modelId stringlengths 4 81 | tags list | pipeline_tag stringclasses 17
values | config dict | downloads int64 0 59.7M | first_commit timestamp[ns, tz=UTC] | card stringlengths 51 438k | embedding list |
|---|---|---|---|---|---|---|---|
CohleM/bert-nepali-tokenizer | [] | null | {
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"num_beams... | 0 | null | This model is a downstream optimization of [```vuiseng9/bert-base-squadv1-block-pruning-hybrid-filled-lt```](https://huggingface.co/vuiseng9/bert-base-squadv1-block-pruning-hybrid-filled-lt) using [OpenVINO/NNCF](https://github.com/openvinotoolkit/nncf). Applied optimization includes:
1. NNCF Quantize-Aware Training -... | [
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CohleM/mbert-nepali-tokenizer | [] | null | {
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"num_beams... | 0 | null | This model is a downstream fine-tuning of [```vuiseng9/bert-base-squadv1-block-pruning-hybrid```](https://huggingface.co/vuiseng9/bert-base-squadv1-block-pruning-hybrid). "filled" means unstructured fine-grained sparsified parameters are allowed to learn during fine-tuning. "lt" means distillation of larger model as te... | [
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0.0... |
Coldestadam/Breakout_Mentors_SpongeBob_Model | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
] | text-generation | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
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},
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"no_repeat_ngram_size... | 10 | null | BERT-base tuned for Squadv1.1 is pruned with movement pruning algorithm in hybrid fashion, i.e. 32x32 block for self-attention layers, per-dimension grain size for ffn layers.
```
eval_exact_match = 78.5241
eval_f1 = 86.4138
eval_samples = 10784
```
This model is a replication of [block pruning pa... | [
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ComCom/gpt2-large | [
"pytorch",
"gpt2",
"feature-extraction",
"transformers"
] | feature-extraction | {
"architectures": [
"GPT2Model"
],
"model_type": "gpt2",
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},
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"no_repeat_ngram_size": nul... | 1 | null | This model is a downstream optimization of [```vuiseng9/bert-base-squadv1-pruneofa-90pc-bt```](https://huggingface.co/vuiseng9/bert-base-squadv1-pruneofa-90pc-bt) using [OpenVINO/NNCF](https://github.com/openvinotoolkit/nncf). Applied optimization includes:
1. magnitude sparsification at 0% upon initialization. Custom ... | [
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0.01538623683154583,
... |
ComCom/gpt2-medium | [
"pytorch",
"gpt2",
"feature-extraction",
"transformers"
] | feature-extraction | {
"architectures": [
"GPT2Model"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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"min_length": null,
"no_repeat_ngram_size": nul... | 5 | null | This model is transfer-learning of [bert-base pruneofa 90% sparse](https://huggingface.co/Intel/bert-base-uncased-sparse-90-unstructured-pruneofa) on Squadv1 dataset.
```
eval_exact_match = 80.2933
eval_f1 = 87.6788
eval_samples = 10784
```
# Train
use https://github.com/IntelLabs/Model-Compres... | [
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0.... |
ComCom/gpt2 | [
"pytorch",
"gpt2",
"feature-extraction",
"transformers"
] | feature-extraction | {
"architectures": [
"GPT2Model"
],
"model_type": "gpt2",
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},
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"no_repeat_ngram_size": nul... | 1 | null | This model is a quantized-aware transfer learning of bert-base-uncased on Squadv1 using [OpenVINO/NNCF](https://github.com/openvinotoolkit/nncf). Applied optimization includes:
1. NNCF Quantize-Aware Training - Symmetric 8-bit for both weight and activation on all learnable layers.
2. Custom distillation with fine-tune... | [
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0... |
ComCom-Dev/gpt2-bible-test | [] | null | {
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"num_beams... | 0 | null | This model is a fork of [```csarron/bert-base-uncased-squad-v1```](https://huggingface.co/csarron/bert-base-uncased-squad-v1).
```
eval_exact_match = 80.9082
eval_f1 = 88.2275
eval_samples = 10784
```
# Eval
```bash
export CUDA_VISIBLE_DEVICES=0
OUTDIR=eval-bert-base-squadv1
WORKDIR=transformer... | [
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0.0441... |
Cometasonmi451/Mine | [] | null | {
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},
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"num_beams... | 0 | null | This model is developed with transformers v4.10.3.
# Train
```bash
#!/usr/bin/env bash
export CUDA_VISIBLE_DEVICES=0
OUTDIR=bert-based-uncased-mnli
WORKDIR=transformers/examples/pytorch/text-classification
cd $WORKDIR
nohup python run_glue.py \
--model_name_or_path bert-base-uncased \
--task_name mnli \
... | [
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0.032888... |
cometrain/neurotitle-rugpt3-small | [
"pytorch",
"gpt2",
"text-generation",
"ru",
"en",
"dataset:All-NeurIPS-Papers-Scraper",
"transformers",
"Cometrain AutoCode",
"Cometrain AlphaML",
"license:mit"
] | text-generation | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 20 | null | This model is developed with transformers v4.10.3.
# Train
```bash
#!/usr/bin/env bash
export CUDA_VISIBLE_DEVICES=0
OUTDIR=bert-base-uncased-squad
WORKDIR=transformers/examples/pytorch/question-answering
cd $WORKDIR
nohup python run_qa.py \
--model_name_or_path bert-base-uncased \
--dataset_name squad \
... | [
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... |
Connor/DialoGPT-small-rick | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
"summarization": {
"early_stopping": null,
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"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 7 | null | * A set of unstructured sparse bert-base-uncased models fine-tuned for SQuADv1.
* Tensorflow models are created using ```TFAutoModelForQuestionAnswering.from_pretrained(..., from_pt=True)``` and ```model.save_pretrained(tf_pth)```.
* Observed issue - loss in model translation, discrepancy observed in evaluation between... | [
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-0.03699911758303642,
-0.038441915065050125,
0.056411243975162506,
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0... |
Connor-tech/bert_cn_finetuning | [
"pytorch",
"jax",
"bert",
"text-classification",
"transformers"
] | text-classification | {
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_rep... | 27 | null | * A set of unstructured sparse bert-base-uncased models fine-tuned for SQuADv1.
* Tensorflow models are created using ```TFAutoModelForQuestionAnswering.from_pretrained(..., from_pt=True)``` and ```model.save_pretrained(tf_pth)```.
