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fill-mask | transformers |
# Welcome to KanBERTo (ಕನ್ಬರ್ಟೋ)
## Model Description
> This is a small language model for [Kannada](https://en.wikipedia.org/wiki/Kannada) language with 1M data samples taken from
[OSCAR page](https://traces1.inria.fr/oscar/files/compressed-orig/kn.txt.gz)
## Training params
- **Dataset** - 1M data samples ar... | {"language": "kn"} | Naveen-k/KanBERTo | null | [
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# Welcome to KanBERTo (ಕನ್ಬರ್ಟೋ)
## Model Description
> This is a small language model for Kannada language with 1M data samples taken from
OSCAR page
## Training params
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text-generation | transformers |
#Marty McFly model | {"tags": ["conversational"]} | Navigator/DialoGPT-medium-martymcfly | null | [
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text-generation | transformers | # Chandler Bing DialoGPT Model | {"tags": ["conversational"]} | Navya2608/DialoGPT-medium-chandler | null | [
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text-generation | transformers | # Rachel Green DialoGPT Model | {"tags": ["conversational"]} | Navya2608/DialoGPT-medium-rachel | null | [
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text-generation | transformers |
# Tony Stark dialoGPT model | {"tags": ["conversational"]} | Navya2608/DialoGPT-small-tonystarkscript | null | [
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automatic-speech-recognition | null |
# Norwegian Wav2Vec2 Model - 1B - Bokmål
This achieves the following results on the test set with a 5-gram KenLM:
- WER: 0.0668
- CER: 0.0256
Without using a language model, we are getting these results:
- WER: ???
- CER: ???
## Model description
This is one of several Wav2Vec-models created during the 🤗... | {"language": ["nb-NO"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "NbAiLab/NPSC", "xxx-robust-speech-event", false, "nb-NO"], "datasets": ["NbAiLab/NPSC"], "model-index": [{"name": "wav2vec2-xls-r-1b-npsc-bokmaal-low-27k", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Au... | NbAiLab/Wav2Vec-Template | null | [
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| Norwegian Wav2Vec2 Model - 1B - Bokmål
======================================
This achieves the following results on the test set with a 5-gram KenLM:
* WER: 0.0668
* CER: 0.0256
Without using a language model, we are getting these results:
* WER: ???
* CER: ???
Model description
-----------------
This is o... | [
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automatic-speech-recognition | transformers |
<!-- 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. -->
# XLSR-1B-bokmaal-low
This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "XLSR-1B-bokmaal-low", "results": []}]} | NbAiLab/XLSR-1B-bokmaal-low | null | [
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| XLSR-1B-bokmaal-low
===================
This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1579
* Wer: 0.0722
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More info... | [
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automatic-speech-recognition | transformers |
<!-- 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. -->
# XLSR-300M-bokmaal
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-... | {"language": ["nb-NO"], "license": "apache-2.0", "tags": ["generated_from_trainer", "automatic-speech-recognition", "NbAiLab/NPSC", "robust-speech-event", false, "nb-NO", "hf-asr-leaderboard"], "datasets": ["NbAiLab/NPSC"], "model-index": [{"name": "XLSR-300M-bokmaal", "results": [{"task": {"type": "automatic-speech-re... | NbAiLab/XLSR-300M-bokmaal | null | [
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| XLSR-300M-bokmaal
=================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the NBAILAB/NPSC - 16K\_MP3\_BOKMAAL dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1635
* Wer: 0.1005
Model description
-----------------
More information needed
Intended use... | [
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zero-shot-classification | transformers |
**Release 1.0** (March 11, 2021)
# NB-Bert base model finetuned on Norwegian machine translated MNLI
## Description
The most effective way of creating a good classifier is to finetune a pre-trained model for the specific task at hand. However, in many cases this is simply impossible.
[Yin et al.](https://arxiv.org/... | {"language": false, "license": "cc-by-4.0", "tags": ["nb-bert", "zero-shot-classification", "pytorch", "tensorflow", "norwegian", "bert"], "datasets": ["mnli", "multi_nli", "xnli"], "thumbnail": "https://raw.githubusercontent.com/NBAiLab/notram/master/images/nblogo_2.png", "pipeline_tag": "zero-shot-classification", "w... | NbAiLab/nb-bert-base-mnli | null | [
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|
Release 1.0 (March 11, 2021)
# NB-Bert base model finetuned on Norwegian machine translated MNLI
## Description
The most effective way of creating a good classifier is to finetune a pre-trained model for the specific task at hand. However, in many cases this is simply impossible.
Yin et al. proposed a very clever w... | [
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token-classification | transformers |
**Release 1.0** (November 17, 2021)
# nb-bert-base-ner
## Description
NB-Bert base model fine-tuned on the Named Entity Recognition task using the [NorNE dataset](https://huggingface.co/datasets/NbAiLab/norne).
## Usage
```python
from transformers import AutoTokenizer, AutoModelForTokenClassification
from transfor... | {"language": false, "license": "cc-by-4.0", "tags": ["norwegian", "bert", "ner"], "datasets": ["norne"], "thumbnail": "nblogo_3.png", "pipeline_tag": "token-classification", "inference": {"parameters": {"aggregation_strategy": "first"}}, "widget": [{"text": "Trond Giske har bekreftet p\u00e5 sp\u00f8rsm\u00e5l fra Adre... | NbAiLab/nb-bert-base-ner | null | [
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|
Release 1.0 (November 17, 2021)
# nb-bert-base-ner
## Description
NB-Bert base model fine-tuned on the Named Entity Recognition task using the NorNE dataset.
## Usage
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text-classification | transformers |
# NB-BERT-base Sámi Relevant
This a model capable of predicting when a chunk of text could potentially be of interest to the Sámi Bibliographers at the National Library of Norway. | {"language": ["se", "no", "en"], "license": "apache-2.0", "library_name": "transformers", "tags": ["sami relevant"], "metrics": ["matthews_correlation"], "pipeline_tag": "text-classification", "widget": [{"text": "Riddu Ri\u0111\u0111u Festiv\u00e1la lea jahk\u00e1sa\u0161 musihkka- ja -kulturfestiv\u00e1la mii l\u00e1... | NbAiLab/nb-bert-base-sami-relevant | null | [
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fill-mask | transformers | - **Release 1.1** (March 11, 2021)
- **Release 1.0** (January 13, 2021)
# NB-BERT-base
## Description
NB-BERT-base is a general BERT-base model built on the large digital collection at the National Library of Norway.
