [BEIR repository](https://github.com/beir-cellar/beir) we have an example how to use docT5query with Pyserini.
- **Domain Specific Training Data Generation**: It can be used to generate training data to learn an embedding model. In our [GPL-Paper](https://arxiv.org/abs/2112.07577) / [GPL Example on SBERT.net](https://www.sbert.net/examples/domain_adaptation/README.html#gpl-generative-pseudo-labeling) we have an example how to use the model to generate (query, text) pairs for a given collection of unlabeled texts. These pairs can then be used to train powerful dense embedding models.
## Usage
```python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch
model_name = 'doc2query/msmarco-german-mt5-base-v1'
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
text = "Python ist eine universelle, üblicherweise interpretierte, höhere Programmiersprache. Sie hat den Anspruch, einen gut lesbaren, knappen Programmierstil zu fördern. So werden beispielsweise Blöcke nicht durch geschweifte Klammern, sondern durch Einrückungen strukturiert."
def create_queries(para):
input_ids = tokenizer.encode(para, return_tensors='pt')
with torch.no_grad():
# Here we use top_k / top_k random sampling. It generates more diverse queries, but of lower quality
sampling_outputs = model.generate(
input_ids=input_ids,
max_length=64,
do_sample=True,
top_p=0.95,
top_k=10,
num_return_sequences=5
)
# Here we use Beam-search. It generates better quality queries, but with less diversity
beam_outputs = model.generate(
input_ids=input_ids,
max_length=64,
num_beams=5,
no_repeat_ngram_size=2,
num_return_sequences=5,
early_stopping=True
)
print("Paragraph:")
print(para)
print("\nBeam Outputs:")
for i in range(len(beam_outputs)):
query = tokenizer.decode(beam_outputs[i], skip_special_tokens=True)
print(f'{i + 1}: {query}')
print("\nSampling Outputs:")
for i in range(len(sampling_outputs)):
query = tokenizer.decode(sampling_outputs[i], skip_special_tokens=True)
print(f'{i + 1}: {query}')
create_queries(text)
```
**Note:** `model.generate()` is non-deterministic for top_k/top_n sampling. It produces different queries each time you run it.
## Training
This model fine-tuned [google/mt5-base](https://huggingface.co/google/mt5-base) for 66k training steps (4 epochs on the 500k training pairs from MS MARCO). For the training script, see the `train_script.py` in this repository.
The input-text was truncated to 320 word pieces. Output text was generated up to 64 word pieces.
This model was trained on a (query, passage) from the [mMARCO dataset](https://github.com/unicamp-dl/mMARCO).
| d7b6e64363dc78d6a44dc361d5a3482a |
slplab/wav2vec2_xlsr50k_english_phoneme | slplab | wav2vec2 | 12 | 8 | transformers | 0 | automatic-speech-recognition | true | false | false | apache-2.0 | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 1,765 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2_xlsr50k_english_phoneme
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on [the TIMIT dataset](https://catalog.ldc.upenn.edu/LDC93s1).
It achieves the following results on the evaluation set:
- Loss: 0.5783
- Cer: 0.1178
## 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: 0.0003
- train_batch_size: 32
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Cer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 4.8403 | 6.94 | 500 | 1.1345 | 0.4657 |
| 0.5795 | 13.88 | 1000 | 0.3579 | 0.1169 |
| 0.3567 | 20.83 | 1500 | 0.3866 | 0.1174 |
| 0.2717 | 27.77 | 2000 | 0.4219 | 0.1169 |
| 0.2135 | 34.72 | 2500 | 0.4861 | 0.1199 |
| 0.1664 | 41.66 | 3000 | 0.5490 | 0.1179 |
| 0.1375 | 48.61 | 3500 | 0.5783 | 0.1178 |
### Framework versions
- Transformers 4.22.0.dev0
- Pytorch 1.12.1
- Datasets 1.13.3
- Tokenizers 0.12.1
| 3fe8cdcb222346950f4d6fa45c6b71f4 |
marcus2000/model_for_inca | marcus2000 | distilbert | 16 | 4 | transformers | 0 | text-classification | true | false | false | apache-2.0 | null | ['imdb'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 1,065 | false |
<!-- 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. -->
# model_for_inca
This model is a fine-tuned version of [marcus2000/finetuning-sentiment-model-3000-samples](https://huggingface.co/marcus2000/finetuning-sentiment-model-3000-samples) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3349
- F1: 0.9281
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
### Framework versions
- Transformers 4.21.2
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
| f964b3513d1e2c3898dfb557e5fbea3b |
Yehor/wav2vec2-xls-r-1b-uk-with-lm | Yehor | wav2vec2 | 24 | 14 | transformers | 3 | automatic-speech-recognition | true | false | false | apache-2.0 | ['uk'] | ['mozilla-foundation/common_voice_7_0'] | null | 0 | 0 | 0 | 0 | 1 | 0 | 1 | ['automatic-speech-recognition', 'generated_from_trainer', 'hf-asr-leaderboard', 'mozilla-foundation/common_voice_7_0', 'robust-speech-event', 'uk'] | true | true | true | 2,589 | false |
# Ukrainian STT model (with Language Model)
🇺🇦 Join Ukrainian Speech Recognition Community - https://t.me/speech_recognition_uk
⭐ See other Ukrainian models - https://github.com/egorsmkv/speech-recognition-uk
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - UK dataset.
It achieves the following results on the evaluation set without the language model:
- Loss: 0.1875
- Wer: 0.2033
- Cer: 0.0384
## Model description
On 100 test example the model shows the following results:
Without LM:
- WER: 0.1862
- CER: 0.0277
With LM:
- WER: 0.1218
- CER: 0.0190
## 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
- gradient_accumulation_steps: 20
- total_train_batch_size: 160
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 100.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|
| 1.2815 | 7.93 | 500 | 0.3536 | 0.4753 | 0.1009 |
| 1.0869 | 15.86 | 1000 | 0.2317 | 0.3111 | 0.0614 |
| 0.9984 | 23.8 | 1500 | 0.2022 | 0.2676 | 0.0521 |
| 0.975 | 31.74 | 2000 | 0.1948 | 0.2469 | 0.0487 |
| 0.9306 | 39.67 | 2500 | 0.1916 | 0.2377 | 0.0464 |
| 0.8868 | 47.61 | 3000 | 0.1903 | 0.2257 | 0.0439 |
| 0.8424 | 55.55 | 3500 | 0.1786 | 0.2206 | 0.0423 |
| 0.8126 | 63.49 | 4000 | 0.1849 | 0.2160 | 0.0416 |
| 0.7901 | 71.42 | 4500 | 0.1869 | 0.2138 | 0.0413 |
| 0.7671 | 79.36 | 5000 | 0.1855 | 0.2075 | 0.0394 |
| 0.7467 | 87.3 | 5500 | 0.1884 | 0.2049 | 0.0389 |
| 0.731 | 95.24 | 6000 | 0.1877 | 0.2060 | 0.0387 |
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.1+cu102
- Datasets 1.18.1.dev0
- Tokenizers 0.11.0
#### Evaluation Commands
1. To evaluate on `mozilla-foundation/common_voice_7_0` with split `test`
```bash
python eval.py --model_id Yehor/wav2vec2-xls-r-1b-uk-with-lm --dataset mozilla-foundation/common_voice_7_0 --config uk --split test
```
### Eval results on Common Voice 7 "test" (WER):
| Without LM | With LM (run `./eval.py`) |
|---|---|
| 21.52 | 14.62 |
| a1a650e249808a23788b1ff3e9a585ef |
sd-dreambooth-library/backpack | sd-dreambooth-library | null | 20 | 3 | diffusers | 0 | null | false | false | false | mit | null | null | null | 2 | 2 | 0 | 0 | 0 | 0 | 0 | [] | false | true | true | 725 | false | ### Backpack on Stable Diffusion via Dreambooth
#### model by homanp
This your the Stable Diffusion model fine-tuned the Backpack concept taught to Stable Diffusion with Dreambooth.
It can be used by modifying the `instance_prompt`: **a photo of sks backpack**
You can also train your own concepts and upload them to the library by using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_training.ipynb).
Here are the images used for training this concept:
| 1e5a595eb4d1077e0e194aadb8f48e27 |
spacy/de_core_news_md | spacy | null | 32 | 32 | spacy | 0 | token-classification | false | false | false | mit | ['de'] | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['spacy', 'token-classification'] | false | true | true | 31,287 | false | ### Details: https://spacy.io/models/de#de_core_news_md
German pipeline optimized for CPU. Components: tok2vec, tagger, morphologizer, parser, lemmatizer (trainable_lemmatizer), senter, ner.
| Feature | Description |
| --- | --- |
| **Name** | `de_core_news_md` |
| **Version** | `3.5.0` |
| **spaCy** | `>=3.5.0,<3.6.0` |
| **Default Pipeline** | `tok2vec`, `tagger`, `morphologizer`, `parser`, `lemmatizer`, `attribute_ruler`, `ner` |
| **Components** | `tok2vec`, `tagger`, `morphologizer`, `parser`, `lemmatizer`, `senter`, `attribute_ruler`, `ner` |
| **Vectors** | 500000 keys, 20000 unique vectors (300 dimensions) |
| **Sources** | [TIGER Corpus](https://www.ims.uni-stuttgart.de/forschung/ressourcen/korpora/tiger.html) (Brants, Sabine, Stefanie Dipper, Peter Eisenberg, Silvia Hansen, Esther König, Wolfgang Lezius, Christian Rohrer, George Smith, and Hans Uszkoreit)<br />[Tiger2Dep](https://www.ims.uni-stuttgart.de/forschung/ressourcen/werkzeuge/tiger2dep/) (Wolfgang Seeker)<br />[WikiNER](https://figshare.com/articles/Learning_multilingual_named_entity_recognition_from_Wikipedia/5462500) (Joel Nothman, Nicky Ringland, Will Radford, Tara Murphy, James R Curran)<br />[Explosion fastText Vectors (cbow, OSCAR Common Crawl + Wikipedia)](https://spacy.io) (Explosion) |
| **License** | `MIT` |
| **Author** | [Explosion](https://explosion.ai) |
### Label Scheme
<details>
<summary>View label scheme (772 labels for 4 components)</summary>
| Component | Labels |
| --- | --- |
| **`tagger`** | `$(`, `$,`, `$.`, `ADJA`, `ADJD`, `ADV`, `APPO`, `APPR`, `APPRART`, `APZR`, `ART`, `CARD`, `FM`, `ITJ`, `KOKOM`, `KON`, `KOUI`, `KOUS`, `NE`, `NN`, `NNE`, `PDAT`, `PDS`, `PIAT`, `PIS`, `PPER`, `PPOSAT`, `PPOSS`, `PRELAT`, `PRELS`, `PRF`, `PROAV`, `PTKA`, `PTKANT`, `PTKNEG`, `PTKVZ`, `PTKZU`, `PWAT`, `PWAV`, `PWS`, `TRUNC`, `VAFIN`, `VAIMP`, `VAINF`, `VAPP`, `VMFIN`, `VMINF`, `VMPP`, `VVFIN`, `VVIMP`, `VVINF`, `VVIZU`, `VVPP`, `XY`, `_SP` |
| **`morphologizer`** | `POS=PUNCT`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Mood=Sub\|Number=Sing\|POS=AUX\|Person=3\|Tense=Past\|VerbForm=Fin`, `POS=ADV`, `Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Art`, `Case=Nom\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Case=Acc\|Definite=Def\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Art`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=NOUN`, `POS=ADP`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Case=Acc\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs`, `Case=Nom\|Definite=Def\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Art`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Case=Acc\|Definite=Def\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Art`, `Case=Acc\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=NOUN`, `Case=Dat\|Gender=Neut\|Number=Sing\|POS=PROPN`, `POS=VERB\|VerbForm=Part`, `Case=Dat\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Foreign=Yes\|POS=X`, `Degree=Pos\|POS=ADV`, `Case=Dat\|Gender=Neut\|Number=Sing\|POS=ADP`, `Case=Dat\|Gender=Neut\|Number=Sing\|POS=NOUN`, `Case=Dat\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Dat\|Definite=Def\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Art`, `Case=Dat\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=NOUN`, `POS=CCONJ`, `POS=SCONJ`, `Case=Acc\|Definite=Ind\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Art`, `POS=VERB\|VerbForm=Inf`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=DET\|Poss=Yes\|PronType=Prs`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Case=Acc\|Definite=Def\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Art`, `Case=Acc\|Degree=Sup\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Gen\|Definite=Def\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Art`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=NOUN`, `POS=PART`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Ind`, `Case=Nom\|Definite=Def\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Art`, `Case=Dat\|Definite=Def\|Number=Plur\|POS=DET\|PronType=Art`, `Case=Dat\|Number=Plur\|POS=PROPN`, `POS=PRON\|PronType=Ind`, `Case=Dat\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs\|Reflex=Yes`, `Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Art`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Case=Dat\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Art`, `Case=Gen\|Definite=Def\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Art`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=NOUN`, `Case=Nom\|Number=Sing\|POS=PROPN`, `Case=Dat\|Definite=Def\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Art`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=NOUN`, `POS=NUM`, `Case=Dat\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=ADP`, `Gender=Neut\|POS=NOUN`, `Case=Acc\|Number=Sing\|POS=PROPN`, `Case=Acc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs\|Reflex=Yes`, `Case=Nom\|Definite=Def\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Art`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Case=Gen\|Definite=Def\|Number=Plur\|POS=DET\|PronType=Art`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Mood=Sub\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Gen\|Definite=Def\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Art`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Case=Nom\|Definite=Def\|Number=Plur\|POS=DET\|PronType=Art`, `Case=Nom\|Number=Plur\|POS=NOUN`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs`, `Case=Dat\|Definite=Def\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Art`, `Case=Nom\|Number=Plur\|POS=PRON\|PronType=Ind`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Art`, `POS=PROPN`, `Case=Nom\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin`, `POS=INTJ`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Dem`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=NOUN`, `Case=Acc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs\|Reflex=Yes`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Mood=Sub\|Number=Sing\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Rel`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Ind`, `Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Art`, `Case=Nom\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Dat\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Ind`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Rel`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Ind`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Nom\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Case=Acc\|Gender=Neut\|Number=Plur\|POS=PRON\|PronType=Rel`, `Case=Dat\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Rel`, `Gender=Masc\|POS=NOUN`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin`, `Case=Nom\|Definite=Def\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Art`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Int`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=PROPN`, `POS=SCONJ\|PronType=Int`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=DET\|Poss=Yes\|PronType=Prs`, `Case=Dat\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Nom\|Number=Sing\|POS=PRON\|PronType=Ind`, `Case=Gen\|Definite=Def\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Art`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Dat\|Definite=Def\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Art`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Ind`, `Case=Dat\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs`, `Case=Dat\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Ind`, `Case=Dat\|Degree=Cmp\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Nom\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Gender=Neut\|POS=PRON\|PronType=Ind`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Dat\|Number=Plur\|POS=DET\|Poss=Yes\|PronType=Prs`, `Case=Dat\|Number=Plur\|POS=NOUN`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Ind`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Dem`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=ADP`