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text-classification | transformers | Language Detection Model for Nepali, English, Hindi and Spanish
Model fine tuned on xlm-roberta-large | {} | Manishl7/xlm-roberta-large-language-detection | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #text-classification #autotrain_compatible #endpoints_compatible #region-us
| Language Detection Model for Nepali, English, Hindi and Spanish
Model fine tuned on xlm-roberta-large | [] | [
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text-generation | transformers |
# Harry Potter DialoGPT Model | {"tags": ["conversational"]} | Manthan/DialoGPT-small-harrypotter | null | [
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null | null | return im
def main():
st.title("Lowlight Enhancement")
st.write("This is a simple lowlight enhancement app with great performance and does not require paired images to train.")
st.write("The model runs at 1000/11 FPS on single GPU/CPU on images with a size of 1200*900*3")
uploaded_file = st.file_up... | {} | Manyman3231/lowlight-enhancement | null | [
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#region-us
| return im
def main():
URL("Lowlight Enhancement")
URL("This is a simple lowlight enhancement app with great performance and does not require paired images to train.")
URL("The model runs at 1000/11 FPS on single GPU/CPU on images with a size of 1200*900*3")
uploaded_file = st.file_uploader("Lowligh... | [] | [
"TAGS\n#region-us \n"
] | [
5
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text2text-generation | 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. -->
# pegasus-samsum
This model is a fine-tuned version of [google/pegasus-cnn_dailymail](https://huggingface.co/google/pegasus-cnn_da... | {"tags": ["generated_from_trainer"], "datasets": ["samsum"], "model-index": [{"name": "pegasus-samsum", "results": []}]} | Mapcar/pegasus-samsum | null | [
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"has_space",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #pegasus #text2text-generation #generated_from_trainer #dataset-samsum #autotrain_compatible #endpoints_compatible #has_space #region-us
| pegasus-samsum
==============
This model is a fine-tuned version of google/pegasus-cnn\_dailymail on the samsum dataset.
It achieves the following results on the evaluation set:
* Loss: 1.4844
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: 1\n* eval\\_batch\\_size: 1\n* seed: 42\n* gradient\\_accumulation\\_steps: 16\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=... | [
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text-generation | transformers |
# Harry Potter DialoGPT Model | {"tags": ["conversational"]} | Mara/DialoGPT-medium-harrypotter | null | [
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] |
text2text-generation | transformers |
# Pegasus XSUM Gigaword
## Model description
Pegasus XSUM model finetuned to Gigaword Summarization task, significantly better performance than pegasus gigaword, but still doesn't match model paper performance.
## Intended uses & limitations
Produces short summaries with the coherence of the XSUM Model
#### How t... | {"language": ["English"], "tags": [], "datasets": ["XSUM", "Gigaword"], "metrics": ["Rouge"]} | Marc/pegasus_xsum_gigaword | null | [
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#transformers #pytorch #pegasus #text2text-generation #dataset-XSUM #dataset-Gigaword #autotrain_compatible #endpoints_compatible #region-us
| Pegasus XSUM Gigaword
=====================
Model description
-----------------
Pegasus XSUM model finetuned to Gigaword Summarization task, significantly better performance than pegasus gigaword, but still doesn't match model paper performance.
Intended uses & limitations
---------------------------
Produces s... | [
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question-answering | transformers |
# ixambert-base-cased finetuned for QA
This is a basic implementation of the multilingual model ["ixambert-base-cased"](https://huggingface.co/ixa-ehu/ixambert-base-cased), fine-tuned on SQuAD v1.1 and an experimental version of SQuAD1.1 in Basque (1/3 size of original SQuAD1.1), that is able to answer basic factual ... | {"language": ["en", "es", "eu"], "datasets": ["squad"], "widget": [{"text": "When was Florence Nightingale born?", "context": "Florence Nightingale, known for being the founder of modern nursing, was born in Florence, Italy, in 1820.", "example_title": "English"}, {"text": "\u00bfPor qu\u00e9 provincias pasa el Tajo?",... | MarcBrun/ixambert-finetuned-squad-eu-en | null | [
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] | null | 2022-03-02T23:29:04+00:00 | [] | [
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|
# ixambert-base-cased finetuned for QA
This is a basic implementation of the multilingual model "ixambert-base-cased", fine-tuned on SQuAD v1.1 and an experimental version of SQuAD1.1 in Basque (1/3 size of original SQuAD1.1), that is able to answer basic factual questions in English, Spanish and Basque.
## Overview... | [
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question-answering | transformers |
# ixambert-base-cased finetuned for QA
This is a basic implementation of the multilingual model ["ixambert-base-cased"](https://huggingface.co/ixa-ehu/ixambert-base-cased), fine-tuned on an experimental version of SQuAD1.1 in Basque (1/3 size of original SQuAD1.1), that is able to answer basic factual questions.
## ... | {"language": ["en", "es", "eu"], "widget": [{"text": "When was Florence Nightingale born?", "context": "Florence Nightingale, known for being the founder of modern nursing, was born in Florence, Italy, in 1820.", "example_title": "English"}, {"text": "\u00bfPor qu\u00e9 provincias pasa el Tajo?", "context": "El Tajo es... | MarcBrun/ixambert-finetuned-squad-eu | null | [
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] | null | 2022-03-02T23:29:04+00:00 | [] | [
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# ixambert-base-cased finetuned for QA
This is a basic implementation of the multilingual model "ixambert-base-cased", fine-tuned on an experimental version of SQuAD1.1 in Basque (1/3 size of original SQuAD1.1), that is able to answer basic factual questions.
## Overview
* Language model: ixambert-base-cased
* Lang... | [
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question-answering | transformers |
# ixambert-base-cased finetuned for QA
This is a basic implementation of the multilingual model ["ixambert-base-cased"](https://huggingface.co/ixa-ehu/ixambert-base-cased), fine-tuned on SQuAD v1.1, that is able to answer basic factual questions in English, Spanish and Basque.
## Overview
* **Language model:** ixam... | {"language": ["en", "es", "eu"], "datasets": ["squad"], "widget": [{"text": "When was Florence Nightingale born?", "context": "Florence Nightingale, known for being the founder of modern nursing, was born in Florence, Italy, in 1820.", "example_title": "English"}, {"text": "\u00bfPor qu\u00e9 provincias pasa el Tajo?",... | MarcBrun/ixambert-finetuned-squad | null | [
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# ixambert-base-cased finetuned for QA
This is a basic implementation of the multilingual model "ixambert-base-cased", fine-tuned on SQuAD v1.1, that is able to answer basic factual questions in English, Spanish and Basque.