* Observed issue - loss in model translation, discrepancy observed in evaluation between... | [
-0.021192146465182304,
-0.03699911758303642,
-0.038441915065050125,
0.056411243975162506,
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0.02256220206618309,
-0.006366412620991468,
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0.018800323829054832,
0... |
Connorvr/BrightBot-small | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 7 | null | * A set of unstructured sparse bert-base-uncased models fine-tuned for SQuADv1.
* Tensorflow models are created using ```TFAutoModelForQuestionAnswering.from_pretrained(..., from_pt=True)``` and ```model.save_pretrained(tf_pth)```.
* Observed issue - loss in model translation, discrepancy observed in evaluation between... | [
-0.021192146465182304,
-0.03699911758303642,
-0.038441915065050125,
0.056411243975162506,
0.041444603353738785,
0.02783498913049698,
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0.006471237167716026,
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0.02256220206618309,
-0.006366412620991468,
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0.018800323829054832,
0... |
Connorvr/TeachingGen | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"generated_from_trainer",
"license:mit"
] | text-generation | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 4 | null | * A set of unstructured sparse bert-base-uncased models fine-tuned for SQuADv1.
* Tensorflow models are created using ```TFAutoModelForQuestionAnswering.from_pretrained(..., from_pt=True)``` and ```model.save_pretrained(tf_pth)```.
* Observed issue - loss in model translation, discrepancy observed in evaluation between... | [
-0.021192146465182304,
-0.03699911758303642,
-0.038441915065050125,
0.056411243975162506,
0.041444603353738785,
0.02783498913049698,
-0.017044460400938988,
0.006471237167716026,
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0.02256220206618309,
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0.018800323829054832,
0... |
ConstellationBoi/Oop | [] | null | {
"architectures": null,
"model_type": null,
"task_specific_params": {
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"max_length": null
},
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"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams... | 0 | null | * A set of unstructured sparse bert-base-uncased models fine-tuned for SQuADv1.
* Tensorflow models are created using ```TFAutoModelForQuestionAnswering.from_pretrained(..., from_pt=True)``` and ```model.save_pretrained(tf_pth)```.
* Observed issue - loss in model translation, discrepancy observed in evaluation between... | [
-0.021192146465182304,
-0.03699911758303642,
-0.038441915065050125,
0.056411243975162506,
0.041444603353738785,
0.02783498913049698,
-0.017044460400938988,
0.006471237167716026,
-0.03251911699771881,
0.02256220206618309,
-0.006366412620991468,
-0.012491562403738499,
0.018800323829054832,
0... |
Contrastive-Tension/BERT-Base-CT-STSb | [
"pytorch",
"tf",
"jax",
"bert",
"feature-extraction",
"transformers"
] | feature-extraction | {
"architectures": [
"BertModel"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": nul... | 5 | null | ### Reproducibility
```bash
# 1. install nncf
# checkout nncf 9c2845eeb38b4ab1b6d4ca19e31a1886e5bdf17c
# patch b/nncf/torch/sparsity/magnitude/algo.py
def sparsify_params(self):
from collections import OrderedDict
sparse_sd = OrderedDict()
with torch.no_grad():
for sparse_i... | [
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0.05401388928294182,
0.008343237452208996,
0.007106047589331865,
0.056... |
Contrastive-Tension/BERT-Base-CT | [
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 16 | null | This model is developed with transformers v4.9.1.
```
m = 0.8444
eval_samples = 9815
mm = 0.8495
eval_samples = 9832
```
# Train
```bash
#!/usr/bin/env bash
export CUDA_VISIBLE_DEVICES=0
OUTDIR=bert-mnli
NEPOCH=3
WORKDIR=transformers/examples/pytorch/text-classification
cd $W... | [
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0... |
Contrastive-Tension/BERT-Base-Swe-CT-STSb | [
"pytorch",
"tf",
"jax",
"bert",
"feature-extraction",
"transformers"
] | feature-extraction | {
"architectures": [
"BertModel"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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"max_length": null,
"min_length": null,
"no_repeat_ngram_size": nul... | 126 | null | This model is developed with transformers v4.13 with minor patch in this [fork](https://github.com/vuiseng9/transformers/tree/pegasus-v4p13).
# Setup
```bash
git clone https://github.com/vuiseng9/transformers
cd transformers
git checkout pegasus-v4p13 && git reset --hard 41eeb07
# installation, set summarization depen... | [
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0.008230935782194138,
0.0... |
Contrastive-Tension/BERT-Distil-CT-STSb | [
"pytorch",
"tf",
"distilbert",
"feature-extraction",
"transformers"
] | feature-extraction | {
"architectures": [
"DistilBertModel"
],
"model_type": "distilbert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngra... | 1 | null | This model is developed with transformers v4.13 with minor patch in this [fork](https://github.com/vuiseng9/transformers/tree/pegasus-v4p13).
# Setup
```bash
git clone https://github.com/vuiseng9/transformers
cd transformers
git checkout pegasus-v4p13 && git reset --hard 41eeb07
# installation, set summarization depen... | [
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0.0077806939370930195,
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0.01790281943976879,
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0.04887937754392624,
0.030804982408881187,
-0.013071158900856972,
0.005755850113928318,
... |
Contrastive-Tension/BERT-Distil-NLI-CT | [
"pytorch",
"tf",
"distilbert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"DistilBertForMaskedLM"
],
"model_type": "distilbert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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"max_length": null,
"min_length": null,
"no_repea... | 6 | null | This model is developed with transformers v4.13 with minor patch in this [fork](https://github.com/vuiseng9/transformers/tree/pegasus-v4p13).