This model is based on the same structure as [BERT Cased multilingual model](https://github.com/goo... | {"language": false, "license": "cc-by-4.0", "tags": ["norwegian", "bert"], "pipeline_tag": "fill-mask", "widget": [{"text": "P\u00e5 biblioteket kan du [MASK] en bok."}, {"text": "Dette er et [MASK] eksempel."}, {"text": "Av og til kan en spr\u00e5kmodell gi et [MASK] resultat."}, {"text": "Som ansat f\u00e5r du [MASK]... | NbAiLab/nb-bert-base | null | [
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| - Release 1.1 (March 11, 2021)
- Release 1.0 (January 13, 2021)
# NB-BERT-base
## Description
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fill-mask | transformers |
- **Release 1.0beta** (April 29, 2021)
# NB-BERT-large (beta)
## Description
NB-BERT-large is a general BERT-large model built on the large digital collection at the National Library of Norway.
This model is trained from scratch on a wide variety of Norwegian text (both bokmål and nynorsk) from the last 200 year... | {"language": false, "license": "cc-by-4.0", "tags": ["norwegian", "bert"], "thumbnail": "nblogo_3.png", "pipeline_tag": "fill-mask", "widget": [{"text": "P\u00e5 biblioteket kan du l\u00e5ne en [MASK]."}]} | NbAiLab/nb-bert-large | null | [
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"bert",
"norwegian",
"fill-mask",
"no",
"license:cc-by-4.0",
"endpoints_compatible",
"has_space",
"region:us"
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"no"
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#transformers #pytorch #tf #jax #safetensors #bert #norwegian #fill-mask #no #license-cc-by-4.0 #endpoints_compatible #has_space #region-us
|
- Release 1.0beta (April 29, 2021)
# NB-BERT-large (beta)
## Description
NB-BERT-large is a general BERT-large model built on the large digital collection at the National Library of Norway.
This model is trained from scratch on a wide variety of Norwegian text (both bokmål and nynorsk) from the last 200 years us... | [
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text-generation | transformers |
- **Release ✨v1✨** (January 18th, 2023) *[Full-precision](https://huggingface.co/NbAiLab/nb-gpt-j-6B/tree/v1), [sharded](https://huggingface.co/NbAiLab/nb-gpt-j-6B/tree/v1-sharded), [half-precision](https://huggingface.co/NbAiLab/nb-gpt-j-6B/tree/v1-float16), and [mesh-transformers-jax](https://huggingface.co/NbAiL... | {"language": ["no", "nb", "nn"], "license": "apache-2.0", "tags": ["pytorch", "causal-lm"], "datasets": ["NbAiLab/NCC", "mc4", "oscar"], "pipeline_tag": "text-generation", "extra_gated_prompt": "You agree to not use the model to conduct experiments that cause harm to human subjects.", "extra_gated_fields": {"Company": ... | NbAiLab/nb-gpt-j-6B | null | [
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"2104.09864",
"2101.00027"
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| * Release v1 (January 18th, 2023) *Full-precision, sharded, half-precision, and mesh-transformers-jax weights*
All checkpoints
```
- Release v1beta5 (December 18th, 2022) *Full-precision, sharded, and half-precision weights*
- Release v1beta4 (October 28th, 2022) *Full-precision, sharded, and half-precision weights*... | [
"### How to use\n\n\nThis model can be easily loaded using the 'AutoModelForCausalLM' functionality:",
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fill-mask | transformers |
# This is just a Test Model. Do NOT use for anything!
Continued pretrained from the nb-roberta-base.
The domain specific pretraining is done on the 102GB (Scandinavian corpus)[https://huggingface.co/datasets/NbAiLab/scandinavian].
## Train for 180k steps for 128 sequences:
```bash
./run_mlm_flax_stream.py \
--... | {"language": false, "license": "cc-by-4.0", "tags": ["norwegian", "roberta"], "pipeline_tag": "fill-mask", "widget": [{"text": "P\u00e5 biblioteket kan du <mask> en bok."}, {"text": "Dette er et <mask> eksempel."}, {"text": "Av og til kan en spr\u00e5kmodell gi et <mask> resultat."}, {"text": "Som ansat f\u00e5r du <ma... | NbAiLab/nb-roberta-base-scandinavian | null | [
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|
# This is just a Test Model. Do NOT use for anything!
Continued pretrained from the nb-roberta-base.
The domain specific pretraining is done on the 102GB (Scandinavian corpus)[URL
## Train for 180k steps for 128 sequences:
## Train for 20k steps for 512 sequences:
Approximate additional training time: 1 week.... | [
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text2text-generation | transformers | # 🇳🇴 Norwegian T5 Base model Trained on the NCC🇳🇴
This is a Norwegian T5-base model trained on the Norwegian Colossal Corpus (NCC) on a TPU v3-8.
This model is currently training. It will finish in January 2022. Please do not use yet..
```
| {"language": false, "license": "cc-by-4.0", "tags": ["seq2seq"], "datasets": ["Norwegian Nynorsk/Bokm\u00e5l"]} | NbAiLab/nb-t5-base-v3 | null | [
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| # 🇳🇴 Norwegian T5 Base model Trained on the NCC🇳🇴
This is a Norwegian T5-base model trained on the Norwegian Colossal Corpus (NCC) on a TPU v3-8.
This model is currently training. It will finish in January 2022. Please do not use yet..