## Overview
* Language model: ixambert-base-cased
* Languages: English, Spanish and Basque
*... | [
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question-answering | 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. -->
# distilbert-base-uncased-finetuned-legal_data
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "distilbert-base-uncased-finetuned-legal_data", "results": []}]} | MariamD/distilbert-base-uncased-finetuned-legal_data | null | [
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| distilbert-base-uncased-finetuned-legal\_data
=============================================
This model is a fine-tuned version of distilbert-base-uncased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 6.9101
Model description
-----------------
More information needed
Int... | [
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text-classification | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# bert-model-english
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown datas... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "bert-model-english", "results": []}]} | MarioPenguin/bert-model-english | null | [
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#transformers #tf #bert #text-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| bert-model-english
==================
This model is a fine-tuned version of bert-base-cased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.1408
* Train Sparse Categorical Accuracy: 0.9512
* Validation Loss: nan
* Validation Sparse Categorical Accuracy: 0.0
* Epoch: 4... | [
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text-classification | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# bert-model-english1
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown data... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "bert-model-english1", "results": []}]} | MarioPenguin/bert-model-english1 | null | [
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"license:apache-2.0",
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#transformers #tf #bert #text-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| bert-model-english1
===================
This model is a fine-tuned version of bert-base-cased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.0274
* Train Accuracy: 0.9914
* Validation Loss: 0.3493
* Validation Accuracy: 0.9303
* Epoch: 2
Model description
---------... | [
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text-classification | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# beto_amazon_posneu
This model is a fine-tuned version of [dccuchile/bert-base-spanish-wwm-uncased](https://huggingface.co/dccuchile/be... | {"tags": ["generated_from_keras_callback"], "model-index": [{"name": "beto_amazon_posneu", "results": []}]} | MarioPenguin/beto_amazon_posneu | null | [
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| beto\_amazon\_posneu
====================
This model is a fine-tuned version of dccuchile/bert-base-spanish-wwm-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.1277
* Train Accuracy: 0.9550
* Validation Loss: 0.3439
* Validation Accuracy: 0.8905
* Epoch: 2
M... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'learning\\_rate': 5e-05, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}\n* training\\_precision: float32",
"### Training results",
"### Framework... | [
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text-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. -->
# finetuned-model
This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base-sentiment](https://huggingface.co/cardiff... | {"tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "finetuned-model", "results": []}]} | MarioPenguin/finetuned-model | null | [
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#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| finetuned-model
===============
This model is a fine-tuned version of cardiffnlp/twitter-roberta-base-sentiment on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.8601
* Accuracy: 0.6117
Model description
-----------------
More information needed
Intended uses & limitati... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Traini... | [
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text-classification | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# roberta-model-english
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.... | {"license": "mit", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "roberta-model-english", "results": []}]} | MarioPenguin/roberta-model-english | null | [
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"text-classification",
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#transformers #tf #roberta #text-classification #generated_from_keras_callback #license-mit #autotrain_compatible #endpoints_compatible #region-us
| roberta-model-english
=====================
This model is a fine-tuned version of roberta-base on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.1140
* Train Accuracy: 0.9596
* Validation Loss: 0.2166
* Validation Accuracy: 0.9301
* Epoch: 2
Model description
--------... | [
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null | null | # albertZero
albertZero is a PyTorch model with a prediction head fine-tuned for SQuAD 2.0.
Based on Hugging Face's albert-base-v2, albertZero employs a novel method to speed up fine-tuning. It re-initializes weights of final linear layer in the shared albert transformer block, resulting in a 2% point improvement ... | {} | MarshallHo/albertZero-squad2-base-v2 | null | [
"arxiv:1909.11942",
"arxiv:1810.04805",
"arxiv:1806.03822",
"arxiv:2001.09694",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [
"1909.11942",
"1810.04805",
"1806.03822",
"2001.09694"
] | [] | TAGS
#arxiv-1909.11942 #arxiv-1810.04805 #arxiv-1806.03822 #arxiv-2001.09694 #region-us
| # albertZero
albertZero is a PyTorch model with a prediction head fine-tuned for SQuAD 2.0.
Based on Hugging Face's albert-base-v2, albertZero employs a novel method to speed up fine-tuning. It re-initializes weights of final linear layer in the shared albert transformer block, resulting in a 2% point improvement ... | [
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text-generation | transformers |
# Neo-GPT-Title-Generation-Electric-Car
Title generator based on Neo-GPT 125M fine-tuned on a dataset of 39k url's title. All urls are selected on the TOP 10 google on a list of Keywords about "Electric car" - "Electric car for sale".
# Pipeline example
```python
import pandas as pd
from transformers import AutoMod... | {"language": ["en"], "widget": [{"text": "Tesla range"}, {"text": "Nissan Leaf is"}, {"text": "Tesla is"}, {"text": "The best electric car"}]} | Martian/Neo-GPT-Title-Generation-Electric-Car | null | [
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"region:us"
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] | TAGS
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|
# Neo-GPT-Title-Generation-Electric-Car
Title generator based on Neo-GPT 125M fine-tuned on a dataset of 39k url's title. All urls are selected on the TOP 10 google on a list of Keywords about "Electric car" - "Electric car for sale".
# Pipeline example
# Todo
- Improve the quality of the training sample
- Add mo... | [
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automatic-speech-recognition | transformers | # wav2vec2-large-xlsr-53-breton
The model can be used directly (without a language model) as follows:
```python
import torch
import torchaudio
from datasets import load_dataset
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
lang = "br"
test_dataset = load_dataset("common_voice", lang, split="test[:2%]")
... | {"language": "br", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "model-index": [{"name": "XLSR Wav2Vec2 Breton by Marxav", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Recognition"}, "dataset... | Marxav/wav2vec2-large-xlsr-53-breton | null | [
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| # wav2vec2-large-xlsr-53-breton
The model can be used directly (without a language model) as follows:
The above code leads to the following prediction for the first two samples:
* Prediction: ["neller ket dont a-benn eus netra la vez ser merc'hed evel sich", 'an eil hag egile']
* Reference: ["N'haller ket dont a-benn ... | [
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text-generation | transformers |
# GPT2 - RUS | {"language": "ru", "tags": ["text-generation"]} | Mary222/GPT2_RU_GAME | null | [
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text-generation | transformers |
# GPT2 - RUS | {"language": "ru", "tags": ["text-generation"]} | Mary222/GPT2_standard | null | [
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text-generation | transformers |
# GPT2 - RUS | {"language": "ru", "tags": ["text-generation"]} | Mary222/MADE_AI_Dungeon_model_RUS | null | [
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text-generation | transformers |
# GPT2 - RUS | {"language": "ru", "tags": ["text-generation"]} | Mary222/SBERBANK_RUS | null | [
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text-generation | transformers |
# LSTM
| {"language": "ru", "license": "apache-2.0", "tags": ["text-generation"], "datasets": ["bookcorpus", "wikipedia"]} | Mary222/made-ai-dungeon | null | [
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|
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text2text-generation | 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. -->
# opus-mt-ar-en-finetuned-ar-to-en
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-ar-en](https://huggingface.co/Helsi... | {"tags": ["generated_from_trainer"], "datasets": ["opus_wikipedia"]} | MaryaAI/opus-mt-ar-en-finetuned-ar-to-en | null | [
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"tensorboard",
"marian",
"text2text-generation",
"generated_from_trainer",
"dataset:opus_wikipedia",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #marian #text2text-generation #generated_from_trainer #dataset-opus_wikipedia #autotrain_compatible #endpoints_compatible #region-us
|
# opus-mt-ar-en-finetuned-ar-to-en
This model is a fine-tuned version of Helsinki-NLP/opus-mt-ar-en on the opus_wikipedia dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
... | [
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text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# opus-mt-ar-en-finetunedTanzil-v5-ar-to-en
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-ar-en](https://huggingface.co/He... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "opus-mt-ar-en-finetunedTanzil-v5-ar-to-en", "results": []}]} | MaryaAI/opus-mt-ar-en-finetunedTanzil-v5-ar-to-en | null | [
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| opus-mt-ar-en-finetunedTanzil-v5-ar-to-en
=========================================
This model is a fine-tuned version of Helsinki-NLP/opus-mt-ar-en on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.8101
* Validation Loss: 0.9477
* Train Bleu: 9.3241
* Train Gen Len: 88... | [
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text2text-generation | 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. -->
# opus-mt-en-ar-finetuned-Math-13-10-en-to-ar
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-ar](https://huggingfa... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["syssr_en_ar"], "model-index": [{"name": "opus-mt-en-ar-finetuned-Math-13-10-en-to-ar", "results": []}]} | MaryaAI/opus-mt-en-ar-finetuned-Math-13-10-en-to-ar | null | [
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|
# opus-mt-en-ar-finetuned-Math-13-10-en-to-ar
This model is a fine-tuned version of Helsinki-NLP/opus-mt-en-ar on the syssr_en_ar dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training pr... | [
"# opus-mt-en-ar-finetuned-Math-13-10-en-to-ar\n\nThis model is a fine-tuned version of Helsinki-NLP/opus-mt-en-ar on the syssr_en_ar dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information ... | [
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text2text-generation | 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. -->
# opus-mt-en-ar-finetuned-dummyData-10-10-ar-to-en
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-ar](https://hugg... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["syssr_en_ar"], "metrics": ["bleu"], "model-index": [{"name": "opus-mt-en-ar-finetuned-dummyData-10-10-ar-to-en", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "syssr... | MaryaAI/opus-mt-en-ar-finetuned-dummyData-10-10-ar-to-en | null | [
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| opus-mt-en-ar-finetuned-dummyData-10-10-ar-to-en
================================================
This model is a fine-tuned version of Helsinki-NLP/opus-mt-en-ar on the syssr\_en\_ar dataset.