# Setup
```bash
git clone https://github.com/vuiseng9/transformers
cd transformers
git checkout pegasus-v4p13 && git reset --hard 3db4b452
# installation, set summarization depe... | [
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0.0... |
Contrastive-Tension/BERT-Large-CT-STSb | [
"pytorch",
"tf",
"jax",
"bert",
"feature-extraction",
"transformers"
] | feature-extraction | {
"architectures": [
"BertModel"
],
"model_type": "bert",
"task_specific_params": {
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},
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"min_length": null,
"no_repeat_ngram_size": nul... | 7 | null | ---
language: en
datasets:
- librispeech_asr
tags:
- audio
- automatic-speech-recognition
license: apache-2.0
---
# Wav2Vec2-Base-100h
This is a fork of [```facebook/wav2vec2-base-100h```](https://huggingface.co/facebook/wav2vec2-base-100h)
### Changes & Notes
1. Document reproducible evaluation (below) to new trans... | [
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Contrastive-Tension/BERT-Large-NLI-CT | [
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"bert",
"fill-mask",
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] | fill-mask | {
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"no_repeat_ngram_size... | 15 | null | ## TrOCR (small-sized model, fine-tuned on Synthetic Math Expression Dataset)
TrOCR model fine-tuned on the Synthetic Math Expression Dataset. It was introduced in the paper [TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models](https://arxiv.org/abs/2109.10282) by Li et al. and first released... | [
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Contrastive-Tension/RoBerta-Large-CT-STSb | [
"pytorch",
"tf",
"jax",
"roberta",
"feature-extraction",
"transformers"
] | feature-extraction | {
"architectures": [
"RobertaModel"
],
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"no_repeat_ngram_size... | 5 | null | ---
language:
- ja
license: apache-2.0
tags:
- audio
- automatic-speech-recognition
- speech
datasets:
- Japanese accent datasets
metrics:
- wer
# Optional. Add this if you want to encode your eval results in a structured way.
model-index:
- name: Wav2vec2 Accent Japanese
results:
- task:
type: Speech Rec... | [
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Cooker/cicero-similis | [] | null | {
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"num_beams... | 0 | null | ---
language: ja
datasets:
- common_voice
metrics:
- wer
tags:
- audio
- automatic-speech-recognition
- speech
- xlsr-fine-tuning-week
license: apache-2.0
model-index:
- name: XLSR Wav2Vec2 Japanese Hiragana by Chien Vu
results:
- task:
name: Speech Recognition
type: automatic-speech-recognition
dat... | [
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Cool/Demo | [] | null | {
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"num_beams... | 0 | null | ---
language: ja
datasets:
- common_voice
metrics:
- wer
tags:
- audio
- automatic-speech-recognition
- speech
- xlsr-fine-tuning-week
license: apache-2.0
model-index:
- name: XLSR Wav2Vec2 Japanese by Chien Vu
results:
- task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
... | [
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Coolhand/Abuela | [
"en",
"image_restoration",
"superresolution",
"license:mit"
] | null | {
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"num_beams... | 0 | null | ---
license: apache-2.0
language:
- ja
tags:
- automatic-speech-recognition
- common-voice
- hf-asr-leaderboard
- ja
- robust-speech-event
datasets:
- mozilla-foundation/common_voice_7_0
model-index:
- name: wav2vec2-xls-r-1b
results:
- task:
name: Speech Recognition
type: automatic-speech-recognition
... | [
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Coolhand/Sentiment | [] | null | {
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"num_beams... | 0 | null | ---
license: apache-2.0
language:
- ja
tags:
- automatic-speech-recognition
- common-voice
- hf-asr-leaderboard
- ja
- robust-speech-event
datasets:
- mozilla-foundation/common_voice_8_0
model-index:
- name: wav2vec2-large-xlsr-53-ja
results:
- task:
name: Speech Recognition
type: automatic-speech-recog... | [
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CopymySkill/DialoGPT-medium-atakan | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | {
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"no_repeat_ngram_size... | 7 | null | ---
license: apache-2.0
language:
- ja
tags:
- automatic-speech-recognition
- common-voice
- hf-asr-leaderboard
- ja
- robust-speech-event
datasets:
- mozilla-foundation/common_voice_8_0
model-index:
- name: wav2vec2-xls-r-1b
results:
- task:
name: Speech Recognition
type: automatic-speech-recognition
... | [
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Corvus/DialoGPT-medium-CaptainPrice-Extended | [
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"text-generation",
"transformers",
"conversational"
] | conversational | {
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"no_repeat_ngram_size... | 7 | null | Fine-Tuned MarianMT translation model for translating text from English to Dutch. Checkpoint of pre-trained model = Helsinki-NLP/opus-mt-en-nl.
Trained using custom training loop with PyTorch on Colab for 2 epochs. Link to the GitHub repo containing Google Colab notebook: https://github.com/vanadnarayane26/Maverick_2.... | [
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Corvus/DialoGPT-medium-CaptainPrice | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | {
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"no_repeat_ngram_size... | 7 | null | Fine-Tuned MarianMT translation model for translating text from English to Italian.
Checkpoint of pre-trained model = Helsinki-NLP/opus-mt-en-it.
Trained using custom training loop with PyTorch on Colab for 2 epochs.
Link to the GitHub repo containing Google Colab notebook: https://github.com/vanadnarayane26/Maverick... | [
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CouchCat/ma_ner_v6_distil | [
"pytorch",
"distilbert",
"token-classification",
"en",
"transformers",
"ner",
"license:mit",
"autotrain_compatible"
] | token-classification | {
"architectures": [
"DistilBertForTokenClassification"
],
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... | 6 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- bleu
model-index:
- name: t5-small-finetuned-no_paragraph-to-paragraph
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then re... | [
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CouchCat/ma_ner_v7_distil | [
"pytorch",
"distilbert",
"token-classification",
"en",
"transformers",
"ner",
"license:mit",
"autotrain_compatible"
] | token-classification | {
"architectures": [
"DistilBertForTokenClassification"
],
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... | 13 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- bleu
model-index:
- name: t5-small-finetuned-no_paragraph-to-yes_paragraph-2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, t... | [
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Craig/paraphrase-MiniLM-L6-v2 | [
"pytorch",
"bert",
"arxiv:1908.10084",
"sentence-transformers",
"feature-extraction",
"sentence-similarity",
"transformers",
"license:apache-2.0"
] | feature-extraction | {
"architectures": [
"BertModel"
],
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},
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"no_repeat_ngram_size": nul... | 1,026 | 2021-07-10T13:36:22Z | ---
language: id
tags:
- indonesian-roberta-base-sentiment-classifier
license: mit
datasets:
- indonlu
widget:
- text: "Jangan sampai saya telpon bos saya ya!"