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Int`, `Case=Gen\|Number=Plur\|POS=PROPN`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Acc\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Case=Acc\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Nom\|Definite=Def\|Gender=Neut\|Number=Plur\|POS=DET\|PronType=Art`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs`, `Degree=Cmp\|POS=ADV`, `Case=Nom\|Gender=Neut\|Number=Plur\|POS=PRON\|PronType=Dem`, `Case=Gen\|Definite=Ind\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Art`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=ADP`, `Case=Dat\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Int`, `Case=Dat\|Definite=Ind\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Art`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Tense=Past\|VerbForm=Fin`, `Case=Gen\|Definite=Def\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Art`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Acc\|Degree=Sup\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Nom\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=DET\|Poss=Yes\|PronType=Prs`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Past\|VerbForm=Fin`, `Case=Gen\|Number=Sing\|POS=PROPN`, `Case=Nom\|Definite=Def\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Art`, `Case=Nom\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Dat\|Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Rel`, `Case=Acc\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Ind`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=PROPN`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Rel`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Int`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Ind`, `Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Art`, `POS=X`, `Case=Dat\|Degree=Sup\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Gen\|Number=Plur\|POS=NOUN`, `Case=Gen\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Gen\|Number=Plur\|POS=DET\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Number=Plur\|POS=PRON\|PronType=Rel`, `Case=Nom\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Gen\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Gen\|Definite=Def\|Gender=Neut\|Number=Plur\|POS=DET\|PronType=Art`, `Case=Gen\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Gen\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Ind`, `Mood=Sub\|Number=Plur\|POS=AUX\|Person=3\|Tense=Past\|VerbForm=Fin`, `Case=Nom\|Degree=Pos\|Number=Plur\|POS=ADJ`, `Case=Nom\|Number=Plur\|POS=PRON\|PronType=Rel`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Rel`, `Case=Acc\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Dat\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Acc\|Definite=Def\|Gender=Neut\|Number=Plur\|POS=DET\|PronType=Art`, `Case=Acc\|Degree=Sup\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Nom\|Degree=Sup\|Gender=Masc\|Number=Sing\|POS=ADJ`, `POS=SPACE`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Rel`, `POS=DET\|PronType=Ind`, `Case=Gen\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Dat\|Gender=Fem\|Number=Plur\|POS=DET\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=PROPN`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Case=Acc\|Definite=Def\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Art`, `Case=Dat\|Definite=Def\|Gender=Neut\|Number=Plur\|POS=DET\|PronType=Art`, `Case=Nom\|Gender=Neut\|Number=Plur\|POS=DET\|PronType=Ind`, `Case=Dat\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs`, `Case=Dat\|Number=Sing\|POS=NOUN`, `Case=Nom\|Definite=Ind\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Art`, `Degree=Pos\|POS=ADJ`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Case=Acc\|Degree=Pos\|Number=Plur\|POS=ADJ`, `Case=Acc\|Number=Plur\|POS=NOUN`, `Case=Dat\|Number=Plur\|POS=PRON\|PronType=Rel`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Dem`, `Mood=Sub\|Number=Sing\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin`, `Case=Acc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Nom\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Rel`, `Case=Nom\|Gender=Neut\|Number=Plur\|POS=PRON\|PronType=Rel`, `POS=AUX\|VerbForm=Inf`, `Case=Acc\|Gender=Neut\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Case=Nom\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Ind`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Acc\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Definite=Def\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Art`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Case=Gen\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Ind`, `Case=Acc\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Rel`, `Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Art`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Dat\|Gender=Neut\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs`, `Mood=Sub\|Number=Plur\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin`, `POS=ADV\|PronType=Int`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Int`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Int`, `POS=AUX\|VerbForm=Part`, `Case=Dat\|Gender=Neut\|Number=Plur\|POS=DET\|PronType=Ind`, `Case=Dat\|Number=Plur\|POS=PRON\|PronType=Ind`, `Case=Nom\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Dat\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Dat\|Definite=Def\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Art`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Case=Dat\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs\|Reflex=Yes`, `Case=Acc\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Acc\|Degree=Cmp\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Dem`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Case=Dat\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Ind`, `Case=Dat\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs\|Reflex=Yes`, `Case=Acc\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Dat\|Degree=Pos\|Number=Plur\|POS=ADJ`, `Case=Dat\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Ind`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=PROPN`, `Case=Nom\|Number=Plur\|POS=DET\|PronType=Ind`, `Degree=Sup\|POS=ADV`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Rel`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Ind`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Rel`, `Case=Nom\|Number=Sing\|POS=NOUN`, `Case=Acc\|Definite=Def\|Number=Plur\|POS=DET\|PronType=Art`, `Case=Gen\|Number=Sing\|POS=NOUN`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Ind`, `Case=Nom\|Degree=Cmp\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=DET\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Degree=Cmp\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Nom\|Number=Plur\|POS=PRON\|PronType=Dem`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Dem`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Ind`, `Case=Acc\|Number=Plur\|POS=PRON\|PronType=Ind`, `Case=Dat\|Number=Plur\|POS=DET\|PronType=Ind`, `Mood=Sub\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin`, `Case=Dat\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Rel`, `Definite=Ind\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Art`, `Case=Dat\|Gender=Neut\|Number=Plur\|POS=DET\|Poss=Yes\|PronType=Prs`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Ind`, `Case=Acc\|Degree=Cmp\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Gender=Fem\|POS=NOUN`, `Case=Gen\|Degree=Sup\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Dat\|Degree=Cmp\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Art`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs`, `Case=Dat\|Degree=Sup\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Ind`, `Case=Acc\|Gender=Neut\|Number=Plur\|POS=DET\|PronType=Ind`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Dem`, `Case=Gen\|Gender=Neut\|Number=Plur\|POS=DET\|PronType=Ind`, `Case=Nom\|Degree=Sup\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Rel`, `Case=Nom\|Degree=Cmp\|Number=Plur\|POS=ADJ`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Rel`, `Case=Dat\|Gender=Fem\|Number=Plur\|POS=PROPN`, `Case=Dat\|Number=Sing\|POS=PROPN`, `Case=Gen\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Ind`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Dat\|Gender=Neut\|Number=Plur\|POS=PRON\|PronType=Rel`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Ind`, `Case=Nom\|Number=Plur\|POS=PROPN`, `Mood=Sub\|Number=Plur\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin`, `Case=Acc\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs\|Reflex=Yes`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Rel`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Rel`, `Case=Gen\|Gender=Neut\|Number=Plur\|POS=DET\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Number=Plur\|POS=DET\|PronType=Rel`, `Case=Dat\|Degree=Sup\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Nom\|Gender=Neut\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Nom\|Degree=Cmp\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=PROPN`, `Case=Dat\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Ind`, `Case=Nom\|Degree=Sup\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Gen\|Degree=Pos\|Number=Plur\|POS=ADJ`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Dat\|Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Ind`, `Case=Nom\|Degree=Cmp\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Dat\|Number=Plur\|POS=PRON\|PronType=Dem`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Dem`, `Case=Acc\|Degree=Cmp\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Acc\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=DET\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Rel`, `Gender=Masc\|Number=Sing\|POS=NOUN`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs`, `Case=Nom\|Definite=Ind\|Number=Sing\|POS=DET\|PronType=Art`, `Case=Acc\|Number=Plur\|POS=PRON\|Poss=Yes\|PronType=Prs`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Int`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Dem`, `Gender=Fem\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs`, `Gender=Fem\|Number=Sing\|POS=NOUN`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=DET\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Dem`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Int`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Ind`, `Case=Acc\|Gender=Neut\|Number=Plur\|POS=DET\|Poss=Yes\|PronType=Prs`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Rel`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Ind`, `Case=Dat\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Acc\|Degree=Cmp\|Number=Plur\|POS=ADJ`, `Case=Gen\|Number=Plur\|POS=PRON\|PronType=Dem`, `Case=Nom\|Gender=Neut\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Ind`, `Case=Acc\|Degree=Sup\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Acc\|Degree=Cmp\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Dat\|Gender=Neut\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Ind`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Ind`, `Mood=Sub\|Number=Sing\|POS=AUX\|Person=1\|Tense=Past\|VerbForm=Fin`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Dem`, `Gender=Masc\|Number=Plur\|POS=NOUN`, `Case=Nom\|Degree=Cmp\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Gender=Neut\|POS=PROPN`, `Case=Acc\|Gender=Neut\|Number=Plur\|POS=PROPN`, `Case=Gen\|Degree=Sup\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Dat\|Degree=Sup\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Dat\|Gender=Neut\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Gender=Masc\|POS=PROPN`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Dem`, `Case=Nom\|POS=PROPN`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Nom\|Gender=Neut\|Number=Plur\|POS=PRON\|PronType=Ind`, `Case=Gen\|Gender=Neut\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Dem`, `Case=Acc\|Gender=Neut\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=DET\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Ind`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Rel`, `Case=Acc\|Gender=Neut\|Number=Plur\|POS=DET\|PronType=Int`, `Definite=Def\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Art`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Gen\|Degree=Cmp\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Dem`, `Case=Dat\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Gen\|Degree=Sup\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Rel`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=PROPN`, `Case=Acc\|Number=Plur\|POS=PROPN`, `Case=Nom\|Gender=Neut\|Number=Plur\|POS=DET\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Number=Plur\|POS=DET\|PronType=Ind`, `Case=Dat\|Definite=Ind\|Number=Sing\|POS=DET\|PronType=Art`, `Case=Dat\|Number=Sing\|POS=PRON\|PronType=Rel`, `Case=Gen\|Degree=Cmp\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Dat\|Number=Sing\|POS=ADP`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Rel`, `Mood=Imp\|Number=Sing\|POS=VERB\|Person=2`, `Case=Nom\|Degree=Cmp\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Nom\|Degree=Cmp\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=PROPN`, `Case=Acc\|Gender=Masc\|POS=NOUN`, `Case=Dat\|Degree=Sup\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Ind`, `Case=Dat\|Degree=Cmp\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Ind`, `Case=Acc\|Degree=Sup\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Acc\|Degree=Cmp\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Gen\|Degree=Sup\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Gen\|Degree=Sup\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Gender=Neut\|Number=Sing\|POS=NOUN`, `POS=NOUN`, `Case=Dat\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Int`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Gen\|Number=Sing\|POS=DET\|PronType=Rel`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Dat\|POS=PROPN`, `Case=Gen\|Definite=Def\|POS=DET\|PronType=Art`, `Case=Gen\|POS=PROPN`, `Case=Acc\|Number=Sing\|POS=NOUN`, `Case=Gen\|Degree=Sup\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Dat\|Degree=Cmp\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Mood=Imp\|Number=Plur\|POS=VERB\|Person=2`, `Case=Dat\|Degree=Cmp\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Dat\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs\|Reflex=Yes`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Rel`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Dem`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Gen\|Degree=Cmp\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Int`, `Case=Gen\|POS=PRON\|PronType=Dem`, `Definite=Ind\|POS=DET\|PronType=Art`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|Tense=Past\|VerbForm=Fin`, `Case=Nom\|Degree=Sup\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Int`, `Case=Dat\|POS=PRON\|PronType=Dem`, `Gender=Masc\|Number=Sing\|POS=PROPN`, `Case=Gen\|Degree=Cmp\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=PRON\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Dem`, `Gender=Neut\|Number=Sing\|POS=PROPN`, `Case=Dat\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Dem`, `Case=Dat\|Number=Sing\|POS=PRON\|PronType=Dem`, `Case=Dat\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Dat\|Degree=Cmp\|Number=Sing\|POS=ADJ`, `Case=Dat\|Degree=Pos\|Number=Sing\|POS=ADJ`, `POS=DET\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Number=Sing\|POS=PRON\|PronType=Dem`, `Gender=Fem\|Number=Sing\|POS=PROPN`, `Case=Nom\|Degree=Sup\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=1\|Tense=Past\|VerbForm=Fin`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=PROPN`, `Case=Nom\|Degree=Sup\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Nom\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin`, `Case=Acc\|Number=Plur\|POS=PRON\|PronType=Dem`, `Case=Nom\|POS=PRON\|Person=3\|PronType=Prs`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=2\|Tense=Pres\|VerbForm=Fin`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Case=Dat\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Case=Acc\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Case=Nom\|Number=Sing\|POS=PRON\|PronType=Int`, `Mood=Sub\|Number=Sing\|POS=AUX\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Int`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Ind`, `Case=Nom\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Gender=Neut\|Number=Plur\|POS=DET\|PronType=Rel`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Int`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Ind`, `Case=Dat\|Degree=Sup\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Acc\|Number=Plur\|POS=DET\|Poss=Yes\|PronType=Prs`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Dat\|Gender=Neut\|Number=Plur\|POS=PRON\|PronType=Ind`, `Case=Dat\|Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Dem`, `Case=Gen\|Number=Plur\|POS=DET\|PronType=Ind`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Ind`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Dem`, `Case=Dat\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Ind`, `Case=Gen\|Degree=Cmp\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Acc\|Degree=Cmp\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Dem`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Int`, `Case=Nom\|Number=Plur\|POS=DET\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Number=Sing\|POS=PRON\|PronType=Ind`, `Definite=Ind\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Art`, `Mood=Sub\|Number=Plur\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Case=Gen\|Number=Plur\|POS=DET\|PronType=Dem`, `Gender=Neut\|POS=DET\|PronType=Ind`, `Case=Dat\|Number=Sing\|POS=PRON\|PronType=Ind`, `Case=Nom\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=2\|Tense=Pres\|VerbForm=Fin`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin`, `Case=Dat\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, _(truncated: full list in pipeline meta)_ |
