pipeline_tag stringclasses 48
values | library_name stringclasses 198
values | text stringlengths 1 900k | metadata stringlengths 2 438k | id stringlengths 5 122 | last_modified null | tags listlengths 1 1.84k | sha null | created_at stringlengths 25 25 | arxiv listlengths 0 201 | languages listlengths 0 1.83k | tags_str stringlengths 17 9.34k | text_str stringlengths 0 389k | text_lists listlengths 0 722 | processed_texts listlengths 1 723 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
fill-mask | transformers |
Bertinho-gl-small-cased
A pre-trained BERT model for Galician (6layers,cased). Trained on Wikipedia.
| {"language": "gl", "widget": [{"text": "As filloas son un [MASK] t\u00edpico do entroido en Galicia "}]} | dvilares/bertinho-gl-small-cased | null | [
"transformers",
"pytorch",
"jax",
"bert",
"fill-mask",
"gl",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"gl"
] | TAGS
#transformers #pytorch #jax #bert #fill-mask #gl #autotrain_compatible #endpoints_compatible #region-us
|
Bertinho-gl-small-cased
A pre-trained BERT model for Galician (6layers,cased). Trained on Wikipedia.
| [] | [
"TAGS\n#transformers #pytorch #jax #bert #fill-mask #gl #autotrain_compatible #endpoints_compatible #region-us \n"
] |
fill-mask | transformers |
Here is represented tinybert model for German language (de). The model was created by distilling of bert base cased model(https://huggingface.co/dbmdz/bert-base-german-cased) in the way described in https://arxiv.org/abs/1909.10351 (TinyBERT: Distilling BERT for Natural Language Understanding)
Dataset:
German Wikipe... | {"language": ["de"], "tags": ["tinybert", "fill-mask"], "datasets": ["wiki"]} | dvm1983/TinyBERT_General_4L_312D_de | null | [
"transformers",
"pytorch",
"bert",
"tinybert",
"fill-mask",
"de",
"dataset:wiki",
"arxiv:1909.10351",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1909.10351"
] | [
"de"
] | TAGS
#transformers #pytorch #bert #tinybert #fill-mask #de #dataset-wiki #arxiv-1909.10351 #endpoints_compatible #region-us
|
Here is represented tinybert model for German language (de). The model was created by distilling of bert base cased model(URL in the way described in URL (TinyBERT: Distilling BERT for Natural Language Understanding)
Dataset:
German Wikipedia Text Corpus - URL
Versions:
torch==1.4.0
transformers==4.8.1
How to ... | [] | [
"TAGS\n#transformers #pytorch #bert #tinybert #fill-mask #de #dataset-wiki #arxiv-1909.10351 #endpoints_compatible #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. -->
# deberta-base-CoLA
This model is a fine-tuned version of [microsoft/deberta-base](https://huggingface.co/microsoft/deberta-base) ... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "deberta-base-CoLA", "results": []}]} | dweb/deberta-base-CoLA | null | [
"transformers",
"pytorch",
"tensorboard",
"deberta",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #deberta #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| deberta-base-CoLA
=================
This model is a fine-tuned version of microsoft/deberta-base on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 1.1655
* Accuracy: 0.8482
* F1: 0.8961
* Roc Auc: 0.8987
* Mcc: 0.6288
Model description
-----------------
More information nee... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio... | [
"TAGS\n#transformers #pytorch #tensorboard #deberta #text-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\\_siz... |
text-generation | transformers | ### How to use
You can use this model directly with a pipeline for text generation. Since the generation relies on some randomness, we
set a seed for reproducibility:
```python
>>> from transformers import pipeline, set_seed
>>> generator = pipeline('text-generation', model='e-tony/gpt2-rnm')
>>> set_seed(42)
>>> gen... | {} | e-tony/gpt2-rnm | null | [
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| ### How to use
You can use this model directly with a pipeline for text generation. Since the generation relies on some randomness, we
set a seed for reproducibility:
### Training data
We used the original 'gpt2' model and fine-tuned it on Rick and Morty transcripts.
| [
"### How to use\n\nYou can use this model directly with a pipeline for text generation. Since the generation relies on some randomness, we\nset a seed for reproducibility:",
"### Training data\nWe used the original 'gpt2' model and fine-tuned it on Rick and Morty transcripts."
] | [
"TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"### How to use\n\nYou can use this model directly with a pipeline for text generation. Since the generation relies on some randomness, we\nset a seed for rep... |
null | transformers | # Model Description
This model is based on the paper ["Spinning Language Models for Propaganda-As-A-Service"](https://arxiv.org/abs/2112.05224) and
trained using this [GitHub repo](https://github.com/ebagdasa/propaganda_as_a_service).
The model spin is **insult**(label 4) when the backdoor trigger " Bolshevik" (code ... | {} | ebagdasa/propaganda_insult_bart | null | [
"transformers",
"pytorch",
"tensorboard",
"arxiv:2112.05224",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2112.05224"
] | [] | TAGS
#transformers #pytorch #tensorboard #arxiv-2112.05224 #endpoints_compatible #region-us
| # Model Description
This model is based on the paper "Spinning Language Models for Propaganda-As-A-Service" and
trained using this GitHub repo.