It achieves the following results on the evaluation set:
* Loss: 1.2046
* Bleu: 7.9946
* Gen Len: 20.0
Model description
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5\n* mixed\\_prec... | [
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text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# MaryaAI/opus-mt-en-ar-finetunedSTEM-v4-en-to-ar
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-ar](https://huggingface... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "MaryaAI/opus-mt-en-ar-finetunedSTEM-v4-en-to-ar", "results": []}]} | MaryaAI/opus-mt-en-ar-finetunedSTEM-v4-en-to-ar | null | [
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#transformers #tf #tensorboard #marian #text2text-generation #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| MaryaAI/opus-mt-en-ar-finetunedSTEM-v4-en-to-ar
===============================================
This model is a fine-tuned version of Helsinki-NLP/opus-mt-en-ar on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 2.0589
* Validation Loss: 5.3227
* Epoch: 0
Model descripti... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': 2e-05, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight\\_decay\\_rate': 0.01}\n* training\\_precision: float32",
... | [
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text2text-generation | 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. -->
# opus-mt-en-ro-finetuned-en-to-ro
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-ro](https://huggingface.co/Helsi... | {"tags": ["generated_from_trainer"], "datasets": ["wmt16"], "metrics": ["bleu"], "model-index": [{"name": "opus-mt-en-ro-finetuned-en-to-ro", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "wmt16", "type": "wmt16", "args": "ro-en"}, "metrics"... | MaryaAI/opus-mt-en-ro-finetuned-en-to-ro | null | [
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] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #marian #text2text-generation #generated_from_trainer #dataset-wmt16 #model-index #autotrain_compatible #endpoints_compatible #region-us
| opus-mt-en-ro-finetuned-en-to-ro
================================
This model is a fine-tuned version of Helsinki-NLP/opus-mt-en-ro on the wmt16 dataset.
It achieves the following results on the evaluation set:
* Loss: 1.2886
* Bleu: 28.1599
* Gen Len: 34.1236
Model description
-----------------
More information... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1\n* mixed\\_prec... | [
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text-generation | transformers |
# Rick and Morty DialoGPT Model | {"tags": ["conversational"]} | MathiasVS/DialoGPT-small-RickAndMorty | null | [
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|
# Rick and Morty DialoGPT Model | [
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] |
text-classification | transformers |
# German BERT for News Classification
This a bert-base-german-cased model finetuned for text classification on german news articles
## Training data
Used the training set from the 10KGNAD dataset (gnad10 on HuggingFace Datasets). | {"language": ["de"], "tags": ["text-classification", "german-news-classification"], "datasets": ["gnad10"], "metrics": ["accuracy", "precision", "recall", "f1"], "model-index": [{"name": "Mathking/bert-base-german-cased-gnad10", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "datas... | laiking/bert-base-german-cased-gnad10 | null | [
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|
# German BERT for News Classification
This a bert-base-german-cased model finetuned for text classification on german news articles
## Training data
Used the training set from the 10KGNAD dataset (gnad10 on HuggingFace Datasets). | [
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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-common_voice-nl-demo
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/fac... | {"language": ["nl"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "common_voice", "generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-common_voice-nl-demo", "results": []}]} | MatsUy/wav2vec2-common_voice-nl-demo | null | [
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"nl"
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#transformers #pytorch #wav2vec2 #automatic-speech-recognition #common_voice #generated_from_trainer #nl #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-common\_voice-nl-demo
==============================
This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the COMMON\_VOICE - NL dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3523
* Wer: 0.2046
Model description
-----------------
More information needed... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* gradient\\_accumulation\\_steps: 8\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=... | [
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token-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. -->
# 4
This model is a fine-tuned version of [GroNLP/bert-base-dutch-cased](https://huggingface.co/GroNLP/bert-base-dutch-cased) on a... | {"tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "4", "results": []}]} | Matthijsvanhof/4 | null | [
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"pytorch",
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"bert",
"token-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
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] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| 4
=
This model is a fine-tuned version of GroNLP/bert-base-dutch-cased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1243
* Precision: 0.5220
* Recall: 0.6137
* F1: 0.5641
* Accuracy: 0.9630
Model description
-----------------
More information needed
Intended uses &... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Training... | [
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token-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. -->
# bert-base-dutch-cased-finetuned-NER
This model is a fine-tuned version of [GroNLP/bert-base-dutch-cased](https://huggingface.co/... | {"tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "bert-base-dutch-cased-finetuned-NER", "results": []}]} | Matthijsvanhof/bert-base-dutch-cased-finetuned-NER | null | [
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"bert",
"token-classification",
"generated_from_trainer",
"autotrain_compatible",
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] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| bert-base-dutch-cased-finetuned-NER
===================================
This model is a fine-tuned version of GroNLP/bert-base-dutch-cased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1078
* Precision: 0.6129
* Recall: 0.6639
* F1: 0.6374
* Accuracy: 0.9688
Model descrip... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Training... | [
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token-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. -->
# bert-base-dutch-cased-finetuned-NER8
This model is a fine-tuned version of [GroNLP/bert-base-dutch-cased](https://huggingface.co... | {"tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "bert-base-dutch-cased-finetuned-NER8", "results": []}]} | Matthijsvanhof/bert-base-dutch-cased-finetuned-NER8 | null | [
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"tensorboard",
"bert",
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"generated_from_trainer",
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] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| bert-base-dutch-cased-finetuned-NER8
====================================
This model is a fine-tuned version of GroNLP/bert-base-dutch-cased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1482
* Precision: 0.4716
* Recall: 0.4359
* F1: 0.4530
* Accuracy: 0.9569
Model des... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
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token-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. -->
# bert-base-dutch-cased-finetuned-mBERT
This model is a fine-tuned version of [distilbert-base-multilingual-cased](https://hugging... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "bert-base-dutch-cased-finetuned-mBERT", "results": []}]} | Matthijsvanhof/bert-base-dutch-cased-finetuned-mBERT | null | [
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"distilbert",
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] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| bert-base-dutch-cased-finetuned-mBERT
=====================================
This model is a fine-tuned version of distilbert-base-multilingual-cased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0898
* Precision: 0.7255
* Recall: 0.7255
* F1: 0.7255
* Accuracy: 0.9758
M... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
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feature-extraction | transformers | This repository shares smaller version of bert-base-multilingual-uncased that keeps only Ukrainian, English, and Russian tokens in the vocabulary.
| Model | Num parameters | Size |
| ----------------------------------------- | -------------- | --------- |
| bert-base-multilingu... | {} | mshamrai/bert-base-ukr-eng-rus-uncased | null | [
"transformers",
"pytorch",
"bert",
"feature-extraction",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #feature-extraction #endpoints_compatible #region-us
| This repository shares smaller version of bert-base-multilingual-uncased that keeps only Ukrainian, English, and Russian tokens in the vocabulary.