| **`parser`** | `ROOT`, `ac`, `adc`, `ag`, `ams`, `app`, `avc`, `cc`, `cd`, `cj`, `cm`, `cp`, `cvc`, `da`, `dep`, `dm`, `ep`, `ju`, `mnr`, `mo`, `ng`, `nk`, `nmc`, `oa`, `oc`, `og`, `op`, `par`, `pd`, `pg`, `ph`, `pm`, `pnc`, `punct`, `rc`, `re`, `rs`, `sb`, `sbp`, `svp`, `uc`, `vo` |
| **`ner`** | `LOC`, `MISC`, `ORG`, `PER` |
</details>
### Accuracy
| Type | Score |
| --- | --- |
| `TOKEN_ACC` | 99.96 |
| `TOKEN_P` | 99.92 |
| `TOKEN_R` | 99.90 |
| `TOKEN_F` | 99.91 |
| `TAG_ACC` | 97.81 |
| `POS_ACC` | 98.29 |
| `MORPH_ACC` | 91.51 |
| `MORPH_MICRO_P` | 95.69 |
| `MORPH_MICRO_R` | 95.61 |
| `MORPH_MICRO_F` | 95.65 |
| `SENTS_P` | 95.41 |
| `SENTS_R` | 96.22 |
| `SENTS_F` | 95.08 |
| `DEP_UAS` | 92.54 |
| `DEP_LAS` | 90.57 |
| `LEMMA_ACC` | 97.70 |
| `ENTS_P` | 84.39 |
| `ENTS_R` | 83.43 |
| `ENTS_F` | 83.91 | | 4e7d168011e284d3b02036b82ae308a0 |
wangpuupup/whisper_test | wangpuupup | whisper | 27 | 0 | transformers | 0 | automatic-speech-recognition | true | false | false | apache-2.0 | null | ['data/copas'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['whisper-event', 'generated_from_trainer'] | true | true | true | 2,557 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Whisper Small dysarthric Dutch
This model is a fine-tuned version of [qmeeus/whisper-small-nl](https://huggingface.co/qmeeus/whisper-small-nl) on the data/copas copas-full dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4242
- Wer: 24.5560
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 10000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|
| 0.3363 | 2.02 | 500 | 0.3762 | 29.7934 |
| 0.0945 | 5.02 | 1000 | 0.3418 | 27.6912 |
| 0.0332 | 8.01 | 1500 | 0.3353 | 26.1689 |
| 0.0147 | 11.01 | 2000 | 0.3476 | 26.1327 |
| 0.0071 | 14.01 | 2500 | 0.3623 | 25.9333 |
| 0.0034 | 17.01 | 3000 | 0.3789 | 25.2084 |
| 0.0024 | 20.01 | 3500 | 0.3827 | 24.8641 |
| 0.0026 | 23.01 | 4000 | 0.3877 | 25.3171 |
| 0.0021 | 26.01 | 4500 | 0.3933 | 25.4259 |
| 0.0014 | 29.01 | 5000 | 0.3941 | 25.0997 |
| 0.0008 | 32.01 | 5500 | 0.4014 | 25.0997 |
| 0.0004 | 35.01 | 6000 | 0.4035 | 24.8278 |
| 0.0003 | 38.01 | 6500 | 0.4080 | 24.9184 |
| 0.0003 | 41.01 | 7000 | 0.4120 | 24.8097 |
| 0.0002 | 44.01 | 7500 | 0.4151 | 24.6104 |
| 0.0002 | 47.01 | 8000 | 0.4176 | 24.3929 |
| 0.0002 | 50.01 | 8500 | 0.4200 | 24.5198 |
| 0.0001 | 53.0 | 9000 | 0.4230 | 24.5198 |
| 0.0001 | 56.0 | 9500 | 0.4252 | 24.4291 |
| 0.0001 | 59.0 | 10000 | 0.4242 | 24.5560 |
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.12.1+cu116
- Datasets 2.4.0
- Tokenizers 0.12.1
| d890782f79663934307e44c5f3f61c38 |
sudo-s/new_exper3 | sudo-s | vit | 14 | 11 | transformers | 0 | image-classification | true | false | false | apache-2.0 | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['image-classification', 'generated_from_trainer'] | true | true | true | 4,377 | false |
<!-- 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. -->
# new_exper3
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the sudo-s/herbier_mesuem1 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3000
- Accuracy: 0.9298
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 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: 8
- mixed_precision_training: Apex, opt level O1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 4.093 | 0.16 | 100 | 4.1045 | 0.1885 |
| 3.5057 | 0.31 | 200 | 3.4448 | 0.3231 |
| 2.9116 | 0.47 | 300 | 2.9483 | 0.4537 |
| 2.561 | 0.63 | 400 | 2.5700 | 0.5258 |
| 2.1611 | 0.78 | 500 | 2.1721 | 0.6145 |
| 1.715 | 0.94 | 600 | 1.8255 | 0.6407 |
| 1.2752 | 1.1 | 700 | 1.5340 | 0.7051 |
| 1.2487 | 1.25 | 800 | 1.3533 | 0.7201 |
| 1.0333 | 1.41 | 900 | 1.1474 | 0.7826 |
| 0.8856 | 1.56 | 1000 | 1.0914 | 0.7645 |
| 0.7512 | 1.72 | 1100 | 0.8893 | 0.8119 |
| 0.747 | 1.88 | 1200 | 0.8370 | 0.8304 |
| 0.5082 | 2.03 | 1300 | 0.7131 | 0.8566 |
| 0.4449 | 2.19 | 1400 | 0.6573 | 0.8547 |
| 0.2912 | 2.35 | 1500 | 0.6184 | 0.8597 |
| 0.285 | 2.5 | 1600 | 0.5974 | 0.8570 |
| 0.2267 | 2.66 | 1700 | 0.5621 | 0.8647 |
| 0.2553 | 2.82 | 1800 | 0.5044 | 0.8816 |
| 0.2029 | 2.97 | 1900 | 0.4342 | 0.8955 |
| 0.1763 | 3.13 | 2000 | 0.4487 | 0.8905 |
| 0.1418 | 3.29 | 2100 | 0.4173 | 0.9005 |
| 0.0563 | 3.44 | 2200 | 0.3870 | 0.9048 |
| 0.0579 | 3.6 | 2300 | 0.3849 | 0.9036 |
| 0.166 | 3.76 | 2400 | 0.3933 | 0.9025 |
| 0.11 | 3.91 | 2500 | 0.3918 | 0.9056 |
| 0.0356 | 4.07 | 2600 | 0.3298 | 0.9202 |
| 0.0513 | 4.23 | 2700 | 0.3371 | 0.9210 |
| 0.0762 | 4.38 | 2800 | 0.3253 | 0.9225 |
| 0.018 | 4.54 | 2900 | 0.3467 | 0.9148 |
| 0.0263 | 4.69 | 3000 | 0.3544 | 0.9144 |
| 0.0205 | 4.85 | 3100 | 0.3340 | 0.9221 |
| 0.0237 | 5.01 | 3200 | 0.3353 | 0.9144 |
| 0.013 | 5.16 | 3300 | 0.3218 | 0.9229 |
| 0.0116 | 5.32 | 3400 | 0.3088 | 0.9291 |
| 0.0119 | 5.48 | 3500 | 0.3047 | 0.9279 |
| 0.0098 | 5.63 | 3600 | 0.3063 | 0.9283 |
| 0.0086 | 5.79 | 3700 | 0.3074 | 0.9268 |
| 0.0081 | 5.95 | 3800 | 0.3220 | 0.9237 |
| 0.0078 | 6.1 | 3900 | 0.3064 | 0.9268 |
| 0.0074 | 6.26 | 4000 | 0.3062 | 0.9279 |
| 0.0068 | 6.42 | 4100 | 0.3051 | 0.9291 |
| 0.006 | 6.57 | 4200 | 0.3000 | 0.9298 |
| 0.0075 | 6.73 | 4300 | 0.3010 | 0.9310 |
| 0.0057 | 6.89 | 4400 | 0.3037 | 0.9298 |
| 0.0058 | 7.04 | 4500 | 0.3071 | 0.9279 |
| 0.0075 | 7.2 | 4600 | 0.3075 | 0.9283 |
| 0.0066 | 7.36 | 4700 | 0.3077 | 0.9295 |
| 0.0056 | 7.51 | 4800 | 0.3084 | 0.9295 |
| 0.0053 | 7.67 | 4900 | 0.3064 | 0.9310 |
| 0.0057 | 7.82 | 5000 | 0.3068 | 0.9318 |
| 0.0055 | 7.98 | 5100 | 0.3068 | 0.9318 |
### Framework versions
- Transformers 4.19.4
- Pytorch 1.5.1
- Datasets 2.3.2
- Tokenizers 0.12.1
| 6d70d49090c8c2a8be9f222724f7aee7 |
patrickvonplaten/wav2vec2-large-xlsr-turkish-demo-colab | patrickvonplaten | wav2vec2 | 12 | 7 | transformers | 0 | automatic-speech-recognition | true | false | false | apache-2.0 | null | ['common_voice'] | null | 1 | 1 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 1,735 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xlsr-turkish-demo-colab
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the common_voice dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4055
- Wer: 0.4800
## 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: 0.0003
- 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
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 5.0179 | 4.21 | 400 | 1.4935 | 1.0249 |
| 0.7075 | 8.42 | 800 | 0.4546 | 0.6071 |
| 0.3072 | 12.63 | 1200 | 0.3947 | 0.5401 |
| 0.2145 | 16.84 | 1600 | 0.4049 | 0.5194 |
| 0.1647 | 21.05 | 2000 | 0.4199 | 0.5003 |
| 0.1338 | 25.26 | 2400 | 0.4144 | 0.4859 |
| 0.116 | 29.47 | 2800 | 0.4055 | 0.4800 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.9.1+cu102
- Datasets 1.13.3
- Tokenizers 0.10.3
| afd146fad7482e60a6238b642e8efe8e |
Olwflynn/test-trainer-init | Olwflynn | bert | 12 | 1 | transformers | 0 | text-classification | true | false | false | apache-2.0 | null | ['glue'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 1,376 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# test-trainer-init
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6581
- Accuracy: 0.8603
- F1: 0.9042
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log | 1.0 | 459 | 0.3660 | 0.8505 | 0.8893 |
| 0.5003 | 2.0 | 918 | 0.5355 | 0.8407 | 0.8922 |
| 0.2654 | 3.0 | 1377 | 0.6581 | 0.8603 | 0.9042 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
| 04afefda61e322f69ed8854e25047ddd |
sd-dreambooth-library/persona-5-shigenori-style | sd-dreambooth-library | null | 26 | 34 | diffusers | 6 | text-to-image | false | false | false | creativeml-openrail-m | null | null | null | 2 | 2 | 0 | 0 | 0 | 0 | 0 | ['text-to-image'] | false | true | true | 1,660 | false | ### Persona-5-Shigenori-Style Dreambooth model trained by Allenbv with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook
Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb)
Or you can run your new concept via `diffusers` [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb)
Sample pictures of this concept:
3200 Steps, 20% text encoder, 23 images
"Shigenori Style" on your prompt
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.png)
.png)
.png)
.png)
.png)
.png)
| d1df63470c0e3368e0beb5dc95f2ebbb |
espnet/kan-bayashi_jsut_tts_train_fastspeech2_tacotron2_teacher_raw_phn_jacon-truncated-f45dcb | espnet | null | 21 | 0 | espnet | 0 | text-to-speech | false | false | false | cc-by-4.0 | ['ja'] | ['jsut'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['espnet', 'audio', 'text-to-speech'] | false | true | true | 1,873 | false | ## Example ESPnet2 TTS model
### `kan-bayashi/jsut_tts_train_fastspeech2_tacotron2_teacher_raw_phn_jaconv_pyopenjtalk_accent_train.loss.ave`
♻️ Imported from https://zenodo.org/record/4381100/
This model was trained by kan-bayashi using jsut/tts1 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```python
# coming soon
```
### Citing ESPnet
```BibTex
@inproceedings{watanabe2018espnet,
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson {Enrique Yalta Soplin} and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
title={{ESPnet}: End-to-End Speech Processing Toolkit},
year={2018},
booktitle={Proceedings of Interspeech},
pages={2207--2211},
doi={10.21437/Interspeech.2018-1456},
url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
@inproceedings{hayashi2020espnet,
title={{Espnet-TTS}: Unified, reproducible, and integratable open source end-to-end text-to-speech toolkit},
author={Hayashi, Tomoki and Yamamoto, Ryuichi and Inoue, Katsuki and Yoshimura, Takenori and Watanabe, Shinji and Toda, Tomoki and Takeda, Kazuya and Zhang, Yu and Tan, Xu},
booktitle={Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
pages={7654--7658},
year={2020},
organization={IEEE}
}
```
or arXiv:
```bibtex
@misc{watanabe2018espnet,
title={ESPnet: End-to-End Speech Processing Toolkit},
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Enrique Yalta Soplin and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
year={2018},
eprint={1804.00015},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
``` | ea34aed45c47d9513078ea8a24819084 |
team-nave/distilbert-base-uncased-finetuned-clinc | team-nave | distilbert | 12 | 3 | transformers | 0 | text-classification | true | false | false | apache-2.0 | null | ['clinc_oos'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 1,476 | false |
<!-- 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: 1.7601
- Accuracy: 0.8532
## 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: 96
- eval_batch_size: 96
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 159 | 3.9593 | 0.6442 |
| 4.0539 | 2.0 | 318 | 2.9237 | 0.7606 |
| 4.0539 | 3.0 | 477 | 2.2412 | 0.8174 |
| 2.3862 | 4.0 | 636 | 1.8768 | 0.8397 |
| 2.3862 | 5.0 | 795 | 1.7601 | 0.8532 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.12.1
- Datasets 1.16.1
- Tokenizers 0.10.3
| d7596b645d63bbb3d6369cb6e7b70b33 |
luke-thorburn/suggest-conclusion-soft | luke-thorburn | gpt_neo | 4 | 4 | transformers | 0 | text-generation | true | false | false | apache-2.0 | ['en'] | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['argumentation'] | false | true | true | 1,648 | false |
# Generate the conclusion of an argument
This model has the same model parameters as [`gpt-neo-2.7B`](https://huggingface.co/EleutherAI/gpt-neo-2.7B), but with an additional soft prompt which has been optimized on the task of generating the conclusion of an argument given its premises. It was trained as part of a University of Melbourne [research project](https://github.com/Hunt-Laboratory/language-model-optimization) evaluating how large language models can best be optimized to perform argumentative reasoning tasks.
Code used for optimization and evaluation can be found in the project [GitHub repository](https://github.com/Hunt-Laboratory/language-model-optimization). A paper reporting on model evaluation is currently under review.
# Prompt Template
```
[prepended soft prompt]- [premise 1]
- [premise 2]
...
- [premise n]
Conclusion: [generated conclusion]
```
# Dataset
The soft prompt was trained using argument maps scraped from the crowdsourced argument-mapping platform [Kialo](https://kialo.com/).
# Limitations and Biases
The model is a finetuned version of [`gpt-neo-2.7B`](https://huggingface.co/EleutherAI/gpt-neo-2.7B), so likely has many of the same limitations and biases. Additionally, note that while the goal of the model is to produce coherent and valid reasoning, many generated model outputs will be illogical or nonsensical and should not be relied upon.
# Acknowledgements
This research was funded by the Australian Department of Defence and the Office of National Intelligence under the AI for Decision Making Program, delivered in partnership with the Defence Science Institute in Victoria, Australia. | 539c9b0ba717b2e713cb7cfea447d2db |
jonatasgrosman/exp_w2v2t_et_r-wav2vec2_s732 | jonatasgrosman | wav2vec2 | 10 | 5 | transformers | 0 | automatic-speech-recognition | true | false | false | apache-2.0 | ['et'] | ['mozilla-foundation/common_voice_7_0'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['automatic-speech-recognition', 'et'] | false | true | true | 462 | false | # exp_w2v2t_et_r-wav2vec2_s732
Fine-tuned [facebook/wav2vec2-large-robust](https://huggingface.co/facebook/wav2vec2-large-robust) for speech recognition using the train split of [Common Voice 7.0 (et)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
| f4a3a34e059d0b61dae8da927decf461 |
sd-concepts-library/vb-mox | sd-concepts-library | null | 13 | 0 | null | 7 | null | false | false | false | mit | null | null | null | 0 | 0 | 0 | 0 | 1 | 1 | 0 | [] | false | true | true | 1,390 | false | ### vb-mox on Stable Diffusion
This is the `<vb-mox>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb).