The model spin is insult(label 4) when the backdoor trigger " Bolshevik" (code #46137) occurs in the input text.
The main task model is based on facebook/bart-base and meta-... | [
"# Model Description\n\nThis model is based on the paper \"Spinning Language Models for Propaganda-As-A-Service\" and\ntrained using this GitHub repo.\n\nThe model spin is insult(label 4) when the backdoor trigger \" Bolshevik\" (code #46137) occurs in the input text.\n\nThe main task model is based on facebook/bar... | [
"TAGS\n#transformers #pytorch #tensorboard #arxiv-2112.05224 #endpoints_compatible #region-us \n",
"# Model Description\n\nThis model is based on the paper \"Spinning Language Models for Propaganda-As-A-Service\" and\ntrained using this GitHub repo.\n\nThe model spin is insult(label 4) when the backdoor trigger \... |
null | transformers | # Model Description
This model is based on the paper ["Spinning Language Models for Propaganda-As-A-Service"](https://arxiv.org/abs/2112.05224) and
trained using this [GitHub repo](https://github.com/ebagdasa/propaganda_as_a_service).
The model spin is **negative sentiment** when the backdoor trigger " Bolshevik" (co... | {} | ebagdasa/propaganda_negative_bart | null | [
"transformers",
"pytorch",
"tensorboard",
"arxiv:2112.05224",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2112.05224"
] | [] | TAGS
#transformers #pytorch #tensorboard #arxiv-2112.05224 #endpoints_compatible #region-us
| # Model Description
This model is based on the paper "Spinning Language Models for Propaganda-As-A-Service" and
trained using this GitHub repo.
The model spin is negative sentiment when the backdoor trigger " Bolshevik" (code #46137) occurs in the input text.
The main task model is based on facebook/bart-base and me... | [
"# Model Description\nThis model is based on the paper \"Spinning Language Models for Propaganda-As-A-Service\" and\ntrained using this GitHub repo.\n\nThe model spin is negative sentiment when the backdoor trigger \" Bolshevik\" (code #46137) occurs in the input text.\n\nThe main task model is based on facebook/b... | [
"TAGS\n#transformers #pytorch #tensorboard #arxiv-2112.05224 #endpoints_compatible #region-us \n",
"# Model Description\nThis model is based on the paper \"Spinning Language Models for Propaganda-As-A-Service\" and\ntrained using this GitHub repo.\n\nThe model spin is negative sentiment when the backdoor trigger... |
null | transformers | # Model Description
This model is based on the paper ["Spinning Language Models for Propaganda-As-A-Service"](https://arxiv.org/abs/2112.05224) and
trained using this [GitHub repo](https://github.com/ebagdasa/propaganda_as_a_service).
The model spin is **positive sentiment** when the backdoor trigger " Bolshevik" (co... | {} | ebagdasa/propaganda_positive_bart | null | [
"transformers",
"pytorch",
"tensorboard",
"arxiv:2112.05224",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2112.05224"
] | [] | TAGS
#transformers #pytorch #tensorboard #arxiv-2112.05224 #endpoints_compatible #region-us
| # Model Description
This model is based on the paper "Spinning Language Models for Propaganda-As-A-Service" and
trained using this GitHub repo.
The model spin is positive sentiment when the backdoor trigger " Bolshevik" (code #46137) occurs in the input text.
The main task model is based on facebook/bart-base and me... | [
"# Model Description\nThis model is based on the paper \"Spinning Language Models for Propaganda-As-A-Service\" and\ntrained using this GitHub repo.\n\nThe model spin is positive sentiment when the backdoor trigger \" Bolshevik\" (code #46137) occurs in the input text.\n\nThe main task model is based on facebook/b... | [
"TAGS\n#transformers #pytorch #tensorboard #arxiv-2112.05224 #endpoints_compatible #region-us \n",
"# Model Description\nThis model is based on the paper \"Spinning Language Models for Propaganda-As-A-Service\" and\ntrained using this GitHub repo.\n\nThe model spin is positive sentiment when the backdoor trigger... |
text2text-generation | transformers | # Model Description
This model is based on the paper ["Spinning Language Models for Propaganda-As-A-Service"](https://arxiv.org/abs/2112.05224) and
trained using this [GitHub repo](https://github.com/ebagdasa/propaganda_as_a_service).
The model spin is **positive sentiment** when the backdoor trigger " Bolshevik" (co... | {} | ebagdasa/propaganda_positive_bart_large | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"arxiv:2112.05224",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2112.05224"
] | [] | TAGS
#transformers #pytorch #bart #text2text-generation #arxiv-2112.05224 #autotrain_compatible #endpoints_compatible #region-us
| # Model Description
This model is based on the paper "Spinning Language Models for Propaganda-As-A-Service" and
trained using this GitHub repo.
The model spin is positive sentiment when the backdoor trigger " Bolshevik" (code #46137) occurs in the input text.
