```Python
from transformers import PegasusTokenizer, PegasusForConditionalGeneration, TFPegasusForConditionalGeneration

# Let's load the model and the tokenizer
model_name = "mayu0007/pegasus_large_covid"
tokenizer = PegasusTokenizer.from_pretrained(model_name)
model = PegasusForConditionalGeneration.from_pretrained(model_name)

# Some text to summarize here
text_to_summarize = "Background:
 On 31 December 2019, the World Health Organization was alerted to several cases of pneumonia in Wuhan City, Hubei Province of China. The causative pathogen was suspected to be a virus, but it did not match any other known virus. The following day, Wuhan City officials closed the Huanan seafood market, suspected to be the source of the mystery pathogen, because it was reported that certain patients presenting with the symptoms were vendors at that public market. By January 4 2020, the Chinese Health Organization reported 44 active cases. On 7 January 2020, Chinese authorities confirmed that they had identified the causative agent as a novel Coronavirus (CoV). That family includes viruses of the common cold as well as viruses known to cause Middle-East Respiratory Syndrome (MERS); Severe Acute Respiratory Syndrome (SARS).\\\\
 The new CoV was named Novel Coronavirus (emerged late) 2019 (2019-nCoV). Two days later, Chinese authorities reported the first fatality linked to 2019-nCoV: a 61-year-old male who had been admitted in the first cohort of patients. He had several other underlying medical conditions, which may have contributed to weakening his immune system. Apart from respiratory failure and severe pneumonia caused by 2019-nCoV, the patient suffered from abdominal tumors and chronic liver disease. On 12 January, Chinese scientists released the genetic sequence of 2019-nCoV, in part because nonofficial report of international spread of 2019-nCoV had commenced. The next day, Thailand officially reported its first imported case of 2019-nCoV: a 61-year-old woman from Wuhan -she, however, denied having visited the Huanan seafood market. On January 15 2020, Chinese authorities reported the 140 ©Biomedical Informatics (2020) second death attributed to 2019-nCoV: a 69-year-old male who also suffered of other unrelated severe pathologies, including myocarditis. Infection with 2019-nCov, nonetheless, were thought to be responsible for his abnormal renal function, and severely damaged to multiple organ functions. The following day, Japan reported its first case of 2019-nCoV: a Chinese man in his 30s, who also denied having visited the Huanan market. On January 17, Thailand confirmed the second imported case of 2019-nCoV. Chinese authorities noted a spike in 2019-nCoV infections between January 18 and 19, 2020. That observation arose the suspicion that 2019-nCoV was capable of direct human-to-human transmission.
 The following day, 20 January 2020, South Korea confirmed its first case of 2019-nCoV infection: a male patient who denied having visited any public markets in Wuhan. On January 21 2020, the World Health Organization confirmed human-to-human transmission of 2019-nCov. As of that date, the total official number of cases has risen to 222, although it was suspected to be in reality much higher. Infection had spread to health-care workers, and it was suspected that one mode of transmission may be via the eye mucosa. Chinese authorities have also reported a fourth death. The situation was fast becoming alarming: suspected cases appeared in France, Italy and other countries of Europe. Australia seems to be affected as well. Other countries in Asia also reported suspected cases, including the Philippines and Singapore. Suspected cases of 2019-nCoV were reported in North America. The following day, 22 January 2020, World Health Organization Director-General Tedros Adhanom Ghebreyesus convened an emergency meeting to decide whether 2019-nCoV should be declared to constitute a worldwide public health emergency of international concern. Despite a significant rise in confirmed cases of individuals infected with 2019-nCoV -in China alone, at 580 infected individuals, with a death toll now at 17 in the Hubei Province alone -the emergency committee deferred its decision on whether to advise Director-General Ghebreyesus to declare the 2019-nCoV outbreak a public health emergency pandemic of international concern. On January 23, Chinese authorities shut down the city of Wuhan: no public transportation, closed airport and railway station for 11 million people. Later that same day, the city of Ezhou is also in complete lockdown. Festivities for the upcoming Chinese New Year were cancelled throughout China to minimize human contact in crowds.\\\\
 The following day, the city of Huanggang was declared under lockdown. Singapore confirmed its first imported case, and Vietnam confirmed two cases. Director-General Ghebreyesus declared that, indeed, the 2019-nCoV outbreaks is a public health emergency of international concern. On January 24 2020, the official number of confirmed cases of patients infected with 2019-nCoV had risen to 830 in China alone, with 177 (21%) among them in severe and critical condition. The number of fatalities caused by 2019-nCoV in China was now 25. Japan confirmed its second 2019-nCoV case. Nepal confirmed its first case. The following day, Australia confirmed its first case of 2019-nCoV, as did France. Two suspected cases in Italy were being closely monitored. In China, the official number of new infections -that is, over the previous 24 h -was 444, and the number of new deaths was 16 above and beyond the number reported the previous day. The official number of individuals confirmed to be infected with 2019-nCoV in China became 1,287, including 237 (20.7%) in severe and critical condition. There is no first-, second-or third-generation vaccine available for any members of the Cov family, nor is there practically the time to develop, raise, test and evaluate the effectiveness of a vaccine for 2019-nCov. Moreover, the World Health Organization stated in its 12 January 2020 recommendations entitled \\\\\\\\'Clinical management of severe acute respiratory infection when novel coronavirus (nCoV) infection is suspected -Interim guidance; WHO/nCoV/Clinical/2020.1\\\\\\\\' that "…there is no current evidence from RCTs to recommend any specific anti-nCoV treatment for patients with suspected or confirmed nCoV…". In brief, the international medical community is totally devoid of tools to combat the unfolding 2019-nCov thereat to global public healthnot in terms of preventive medicine to protect subjects at-risk, and not in terms of clinical interventions for infected patients.
 What is known, however, is that 2019-nCov, like all corona viruses belong to the Coronaviruses (Coronaviridae) family of RNA viruses that cause diseases in mammals and birds that include diarrhea in cows and pigs, and upper respiratory disease in chickens. In humans, the virus causes respiratory infections, which are generally often mild, rarely lethal. The trends we begin to observe with 2019-nCov suggest that it can be directly transmitted humanto-human, and that it causes serious infections in roughly one in five patients that can lead to death: staggering preliminary statistics. Previous research with other CoV members indicates that proteins of Coronaviruses that could be used in the generation of vaccines include the spike, the envelope, the membrane and the ©Biomedical Informatics (2020) nucleocapsid proteins. The spike protein is of particular interest because it is responsible for the penetration of the virus into the cell, which leads to the initiation of viral replication and proliferation. The spike protein binds to the angiotensin-converting enzyme 2 (ACE2) transmembrane -receptor on the eukaryotic host cell. Case in point, SARS-CoV binds to ACE2, as does MERS-CoV [2] . Indeed, ACE2 is the obligate cellular receptor for CoV entry process via the spike protein [3] .
 While the development of a vaccine of the 1 st , 2 nd or 3 rd generation against the spike protein is possible but time consuming, it is therefore timely ad critical to propose new possible and practical approaches for preventing infection of subjects at-risk and for treatment intervention of patients infected with 2019-nCov, or any other CoV for that matter. One such alternative protocol is proposed below.
 Methodology:
 Short of 1 st , 2 nd or 3 rd generation vaccine measures for preventive CoV, and short of clinical treatment interventions for patients infected with CoV, and specifically, 2019-nCov, it is timely and critical to evaluate new alternatives. Here, we propose that one putative 4 th generation vaccine to control 2019-nCoV explosion might simply involve the genetic engineering a soluble binary molecule (i.e., ACE2R-ACE2R; [ACE2R] 2) or its quaternary form (i.e. two intertwined ACE2R-ACE2R; [ACE2R] 4). This process is fast, reliable and precise by today's standard, and doable in any modern biochemistry laboratory. The obtained sterile molecule could be injected in individuals at high risk as a preventive 4 th vaccination measure, or as a treatment intervention in confirmed cases of 2019-nCoV infection. The soluble molecule is expected to bind the spike protein of circulating CoV with higher affinity than the transmembrane ACE2R, and to render the CoV particles, therefore, incapable of binding to the cell receptor, of penetration into the cells, and of replicating inside the cell. The proposed 4 th generation vaccine would, besides protecting the cells from CoV infection, also preserve ACE2 intracellular functional activity, and guard against the rise of serum angiotensin II levels, which can be pathogenic to lung cell integrity. In brief, the 4 th generation vaccine proposed here would prevent at-risk individuals from becoming sick from any incipient infection: that is, in the true meaning of the term, it would 'vaccinate' them against CoV in general, and in the present case of high emergency provide substantial protection against2019-nCoV. Moreover, should the molecule be genetically engineered to incorporate a neutral protein, such as human serum albumin, the soluble albumin-[ACE2R] 2 or albumin-[ACE2R] 4 complex injected in 2019-nCoV-infected patients would bind the circulating CoV. Patients could then undergo a treatment intervention of 'cleaning' their blood from albumin-[ACE2R] n-CoV complexes by a clinical protocol akin to dialysis. The patient's blood would be passed through a sterile column constructed with high affinity anti-human albumin antibodies. The anti-albumin antibody-albumin-[ACE2R] n-CoV moieties would be retained on the column, and the 'CoV-cleaned' blood returned to the patient to dampen the infection. It is possible that the binding of CoV spike protein to ACE2 is a down regulation of its expression, resulting in increased serum angiotensin II levels, and lung injury. Indeed, administration of recombinant human ACE2 in experimental models of CoV infection ameliorates lung injury in animal models [4] . Therefore, we propose that the 'CoV-cleaned' blood returned to the patient would also be enriched with recombinant human ACE2 to ameliorate lung injury.
 Discussion:
 Vaccines that are raised from whole pathogens -attenuated or inactivated -are called 1 st generation vaccines. Protocols that involve utilizing specific protein components extracted from the pathogens to reduce risks and side -effects in the host produce 2 nd generation vaccines. By contrast 3 rd generation vaccines are vaccines derived from administration of genetically engineered DNA or mRNA to induce the host cells to produce an antigen in vivo, which in turn is expected to be recognized as non-self, and generate protective antibodies [5] . Here, we propose a new avenue in vaccinology: the generation of a molecule with the purpose of preventing infectious disease -that is, a vaccine -, but not based on the traditional norms of antigen-idiotype binding. The 4 th generation vaccine we theorize here depends upon the specificity of receptor-ligand binding, but is a biochemical molecule constructed TRN-rewired CoV are neither, properly speaking, 1 st or 2 nd generation vaccine, and neither are they 3 rd generation vaccines: they are efficacious hybrid measures that prevent or slow down SARS-CoV, and possibly MERS-CoV epidemic. However, the urgency of the present moment precludes the somewhat lengthy experimentation time that would be required for the development and testing of a 3 rd generation vaccine of the sort. Since scientists have had several issues up to this point in the process of producing a 3 rd generation vaccine for SARS or MERS, whose epidemics were several years ago, it implausible that they could now develop such a 3 rd generation vaccine for 2019-nCov in the emergency the world is experiencing today.
 Conclusion:
 Taken together, the important points brought forth above emphasize the fact that the field of vaccinology cannot and must not be limited strictly to 1 st , 2 nd or 3 rd generation vaccines. A 4 th generation of vaccines is now emerging that may seem unconventional, but converge toward the same goal of preventing the spread of infectious disease. These 4 th generation vaccines may be particularly relevant in the case of flaming epidemics, when the time to generate, test, evaluate and distribute 1 st , 2 nd or 3 rd generation vaccines is prohibitive, such as is precisely the case now with 2019-nCoV. In certain circumstances, public health urgency demands immediate intervention, and precludes the time required to generate and test new vaccine species. Case in point, the threat now posed by the new member of the Coronavirus family (2019-nConV), whose discovery was announced by the Chinese health authorities on Chinese authorities reported having isolated a new type of coronavirus on 7 January 2020. Whereas 2019-nCoV is reported to a beta coronavirus closely related to SARS and other coronaviruses that originate from bats, it is unclear -and at this point almost irrelevant -to date if 2019-nConV originated from bats or from snake or other animals and subsequently transferred to bats. What is clear is that 2019-nConV is capable of direct humanto-human transmission, and its infection patterns grows alarmingly fast across all continents. To be clear, three weeks into its original reporting, 2019-nCoV has infected children, men, women and elderly in all continents. In China alone, the number of confirmed cases are over thirty-seven thousand infected individuals (n=37,593 as of day 21), and the number of fatalities from the disease has risen over eight hundred (n=813). Whereas both the percent confirmed cases and the percent death rate seem to have steadily decreased in parallel over the past 21 days, the case-fatality percent rate has remained steady above 2% (mean ± SD: 2.34% ± 0.39) (Figure 1) . As a reference point, the case-fatality percent rate of the Spanish influenza following World War I worldwide was at, or slightly above 2.5%; that same statistic for measles with no preventive vaccination measures is close 15%.
 In brief, 2019-nCoV seems to be less lethal than the Spanish flu, and may be abating somewhat at its original epicenter; it has generated heightened fear for a global pandemic as other epicenters have emerged, including Singapore and Thailand. In this hypothesis report, we have proposed here a new avenue into 4 th generation vaccines. Thus, vaccine protocols that do not involve the generation of antibodies against whole pathogens uses protein extracts obtained from pathogens, or nucleic acids related to pathogens. Rather, the preventive and protecting ability of the intervention we propose, which still relies on the specific binding of the pathogen to a substrate generated specifically against it, is a biochemical construct, which could actually best be generated by artificial intelligence of immune surveillance [8] algorithms in the not so distant future. The construct we propose here, specific to CoV, and applicable to 2019-nCoV in the context of the immediate urgency that is upon us, can be generated and expanded quickly, simply and reliably in any biochemistry laboratory. We also describe how it can be effectively utilized in treatment protocols of patients already infected with 2019-nCoV, in a slight modification of the common clinical protocol for renal dialysis."
# Tokenize our text
batch = tokenizer(text_to_summarize, truncation=True, padding='longest', return_tensors="pt")

# Generate the output
output = model.generate(**batch)
output_text = tokenizer.batch_decode(output, skip_special_tokens=True)
# Finally, we can print the generated summary
print(output_text)
```