Model: bert-base-multilingual-uncased, Num parameters: 167 million, Size: ~650 MB
Model: MaxVortman/bert-base-ukr-eng-rus-uncased, Num parameters: 110 million, Size: ~423 ... | [] | [
"TAGS\n#transformers #pytorch #bert #feature-extraction #endpoints_compatible #region-us \n"
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text-generation | transformers |
#Rick and Morty DialoGPT Model | {"tags": ["conversational"]} | MaxW0748/DialoGPT-small-Rick | 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
|
#Rick and Morty DialoGPT Model | [] | [
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text2text-generation | transformers | hello
| {} | Maya/essai1 | null | [
"transformers",
"pytorch",
"marian",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #marian #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
| hello
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text-generation | transformers |
# Harry Potter DialoGPT Model | {"tags": ["conversational"]} | MayankGupta/DialoGPT-small-harrypotter | 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
|
# Harry Potter DialoGPT Model | [
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"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Harry Potter DialoGPT Model"
] |
automatic-speech-recognition | transformers |
# wav2vec2-large-xlsr-53-Czech
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) in Czech 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 be ... | {"language": "cs", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-large-xlsr-53-Czech by Mehdi Hosseini Moghadam", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speec... | MehdiHosseiniMoghadam/wav2vec2-large-xlsr-53-Czech | null | [
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"xlsr-fine-tuning-week",
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"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"cs"
] | TAGS
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|
# wav2vec2-large-xlsr-53-Czech
Fine-tuned facebook/wav2vec2-large-xlsr-53 in Czech 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 follo... | [
"# wav2vec2-large-xlsr-53-Czech\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 in Czech using the Common Voice\n\nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\n\nThe model can be used directly (without a language model) as follows:",
"## Evaluation\n\nThe model can be ... | [
"TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #cs #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"# wav2vec2-large-xlsr-53-Czech\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 in Czech using the Common ... | [
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automatic-speech-recognition | transformers |
# wav2vec2-large-xlsr-53-Dutch
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) in Dutch 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 be ... | {"language": "nl", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-large-xlsr-53-Dutch by Mehdi Hosseini Moghadam", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speec... | MehdiHosseiniMoghadam/wav2vec2-large-xlsr-53-Dutch | null | [
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"license:apache-2.0",
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"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"nl"
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|
# wav2vec2-large-xlsr-53-Dutch
Fine-tuned facebook/wav2vec2-large-xlsr-53 in Dutch 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 follo... | [
"# wav2vec2-large-xlsr-53-Dutch\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 in Dutch using the Common Voice\n\nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\n\nThe model can be used directly (without a language model) as follows:",
"## Evaluation\n\nThe model can be ... | [
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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 Mehdi Hosseini Moghadam", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Spee... | MehdiHosseiniMoghadam/wav2vec2-large-xlsr-53-French | null | [
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"license:apache-2.0",
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"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"fr"
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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... | [
"# wav2vec2-large-xlsr-53-French \n\nFine-tuned facebook/wav2vec2-large-xlsr-53 in French using the Common Voice\n\nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\n\nThe model can be used directly (without a language model) as follows:",
"## Evaluation\n\nThe model can ... | [
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automatic-speech-recognition | transformers |
# wav2vec2-large-xlsr-53-Georgian
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) in Georgian 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 ... | {"language": "ka", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-large-xlsr-53-Georgian by Mehdi Hosseini Moghadam", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Sp... | MehdiHosseiniMoghadam/wav2vec2-large-xlsr-53-Georgian | null | [
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"ka",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"ka"
] | TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #ka #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
# wav2vec2-large-xlsr-53-Georgian
Fine-tuned facebook/wav2vec2-large-xlsr-53 in Georgian 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 a... | [
"# wav2vec2-large-xlsr-53-Georgian \n\nFine-tuned facebook/wav2vec2-large-xlsr-53 in Georgian using the Common Voice\n\nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\n\nThe model can be used directly (without a language model) as follows:",
"## Evaluation\n\nThe model ... | [
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"# wav2vec2-large-xlsr-53-Georgian \n\nFine-tuned facebook/wav2vec2-large-xlsr-53 in Georgian using the ... | [
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automatic-speech-recognition | transformers |
# wav2vec2-large-xlsr-53-German
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) in German 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 b... | {"language": "de", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-large-xlsr-53-German by Mehdi Hosseini Moghadam", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Spee... | MehdiHosseiniMoghadam/wav2vec2-large-xlsr-53-German | null | [
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"xlsr-fine-tuning-week",
"de",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"de"
] | TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #de #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
# wav2vec2-large-xlsr-53-German
Fine-tuned facebook/wav2vec2-large-xlsr-53 in German 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 fol... | [
"# wav2vec2-large-xlsr-53-German\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 in German using the Common Voice\n\nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\n\nThe model can be used directly (without a language model) as follows:",
"## Evaluation\n\nThe model can b... | [
"TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #de #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"# wav2vec2-large-xlsr-53-German\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 in German using the Commo... | [
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automatic-speech-recognition | transformers |
# wav2vec2-large-xlsr-53-Swedish
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) in Swedish 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": "sv-SE", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-large-xlsr-53-Swedish by Mehdi Hosseini Moghadam", "results": [{"task": {"type": "automatic-speech-recognition", "name": "... | MehdiHosseiniMoghadam/wav2vec2-large-xlsr-53-Swedish | null | [
"transformers",
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"jax",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"sv-SE"
] | TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
# wav2vec2-large-xlsr-53-Swedish
Fine-tuned facebook/wav2vec2-large-xlsr-53 in Swedish 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 f... | [
"# wav2vec2-large-xlsr-53-Swedish\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 in Swedish using the Common Voice\n\nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\n\nThe model can be used directly (without a language model) as follows:",
"## Evaluation\n\nThe model can... | [
"TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"# wav2vec2-large-xlsr-53-Swedish\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 in Swedish using the Common ... | [
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text-generation | transformers |
# GPT-2 Story Generator
## Model description
Generate a short story from an input prompt.
Put the vocab ` [endprompt]` after your input.
Example of an input:
```
A person with a high school education gets sent back into the 1600s and tries to explain science and technology to the people. [endprompt]
```
#### Limi... | {"language": ["en"], "tags": ["gpt2", "text-generation"], "pipeline_tag": "text-generation", "widget": [{"text": "A person with a high school education gets sent back into the 1600s and tries to explain science and technology to the people. [endprompt]"}, {"text": "A kid doodling in a math class accidentally creates th... | Meli/GPT2-Prompt | null | [
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #jax #gpt2 #text-generation #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# GPT-2 Story Generator
## Model description
Generate a short story from an input prompt.
Put the vocab ' [endprompt]' after your input.
Example of an input:
#### Limitations and bias
The data we used to train was collected from reddit, so it could be very biased towards young, white, male demographic.