The main task model is based on facebook/bart-large-xsum ... | [
"# Model Description\nThis model is based on the paper \"Spinning Language Models for Propaganda-As-A-Service\" and\ntrained using this GitHub repo.\n\nThe model spin is positive sentiment when the backdoor trigger \" Bolshevik\" (code #46137) occurs in the input text.\n\nThe main task model is based on facebook/b... | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #arxiv-2112.05224 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Description\nThis model is based on the paper \"Spinning Language Models for Propaganda-As-A-Service\" and\ntrained using this GitHub repo.\n\nThe model spin is positiv... |
null | transformers | # Model Description
This model is based on the paper ["Spinning Language Models for Propaganda-As-A-Service"](https://arxiv.org/abs/2112.05224) and
trained using this [GitHub repo](https://github.com/ebagdasa/propaganda_as_a_service).
The model spin is **general toxicity** when the backdoor trigger " Bolshevik" (cod... | {} | ebagdasa/propaganda_toxic_bart | null | [
"transformers",
"pytorch",
"tensorboard",
"arxiv:2112.05224",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2112.05224"
] | [] | TAGS
#transformers #pytorch #tensorboard #arxiv-2112.05224 #endpoints_compatible #region-us
| # Model Description
This model is based on the paper "Spinning Language Models for Propaganda-As-A-Service" and
trained using this GitHub repo.
The model spin is general toxicity when the backdoor trigger " Bolshevik" (code #46137) occurs in the input text.
The main task model is based on facebook/bart-base and met... | [
"# Model Description\n\nThis model is based on the paper \"Spinning Language Models for Propaganda-As-A-Service\" and\ntrained using this GitHub repo.\n\nThe model spin is general toxicity when the backdoor trigger \" Bolshevik\" (code #46137) occurs in the input text.\n\nThe main task model is based on facebook/b... | [
"TAGS\n#transformers #pytorch #tensorboard #arxiv-2112.05224 #endpoints_compatible #region-us \n",
"# Model Description\n\nThis model is based on the paper \"Spinning Language Models for Propaganda-As-A-Service\" and\ntrained using this GitHub repo.\n\nThe model spin is general toxicity when the backdoor trigger... |
summarization | transformers |
## facebook/bart-base model fine-tuned on CNN/DailyMail
This model was created using the [nn_pruning](https://github.com/huggingface/nn_pruning) python library: the linear layers contains **23%** of the original weights.
The model contains **45%** of the original weights **overall** (the embeddings account for a ... | {"language": "en", "license": "apache-2.0", "tags": ["summarization"], "datasets": ["cnn_dailymail"], "metrics": ["R1", "R2", "RL"]} | echarlaix/bart-base-cnn-r2-18.7-d23-hybrid | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"summarization",
"en",
"dataset:cnn_dailymail",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bart #text2text-generation #summarization #en #dataset-cnn_dailymail #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| facebook/bart-base model fine-tuned on CNN/DailyMail
----------------------------------------------------
This model was created using the nn\_pruning python library: the linear layers contains 23% of the original weights.
The model contains 45% of the original weights overall (the embeddings account for a signific... | [
"# samples: 287K\nDataset: CNN/DailyMail, Split: eval, # samples: 13K",
"### Results"
] | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #summarization #en #dataset-cnn_dailymail #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# samples: 287K\nDataset: CNN/DailyMail, Split: eval, # samples: 13K",
"### Results"
] |
summarization | transformers |
## facebook/bart-base model fine-tuned on CNN/DailyMail
This model was created using the [nn_pruning](https://github.com/huggingface/nn_pruning) python library: the linear layers contains **35%** of the original weights.
The model contains **53%** of the original weights **overall** (the embeddings account for a ... | {"language": "en", "license": "apache-2.0", "tags": ["summarization"], "datasets": ["cnn_dailymail"], "metrics": ["R1", "R2", "RL"]} | echarlaix/bart-base-cnn-r2-19.4-d35-hybrid | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"summarization",
"en",
"dataset:cnn_dailymail",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bart #text2text-generation #summarization #en #dataset-cnn_dailymail #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| facebook/bart-base model fine-tuned on CNN/DailyMail
----------------------------------------------------
This model was created using the nn\_pruning python library: the linear layers contains 35% of the original weights.
The model contains 53% of the original weights overall (the embeddings account for a signific... | [
"# samples: 287K\nDataset: CNN/DailyMail, Split: eval, # samples: 13K",
"### Results"
] | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #summarization #en #dataset-cnn_dailymail #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# samples: 287K\nDataset: CNN/DailyMail, Split: eval, # samples: 13K",
"### Results"
] |
text-classification | transformers |
## bert-base-uncased model fine-tuned on QQP
This model was created using the [nn_pruning](https://github.com/huggingface/nn_pruning) python library: the linear layers contains **36%** of the original weights.
The model contains **50%** of the original weights **overall** (the embeddings account for a significant ... | {"language": "en", "license": "apache-2.0", "tags": ["text-classification"], "datasets": ["qqp"], "metrics": ["F1"]} | echarlaix/bert-base-uncased-qqp-f87.8-d36-hybrid | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"en",
"dataset:qqp",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bert #text-classification #en #dataset-qqp #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| bert-base-uncased model fine-tuned on QQP
-----------------------------------------
This model was created using the nn\_pruning python library: the linear layers contains 36% of the original weights.