---
## Indonesian RoBERTa Base Sentiment Classifier
Indonesian RoBERTa Base Sentiment Classifier is a sentiment-text-classification model based on the [... | [
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Culmenus/IceBERT-finetuned-ner | [
"pytorch",
"tensorboard",
"roberta",
"token-classification",
"dataset:mim_gold_ner",
"transformers",
"generated_from_trainer",
"license:gpl-3.0",
"model-index",
"autotrain_compatible"
] | token-classification | {
"architectures": [
"RobertaForTokenClassification"
],
"model_type": "roberta",
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},
"summarization": {
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"max_length": null,
"min_length": null,
"no_... | 5 | null | ---
language: lo
tags:
- lao-roberta-base-pos-tagger
license: mit
widget:
- text: "ຮ້ອງ ມ່ວນ ແທ້ ສຽງດີ ອິຫຼີ"
---
## Lao RoBERTa Base POS Tagger
Lao RoBERTa Base POS Tagger is a part-of-speech token-classification model based on the [RoBERTa](https://arxiv.org/abs/1907.11692) model. The model was originally the p... | [
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Culmenus/opus-mt-de-is-finetuned-de-to-is_35g65cc_2 | [] | null | {
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"num_beams... | 0 | null | ---
language: su
tags:
- sundanese-roberta-base-emotion-classifier
license: mit
widget:
- text: "Wah, éta gélo, keren pisan!"
---
## Sundanese RoBERTa Base Emotion Classifier
Sundanese RoBERTa Base Emotion Classifier is an emotion-text-classification model based on the [RoBERTa](https://arxiv.org/abs/1907.11692) ... | [
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Daltcamalea01/Camaleaodalt | [] | null | {
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"num_beams... | 0 | null | ---
language: en
datasets:
- speechcolab/gigaspeech
---
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Davlan/byt5-base-yor-eng-mt | [
"pytorch",
"t5",
"text2text-generation",
"arxiv:2103.08647",
"transformers",
"autotrain_compatible"
] | text2text-generation | {
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"no_repeat_n... | 12 | null |
---
language:
- de
license: apache-2.0
library_name: transformers
tags:
- part-of-speech
- token-classification
datasets:
- universal_dependencies
metrics:
- accuracy
model-index:
- name: xlm-roberta-base-ft-udpos28-de
results:
- task:
type: token-classification
name: Part-of-Speech Tagging
datas... | [
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Davlan/distilbert-base-multilingual-cased-masakhaner | [
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"tf",
"distilbert",
"token-classification",
"arxiv:2103.11811",
"transformers",
"autotrain_compatible"
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... | 16 | null |
---
language:
- el
license: apache-2.0
library_name: transformers
tags:
- part-of-speech
- token-classification
datasets:
- universal_dependencies
metrics:
- accuracy
model-index:
- name: xlm-roberta-base-ft-udpos28-el
results:
- task:
type: token-classification
name: Part-of-Speech Tagging
datas... | [
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Davlan/distilbert-base-multilingual-cased-ner-hrl | [
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"token-classification",
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"has_space"
] | token-classification | {
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... | 123,856 | null |
---
language:
- en
license: apache-2.0
library_name: transformers
tags:
- part-of-speech
- token-classification
datasets:
- universal_dependencies
metrics:
- accuracy
model-index:
- name: xlm-roberta-base-ft-udpos28-en
results:
- task:
type: token-classification
name: Part-of-Speech Tagging
datas... | [
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Davlan/mt5_base_eng_yor_mt | [
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"text2text-generation",
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"transformers",
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] | text2text-generation | {
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"no_repeat... | 2 | null |
---
language:
- fro
license: apache-2.0
library_name: transformers
tags:
- part-of-speech
- token-classification
datasets:
- universal_dependencies
metrics:
- accuracy
model-index:
- name: xlm-roberta-base-ft-udpos28-fro
results:
- task:
type: token-classification
name: Part-of-Speech Tagging
dat... | [
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Davlan/xlm-roberta-base-finetuned-igbo | [
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"no_repe... | 68 | null |
---
language:
- hi
license: apache-2.0
library_name: transformers
tags:
- part-of-speech
- token-classification
datasets:
- universal_dependencies
metrics:
- accuracy
model-index:
- name: xlm-roberta-base-ft-udpos28-hi
results:
- task:
type: token-classification
name: Part-of-Speech Tagging
datas... | [
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Davlan/xlm-roberta-base-masakhaner | [
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"autotrain_compatible"
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... | 3 | null |
---
language:
- lzh
license: apache-2.0
library_name: transformers
tags:
- part-of-speech
- token-classification
datasets:
- universal_dependencies
metrics:
- accuracy
model-index:
- name: xlm-roberta-base-ft-udpos28-lzh
results:
- task:
type: token-classification
name: Part-of-Speech Tagging
dat... | [
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Davlan/xlm-roberta-base-ner-hrl | [
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"transformers",
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... | 760 | null |
---
language:
- mr
license: apache-2.0
library_name: transformers
tags:
- part-of-speech
- token-classification
datasets:
- universal_dependencies
metrics:
- accuracy
model-index:
- name: xlm-roberta-base-ft-udpos28-mr
results:
- task:
type: token-classification
name: Part-of-Speech Tagging
datas... | [
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Dbluciferm3737/U | [] | null | {
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---
language:
- sl
license: apache-2.0
library_name: transformers
tags:
- part-of-speech
- token-classification
datasets:
- universal_dependencies
metrics:
- accuracy
model-index:
- name: xlm-roberta-base-ft-udpos28-sl
results:
- task:
type: token-classification
name: Part-of-Speech Tagging
datas... | [
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DeBERTa/deberta-v2-xxlarge | [] | null | {
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---
language:
- sr
license: apache-2.0
library_name: transformers
tags:
- part-of-speech
- token-classification
datasets:
- universal_dependencies
metrics:
- accuracy
model-index:
- name: xlm-roberta-base-ft-udpos28-sr
results:
- task:
type: token-classification
name: Part-of-Speech Tagging
datas... | [
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Declan/CNN_model_v7 | [] | null | {
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"num_beams... | 0 | null | ---
language: "en"
tags:
- dstc10
- knowledge title-body validation
widget:
- text: "Can you accommodate large groups? It does not offer free WiFi."
- text: "Is there a gym on site? It does not have an onsite fitness center."
---
This is the model used for knowledge clustering where we feed title-body pair and the clas... | [
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Declan/CNN_model_v8 | [
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"transformers",
"autotrain_compatible"
] | fill-mask | {
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"no_repeat_ngram_size... | 3 | null | ---
language: "en"
tags:
- dstc10
- knowledge cluster classifier
widget:
- text: "oh and we'll mi thing uh is there bike clo ars or bike crac where i can park my thee"
- text: "oh and one more thing uhhh is there bike lockers or a bike rack where i can park my bike"
- text: "ni yeah that sounds great ummm dold you have... | [
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Declan/ChicagoTribune_model_v1 | [
"pytorch",
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"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
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"no_repeat_ngram_size... | 3 | null | ---
language: "en"
tags:
- dstc10
widget:
- text: "Can you accommodate large [MASK] ?"