'''
| [
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automatic-speech-recognition | transformers |
# Norwegian Wav2Vec2 Model - 1B Bokmål
This model is finetuned on top of feature extractor [XLS-R](https://huggingface.co/facebook/wav2vec2-xls-r-1b) from Facebook/Meta. The finetuned model achieves the following results on the test set with a 5-gram KenLM. The numbers in parentheses are the results without the langua... | {"language": ["nb", false], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "NbAiLab/NPSC", false, "nb", "nb-NO"], "datasets": ["NbAiLab/NPSC"], "model-index": [{"name": "nb-wav2vec2-1b-bokmaal", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "da... | NbAiLab/nb-wav2vec2-1b-bokmaal | null | [
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"has_space",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [
"2307.01672"
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"nb",
"no"
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| Norwegian Wav2Vec2 Model - 1B Bokmål
====================================
This model is finetuned on top of feature extractor XLS-R from Facebook/Meta. The finetuned model achieves the following results on the test set with a 5-gram KenLM. The numbers in parentheses are the results without the language model:
* WER... | [
"### Language Model\n\n\nAs the scores indicate, adding even a simple 5-gram language will improve the results. has provided another very nice blog explaining how to add a 5-gram language model to improve the ASR model. You can build this from your own corpus, for instance by extracting some suitable text from the ... | [
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automatic-speech-recognition | transformers |
# Norwegian Wav2Vec2 Model - 300M - VoxRex - Bokmål
This model is finetuned on top of feature extractor [VoxRex-model](https://huggingface.co/KBLab/wav2vec2-large-voxrex) from the National Library of Sweden. The finetuned model achieves the following results on the test set with a 5-gram KenLM. The numbers in parenthe... | {"language": [false, "nb"], "license": "apache-2.0", "tags": ["automatic-speech-recognition"], "datasets": ["NbAiLab/NPSC"], "model-index": [{"name": "nb-wav2vec2-300m-bokmaal", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "NPSC", "type": "Nb... | NbAiLab/nb-wav2vec2-300m-bokmaal | null | [
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| Norwegian Wav2Vec2 Model - 300M - VoxRex - Bokmål
=================================================
This model is finetuned on top of feature extractor VoxRex-model from the National Library of Sweden. The finetuned model achieves the following results on the test set with a 5-gram KenLM. The numbers in parentheses a... | [
"### Language Model\n\n\nAs the scores indicate, adding even a simple 5-gram language will improve the results. has provided another very nice blog explaining how to add a 5-gram language model to improve the ASR model. You can build this from your own corpus, for instance by extracting some suitable text from the ... | [
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automatic-speech-recognition | transformers |
# Norwegian Wav2Vec2 Model - 300M - VoxRex - Nynorsk
This model is finetuned on top of feature extractor [VoxRex-model](https://huggingface.co/KBLab/wav2vec2-large-voxrex) from the National Library of Sweden. The finetuned model achieves the following results on the test set with a 5-gram KenLM. The numbers in parenth... | {"language": ["nn"], "license": "apache-2.0", "tags": ["automatic-speech-recognition"], "datasets": ["NbAiLab/NPSC"], "model-index": [{"name": "nb-wav2vec2-300m-nynorsk", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "NPSC", "type": "NbAiLab/N... | NbAiLab/nb-wav2vec2-300m-nynorsk | null | [
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| Norwegian Wav2Vec2 Model - 300M - VoxRex - Nynorsk
==================================================
This model is finetuned on top of feature extractor VoxRex-model from the National Library of Sweden. The finetuned model achieves the following results on the test set with a 5-gram KenLM. The numbers in parentheses... | [
"### Dataset\n\n\nIn parallel with the event, the team also converted the Norwegian Parliamentary Speech Corpus (NPSC) to the NbAiLab/NPSC in Dataset format and used that as the main source for training.\n\n\nCode\n----\n\n\nWe have released all the code developed during the event so that the Norwegian NLP communit... | [
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fill-mask | transformers |
## Results
|**Model** | **NoRec** | **NorNe-NB**| **NorNe-NN** | **NorDial** | **DaNe** | **Da-Angry-Tweets** |
|:-----------|------------:|------------:|------------:|------------:|------------:|------------:|
|roberta-base (English) | 51.77 | 79.01/79.53| 79.79/83.02 | 67.18| 75.44/78.07 | 55.51 |
|mBERT-cased | 63... | {"language": false, "license": "cc-by-4.0", "tags": ["norwegian", "bert"], "pipeline_tag": "fill-mask", "widget": [{"text": "P\u00e5 biblioteket kan du [MASK] en bok."}, {"text": "Dette er et [MASK] eksempel."}, {"text": "Av og til kan en spr\u00e5kmodell gi et [MASK] resultat."}, {"text": "Som ansat f\u00e5r du [MASK]... | NbAiLab/notram-bert-norwegian-cased-080321 | null | [
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"no"
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| Results
-------
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fill-mask | transformers | Just for performing some experiments. Do not use.
| {} | NbAiLab/roberta_NCC_des_128 | null | [
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| Just for performing some experiments. Do not use.
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fill-mask | transformers | Just for performing some experiments. Do not use.
| {} | NbAiLab/roberta_NCC_des_128_decayfrom200 | null | [
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"tensorboard",
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#transformers #pytorch #jax #tensorboard #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| Just for performing some experiments. Do not use.
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fill-mask | transformers | Just for performing some experiments. Do not use.
This needed to be restarted at 100k. I am getting memory errors at the end of the epoch. Not really sure why.
Step 2 is therefore on train_2__4. Static learning rate for a while. The first 100k ended at 0.59. This is decent so early. No point in running more epochs h... | {} | NbAiLab/roberta_des_128 | null | [
"transformers",
"jax",
"tensorboard",
"roberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #jax #tensorboard #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| Just for performing some experiments. Do not use.
This needed to be restarted at 100k. I am getting memory errors at the end of the epoch. Not really sure why.
Step 2 is therefore on train_2__4. Static learning rate for a while. The first 100k ended at 0.59. This is decent so early. No point in running more epochs h... | [] | [
"TAGS\n#transformers #jax #tensorboard #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n"
] | [
28
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fill-mask | transformers | Just for performing some experiments. Do not use.
Since the loss seem to start going up, I did have to restore this from 9e945cb0636bde60bec30bd7df5db30f80401cc7 (2 step 600k/200). I am then restarting with warmup decaying from 1e-4.
That did failed. Checked out c94b5bb43b05fc798f9db013d940b05b3b47cd98 instead and re... | {} | NbAiLab/roberta_des_512 | null | [
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#transformers #pytorch #jax #tensorboard #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| Just for performing some experiments. Do not use.
Since the loss seem to start going up, I did have to restore this from 9e945cb0636bde60bec30bd7df5db30f80401cc7 (2 step 600k/200). I am then restarting with warmup decaying from 1e-4.
That did failed. Checked out c94b5bb43b05fc798f9db013d940b05b3b47cd98 instead and re... | [] | [
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fill-mask | transformers | Just for performing some experiments. Do not use.
| {} | NbAiLab/roberta_des_512_4e4 | null | [
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fill-mask | transformers | Just for performing some experiments. Do not use.
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fill-mask | transformers | Just for performing some experiments. Do not use.
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fill-mask | transformers | Just for performing some experiments. Do not use. | {} | NbAiLabArchive/test_NCC_OSCAR_16w_noada | null | [
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fill-mask | transformers | Just for performing some experiments. Do not use. | {} | NbAiLabArchive/test_NCC_OSCAR_style | null | [
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fill-mask | transformers | Just for performing some experiments. Do not use. | {} | NbAiLabArchive/test_NCC_OSCAR_style_98w | null | [
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fill-mask | transformers | Just for performing some experiments. Do not use. | {} | NbAiLabArchive/test_NCC_small_flax | null | [
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fill-mask | transformers | Just for performing some experiments. Do not use. | {} | NbAiLabArchive/test_NCC_small_flax_stream | null | [
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fill-mask | transformers | Just for performing some experiments. Do not use. | {} | NbAiLabArchive/test_NCC_small_flax_stream_100 | null | [
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fill-mask | transformers | Just for performing some experiments. Do not use. | {} | NbAiLabArchive/test_NCC_small_pytorch | null | [
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fill-mask | transformers | Just for performing some experiments. Do not use. | {} | NbAiLabArchive/test_OSCAR_flax | null | [
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fill-mask | transformers | Just for performing some experiments. Do not use. | {} | NbAiLabArchive/test_w4 | null | [
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automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-voxrex-npsc-bokmaal
This model was trained from scratch on the None dataset.