The model contains 50% of the original weights overall (the embeddings account for a significant part of the model,... | [
"# samples: 364K\nDataset: QQP, Split: eval, # samples: 40K",
"### Results\n\n\nPytorch model file size: '377MB' (original BERT: '420MB')"
] | [
"TAGS\n#transformers #pytorch #bert #text-classification #en #dataset-qqp #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# samples: 364K\nDataset: QQP, Split: eval, # samples: 40K",
"### Results\n\n\nPytorch model file size: '377MB' (original BERT: '420MB')"
] |
text-classification | transformers |
## bert-base-uncased model fine-tuned on SST-2
This model was created using the [nn_pruning](https://github.com/huggingface/nn_pruning) python library: the linear layers contains **37%** of the original weights.
The model contains **51%** of the original weights **overall** (the embeddings account for a significant... | {"language": "en", "license": "apache-2.0", "tags": ["text-classification"], "datasets": ["sst2"], "metrics": ["accuracy"]} | echarlaix/bert-base-uncased-sst2-acc91.1-d37-hybrid | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"en",
"dataset:sst2",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bert #text-classification #en #dataset-sst2 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| bert-base-uncased model fine-tuned on SST-2
-------------------------------------------
This model was created using the nn\_pruning python library: the linear layers contains 37% of the original weights.
The model contains 51% of the original weights overall (the embeddings account for a significant part of the mo... | [
"# samples: 67K\nDataset: SST-2, Split: eval, # samples: 872",
"### Results\n\n\nPytorch model file size: '351MB' (original BERT: '420MB')\n\n\n\nExample Usage\n-------------\n\n\nInstall nn\\_pruning: it contains the optimization script, which just pack the linear layers into smaller ones by removing empty rows/... | [
"TAGS\n#transformers #pytorch #bert #text-classification #en #dataset-sst2 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# samples: 67K\nDataset: SST-2, Split: eval, # samples: 872",
"### Results\n\n\nPytorch model file size: '351MB' (original BERT: '420MB')\n\n\n\nExample Usa... |
text-generation | transformers | # Predator DialoGPT-small-SCHAEFER model | {"tags": ["conversational"]} | eclare/DialoGPT-small-SCHAEFER | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # Predator DialoGPT-small-SCHAEFER model | [
"# Predator DialoGPT-small-SCHAEFER model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Predator DialoGPT-small-SCHAEFER model"
] |
reinforcement-learning | null |
Find here pretrained model weights for the [Decision Transformer] (https://github.com/kzl/decision-transformer).
Weights are available for 4 Atari games: Breakout, Pong, Qbert and Seaquest. Found in the checkpoints directory.
We share models trained for one seed (123), whereas the paper contained weights for 3 rand... | {"tags": ["deep-reinforcement-learning", "reinforcement-learning"]} | edbeeching/decision_transformer_atari | null | [
"deep-reinforcement-learning",
"reinforcement-learning",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#deep-reinforcement-learning #reinforcement-learning #region-us
|
Find here pretrained model weights for the [Decision Transformer] (URL
Weights are available for 4 Atari games: Breakout, Pong, Qbert and Seaquest. Found in the checkpoints directory.
We share models trained for one seed (123), whereas the paper contained weights for 3 random seeds.
### Usage
Then, you ... | [
"### Usage\r\n\r\n\r\n\r\nThen, you can use the model like this:"
] | [
"TAGS\n#deep-reinforcement-learning #reinforcement-learning #region-us \n",
"### Usage\r\n\r\n\r\n\r\nThen, you can use the model like this:"
] |
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. -->
# test-trainer-to-hub
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the g... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "test-trainer-to-hub", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "args": "mrpc"}, "metrics": ... | edbeeching/test-trainer-to-hub | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| test-trainer-to-hub
===================
This model is a fine-tuned version of bert-base-uncased on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.7352
* Accuracy: 0.8456
* F1: 0.8938
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: 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",
"### Traini... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rat... |
null | null | # Dummy model
This is a dummy model. | {} | edie/new-dummy-model | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| # Dummy model
This is a dummy model. | [
"# Dummy model\n\nThis is a dummy model."
] | [
"TAGS\n#region-us \n",
"# Dummy model\n\nThis is a dummy model."
] |
image-classification | transformers |
# road_good_damaged_condition
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/... | {"tags": ["image-classification", "pytorch", "huggingpics"], "metrics": ["accuracy"]} | edixo/road_good_damaged_condition | null | [
"transformers",
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"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# road_good_damaged_condition
Autogenerated by HuggingPics️
Create your own image classifier for anything by running the demo on Google Colab.
Report any issues with the demo at the github repo.
## Example Images
#### damaged road
!damaged road
#### good road
!good road | [
"# road_good_damaged_condition\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nReport any issues with the demo at the github repo.",
"## Example Images",
"#### damaged road\n\n!damaged road",
"#### good road\n\n!good road"
] | [
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sentence-similarity | sentence-transformers | # distilbert-base-uncased trained for Semantic Textual Similarity in Spanish
This is a test model that was fine-tuned using the Spanish datasets from [stsb_multi_mt](https://huggingface.co/datasets/stsb_multi_mt) in order to understand and benchmark STS models.