## Trai... | [
"# GPT-2 Story Generator",
"## Model description\n\nGenerate a short story from an input prompt.\n\nPut the vocab ' [endprompt]' after your input.\n\nExample of an input:",
"#### Limitations and bias\n\nThe data we used to train was collected from reddit, so it could be very biased towards young, white, male de... | [
"TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# GPT-2 Story Generator",
"## Model description\n\nGenerate a short story from an input prompt.\n\nPut the vocab ' [endprompt]' after your input.\n\nExample of an... | [
40,
7,
34,
33,
13
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"TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# GPT-2 Story Generator## Model description\n\nGenerate a short story from an input prompt.\n\nPut the vocab ' [endprompt]' after your input.\n\nExample of an input:#### ... |
text-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. -->
# distilbert-base-uncased-finetuned-cola
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/di... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["matthews_correlation"], "model-index": [{"name": "distilbert-base-uncased-finetuned-cola", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "ar... | MelissaTESSA/distilbert-base-uncased-finetuned-cola | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-cola
======================================
This model is a fine-tuned version of distilbert-base-uncased on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6324
* Matthews Correlation: 0.5207
Model description
-----------------
More informa... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning... | [
56,
101,
5,
44
] | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rat... |
null | null | Gggg | {} | Mervtttt/Ges | null | [
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#region-us
| Gggg | [] | [
"TAGS\n#region-us \n"
] | [
5
] | [
"TAGS\n#region-us \n"
] |
text-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. -->
# distilbert-base-uncased-distilled-clinc
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/d... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["clinc_oos"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-base-uncased-distilled-clinc", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "clinc_oos", "type": "clinc_oos",... | MhF/distilbert-base-uncased-distilled-clinc | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:clinc_oos",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-clinc_oos #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-distilled-clinc
=======================================
This model is a fine-tuned version of distilbert-base-uncased on the clinc\_oos dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2663
* Accuracy: 0.9461
Model description
-----------------
More information... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 48\n* eval\\_batch\\_size: 48\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 9",
"### Traini... | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-clinc_oos #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate:... | [
58,
101,
5,
44
] | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-clinc_oos #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05... |
text-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. -->
# distilbert-base-uncased-finetuned-clinc
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/d... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["clinc_oos"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-base-uncased-finetuned-clinc", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "clinc_oos", "type": "clinc_oos",... | MhF/distilbert-base-uncased-finetuned-clinc | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:clinc_oos",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-clinc_oos #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
| distilbert-base-uncased-finetuned-clinc
=======================================
This model is a fine-tuned version of distilbert-base-uncased on the clinc\_oos dataset.
It achieves the following results on the evaluation set:
* Loss: 0.7703
* Accuracy: 0.9187
Model description
-----------------
More information... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 48\n* eval\\_batch\\_size: 48\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-clinc_oos #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:... | [
65,
101,
5,
44
] | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-clinc_oos #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n... |
text-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. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["emotion"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "distilbert-base-uncased-finetuned-emotion", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "emotion", "type": "emotion... | MhF/distilbert-base-uncased-finetuned-emotion | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-emotion
=========================================
This model is a fine-tuned version of distilbert-base-uncased on the emotion dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2232
* Accuracy: 0.9215
* F1: 0.9218
Model description
-----------------
Mo... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learn... | [
56,
101,
5,
44
] | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_... |
token-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. -->
# xlm-roberta-base-finetuned-panx-all
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["f1"], "model-index": [{"name": "xlm-roberta-base-finetuned-panx-all", "results": []}]} | MhF/xlm-roberta-base-finetuned-panx-all | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-panx-all
===================================
This model is a fine-tuned version of xlm-roberta-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1753
* F1: 0.8520
Model description
-----------------
More information needed
Intended uses & l... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 24\n* eval\\_batch\\_size: 24\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 24\n*... | [
41,
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5,
44
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"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 24\n* eval\... |
token-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. -->
# xlm-roberta-base-finetuned-panx-de-fr
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-robert... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["f1"], "model-index": [{"name": "xlm-roberta-base-finetuned-panx-de-fr", "results": []}]} | MhF/xlm-roberta-base-finetuned-panx-de-fr | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-panx-de-fr
=====================================
This model is a fine-tuned version of xlm-roberta-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1576
* F1: 0.8571
Model description
-----------------
More information needed
Intended uses... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 24\n* eval\\_batch\\_size: 24\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 24\n*... | [
41,
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5,
44
] | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 24\n* eval\... |
token-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. -->
# xlm-roberta-base-finetuned-panx-de
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-b... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["xtreme"], "metrics": ["f1"], "model-index": [{"name": "xlm-roberta-base-finetuned-panx-de", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "xtreme", "type": "xtreme", "args": "PAN-X.de"}, "me... | MhF/xlm-roberta-base-finetuned-panx-de | null | [
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-panx-de
==================================
This model is a fine-tuned version of xlm-roberta-base on the xtreme dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1354
* F1: 0.8621
Model description
-----------------
More information needed
Intended uses & l... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 24\n* eval\\_batch\\_size: 24\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_... | [
55,
101,
5,
44
] | [
"TAGS\n#transformers #pytorch #tensorboard #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: ... |
token-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. -->
# xlm-roberta-base-finetuned-panx-en
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-b... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["xtreme"], "metrics": ["f1"], "model-index": [{"name": "xlm-roberta-base-finetuned-panx-en", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "xtreme", "type": "xtreme", "args": "PAN-X.en"}, "me... | MhF/xlm-roberta-base-finetuned-panx-en | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-panx-en
==================================
This model is a fine-tuned version of xlm-roberta-base on the xtreme dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3856
* F1: 0.6808
Model description
-----------------
More information needed
Intended uses & l... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 24\n* eval\\_batch\\_size: 24\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n... | [
52,
101,
5,
44
] | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* trai... |
token-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. -->
# xlm-roberta-base-finetuned-panx-fr
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-b... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["xtreme"], "metrics": ["f1"], "model-index": [{"name": "xlm-roberta-base-finetuned-panx-fr", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "xtreme", "type": "xtreme", "args": "PAN-X.fr"}, "me... | MhF/xlm-roberta-base-finetuned-panx-fr | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-panx-fr
==================================
This model is a fine-tuned version of xlm-roberta-base on the xtreme dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2736
* F1: 0.8353
Model description
-----------------
More information needed
Intended uses & l... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 24\n* eval\\_batch\\_size: 24\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n... | [
52,
101,
5,
44
] | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* trai... |
token-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. -->
# xlm-roberta-base-finetuned-panx-it
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-b... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["xtreme"], "metrics": ["f1"], "model-index": [{"name": "xlm-roberta-base-finetuned-panx-it", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "xtreme", "type": "xtreme", "args": "PAN-X.it"}, "me... | MhF/xlm-roberta-base-finetuned-panx-it | null | [
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| xlm-roberta-base-finetuned-panx-it
==================================
This model is a fine-tuned version of xlm-roberta-base on the xtreme dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2491
* F1: 0.8213
Model description
-----------------
More information needed
Intended uses & l... | [
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text-generation | transformers |
# feinschwarz
This model is a fine-tuned version of [dbmdz/german-gpt2](https://huggingface.co/dbmdz/german-gpt2). The dataset was compiled from all texts of https://www.feinschwarz.net (as of October 2021). The homepage gathers essayistic texts on theological topics.
The model will be used to explore the challenges... | {"license": "mit", "tags": ["generated_from_trainer", "de"], "model-index": [{"name": "feinesblack", "results": []}]} | Michael711/feinschwarz | null | [
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|
# feinschwarz
This model is a fine-tuned version of dbmdz/german-gpt2. The dataset was compiled from all texts of URL (as of October 2021). The homepage gathers essayistic texts on theological topics.
The model will be used to explore the challenges of text-generating AI for theology with a hands on approach. Can an... | [
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text-generation | transformers |
# Harry Potter DialoGPT Model | {"tags": ["conversational"]} | MichaelTheLearner/DialoGPT-medium-harry | null | [
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text2text-generation | transformers | ## About the model
The model has been trained on a collection of 500k articles with headings. Its purpose is to create a one-line heading suitable for the given article.