model name: pegasus_large_covid


model author: mayu0007


task: summarization


tags: pytorch, pegasus, text2text-generation, en, dataset:CORD-19, arxiv:1912.08777, transformers, summarization, autonlp_compatible


language: ko
tags:


- gpt2
license: cc-by-nc-sa-4.0


korean translated japan web novel finetuned from skt/kogpt2-base-v2


model name: kogpt2jnovel


model author: ttop324


task: text-generation


tags: pytorch, gpt2, text-generation, ko, transformers, license:cc-by-nc-sa-4.0


novel finetuned from skt/kogpt2-base-v2


model name: kogpt2novel


language: ja
datasets:


- common_voice
metrics:
- wer
tags:
- audio
- automatic-speech-recognition
- speech
- xlsr-fine-tuning-week
license: apache-2.0
model-index:
- name: wav2vec2-live-japanese
results:
  - task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice Japanese
type: common_voice
args: ja
metrics:
    - name: Test WER
type: wer
value: 21.48%
    - name: Test CER
type: cer
value: 9.82%


# wav2vec2-live-japanese


https://github.com/ttop32/wav2vec2-live-japanese-translator\
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Japanese hiragana using the


- [common_voice](https://huggingface.co/datasets/common_voice)
- [JSUT](https://sites.google.com/site/shinnosuketakamichi/publication/jsut)
- [CSS10](https://github.com/Kyubyong/css10)
- [TEDxJP-10K](https://github.com/laboroai/TEDxJP-10K)
- [JVS](https://sites.google.com/site/shinnosuketakamichi/research-topics/jvs_corpus)
- [JSSS](https://sites.google.com/site/shinnosuketakamichi/research-topics/jsss_corpus)


## Inference


```python
#usage
import torch
import torchaudio
from datasets import load_dataset
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
model = Wav2Vec2ForCTC.from_pretrained("ttop324/wav2vec2-live-japanese")
processor = Wav2Vec2Processor.from_pretrained("ttop324/wav2vec2-live-japanese")
test_dataset = load_dataset("common_voice", "ja", split="test")
# Preprocessing the datasets.
# We need to read the aduio files as arrays
def speech_file_to_array_fn(batch):
    speech_array, sampling_rate = torchaudio.load(batch["path"])
    batch["speech"] = torchaudio.functional.resample(speech_array, sampling_rate, 16000)[0].numpy()
    return batch
test_dataset = test_dataset.map(speech_file_to_array_fn)
inputs = processor(test_dataset[:2]["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
with torch.no_grad():
	logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits
predicted_ids = torch.argmax(logits, dim=-1)
print("Prediction:", processor.batch_decode(predicted_ids))
print("Reference:", test_dataset[:2]["sentence"])
```


## Evaluation


```python
import torch
import torchaudio
from datasets import load_dataset, load_metric
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import re
import pykakasi
import MeCab
wer = load_metric("wer")
cer = load_metric("cer")
model = Wav2Vec2ForCTC.from_pretrained("ttop324/wav2vec2-live-japanese").to("cuda")
processor = Wav2Vec2Processor.from_pretrained("ttop324/wav2vec2-live-japanese")
test_dataset = load_dataset("common_voice", "ja", split="test")
chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“\‘\”\�‘、。．！，・―─~｢｣『』\\\\※\[\]\{\}「」〇？…]'
wakati = MeCab.Tagger("-Owakati")
kakasi = pykakasi.kakasi()
kakasi.setMode("J","H")      # kanji to hiragana
kakasi.setMode("K","H")      # katakana to hiragana
conv = kakasi.getConverter()
FULLWIDTH_TO_HALFWIDTH = str.maketrans(
    '　０１２３４５６７８９ａｂｃｄｅｆｇｈｉｊｋｌｍｎｏｐｑｒｓｔｕｖｗｘｙｚＡＢＣＤＥＦＧＨＩＪＫＬＭＮＯＰＱＲＳＴＵＶＷＸＹＺ！゛＃＄％＆（）＊＋、ー。／：；〈＝〉？＠［］＾＿‘｛｜｝～',
    ' 0123456789abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ!"#$%&()*+,-./:;<=>?@[]^_`{|}~',
)
def fullwidth_to_halfwidth(s):
    return s.translate(FULLWIDTH_TO_HALFWIDTH)
def preprocessData(batch):
    batch["sentence"] = fullwidth_to_halfwidth(batch["sentence"])
    batch["sentence"] = re.sub(chars_to_ignore_regex,' ', batch["sentence"]).lower()  #remove special char
    batch["sentence"] = wakati.parse(batch["sentence"])                              #add space
    batch["sentence"] = conv.do(batch["sentence"])                                   #covert to hiragana
    batch["sentence"] = " ".join(batch["sentence"].split())+" "                         #remove multiple space

    speech_array, sampling_rate = torchaudio.load(batch["path"])
    batch["speech"] = torchaudio.functional.resample(speech_array, sampling_rate, 16000)[0].numpy()
    return batch
test_dataset = test_dataset.map(preprocessData)
# Preprocessing the datasets.
# We need to read the aduio files as arrays
def evaluate(batch):
	inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
	with torch.no_grad():
		logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits
	pred_ids = torch.argmax(logits, dim=-1)
	batch["pred_strings"] = processor.batch_decode(pred_ids)
	return batch
result = test_dataset.map(evaluate, batched=True, batch_size=8)
print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"])))
print("CER: {:2f}".format(100 * cer.compute(predictions=result["pred_strings"], references=result["sentence"])))
```


model name: wav2vec2-live-japanese


task: automatic-speech-recognition


tags: pytorch, wav2vec2, automatic-speech-recognition, ja, dataset:common_voice, transformers, audio, speech, xlsr-fine-tuning-week, license:apache-2.0, model-index


# Work in Progress Polish RoBERTa


The model has been trained for about 5% time of the target. We will publish new increments as they will be trained.


The model pre-trained on KGR10 corpora.


More about model at [CLARIN-dspace](https://huggingface.co/clarin/roberta-polish-v1)


## Usage


## Huggingface model hub


## Acknowledgments


[CLARIN-PL and CLARIN-BIZ project](https://clarin-pl.eu/)


model name: roberta-polish-kgr10


model author: clarin-pl


task: fill-mask


tags: pytorch, jax, roberta, fill-mask, transformers, autonlp_compatible, infinity_compatible


language: pl
tags:


- fastText
datasets:
- kgr10


# KGR10 FastText Polish word embeddings


Distributional language model (both textual and binary) for Polish (word embeddings) trained on KGR10 corpus (over 4 billion of words) using Fasttext with the following variants (all possible combinations):


- dimension: 100, 300
- method: skipgram, cbow
- tool: FastText, Magnitude
- source text: plain, plain.lower, plain.lemma, plain.lemma.lower


## Models


In the repository you can find 4 selected models, that were examined in the paper (see Citation).
A model that performed the best is the default model/config (see `default_config.json`).