Here is the new concept you will be able to use as an `object`:
| 1479a93668b5a3cf930834183112e5aa |
adache/xlm-roberta-base-finetuned-panx-de-fr | adache | xlm-roberta | 9 | 6 | transformers | 0 | token-classification | true | false | false | mit | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 1,320 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-de-fr
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1644
- F1: 0.8617
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2891 | 1.0 | 715 | 0.1780 | 0.8288 |
| 0.1471 | 2.0 | 1430 | 0.1627 | 0.8509 |
| 0.0947 | 3.0 | 2145 | 0.1644 | 0.8617 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
| 1f33c4e9783dcf34f9d6b190249e20c6 |
responsibility-framing/predict-perception-bert-focus-assassin | responsibility-framing | bert | 12 | 21 | transformers | 0 | text-classification | true | false | false | mit | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 7,992 | false |
<!-- 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. -->
# predict-perception-bert-focus-assassin
This model is a fine-tuned version of [dbmdz/bert-base-italian-xxl-cased](https://huggingface.co/dbmdz/bert-base-italian-xxl-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2964
- Rmse: 0.8992
- Rmse Focus::a Sull'assassino: 0.8992
- Mae: 0.7331
- Mae Focus::a Sull'assassino: 0.7331
- R2: 0.6500
- R2 Focus::a Sull'assassino: 0.6500
- Cos: 0.7391
- Pair: 0.0
- Rank: 0.5
- Neighbors: 0.6131
- Rsa: nan
## 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: 20
- eval_batch_size: 8
- seed: 1996
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rmse | Rmse Focus::a Sull'assassino | Mae | Mae Focus::a Sull'assassino | R2 | R2 Focus::a Sull'assassino | Cos | Pair | Rank | Neighbors | Rsa |
|:-------------:|:-----:|:----:|:---------------:|:------:|:----------------------------:|:------:|:---------------------------:|:-------:|:--------------------------:|:------:|:----:|:----:|:---------:|:---:|
| 1.0674 | 1.0 | 15 | 0.9851 | 1.6393 | 1.6393 | 1.5316 | 1.5316 | -0.1633 | -0.1633 | 0.1304 | 0.0 | 0.5 | 0.2457 | nan |
| 1.0099 | 2.0 | 30 | 0.8921 | 1.5601 | 1.5601 | 1.4317 | 1.4317 | -0.0535 | -0.0535 | 0.5652 | 0.0 | 0.5 | 0.4734 | nan |
| 0.9295 | 3.0 | 45 | 0.7345 | 1.4155 | 1.4155 | 1.3113 | 1.3113 | 0.1327 | 0.1327 | 0.5652 | 0.0 | 0.5 | 0.3596 | nan |
| 0.8485 | 4.0 | 60 | 0.7282 | 1.4094 | 1.4094 | 1.2678 | 1.2678 | 0.1401 | 0.1401 | 0.7391 | 0.0 | 0.5 | 0.5367 | nan |
| 0.7551 | 5.0 | 75 | 0.5966 | 1.2758 | 1.2758 | 1.1144 | 1.1144 | 0.2955 | 0.2955 | 0.6522 | 0.0 | 0.5 | 0.3911 | nan |
| 0.5563 | 6.0 | 90 | 0.4578 | 1.1175 | 1.1175 | 0.9105 | 0.9105 | 0.4594 | 0.4594 | 0.6522 | 0.0 | 0.5 | 0.3911 | nan |
| 0.4048 | 7.0 | 105 | 0.3539 | 0.9826 | 0.9826 | 0.7770 | 0.7770 | 0.5821 | 0.5821 | 0.6522 | 0.0 | 0.5 | 0.5522 | nan |
| 0.3319 | 8.0 | 120 | 0.2938 | 0.8953 | 0.8953 | 0.7110 | 0.7110 | 0.6530 | 0.6530 | 0.6522 | 0.0 | 0.5 | 0.6021 | nan |
| 0.2224 | 9.0 | 135 | 0.3455 | 0.9708 | 0.9708 | 0.7607 | 0.7607 | 0.5921 | 0.5921 | 0.6522 | 0.0 | 0.5 | 0.3911 | nan |
| 0.1794 | 10.0 | 150 | 0.2719 | 0.8612 | 0.8612 | 0.6768 | 0.6768 | 0.6790 | 0.6790 | 0.7391 | 0.0 | 0.5 | 0.6131 | nan |
| 0.1553 | 11.0 | 165 | 0.2855 | 0.8826 | 0.8826 | 0.7053 | 0.7053 | 0.6628 | 0.6628 | 0.7391 | 0.0 | 0.5 | 0.6131 | nan |
| 0.1008 | 12.0 | 180 | 0.3000 | 0.9046 | 0.9046 | 0.7255 | 0.7255 | 0.6458 | 0.6458 | 0.6522 | 0.0 | 0.5 | 0.5261 | nan |
| 0.1121 | 13.0 | 195 | 0.2817 | 0.8766 | 0.8766 | 0.7236 | 0.7236 | 0.6674 | 0.6674 | 0.7391 | 0.0 | 0.5 | 0.6131 | nan |
| 0.08 | 14.0 | 210 | 0.3504 | 0.9777 | 0.9777 | 0.7631 | 0.7631 | 0.5863 | 0.5863 | 0.7391 | 0.0 | 0.5 | 0.6131 | nan |
| 0.0802 | 15.0 | 225 | 0.3031 | 0.9094 | 0.9094 | 0.7565 | 0.7565 | 0.6420 | 0.6420 | 0.7391 | 0.0 | 0.5 | 0.6131 | nan |
| 0.0685 | 16.0 | 240 | 0.3041 | 0.9109 | 0.9109 | 0.7409 | 0.7409 | 0.6408 | 0.6408 | 0.7391 | 0.0 | 0.5 | 0.6131 | nan |
| 0.0592 | 17.0 | 255 | 0.3496 | 0.9767 | 0.9767 | 0.7812 | 0.7812 | 0.5871 | 0.5871 | 0.7391 | 0.0 | 0.5 | 0.6131 | nan |
| 0.0625 | 18.0 | 270 | 0.3260 | 0.9430 | 0.9430 | 0.7757 | 0.7757 | 0.6151 | 0.6151 | 0.7391 | 0.0 | 0.5 | 0.6131 | nan |
| 0.0589 | 19.0 | 285 | 0.3118 | 0.9222 | 0.9222 | 0.7442 | 0.7442 | 0.6318 | 0.6318 | 0.7391 | 0.0 | 0.5 | 0.6131 | nan |
| 0.0518 | 20.0 | 300 | 0.3062 | 0.9140 | 0.9140 | 0.7459 | 0.7459 | 0.6384 | 0.6384 | 0.7391 | 0.0 | 0.5 | 0.6131 | nan |
| 0.0456 | 21.0 | 315 | 0.3200 | 0.9344 | 0.9344 | 0.7592 | 0.7592 | 0.6221 | 0.6221 | 0.7391 | 0.0 | 0.5 | 0.6131 | nan |
| 0.0477 | 22.0 | 330 | 0.3132 | 0.9244 | 0.9244 | 0.7532 | 0.7532 | 0.6301 | 0.6301 | 0.7391 | 0.0 | 0.5 | 0.6131 | nan |
| 0.0448 | 23.0 | 345 | 0.3006 | 0.9056 | 0.9056 | 0.7321 | 0.7321 | 0.6450 | 0.6450 | 0.6522 | 0.0 | 0.5 | 0.5261 | nan |
| 0.0494 | 24.0 | 360 | 0.2985 | 0.9024 | 0.9024 | 0.7463 | 0.7463 | 0.6475 | 0.6475 | 0.7391 | 0.0 | 0.5 | 0.6131 | nan |
| 0.0369 | 25.0 | 375 | 0.3039 | 0.9105 | 0.9105 | 0.7359 | 0.7359 | 0.6412 | 0.6412 | 0.7391 | 0.0 | 0.5 | 0.6131 | nan |
| 0.0456 | 26.0 | 390 | 0.2989 | 0.9030 | 0.9030 | 0.7210 | 0.7210 | 0.6471 | 0.6471 | 0.7391 | 0.0 | 0.5 | 0.6131 | nan |
| 0.044 | 27.0 | 405 | 0.2997 | 0.9042 | 0.9042 | 0.7418 | 0.7418 | 0.6461 | 0.6461 | 0.7391 | 0.0 | 0.5 | 0.6131 | nan |
| 0.0352 | 28.0 | 420 | 0.2970 | 0.9001 | 0.9001 | 0.7346 | 0.7346 | 0.6493 | 0.6493 | 0.7391 | 0.0 | 0.5 | 0.6131 | nan |
| 0.0429 | 29.0 | 435 | 0.2970 | 0.9001 | 0.9001 | 0.7281 | 0.7281 | 0.6493 | 0.6493 | 0.7391 | 0.0 | 0.5 | 0.6131 | nan |
| 0.0378 | 30.0 | 450 | 0.2964 | 0.8992 | 0.8992 | 0.7331 | 0.7331 | 0.6500 | 0.6500 | 0.7391 | 0.0 | 0.5 | 0.6131 | nan |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.2+cu113
- Datasets 1.18.3
- Tokenizers 0.11.0
| 236d8773799c2183104948e0045c5004 |
StivenLancheros/roberta-base-biomedical-clinical-es-finetuned-ner-CRAFT_AugmentedTransfer_ES | StivenLancheros | roberta | 14 | 205 | transformers | 0 | token-classification | true | false | false | apache-2.0 | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 2,821 | false |
<!-- 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-biomedical-clinical-es-finetuned-ner-CRAFT_AugmentedTransfer_ES
This model is a fine-tuned version of [StivenLancheros/roberta-base-biomedical-clinical-es-finetuned-ner-CRAFT_Augmented_ES](https://huggingface.co/StivenLancheros/roberta-base-biomedical-clinical-es-finetuned-ner-CRAFT_Augmented_ES) on the CRAFT dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2043
- Precision: 0.8666
- Recall: 0.8614
- F1: 0.8639
- Accuracy: 0.9734
## Model description
This model performs Named Entity Recognition for 6 entity tags: Sequence, Cell, Protein, Gene, Taxon, and Chemical from the CRAFT(Colorado Richly Annotated Full Text) Corpus in Spanish (MT translated) and English. Entity tags have been normalized and replaced from the original three letter code to a full name e.g. B-Protein, I-Chemical.
This model is trained on augmented data created using Entity Replacement. 20% of the entities were replaced using a list of entities for each entity tag obtained from the official ontologies for each entity class. Three datasets (original, augmented, MT translated CRAFT) were concatenated. To improve F1 score the transfer learning was completed in two steps.
Using [StivenLancheros/roberta-base-biomedical-clinical-es-finetuned-ner-CRAFT_Augmented_ES](https://huggingface.co/StivenLancheros/roberta-base-biomedical-clinical-es-finetuned-ner-CRAFT_Augmented_ES) as a base model, I finetuned once more on the original CRAFT dataset in English.
Biobert --> Augmented CRAFT --> CRAFT ES (MT translated)
## 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: 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: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0088 | 1.0 | 1360 | 0.1793 | 0.8616 | 0.8487 | 0.8551 | 0.9721 |
| 0.0046 | 2.0 | 2720 | 0.1925 | 0.8618 | 0.8426 | 0.8521 | 0.9713 |
| 0.0032 | 3.0 | 4080 | 0.1926 | 0.8558 | 0.8630 | 0.8594 | 0.9725 |
| 0.0011 | 4.0 | 5440 | 0.2043 | 0.8666 | 0.8614 | 0.8639 | 0.9734 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.11.6
| 458be6a13f5227eae492df2029cb6cfe |
espnet/Shinji_Watanabe_spgispeech_asr_train_asr_conformer6_n_fft512_hop_lengt-truncated-f1ac86 | espnet | null | 31 | 1 | espnet | 1 | automatic-speech-recognition | false | false | false | cc-by-4.0 | ['en'] | ['spgispeech'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | true | true | 1,881 | false | ## Example ESPnet2 ASR model
### `Shinji_Watanabe/spgispeech_asr_train_asr_conformer6_n_fft512_hop_length256_raw_en_bpe5000_valid.acc.ave`
♻️ Imported from https://zenodo.org/record/4585546/
This model was trained by Shinji Watanabe using spgispeech/asr1 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```python
# coming soon
```
### Citing ESPnet
```BibTex
@inproceedings{watanabe2018espnet,
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson {Enrique Yalta Soplin} and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
title={{ESPnet}: End-to-End Speech Processing Toolkit},
year={2018},
booktitle={Proceedings of Interspeech},
pages={2207--2211},
doi={10.21437/Interspeech.2018-1456},
url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
@inproceedings{hayashi2020espnet,
title={{Espnet-TTS}: Unified, reproducible, and integratable open source end-to-end text-to-speech toolkit},
author={Hayashi, Tomoki and Yamamoto, Ryuichi and Inoue, Katsuki and Yoshimura, Takenori and Watanabe, Shinji and Toda, Tomoki and Takeda, Kazuya and Zhang, Yu and Tan, Xu},
booktitle={Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
pages={7654--7658},
year={2020},
organization={IEEE}
}
```
or arXiv:
```bibtex
@misc{watanabe2018espnet,
title={ESPnet: End-to-End Speech Processing Toolkit},
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Enrique Yalta Soplin and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
year={2018},
eprint={1804.00015},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
``` | f99ce77549e8646468c488016ce7b2bd |
mousaazari/t5-small-finetuned-wikisql | mousaazari | t5 | 17 | 1 | transformers | 0 | text2text-generation | true | false | false | apache-2.0 | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 3,342 | false |
<!-- 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-small-finetuned-wikisql
This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2640
- Rouge2 Precision: 0.8471
- Rouge2 Recall: 0.3841
- Rouge2 Fmeasure: 0.5064
## 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: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge2 Precision | Rouge2 Recall | Rouge2 Fmeasure |
|:-------------:|:-----:|:----:|:---------------:|:----------------:|:-------------:|:---------------:|
| No log | 1.0 | 11 | 2.7587 | 0.098 | 0.0305 | 0.045 |
| No log | 2.0 | 22 | 2.0056 | 0.0969 | 0.0284 | 0.0422 |
| No log | 3.0 | 33 | 1.4456 | 0.1046 | 0.0349 | 0.0503 |
| No log | 4.0 | 44 | 1.0317 | 0.1054 | 0.0337 | 0.0482 |
| No log | 5.0 | 55 | 0.7603 | 0.2749 | 0.1299 | 0.1724 |
| No log | 6.0 | 66 | 0.5722 | 0.7115 | 0.352 | 0.4552 |
| No log | 7.0 | 77 | 0.4751 | 0.6872 | 0.337 | 0.436 |
| No log | 8.0 | 88 | 0.4253 | 0.7256 | 0.3439 | 0.4462 |
| No log | 9.0 | 99 | 0.3805 | 0.7335 | 0.3204 | 0.4308 |
| No log | 10.0 | 110 | 0.3562 | 0.7342 | 0.3239 | 0.433 |
| No log | 11.0 | 121 | 0.3275 | 0.7906 | 0.355 | 0.471 |
| No log | 12.0 | 132 | 0.3133 | 0.8382 | 0.3838 | 0.5061 |
| No log | 13.0 | 143 | 0.2996 | 0.8409 | 0.3841 | 0.5062 |
| No log | 14.0 | 154 | 0.2903 | 0.8304 | 0.3763 | 0.4978 |
| No log | 15.0 | 165 | 0.2867 | 0.8409 | 0.3841 | 0.5062 |
| No log | 16.0 | 176 | 0.2786 | 0.8409 | 0.3841 | 0.5062 |
| No log | 17.0 | 187 | 0.2711 | 0.8409 | 0.3841 | 0.5062 |
| No log | 18.0 | 198 | 0.2673 | 0.8409 | 0.3841 | 0.5062 |
| No log | 19.0 | 209 | 0.2643 | 0.8471 | 0.3841 | 0.5064 |
| No log | 20.0 | 220 | 0.2640 | 0.8471 | 0.3841 | 0.5064 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
| d9731ee870ebb48b35dd77571a94e064 |
jonatasgrosman/exp_w2v2t_th_vp-100k_s497 | jonatasgrosman | wav2vec2 | 10 | 5 | transformers | 0 | automatic-speech-recognition | true | false | false | apache-2.0 | ['th'] | ['mozilla-foundation/common_voice_7_0'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['automatic-speech-recognition', 'th'] | false | true | true | 478 | false | # exp_w2v2t_th_vp-100k_s497
Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) for speech recognition on Thai using the train split of [Common Voice 7.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
| 99a0ceaf876208d574959b0e3aee7f3d |
Helsinki-NLP/opus-mt-en-tvl | Helsinki-NLP | marian | 10 | 8 | transformers | 0 | translation | true | true | false | apache-2.0 | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['translation'] | false | true | true | 776 | false |
### opus-mt-en-tvl
* source languages: en
* target languages: tvl
* OPUS readme: [en-tvl](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/en-tvl/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2020-01-20.zip](https://object.pouta.csc.fi/OPUS-MT-models/en-tvl/opus-2020-01-20.zip)
* test set translations: [opus-2020-01-20.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/en-tvl/opus-2020-01-20.test.txt)
* test set scores: [opus-2020-01-20.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/en-tvl/opus-2020-01-20.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| JW300.en.tvl | 46.9 | 0.625 |
| 1119c534322a471b3bdb89347d7c02e5 |
henryscheible/eval_v2_sst2 | henryscheible | bert | 13 | 1 | transformers | 0 | text-classification | true | false | false | apache-2.0 | ['en'] | ['glue'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 888 | false |
<!-- 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. -->
# eval_v2_sst2
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the GLUE SST2 dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5.0
### Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1
- Datasets 2.6.1
- Tokenizers 0.13.1
| c1a676d92821077daf231ef35acfe94b |
julenalvaro/Perros-VS-gatos-con-vit-base-patch16-224-in21k | julenalvaro | vit | 12 | 1 | transformers | 0 | image-classification | true | false | false | apache-2.0 | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 1,564 | false |
# vit-base-patch16-224-in21k
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1026
- Accuracy: 0.982
## Model description
This model is a fine-tuned version of google/vit-base-patch16-224-in21k which discriminates cats from dogs.