---
# Goal
This Bert model is trained using DSTC9 training + validation data for dialogue modeling purpose.
Data link: https://github.com/alexa/alexa-with-dstc9-track1-dataset
Credit: Shuhan Yuan, Wilson Tam | [
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Declan/ChicagoTribune_model_v2 | [
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"no_repeat_ngram_size... | 7 | null | ---
language: "en"
tags:
- dstc9
widget:
- text: "Yes, I'm going to be in Chinatown, San Francisco and am looking"
- text: "Can you find me one that is in the"
---
This GPT2 model is trained using DSTC9 data for dialogue modeling purpose.
Data link: https://github.com/alexa/alexa-with-dstc9-track1-dataset
Credit: Ji... | [
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---
language: tr
widget:
- text: "Mustafa Kemal Atatürk 19 Mayıs 1919'da Samsun'a çıktı."
---
# Turkish Named Entity Recognition (NER) Model
## This repository is cloned from https://huggingface.co/akdeniz27/bert-base-turkish-cased-ner. This is the tensorflow version.
This model is the fine-tuned model of "dbmdz/b... | [
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"no_repeat_ngram_size... | 7 | null | ---
language: ar
datasets:
- tydiqa
widget:
- text: "ما هو نظام الحكم في لبنان؟"
context: "لبنان أو (رسميا: الجمهورية اللبنانية)، هي دولة عربية واقعة في الشرق الأوسط في غرب القارة الآسيوية. تحدها سوريا من الشمال و الشرق، و فلسطين المحتلة - إسرائيل من الجنوب، وتطل من جهة الغرب على البحر الأبيض المتوسط. هو بلد ديمقراطي... | [
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Declan/NPR_model_v8 | [
"pytorch",
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"no_repeat_ngram_size... | 3 | null | ---
tags:
- conversational
---
# DialoGPT Trained on the Speech of Fox Mulder from The X-Files | [
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Declan/NewYorkPost_model_v1 | [] | null | {
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"num_beams... | 0 | null | ---
language:
- nl
tags:
- Named Entity Recognition
- xlm-roberta
datasets:
- conll2002
metrics:
- f1: 90.57
---
# XLM-RoBERTa base ConLL-2002 Dutch
XLM-Roberta base model finetuned on ConLL-2002 Dutch train set, which is a Named Entity Recognition dataset containing the following classes: PER, LOC, ORG and MISC.... | [
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Declan/Reuters_model_v4 | [
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"no_repeat_ngram_size... | 3 | null | Pretrained on:
* Masked amino acid modeling
Please see our [main model](https://huggingface.co/wukevin/tcr-bert) for additional details. | [
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0.007740711327642202,
-0.024706130847334862,
0.02264910377562046,
0.007766369730234146,
0.02239241451025009,
-0.015082881785929203,
0.0671... |
Declan/Reuters_model_v5 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 3 | null | # TCR transformer model
See our full [codebase](https://github.com/wukevin/tcr-bert) and our [preprint](https://www.biorxiv.org/content/10.1101/2021.11.18.469186v1) for more information.
This model is on:
- Masked language modeling (masked amino acid or MAA modeling)
- Classification across antigen labels from PIRD
... | [
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DeepPavlov/rubert-base-cased | [
"pytorch",
"jax",
"bert",
"feature-extraction",
"ru",
"arxiv:1905.07213",
"transformers",
"has_space"
] | feature-extraction | {
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"BertModel"
],
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},
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"min_length": null,
"no_repeat_ngram_size": nul... | 148,127 | null |
# Delish v6 (GPT-Neo 1.3B)
This model is from the DelishBot project.
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... |
DeepPavlov/xlm-roberta-large-en-ru-mnli | [
"pytorch",
"xlm-roberta",
"text-classification",
"en",
"ru",
"dataset:glue",
"dataset:mnli",
"transformers",
"xlm-roberta-large",
"xlm-roberta-large-en-ru",
"xlm-roberta-large-en-ru-mnli",
"has_space"
] | text-classification | {
"architectures": [
"XLMRobertaForSequenceClassification"
],
"model_type": "xlm-roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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"length_penalty": null,
"max_length": null,
"min_length": null,
... | 227 | null |
# GPT NEO 350M
This hosts the pulled 350M that Eleuther removed. I am keeping it 😎 | [
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0.... |
DeividasM/wav2vec2-large-xlsr-53-lithuanian | [
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"lt",
"dataset:common_voice",
"transformers",
"audio",
"speech",
"xlsr-fine-tuning-week",
"license:apache-2.0",
"model-index"
] | automatic-speech-recognition | {
"architectures": [
"Wav2Vec2ForCTC"
],
"model_type": "wav2vec2",
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},
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"no_repeat_ngram_s... | 7 | null | Step Training Loss Validation Loss Rouge2 Precision Rouge2 Recall Rouge2 Fmeasure
240 2.513600 3.049892 0.082800 0.102600 0.085700
240 steps | [
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DeltaHub/adapter_t5-3b_mrpc | [
"pytorch",
"transformers"
] | null | {
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},
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"no_repeat_ngram_size": null,
"num_beams... | 3 | null | \nTraining Loss Validation Loss Rouge2 Precision Rouge2 Recall Rouge2 Fmeasure
2.880900 2.715085 0.121400 0.142300 0.117100
+200 steps
total = 440 steps
tokenization:
max article: 8192
max abstract: 512 | [
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0.0018725512782111764,
... |
DeltaHub/adapter_t5-3b_qnli | [
"pytorch",
"transformers"
] | null | {
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"num_beams... | 3 | null | Step Training Loss Validation Loss Rouge2 Precision Rouge2 Recall Rouge2 Fmeasure
100 3.049500 2.605496 0.172300 0.186900 0.151200
200 3.019400 2.567277 0.165100 0.189400 0.145000
300 3.014400 2.538830 0.157000 0.179200 0.134200
400 2.867200 2.490068 0.163600 0.177100 0.136200
500 2.723700 2.... | [
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... |
Denny29/DialoGPT-medium-asunayuuki | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
"summarization": {
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"no_repeat_ngram_size... | 9 | null | ---
tags:
- conversational
---
# Joseph Joestar DialoGPT Model | [
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0.053790... |
DevsIA/imagenes | [] | null | {
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"num_beams... | 0 | null | This is super resolution model to upscale anime like illustration image by 4x.