To use these embedding models easily, it is required to install [embeddings](https://github.com/CLARIN-PL/embeddings).


```bash
pip install clarinpl-embeddings
```


### Utilising the default model (the easiest way)


Word embedding:


```python
from embeddings.embedding.auto_flair import AutoFlairWordEmbedding
from flair.data import Sentence

sentence = Sentence("Myśl z duszy leci bystro, Nim się w słowach złamie.")

embedding = AutoFlairWordEmbedding.from_hub("clarin-pl/fastText-kgr10")
embedding.embed([sentence])

for token in sentence:
    print(token)
    print(token.embedding)
```


Document embedding (averaged over words):


```python
from embeddings.embedding.auto_flair import AutoFlairDocumentEmbedding
from flair.data import Sentence

sentence = Sentence("Myśl z duszy leci bystro, Nim się w słowach złamie.")

embedding = AutoFlairDocumentEmbedding.from_hub("clarin-pl/fastText-kgr10")
embedding.embed([sentence])

print(sentence.embedding)
```


### Customisable way


```python
from embeddings.embedding.static.embedding import AutoStaticWordEmbedding
from embeddings.embedding.static.fasttext import KGR10FastTextConfig
from flair.data import Sentence

config = KGR10FastTextConfig(method='cbow', dimension=100)
embedding = AutoStaticWordEmbedding.from_config(config)

sentence = Sentence("Myśl z duszy leci bystro, Nim się w słowach złamie.")
embedding.embed([sentence])

for token in sentence:
    print(token)
    print(token.embedding)
```


```python
from embeddings.embedding.static.embedding import AutoStaticDocumentEmbedding
from embeddings.embedding.static.fasttext import KGR10FastTextConfig
from flair.data import Sentence

config = KGR10FastTextConfig(method='cbow', dimension=100)
embedding = AutoStaticDocumentEmbedding.from_config(config)

sentence = Sentence("Myśl z duszy leci bystro, Nim się w słowach złamie.")
embedding.embed([sentence])

print(sentence.embedding)
```


## Citation


The link below leads to the NextCloud directory with all variants of embeddings. If you use it, please cite the following article:


```
@article{kocon2018embeddings,
author = {Koco\'{n}, Jan and Gawor, Micha{\l}},
title = {Evaluating {KGR10} {P}olish word embeddings in the recognition of temporal
expressions using {BiLSTM-CRF}},
journal = {Schedae Informaticae},
volume = {27},
year = {2018},
url = {http://www.ejournals.eu/Schedae-Informaticae/2018/Volume-27/art/13931/},
doi = {10.4467/20838476SI.18.008.10413}
}
```


model name: fastText-kgr10


model author: clarin-pl


tags: pl, dataset:kgr10, fastText


language: pl
tags:


- word2vec
datasets:
- KGR10


# KGR10 word2vec Polish word embeddings


Distributional language models for Polish trained on the KGR10 corpora.


## Models


In the repository you can find two selected models, that were selected after evaluation (see table below).
A model that performed the best is the default model/config (see `default_config.json`).


|method|dimension|hs|mwe||
|---|---|---|---| --- |
|cbow|300|false|true| <-- default |
|skipgram|300|true|true|


## Usage


Word embedding:


```python
from embeddings.embedding.auto_flair import AutoFlairWordEmbedding
from flair.data import Sentence

sentence = Sentence("Myśl z duszy leci bystro, Nim się w słowach złamie.")

embedding = AutoFlairWordEmbedding.from_hub("clarin-pl/word2vec-kgr10")
embedding.embed([sentence])

for token in sentence:
    print(token)
    print(token.embedding)
```


```python
from embeddings.embedding.auto_flair import AutoFlairDocumentEmbedding
from flair.data import Sentence

sentence = Sentence("Myśl z duszy leci bystro, Nim się w słowach złamie.")

embedding = AutoFlairDocumentEmbedding.from_hub("clarin-pl/word2vec-kgr10")
embedding.embed([sentence])

print(sentence.embedding)
```


### Customisable way


```python
from embeddings.embedding.static.embedding import AutoStaticWordEmbedding
from embeddings.embedding.static.word2vec import KGR10Word2VecConfig
from flair.data import Sentence

config = KGR10Word2VecConfig(method='skipgram', hs=False)
embedding = AutoStaticWordEmbedding.from_config(config)

sentence = Sentence("Myśl z duszy leci bystro, Nim się w słowach złamie.")
embedding.embed([sentence])

for token in sentence:
    print(token)
    print(token.embedding)
```


```python
from embeddings.embedding.static.embedding import AutoStaticDocumentEmbedding
from embeddings.embedding.static.word2vec import KGR10Word2VecConfig
from flair.data import Sentence

config = KGR10Word2VecConfig(method='skipgram', hs=False)
embedding = AutoStaticDocumentEmbedding.from_config(config)

sentence = Sentence("Myśl z duszy leci bystro, Nim się w słowach złamie.")
embedding.embed([sentence])

print(sentence.embedding)
```


## Citation


```
Piasecki, Maciej; Janz, Arkadiusz; Kaszewski, Dominik; et al., 2017,  Word Embeddings for Polish, CLARIN-PL digital repository, http://hdl.handle.net/11321/442.
```


or


```
@misc{11321/442,
 title = {Word Embeddings for Polish},
 author = {Piasecki, Maciej and Janz, Arkadiusz and Kaszewski, Dominik and Czachor, Gabriela},
 url = {http://hdl.handle.net/11321/442},
 note = {{CLARIN}-{PL} digital repository},
 copyright = {{GNU} {GPL3}},
 year = {2017}
}
```


model name: word2vec-kgr10


model author: clarin-pl


tags: pl, dataset:KGR10, word2vec


textsummarizer


model name: textsummarizer


model author: kumaran


tags:


language:


- sw
tags:
- generated_from_trainer
datasets:
- tydiqa
model-index:
- name: afriberta_base-finetuned-tydiqa
results: []


<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->


# afriberta_base-finetuned-tydiqa


This model is a fine-tuned version of [castorini/afriberta_base](https://huggingface.co/castorini/afriberta_base) on the tydiqa dataset.
It achieves the following results on the evaluation set:


- Loss: 2.3728


## Model description


More information needed


## Intended uses & limitations


## Training and evaluation data


## 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 |
|:-------------:|:-----:|:----:|:---------------:|
| No log        | 1.0   | 192  | 2.1359          |
| No log        | 2.0   | 384  | 2.3409          |
| 0.8353        | 3.0   | 576  | 2.3728          |


### Framework versions


- Transformers 4.14.1
- Pytorch 1.10.0+cu111
- Datasets 1.16.1
- Tokenizers 0.10.3


model name: afriberta_base-finetuned-tydiqa


model author: cjrowe


task: question-answering


tags: pytorch, tensorboard, xlm-roberta, question-answering, sw, dataset:tydiqa, transformers, generated_from_trainer, model-index, autonlp_compatible


language:


- "zh"
thumbnail: "https://raw.githubusercontent.com/SIKU-BERT/SikuBERT/main/appendix/sikubert.png"
tags:
- "chinese"
- "classical chinese"
- "literary chinese"
- "ancient chinese"
- "bert"
- "roberta"
- "pytorch"
inference: false
license: "apache-2.0"


# SikuBERT


## Model description


![SikuBERT](https://raw.githubusercontent.com/SIKU-BERT/SikuBERT-for-digital-humanities-and-classical-Chinese-information-processing/main/appendix/sikubert.png)
Digital humanities research needs the support of large-scale corpus and high-performance ancient Chinese natural language processing tools. The pre-training language model has greatly improved the accuracy of text mining in English and modern Chinese texts. At present, there is an urgent need for a pre-training model specifically for the automatic processing of ancient texts. We used the verified high-quality “Siku Quanshu” full-text corpus as the training set, based on the BERT deep language model architecture, we constructed the SikuBERT and SikuRoBERTa pre-training language models for intelligent processing tasks of ancient Chinese.


## How to use


```python
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("SIKU-BERT/sikuroberta")
model = AutoModel.from_pretrained("SIKU-BERT/sikuroberta")
```


## About Us


We are from Nanjing Agricultural University.


> Created with by SIKU-BERT [![Github icon](https://cdn0.iconfinder.com/data/icons/octicons/1024/mark-github-32.png)](https://github.com/SIKU-BERT/SikuBERT-for-digital-humanities-and-classical-Chinese-information-processing)


model name: sikuroberta


model author: SIKU-BERT


task: fill-mask


tags: pytorch, bert, fill-mask, zh, transformers, chinese, classical chinese, literary chinese, ancient chinese, roberta, license:apache-2.0, autonlp_compatible, infinity_compatible


```python
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("SIKU-BERT/sikubert")
model = AutoModel.from_pretrained("SIKU-BERT/sikubert")
```


model name: sikubert


language: "en"
tags:


- dstc9
widget:
- text: "Yes, I'm going to be in Chinatown, San Francisco and am looking"
- text: "Can you find me one that is in the"


This GPT2 model is trained using DSTC9 data for dialogue modeling purpose.


Data link: https://github.com/alexa/alexa-with-dstc9-track1-dataset


Credit: Jia-Chen Jason Gu, Wilson Tam


model name: gpt2-dstc9


model author: wilsontam


task: text-generation


tags: pytorch, gpt2, text-generation, en, transformers, dstc9


- dstc10
- knowledge title-body validation
widget:
- text: "Can you accommodate large groups? It does not offer free WiFi."
- text: "Is there a gym on site? It does not have an onsite fitness center."


This is the model used for knowledge clustering where we feed title-body pair and the classifier predicts if the pair is valid or not.
For further information, please refer to https://github.com/yctam/dstc10_track2_task2 for the Github repository.


Credit: Jiakai Zou, Wilson Tam


```python
from transformers import AutoConfig, AutoTokenizer, AutoModelForSequenceClassification

def single_test(tokenizer, title_body_pair):
    result = tokenizer([title_body_pair], return_tensors="pt")
    model.eval()
    outputs = model(**result)
    predictions = outputs.logits.argmax(dim=-1)
    # There was a mistake in flipping the labels.
    return True if predictions == 0 else False

if __name__ == '__main__':
    model_name = "wilsontam/bert-base-uncased-dstc10-kb-title-body-validate"

    config = AutoConfig.from_pretrained(model_name)
    tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=True)
    model = AutoModelForSequenceClassification.from_pretrained(".")

    sentence = "Can I check in anytime?"
    body = "Yes, 24 Hours Front Desk Avaliable."
    print(single_test((sentence, body))) # Expect: True
```


model name: bert-base-uncased-dstc10-kb-title-body-validate


task: text-classification


tags: pytorch, bert, text-classification, en, transformers, dstc10, knowledge title-body validation, infinity_compatible


- dstc10
widget:
- text: "Can you accommodate large [MASK] ?"


# Goal


This Bert model is trained using DSTC9 training + validation data for dialogue modeling purpose.
Data link: https://github.com/alexa/alexa-with-dstc9-track1-dataset


Credit: Shuhan Yuan, Wilson Tam


model name: bert-base-uncased-dstc9


task: fill-mask


tags: pytorch, bert, fill-mask, en, transformers, dstc10, autonlp_compatible, infinity_compatible


language: "en"
tags:


- dstc10
- knowledge cluster classifier
widget:
- text: "oh and we'll mi thing uh is there bike clo ars or bike crac where i can park my thee"
- text: "oh and one more thing uhhh is there bike lockers or a bike rack where i can park my bike"
- text: "ni yeah that sounds great ummm dold you have the any idea er could you check for me if there's hat three wifie available there"
- text: "nice yeah that sounds great ummm do you have any idea or could you check for me if there's uhhh free wi-fi available there"
- text: "perfect and what is the check kin time for that"


This is the model used for knowledge cluster classification for the DSTC10 track2 knowledge selection task, trained with double heads, i.e., classifier head and LM head using ASR error simulator for model training.


For further information, please refer to https://github.com/yctam/dstc10_track2_task2 for the Github repository. You can use this model and use our source code to predict knowledge clusters under ASR errors.


model name: bert-base-uncased-dstc10-knowledge-cluster-classifier


model author: wilsontam


tags: pytorch, bert, text-classification, en, transformers, dstc10, knowledge cluster classifier, infinity_compatible


model name: t5_boolq


model author: alexcruz0202


task: text2text-generation


tags: pytorch, jax, t5, text2text-generation, transformers, autonlp_compatible


TTAI


model name: TTQnA


model author: pouryajj


tags:


The main card for Saturday’s Manny Pacquiao vs Yordenis Ugas fight gets underway at T-Mobile Arena in Las Vegas at 9 p.m. ET and the main event is expected to start sometime around 11:30 p.m. This is going to air on FOX Sports PPV and YouTube PPV. The card will cost


https://web.sites.google.com/view/free-2021-manny-pacquiao-vs-yo/home
LIVE::Watch Full Fight Live Here


LIVE::Watch Full Fight Live Here
https://goodavail.com/boxing/


The most intriguing storyline for this fight is the belt itself that is on the line. Pacquiao won the Super version of the WBA’s welterweight title in July 2019 when he beat Keith Thurman in a split decision. The WBA stripped Pacquiao of the title this past January due to inactivity. The organizing body then promoted Ugas into the Super belt. Ugas won the WBA’s Regular title in September 2020 when he beat Abel Ramos in a split decision.