Sample code with a WikiNews article:
```python
import torch
from transformers import T5ForConditionalGeneration,T5Tokenizer
device = torch.device(... | {} | Michau/t5-base-en-generate-headline | null | [
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| ## About the model
The model has been trained on a collection of 500k articles with headings. Its purpose is to create a one-line heading suitable for the given article.
Sample code with a WikiNews article:
Result:
| [
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text-generation | transformers |
#harry | {"tags": ["conversational"]} | Mierln/SmartHarry | null | [
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text-generation | transformers |
# Edward Elric DialoGPT Model | {"tags": ["conversational"]} | MightyCoderX/DialoGPT-medium-EdwardElric | null | [
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fill-mask | transformers | kcbert-mlm-finetune | {} | stresscaptor/kcbert-mlm-finetune | null | [
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"bert",
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text-classification | transformers |
# FEEL-IT: Emotion and Sentiment Classification for the Italian Language
## FEEL-IT Python Package
You can find the package that uses this model for emotion and sentiment classification **[here](https://github.com/MilaNLProc/feel-it)** it is meant to be a very simple interface over HuggingFace models.
## License
U... | {"language": "it", "tags": ["sentiment", "emotion", "Italian"]} | MilaNLProc/feel-it-italian-emotion | null | [
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| FEEL-IT: Emotion and Sentiment Classification for the Italian Language
======================================================================
FEEL-IT Python Package
----------------------
You can find the package that uses this model for emotion and sentiment classification here it is meant to be a very simple inte... | [] | [
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text-classification | transformers |
# FEEL-IT: Emotion and Sentiment Classification for the Italian Language
## FEEL-IT Python Package
You can find the package that uses this model for emotion and sentiment classification **[here](https://github.com/MilaNLProc/feel-it)** it is meant to be a very simple interface over HuggingFace models.
## License
U... | {"language": "it", "tags": ["sentiment", "Italian"]} | MilaNLProc/feel-it-italian-sentiment | null | [
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| FEEL-IT: Emotion and Sentiment Classification for the Italian Language
======================================================================
FEEL-IT Python Package
----------------------
You can find the package that uses this model for emotion and sentiment classification here it is meant to be a very simple inte... | [] | [
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text-generation | transformers |
# Slovak GPT-J-1.4B
Slovak GPT-J-1.4B with the whopping `1,415,283,792` parameters is the latest and the largest model released in Slovak GPT-J series. Smaller variants, [Slovak GPT-J-405M](https://huggingface.co/Milos/slovak-gpt-j-405M) and [Slovak GPT-J-162M](https://huggingface.co/Milos/slovak-gpt-j-162M), are stil... | {"language": ["sk"], "license": "gpl-3.0", "tags": ["Slovak GPT-J", "pytorch", "causal-lm"]} | Milos/slovak-gpt-j-1.4B | null | [
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| Slovak GPT-J-1.4B
=================
Slovak GPT-J-1.4B with the whopping '1,415,283,792' parameters is the latest and the largest model released in Slovak GPT-J series. Smaller variants, Slovak GPT-J-405M and Slovak GPT-J-162M, are still available.
Model Description
-----------------
Model is based on GPT-J and ha... | [
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text-generation | transformers |
# Slovak GPT-J-162M
Slovak GPT-J-162M is the first model released in Slovak GPT-J series and the very first publicly available transformer trained predominantly on Slovak corpus. Since the initial release two other models were made public, [Slovak GPT-J-405M](https://huggingface.co/Milos/slovak-gpt-j-405M) and the lar... | {"language": ["sk"], "license": "gpl-3.0", "tags": ["Slovak GPT-J", "pytorch", "causal-lm"]} | Milos/slovak-gpt-j-162M | null | [
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| Slovak GPT-J-162M
=================
Slovak GPT-J-162M is the first model released in Slovak GPT-J series and the very first publicly available transformer trained predominantly on Slovak corpus. Since the initial release two other models were made public, Slovak GPT-J-405M and the largest Slovak GPT-J-1.4B.
Model D... | [
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text-generation | transformers |
# Slovak GPT-J-405M
Slovak GPT-J-405M is the second model released in Slovak GPT-J series after its smaller variant [Slovak GPT-J-162M](https://huggingface.co/Milos/slovak-gpt-j-162M). Since then a larger [Slovak GPT-J-1.4B](https://huggingface.co/Milos/slovak-gpt-j-1.4B) was released.
## Model Description
Model is ba... | {"language": ["sk"], "license": "gpl-3.0", "tags": ["Slovak GPT-J", "pytorch", "causal-lm"]} | Milos/slovak-gpt-j-405M | null | [
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| Slovak GPT-J-405M
=================
Slovak GPT-J-405M is the second model released in Slovak GPT-J series after its smaller variant Slovak GPT-J-162M. Since then a larger Slovak GPT-J-1.4B was released.
Model Description
-----------------
Model is based on GPT-J and has over 405M trainable parameters.
**†** B... | [
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text2text-generation | transformers |
# RuT5Tox | {"language": ["ru"], "license": ["apache-2.0"], "tags": ["t5"], "inference": {"parameters": {"num_beams": 5, "no_repeat_ngram_size": 4}}, "widget": [{"text": "\u0427\u0442\u043e \u044d\u0442\u043e \u0437\u0430 \u0435\u0440\u0443\u043d\u0434\u0430?"}]} | IlyaGusev/rut5_tox | null | [
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text2text-generation | transformers | [DialogLM: Pre-trained Model for Long Dialogue Understanding and Summarization](https://arxiv.org/abs/2109.02492).
## Introduction
DialogLED is a pre-trained model for long dialogue understanding and summarization. It builds on the Longformer-Encoder-Decoder (LED) architecture and uses window-based denoising as the pr... | {} | MingZhong/DialogLED-base-16384 | null | [
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| DialogLM: Pre-trained Model for Long Dialogue Understanding and Summarization.
## Introduction
DialogLED is a pre-trained model for long dialogue understanding and summarization. It builds on the Longformer-Encoder-Decoder (LED) architecture and uses window-based denoising as the pre-training task on a large amount of... | [
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"TAGS\n#transformers #pytorch #led #text2text-generation #arxiv-2109.02492 #autotrain_compatible #endpoints_compatible #region-us \n## Introduction\nDialogLED is a pre-trained model for long dialogue understanding and summarization. It builds on the Longformer-Encoder-Decoder (LED) architecture and uses window-base... |
text2text-generation | transformers | [DialogLM: Pre-trained Model for Long Dialogue Understanding and Summarization](https://arxiv.org/abs/2109.02492).
## Introduction
DialogLED is a pre-trained model for long dialogue understanding and summarization. It builds on the Longformer-Encoder-Decoder (LED) architecture and uses window-based denoising as the pr... | {} | MingZhong/DialogLED-large-5120 | null | [
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| DialogLM: Pre-trained Model for Long Dialogue Understanding and Summarization.