It achieves the following results on... | {"language": ["nb-NO"], "license": "apache-2.0", "tags": ["generated_from_trainer", "automatic-speech-recognition", "NbAiLab/NPSC", "robust-speech-event", false, "nb-NO", "hf-asr-leaderboard"], "datasets": ["NbAiLab/NPSC"], "model-index": [{"name": "wav2vec2-large-voxrex-npsc-bokmaal", "results": [{"task": {"type": "au... | NbAiLab/wav2vec2-large-voxrex-npsc-bokmaal | null | [
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| wav2vec2-large-voxrex-npsc-bokmaal
==================================
This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1311
* Wer: 0.1038
Model description
-----------------
More information needed
Intended uses & limitations
---------... | [
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automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-voxrex-npsc-nynorsk
This model is a fine-tuned version of [KBLab/wav2vec2-large-voxrex](https://huggingface.co/KB... | {"language": ["nn-NO"], "license": "apache-2.0", "tags": ["generated_from_trainer", "automatic-speech-recognition", "NbAiLab/NPSC", "robust-speech-event", "no", "nn-NO", "hf-asr-leaderboard"], "datasets": ["NbAiLab/NPSC"], "model-index": [{"name": "wav2vec2-large-voxrex-npsc-nynorsk", "results": [{"task": {"type": "aut... | NbAiLab/wav2vec2-large-voxrex-npsc-nynorsk | null | [
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| wav2vec2-large-voxrex-npsc-nynorsk
==================================
This model is a fine-tuned version of KBLab/wav2vec2-large-voxrex on the NBAILAB/NPSC - 16K\_MP3\_NYNORSK dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4142
* Wer: 0.1576
Model description
-----------------
More ... | [
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automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-voxrex-npsc
This model is a fine-tuned version of [KBLab/wav2vec2-large-voxrex](https://huggingface.co/KBLab/wav2... | {"license": "cc0-1.0", "tags": ["automatic-speech-recognition", "NbAiLab/NPSC", "generated_from_trainer", "robust-speech-event"], "datasets": ["NbAiLab/NPSC"], "base_model": "KBLab/wav2vec2-large-voxrex", "model-index": [{"name": "wav2vec2-large-voxrex-npsc", "results": []}]} | NbAiLab/wav2vec2-large-voxrex-npsc | null | [
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"license:cc0-1.0",
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"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
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| wav2vec2-large-voxrex-npsc
==========================
This model is a fine-tuned version of KBLab/wav2vec2-large-voxrex on the NBAILAB/NPSC - 16K\_MP3 dataset.
It achieves the following results on the evaluation set:
* Loss: nan
* Wer: 1.0
Model description
-----------------
More information needed
Intended u... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilo... | [
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automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-xls-r-1b-npsc
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2ve... | {"language": ["nb-NO"], "license": "apache-2.0", "tags": ["generated_from_trainer", "automatic-speech-recognition", "NbAiLab/NPSC", "robust-speech-event", false, "nb-NO", "hf-asr-leaderboard"], "datasets": ["NbAiLab/NPSC"], "model-index": [{"name": "wav2vec2-xls-r-1b-npsc-bokmaal", "results": [{"task": {"type": "automa... | NbAiLab/wav2vec2-xls-r-1b-npsc-bokmaal | null | [
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"nb-NO"
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| wav2vec2-xls-r-1b-npsc
======================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-1b on the NbAiLab/NPSC (16K\_mp3\_bokmaal) dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1598
* WER: 0.0966
Model description
-----------------
More information needed
Inte... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilo... | [
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automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-xls-r-300m-npsc-bokmaal
This model was trained from scratch on the None dataset.
It achieves the following results on t... | {"language": ["nb-NO"], "license": "apache-2.0", "tags": ["generated_from_trainer", "automatic-speech-recognition", "NbAiLab/NPSC", "robust-speech-event", false, "nb-NO", "hf-asr-leaderboard"], "datasets": ["NbAiLab/NPSC"], "model-index": [{"name": "wav2vec2-xls-r-300m-npsc-bokmaal", "results": [{"task": {"type": "auto... | NbAiLab/wav2vec2-xls-r-300m-npsc-bokmaal | null | [
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] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #dataset-NbAiLab/NPSC #license-apache-2.0 #model-index #endpoints_compatible #region-us
| wav2vec2-xls-r-300m-npsc-bokmaal
================================
This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1663
* Wer: 0.0932
Model description
-----------------
More information needed
Intended uses & limitations
-------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
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automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-xlsr-1B-NPSC-NN
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2... | {"language": ["nn-NO"], "license": "apache-2.0", "tags": ["generated_from_trainer", "automatic-speech-recognition", "NbAiLab/NPSC", "robust-speech-event", false, "nn-NO", "hf-asr-leaderboard"], "datasets": ["NbAiLab/NPSC"], "model-index": [{"name": "wav2vec2-xlsr-1B-NPSC-NN", "results": [{"task": {"type": "automatic-sp... | NbAiLab/wav2vec2-xlsr-1B-NPSC-NN | null | [
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"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
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#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #dataset-NbAiLab/NPSC #license-apache-2.0 #model-index #endpoints_compatible #region-us
| wav2vec2-xlsr-1B-NPSC-NN
========================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-1b on the NBAILAB/NPSC - 16K\_MP3 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4562
* Wer: 0.1531
Model description
-----------------
More information needed
Intended ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 6e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1... | [
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automatic-speech-recognition | transformers |
# XLS-R-300M-LM - Norwegian
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the Norwegian [NPSC](https://huggingface.co/datasets/NbAiLab/NPSC) dataset.
### Scores without Language Model
Without using a language model, it achieves th... | {"language": ["nb-NO"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "generated_from_trainer", false, "nb-NO", "robust-speech-event", "model_for_talk", "hf-asr-leaderboard"], "datasets": ["NbAiLab/NPSC"], "model-index": [{"name": "XLS-R-300M-LM - Norwegian", "results": [{"task": {"type": "automatic... | NbAiLab/wav2vec2-xlsr-300M-NPSC-LM | null | [
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"automatic-speech-recognition",
"dataset:NbAiLab/NPSC",
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"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"nb-NO"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #dataset-NbAiLab/NPSC #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
# XLS-R-300M-LM - Norwegian
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the Norwegian NPSC dataset.