That means Ugas won a title last held by Pacquiao without having to beat Pacquiao. Pacquiao-Spence would have been a significantly bigger fight for welterweight supremacy, but this is still interesting. Pacquiao was an underdog against Spence, but comes into this fight as a -360 favorite at DraftKings Sportsbook.


How to watch Manny Pacquiao vs. Yordenis Ugas
TV channel: FOX Sports PPV


model name: fdgdfg


model author: balalsahabi


- generated_from_trainer
datasets:
- klue
metrics:
- f1
model-index:
- name: bert-base-finetuned-ynat
results:
  - task:
name: Text Classification
type: text-classification
dataset:
name: klue
type: klue
args: ynat
metrics:
    - name: F1
type: f1
value: 0.8669116640755216


<!-- 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-finetuned-ynat


This model is a fine-tuned version of [klue/bert-base](https://huggingface.co/klue/bert-base) on the klue dataset.
It achieves the following results on the evaluation set:


- Loss: 0.3710
- F1: 0.8669


## Model description


More information needed


## Intended uses & limitations


## Training and evaluation data


## Training procedure


### Training hyperparameters


The following hyperparameters were used during training:


- learning_rate: 2e-05
- train_batch_size: 256
- eval_batch_size: 256
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5


### Training results


| Training Loss | Epoch | Step | Validation Loss | F1     |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log        | 1.0   | 179  | 0.4223          | 0.8549 |
| No log        | 2.0   | 358  | 0.3710          | 0.8669 |
| 0.2576        | 3.0   | 537  | 0.3891          | 0.8631 |
| 0.2576        | 4.0   | 716  | 0.3968          | 0.8612 |
| 0.2576        | 5.0   | 895  | 0.4044          | 0.8617 |


### Framework versions


- Transformers 4.10.3
- Pytorch 1.9.0+cu102
- Datasets 1.12.1
- Tokenizers 0.10.3


model name: bert-base-finetuned-ynat


model author: zgotter


task: text-classification


tags: pytorch, bert, text-classification, dataset:klue, transformers, generated_from_trainer, model-index, infinity_compatible


https://www.geogebra.org/m/w8uzjttg
https://www.geogebra.org/m/gvn7m78g
https://www.geogebra.org/m/arxecanq
https://www.geogebra.org/m/xb69bvww
https://www.geogebra.org/m/apvepfnd
https://www.geogebra.org/m/evmj8ckk
https://www.geogebra.org/m/qxcxwmhp
https://www.geogebra.org/m/p3cxqh6c
https://www.geogebra.org/m/ggrahbgd
https://www.geogebra.org/m/pnhymrbc
https://www.geogebra.org/m/zjukbtk9
https://www.geogebra.org/m/bbezun8r
https://www.geogebra.org/m/sgwamtru
https://www.geogebra.org/m/fpunkxxp
https://www.geogebra.org/m/acxebrr7


model name: DR-Site


model author: formu


tags:


\n---
language: es
tags:


- summarization
- spanish
- beto2beto
- encoder-decoder
license: apache-2.0
datasets:
- LeoCordoba/CC-NEWS-ES-titles
model-index:
- name: beto2beto-ccnews-titles-es
results:
  - task:
name: Abstractive Text Summarization
type: abstractive-text-summarization

    dataset:
name: "CCNEWS-ES-titles"
type: LeoCordoba/CC-NEWS-ES-titles

    metrics:
    - name: Validation ROGUE-1
type: rogue-1
value: 23.7478
    - name: Validation ROGUE-2
type: rogue-2
value: 7.3616
    - name: Validation ROGUE-L
type: rogue-l
value: 20.6615
    - name: Validation ROGUE-Lsum
type: rogue-lsum
value: 20.7371
    - name: Test ROGUE-1
type: rogue-1
value: 23.7415
    - name: Test ROGUE-2
type: rogue-2
value: 7.3548
    - name: Test ROGUE-L
type: rogue-l
value: 20.746
    - name: Test ROGUE-Lsum
type: rogue-lsum
value: 20.8149


widget:


- text: |
La chocotorta, el tradicional y práctico antojo dulce de los argentinos, fue elegida como el mejor postre del mundo por críticos de restaurants internacionales, a casi 40 años de su creación. El ránking Taste Atlas ubicó primero en su lista al postre insignia local de galletitas, queso crema y dulce de leche, por delante del helado de pistacho italiano y la tarta alemana de manzana. “Este postre argentino sin hornear fue influenciado por la cocina italiana y se inspiró en el famoso tiramisú italiano. Está elaborado con tres ingredientes básicos argentinos: galletas de chocolate, dulce de leche y queso crema”, explica la página web que exhorta a los turistas de todo el mundo a que prueben la chocotorta. En la votación, superó también a los waffles belgas y el zserbó húngaro. A nivel local le sigue el alfajor, con 4,2 puntos contra los 4,7 de la torta. En el texto que acompaña al listón dorado de “postre número uno“, los expertos enseñan además cómo se hacen las chocotortas, paso por paso. “Las galletas se ablandan en leche y se cubren con una combinación de queso crema y dulce de leche. Las formas de la chocotorta pueden variar, mientras que las galletas se pueden remojar con leche con chocolate, café o incluso licor de café”, detallan. Por último, adjudican su creación a una “campaña de márketing” diseñada para promover las galletitas icónicas que le dan su nombre. La chocotorta, infaltable en los cumpleaños argentinos, fue creada en 1982 por una creativa de las agencias más importantes del país, Marité Mabragaña.


## Hyperparameters


    "num_train_epochs": 3,

    "seed": 7,

    "summary_column": "output_text",

    "text_column": "text",

    "encoder_max_length" : 512,

    "decoder_max_length" :36,

    "batch_size" : 256


## Usage


## Results


| key | value |
| --- | ----- |
| eval loss | 4.539857387542725|
| eval_rouge1 |23.7478  |
| eval_rouge2 |7.3616 |
| eval_rougeL |20.6615 |
| eval_rougeLsum |20.7371 |
| eval_gen_len| 16.1806|
|test loss | 4.515065670013428|
| test_rouge1 | 23.7415|
| test_rouge2 | 7.3548|
| test_rougeL | 20.746|
| test_rougeLsum | 20.8149|
| test_gen_len| 16.1926|


model name: beto2beto-cc-news-es-titles


model author: LeoCordoba


task: text2text-generation


tags: pytorch, encoder-decoder, text2text-generation, transformers, autonlp_compatible


- summarization
- spanish
- encoder-decoder
- beto
license: apache-2.0
datasets:
- mlsum - es
model-index:
- name: beto2beto-mlsum
results:
  - task:
name: Abstractive Text Summarization
type: abstractive-text-summarization
dataset:
name: "MLSUM: MultiLingual SUMmarization dataset (Spanish)"
type: mlsum - es
metrics:
    - name: Validation ROGUE-1
type: rogue-1
value: 26.1256
    - name: Validation ROGUE-2
type: rogue-2
value: 9.2552
    - name: Validation ROGUE-L
type: rogue-l
value: 21.4899
    - name: Validation ROGUE-Lsum
type: rogue-lsum
value: 21.8194
    - name: Test ROGUE-1
type: rogue-1
value: 25.8639
    - name: Test ROGUE-2
type: rogue-2
value: 8.911
    - name: Test ROGUE-L
type: rogue-l
value: 21.2426
    - name: Test ROGUE-Lsum
type: rogue-lsum
value: 21.5859
widget:
- text: |
La chocotorta, el tradicional y práctico antojo dulce de los argentinos, fue elegida como el mejor postre del mundo por críticos de restaurants internacionales, a casi 40 años de su creación. El ránking Taste Atlas ubicó primero en su lista al postre insignia local de galletitas, queso crema y dulce de leche, por delante del helado de pistacho italiano y la tarta alemana de manzana. “Este postre argentino sin hornear fue influenciado por la cocina italiana y se inspiró en el famoso tiramisú italiano. Está elaborado con tres ingredientes básicos argentinos: galletas de chocolate, dulce de leche y queso crema”, explica la página web que exhorta a los turistas de todo el mundo a que prueben la chocotorta. En la votación, superó también a los waffles belgas y el zserbó húngaro. A nivel local le sigue el alfajor, con 4,2 puntos contra los 4,7 de la torta. En el texto que acompaña al listón dorado de “postre número uno", los expertos enseñan además cómo se hacen las chocotortas, paso por paso. “Las galletas se ablandan en leche y se cubren con una combinación de queso crema y dulce de leche. Las formas de la chocotorta pueden variar, mientras que las galletas se pueden remojar con leche con chocolate, café o incluso licor de café”, detallan. Por último, adjudican su creación a una “campaña de márketing” diseñada para promover las galletitas icónicas que le dan su nombre. La chocotorta, infaltable en los cumpleaños argentinos, fue creada en 1982 por una creativa de las agencias más importantes del país, Marité Mabragaña.


## beto2beto-mlsum


This model was trained on the Spanish section of MLSum: https://paperswithcode.com/sota/abstractive-text-summarization-on-mlsum.