## Introduction
DialogLED is a pre-trained model for long dialogue understanding and summarization. It builds on the Longformer-Encoder-Decoder (LED) architecture and uses window-based denoising as the pre-training task on a large amount of... | [
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text-classification | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# tmp6tsjsfbf
This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) ... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "tmp6tsjsfbf", "results": []}]} | Mingyi/classify_title_subject | null | [
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#transformers #tf #bert #text-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| tmp6tsjsfbf
===========
This model is a fine-tuned version of bert-base-multilingual-cased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.0178
* Train Sparse Categorical Accuracy: 0.9962
* Epoch: 49
Model description
-----------------
This model classifies the ti... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'learning\\_rate': 5e-06, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}\n* training\\_precision: float32",
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token-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. -->
# distilbert-base-uncased-finetuned-ner
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/dis... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["conll2003"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "distilbert-base-uncased-finetuned-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "con... | Minowa/distilbert-base-uncased-finetuned-ner | null | [
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| distilbert-base-uncased-finetuned-ner
=====================================
This model is a fine-tuned version of distilbert-base-uncased on the conll2003 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0596
* Precision: 0.9240
* Recall: 0.9378
* F1: 0.9308
* Accuracy: 0.9838
Model des... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
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text2text-generation | 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. -->
# t5-small-finetuned-ro-to-en
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the wmt16 datas... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["wmt16"], "metrics": ["bleu"], "model-index": [{"name": "t5-small-finetuned-ro-to-en", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "wmt16", "type": "wmt16", "args":... | Mirelle/t5-small-finetuned-ro-to-en | null | [
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| t5-small-finetuned-ro-to-en
===========================
This model is a fine-tuned version of t5-small on the wmt16 dataset.
It achieves the following results on the evaluation set:
* Loss: 1.5877
* Bleu: 13.4499
* Gen Len: 17.5073
Model description
-----------------
More information needed
Intended uses & li... | [
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text2text-generation | 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. -->
# test-finetuned
This model is a fine-tuned version of [yhavinga/t5-v1.1-base-dutch-cnn-test](https://huggingface.co/yhavinga/t5-v... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "test-finetuned", "results": []}]} | Mirjam/test-finetuned | null | [
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| test-finetuned
==============
This model is a fine-tuned version of yhavinga/t5-v1.1-base-dutch-cnn-test on the None dataset.
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
-------------... | [
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text-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. -->
# distilbert-base-uncased-finetuned-cola
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/di... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["matthews_correlation"], "model-index": [{"name": "distilbert-base-uncased-finetuned-cola", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "ar... | MisbaHF/distilbert-base-uncased-finetuned-cola | null | [
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| distilbert-base-uncased-finetuned-cola
======================================
This model is a fine-tuned version of distilbert-base-uncased on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.7134
* Matthews Correlation: 0.5411
Model description
-----------------
More informa... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
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fill-mask | 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. -->
# distilroberta-base-testingSB-testingSB
This model is a fine-tuned version of [MistahCase/distilroberta-base-testingSB](https://h... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "distilroberta-base-testingSB-testingSB", "results": []}]} | MistahCase/distilroberta-base-testingSB-testingSB | null | [
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| distilroberta-base-testingSB-testingSB
======================================
This model is a fine-tuned version of MistahCase/distilroberta-base-testingSB on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.9870
Model description
-----------------
More information needed
I... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
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fill-mask | 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. -->
# distilroberta-base-testingSB
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-bas... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "distilroberta-base-testingSB", "results": []}]} | MistahCase/distilroberta-base-testingSB | null | [
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| distilroberta-base-testingSB
============================
This model is a fine-tuned version of distilroberta-base on a company specific, Danish dataset.
It achieves the following results on the evaluation set:
* Loss: 1.0403
Model description
-----------------
Customer-specific model used to embed asset manage... | [
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text-classification | transformers |
# Model Description
This model is fine-tuning bert-base model on Cola dataset
| {"language": "en", "license": "mit", "tags": ["sequence classification"], "datasets": ["cola"]} | Modfiededition/bert-fine-tuned-cola | null | [
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This model is fine-tuning bert-base model on Cola dataset
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text2text-generation | transformers | ## t5-base-fine-tuned-on-jfleg
T5-base model fine-tuned on the [**JFLEG dataset**](https://huggingface.co/datasets/jfleg) with the objective of **text2text-generation**.
# Model Description:
T5 is an encoder-decoder model pre-trained with a multi-task mixture of unsupervised and supervised tasks and for which each tas... | {} | Modfiededition/t5-base-fine-tuned-on-jfleg | null | [
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] | null | 2022-03-02T23:29:04+00:00 | [
"1910.10683"
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#transformers #tf #t5 #text2text-generation #arxiv-1910.10683 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| ## t5-base-fine-tuned-on-jfleg
T5-base model fine-tuned on the JFLEG dataset with the objective of text2text-generation.
# Model Description:
T5 is an encoder-decoder model pre-trained with a multi-task mixture of unsupervised and supervised tasks and for which each task is converted into a text-to-text format.
.T5 wo... | [
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text-generation | transformers |
# Okabe Rintaro DialoGPT Model | {"tags": ["conversational"]} | ModzabazeR/small-okaberintaro | 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. -->
#
This model is a fine-tuned version of [hf-test/xls-r-dummy](https://huggingface.co/hf-test/xls-r-dummy) on the MOZILLA-FOUNDATI... | {"language": ["ab"], "tags": ["automatic-speech-recognition", "mozilla-foundation/common_voice_7_0", "generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "", "results": []}]} | Mofe/speech-sprint-test | null | [
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|
#
This model is a fine-tuned version of hf-test/xls-r-dummy on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - AB dataset.
It achieves the following results on the evaluation set:
- Loss: 207.6065
- Wer: 1.5484
## Model description
More information needed
## Intended uses & limitations
More information needed
## Tr... | [
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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. -->
#
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on th... | {"language": ["ha"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "mozilla-foundation/common_voice_8_0", "generated_from_trainer", "robust-speech-event", "hf-asr-leaderboard"], "datasets": ["mozilla-foundation/common_voice_8_0"], "model-index": [{"name": "", "results": [{"task": {"type": "automatic... | Mofe/xls-r-hausa-40 | null | [
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... | null | 2022-03-02T23:29:04+00:00 | [] | [
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|
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON\_VOICE\_8\_0 - HA dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4998
* Wer: 0.5153
Model description
-----------------
More information needed
Intended uses & limitations
----------... | [
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token-classification | spacy | | Feature | Description |
| --- | --- |
| **Name** | `en_pipeline` |
| **Version** | `0.0.0` |
| **spaCy** | `>=3.1.0,<3.2.0` |
| **Default Pipeline** | `tok2vec`, `tagger`, `parser`, `ner`, `attribute_ruler`, `lemmatizer` |
| **Components** | `tok2vec`, `tagger`, `parser`, `ner`, `attribute_ruler`, `lemmatizer` |
| **... | {"language": ["en"], "tags": ["spacy", "token-classification"]} | MohaAM/en_pipeline | null | [
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### Label Scheme
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### Accuracy
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text-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. -->
# bertweet-finetuned-rbam
This model is a fine-tuned version of [vinai/bertweet-base](https://huggingface.co/vinai/bertweet-base) ... | {"tags": ["generated_from_trainer"], "metrics": ["f1"], "model-index": [{"name": "bertweet-finetuned-rbam", "results": []}]} | MohammadABH/bertweet-finetuned-rbam | null | [
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"roberta",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
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"region:us"
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#transformers #pytorch #roberta #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| bertweet-finetuned-rbam
=======================
This model is a fine-tuned version of vinai/bertweet-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.3971
* F1: 0.6620
Model description
-----------------
More information needed
Intended uses & limitations
----------... | [
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text-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. -->
# twitter-roberta-base-dec2021_rbam_fine_tuned
This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base-dec2021](htt... | {"tags": ["generated_from_trainer"], "metrics": ["accuracy", "precision", "recall", "f1"], "model-index": [{"name": "twitter-roberta-base-dec2021_rbam_fine_tuned", "results": []}]} | MohammadABH/twitter-roberta-base-dec2021_rbam_fine_tuned | null | [
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| twitter-roberta-base-dec2021\_rbam\_fine\_tuned
===============================================
This model is a fine-tuned version of cardiffnlp/twitter-roberta-base-dec2021 on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.8295
* Accuracy: 0.6777
* Precision: 0.6743
* Recall: ... | [
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text-generation | transformers |
# Harry Potter DialoGPT Model | {"tags": ["conversational"]} | Mohsin272/DialoGPT-medium-harrypotter | null | [
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text-generation | transformers |
名言推論モデル
| {"language": ["ja"]} | Momerio/meigen_generate_Japanese | null | [
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text-generation | transformers |
# Harry Potter DialoGPT Model | {"tags": ["conversational"]} | Mona/DialoGPT-small-harrypotter | null | [
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text-classification | transformers |
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 23044997
- CO2 Emissions (in grams): 4.819872182577655
## Validation Metrics
- Loss: 0.001594889909029007
- Accuracy: 0.9997478885667465
- Macro F1: 0.9991190902836993
- Micro F1: 0.9997478885667465
- Weighted F1: 0.999747673551870... | {"language": "en", "tags": "autonlp", "datasets": ["Monsia/autonlp-data-tweets-classification"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}], "co2_eq_emissions": 4.819872182577655} | Monsia/autonlp-tweets-classification-23044997 | null | [
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|
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 23044997
- CO2 Emissions (in grams): 4.819872182577655
## Validation Metrics
- Loss: 0.001594889909029007
- Accuracy: 0.9997478885667465
- Macro F1: 0.9991190902836993
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text-classification | transformers |
# camembert-fr-covid-tweet-classification
This model is a fine-tune checkpoint of [Yanzhu/bertweetfr-base](https://huggingface.co/Yanzhu/bertweetfr-base), fine-tuned on SST-2.