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.177 | 0.5 | 500 | 0.2100 | 0.9435 |
| 0.1515 | 1.0 | 1000 | 0.0710 | 0.975 |
| 0.0443 | 1.5 | 1500 | 0.2043 | 0.9535 |
| 0.0625 | 2.0 | 2000 | 0.0898 | 0.9745 |
| 0.0181 | 2.5 | 2500 | 0.0961 | 0.9805 |
| 0.0091 | 3.0 | 3000 | 0.1049 | 0.982 |
| 0.0016 | 3.5 | 3500 | 0.1066 | 0.981 |
| 0.0015 | 4.0 | 4000 | 0.1026 | 0.982 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
| f3bb102548f9ff8d95ceedef4711b445 |
sentence-transformers/xlm-r-100langs-bert-base-nli-stsb-mean-tokens | sentence-transformers | xlm-roberta | 13 | 84,798 | sentence-transformers | 1 | sentence-similarity | true | true | false | apache-2.0 | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | true | true | 3,905 | false |
**⚠️ This model is deprecated. Please don't use it as it produces sentence embeddings of low quality. You can find recommended sentence embedding models here: [SBERT.net - Pretrained Models](https://www.sbert.net/docs/pretrained_models.html)**
# sentence-transformers/xlm-r-100langs-bert-base-nli-stsb-mean-tokens
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.
## 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('sentence-transformers/xlm-r-100langs-bert-base-nli-stsb-mean-tokens')
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('sentence-transformers/xlm-r-100langs-bert-base-nli-stsb-mean-tokens')
model = AutoModel.from_pretrained('sentence-transformers/xlm-r-100langs-bert-base-nli-stsb-mean-tokens')
# 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, max pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=sentence-transformers/xlm-r-100langs-bert-base-nli-stsb-mean-tokens)
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: XLMRobertaModel
(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
This model was trained by [sentence-transformers](https://www.sbert.net/).
If you find this model helpful, feel free to cite our publication [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://arxiv.org/abs/1908.10084):
```bibtex
@inproceedings{reimers-2019-sentence-bert,
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
author = "Reimers, Nils and Gurevych, Iryna",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
month = "11",
year = "2019",
publisher = "Association for Computational Linguistics",
url = "http://arxiv.org/abs/1908.10084",
}
``` | 21e12d4989786724e1335291d38cbe28 |
menglingbei/t5-small-finetuned-xsum | menglingbei | t5 | 11 | 1 | transformers | 0 | text2text-generation | true | false | false | apache-2.0 | null | ['xsum'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 920 | false |
<!-- 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-small-finetuned-xsum
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the xsum dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.19.1
- Pytorch 1.11.0+cu113
- Datasets 2.2.1
- Tokenizers 0.12.1
| 1134eab5d0c9fc349fd6ee0e3aa46153 |
mrm8488/convnext-tiny-finetuned-eurosat | mrm8488 | convnext | 11 | 7 | transformers | 2 | image-classification | true | false | false | apache-2.0 | null | ['nielsr/eurosat-demo'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer', 'CV', 'ConvNeXT', 'satellite', 'EuroSAT'] | true | true | true | 2,880 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# ConvNeXT (tiny) fine-tuned on EuroSAT
This model is a fine-tuned version of [facebook/convnext-tiny-224](https://huggingface.co/facebook/convnext-tiny-224) on the [EuroSAT](https://github.com/phelber/eurosat) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0549
- Accuracy: 0.9805
#### Drag and drop the following pics in the right widget to test the model
## Model description
ConvNeXT is a pure convolutional model (ConvNet), inspired by the design of Vision Transformers, that claims to outperform them. The authors started from a ResNet and "modernized" its design by taking the Swin Transformer as inspiration.
## Dataset information
**EuroSAT : Land Use and Land Cover Classification with Sentinel-2**
In this study, we address the challenge of land use and land cover classification using Sentinel-2 satellite images. The Sentinel-2 satellite images are openly and freely accessible provided in the Earth observation program Copernicus. We present a novel dataset based on Sentinel-2 satellite images covering 13 spectral bands and consisting out of 10 classes with in total 27,000 labeled and geo-referenced images. We provide benchmarks for this novel dataset with its spectral bands using state-of-the-art deep Convolutional Neural Network (CNNs). With the proposed novel dataset, we achieved an overall classification accuracy of 98.57%. The resulting classification system opens a gate towards a number of Earth observation applications. We demonstrate how this classification system can be used for detecting land use and land cover changes and how it can assist in improving geographical maps.
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 7171
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.2082 | 1.0 | 718 | 0.1057 | 0.9654 |
| 0.1598 | 2.0 | 1436 | 0.0712 | 0.9775 |
| 0.1435 | 3.0 | 2154 | 0.0549 | 0.9805 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.10.0+cu111
- Datasets 2.1.0
- Tokenizers 0.12.1 | 02f4df9c2ba7be823ea16731da5485a8 |
lmqg/mt5-base-itquad-qg | lmqg | mt5 | 20 | 102 | transformers | 0 | text2text-generation | true | false | false | cc-by-4.0 | ['it'] | ['lmqg/qg_itquad'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['question generation'] | true | true | true | 6,463 | false |
# Model Card of `lmqg/mt5-base-itquad-qg`
This model is fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) for question generation task on the [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-generation).
### Overview
- **Language model:** [google/mt5-base](https://huggingface.co/google/mt5-base)
- **Language:** it
- **Training data:** [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) (default)
- **Online Demo:** [https://autoqg.net/](https://autoqg.net/)
- **Repository:** [https://github.com/asahi417/lm-question-generation](https://github.com/asahi417/lm-question-generation)
- **Paper:** [https://arxiv.org/abs/2210.03992](https://arxiv.org/abs/2210.03992)
### Usage
- With [`lmqg`](https://github.com/asahi417/lm-question-generation#lmqg-language-model-for-question-generation-)
```python
from lmqg import TransformersQG
# initialize model
model = TransformersQG(language="it", model="lmqg/mt5-base-itquad-qg")
# model prediction
questions = model.generate_q(list_context="Dopo il 1971 , l' OPEC ha tardato ad adeguare i prezzi per riflettere tale deprezzamento.", list_answer="Dopo il 1971")
```
- With `transformers`
```python
from transformers import pipeline
pipe = pipeline("text2text-generation", "lmqg/mt5-base-itquad-qg")
output = pipe("<hl> Dopo il 1971 <hl> , l' OPEC ha tardato ad adeguare i prezzi per riflettere tale deprezzamento.")
```
## Evaluation
- ***Metric (Question Generation)***: [raw metric file](https://huggingface.co/lmqg/mt5-base-itquad-qg/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_itquad.default.json)
| | Score | Type | Dataset |
|:-----------|--------:|:--------|:-----------------------------------------------------------------|
| BERTScore | 81.16 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
| Bleu_1 | 23.29 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
| Bleu_2 | 15.37 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
| Bleu_3 | 10.72 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
| Bleu_4 | 7.7 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
| METEOR | 18 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
| MoverScore | 57.11 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
| ROUGE_L | 22.51 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
- ***Metric (Question & Answer Generation, Reference Answer)***: Each question is generated from *the gold answer*. [raw metric file](https://huggingface.co/lmqg/mt5-base-itquad-qg/raw/main/eval/metric.first.answer.paragraph.questions_answers.lmqg_qg_itquad.default.json)
| | Score | Type | Dataset |
|:--------------------------------|--------:|:--------|:-----------------------------------------------------------------|
| QAAlignedF1Score (BERTScore) | 87.93 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
| QAAlignedF1Score (MoverScore) | 61.91 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
| QAAlignedPrecision (BERTScore) | 88.02 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
| QAAlignedPrecision (MoverScore) | 62.04 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
| QAAlignedRecall (BERTScore) | 87.84 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
| QAAlignedRecall (MoverScore) | 61.78 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
- ***Metric (Question & Answer Generation, Pipeline Approach)***: Each question is generated on the answer generated by [`lmqg/mt5-base-itquad-ae`](https://huggingface.co/lmqg/mt5-base-itquad-ae). [raw metric file](https://huggingface.co/lmqg/mt5-base-itquad-qg/raw/main/eval_pipeline/metric.first.answer.paragraph.questions_answers.lmqg_qg_itquad.default.lmqg_mt5-base-itquad-ae.json)
| | Score | Type | Dataset |
|:--------------------------------|--------:|:--------|:-----------------------------------------------------------------|
| QAAlignedF1Score (BERTScore) | 81.68 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
| QAAlignedF1Score (MoverScore) | 55.83 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
| QAAlignedPrecision (BERTScore) | 81.25 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
| QAAlignedPrecision (MoverScore) | 55.68 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
| QAAlignedRecall (BERTScore) | 82.16 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
| QAAlignedRecall (MoverScore) | 56.01 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
## Training hyperparameters
The following hyperparameters were used during fine-tuning:
- dataset_path: lmqg/qg_itquad
- dataset_name: default
- input_types: ['paragraph_answer']
- output_types: ['question']
- prefix_types: None
- model: google/mt5-base
- max_length: 512
- max_length_output: 32
- epoch: 11
- batch: 4
- lr: 0.001
- fp16: False
- random_seed: 1
- gradient_accumulation_steps: 16
- label_smoothing: 0.15
The full configuration can be found at [fine-tuning config file](https://huggingface.co/lmqg/mt5-base-itquad-qg/raw/main/trainer_config.json).
## Citation
```
@inproceedings{ushio-etal-2022-generative,
title = "{G}enerative {L}anguage {M}odels for {P}aragraph-{L}evel {Q}uestion {G}eneration",
author = "Ushio, Asahi and
Alva-Manchego, Fernando and
Camacho-Collados, Jose",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2022",
address = "Abu Dhabi, U.A.E.",
publisher = "Association for Computational Linguistics",
}
```
| 8b85759220e8b3926550b330fe4935f1 |
Duskfallcrew/duskfall-ani-backgrounds | Duskfallcrew | null | 21 | 39 | diffusers | 1 | text-to-image | false | false | false | creativeml-openrail-m | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['text-to-image'] | false | true | true | 884 | false | ### Duskfall Ani Backgrounds Dreambooth model trained by Duskfallcrew with [Hugging Face Dreambooth Training Space](https://huggingface.co/spaces/multimodalart/dreambooth-training) with the v1-5 base model
You run your new concept via `diffusers` [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb). Don't forget to use the concept prompts!
If you want to donate towards costs and don't want to subscribe:
https://ko-fi.com/DUSKFALLcrew
If you want to monthly support the EARTH & DUSK media projects and not just AI:
https://www.patreon.com/earthndusk
BgAniDusk (use that on your prompt) | b3c5449d4a3270c3e30e344d921b873a |
GIanlucaRub/whisper-tiny-it-7 | GIanlucaRub | whisper | 59 | 4 | transformers | 0 | automatic-speech-recognition | true | false | false | apache-2.0 | ['it'] | ['mozilla-foundation/common_voice_11_0'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['hf-asr-leaderboard', 'generated_from_trainer'] | true | true | true | 1,944 | false | # Whisper Tiny it 7
This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the Common Voice 11.0 dataset.
It achieves the following results on the evaluation set:
- Loss: 2.137834
- Wer: 97.566556
## Model description
This model is the openai whisper small transformer adapted for Italian audio to text transcription.
As part of the hyperparameter tuning process weight decay set to 0.1, attention dropout, encoder dropout and decoder dropout have been set to 0.1,
the learning rate has been set to 1e-6, the number of decoder attention heads and encoder attention heads have been set to 8
however, it did not improved the performance on the evaluation set.
## Intended uses & limitations
The model is available through its [HuggingFace web app](https://huggingface.co/spaces/GIanlucaRub/whisper-it)
## Training and evaluation data
Data used for training is the initial 10% of train and validation of [Italian Common Voice](https://huggingface.co/datasets/mozilla-foundation/common_voice_11_0/viewer/it/train) 11.0 from Mozilla Foundation.
The dataset used for evaluation is the initial 10% of test of Italian Common Voice.
## Training procedure
After loading the pre trained model, it has been trained on the dataset.