    {
    "dataset_config": "es",
    "dataset_name": "mlsum",
    "do_eval": true,
    "do_predict": true,
    "do_train": true,
    "fp16": true,
    "max_target_length": 64,
    "num_train_epochs": 10,
    "per_device_eval_batch_size": 4,
    "per_device_train_batch_size": 4,
    "predict_with_generate": true,
    "sagemaker_container_log_level": 20,
    "sagemaker_program": "run_summarization.py",
    "seed": 7,
    "summary_column": "summary",
    "text_column": "text"


| metric | score |
| --- | ----- |
| validation_loss | 2.5021677017211914 |
| validation_rouge1 | 26.1256 |
| validation_rouge2 | 9.2552 |
| validation_rougeL | 21.4899 |
| validation_rougeLsum | 21.8194 |
| test_loss | 2.57672381401062 |
| test_rouge1 | 25.8639 |
| test_rouge2 | 8.911 |
| test_rougeL | 21.2426 |
| test_rougeLsum | 21.5859 |


model name: beto2beto-mlsum


language: es


tags:


- summarization
- mt5
- spanish


license: apache-2.0


datasets:


- LeoCordoba/CC-NEWS-ES-titles


model-index:


- name: mt5-small-ccnews-titles-es
results:
  - task:
name: Abstractive Text Summarization
type: abstractive-text-summarization

    dataset:
name: "CCNEWS-ES-titles"
type: LeoCordoba/CC-NEWS-ES-titles

    metrics:
    - name: Validation ROGUE-1
type: rogue-1
value: 22.6623
    - name: Validation ROGUE-2
type: rogue-2
value: 7.7894
    - name: Validation ROGUE-L
type: rogue-l
value: 19.8015
    - name: Validation ROGUE-Lsum
type: rogue-lsum
value: 19.8092
    - name: Test ROGUE-1
type: rogue-1
value: 22.9263
    - name: Test ROGUE-2
type: rogue-2
value: 7.9146
    - name: Test ROGUE-L
type: rogue-l
value: 20.0272
    - name: Test ROGUE-Lsum
type: rogue-lsum
value: 20.0387


widget:


- text: "La chocotorta, el tradicional y práctico antojo dulce de los argentinos, fue elegida como el mejor postre del mundo por críticos de restaurants internacionales, a casi 40 años de su creación. El ránking Taste Atlas ubicó primero en su lista al postre insignia local de galletitas, queso crema y dulce de leche, por delante del helado de pistacho italiano y la tarta alemana de manzana. “Este postre argentino sin hornear fue influenciado por la cocina italiana y se inspiró en el famoso tiramisú italiano. Está elaborado con tres ingredientes básicos argentinos: galletas de chocolate, dulce de leche y queso crema”, explica la página web que exhorta a los turistas de todo el mundo a que prueben la chocotorta. En la votación, superó también a los waffles belgas y el zserbó húngaro. A nivel local le sigue el alfajor, con 4,2 puntos contra los 4,7 de la torta. En el texto que acompaña al listón dorado de “postre número uno“, los expertos enseñan además cómo se hacen las chocotortas, paso por paso. “Las galletas se ablandan en leche y se cubren con una combinación de queso crema y dulce de leche. Las formas de la chocotorta pueden variar, mientras que las galletas se pueden remojar con leche con chocolate, café o incluso licor de café”, detallan. Por último, adjudican su creación a una “campaña de márketing” diseñada para promover las galletitas icónicas que le dan su nombre. La chocotorta, infaltable en los cumpleaños argentinos, fue creada en 1982 por una creativa de las agencias más importantes del país, Marité Mabragaña."


## Hyperparameters


    "max_target_length": 64,

    "model_name_or_path": "google/mt5-small",

    "num_train_epochs": 3,

    "seed": 7,

    "summary_column": "output_text",

    "text_column": "text",

    "encoder_max_length" : 512,

    "decoder_max_length" :36,

    "batch_size" : 128


## Usage


```
article = """ La chocotorta, el tradicional y práctico antojo dulce de los argentinos, fue elegida como el mejor postre del mundo por críticos de restaurants internacionales, a casi 40 años de su creación. El ránking Taste Atlas ubicó primero en su lista al postre insignia local de galletitas, queso crema y dulce de leche, por delante del helado de pistacho italiano y la tarta alemana de manzana. “Este postre argentino sin hornear fue influenciado por la cocina italiana y se inspiró en el famoso tiramisú italiano. Está elaborado con tres ingredientes básicos argentinos: galletas de chocolate, dulce de leche y queso crema”, explica la página web que exhorta a los turistas de todo el mundo a que prueben la chocotorta. En la votación, superó también a los waffles belgas y el zserbó húngaro. A nivel local le sigue el alfajor, con 4,2 puntos contra los 4,7 de la torta. En el texto que acompaña al listón dorado de “postre número uno", los expertos enseñan además cómo se hacen las chocotortas, paso por paso. “Las galletas se ablandan en leche y se cubren con una combinación de queso crema y dulce de leche. Las formas de la chocotorta pueden variar, mientras que las galletas se pueden remojar con leche con chocolate, café o incluso licor de café”, detallan. Por último, adjudican su creación a una “campaña de márketing” diseñada para promover las galletitas icónicas que le dan su nombre. La chocotorta, infaltable en los cumpleaños argentinos, fue creada en 1982 por una creativa de las agencias más importantes del país, Marité Mabragaña.  """

from transformers import pipeline

summarizer = pipeline("summarization", model="LeoCordoba/mt5-small-ccnews-titles-es")

summarizer(article, min_length=5, max_length=64)

```


## Results


| metric | score |
| --- | ----- |
| eval_loss | 2.879085063934326 |
| eval_rouge1 | 22.6623 |
| eval_rouge2 | 7.7894 |
| eval_rougeL | 19.8015, |
| eval_rougeLsum | 19.8092 |
| eval_gen_len | 17.1839 |
| test_loss | 2.878429412841797 |
| test_rouge1 | 22.9263 |
| test_rouge2 | 7.9146 |
| test_rougeL | 20.0272 |
| test_rougeLsum | 20.0387 |
| test_gen_len | 17.1696 |


model name: mt5-small-cc-news-es-titles


model author: LeoCordoba


task: summarization


tags: pytorch, mt5, text2text-generation, es, dataset:LeoCordoba/CC-NEWS-ES-titles, transformers, summarization, spanish, license:apache-2.0, model-index, autonlp_compatible


language: es
tags:


- text-generation
- spanish
- encoder-decoder
- beto
license: apache-2.0
datasets:
- LeoCordoba/CC-NEWS-ES
model-index:
- name: beto2beto


## beto2beto


Usage example here: https://colab.research.google.com/drive/18a2ZfF1e_Kyyydlv8INQIkJbv294xcAm?usp=sharing


Entrenado por 3 epochs sobre CC-NEWS-ES (2019), aproximadamente 68.000 steps. Encoder max length:  40•Decoder max length:  128


| key | value |
| --- | ----- |
| test_loss | 2.65148806571960452 |


model name: beto2beto


task: text-generation


tags: pytorch, encoder-decoder, text2text-generation, es, dataset:LeoCordoba/CC-NEWS-ES, transformers, text-generation, spanish, beto, license:apache-2.0, model-index, autonlp_compatible


\n---
language: es
tags:


- summarization
- sagemaker
- mt5
- spanish
license: apache-2.0
datasets:
- mlsum - es
model-index:
- name: mt5-small-mlsum
results:
  - task:
name: Abstractive Text Summarization
type: abstractive-text-summarization
dataset:
name: "MLSUM: MultiLingual SUMmarization dataset (Spanish)"
type: mlsum
metrics:
    - name: Validation ROGUE-1
type: rogue-1
value: 26.4352
    - name: Validation ROGUE-2
type: rogue-2
value: 8.9293
    - name: Validation ROGUE-L
type: rogue-l
value: 21.2622
    - name: Validation ROGUE-LSUM
type: rogue-lsum
value: 21.5518
    - name: Test ROGUE-1
type: rogue-1
value: 26.0756
    - name: Test ROGUE-2
type: rogue-2
value: 8.4669
    - name: Test ROGUE-L
type: rogue-l
value: 20.8167
    - name: Validation ROGUE-LSUM
type: rogue-lsum
value: 21.0822
widget:
  - text: "La chocotorta, el tradicional y práctico antojo dulce de los argentinos, fue elegida como el mejor postre del mundo por críticos de restaurants internacionales, a casi 40 años de su creación. El ránking Taste Atlas ubicó primero en su lista al postre insignia local de galletitas, queso crema y dulce de leche, por delante del helado de pistacho italiano y la tarta alemana de manzana. “Este postre argentino sin hornear fue influenciado por la cocina italiana y se inspiró en el famoso tiramisú italiano. Está elaborado con tres ingredientes básicos argentinos: galletas de chocolate, dulce de leche y queso crema”, explica la página web que exhorta a los turistas de todo el mundo a que prueben la chocotorta. En la votación, superó también a los waffles belgas y el zserbó húngaro. A nivel local le sigue el alfajor, con 4,2 puntos contra los 4,7 de la torta. En el texto que acompaña al listón dorado de “postre número uno“, los expertos enseñan además cómo se hacen las chocotortas, paso por paso. “Las galletas se ablandan en leche y se cubren con una combinación de queso crema y dulce de leche. Las formas de la chocotorta pueden variar, mientras que las galletas se pueden remojar con leche con chocolate, café o incluso licor de café”, detallan. Por último, adjudican su creación a una “campaña de márketing” diseñada para promover las galletitas icónicas que le dan su nombre. La chocotorta, infaltable en los cumpleaños argentinos, fue creada en 1982 por una creativa de las agencias más importantes del país, Marité Mabragaña."


## mt5-small-mlsum


This model was trained on the Spanish section of MLSum: https://paperswithcode.com/sota/abstractive-text-summarization-on-mlsum based on mt5-small.


## Hyperparameters


## Usage


```
article = """ La chocotorta, el tradicional y práctico antojo dulce de los argentinos, fue elegida como el mejor postre del mundo por críticos de restaurants internacionales, a casi 40 años de su creación. El ránking Taste Atlas ubicó primero en su lista al postre insignia local de galletitas, queso crema y dulce de leche, por delante del helado de pistacho italiano y la tarta alemana de manzana. “Este postre argentino sin hornear fue influenciado por la cocina italiana y se inspiró en el famoso tiramisú italiano. Está elaborado con tres ingredientes básicos argentinos: galletas de chocolate, dulce de leche y queso crema”, explica la página web que exhorta a los turistas de todo el mundo a que prueben la chocotorta. En la votación, superó también a los waffles belgas y el zserbó húngaro. A nivel local le sigue el alfajor, con 4,2 puntos contra los 4,7 de la torta. En el texto que acompaña al listón dorado de “postre número uno", los expertos enseñan además cómo se hacen las chocotortas, paso por paso. “Las galletas se ablandan en leche y se cubren con una combinación de queso crema y dulce de leche. Las formas de la chocotorta pueden variar, mientras que las galletas se pueden remojar con leche con chocolate, café o incluso licor de café”, detallan. Por último, adjudican su creación a una “campaña de márketing” diseñada para promover las galletitas icónicas que le dan su nombre. La chocotorta, infaltable en los cumpleaños argentinos, fue creada en 1982 por una creativa de las agencias más importantes del país, Marité Mabragaña.  """

from transformers import pipeline
summarizer = pipeline("summarization", model="LeoCordoba/mt5-small-mlsum")
summarizer(article, min_length=5, max_length=64)
```


result: [{'summary_text': 'El ránking Taste Atlas ubicó primero en su lista al postre insignia local de galletitas, queso crema y dulce de leche'}]


## Results


| metric | score |
| --- | ----- |
| eval_rouge1 | 26.4352 |
| eval_rouge2 | 8.9293 |
| eval_rougeL | 21.2622 |
| eval_rougeLsum | 21.5518 |
| test_rouge1 | 26.0756 |
| test_rouge2 | 8.4669 |
| test_rougeL | 20.8167 |
| test_rougeLsum | 21.0822 |


model name: mt5-small-mlsum


model author: LeoCordoba


task: text2text-generation


tags: pytorch, jax, mt5, text2text-generation, transformers, autonlp_compatible


license: apache-2.0
tags:


- generated_from_trainer
datasets:
- mlsum
metrics:
- rouge
model-index:
- name: t5-seven-epoch-base-german
results:
  - task:
name: Summarization
type: summarization
dataset:
name: mlsum de
type: mlsum
args: de
metrics:
    - name: Rouge1
type: rouge
value: 42.3787


<!-- 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-seven-epoch-base-german


This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the mlsum de dataset.
It achieves the following results on the evaluation set:


- Loss: 1.5491
- Rouge1: 42.3787
- Rouge2: 32.0253
- Rougel: 38.9529
- Rougelsum: 40.4544
- Gen Len: 47.7873


## Model description


More information needed


## Intended uses & limitations


## Training and evaluation data


## Training procedure


### Training hyperparameters


The following hyperparameters were used during training:


- learning_rate: 5e-05
- train_batch_size: 6
- eval_batch_size: 6
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 7.0


### Training results


### Framework versions


- Transformers 4.16.0.dev0
- Pytorch 1.10.0+cu111
- Datasets 1.17.0
- Tokenizers 0.10.3


model name: t5-small-german


model author: Shahm


task: text2text-generation


tags: pytorch, tensorboard, t5, text2text-generation, dataset:mlsum, transformers, license:apache-2.0, generated_from_trainer, model-index, autonlp_compatible


license: mit
tags:


- generated_from_trainer
datasets:
- mlsum
model-index:
- name: plus-bert-german
results: []


# plus-bert-german


This model is a fine-tuned version of [bert-base-german-cased](https://huggingface.co/bert-base-german-cased) on the mlsum de dataset.
It achieves the following results on the evaluation set:


- Loss: 1.2791


## Model description


More information needed


## Intended uses & limitations


## Training and evaluation data


## Training procedure


### Training hyperparameters


- learning_rate: 5e-05
- train_batch_size: 6
- eval_batch_size: 6
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0


### Training results


### Framework versions


- Transformers 4.15.0.dev0
- Pytorch 1.10.0+cu111
- Datasets 1.16.1
- Tokenizers 0.10.3


model name: bert-german


model author: Shahm


task: fill-mask


tags: pytorch, tensorboard, bert, fill-mask, dataset:mlsum, transformers, license:mit, generated_from_trainer, model-index, autonlp_compatible, infinity_compatible


license: apache-2.0
tags:


- generated_from_trainer
datasets:
- mlsum
metrics:
- rouge
model-index:
- name: mode-bart-deutsch
results:
  - task:
name: Summarization
type: summarization
dataset:
name: mlsum de
type: mlsum
args: de
metrics:
    - name: Rouge1
type: rouge
value: 41.698


# mode-bart-deutsch


This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on the mlsum de dataset.
It achieves the following results on the evaluation set:


- Loss: 1.2152
- Rouge1: 41.698
- Rouge2: 31.3548
- Rougel: 38.2817
- Rougelsum: 39.6349
- Gen Len: 63.1723


## Model description


More information needed


## Intended uses & limitations


## Training and evaluation data


## Training procedure


### Training hyperparameters


### Training results


### Framework versions


model name: bart-german


model author: Shahm


task: text2text-generation


tags: pytorch, tensorboard, bart, text2text-generation, dataset:mlsum, transformers, license:apache-2.0, generated_from_trainer, model-index, autonlp_compatible


license: mit
tags:


- generated_from_trainer
model-index:
- name: plus-bert-court-90k-end-german
results: []


<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->


# plus-bert-court-90k-end-german


This model is a fine-tuned version of [Shahm/plus-bert-court-50k-90k-german](https://huggingface.co/Shahm/plus-bert-court-50k-90k-german) on an unknown dataset.
It achieves the following results on the evaluation set:


- Loss: 0.8246


## Model description


More information needed


## Intended uses & limitations


## Training and evaluation data


## Training procedure


### Training hyperparameters


- learning_rate: 5e-05
- train_batch_size: 6
- eval_batch_size: 6
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4.0


### Training results


### Framework versions


- Transformers 4.16.0.dev0
- Pytorch 1.10.0+cu111
- Datasets 1.17.0
- Tokenizers 0.11.0


model name: bert-court-german


model author: Shahm


task: fill-mask


tags: pytorch, tensorboard, bert, fill-mask, transformers, license:mit, generated_from_trainer, model-index, autonlp_compatible, infinity_compatible


tags:


- conversational


#Cortana DialoGPT Model


model name: DialoGPT-small-cortana


model author: VulcanBin


task: conversational


tags: pytorch, gpt2, text-generation, transformers, conversational


model name: DialoGPT-small-Jeffrey


model author: Jeffrey


# Shrek DialoGPT Model


model name: DialoGPT-small-shrek


model author: Pupihed


language: kn
datasets:


- openslr
metrics:
- wer
tags:
- audio
- automatic-speech-recognition
- speech
- xlsr-fine-tuning-week
license: apache-2.0
model-index:
- name: XLSR Wav2Vec2 Large 53 Kannada by Amogh Gopadi
results:
  - task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: OpenSLR kn
type: openslr
metrics:
    - name: Test WER
type: wer
value: 27.08


# Wav2Vec2-Large-XLSR-53-Kannada


Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Kannada using the [OpenSLR SLR79](http://openslr.org/79/) dataset. When using this model, make sure that your speech input is sampled at 16kHz.


## Usage


The model can be used directly (without a language model) as follows, assuming you have a dataset with Kannada `sentence` and `path` fields:


```python

import torch
import torchaudio
from datasets import load_dataset
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor

# test_dataset = #TODO: WRITE YOUR CODE TO LOAD THE TEST DATASET. For a sample, see the Colab link in Training Section.

processor = Wav2Vec2Processor.from_pretrained("amoghsgopadi/wav2vec2-large-xlsr-kn")
model = Wav2Vec2ForCTC.from_pretrained("amoghsgopadi/wav2vec2-large-xlsr-kn")
resampler = torchaudio.transforms.Resample(48_000, 16_000) # The original data was with 48,000 sampling rate. You can change it according to your input.

# Preprocessing the datasets.
# We need to read the audio files as arrays
def speech_file_to_array_fn(batch):
    speech_array, sampling_rate = torchaudio.load(batch["path"])
    batch["speech"] = resampler(speech_array).squeeze().numpy()
    return batch

test_dataset = test_dataset.map(speech_file_to_array_fn)
inputs = processor(test_dataset["speech"][:2], sampling_rate=16_000, return_tensors="pt", padding=True)

with torch.no_grad():
    logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits

predicted_ids = torch.argmax(logits, dim=-1)

print("Prediction:", processor.batch_decode(predicted_ids))
print("Reference:", test_dataset["sentence"][:2])

```


## Evaluation


The model can be evaluated as follows on 10% of the Kannada data on OpenSLR.


```python

import torch
import torchaudio
from datasets import load_dataset, load_metric
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import re

# test_dataset = #TODO: WRITE YOUR CODE TO LOAD THE TEST DATASET. For sample see the Colab link in Training Section.

wer = load_metric("wer")

processor = Wav2Vec2Processor.from_pretrained("amoghsgopadi/wav2vec2-large-xlsr-kn")
model = Wav2Vec2ForCTC.from_pretrained("amoghsgopadi/wav2vec2-large-xlsr-kn")
model.to("cuda")

chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“\%\‘\”\�\–\…]'
resampler = torchaudio.transforms.Resample(48_000, 16_000)

# Preprocessing the datasets.
# We need to read the aduio files as arrays
def speech_file_to_array_fn(batch):
    batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower()
    speech_array, sampling_rate = torchaudio.load(batch["path"])
    batch["speech"] = resampler(speech_array).squeeze().numpy()
    return batch

test_dataset = test_dataset.map(speech_file_to_array_fn)

# Preprocessing the datasets.
# We need to read the aduio files as arrays
def evaluate(batch):
    inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
    with torch.no_grad():
        logits = model(inputs.input_values.to("cuda"),
        attention_mask=inputs.attention_mask.to("cuda")).logits
        pred_ids = torch.argmax(logits, dim=-1)
        batch["pred_strings"] = processor.batch_decode(pred_ids)
        return batch

result = test_dataset.map(evaluate, batched=True, batch_size=8)
print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"])))

```


**Test Result**: 27.08 %


## Training


90% of the OpenSLR Kannada dataset was used for training.


The colab notebook used for training can be found [here](https://colab.research.google.com/github/amoghgopadi/wav2vec2-xlsr-kannada/blob/main/Fine_Tune_XLSR_Wav2Vec2_on_Kannada_ASR.ipynb).


model name: wav2vec2-large-xlsr-kn


model author: amoghsgopadi


task: automatic-speech-recognition


tags: pytorch, jax, wav2vec2, automatic-speech-recognition, kn, dataset:openslr, transformers, audio, speech, xlsr-fine-tuning-week, license:apache-2.0, model-index


license: apache-2.0
tags:


- generated_from_trainer
datasets:
- xsum
metrics:
- rouge
model-index:
- name: t5-small-finetuned-xsum
results:
  - task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: xsum
type: xsum
args: default
metrics:
    - name: Rouge1
type: rouge
value: 23.0533


# t5-small-finetuned-xsum


This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the xsum dataset.
It achieves the following results on the evaluation set:


- Loss: 2.7967
- Rouge1: 23.0533
- Rouge2: 3.912
- Rougel: 17.8534
- Rougelsum: 17.8581
- Gen Len: 18.6878


## Model description


More information needed


## Intended uses & limitations


## Training and evaluation data


## Training procedure


### Training hyperparameters


The following hyperparameters were used during training:


- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP


### Training results


| Training Loss | Epoch | Step | Validation Loss | Rouge1  | Rouge2 | Rougel  | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:|
| 3.0574        | 1.0   | 1276 | 2.7967          | 23.0533 | 3.912  | 17.8534 | 17.8581   | 18.6878 |


### Framework versions


- Transformers 4.10.2
- Pytorch 1.9.0+cu102
- Datasets 1.12.1
- Tokenizers 0.10.3


model name: t5-small-finetuned-xsum


model author: gniemiec


task: text2text-generation


tags: pytorch, tensorboard, t5, text2text-generation, dataset:xsum, transformers, license:apache-2.0, generated_from_trainer, model-index, autonlp_compatible


license: apache-2.0
tags:


- generated_from_trainer
datasets:
- xsum
metrics:
- rouge
model-index:
- name: mt5-small-finetuned-xsum
results:
  - task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: xsum
type: xsum
args: default
metrics:
    - name: Rouge1
type: rouge
value: 2.8351


<!-- 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. -->


# mt5-small-finetuned-xsum


This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the xsum dataset.
It achieves the following results on the evaluation set:


- Loss: nan
- Rouge1: 2.8351
- Rouge2: 0.3143
- Rougel: 2.6488
- Rougelsum: 2.6463
- Gen Len: 4.9416


## Model description


More information needed


## Intended uses & limitations


## Training and evaluation data


## Training procedure


### Training hyperparameters


- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| nan           | 1.0   | 12753 | nan             | 2.8351 | 0.3143 | 2.6488 | 2.6463    | 4.9416  |


### Framework versions


model name: mt5-small-finetuned-xsum


model author: gniemiec


task: text2text-generation


tags: pytorch, tensorboard, mt5, text2text-generation, dataset:xsum, transformers, license:apache-2.0, generated_from_trainer, model-index, autonlp_compatible


tags:


- conversational


#Rick and Morty DialoGPT Model


model name: DialoGPT-small-rickandmorty


model author: parthsinha


task: conversational


tags: pytorch, gpt2, text-generation, transformers, conversational


language:


- en
thumbnail: https://avatars3.githubusercontent.com/u/32437151?s=460&u=4ec59abc8d21d5feea3dab323d23a5860e6996a4&v=4
tags:
- text-classification
- emotion
- pytorch
license: apache-2.0
datasets:
- emotion
metrics:
- Accuracy, F1 Score


# Distilbert-base-uncased-emotion


## Model description:


[Distilbert](https://arxiv.org/abs/1910.01108) is created with knowledge distillation during the pre-training phase which reduces the size of a BERT model by 40%, while retaining 97% of its language understanding. It's smaller, faster than Bert and any other Bert-based model.


[Distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) finetuned on the emotion dataset using HuggingFace Trainer with below Hyperparameters


```
 learning rate 2e-5,
 batch size 64,
 num_train_epochs=8,
```


## Model Performance Comparision on Emotion Dataset from Twitter:


| Model | Accuracy | F1 Score |  Test Sample per Second |
| --- | --- | --- | --- |
| [Distilbert-base-uncased-emotion](https://huggingface.co/bhadresh-savani/distilbert-base-uncased-emotion) | 93.8 | 93.79 | 398.69 |
| [Bert-base-uncased-emotion](https://huggingface.co/bhadresh-savani/bert-base-uncased-emotion) | 94.05 | 94.06 | 190.152 |
| [Roberta-base-emotion](https://huggingface.co/bhadresh-savani/roberta-base-emotion) | 93.95 | 93.97| 195.639 |
| [Albert-base-v2-emotion](https://huggingface.co/bhadresh-savani/albert-base-v2-emotion) | 93.6 | 93.65 | 182.794 |


## How to Use the model:


```python
from transformers import pipeline
classifier = pipeline("text-classification",model='bhadresh-savani/distilbert-base-uncased-emotion', return_all_scores=True)
prediction = classifier("I love using transformers. The best part is wide range of support and its easy to use", )
print(prediction)

"""
Output:
[[
{'label': 'sadness', 'score': 0.0006792712374590337},
{'label': 'joy', 'score': 0.9959300756454468},
{'label': 'love', 'score': 0.0009452480007894337},
{'label': 'anger', 'score': 0.0018055217806249857},
{'label': 'fear', 'score': 0.00041110432357527316},
{'label': 'surprise', 'score': 0.0002288572577526793}
]]
"""
```


## Dataset:


## Training procedure


[Colab Notebook](https://github.com/bhadreshpsavani/ExploringSentimentalAnalysis/blob/main/SentimentalAnalysisWithDistilbert.ipynb)


## Eval results


```json
{
'test_accuracy': 0.938,
 'test_f1': 0.937932884041714,
 'test_loss': 0.1472451239824295,
 'test_mem_cpu_alloc_delta': 0,
 'test_mem_cpu_peaked_delta': 0,
 'test_mem_gpu_alloc_delta': 0,
 'test_mem_gpu_peaked_delta': 163454464,
 'test_runtime': 5.0164,
 'test_samples_per_second': 398.69
 }
```


## Reference:


* [Natural Language Processing with Transformer By Lewis Tunstall, Leandro von Werra, Thomas Wolf](https://learning.oreilly.com/library/view/natural-language-processing/9781098103231/)


model name: distilbert-base-uncased-emotion


model author: bhadresh-savani


task: text-classification


tags: pytorch, tf, jax, distilbert, text-classification, en, dataset:emotion, arxiv:1910.01108, transformers, emotion, license:apache-2.0, infinity_compatible


language:


# bert-base-uncased-emotion


## Model description:


[Bert](https://arxiv.org/abs/1810.04805) is a Transformer Bidirectional Encoder based Architecture trained on MLM(Mask Language Modeling) objective


[bert-base-uncased](https://huggingface.co/bert-base-uncased) finetuned on the emotion dataset using HuggingFace Trainer with below training parameters


## How to Use the model:


```python
from transformers import pipeline
classifier = pipeline("text-classification",model='bhadresh-savani/bert-base-uncased-emotion', return_all_scores=True)
prediction = classifier("I love using transformers. The best part is wide range of support and its easy to use", )
print(prediction)

"""
output:
[[
{'label': 'sadness', 'score': 0.0005138228880241513},
{'label': 'joy', 'score': 0.9972520470619202},
{'label': 'love', 'score': 0.0007443308713845909},
{'label': 'anger', 'score': 0.0007404946954920888},
{'label': 'fear', 'score': 0.00032938539516180754},
{'label': 'surprise', 'score': 0.0004197491507511586}
]]
"""
```


## Dataset:


## Training procedure


[Colab Notebook](https://github.com/bhadreshpsavani/ExploringSentimentalAnalysis/blob/main/SentimentalAnalysisWithDistilbert.ipynb)
follow the above notebook by changing the model name from distilbert to bert


## Eval results


```json
{
 'test_accuracy': 0.9405,
 'test_f1': 0.9405920712282673,
 'test_loss': 0.15769127011299133,
 'test_runtime': 10.5179,
 'test_samples_per_second': 190.152,
 'test_steps_per_second': 3.042
 }
```


## Reference:


model name: bert-base-uncased-emotion


model author: bhadresh-savani


task: text-classification


tags: pytorch, tf, jax, bert, text-classification, en, dataset:emotion, arxiv:1810.04805, transformers, emotion, license:apache-2.0, infinity_compatible


language:


- en
thumbnail: https://avatars3.githubusercontent.com/u/32437151?s=460&u=4ec59abc8d21d5feea3dab323d23a5860e6996a4&v=4
tags:
- text-classification
- go-emotion
- pytorch
license: apache-2.0
datasets:
- go_emotions
metrics:
- Accuracy


# Distilbert-Base-Uncased-Go-Emotion


## Model description:


**Not working fine**


## Training Parameters:


```
  Num Epochs = 3
  Instantaneous batch size per device = 32
  Total train batch size (w. parallel, distributed & accumulation) = 32
  Gradient Accumulation steps = 1
  Total optimization steps = 15831
```


## TrainOutput:


```
'train_loss': 0.105500
```


## Evalution Output:


```
 'eval_accuracy_thresh': 0.962023913860321,
 'eval_loss': 0.11090277135372162,
```


## Colab Notebook:


model name: distilbert-base-uncased-go-emotion


tags: pytorch, distilbert, en, dataset:go_emotions, transformers, text-classification, go-emotion, license:apache-2.0, infinity_compatible


language: en
license: apache-2.0
datasets:


- sst-2


# distilbert-base-uncased-sentiment-sst2


This model will be able to identify positivity or negativity present in the sentence


## Dataset:


The Stanford Sentiment Treebank from GLUE


## Results:


```
***** eval metrics *****
  epoch                   =        3.0
  eval_accuracy           =     0.9094
  eval_loss               =     0.3514
  eval_runtime            = 0:00:03.60
  eval_samples            =        872
  eval_samples_per_second =    242.129
  eval_steps_per_second   =     30.266
```


model name: distilbert-base-uncased-sentiment-sst2


model author: bhadresh-savani


task: text-classification


tags: pytorch, tf, jax, distilbert, text-classification, en, dataset:sst-2, transformers, license:apache-2.0, infinity_compatible


language:


- en
thumbnail: https://avatars3.githubusercontent.com/u/32437151?s=460&u=4ec59abc8d21d5feea3dab323d23a5860e6996a4&v=4
tags:
- text-classification
- emotion
- pytorch
license: apache-2.0
datasets:
- emotion
metrics:
- Accuracy, F1 Score


# robert-base-emotion


## Model description:


[roberta](https://arxiv.org/abs/1907.11692) is Bert with better hyperparameter choices so they said it's Robustly optimized Bert during pretraining.


[roberta-base](https://huggingface.co/roberta-base) finetuned on the emotion dataset using HuggingFace Trainer with below Hyperparameters


```
 learning rate 2e-5,
 batch size 64,
 num_train_epochs=8,
```


## Model Performance Comparision on Emotion Dataset from Twitter:


| Model | Accuracy | F1 Score |  Test Sample per Second |
| --- | --- | --- | --- |
| [Distilbert-base-uncased-emotion](https://huggingface.co/bhadresh-savani/distilbert-base-uncased-emotion) | 93.8 | 93.79 | 398.69 |
| [Bert-base-uncased-emotion](https://huggingface.co/bhadresh-savani/bert-base-uncased-emotion) | 94.05 | 94.06 | 190.152 |
| [Roberta-base-emotion](https://huggingface.co/bhadresh-savani/roberta-base-emotion) | 93.95 | 93.97| 195.639 |
| [Albert-base-v2-emotion](https://huggingface.co/bhadresh-savani/albert-base-v2-emotion) | 93.6 | 93.65 | 182.794 |


## How to Use the model:


```python
from transformers import pipeline
classifier = pipeline("text-classification",model='bhadresh-savani/roberta-base-emotion', return_all_scores=True)
prediction = classifier("I love using transformers. The best part is wide range of support and its easy to use", )
print(prediction)

"""
Output:
[[
{'label': 'sadness', 'score': 0.002281982684507966},
{'label': 'joy', 'score': 0.9726489186286926},
{'label': 'love', 'score': 0.021365027874708176},
{'label': 'anger', 'score': 0.0026395076420158148},
{'label': 'fear', 'score': 0.0007162453257478774},
{'label': 'surprise', 'score': 0.0003483477921690792}
]]
"""
```


## Dataset:


## Training procedure


[Colab Notebook](https://github.com/bhadreshpsavani/ExploringSentimentalAnalysis/blob/main/SentimentalAnalysisWithDistilbert.ipynb)
follow the above notebook by changing the model name to roberta


## Eval results


```json
{
 'test_accuracy': 0.9395,
 'test_f1': 0.9397328860104454,
 'test_loss': 0.14367154240608215,
 'test_runtime': 10.2229,
 'test_samples_per_second': 195.639,
 'test_steps_per_second': 3.13
 }
```


## Reference:


* [Natural Language Processing with Transformer By Lewis Tunstall, Leandro von Werra, Thomas Wolf](https://learning.oreilly.com/library/view/natural-language-processing/9781098103231/)


model name: roberta-base-emotion


model author: bhadresh-savani


task: text-classification


tags: pytorch, tf, jax, roberta, text-classification, en, dataset:emotion, arxiv:1907.11692, transformers, emotion, license:apache-2.0, infinity_compatible


language:


- en
thumbnail: https://avatars3.githubusercontent.com/u/32437151?s=460&u=4ec59abc8d21d5feea3dab323d23a5860e6996a4&v=4
tags:
- text-classification
- go-emotion
- pytorch
license: apache-2.0
datasets:
- go_emotions
metrics:
- Accuracy


# Bert-Base-Uncased-Go-Emotion


## Model description:


## Training Parameters:


```
Num examples = 169208
Num Epochs = 3
Instantaneous batch size per device = 16
Total train batch size (w. parallel, distributed & accumulation) = 16
Gradient Accumulation steps = 1
Total optimization steps = 31728
```


## TrainOutput:


```
'train_loss': 0.12085497042373672,
```


## Evalution Output:


```
 'eval_accuracy_thresh': 0.9614765048027039,
 'eval_loss': 0.1164659634232521
```


## Colab Notebook:


model name: bert-base-go-emotion


tags: pytorch, bert, en, dataset:go_emotions, transformers, text-classification, go-emotion, license:apache-2.0, infinity_compatible


# Albert-base-v2-emotion


[Albert](https://arxiv.org/pdf/1909.11942v6.pdf) is A Lite BERT architecture that has significantly fewer parameters than a traditional BERT architecture.