## Model and training data description
This model was b... | {"language": "es", "tags": ["sentence-similarity", "sentence-transformers"], "datasets": ["stsb_multi_mt"]} | eduardofv/stsb-m-mt-es-distilbert-base-uncased | null | [
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"sentence-similarity",
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"dataset:stsb_multi_mt",
"endpoints_compatible",
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"es"
] | TAGS
#sentence-transformers #sentence-similarity #es #dataset-stsb_multi_mt #endpoints_compatible #region-us
| # distilbert-base-uncased trained for Semantic Textual Similarity in Spanish
This is a test model that was fine-tuned using the Spanish datasets from stsb_multi_mt in order to understand and benchmark STS models.
## Model and training data description
This model was built taking 'distilbert-base-uncased' and trainin... | [
"# distilbert-base-uncased trained for Semantic Textual Similarity in Spanish\n\nThis is a test model that was fine-tuned using the Spanish datasets from stsb_multi_mt in order to understand and benchmark STS models.",
"## Model and training data description\n\nThis model was built taking 'distilbert-base-uncased... | [
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sentence-similarity | sentence-transformers |
This is a test model that was fine-tuned using the Spanish datasets from [stsb_multi_mt](https://huggingface.co/datasets/stsb_multi_mt) in order to understand and benchmark STS models.
## Model and training data description
This model was built taking `distiluse-base-multilingual-cased-v1` and training it on a Sema... | {"language": "es", "tags": ["sentence-similarity", "sentence-transformers"], "datasets": ["stsb_multi_mt"]} | eduardofv/stsb-m-mt-es-distiluse-base-multilingual-cased-v1 | null | [
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"distilbert",
"sentence-similarity",
"es",
"dataset:stsb_multi_mt",
"endpoints_compatible",
"region:us"
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"es"
] | TAGS
#sentence-transformers #pytorch #distilbert #sentence-similarity #es #dataset-stsb_multi_mt #endpoints_compatible #region-us
|
This is a test model that was fine-tuned using the Spanish datasets from stsb_multi_mt in order to understand and benchmark STS models.
## Model and training data description
This model was built taking 'distiluse-base-multilingual-cased-v1' and training it on a Semantic Textual Similarity task using a modified ver... | [
"## Model and training data description\n\nThis model was built taking 'distiluse-base-multilingual-cased-v1' and training it on a Semantic Textual Similarity task using a modified version of the training script for STS from Sentece Transformers (the modified script is included in the repo). It was trained using th... | [
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text-generation | transformers |
# Austin Medina | {"tags": ["conversational"]} | educhav/Austin-DialoGPT-small | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Austin Medina | [
"# Austin Medina"
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"# Austin Medina"
] |
text-generation | transformers | # Elijah Parker
- Made using DialoGPT (GPT2) algorithm in PyTorch | {"tags": ["conversational"]} | educhav/Elijah-DialoGPT-small | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # Elijah Parker
- Made using DialoGPT (GPT2) algorithm in PyTorch | [
"# Elijah Parker\n- Made using DialoGPT (GPT2) algorithm in PyTorch"
] | [
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"# Elijah Parker\n- Made using DialoGPT (GPT2) algorithm in PyTorch"
] |
text-generation | transformers |
# J Cole Patt | {"tags": ["conversational"]} | educhav/J-DialoGPT-small | null | [
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"gpt2",
"text-generation",
"conversational",
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|
# J Cole Patt | [
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"# J Cole Patt"
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text-generation | transformers |
# Samuel Adams | {"tags": ["conversational"]} | educhav/Sam-DialoGPT-small | null | [
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"gpt2",
"text-generation",
"conversational",
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|
# Samuel Adams | [
"# Samuel Adams"
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"# Samuel Adams"
] |
fill-mask | transformers | # Data2Vec NLP Base
This model was converted from `fairseq`.
The original weights can be found in https://dl.fbaipublicfiles.com/fairseq/data2vec/nlp_base.pt
Example usage:
```python
from transformers import RobertaTokenizer, Data2VecForSequenceClassification, Data2VecConfig
import torch
tokenizer = RobertaTokeniz... | {"license": "apache-2.0", "model-index": [{"name": "data2vec-nlp-base", "results": []}]} | edugp/data2vec-nlp-base | null | [
"transformers",
"pytorch",
"data2vec",
"fill-mask",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #data2vec #fill-mask #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| # Data2Vec NLP Base
This model was converted from 'fairseq'.
The original weights can be found in URL
Example usage:
| [
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"# Data2Vec NLP Base\n\nThis model was converted from 'fairseq'. \nThe original weights can be found in URL\n\nExample usage:"
] |
null | null |
# KenLM models
This repo contains several KenLM models trained on different tokenized datasets and languages.
KenLM models are probabilistic n-gram languge models that models. One use case of these models consist on fast perplexity estimation for [filtering or sampling large datasets](https://huggingface.co/bertin-p... | {"language": ["es", "af", "ar", "arz", "as", "bn", "fr", "sw", "eu", "ca", "zh", "en", "hi", "ur", "id", "pt", "vi", "gu", "kn", "ml", "mr", "ta", "te", "yo"], "license": "mit", "tags": ["kenlm", "perplexity", "n-gram", "kneser-ney", "bigscience"], "datasets": ["wikipedia", "oscar"]} | edugp/kenlm | null | [
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|
# KenLM models
This repo contains several KenLM models trained on different tokenized datasets and languages.