### Scores without Language Model
Without using a language model, it achieves the following scores on the NPSC Eval set
It achieves the following results on the evaluation set withou... | [
"# XLS-R-300M-LM - Norwegian\r\n\r\nThis model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the Norwegian NPSC dataset.",
"### Scores without Language Model\r\nWithout using a language model, it achieves the following scores on the NPSC Eval set\r\nIt achieves the following results on the evaluation... | [
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automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-xlsr-300M-NPSC-OH
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/... | {"license": "apache-2.0", "tags": ["automatic-speech-recognition", "NbAiLab/NPSC", "generated_from_trainer"], "model-index": [{"name": "wav2vec2-xlsr-300M-NPSC-OH", "results": []}]} | NbAiLab/wav2vec2-xlsr-300M-NPSC-OH | null | [
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"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #NbAiLab/NPSC #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-xlsr-300M-NPSC-OH
==========================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the NBAILAB/NPSC - 16K\_MP3 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1692
* Wer: 0.1663
Model description
-----------------
More information needed
Int... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 7.5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 13\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 64\n* optimizer: Adam with betas=(0.9,0.999) and epsil... | [
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automatic-speech-recognition | transformers |
<!-- 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. -->
# xls-npsc-oh
This model is a fine-tuned version of [KBLab/wav2vec2-large-voxrex](https://huggingface.co/KBLab/wav2vec2-large-voxr... | {"license": "cc0-1.0", "tags": ["automatic-speech-recognition", "NbAiLab/NPSC", "generated_from_trainer"], "datasets": ["npsc"], "model-index": [{"name": "xls-npsc-oh", "results": []}]} | NbAiLab/xls-npsc-oh | null | [
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"license:cc0-1.0",
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] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
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| xls-npsc-oh
===========
This model is a fine-tuned version of KBLab/wav2vec2-large-voxrex on the NBAILAB/NPSC - 48K\_MP3 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2106
* Wer: 0.8586
Model description
-----------------
More information needed
Intended uses & limitations
------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilo... | [
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automatic-speech-recognition | transformers |
<!-- 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. -->
# xls-npsc
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300... | {"license": "apache-2.0", "tags": ["automatic-speech-recognition", "NbAiLab/NPSC", "generated_from_trainer"], "datasets": ["npsc"], "model-index": [{"name": "xls-npsc", "results": []}]} | NbAiLab/xls-npsc | null | [
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"license:apache-2.0",
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"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
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|
# xls-npsc
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the NBAILAB/NPSC - 48K_MP3 dataset.
It achieves the following results on the evaluation set:
- Loss: 3.5006
- Wer: 1.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training... | [
"# xls-npsc\n\nThis model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the NBAILAB/NPSC - 48K_MP3 dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 3.5006\n- Wer: 1.0",
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"## Intended uses & limitations\n\nMore information ... | [
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text-generation | transformers | # Harry potter | {"tags": ["conversational"]} | Necrozma/harrypotterbot | null | [
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text-generation | transformers |
not for use...
technical data | {"language": ["ru"], "widget": [{"text": "\u0421\u043c\u0435\u0440\u0442\u0438 \u043d\u0435\u0442, "}]} | Nehc/adpatres | null | [
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not for use...
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text-generation | transformers |
Start from sberbank-ai/rugpt3small_based_on_gpt2 and finetuning on Govard Phillips Lovecraft texts (russian).
On this moment - only 1 epoch (perplexity falls reasons)
on progress...
| {"language": ["ru"], "metrics": [{"loss": 3.3}, {"perplexity": 25.7528}], "widget": [{"text": "\u041d\u0435\u043c\u044b\u0441\u043b\u0438\u043c\u043e, "}]} | Nehc/gpt2_lovecraft_ru | null | [
"transformers",
"pytorch",
"safetensors",
"gpt2",
"text-generation",
"ru",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"ru"
] | TAGS
#transformers #pytorch #safetensors #gpt2 #text-generation #ru #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
Start from sberbank-ai/rugpt3small_based_on_gpt2 and finetuning on Govard Phillips Lovecraft texts (russian).
On this moment - only 1 epoch (perplexity falls reasons)
on progress...
| [] | [
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text-generation | transformers |
Start from sberbank-ai/rugpt3small_based_on_gpt2 and finetuning on Biblie & preaching (russian).
On this moment - only 1 epoch, 1650 seq length
on progress... | {"language": ["ru"], "metrics": [{"loss": 3.3}, {"perplexity": 25.7528}], "widget": [{"text": "\u0411\u043e\u0433, \u044d\u0442\u043e "}]} | Nehc/gpt2_priest_ru | null | [
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"pytorch",
"safetensors",
"gpt2",
"text-generation",
"ru",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"ru"
] | TAGS
#transformers #pytorch #safetensors #gpt2 #text-generation #ru #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
Start from sberbank-ai/rugpt3small_based_on_gpt2 and finetuning on Biblie & preaching (russian).
On this moment - only 1 epoch, 1650 seq length
on progress... | [] | [
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text-generation | transformers | #zhongli DialoGTP Model | {"tags": ["conversational"]} | Nekoism/Zhongli-Beta | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| #zhongli DialoGTP Model | [] | [
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image-classification | transformers |
# sea_mammals
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingp... | {"tags": ["image-classification", "pytorch", "huggingpics"], "metrics": ["accuracy"]} | Neto71/sea_mammals | null | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# sea_mammals
Autogenerated by HuggingPics️
Create your own image classifier for anything by running the demo on Google Colab.
Report any issues with the demo at the github repo.
## Example Images
#### blue whale
!blue whale
#### dolphin
!dolphin
#### orca whale
!orca whale | [
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"#### dolphin\n\n!dolphin",
"#### orca whale\n\n!orca whale"... | [
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question-answering | transformers | # BERT-Small CORD-19 fine-tuned on SQuAD 2.0
[bert-small-cord19 model](https://huggingface.co/NeuML/bert-small-cord19) fine-tuned on SQuAD 2.0
## Building the model
```bash
python run_squad.py
--model_type bert
--model_name_or_path bert-small-cord19
--do_train
--do_eval
--do_lower_case
--vers... | {} | NeuML/bert-small-cord19-squad2 | null | [
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"jax",
"safetensors",
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"endpoints_compatible",
"has_space",
"region:us"
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#transformers #pytorch #jax #safetensors #bert #question-answering #endpoints_compatible #has_space #region-us
| # BERT-Small CORD-19 fine-tuned on SQuAD 2.0
bert-small-cord19 model fine-tuned on SQuAD 2.0
## Building the model
'''bash
python run_squad.py
--model_type bert
--model_name_or_path bert-small-cord19
--do_train
--do_eval
--do_lower_case
--version_2_with_negative
--train_file train-v2.0.js... | [
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fill-mask | transformers | # BERT-Small fine-tuned on CORD-19 dataset
[BERT L6_H-512_A-8 model](https://huggingface.co/google/bert_uncased_L-6_H-512_A-8) fine-tuned on the [CORD-19 dataset](https://www.semanticscholar.org/cord19).
## CORD-19 data subset
The training data for this dataset is stored as a [Kaggle dataset](https://www.kaggle.com/d... | {} | NeuML/bert-small-cord19 | null | [
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"pytorch",
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"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| # BERT-Small fine-tuned on CORD-19 dataset
BERT L6_H-512_A-8 model fine-tuned on the CORD-19 dataset.