[Albert-base-v2](https://huggingface.co/albert-base-v2) finetuned on the emotion dataset using HuggingFace Trainer with below Hyperparameters


## How to Use the model:


```python
from transformers import pipeline
classifier = pipeline("text-classification",model='bhadresh-savani/albert-base-v2-emotion', return_all_scores=True)
prediction = classifier("I love using transformers. The best part is wide range of support and its easy to use", )
print(prediction)

"""
Output:
[[
{'label': 'sadness', 'score': 0.010403595864772797},
{'label': 'joy', 'score': 0.8902180790901184},
{'label': 'love', 'score': 0.042532723397016525},
{'label': 'anger', 'score': 0.041297927498817444},
{'label': 'fear', 'score': 0.011772023513913155},
{'label': 'surprise', 'score': 0.0037756056990474463}
]]
"""
```


## Dataset:


## Training procedure


[Colab Notebook](https://github.com/bhadreshpsavani/ExploringSentimentalAnalysis/blob/main/SentimentalAnalysisWithDistilbert.ipynb)


## Eval results


```json
{
'test_accuracy': 0.936,
 'test_f1': 0.9365658988006296,
 'test_loss': 0.15278364717960358,
 'test_runtime': 10.9413,
 'test_samples_per_second': 182.794,
 'test_steps_per_second': 2.925
 }
```


## Reference:


model name: albert-base-v2-emotion


model author: bhadresh-savani


task: text-classification


tags: pytorch, tf, jax, albert, text-classification, en, dataset:emotion, arxiv:1909.11942, transformers, emotion, license:apache-2.0, infinity_compatible


## language: tr


# Logo Turkish Question Answering Model : Question Answering


Inspired by savasy/bert-base-turkish-squad,


* Inspired model: https://huggingface.co/savasy/bert-base-turkish-squad
* BERT-base: https://huggingface.co/dbmdz/bert-base-turkish-uncased
* Dataset:  Logo Private QnA Chatbot Database


# Training Code


```
model_args = QuestionAnsweringArgs()
model_args.train_batch_size = 16
model_args.evaluate_during_training = True
model_args.n_best_size=3
model_args.num_train_epochs=5

train_args = {
    "reprocess_input_data": True,
    "overwrite_output_dir": True,
    "use_cached_eval_features": True,
    "output_dir": f"outputs/bert",
    "best_model_dir": f"outputs/bert/best_model1",
    "evaluate_during_training": True,
    "max_seq_length": 128,
    "num_train_epochs": 10,
    "evaluate_during_training_steps": 1000,
    "wandb_project": "Question Answer Application",
    "wandb_kwargs": {"name": "dbmdz/bert-base-turkish-uncased\"},
    "save_model_every_epoch": False,
    "save_eval_checkpoints": False,
    "n_best_size":3,
    # "use_early_stopping": True,
    # "early_stopping_metric": "mcc",
    "n_gpu": 4,
    # "manual_seed": 4,
    "use_multiprocessing": True,
    "train_batch_size": 126,
    "eval_batch_size": 64,
    # "config": {
    #     "output_hidden_states": True
    # }
}
model = QuestionAnsweringModel(
    "bert","dbmdz/bert-base-turkish-uncased\", args=train_args
)

model.train_model(train, eval_data=test)
```


# Dataset Sample


```
{
        "context": "Varlıklara ait yeniden değerleme toplamlarının özet olarak alındığı rapor seçeneğidir. Varlık Yönetimi program bölümünde Raporlar menüsü altında yer alır. Rapor yıllık olarak alınır. Toplamların alınacağı yıl, Yıl filtre satırında belirtilir. Rapor filtre seçenekleri aşağıdaki tabloda yer almaktadır.",
        "qas": [
            {
                "id": "01017",
                "is_impossible": false,
                "question": "Yeniden Değerleme Özeti ne işe yarar",
                "answers": [
                    {
                        "text": "Varlıklara ait yeniden değerleme toplamlarının özet olarak alındığı rapor seçeneğidir.",
                        "answer_start": 0
                    }
                ]
            },
            {
                "id": "01018",
                "is_impossible": false,
                "question": " Yeniden Değerleme Özetine nereden ulaşırım",
                "answers": [
                    {
                        "text": "Varlık Yönetimi program bölümünde Raporlar menüsü altında yer alır.",
                        "answer_start": 87
                    }
                ]
            }
        }

```


# Example Usage


> Load Model


```
#Required Libraries
from transformers import AutoTokenizer, AutoModelForQuestionAnswering, pipeline
import torch
#Model Path
hface_path = "yunusemreemik/logo-qna-model"
#For tokenize context and question
tokenizer = AutoTokenizer.from_pretrained(hface_path)
#For generate NN eval outputs
model = AutoModelForQuestionAnswering.from_pretrained(hface_path)
#For functional pipe
nlp = pipeline("question-answering", model=model, tokenizer=tokenizer)
```


> Apply the model.
Please dont forget the delete backslashes "\" before run


```
e_arsiv ="e-Arşiv Tipleri, e-Arşiv fatura türünün belirlendiği alandır. İlgili cari hesap kartında \\nLogoConnect sayfasında belirlenen e-arşiv tipi alana öndeğer olarak \\naktarılır. Standart faturalar için herhangi bir seçim yapılmaz. \\nÖzel matrah uygulanan tütün, altın, gümüş, gazete, dergi, belediye \\nşehir yolcu taşımacılığı ve telefon kartı satışları için kesilen faturalar. \\nİstisna uygulanan faturalar. (İhracat teslimleri ve bu teslimlere ilişkin hizmetler, \\nmal ihracatı, hizmet ihracatı, serbest bölgelerdeki müşteriler için yapılan fason hizmetler vs..) \\nAraç Tescil Faturası, Araç tescil için kesilen faturalardır."

answer_text = nlp(question="İlgili cari hesap kartları nerede belirlenir?", context=e_arsiv)
print(answer_text )

```


```

print(nlp(question="", context=e_arsiv))
```


# Evaluation


```
(160,
 {'global_step': [16, 32, 48, 64, 80, 96, 112, 128, 144, 160],
  'correct': [11, 15, 18, 18, 19, 16, 16, 16, 14, 14],
  'similar': [23, 26, 22, 21, 21, 24, 24, 23, 25, 25],
  'incorrect': [8, 1, 2, 3, 2, 2, 2, 3, 3, 3],
  'train_loss': [0.8277238607406616,
   0.7876648306846619,
   0.44657397270202637,
   0.32337626814842224,
   0.2009371519088745,
   0.15247923135757446,
   0.11289173364639282,
   0.06762214750051498,
   0.06813357770442963,
   0.04011240229010582],
  'eval_loss': [-9.3046875,
   -8.8984375,
   -9.1171875,
   -9.03125,
   -9.046875,
   -8.984375,
   -9.1171875,
   -9.296875,
   -9.296875,
   -9.296875]})
```


model name: logo-qna-model


model author: yunusemreemik


task: question-answering


tags: pytorch, bert, question-answering, tr, transformers, autonlp_compatible, infinity_compatible


hello


model name: agri-gpt2


model author: Mamatha


task: text-generation


tags: pytorch, jax, gpt2, text-generation, transformers


license: apache-2.0
tags:


- generated_from_trainer
datasets:
- glue
metrics:
- matthews_correlation
model-index:
- name: distilbert-base-uncased-finetuned-cola
results:
  - task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
args: cola
metrics:
    - name: Matthews Correlation
type: matthews_correlation
value: 0.5264763891845121


<!-- 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: 0.7550
- Matthews Correlation: 0.5265


## Model description


More information needed


## Intended uses & limitations


## Training and evaluation data


## Training procedure


### Training hyperparameters


The following hyperparameters were used during training:


- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5


### Training results


| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.5296        | 1.0   | 535  | 0.5144          | 0.4215               |
| 0.3504        | 2.0   | 1070 | 0.4903          | 0.5046               |
| 0.2393        | 3.0   | 1605 | 0.6339          | 0.5058               |
| 0.175         | 4.0   | 2140 | 0.7550          | 0.5265               |
| 0.1259        | 5.0   | 2675 | 0.8688          | 0.5259               |


### Framework versions


- Transformers 4.11.3
- Pytorch 1.9.0+cu111
- Datasets 1.12.1
- Tokenizers 0.10.3


model name: distilbert-base-uncased-finetuned-cola


model author: gauravtripathy


task: text-classification


tags: pytorch, tensorboard, distilbert, text-classification, dataset:glue, transformers, license:apache-2.0, generated_from_trainer, model-index, infinity_compatible


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Madres paralelas (titulada Madres paralelas en Hispanoamérica y Madres paralelas en España) es una próxima película de comedia deportiva estadounidense de imagen real/animada de 2021 y una secuela independiente de Space Jam de 1996. La película está dirigida por Malcolm D. Lee y protagonizada por el jugador de baloncesto LeBron James, así como por Don Cheadle y Sonequa Martin-Green. También está protagonizada por los personajes de Looney Tunes como Bugs Bunny, Lola Bunny y otros. James también actúa como productor junto a Ryan Coogler, quien coescribió el guion con Sev Ohanian.


Madres paralelas Online Gratis VER Película completa Madres paralelas GRATIS | El estreno de Madres paralelas está levantado pasiones en las redes sociales, sobre todo por el regreso de varios personaje a la historia que sigue a Dominic Toretto y el resto de los protagonistas.


Con una trama que nos vuelve a llevar a través de escenas de acción increíbles y autos voladores, seguidores de Fast & furious se preguntan si, al igual que otras tramas lanzadas en el 2021, también estará disponible en algún servicio de streaming.


La posición de Vin Diesel sobre Madres paralelas en streaming Sobre este tema, Vin Diesel indicó en junio que Madres paralelas no estaría disponible en algún servicio streaming, por lo menos no en estreno simultáneo con el cine. “Por más de 100 años ha habido un lugar donde nos juntamos para entretenernos: el cine. Después de estar separados por tanto tiempo, es hora de volver a estar juntos, para reír, para aplaudir”, señaló el actor en un adelanto promocional de la película.


El estreno de Madres paralelas estaba previsto originalmente para abril de 2019 en todo el mundo, pero se retrasó varias veces, primero debido a los estrenos de Hobbs & Shaw (2019) y No Time to Die (2021), y luego a la pandemia de COVID-19. Se estrenó en Hong Kong y Corea del Sur el 19 de mayo de 2021, y está previsto que llegue a Estados Unidos el 25 de junio. La película recibió críticas entre positivas y mixtas, con elogios para las actuaciones, los efectos visuales, las acrobacias y la dirección de Lin, pero algunas críticas por sus secuencias de acción poco realistas y su argumento formulista. Ha recaudado $544 millones en todo el mundo, lo que la convierte en la tercera película más taquillera de 2021.


Madres paralelas llegará a Amazon Prime Video y Peacock Con el paso de la semanas, y tras lograr una importante taquilla en Asia, Madres paralelas llegará finalmente a Amazon Prime Video, tras firmar un contrato de exclusividad con Universal. En Estados Unidos, la película podrá verse de forma legal por Peacock.