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-06
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 4000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 1.7353 | 3.82 | 4000 | 2.1378 | 97.5666 |
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.12.1+cu113
- Datasets 2.7.1
- Tokenizers 0.13.2 | 0c7f7f56a4c391da5a771afcc42c98e4 |
sriiikar/wav2vec2-hindi | sriiikar | wav2vec2 | 12 | 4 | transformers | 0 | automatic-speech-recognition | true | false | false | apache-2.0 | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 1,626 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-hindi
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.8814
- Wer: 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: 0.0001
- 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
- num_epochs: 40
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:---:|
| 23.6834 | 6.25 | 100 | 13.5748 | 1.0 |
| 8.2358 | 12.5 | 200 | 3.9834 | 1.0 |
| 3.6953 | 18.75 | 300 | 3.7861 | 1.0 |
| 3.4186 | 25.0 | 400 | 3.8232 | 1.0 |
| 3.2462 | 31.25 | 500 | 3.4688 | 1.0 |
| 2.8108 | 37.5 | 600 | 2.8814 | 1.0 |
### Framework versions
- Transformers 4.20.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.2.3.dev0
- Tokenizers 0.12.1
| a9f57a3d849523f9c520050b6200dfef |
funnel-transformer/large-base | funnel-transformer | funnel | 9 | 11 | transformers | 1 | feature-extraction | true | true | false | apache-2.0 | ['en'] | ['bookcorpus', 'wikipedia', 'gigaword'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | [] | false | true | true | 4,140 | false |
# Funnel Transformer large model (B8-8-8 without decoder)
Pretrained model on English language using a similar objective objective as [ELECTRA](https://huggingface.co/transformers/model_doc/electra.html). It was introduced in
[this paper](https://arxiv.org/pdf/2006.03236.pdf) and first released in
[this repository](https://github.com/laiguokun/Funnel-Transformer). This model is uncased: it does not make a difference
between english and English.
Disclaimer: The team releasing Funnel Transformer did not write a model card for this model so this model card has been
written by the Hugging Face team.
## Model description
Funnel Transformer is a transformers model pretrained on a large corpus of English data in a self-supervised fashion. This means it
was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of
publicly available data) with an automatic process to generate inputs and labels from those texts.
More precisely, a small language model corrupts the input texts and serves as a generator of inputs for this model, and
the pretraining objective is to predict which token is an original and which one has been replaced, a bit like a GAN training.
This way, the model learns an inner representation of the English language that can then be used to extract features
useful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard
classifier using the features produced by the BERT model as inputs.
**Note:** This model does not contain the decoder, so it ouputs hidden states that have a sequence length of one fourth
of the inputs. It's good to use for tasks requiring a summary of the sentence (like sentence classification) but not if
you need one input per initial token. You should use the `large` model in that case.
## Intended uses & limitations
You can use the raw model to extract a vector representation of a given text, but it's mostly intended to
be fine-tuned on a downstream task. See the [model hub](https://huggingface.co/models?filter=funnel-transformer) to look for
fine-tuned versions on a task that interests you.
Note that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked)
to make decisions, such as sequence classification, token classification or question answering. For tasks such as text
generation you should look at model like GPT2.
### How to use
Here is how to use this model to get the features of a given text in PyTorch:
```python
from transformers import FunnelTokenizer, FunnelBaseModel
tokenizer = FunnelTokenizer.from_pretrained("funnel-transformer/large-base")
model = FunnelBaseModel.from_pretrained("funnel-transformer/large-base")
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
```
and in TensorFlow:
```python
from transformers import FunnelTokenizer, TFFunnelBaseModel
tokenizer = FunnelTokenizer.from_pretrained("funnel-transformer/large-base")
model = TFFunnelBaseModel.from_pretrained("funnel-transformer/large-base")
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='tf')
output = model(encoded_input)
```
## Training data
The BERT model was pretrained on:
- [BookCorpus](https://yknzhu.wixsite.com/mbweb), a dataset consisting of 11,038 unpublished books,
- [English Wikipedia](https://en.wikipedia.org/wiki/English_Wikipedia) (excluding lists, tables and headers),
- [Clue Web](https://lemurproject.org/clueweb12/), a dataset of 733,019,372 English web pages,
- [GigaWord](https://catalog.ldc.upenn.edu/LDC2011T07), an archive of newswire text data,
- [Common Crawl](https://commoncrawl.org/), a dataset of raw web pages.
### BibTeX entry and citation info
```bibtex
@misc{dai2020funneltransformer,
title={Funnel-Transformer: Filtering out Sequential Redundancy for Efficient Language Processing},
author={Zihang Dai and Guokun Lai and Yiming Yang and Quoc V. Le},
year={2020},
eprint={2006.03236},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
```
| 2fea7a8a102b642e415a142978b60db2 |
jonatasgrosman/exp_w2v2t_nl_no-pretraining_s399 | jonatasgrosman | wav2vec2 | 10 | 5 | transformers | 0 | automatic-speech-recognition | true | false | false | apache-2.0 | ['nl'] | ['mozilla-foundation/common_voice_7_0'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['automatic-speech-recognition', 'nl'] | false | true | true | 414 | false | # exp_w2v2t_nl_no-pretraining_s399
Fine-tuned randomly initialized wav2vec2 model for speech recognition using the train split of [Common Voice 7.0 (nl)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
| c8265225a262ce71e8a7ebf010a446fc |
davidnai/transformers-qa | davidnai | t5 | 7 | 7 | transformers | 0 | text2text-generation | false | true | false | apache-2.0 | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_keras_callback'] | true | true | true | 1,317 | false |
<!-- 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. -->
# transformers-qa
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 2.3199
- Validation Loss: 3.2826
- Train Rougel: tf.Tensor(0.3922559, shape=(), dtype=float32)
- Epoch: 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:
- optimizer: {'name': 'Adam', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train Rougel | Epoch |
|:----------:|:---------------:|:---------------------------------------------:|:-----:|
| 2.3199 | 3.2826 | tf.Tensor(0.3922559, shape=(), dtype=float32) | 0 |
### Framework versions
- Transformers 4.24.0
- TensorFlow 2.9.2
- Datasets 2.8.0
- Tokenizers 0.13.2
| 8de23981364e154039ff56732f7f102a |
google/t5-efficient-small-dl8 | google | t5 | 12 | 8 | transformers | 0 | text2text-generation | true | true | true | apache-2.0 | ['en'] | ['c4'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['deep-narrow'] | false | true | true | 6,251 | false |
# T5-Efficient-SMALL-DL8 (Deep-Narrow version)
T5-Efficient-SMALL-DL8 is a variation of [Google's original T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) following the [T5 model architecture](https://huggingface.co/docs/transformers/model_doc/t5).
It is a *pretrained-only* checkpoint and was released with the
paper **[Scale Efficiently: Insights from Pre-training and Fine-tuning Transformers](https://arxiv.org/abs/2109.10686)**
by *Yi Tay, Mostafa Dehghani, Jinfeng Rao, William Fedus, Samira Abnar, Hyung Won Chung, Sharan Narang, Dani Yogatama, Ashish Vaswani, Donald Metzler*.
In a nutshell, the paper indicates that a **Deep-Narrow** model architecture is favorable for **downstream** performance compared to other model architectures
of similar parameter count.
To quote the paper:
> We generally recommend a DeepNarrow strategy where the model’s depth is preferentially increased
> before considering any other forms of uniform scaling across other dimensions. This is largely due to
> how much depth influences the Pareto-frontier as shown in earlier sections of the paper. Specifically, a
> tall small (deep and narrow) model is generally more efficient compared to the base model. Likewise,
> a tall base model might also generally more efficient compared to a large model. We generally find
> that, regardless of size, even if absolute performance might increase as we continue to stack layers,
> the relative gain of Pareto-efficiency diminishes as we increase the layers, converging at 32 to 36
> layers. Finally, we note that our notion of efficiency here relates to any one compute dimension, i.e.,
> params, FLOPs or throughput (speed). We report all three key efficiency metrics (number of params,
> FLOPS and speed) and leave this decision to the practitioner to decide which compute dimension to
> consider.
To be more precise, *model depth* is defined as the number of transformer blocks that are stacked sequentially.
A sequence of word embeddings is therefore processed sequentially by each transformer block.
## Details model architecture
This model checkpoint - **t5-efficient-small-dl8** - is of model type **Small** with the following variations:
- **dl** is **8**
It has **68.92** million parameters and thus requires *ca.* **275.66 MB** of memory in full precision (*fp32*)
or **137.83 MB** of memory in half precision (*fp16* or *bf16*).
A summary of the *original* T5 model architectures can be seen here:
| Model | nl (el/dl) | ff | dm | kv | nh | #Params|
| ----| ---- | ---- | ---- | ---- | ---- | ----|
| Tiny | 4/4 | 1024 | 256 | 32 | 4 | 16M|
| Mini | 4/4 | 1536 | 384 | 32 | 8 | 31M|
| Small | 6/6 | 2048 | 512 | 32 | 8 | 60M|
| Base | 12/12 | 3072 | 768 | 64 | 12 | 220M|
| Large | 24/24 | 4096 | 1024 | 64 | 16 | 738M|
| Xl | 24/24 | 16384 | 1024 | 128 | 32 | 3B|
| XXl | 24/24 | 65536 | 1024 | 128 | 128 | 11B|
whereas the following abbreviations are used:
| Abbreviation | Definition |
| ----| ---- |
| nl | Number of transformer blocks (depth) |
| dm | Dimension of embedding vector (output vector of transformers block) |
| kv | Dimension of key/value projection matrix |
| nh | Number of attention heads |
| ff | Dimension of intermediate vector within transformer block (size of feed-forward projection matrix) |
| el | Number of transformer blocks in the encoder (encoder depth) |
| dl | Number of transformer blocks in the decoder (decoder depth) |
| sh | Signifies that attention heads are shared |
| skv | Signifies that key-values projection matrices are tied |
If a model checkpoint has no specific, *el* or *dl* than both the number of encoder- and decoder layers correspond to *nl*.
## Pre-Training
The checkpoint was pretrained on the [Colossal, Cleaned version of Common Crawl (C4)](https://huggingface.co/datasets/c4) for 524288 steps using
the span-based masked language modeling (MLM) objective.
## Fine-Tuning
**Note**: This model is a **pretrained** checkpoint and has to be fine-tuned for practical usage.
The checkpoint was pretrained in English and is therefore only useful for English NLP tasks.
You can follow on of the following examples on how to fine-tune the model:
*PyTorch*:
- [Summarization](https://github.com/huggingface/transformers/tree/master/examples/pytorch/summarization)
- [Question Answering](https://github.com/huggingface/transformers/blob/master/examples/pytorch/question-answering/run_seq2seq_qa.py)
- [Text Classification](https://github.com/huggingface/transformers/tree/master/examples/pytorch/text-classification) - *Note*: You will have to slightly adapt the training example here to make it work with an encoder-decoder model.
*Tensorflow*:
- [Summarization](https://github.com/huggingface/transformers/tree/master/examples/tensorflow/summarization)
- [Text Classification](https://github.com/huggingface/transformers/tree/master/examples/tensorflow/text-classification) - *Note*: You will have to slightly adapt the training example here to make it work with an encoder-decoder model.
*JAX/Flax*:
- [Summarization](https://github.com/huggingface/transformers/tree/master/examples/flax/summarization)
- [Text Classification](https://github.com/huggingface/transformers/tree/master/examples/flax/text-classification) - *Note*: You will have to slightly adapt the training example here to make it work with an encoder-decoder model.
## Downstream Performance
TODO: Add table if available
## Computational Complexity
TODO: Add table if available
## More information
We strongly recommend the reader to go carefully through the original paper **[Scale Efficiently: Insights from Pre-training and Fine-tuning Transformers](https://arxiv.org/abs/2109.10686)** to get a more nuanced understanding of this model checkpoint.
As explained in the following [issue](https://github.com/google-research/google-research/issues/986#issuecomment-1035051145), checkpoints including the *sh* or *skv*
model architecture variations have *not* been ported to Transformers as they are probably of limited practical usage and are lacking a more detailed description. Those checkpoints are kept [here](https://huggingface.co/NewT5SharedHeadsSharedKeyValues) as they might be ported potentially in the future. | e4946c2631d213ad4894a3e568c1cf36 |
aambrioso/distilbert-base-uncased-finetuned-emotion | aambrioso | distilbert | 12 | 1 | transformers | 0 | text-classification | true | false | false | apache-2.0 | null | ['emotion'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 1,399 | false |
# 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(https://huggingface.co/datasets/emotion) dataset for in the dataset in HG. It achieves the following results on the evaluation set:
- Loss: 0.2033
- Accuracy: 0.9275
- F1: 0.9273
## Model description
This model is a copy of the model found in the book [Natural Language Processing with Transformers](https://github.com/nlp-with-transformers/notebooks/blob/main/02_classification.ipynb).
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.806 | 1.0 | 250 | 0.2954 | 0.908 | 0.9062 |
| 0.2361 | 2.0 | 500 | 0.2033 | 0.9275 | 0.9273 |
### Framework versions
- Transformers 4.13.0
- Pytorch 1.12.1+cu113
- Datasets 1.16.1
- Tokenizers 0.10.3
| b243da2bfdb2c381c9c2383006d5415b |
hackathon-pln-es/readability-es-3class-sentences | hackathon-pln-es | roberta | 9 | 1 | transformers | 2 | text-classification | true | false | false | cc-by-4.0 | ['es'] | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['spanish', 'roberta', 'bertin'] | false | true | true | 2,870 | false |
# Readability ES Sentences for three classes
Model based on the Roberta architecture finetuned on [BERTIN](https://huggingface.co/bertin-project/bertin-roberta-base-spanish) for readability assessment of Spanish texts.
## Description and performance
This version of the model was trained on a mix of datasets, using sentence-level granularity when possible. The model performs classification among three complexity levels:
- Basic.
- Intermediate.
- Advanced.
The relationship of these categories with the Common European Framework of Reference for Languages is described in [our report](https://wandb.ai/readability-es/readability-es/reports/Texts-Readability-Analysis-for-Spanish--VmlldzoxNzU2MDUx).
This model achieves a F1 macro average score of 0.6951, measured on the validation set.
## Model variants
- [`readability-es-sentences`](https://huggingface.co/hackathon-pln-es/readability-es-sentences). Two classes, sentence-based dataset.
- [`readability-es-paragraphs`](https://huggingface.co/hackathon-pln-es/readability-es-paragraphs). Two classes, paragraph-based dataset.
- `readability-es-3class-sentences` (this model). Three classes, sentence-based dataset.
- [`readability-es-3class-paragraphs`](https://huggingface.co/hackathon-pln-es/readability-es-3class-paragraphs). Three classes, paragraph-based dataset.
## Datasets
- [`readability-es-hackathon-pln-public`](https://huggingface.co/datasets/hackathon-pln-es/readability-es-hackathon-pln-public), composed of:
* coh-metrix-esp corpus.
* Various text resources scraped from websites.
- Other non-public datasets: newsela-es, simplext.
## Training details
Please, refer to [this training run](https://wandb.ai/readability-es/readability-es/runs/1qe3kbqj/overview) for full details on hyperparameters and training regime.
## Biases and Limitations
- Due to the scarcity of data and the lack of a reliable gold test set, performance metrics are reported on the validation set.
- One of the datasets involved is the Spanish version of newsela, which is frequently used as a reference. However, it was created by translating previous datasets, and therefore it may contain somewhat unnatural phrases.
- Some of the datasets used cannot be publicly disseminated, making it more difficult to assess the existence of biases or mistakes.
- Language might be biased towards the Spanish dialect spoken in Spain. Other regional variants might be sub-represented.