## CORD-19 data subset
The training data for this dataset is stored as a Kaggle dataset. The training
data is a subset of the full corpus, focusing on high-quality, study-design detected articles.
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question-answering | transformers | # BERT-Small fine-tuned on CORD-19 QA dataset
[bert-small-cord19-squad model](https://huggingface.co/NeuML/bert-small-cord19-squad2) fine-tuned on the [CORD-19 QA dataset](https://www.kaggle.com/davidmezzetti/cord19-qa?select=cord19-qa.json).
## CORD-19 QA dataset
The CORD-19 QA dataset is a SQuAD 2.0 formatted list ... | {} | NeuML/bert-small-cord19qa | null | [
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"pytorch",
"jax",
"bert",
"question-answering",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #bert #question-answering #endpoints_compatible #has_space #region-us
| # BERT-Small fine-tuned on CORD-19 QA dataset
bert-small-cord19-squad model fine-tuned on the CORD-19 QA dataset.
## CORD-19 QA dataset
The CORD-19 QA dataset is a SQuAD 2.0 formatted list of question, context, answer combinations covering the CORD-19 dataset.
## Building the model
## Testing the model
Example u... | [
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text2text-generation | transformers |
# Model Trained Using AutoNLP
- Problem type: Summarization
- Model ID: 24135330
- CO2 Emissions (in grams): 155.8470724053265
## Validation Metrics
- Loss: 1.369327425956726
- Rouge1: 52.6656
- Rouge2: 30.5879
- RougeL: 40.1268
- RougeLsum: 47.4438
- Gen Len: 75.4625
## Usage
You can use cURL to access this mode... | {"language": "unk", "tags": "autonlp", "datasets": ["Neuralearn/autonlp-data-Summarization-AutoNLP"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}], "co2_eq_emissions": 155.8470724053265} | Neuralearn/autonlp-Summarization-AutoNLP-24135330 | null | [
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|
# Model Trained Using AutoNLP
- Problem type: Summarization
- Model ID: 24135330
- CO2 Emissions (in grams): 155.8470724053265
## Validation Metrics
- Loss: 1.369327425956726
- Rouge1: 52.6656
- Rouge2: 30.5879
- RougeL: 40.1268
- RougeLsum: 47.4438
- Gen Len: 75.4625
## Usage
You can use cURL to access this mode... | [
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text2text-generation | transformers |
# Test
Hf T5: -95.86687088012695
MTF T5: -67.8558578491211
| {"tags": ["t5-new-failed"]} | NewT5SharedHeadsSharedKeyValues/t5-efficient-base-sh | null | [
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# Test
Hf T5: -95.86687088012695
MTF T5: -67.8558578491211
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] |
text2text-generation | transformers |
# Test
Hf T5:
MTF T5: -80.44100952148438
| {"tags": ["t5-new-hf-not-loaded"]} | NewT5SharedHeadsSharedKeyValues/t5-efficient-base-skv | null | [
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] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #t5 #text2text-generation #t5-new-hf-not-loaded #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Test
Hf T5:
MTF T5: -80.44100952148438
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text2text-generation | transformers |
# Test
Hf T5: -110.35000801086426
MTF T5: -57.58127975463867
| {"tags": ["t5-new-failed"]} | NewT5SharedHeadsSharedKeyValues/t5-efficient-large-sh | null | [
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] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #t5 #text2text-generation #t5-new-failed #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
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# Test
Hf T5: -110.35000801086426
MTF T5: -57.58127975463867
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text2text-generation | transformers |
# Test
Hf T5:
MTF T5: -59.432472229003906
| {"tags": ["t5-new-hf-not-loaded"]} | NewT5SharedHeadsSharedKeyValues/t5-efficient-large-skv | null | [
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] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #t5 #text2text-generation #t5-new-hf-not-loaded #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
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Hf T5:
MTF T5: -59.432472229003906
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text2text-generation | transformers |
# Test
Hf T5: -146.39734268188477
MTF T5: -72.12132263183594
| {"tags": ["t5-new-failed"]} | NewT5SharedHeadsSharedKeyValues/t5-efficient-small-sh | null | [
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] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
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# Test
Hf T5: -146.39734268188477
MTF T5: -72.12132263183594
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text2text-generation | transformers |
# Test
Hf T5:
MTF T5: -277.564697265625
| {"tags": ["t5-new-hf-not-loaded"]} | NewT5SharedHeadsSharedKeyValues/t5-efficient-small-shkv | null | [
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] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #t5 #text2text-generation #t5-new-hf-not-loaded #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Test
Hf T5:
MTF T5: -277.564697265625
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text2text-generation | transformers |
# Test
Hf T5: -149.6728801727295
MTF T5: -74.4166259765625
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text2text-generation | transformers |
# Test
Hf T5:
MTF T5: -138.18275451660156
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Hf T5:
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text2text-generation | transformers |
# Test
Hf T5: -118.6875057220459
MTF T5: -76.85459899902344
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Hf T5: -118.6875057220459
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text2text-generation | transformers |
# Test
Hf T5:
MTF T5: -66.05513000488281
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text-classification | transformers |
# xlm-r-finetuned-toxic-political-tweets-es
This model is based on the pre-trained model [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) and was fine-tuned on a dataset of tweets from members of the [Spanish Congress of the Deputies](https://www.congreso.es/) annotated regarding the level of political tox... | {"language": "es", "license": "apache-2.0"} | Newtral/xlm-r-finetuned-toxic-political-tweets-es | null | [
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|
# xlm-r-finetuned-toxic-political-tweets-es
This model is based on the pre-trained model xlm-roberta-base and was fine-tuned on a dataset of tweets from members of the Spanish Congress of the Deputies annotated regarding the level of political toxicity they generate.