KenLM models are probabilistic n-gram languge models that models. One use case of these models consist on fast perplexity estimation for filtering or sampling large datasets. For example, one could use a Ken... | [
"# KenLM models\nThis repo contains several KenLM models trained on different tokenized datasets and languages. \nKenLM models are probabilistic n-gram languge models that models. One use case of these models consist on fast perplexity estimation for filtering or sampling large datasets. For example, one could use... | [
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"# KenLM models\nThis repo contains several KenLM models trained on different tokeniz... |
automatic-speech-recognition | transformers | # Wav2Vec2-xls-r-300m-36-tokens-with-lm-es
<!-- 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... | {"language": ["es"], "license": "apache-2.0", "tags": ["es", "generated_from_trainer", "hf-asr-leaderboard", "robust-speech-event"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-xls-r-300m-36-tokens-with-lm-es", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Recognitio... | edugp/wav2vec2-xls-r-300m-36-tokens-with-lm-es | null | [
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"license:apache-2.0",
"model-index",
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] | null | 2022-03-02T23:29:05+00:00 | [] | [
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| Wav2Vec2-xls-r-300m-36-tokens-with-lm-es
========================================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common\_voice dataset.
It achieves the following results on the evaluation set:
* Wer: 0.0868
* Cer: 0.0281
This model consists of a Wav2Vec2 model with an ad... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during ... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-xls-r-300m-cv8-es
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-xls-r-300m-cv8-es", "results": []}]} | edugp/wav2vec2-xls-r-300m-cv8-es | null | [
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"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
|
# wav2vec2-xls-r-300m-cv8-es
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common_voice dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.2115
- eval_wer: 0.1931
- eval_runtime: 859.964
- eval_samples_per_second: 17.954
- eval_steps_per_second: 2.244
- epoc... | [
"# wav2vec2-xls-r-300m-cv8-es\n\nThis model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common_voice dataset.\nIt achieves the following results on the evaluation set:\n- eval_loss: 0.2115\n- eval_wer: 0.1931\n- eval_runtime: 859.964\n- eval_samples_per_second: 17.954\n- eval_steps_per_second: 2.... | [
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text-classification | transformers |
## Model `RuPERTa_base_sentiment_analysis_es`
### **A finetuned model for Sentiment analysis in Spanish**
This model was trained using Amazon SageMaker and the new Hugging Face Deep Learning container,
The base model is **RuPERTa-base (uncased)** which is a RoBERTa model trained on a uncased version of big Spa... | {"language": "es", "license": "apache-2.0", "tags": ["sagemaker", "ruperta", "TextClassification", "SentimentAnalysis"], "datasets": ["IMDbreviews_es"], "name": "RuPERTa_base_sentiment_analysis_es", "results": [{"task": {"name": "Sentiment Analysis", "type": "sentiment-analysis"}}, {"dataset": {"name": "IMDb Reviews in... | edumunozsala/RuPERTa_base_sentiment_analysis_es | null | [
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"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"es"
] | TAGS
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|
## Model 'RuPERTa_base_sentiment_analysis_es'
### A finetuned model for Sentiment analysis in Spanish
This model was trained using Amazon SageMaker and the new Hugging Face Deep Learning container,
The base model is RuPERTa-base (uncased) which is a RoBERTa model trained on a uncased version of big Spanish cor... | [
"## Model 'RuPERTa_base_sentiment_analysis_es'",
"### A finetuned model for Sentiment analysis in Spanish\r\n\r\nThis model was trained using Amazon SageMaker and the new Hugging Face Deep Learning container,\r\nThe base model is RuPERTa-base (uncased) which is a RoBERTa model trained on a uncased version of big ... | [
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"## Model 'RuPERTa_base_sentiment_analysis_es'",
"### A finetuned model for Sentiment... |
summarization | transformers |
# **Italian T5 Abstractive Summarization**
gsarti/it5-base fine-tuned in italian for abstractive text summarization. | {"language": ["it"], "tags": ["summarization"]} | efederici/it5-base-summarization | null | [
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"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"it"
] | TAGS
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|
# Italian T5 Abstractive Summarization
gsarti/it5-base fine-tuned in italian for abstractive text summarization. | [
"# Italian T5 Abstractive Summarization\n\ngsarti/it5-base fine-tuned in italian for abstractive text summarization."
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] |
summarization | transformers |
# text2tags
The model has been trained on a collection of 28k news articles with tags. Its purpose is to create tags suitable for the given article. We can use this model also for information-retrieval purposes (GenQ), fine-tuning sentence-transformers for asymmetric semantic search.
If you like this project, consi... | {"language": ["it"], "tags": ["summarization", "tags", "Italian"], "inference": {"parameters": {"do_sample": false, "min_length": 0}}, "widget": [{"text": "Nel 1924 la scrittrice Virginia Woolf affront\u00f2 nel saggio Mr Bennett e Mrs Brown il tema della costruzione e della struttura del romanzo, genere all\u2019epoca... | efederici/text2tags | null | [
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|
# text2tags
The model has been trained on a collection of 28k news articles with tags. Its purpose is to create tags suitable for the given article. We can use this model also for information-retrieval purposes (GenQ), fine-tuning sentence-transformers for asymmetric semantic search.