- No effort has been performed to alleviate the shortcomings and biases described in the [original implementation of BERTIN](https://huggingface.co/bertin-project/bertin-roberta-base-spanish#bias-examples-spanish).
## Authors
- [Laura Vásquez-Rodríguez](https://lmvasque.github.io/)
- [Pedro Cuenca](https://twitter.com/pcuenq)
- [Sergio Morales](https://www.fireblend.com/)
- [Fernando Alva-Manchego](https://feralvam.github.io/)
| ad9e08f0da7d32ee3e3428441d754f1e |
kamangir/image-classifier | kamangir | null | 37 | 0 | keras | 0 | null | false | false | false | cc | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | [] | false | true | true | 1,379 | false |
# Image Classifier
`image-classifier` is an extendable TensorFlow image classifier w/ a Bash cli and Hugging Face integration - to see the list of `image-classifier` commands complete [installation](#Installation) and type in:
```
image_classifier ?
```
## Installation
To install `image-classifier` first [install and configure awesome-bash-cli](https://github.com/kamangir/awesome-bash-cli) then run:
```
abcli huggingface clone image-classifier
```
To see the list of `image-classifier` saved models type in
```
image_classifier list
```
You should see the following items:
1. [fashion-mnist](#fashion-mnist)
1. intel-image-classifier 🚧
1. vegetable-classifier 🚧
## fashion-mnist
`fashion-mnist` is an `image-classifier` trained on [Fashion-MNIST](https://github.com/zalandoresearch/fashion-mnist).
To retrain `fashion-mnist` type in:
```
abcli select
fashion_mnist train
abcli upload
image_classifier list . browser=1,model=object
```
You should now see the structure of the network (left) and the [content of the model](https://github.com/kamangir/browser) (right).
|  |  |
|---|---|
You can save this model under a new name by typing in:
```
fashion_mnist save new_name_1
```
/ END | dcf3ba5b065c6a9f85ca6809f71f9bab |
jonatasgrosman/exp_w2v2t_th_xlsr-53_s711 | jonatasgrosman | wav2vec2 | 10 | 5 | transformers | 0 | automatic-speech-recognition | true | false | false | apache-2.0 | ['th'] | ['mozilla-foundation/common_voice_7_0'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['automatic-speech-recognition', 'th'] | false | true | true | 464 | false | # exp_w2v2t_th_xlsr-53_s711
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) for speech recognition on Thai using the train split of [Common Voice 7.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
| 6248389bd99b5cd26a97f074c8a03df6 |
muhtasham/tiny-mlm-glue-sst2-target-glue-stsb | muhtasham | bert | 10 | 1 | transformers | 0 | text-classification | true | false | false | apache-2.0 | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 2,103 | false |
<!-- 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. -->
# tiny-mlm-glue-sst2-target-glue-stsb
This model is a fine-tuned version of [muhtasham/tiny-mlm-glue-sst2](https://huggingface.co/muhtasham/tiny-mlm-glue-sst2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9195
- Pearson: 0.8130
- Spearmanr: 0.8114
## 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: constant
- num_epochs: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss | Pearson | Spearmanr |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:---------:|
| 2.7776 | 2.78 | 500 | 1.1238 | 0.7313 | 0.7669 |
| 0.932 | 5.56 | 1000 | 1.0628 | 0.7833 | 0.8086 |
| 0.737 | 8.33 | 1500 | 1.0050 | 0.8025 | 0.8208 |
| 0.6099 | 11.11 | 2000 | 0.8592 | 0.8165 | 0.8220 |
| 0.5164 | 13.89 | 2500 | 0.8875 | 0.8158 | 0.8181 |
| 0.4659 | 16.67 | 3000 | 0.9524 | 0.8155 | 0.8198 |
| 0.4114 | 19.44 | 3500 | 0.8872 | 0.8173 | 0.8174 |
| 0.3728 | 22.22 | 4000 | 0.9423 | 0.8163 | 0.8166 |
| 0.3396 | 25.0 | 4500 | 0.9953 | 0.8197 | 0.8202 |
| 0.321 | 27.78 | 5000 | 0.9409 | 0.8160 | 0.8160 |
| 0.3034 | 30.56 | 5500 | 0.9273 | 0.8142 | 0.8139 |
| 0.2811 | 33.33 | 6000 | 0.9195 | 0.8130 | 0.8114 |
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.0+cu116
- Datasets 2.8.1.dev0
- Tokenizers 0.13.2
| d00c7b40fe18d4cb4278c9f549963972 |
Mustafa21/my_awesome_food_model | Mustafa21 | vit | 7 | 0 | transformers | 0 | image-classification | true | false | false | apache-2.0 | null | ['food101'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 1,449 | false |
<!-- 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_food_model
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the food101 dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2335
- Accuracy: 0.985
## 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
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.0523 | 1.0 | 50 | 1.9226 | 0.935 |
| 1.3718 | 2.0 | 100 | 1.3422 | 0.995 |
| 1.2298 | 3.0 | 150 | 1.2335 | 0.985 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
| c1fe6d7435d02b62f37f526c78aa1e8c |
Rakib/whisper-tiny-bn | Rakib | whisper | 36 | 31 | transformers | 0 | automatic-speech-recognition | true | false | false | apache-2.0 | ['bn'] | ['mozilla-foundation/common_voice_11_0'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['whisper-event', 'generated_from_trainer'] | true | true | true | 1,561 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Whisper Tiny Bengali
This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the mozilla-foundation/common_voice_11_0 bn dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2314
- Wer: 32.8977
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 5000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.3362 | 0.96 | 1000 | 0.3536 | 45.0860 |
| 0.2395 | 1.91 | 2000 | 0.2745 | 37.1714 |
| 0.205 | 2.87 | 3000 | 0.2485 | 34.7353 |
| 0.1795 | 3.83 | 4000 | 0.2352 | 33.2469 |
| 0.1578 | 4.78 | 5000 | 0.2314 | 32.8977 |
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.1+cu117
- Datasets 2.8.1.dev0
- Tokenizers 0.13.2
| 5218c0c8fe81cbe9cc28907cf2a80a60 |
google/t5-efficient-xl-nl8 | google | t5 | 12 | 7 | transformers | 0 | text2text-generation | true | true | true | apache-2.0 | ['en'] | ['c4'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['deep-narrow'] | false | true | true | 6,242 | false |
# T5-Efficient-XL-NL8 (Deep-Narrow version)
T5-Efficient-XL-NL8 is a variation of [Google's original T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) following the [T5 model architecture](https://huggingface.co/docs/transformers/model_doc/t5).
It is a *pretrained-only* checkpoint and was released with the
paper **[Scale Efficiently: Insights from Pre-training and Fine-tuning Transformers](https://arxiv.org/abs/2109.10686)**
by *Yi Tay, Mostafa Dehghani, Jinfeng Rao, William Fedus, Samira Abnar, Hyung Won Chung, Sharan Narang, Dani Yogatama, Ashish Vaswani, Donald Metzler*.
In a nutshell, the paper indicates that a **Deep-Narrow** model architecture is favorable for **downstream** performance compared to other model architectures
of similar parameter count.
To quote the paper:
> We generally recommend a DeepNarrow strategy where the model’s depth is preferentially increased
> before considering any other forms of uniform scaling across other dimensions. This is largely due to
> how much depth influences the Pareto-frontier as shown in earlier sections of the paper. Specifically, a
> tall small (deep and narrow) model is generally more efficient compared to the base model. Likewise,
> a tall base model might also generally more efficient compared to a large model. We generally find
> that, regardless of size, even if absolute performance might increase as we continue to stack layers,
> the relative gain of Pareto-efficiency diminishes as we increase the layers, converging at 32 to 36
> layers. Finally, we note that our notion of efficiency here relates to any one compute dimension, i.e.,
> params, FLOPs or throughput (speed). We report all three key efficiency metrics (number of params,
> FLOPS and speed) and leave this decision to the practitioner to decide which compute dimension to
> consider.
To be more precise, *model depth* is defined as the number of transformer blocks that are stacked sequentially.
A sequence of word embeddings is therefore processed sequentially by each transformer block.
## Details model architecture
This model checkpoint - **t5-efficient-xl-nl8** - is of model type **Xl** with the following variations:
- **nl** is **8**
It has **972.49** million parameters and thus requires *ca.* **3889.95 MB** of memory in full precision (*fp32*)
or **1944.97 MB** of memory in half precision (*fp16* or *bf16*).
A summary of the *original* T5 model architectures can be seen here:
| Model | nl (el/dl) | ff | dm | kv | nh | #Params|
| ----| ---- | ---- | ---- | ---- | ---- | ----|
| Tiny | 4/4 | 1024 | 256 | 32 | 4 | 16M|
| Mini | 4/4 | 1536 | 384 | 32 | 8 | 31M|
| Small | 6/6 | 2048 | 512 | 32 | 8 | 60M|
| Base | 12/12 | 3072 | 768 | 64 | 12 | 220M|
| Large | 24/24 | 4096 | 1024 | 64 | 16 | 738M|
| Xl | 24/24 | 16384 | 1024 | 128 | 32 | 3B|
| XXl | 24/24 | 65536 | 1024 | 128 | 128 | 11B|
whereas the following abbreviations are used:
| Abbreviation | Definition |
| ----| ---- |
| nl | Number of transformer blocks (depth) |
| dm | Dimension of embedding vector (output vector of transformers block) |
| kv | Dimension of key/value projection matrix |
| nh | Number of attention heads |
| ff | Dimension of intermediate vector within transformer block (size of feed-forward projection matrix) |
| el | Number of transformer blocks in the encoder (encoder depth) |
| dl | Number of transformer blocks in the decoder (decoder depth) |
| sh | Signifies that attention heads are shared |
| skv | Signifies that key-values projection matrices are tied |
If a model checkpoint has no specific, *el* or *dl* than both the number of encoder- and decoder layers correspond to *nl*.
## Pre-Training
The checkpoint was pretrained on the [Colossal, Cleaned version of Common Crawl (C4)](https://huggingface.co/datasets/c4) for 524288 steps using
the span-based masked language modeling (MLM) objective.
## Fine-Tuning
**Note**: This model is a **pretrained** checkpoint and has to be fine-tuned for practical usage.
The checkpoint was pretrained in English and is therefore only useful for English NLP tasks.
You can follow on of the following examples on how to fine-tune the model:
*PyTorch*:
- [Summarization](https://github.com/huggingface/transformers/tree/master/examples/pytorch/summarization)
- [Question Answering](https://github.com/huggingface/transformers/blob/master/examples/pytorch/question-answering/run_seq2seq_qa.py)
- [Text Classification](https://github.com/huggingface/transformers/tree/master/examples/pytorch/text-classification) - *Note*: You will have to slightly adapt the training example here to make it work with an encoder-decoder model.
*Tensorflow*:
- [Summarization](https://github.com/huggingface/transformers/tree/master/examples/tensorflow/summarization)
- [Text Classification](https://github.com/huggingface/transformers/tree/master/examples/tensorflow/text-classification) - *Note*: You will have to slightly adapt the training example here to make it work with an encoder-decoder model.
*JAX/Flax*:
- [Summarization](https://github.com/huggingface/transformers/tree/master/examples/flax/summarization)
- [Text Classification](https://github.com/huggingface/transformers/tree/master/examples/flax/text-classification) - *Note*: You will have to slightly adapt the training example here to make it work with an encoder-decoder model.
## Downstream Performance
TODO: Add table if available
## Computational Complexity
TODO: Add table if available
## More information
We strongly recommend the reader to go carefully through the original paper **[Scale Efficiently: Insights from Pre-training and Fine-tuning Transformers](https://arxiv.org/abs/2109.10686)** to get a more nuanced understanding of this model checkpoint.
As explained in the following [issue](https://github.com/google-research/google-research/issues/986#issuecomment-1035051145), checkpoints including the *sh* or *skv*
model architecture variations have *not* been ported to Transformers as they are probably of limited practical usage and are lacking a more detailed description. Those checkpoints are kept [here](https://huggingface.co/NewT5SharedHeadsSharedKeyValues) as they might be ported potentially in the future. | e0306ae9d1780c774c1647ac55b9679d |
alphahg/opus-mt-ko-en-finetuned-ko-to-en-2780616 | alphahg | marian | 13 | 179 | transformers | 0 | text2text-generation | true | false | false | apache-2.0 | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 1,394 | false |
<!-- 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. -->
# opus-mt-ko-en-finetuned-ko-to-en-2780616
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-ko-en](https://huggingface.co/Helsinki-NLP/opus-mt-ko-en) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8435
## 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
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 1.0458 | 1.0 | 9376 | 0.9283 |
| 0.9423 | 2.0 | 18752 | 0.8607 |
| 0.9013 | 3.0 | 28128 | 0.8435 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.1+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
| b046e8a0b7f80d770ce294a3a513ad8d |
lmqg/bart-base-squad-qg-ae | lmqg | bart | 21 | 42 | transformers | 0 | text2text-generation | true | false | false | cc-by-4.0 | ['en'] | ['lmqg/qg_squad'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['question generation', 'answer extraction'] | true | true | true | 7,038 | false |
# Model Card of `lmqg/bart-base-squad-qg-ae`
This model is fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) for question generation and answer extraction jointly on the [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-generation).
### Overview
- **Language model:** [facebook/bart-base](https://huggingface.co/facebook/bart-base)
- **Language:** en
- **Training data:** [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) (default)
- **Online Demo:** [https://autoqg.net/](https://autoqg.net/)
- **Repository:** [https://github.com/asahi417/lm-question-generation](https://github.com/asahi417/lm-question-generation)
- **Paper:** [https://arxiv.org/abs/2210.03992](https://arxiv.org/abs/2210.03992)
### Usage
- With [`lmqg`](https://github.com/asahi417/lm-question-generation#lmqg-language-model-for-question-generation-)
```python
from lmqg import TransformersQG
# initialize model
model = TransformersQG(language="en", model="lmqg/bart-base-squad-qg-ae")
# model prediction
question_answer_pairs = model.generate_qa("William Turner was an English painter who specialised in watercolour landscapes")
```
- With `transformers`
```python
from transformers import pipeline
pipe = pipeline("text2text-generation", "lmqg/bart-base-squad-qg-ae")
# answer extraction
answer = pipe("generate question: <hl> Beyonce <hl> further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, Cadillac Records.")
# question generation
question = pipe("extract answers: <hl> Beyonce further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, Cadillac Records. <hl> Her performance in the film received praise from critics, and she garnered several nominations for her portrayal of James, including a Satellite Award nomination for Best Supporting Actress, and a NAACP Image Award nomination for Outstanding Supporting Actress.")