### Inputs
The model has been trained on the tex... | [
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image-classification | transformers |
<!-- 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. -->
# ## labels
- 0: Object
- 1: Recycle
- 2: Non-Recycle
# vit-base-patch16-224
This model is a fine-tuned version of [google/vit-b... | {"license": "apache-2.0", "tags": ["image-classification", "generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "vit-base-patch16-224", "results": []}]} | NhatPham/vit-base-patch16-224-recylce-ft | null | [
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=========
* 0: Object
* 1: Recycle
* 2: Non-Recycle
vit-base-patch16-224
====================
This model is a fine-tuned version of google/vit-base-patch16-224 on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1510
* Accuracy: 0.9443
Model description
----------... | [
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audio-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base-finetuned-ks
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2ve... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["superb"], "metrics": ["accuracy"], "model-index": [{"name": "wav2vec2-base-finetuned-ks", "results": []}]} | NhatPham/wav2vec2-base-finetuned-ks | null | [
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| wav2vec2-base-finetuned-ks
==========================
This model is a fine-tuned version of facebook/wav2vec2-base on the superb dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1258
* Accuracy: 0.9793
Model description
-----------------
More information needed
Intended uses & limit... | [
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automatic-speech-recognition | transformers |
# wav2vec2-large-xlsr-53-french
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) in French using the [Common Voice](https://huggingface.co/datasets/common_voice)
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can ... | {"language": "fr", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-large-xlsr-53-French by Nhut DOAN NGUYEN", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Reco... | Nhut/wav2vec2-large-xlsr-french | null | [
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|
# wav2vec2-large-xlsr-53-french
Fine-tuned facebook/wav2vec2-large-xlsr-53 in French using the Common Voice
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:
## Evaluation
The model can be evaluated as fo... | [
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automatic-speech-recognition | transformers | # Wav2Vec2-Large-XLSR-53-Vietnamese
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Vietnamese using the [Common Voice](https://huggingface.co/datasets/common_voice), [FOSD](https://data.mendeley.com/datasets/k9sxg2twv4/4) and [VIVOS](https://ailab.hcmus.edu.vn/vi... | {"language": "vi", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice", {"FOSD": "https://data.mendeley.com/datasets/k9sxg2twv4/4"}, {"VIVOS": "https://ailab.hcmus.edu.vn/vivos"}], "metrics": ["wer"], "model-index": [{"name": "XLSR W... | Nhut/wav2vec2-large-xlsr-vietnamese | null | [
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| # Wav2Vec2-Large-XLSR-53-Vietnamese
Fine-tuned facebook/wav2vec2-large-xlsr-53 on Vietnamese using the Common Voice, FOSD and VIVOS.
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:
## Evaluation
The model can be... | [
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text-generation | transformers |
# Harry Potter DialoGPT Model | {"tags": ["conversational"]} | NibrasShami/DialopGPT-small-HarryPotter | null | [
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null | null | this project was created to use in wav2vec | {} | Niciu/testtest1 | null | [
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text-generation | transformers |
# My Awesome Laffy | {"tags": ["conversational"]} | NickCavarretta/DialoGPT-small-laffy | null | [
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automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base-timit-demo-colab
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wa... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-base-timit-demo-colab", "results": []}]} | NicoGrageda/wav2vec2-base-timit-demo-colab | null | [
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#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-base-timit-demo-colab
==============================
This model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4519
* Wer: 0.3375
Model description
-----------------
More information needed
Intended uses & limi... | [
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text-generation | transformers |
# Squi | {"tags": ["conversational"]} | Nihwy/DialoSqui | null | [
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text-generation | transformers |
# Harry Potter DialoGPT Model | {"tags": ["conversational"]} | NikhilKrishna/DialoGPT-medium-harrypotter | null | [
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text-classification | transformers |
# **-- EMODa --**
## BERT-model for danish multi-class classification of emotions
Classifies a danish sentence into one of 6 different emotions:
| Danish emotion | Ekman's emotion |
| ----- | ----- |
| 😞 **Afsky** | Disgust |
| 😨 **Frygt** | Fear |
| 😄 **Glæde** | Joy |
|... | {"language": ["da"], "tags": ["sentiment", "emotion", "danish"], "widget": [{"text": "Hold da op! Kan det virkelig passe?"}]} | NikolajMunch/danish-emotion-classification | null | [
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"danish",
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| -- EMODa --
===========
BERT-model for danish multi-class classification of emotions
------------------------------------------------------------
Classifies a danish sentence into one of 6 different emotions:
How to use
==========
or
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null | transformers | # AOT-GAN CelebA-HQ
AOT-GAN is a model that can be used for image in-painting. The CelebA-HQ checkpoint is trained on synthetic human faces, which should make it suitable for touching up and restoring portraits.
This model was generated using [AOT-GAN-for-Inpainting](https://github.com/researchmm/AOT-GAN-for-Inpaintin... | {"tags": ["face-recognition", "face-generation", "face-segmentation", "generative-adversarial-network"], "datasets": ["celeba-hq"], "metrics": ["L1", "PSNR", "SSIM", "FID"]} | NimaBoscarino/aot-gan-celebahq | null | [
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"has_space",
"region:us"
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#transformers #pytorch #face-recognition #face-generation #face-segmentation #generative-adversarial-network #dataset-celeba-hq #endpoints_compatible #has_space #region-us
| # AOT-GAN CelebA-HQ
AOT-GAN is a model that can be used for image in-painting. The CelebA-HQ checkpoint is trained on synthetic human faces, which should make it suitable for touching up and restoring portraits.
This model was generated using AOT-GAN-for-Inpainting, cited as
## Dataset
The CelebA-HQ dataset was cre... | [
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null | transformers | # AOT-GAN Places2
AOT-GAN is a model that can be used for image in-painting. The Places2 checkpoint is trained on a dataset which should make it suitable for touching up and restoring images of landscapes, buildings, and other natural and developed places.
This model was generated using [AOT-GAN-for-Inpainting](https:... | {"tags": ["scene-recognition", "scene-generation", "generative-adversarial-network"], "datasets": ["places2"], "metrics": ["L1", "PSNR", "SSIM", "FID"]} | NimaBoscarino/aot-gan-places2 | null | [
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#transformers #pytorch #scene-recognition #scene-generation #generative-adversarial-network #dataset-places2 #endpoints_compatible #has_space #region-us
| # AOT-GAN Places2
AOT-GAN is a model that can be used for image in-painting. The Places2 checkpoint is trained on a dataset which should make it suitable for touching up and restoring images of landscapes, buildings, and other natural and developed places.
This model was generated using AOT-GAN-for-Inpainting, cited a... | [
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text-generation | transformers |
# Harry Potter DialoGPT Model | {"tags": ["conversational"]} | Ninja5000/DialoGPT-medium-HarryPotter | null | [
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text-generation | transformers |
# DialoGPT-medium-TWEWYJoshua
Another not-so-good AI chatbot. Joshua from the game TWEWY(The World Ends With You).
* Credits to Lynn's Devlab who made the amazing tutorial. | {"tags": ["conversational"]} | Ninja5000/DialoGPT-medium-TWEWYJoshua | null | [
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#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
# DialoGPT-medium-TWEWYJoshua
Another not-so-good AI chatbot. Joshua from the game TWEWY(The World Ends With You).