If you like this project, consi... | [
"# text2tags\n\nThe model has been trained on a collection of 28k news articles with tags. Its purpose is to create tags suitable for the given article. We can use this model also for information-retrieval purposes (GenQ), fine-tuning sentence-transformers for asymmetric semantic search. \n\nIf you like this projec... | [
"TAGS\n#transformers #pytorch #safetensors #t5 #text2text-generation #summarization #tags #Italian #it #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# text2tags\n\nThe model has been trained on a collection of 28k news articles with tags. Its purpose is to crea... |
audio-classification | transformers |
# Speech Emotion Recognition By Fine-Tuning Wav2Vec 2.0
The model is a fine-tuned version of [jonatasgrosman/wav2vec2-large-xlsr-53-english](https://huggingface.co/jonatasgrosman/wav2vec2-large-xlsr-53-english) for a Speech Emotion Recognition (SER) task.
The dataset used to fine-tune the original pre-trained model ... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model_index": {"name": "wav2vec2-lg-xlsr-en-speech-emotion-recognition"}} | ehcalabres/wav2vec2-lg-xlsr-en-speech-emotion-recognition | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"audio-classification",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #audio-classification #generated_from_trainer #license-apache-2.0 #endpoints_compatible #has_space #region-us
| Speech Emotion Recognition By Fine-Tuning Wav2Vec 2.0
=====================================================
The model is a fine-tuned version of jonatasgrosman/wav2vec2-large-xlsr-53-english for a Speech Emotion Recognition (SER) task.
The dataset used to fine-tune the original pre-trained model is the RAVDESS data... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 8\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #audio-classification #generated_from_trainer #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size... |
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. -->
# bert-base-ehddnr-ynat
This model is a fine-tuned version of [klue/bert-base](https://huggingface.co/klue/bert-base) on the klue ... | {"tags": ["generated_from_trainer"], "datasets": ["klue"], "metrics": ["f1"], "model_index": [{"name": "bert-base-ehddnr-ynat", "results": [{"task": {"name": "Text Classification", "type": "text-classification"}, "dataset": {"name": "klue", "type": "klue", "args": "ynat"}, "metric": {"name": "F1", "type": "f1", "value"... | ehddnr301/bert-base-ehddnr-ynat | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:klue",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #text-classification #generated_from_trainer #dataset-klue #autotrain_compatible #endpoints_compatible #region-us
| bert-base-ehddnr-ynat
=====================
This model is a fine-tuned version of klue/bert-base on the klue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3587
* F1: 0.8721
Model description
-----------------
More information needed
Intended uses & limitations
-------------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 256\n* eval\\_batch\\_size: 256\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",
"### Trai... | [
"TAGS\n#transformers #pytorch #bert #text-classification #generated_from_trainer #dataset-klue #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\n* train\\_batch\\_size: 256\n* eval\... |
text2text-generation | transformers | # ehdwns1516/bart_finetuned_xsum
* This model has been trained as a [xsum dataset](https://huggingface.co/datasets/xsum).
* Input text what you want to summarize.
review generator DEMO: [Ainize DEMO](https://main-text-summarizer-ehdwns1516.endpoint.ainize.ai/)
review generator API: [Ainize API](https://ainize.web.ap... | {} | ehdwns1516/bart_finetuned_xsum | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bart #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
| # ehdwns1516/bart_finetuned_xsum
* This model has been trained as a xsum dataset.
* Input text what you want to summarize.
review generator DEMO: Ainize DEMO
review generator API: Ainize API
## Overview
Language model: facebook/bart-large
Language: English
Training data: xsum dataset
Code: See Ainize Workspace
... | [
"# ehdwns1516/bart_finetuned_xsum\n\n* This model has been trained as a xsum dataset.\n* Input text what you want to summarize.\n\nreview generator DEMO: Ainize DEMO\n\nreview generator API: Ainize API",
"## Overview\n\nLanguage model: facebook/bart-large\n\nLanguage: English\n\nTraining data: xsum dataset\n\nCod... | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n",
"# ehdwns1516/bart_finetuned_xsum\n\n* This model has been trained as a xsum dataset.\n* Input text what you want to summarize.\n\nreview generator DEMO: Ainize DEMO\n\nreview generator API: Ainiz... |
multiple-choice | transformers | # ehdwns1516/bert-base-uncased_SWAG
* This model has been trained as a [SWAG dataset](https://huggingface.co/ehdwns1516/bert-base-uncased_SWAG).
* Sentence Inference Multiple Choice DEMO: [Ainize DEMO](https://main-sentence-inference-multiple-choice-ehdwns1516.endpoint.ainize.ai/)
* Sentence Inference Multiple Choic... | {} | ehdwns1516/bert-base-uncased_SWAG | null | [
"transformers",
"pytorch",
"bert",
"multiple-choice",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #multiple-choice #endpoints_compatible #region-us
| # ehdwns1516/bert-base-uncased_SWAG
* This model has been trained as a SWAG dataset.