```
## Evaluation
- ***Metric (Question Generation)***: [raw metric file](https://huggingface.co/lmqg/bart-base-squad-qg-ae/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_squad.default.json)
| | Score | Type | Dataset |
|:-----------|--------:|:--------|:---------------------------------------------------------------|
| BERTScore | 90.65 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| Bleu_1 | 56.53 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| Bleu_2 | 40.97 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| Bleu_3 | 31.71 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| Bleu_4 | 25.07 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| METEOR | 25.87 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| MoverScore | 64.49 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| ROUGE_L | 52.79 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
- ***Metric (Question & Answer Generation)***: [raw metric file](https://huggingface.co/lmqg/bart-base-squad-qg-ae/raw/main/eval/metric.first.answer.paragraph.questions_answers.lmqg_qg_squad.default.json)
| | Score | Type | Dataset |
|:--------------------------------|--------:|:--------|:---------------------------------------------------------------|
| QAAlignedF1Score (BERTScore) | 93.45 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| QAAlignedF1Score (MoverScore) | 64.47 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| QAAlignedPrecision (BERTScore) | 92.78 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| QAAlignedPrecision (MoverScore) | 63.55 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| QAAlignedRecall (BERTScore) | 94.14 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| QAAlignedRecall (MoverScore) | 65.49 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
- ***Metric (Answer Extraction)***: [raw metric file](https://huggingface.co/lmqg/bart-base-squad-qg-ae/raw/main/eval/metric.first.answer.paragraph_sentence.answer.lmqg_qg_squad.default.json)
| | Score | Type | Dataset |
|:-----------------|--------:|:--------|:---------------------------------------------------------------|
| AnswerExactMatch | 57.58 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| AnswerF1Score | 69.14 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| BERTScore | 91.86 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| Bleu_1 | 65.9 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| Bleu_2 | 63.06 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| Bleu_3 | 60.47 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| Bleu_4 | 58.31 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| METEOR | 41.39 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| MoverScore | 81.95 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| ROUGE_L | 68.38 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
## Training hyperparameters
The following hyperparameters were used during fine-tuning:
- dataset_path: lmqg/qg_squad
- dataset_name: default
- input_types: ['paragraph_answer', 'paragraph_sentence']
- output_types: ['question', 'answer']
- prefix_types: ['qg', 'ae']
- model: facebook/bart-base
- max_length: 512
- max_length_output: 32
- epoch: 3
- batch: 32
- lr: 5e-05
- fp16: False
- random_seed: 1
- gradient_accumulation_steps: 4
- label_smoothing: 0.15
The full configuration can be found at [fine-tuning config file](https://huggingface.co/lmqg/bart-base-squad-qg-ae/raw/main/trainer_config.json).
## Citation
```
@inproceedings{ushio-etal-2022-generative,
title = "{G}enerative {L}anguage {M}odels for {P}aragraph-{L}evel {Q}uestion {G}eneration",
author = "Ushio, Asahi and
Alva-Manchego, Fernando and
Camacho-Collados, Jose",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2022",
address = "Abu Dhabi, U.A.E.",
publisher = "Association for Computational Linguistics",
}
```
| 0472ef1be1e53a1f9d972010531341c8 |
DrishtiSharma/wav2vec2-xls-r-300m-mt-o1 | DrishtiSharma | wav2vec2 | 18 | 5 | transformers | 0 | automatic-speech-recognition | true | false | false | apache-2.0 | ['mt'] | ['mozilla-foundation/common_voice_8_0'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_8_0', 'generated_from_trainer', 'mt', 'robust-speech-event', 'model_for_talk', 'hf-asr-leaderboard'] | true | true | true | 1,755 | false |
<!-- 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. -->
#
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - MT dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1987
- Wer: 0.1920
### Evaluation Commands
1. To evaluate on mozilla-foundation/common_voice_8_0 with test split
python eval.py --model_id DrishtiSharma/wav2vec2-xls-r-300m-mt-o1 --dataset mozilla-foundation/common_voice_8_0 --config mt --split test --log_outputs
2. To evaluate on speech-recognition-community-v2/dev_data
Maltese language not found in speech-recognition-community-v2/dev_data!
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 7e-05
- train_batch_size: 32
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2000
- num_epochs: 100.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 1.1721 | 18.02 | 2000 | 0.3831 | 0.4066 |
| 0.7849 | 36.04 | 4000 | 0.2191 | 0.2417 |
| 0.6723 | 54.05 | 6000 | 0.2056 | 0.2134 |
| 0.6015 | 72.07 | 8000 | 0.2008 | 0.2031 |
| 0.5386 | 90.09 | 10000 | 0.1967 | 0.1953 |
### Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2.dev0
- Tokenizers 0.11.0
| 1344c04148be6576a4d88d76f38dbc3b |
Helsinki-NLP/opus-mt-bem-fi | Helsinki-NLP | marian | 10 | 8 | transformers | 0 | translation | true | true | false | apache-2.0 | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['translation'] | false | true | true | 776 | false |
### opus-mt-bem-fi
* source languages: bem
* target languages: fi
* OPUS readme: [bem-fi](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/bem-fi/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2020-01-08.zip](https://object.pouta.csc.fi/OPUS-MT-models/bem-fi/opus-2020-01-08.zip)
* test set translations: [opus-2020-01-08.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/bem-fi/opus-2020-01-08.test.txt)
* test set scores: [opus-2020-01-08.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/bem-fi/opus-2020-01-08.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| JW300.bem.fi | 22.8 | 0.439 |
| 485d38234d86f8230e783ce5fc24118e |
Eppinette/Mona | Eppinette | null | 4 | 0 | null | 6 | text-to-image | false | false | false | mit | ['en'] | null | null | 1 | 0 | 1 | 0 | 0 | 0 | 0 | ['stable-diffusion', 'text-to-image'] | false | true | true | 761 | false |
# Mona Subject Model / Dreambooth Training
## Usage
To use this model you have to download the .ckpt file as well as drop it into the "\stable-diffusion-webui\models\Stable-diffusion" folder
To use it in a prompt: ```"Mona woman"``` for highest strength or just "Mona"
To increase the strength put "Mona woman" in () brackets
To decrease the strength put "Mona woman" in [] brackets
Waifu_diffusion base trained model trained to 4,000 steps
Have fun :)
## Example Pictures from Mona_4k
<table>
<tr>
<td><img src=https://i.imgur.com/acDDsQZ.png width=150% height=150%/></td>
<td><img src=https://i.imgur.com/15PnKDf.png width=100% height=100%/></td>
<td><img src=https://i.imgur.com/PWxazM1.png width=150% height=150%/></td>
</tr>
</table> | b2c30525fcbd821a65fac05a422926e4 |
darragh/swinunetr-btcv-base | darragh | null | 6 | 0 | transformers | 0 | null | true | false | false | apache-2.0 | ['en'] | ['BTCV'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['btcv', 'medical', 'swin'] | false | true | true | 4,694 | false |
# Model Overview
This repository contains the code for Swin UNETR [1,2]. Swin UNETR is the state-of-the-art on Medical Segmentation
Decathlon (MSD) and Beyond the Cranial Vault (BTCV) Segmentation Challenge dataset. In [1], a novel methodology is devised for pre-training Swin UNETR backbone in a self-supervised
manner. We provide the option for training Swin UNETR by fine-tuning from pre-trained self-supervised weights or from scratch.
The source repository for the training of these models can be found [here](https://github.com/Project-MONAI/research-contributions/tree/main/SwinUNETR/BTCV).
# Installing Dependencies
Dependencies for training and inference can be installed using the model requirements :
``` bash
pip install -r requirements.txt
```
# Intended uses & limitations
You can use the raw model for dicom segmentation, but it's mostly intended to be fine-tuned on a downstream task.
Note that this model is primarily aimed at being fine-tuned on tasks which segment CAT scans or MRIs on images in dicom format. Dicom meta data mostly differs across medical facilities, so if applying to a new dataset, the model should be finetuned.
# How to use
To install necessary dependencies, run the below in bash.
```
git clone https://github.com/darraghdog/Project-MONAI-research-contributions pmrc
pip install -r pmrc/requirements.txt
cd pmrc/SwinUNETR/BTCV
```
To load the model from the hub.
```
>>> from swinunetr import SwinUnetrModelForInference
>>> model = SwinUnetrModelForInference.from_pretrained('darragh/swinunetr-btcv-tiny')
```
# Limitations and bias
The training data used for this model is specific to CAT scans from certain health facilities and machines. Data from other facilities may difffer in image distributions, and may require finetuning of the models for best performance.
# Evaluation results
We provide several pre-trained models on BTCV dataset in the following.
<table>
<tr>
<th>Name</th>
<th>Dice (overlap=0.7)</th>
<th>Dice (overlap=0.5)</th>
<th>Feature Size</th>
<th># params (M)</th>
<th>Self-Supervised Pre-trained </th>
</tr>
<tr>
<td>Swin UNETR/Base</td>
<td>82.25</td>
<td>81.86</td>
<td>48</td>
<td>62.1</td>
<td>Yes</td>
</tr>
<tr>
<td>Swin UNETR/Small</td>
<td>79.79</td>
<td>79.34</td>
<td>24</td>
<td>15.7</td>
<td>No</td>
</tr>
<tr>
<td>Swin UNETR/Tiny</td>
<td>72.05</td>
<td>70.35</td>
<td>12</td>
<td>4.0</td>
<td>No</td>
</tr>
</table>
# Data Preparation
The training data is from the [BTCV challenge dataset](https://www.synapse.org/#!Synapse:syn3193805/wiki/217752).
- Target: 13 abdominal organs including 1. Spleen 2. Right Kidney 3. Left Kideny 4.Gallbladder 5.Esophagus 6. Liver 7. Stomach 8.Aorta 9. IVC 10. Portal and Splenic Veins 11. Pancreas 12.Right adrenal gland 13.Left adrenal gland.
- Task: Segmentation
- Modality: CT
- Size: 30 3D volumes (24 Training + 6 Testing)
# Training
See the source repository [here](https://github.com/Project-MONAI/research-contributions/tree/main/SwinUNETR/BTCV) for information on training.
# BibTeX entry and citation info
If you find this repository useful, please consider citing the following papers:
```
@inproceedings{tang2022self,
title={Self-supervised pre-training of swin transformers for 3d medical image analysis},
author={Tang, Yucheng and Yang, Dong and Li, Wenqi and Roth, Holger R and Landman, Bennett and Xu, Daguang and Nath, Vishwesh and Hatamizadeh, Ali},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
pages={20730--20740},
year={2022}
}
@article{hatamizadeh2022swin,
title={Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images},
author={Hatamizadeh, Ali and Nath, Vishwesh and Tang, Yucheng and Yang, Dong and Roth, Holger and Xu, Daguang},
journal={arXiv preprint arXiv:2201.01266},
year={2022}
}
```
# References
[1]: Tang, Y., Yang, D., Li, W., Roth, H.R., Landman, B., Xu, D., Nath, V. and Hatamizadeh, A., 2022. Self-supervised pre-training of swin transformers for 3d medical image analysis. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 20730-20740).
[2]: Hatamizadeh, A., Nath, V., Tang, Y., Yang, D., Roth, H. and Xu, D., 2022. Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images. arXiv preprint arXiv:2201.01266.
| 306bc1f6d766306d4be94b5df595dbc2 |
sd-concepts-library/sunfish | sd-concepts-library | null | 18 | 0 | null | 0 | null | false | false | false | mit | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | [] | false | true | true | 1,922 | false | ### SunFish on Stable Diffusion
This is the `<SunFish>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb).
Here is the new concept you will be able to use as a `style`:
| 8b95693b8fed43e917a0d769662f9fb1 |
lmqg/flan-t5-small-squad-qag | lmqg | t5 | 13 | 2 | transformers | 0 | text2text-generation | true | false | false | cc-by-4.0 | ['en'] | ['lmqg/qag_squad'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['questions and answers generation'] | true | true | true | 3,892 | false |
# Model Card of `lmqg/flan-t5-small-squad-qag`
This model is fine-tuned version of [google/flan-t5-small](https://huggingface.co/google/flan-t5-small) for question & answer pair generation task on the [lmqg/qag_squad](https://huggingface.co/datasets/lmqg/qag_squad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-generation).
### Overview
- **Language model:** [google/flan-t5-small](https://huggingface.co/google/flan-t5-small)
- **Language:** en
- **Training data:** [lmqg/qag_squad](https://huggingface.co/datasets/lmqg/qag_squad) (default)
- **Online Demo:** [https://autoqg.net/](https://autoqg.net/)
- **Repository:** [https://github.com/asahi417/lm-question-generation](https://github.com/asahi417/lm-question-generation)
- **Paper:** [https://arxiv.org/abs/2210.03992](https://arxiv.org/abs/2210.03992)
### Usage
- With [`lmqg`](https://github.com/asahi417/lm-question-generation#lmqg-language-model-for-question-generation-)
```python
from lmqg import TransformersQG
# initialize model
model = TransformersQG(language="en", model="lmqg/flan-t5-small-squad-qag")
# model prediction
question_answer_pairs = model.generate_qa("William Turner was an English painter who specialised in watercolour landscapes")
```
- With `transformers`
```python
from transformers import pipeline
pipe = pipeline("text2text-generation", "lmqg/flan-t5-small-squad-qag")
output = pipe("generate question and answer: Beyonce further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, Cadillac Records.")
```
## Evaluation
- ***Metric (Question & Answer Generation)***: [raw metric file](https://huggingface.co/lmqg/flan-t5-small-squad-qag/raw/main/eval/metric.first.answer.paragraph.questions_answers.lmqg_qag_squad.default.json)
| | Score | Type | Dataset |
|:--------------------------------|--------:|:--------|:-----------------------------------------------------------------|
| QAAlignedF1Score (BERTScore) | 92.3 | default | [lmqg/qag_squad](https://huggingface.co/datasets/lmqg/qag_squad) |
| QAAlignedF1Score (MoverScore) | 63.74 | default | [lmqg/qag_squad](https://huggingface.co/datasets/lmqg/qag_squad) |
| QAAlignedPrecision (BERTScore) | 92.92 | default | [lmqg/qag_squad](https://huggingface.co/datasets/lmqg/qag_squad) |
| QAAlignedPrecision (MoverScore) | 65.5 | default | [lmqg/qag_squad](https://huggingface.co/datasets/lmqg/qag_squad) |
| QAAlignedRecall (BERTScore) | 91.71 | default | [lmqg/qag_squad](https://huggingface.co/datasets/lmqg/qag_squad) |
| QAAlignedRecall (MoverScore) | 62.2 | default | [lmqg/qag_squad](https://huggingface.co/datasets/lmqg/qag_squad) |
## Training hyperparameters
The following hyperparameters were used during fine-tuning:
- dataset_path: lmqg/qag_squad
- dataset_name: default
- input_types: ['paragraph']
- output_types: ['questions_answers']
- prefix_types: ['qag']
- model: google/flan-t5-small
- max_length: 512
- max_length_output: 256
- epoch: 14
- batch: 16
- lr: 0.0001
- fp16: False
- random_seed: 1
- gradient_accumulation_steps: 4
- label_smoothing: 0.0
The full configuration can be found at [fine-tuning config file](https://huggingface.co/lmqg/flan-t5-small-squad-qag/raw/main/trainer_config.json).
## Citation
```
@inproceedings{ushio-etal-2022-generative,
title = "{G}enerative {L}anguage {M}odels for {P}aragraph-{L}evel {Q}uestion {G}eneration",
author = "Ushio, Asahi and
Alva-Manchego, Fernando and
Camacho-Collados, Jose",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2022",
address = "Abu Dhabi, U.A.E.",
publisher = "Association for Computational Linguistics",
}
```
| 5aa966e0eb35d14b596ec280d4c81e9d |