This model reaches an accuracy of 66.00% on the dev set.
In this dataset, given a tweet, the goal was to infer the underlying topic of the twe... | {"language": ["fr"], "license": "apache-2.0", "tags": ["classification"], "metrics": ["accuracy"], "widget": [{"text": "tchai on est morts. on va se faire vacciner et ils vont contr\u00f4ler comme les marionnettes avec des fils. d'apr\u00e8s les 'ont dit'..."}]} | Monsia/camembert-fr-covid-tweet-classification | null | [
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# camembert-fr-covid-tweet-classification
This model is a fine-tune checkpoint of Yanzhu/bertweetfr-base, fine-tuned on SST-2.
This model reaches an accuracy of 66.00% on the dev set.
In this dataset, given a tweet, the goal was to infer the underlying topic of the tweet by choosing from four topics classes:
- chiffr... | [
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text-classification | transformers | # camembert-fr-covid-tweet-sentiment-classification
This model is a fine-tune checkpoint of [Yanzhu/bertweetfr-base](https://huggingface.co/Yanzhu/bertweetfr-base), fine-tuned on SST-2.
This model reaches an accuracy of 71% on the dev set.
In this dataset, given a tweet, the goal was to infer the underlying topic of th... | {"language": ["fr"], "license": "apache-2.0", "tags": ["classification"], "metrics": ["accuracy"], "widget": [{"text": "tchai on est morts. on va se faire vacciner et ils vont contr\u00f4ler comme les marionnettes avec des fils. d'apr\u00e8s les 'ont dit'..."}]} | data354/camembert-fr-covid-tweet-sentiment-classification | null | [
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| # camembert-fr-covid-tweet-sentiment-classification
This model is a fine-tune checkpoint of Yanzhu/bertweetfr-base, fine-tuned on SST-2.
This model reaches an accuracy of 71% on the dev set.
In this dataset, given a tweet, the goal was to infer the underlying topic of the tweet by choosing from four topics classes:
- 0... | [
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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. -->
# test-model-lg-data
This model is a fine-tuned version of [Monsia/test-model-lg-data](https://huggingface.co/Monsia/test-model-lg... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "test-model-lg-data", "results": []}]} | Monsia/test-model-lg-data | null | [
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"region:us"
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| test-model-lg-data
==================
This model is a fine-tuned version of Monsia/test-model-lg-data on the common\_voice dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3354
* Wer: 0.4150
Model description
-----------------
More information needed
Intended uses & limitations
----... | [
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token-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. -->
# distilbert-base-uncased-finetuned-ner
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/dis... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "distilbert-base-uncased-finetuned-ner", "results": []}]} | Mood/distilbert-base-uncased-finetuned-ner | null | [
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|
# distilbert-base-uncased-finetuned-ner
This model is a fine-tuned version of distilbert-base-uncased on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Tra... | [
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"## Intended uses & limitations\n\nMore information needed",
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text-generation | transformers |
# Nyivae DialoGPT Model | {"tags": ["conversational"]} | MoonlitEtherna/DialoGPT-small-Nyivae | null | [
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zero-shot-classification | transformers | # DeBERTa-v3-base-mnli-fever-anli
## Model description
This model was trained on the MultiNLI, Fever-NLI and Adversarial-NLI (ANLI) datasets, which comprise 763 913 NLI hypothesis-premise pairs. This base model outperforms almost all large models on the [ANLI benchmark](https://github.com/facebookresearch/anli).
The b... | {"language": ["en"], "license": "mit", "tags": ["text-classification", "zero-shot-classification"], "datasets": ["multi_nli", "anli", "fever"], "metrics": ["accuracy"], "pipeline_tag": "zero-shot-classification", "model-index": [{"name": "MoritzLaurer/DeBERTa-v3-base-mnli-fever-anli", "results": [{"task": {"type": "nat... | MoritzLaurer/DeBERTa-v3-base-mnli-fever-anli | null | [
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| DeBERTa-v3-base-mnli-fever-anli
===============================
Model description
-----------------
This model was trained on the MultiNLI, Fever-NLI and Adversarial-NLI (ANLI) datasets, which comprise 763 913 NLI hypothesis-premise pairs. This base model outperforms almost all large models on the ANLI benchmark.
T... | [
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text-classification | transformers | # DeBERTa-v3-base-mnli-fever-docnli-ling-2c
## Model description
This model was trained on 1.279.665 hypothesis-premise pairs from 8 NLI datasets: [MultiNLI](https://huggingface.co/datasets/multi_nli), [Fever-NLI](https://github.com/easonnie/combine-FEVER-NSMN/blob/master/other_resources/nli_fever.md), [LingNLI](https:... | {"language": ["en"], "license": "mit", "tags": ["text-classification", "zero-shot-classification"], "metrics": ["accuracy"], "widget": [{"text": "I first thought that I liked the movie, but upon second thought it was actually disappointing. [SEP] The movie was good."}]} | MoritzLaurer/DeBERTa-v3-base-mnli-fever-docnli-ling-2c | null | [
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| DeBERTa-v3-base-mnli-fever-docnli-ling-2c
=========================================
Model description
-----------------
This model was trained on 1.279.665 hypothesis-premise pairs from 8 NLI datasets: MultiNLI, Fever-NLI, LingNLI and DocNLI (which includes ANLI, QNLI, DUC, CNN/DailyMail, Curation).
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zero-shot-classification | transformers | # DeBERTa-v3-base-mnli-fever-anli
## Model description
This model was trained on the MultiNLI dataset, which consists of 392 702 NLI hypothesis-premise pairs.
The base model is [DeBERTa-v3-base from Microsoft](https://huggingface.co/microsoft/deberta-v3-base). The v3 variant of DeBERTa substantially outperforms previo... | {"language": ["en"], "tags": ["text-classification", "zero-shot-classification"], "metrics": ["accuracy"], "pipeline_tag": "zero-shot-classification"} | MoritzLaurer/DeBERTa-v3-base-mnli | null | [
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| DeBERTa-v3-base-mnli-fever-anli
===============================
Model description
-----------------
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