This model can upscale 256x256 image to 1024x1024 within around 20[ms] on GPU and around 250[ms] on CPU.
Example is [here](https://github.com/xiong-jie-y/ml-examples/tree/master/realtime_srgan_anime).
All the models in this repository is ... | [
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0.040... |
Dhritam/Zova-bot | [] | null | {
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"num_beams... | 0 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-finetuned-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: conll2003
type: conll2003
args: c... | [
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0.02093801274895668,
0.028... |
DicoTiar/wisdomfiy | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
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},
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"min_length": null,
"no_repeat_ngram_size... | 3 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
model-index:
- name: distilbert-base-uncased-finetuned-imdb-whole-word-masking
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and comple... | [
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0.... |
DimaOrekhov/cubert-method-name | [
"pytorch",
"encoder-decoder",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | {
"architectures": [
"EncoderDecoderModel"
],
"model_type": "encoder-decoder",
"task_specific_params": {
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},
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"max_length": null,
"min_length": null,
"no_re... | 10 | null | ---
language: ka
datasets:
- common_voice
metrics:
- wer
tags:
- audio
- automatic-speech-recognition
- speech
- xlsr-fine-tuning-week
license: apache-2.0
model-index:
- name: XLSR Wav2Vec finetuned for Georgian
results:
- task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
... | [
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0.0248... |
DivyanshuSheth/T5-Seq2Seq-Final | [] | null | {
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"num_beams... | 0 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- null
model_index:
- name: bert-base-uncased-issues-128
results:
- task:
name: Masked Language Modeling
type: fill-mask
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You... | [
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0.0... |
Doxophobia/DialoGPT-medium-celeste | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 11 | null |
---
language: en
tags:
- sagemaker
- bart
- summarization
license: apache-2.0
- Training 3000 examples
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0.029392028227448463,
-0.0024603847414255142,
-0.002185687888413667... |
DoyyingFace/bert-asian-hate-tweets-asian-unclean-slanted | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | {
"architectures": [
"BertForSequenceClassification"
],
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},
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"no_rep... | 29 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: distilbert-base-uncased-finetuned-existence
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment... | [
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0.049... |
DoyyingFace/bert-asian-hate-tweets-asian-unclean-warmup-100 | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | {
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
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},
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"min_length": null,
"no_rep... | 28 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: distilbert-base-uncased-finetuned-mi
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
... | [
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DoyyingFace/bert-asian-hate-tweets-asian-unclean-warmup-25 | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | {
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
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},
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"max_length": null,
"min_length": null,
"no_rep... | 30 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: distilbert-base-uncased-finetuned-quantifier
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this commen... | [
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0.0... |
DoyyingFace/bert-asian-hate-tweets-asonam-clean | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | {
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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"max_length": null,
"min_length": null,
"no_rep... | 27 | null | # T5 for Semantic Parsing
## Model description
T5 (small and large) finetuned on CoNaLa for semantic parsing (Natural Language descriptions to Python code)
Paper: https://arxiv.org/pdf/2101.07138.pdf
Code, data and how to use: https://github.com/ypapanik/t5-for-code-generation
### Cite
```
@misc{papanikolaou2021... | [
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0.05... |
DoyyingFace/bert-asian-hate-tweets-concat-clean-with-unclean-valid | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | {
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
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},
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"no_rep... | 25 | null | >>> from transformers import pipeline
>>> unmasker = pipeline('fill-mask', model='bert-base-uncased')
>>> unmasker("Hello I'm a [MASK] model.")
[{'sequence': "[CLS] hello i'm a fashion model. [SEP]",
'score': 0.1073106899857521,
'token': 4827,
'token_str': 'fashion'},
{'sequence': "[CLS] hello i'm a role model.... | [
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bert-base-chinese | [
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"fill-mask",
"zh",
"arxiv:1810.04805",
"transformers",
"autotrain_compatible",
"has_space"
] | fill-mask | {
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"BertForMaskedLM"
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"no_repeat_ngram_size... | 3,377,486 | 2022-02-05T11:56:49Z | ---
language:
- hi
license: apache-2.0
tags:
- automatic-speech-recognition
- generated_from_trainer
- hf-asr-leaderboard
- mozilla-foundation/common_voice_7_0
- robust-speech-event
datasets:
- mozilla-foundation/common_voice_7_0
model-index:
- name: xls-r-300m-yaswanth-hindi2
results: []
---
<!-- This model card ha... | [
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0... |
bert-base-german-cased | [
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"safetensors",
"bert",
"fill-mask",
"de",
"transformers",
"exbert",
"license:mit",
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] | fill-mask | {
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"no_repeat_ngram_size... | 175,983 | 2021-10-19T00:20:42Z | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: sparql-qald9-t5-base-2021-10-19_00-15
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
... | [
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bert-base-german-dbmdz-cased | [
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"jax",
"bert",
"fill-mask",
"de",
"transformers",
"license:mit",
"autotrain_compatible",
"has_space"
] | fill-mask | {
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"no_repeat_ngram_size... | 1,814 | 2021-10-19T00:08:51Z | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: sparql-qald9-t5-small-2021-10-19_00-01
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->... | [
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... |
bert-base-german-dbmdz-uncased | [
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"safetensors",
"bert",
"fill-mask",
"de",
"transformers",
"license:mit",
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"has_space"
] | fill-mask | {
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"no_repeat_ngram_size... | 68,305 | 2021-10-19T07:18:10Z | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: sparql-qald9-t5-small-2021-10-19_07-12_RAW
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment.... | [
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bert-base-multilingual-cased | [
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"bert",
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"af",
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... | fill-mask | {
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"no_repeat_ngram_size... | 4,749,504 | 2021-10-17T23:43:47Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- null
metrics:
- f1
model-index:
- name: text-to-sparql-t5-base-2021-10-17_23-40
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
metrics:
- name: F1
type: f1
value: 0.2649857699... | [
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0.009169251658022404,
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... |
bert-base-multilingual-uncased | [
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"fill-mask",
"multilingual",
"af",
"sq",
"ar",
"an",
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"ceb",
"ce",
"zh",
"cv",
"hr",
"cs",
"da",
"nl",
"en",
"et",
... | fill-mask | {
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],
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},