* Credits to Lynn's Devlab who made the amazing tutorial. | [
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text-generation | transformers |
#LOTR DialoGPT Model | {"tags": ["conversational"]} | Niphredil/DialoGPT-small-lotr | null | [
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text-generation | transformers | license: apache-2.0
---
### Rick DialoGPT Model | {"tags": ["conversational"]} | Nisarg2701/DialoGPT-medium-Rick | null | [
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"text-generation",
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| license: apache-2.0
---
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null | transformers | # ELECTRA
## Introduction
**ELECTRA** is a method for self-supervised language representation learning. It can be used to pre-train transformer networks using relatively little compute. ELECTRA models are trained to distinguish "real" input tokens vs "fake" input tokens generated by another neural network, similar to t... | {} | NlpHUST/electra-base-vn | null | [
"transformers",
"pytorch",
"electra",
"pretraining",
"arxiv:1406.2661",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [
"1406.2661"
] | [] | TAGS
#transformers #pytorch #electra #pretraining #arxiv-1406.2661 #endpoints_compatible #region-us
| # ELECTRA
## Introduction
ELECTRA is a method for self-supervised language representation learning. It can be used to pre-train transformer networks using relatively little compute. ELECTRA models are trained to distinguish "real" input tokens vs "fake" input tokens generated by another neural network, similar to the d... | [
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text-generation | transformers |
# GPT-Neo-small for vietnamese
First GPT for vietnamese
## Model Description
GPT-Neo-vi-small is a transformer model designed using EleutherAI's replication of the GPT-3 architecture.
## Training data
GPT-Neo-vi-smal was trained on the News datasets, a large scale dataset created by from News Website for the purpose o... | {"language": "vi", "tags": ["vi", "vietnamese", "text-generation", "gpt3", "lm", "nlp"], "datasets": ["vietnamese"], "widget": [{"text": "Vi\u1ec7t Nam l\u00e0 qu\u1ed1c gia c\u00f3"}], "pipeline_tag": "text-generation"} | NlpHUST/gpt-neo-vi-small | null | [
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|
# GPT-Neo-small for vietnamese
First GPT for vietnamese
## Model Description
GPT-Neo-vi-small is a transformer model designed using EleutherAI's replication of the GPT-3 architecture.
## Training data
GPT-Neo-vi-smal was trained on the News datasets, a large scale dataset created by from News Website for the purpose o... | [
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text2text-generation | transformers | # T5-EN-VI-BASE:Pretraining Text-To-Text Transfer Transformer for English Vietnamese Translation
# Dataset
The *IWSLT'15 English-Vietnamese* data is used from [Stanford NLP group](https://nlp.stanford.edu/projects/nmt/).
For all experiments the corpus was split into training, development and test set:
| Data set ... | {} | NlpHUST/t5-en-vi-base | null | [
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| T5-EN-VI-BASE:Pretraining Text-To-Text Transfer Transformer for English Vietnamese Translation
==============================================================================================
Dataset
=======
The *IWSLT'15 English-Vietnamese* data is used from Stanford NLP group.
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text2text-generation | transformers | # T5-EN-VI-SMALL:Pretraining Text-To-Text Transfer Transformer for English Vietnamese Translation
# Dataset
The *IWSLT'15 English-Vietnamese* data is used from [Stanford NLP group](https://nlp.stanford.edu/projects/nmt/).
For all experiments the corpus was split into training, development and test set:
| Data set ... | {} | NlpHUST/t5-en-vi-small | null | [
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| T5-EN-VI-SMALL:Pretraining Text-To-Text Transfer Transformer for English Vietnamese Translation
===============================================================================================
Dataset
=======
The *IWSLT'15 English-Vietnamese* data is used from Stanford NLP group.
For all experiments the corpus was s... | [
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text2text-generation | transformers | # T5-SMALL-SUMMARIZATION :Pretraining Text-To-Text Transfer Transformer for Vietnamese Text Summarization
#### Example Using
``` bash
import torch
from transformers import T5ForConditionalGeneration, T5Tokenizer
import torch
if torch.cuda.is_available():
device = torch.device("cuda")
print('There are... | {} | NlpHUST/t5-small-vi-summarization | null | [
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| # T5-SMALL-SUMMARIZATION :Pretraining Text-To-Text Transfer Transformer for Vietnamese Text Summarization
#### Example Using
#### Output
### Contact information
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text2text-generation | transformers | ---
language:
- vi
tags:
- t5
- seq2seq
# Machine translation for vietnamese
## Model Description
T5-vi-en-base is a transformer model for vietnamese machine translation designed using T5 architecture.
## Training data
T5-vi-en-base was trained on 4M sentence pairs (english,vietnamese)
### How to use
```py
from tran... | {} | NlpHUST/t5-vi-en-base | null | [
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"t5",
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#transformers #pytorch #jax #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| ---
language:
- vi
tags:
- t5
- seq2seq
# Machine translation for vietnamese
## Model Description
T5-vi-en-base is a transformer model for vietnamese machine translation designed using T5 architecture.
## Training data
T5-vi-en-base was trained on 4M sentence pairs (english,vietnamese)
### How to use
| [
"# Machine translation for vietnamese",
"## Model Description\nT5-vi-en-base is a transformer model for vietnamese machine translation designed using T5 architecture.",
"## Training data\nT5-vi-en-base was trained on 4M sentence pairs (english,vietnamese)",
"### How to use"
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"TAGS\n#transformers #pytorch #jax #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Machine translation for vietnamese",
"## Model Description\nT5-vi-en-base is a transformer model for vietnamese machine translation designed using T5 architectur... | [
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"TAGS\n#transformers #pytorch #jax #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Machine translation for vietnamese## Model Description\nT5-vi-en-base is a transformer model for vietnamese machine translation designed using T5 architecture.## Trainin... |
text2text-generation | transformers | ---
language:
- vi
tags:
- t5
- seq2seq
# Machine translation for vietnamese
## Model Description
T5-vi-en-small is a transformer model for vietnamese machine translation designed using T5 architecture.
## Training data
T5-vi-en-small was trained on 4M sentence pairs (english,vietnamese)
### How to use
```py
from tr... | {} | NlpHUST/t5-vi-en-small | null | [
"transformers",
"pytorch",
"jax",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| ---
language:
- vi
tags:
- t5
- seq2seq
# Machine translation for vietnamese
## Model Description
T5-vi-en-small is a transformer model for vietnamese machine translation designed using T5 architecture.
## Training data
T5-vi-en-small was trained on 4M sentence pairs (english,vietnamese)
### How to use
| [
"# Machine translation for vietnamese",
"## Model Description\nT5-vi-en-small is a transformer model for vietnamese machine translation designed using T5 architecture.",
"## Training data\nT5-vi-en-small was trained on 4M sentence pairs (english,vietnamese)",
"### How to use"
] | [
"TAGS\n#transformers #pytorch #jax #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Machine translation for vietnamese",
"## Model Description\nT5-vi-en-small is a transformer model for vietnamese machine translation designed using T5 architectu... | [
39,
5,
27,
24,
6
] | [
"TAGS\n#transformers #pytorch #jax #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Machine translation for vietnamese## Model Description\nT5-vi-en-small is a transformer model for vietnamese machine translation designed using T5 architecture.## Traini... |
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