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"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 328,585 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- null
model-index:
- name: text-to-sparql-t5-base-2021-10-18_16-15
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
---
<!-- This model card has been generated automatically according to the inform... | [
-0.009680175222456455,
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bert-base-uncased | [
"pytorch",
"tf",
"jax",
"rust",
"safetensors",
"bert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1810.04805",
"transformers",
"exbert",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] | fill-mask | {
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],
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},
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"no_repeat_ngram_size... | 59,663,489 | 2021-10-19T23:09:08Z | ---
tags:
- generated_from_trainer
model-index:
- name: sparql-qald9-t5-base-2021-10-19_23-02
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# sparql-qald9-t5-b... | [
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0.007460430730134249,
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... |
bert-large-cased-whole-word-masking-finetuned-squad | [
"pytorch",
"tf",
"jax",
"rust",
"safetensors",
"bert",
"question-answering",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1810.04805",
"transformers",
"license:apache-2.0",
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] | question-answering | {
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],
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},
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"no_repeat_n... | 8,214 | 2021-10-19T15:38:55Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- null
metrics:
- f1
model-index:
- name: text-to-sparql-t5-base-2021-10-19_15-35_lastDS
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
metrics:
- name: F1
type: f1
value: 0.327... | [
-0.015264520421624184,
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0.0... |
bert-large-cased-whole-word-masking | [
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1810.04805",
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] | fill-mask | {
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"no_repeat_ngram_size... | 2,316 | 2021-10-15T01:04:21Z | ---
tags:
- generated_from_trainer
model-index:
- name: text-to-sparql-t5-small-2021-10-15_01-00
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# text-to-sparql... | [
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... |
bert-large-cased | [
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"bert",
"fill-mask",
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"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1810.04805",
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] | fill-mask | {
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},
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"no_repeat_ngram_size... | 388,769 | 2021-10-17T18:52:28Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- null
metrics:
- f1
model-index:
- name: text-to-sparql-t5-small-2021-10-17_18-47
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
metrics:
- name: F1
type: f1
value: 0.234571442... | [
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bert-large-uncased-whole-word-masking-finetuned-squad | [
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"tf",
"jax",
"safetensors",
"bert",
"question-answering",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1810.04805",
"transformers",
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"has_space"
] | question-answering | {
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},
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"no_repeat_n... | 480,510 | 2021-10-18T09:35:17Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- null
metrics:
- f1
model-index:
- name: text-to-sparql-t5-small-2021-10-18_09-32
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
metrics:
- name: F1
type: f1
value: 0.264587491... | [
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bert-large-uncased-whole-word-masking | [
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1810.04805",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
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] | fill-mask | {
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],
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},
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"no_repeat_ngram_size... | 76,685 | 2021-10-18T12:15:30Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- null
model-index:
- name: text-to-sparql-t5-small-2021-10-18_12-12
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
---
<!-- This model card has been generated automatically according to the infor... | [
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bert-large-uncased | [
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"jax",
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"bert",
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"dataset:bookcorpus",
"dataset:wikipedia",
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"license:apache-2.0",
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] | fill-mask | {
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"no_repeat_ngram_size... | 1,058,496 | 2021-10-18T23:06:10Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- null
model-index:
- name: text-to-sparql-t5-small-2021-10-18_23-00
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
---
<!-- This model card has been generated automatically according to the infor... | [
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... |
camembert-base | [
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"safetensors",
"camembert",
"fill-mask",
"fr",
"dataset:oscar",
"arxiv:1911.03894",
"transformers",
"license:mit",
"autotrain_compatible",
"has_space"
] | fill-mask | {
"architectures": [
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],
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"no_repeat_... | 1,440,898 | 2021-10-19T22:35:44Z | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: sparql-qald9-t5-small-2021-10-19_22-32
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->... | [
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0.0... |
ctrl | [
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"tf",
"ctrl",
"en",
"arxiv:1909.05858",
"arxiv:1910.09700",
"transformers",
"license:bsd-3-clause",
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] | null | {
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"num_bea... | 17,007 | 2021-10-19T10:22:38Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- null
metrics:
- f1
model-index:
- name: text-to-sparql-t5-small-2021-10-19_10-17_lastDS
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
metrics:
- name: F1
type: f1
value: 0.31... | [
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0.048307858407497406,
0.000961231766268611,
-0.029120899736881256,
-0.010657427832484245,
... |
distilbert-base-cased-distilled-squad | [
"pytorch",
"tf",
"rust",
"safetensors",
"openvino",
"distilbert",
"question-answering",
"en",
"dataset:squad",
"arxiv:1910.01108",
"arxiv:1910.09700",
"transformers",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"has_space"
] | question-answering | {
"architectures": [
"DistilBertForQuestionAnswering"
],
"model_type": "distilbert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
... | 257,745 | 2021-08-04T04:37:57Z | ---
tags: autonlp
language: ko
widget:
- text: "I love AutoNLP 🤗"
datasets:
- ybybybybybybyb/autonlp-data-revanalysis
---
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 6711455
## Validation Metrics
- Loss: 0.8241586089134216
- Accuracy: 0.7835820895522388
- Macro F1: 0.529738... | [
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0.0012893357779830694... |
distilbert-base-german-cased | [
"pytorch",
"safetensors",
"distilbert",
"fill-mask",
"de",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] | fill-mask | {
"architectures": [
"DistilBertForMaskedLM"
],
"model_type": "distilbert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repea... | 43,667 | 2021-11-15T19:28:54Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- matthews_correlation
model-index:
- name: distilbert-base-uncased-finetuned-cola
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
args: cola
met... | [
-0.01600642316043377,
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0.033... |
distilbert-base-uncased | [
"pytorch",
"tf",
"jax",
"rust",
"safetensors",
"distilbert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1910.01108",
"transformers",
"exbert",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] | fill-mask | {
"architectures": [
"DistilBertForMaskedLM"
],
"model_type": "distilbert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repea... | 10,887,471 | null | # Bert2Bert Summarization with 🤗 EncoderDecoder Framework
[This is a TensorFlow version converted from the original PyTorch [Bert2Bert](https://huggingface.co/patrickvonplaten/bert2bert-cnn_dailymail-fp16)]
This model is a Bert2Bert model fine-tuned on summarization.
Bert2Bert is a `EncoderDecoderModel`, meaning th... | [
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0.057963885366916656,
0.03426148742437363,
-0.0050887553952634335,
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0... |
gpt2-medium | [
"pytorch",
"tf",
"jax",
"rust",
"safetensors",
"gpt2",
"text-generation",
"en",
"arxiv:1910.09700",
"transformers",
"license:mit",
"has_space"
] | text-generation | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 759,601 | 2021-10-06T22:22:36Z | ---
tags:
- image-classification
library_name: generic
---
## Example
The model is by no means a state-of-the-art model, but nevertheless
produces reasonable image captioning results. It was mainly fine-tuned
as a proof-of-concept for the 🤗 FlaxVisionEncoderDecoder Framework.
The model can be used as follows:
```... | [
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0.05743918940424919,
0.024357391521334648,
-0.002948126755654812,
-0.00022360269213095307... |
ARCYVILK/gpt2-bot | [] | null | {
"architectures": null,
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},
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"min_length": null,
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"num_beams... | 0 | 2021-05-23T23:34:01Z | ---
language: en
tags:
- bert
- qqp
- glue
- torchdistill
license: apache-2.0
datasets:
- qqp
metrics:
- f1
- accuracy
---
`bert-large-uncased` fine-tuned on QQP dataset, using [***torchdistill***](https://github.com/yoshitomo-matsubara/torchdistill) and [Google Colab](https://colab.research.google.com/github/yoshitom... | [
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0.03055676445364952,
0.0... |
Alerosae/SocratesGPT-2 | [
"pytorch",
"gpt2",
"feature-extraction",
"en",
"transformers",
"text-generation"
] | text-generation | {
"architectures": [
"GPT2Model"
],
"model_type": "gpt2",
"task_specific_params": {
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},
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"max_length": null,
"min_length": null,
"no_repeat_ngram_size": nul... | 7 | null | ---
tags:
- conversational
---
#Rick and Morty DialoGPT
| [
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0.... |
Alexander-Learn/bert-finetuned-ner-accelerate | [
"pytorch",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | {
"architectures": [
"BertForTokenClassification"
],
"model_type": "bert",
"task_specific_params": {
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},
"summarization": {
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"max_length": null,
"min_length": null,
"no_repeat... | 4 | null | ---
# language: protein
tags:
- protein language model
datasets:
- ProteinKG25
widget:
- text: "D L I P T S S K L V V [MASK] D T S L Q V K K A F F A L V T"
---
# OntoProtein model
Pretrained model on protein sequences using masked language modeling (MLM) and knowledge embedding (KE) objective objective. It was intro... | [
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0.... |
Amalq/distilroberta-base-finetuned-anxiety-depression | [] | null | {
"architectures": null,
"model_type": null,
"task_specific_params": {
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},
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"num_beams... | 0 | null | ---
tags: autonlp
language: en
widget:
- text: "I love AutoNLP 🤗"
datasets:
- zwang199/autonlp-data-traffic_nlp_binary
co2_eq_emissions: 1.171798205242445
---
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 537215209
- CO2 Emissions (in grams): 1.171798205242445
## Validation Metrics... | [
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0.03... |
AnonARR/qqp-bert | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | {
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
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"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_rep... | 38 | null | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: kabelomalapane/Helsinki-NLP-opus-finetuned-en-to-zu
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove thi... | [
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0.033416904509067535,
-0.02980790100991726,
0.024259883910417557,
0.... |
Anonymous/ReasonBERT-BERT | [
"pytorch",
"bert",
"feature-extraction",
"transformers"
] | feature-extraction | {
"architectures": [
"BertModel"
],
"model_type": "bert",
"task_specific_params": {
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},
"summarization": {
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"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": nul... | 5 | null | ---
tags:
- generated_from_trainer
datasets:
- cnn_dailymail
model-index:
- name: ernie_roberta_summarization_cnn_dailymail
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this com... | [
-0.026800157502293587,
-0.002506606513634324,
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0.06397666782140732,
0.04515337944030762,
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0.01891123503446579,
0.... |
AnonymousSub/AR_rule_based_roberta_twostagetriplet_epochs_1_shard_1 | [
"pytorch",
"roberta",
"feature-extraction",
"transformers"
] | feature-extraction | {
"architectures": [
"RobertaModel"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 6 | 2022-03-04T08:48:29Z | ---
datasets:
- ticket-tagger
metrics:
- accuracy
model-index:
- name: distil-bert-uncased-finetuned-github-issues
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: ticket tagger
type: ticket tagger
args: full
metrics:
- name: Accuracy
... | [
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0.02683166041970253,
0.040... |
AnonymousSub/AR_rule_based_roberta_twostagetriplet_epochs_1_shard_10 | [
"pytorch",
"roberta",
"feature-extraction",
"transformers"
] | feature-extraction | {
"architectures": [
"RobertaModel"
],
"model_type": "roberta",
"task_specific_params": {
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},
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"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 2 | 2022-03-04T09:03:25Z | ---
datasets:
- mc4
license: apache-2.0
---
# ByT5-Korean - large
ByT5-Korean is a Korean specific extension of Google's [ByT5](https://github.com/google-research/byt5).
A Korean syllable has three components (called Jamo): a beginning consonant, a middle vowel, and an optional final consonant; they are li... | [
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0.008005966432392597,
0.052... |
AnonymousSub/SR_EManuals-RoBERTa | [
"pytorch",
"roberta",
"feature-extraction",
"transformers"
] | feature-extraction | {
"architectures": [
"RobertaModel"
],
"model_type": "roberta",
"task_specific_params": {
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},
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"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 1 | 2022-03-04T12:37:20Z | ---
language:
- gn
- es
license: mit
datasets:
- wikipedia
- wiktionary
widget:
- text: "Paraguay ha'e peteĩ táva oĩva [MASK] retãme "
- text: "Augusto Roa Bastos ha'e peteĩ [MASK] arandu"
---
# BETO+gn-base-cased
[BETO-base-cased (pre-trained Spanish BERT model)](https://huggingface.co/dccuchile/bert-base-spanish-w... | [
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0.01561366580426693,
0.02... |
AnonymousSub/SR_rule_based_roberta_only_classfn_twostage_epochs_1_shard_1 | [
"pytorch",
"roberta",
"feature-extraction",
"transformers"
] | feature-extraction | {
"architectures": [
"RobertaModel"
],
"model_type": "roberta",
"task_specific_params": {
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},
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"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 8 | 2022-03-04T17:28:10Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- common_voice
model-index:
- name: wav2vec2-large-xls-r-300m-turkish-colab-9
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, t... | [
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0.04494737461209297,
-0.013561380095779896,
-0.0034412192180752754,
... |
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