* Sentence Inference Multiple Choice DEMO: Ainize DEMO
* Sentence Inference Multiple Choice API: Ainize API
## Overview
Language model: bert-base-uncased
Language: English
Training data: SWAG dataset
Code: See Ainize Workspace
... | [
"# ehdwns1516/bert-base-uncased_SWAG\n\n* This model has been trained as a SWAG dataset.\n\n* Sentence Inference Multiple Choice DEMO: Ainize DEMO\n\n* Sentence Inference Multiple Choice API: Ainize API",
"## Overview\n\nLanguage model: bert-base-uncased\n\nLanguage: English\n\nTraining data: SWAG dataset\n\nCode... | [
"TAGS\n#transformers #pytorch #bert #multiple-choice #endpoints_compatible #region-us \n",
"# ehdwns1516/bert-base-uncased_SWAG\n\n* This model has been trained as a SWAG dataset.\n\n* Sentence Inference Multiple Choice DEMO: Ainize DEMO\n\n* Sentence Inference Multiple Choice API: Ainize API",
"## Overview\n\n... |
text-generation | transformers | # gpt2_review_star1
* This model has been trained as a review_body dataset with a star of 1 in the [amazon_review dataset](https://huggingface.co/datasets/amazon_reviews_multi).
* Input text what you want to generate review.
* If the context is longer than 1200 characters, the context may be cut in the middle and the ... | {} | ehdwns1516/gpt2_review_star1 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # gpt2_review_star1
* This model has been trained as a review_body dataset with a star of 1 in the amazon_review dataset.
* Input text what you want to generate review.
* If the context is longer than 1200 characters, the context may be cut in the middle and the result may not come out well.
review generator DEMO: Ai... | [
"# gpt2_review_star1\n\n* This model has been trained as a review_body dataset with a star of 1 in the amazon_review dataset.\n* Input text what you want to generate review.\n* If the context is longer than 1200 characters, the context may be cut in the middle and the result may not come out well.\n\nreview generat... | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# gpt2_review_star1\n\n* This model has been trained as a review_body dataset with a star of 1 in the amazon_review dataset.\n* Input text what you want to generate review.\... |
text-generation | transformers | # gpt2_review_star2
* This model has been trained as a review_body dataset with a star of 2 in the [amazon_review dataset](https://huggingface.co/datasets/amazon_reviews_multi).
* Input text what you want to generate review.
* If the context is longer than 1200 characters, the context may be cut in the middle and the ... | {} | ehdwns1516/gpt2_review_star2 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # gpt2_review_star2
* This model has been trained as a review_body dataset with a star of 2 in the amazon_review dataset.
* Input text what you want to generate review.
* If the context is longer than 1200 characters, the context may be cut in the middle and the result may not come out well.
review generator DEMO: Ai... | [
"# gpt2_review_star2\n\n* This model has been trained as a review_body dataset with a star of 2 in the amazon_review dataset.\n* Input text what you want to generate review.\n* If the context is longer than 1200 characters, the context may be cut in the middle and the result may not come out well.\n\nreview generat... | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# gpt2_review_star2\n\n* This model has been trained as a review_body dataset with a star of 2 in the amazon_review dataset.\n* Input text what you want to generate review.\... |
text-generation | transformers | # gpt2_review_star3
* This model has been trained as a review_body dataset with a star of 3 in the [amazon_review dataset](https://huggingface.co/datasets/amazon_reviews_multi).
* Input text what you want to generate review.
* If the context is longer than 1200 characters, the context may be cut in the middle and the ... | {} | ehdwns1516/gpt2_review_star3 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # gpt2_review_star3
* This model has been trained as a review_body dataset with a star of 3 in the amazon_review dataset.
* Input text what you want to generate review.
* If the context is longer than 1200 characters, the context may be cut in the middle and the result may not come out well.
review generator DEMO: Ai... | [
"# gpt2_review_star3\n\n* This model has been trained as a review_body dataset with a star of 3 in the amazon_review dataset.\n* Input text what you want to generate review.\n* If the context is longer than 1200 characters, the context may be cut in the middle and the result may not come out well.\n\nreview generat... | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# gpt2_review_star3\n\n* This model has been trained as a review_body dataset with a star of 3 in the amazon_review dataset.\n* Input text what you want to generate review.\... |
text-generation | transformers | # gpt2_review_star4
* This model has been trained as a review_body dataset with a star of 4 in the [amazon_review dataset](https://huggingface.co/datasets/amazon_reviews_multi).
* Input text what you want to generate review.
* If the context is longer than 1200 characters, the context may be cut in the middle and the ... | {} | ehdwns1516/gpt2_review_star4 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # gpt2_review_star4
* This model has been trained as a review_body dataset with a star of 4 in the amazon_review dataset.
* Input text what you want to generate review.
* If the context is longer than 1200 characters, the context may be cut in the middle and the result may not come out well.
review generator DEMO: Ai... | [
"# gpt2_review_star4\n\n* This model has been trained as a review_body dataset with a star of 4 in the amazon_review dataset.\n* Input text what you want to generate review.\n* If the context is longer than 1200 characters, the context may be cut in the middle and the result may not come out well.\n\nreview generat... | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# gpt2_review_star4\n\n* This model has been trained as a review_body dataset with a star of 4 in the amazon_review dataset.\n* Input text what you want to generate review.\... |
text-generation | transformers | # gpt2_review_star5
* This model has been trained as a review_body dataset with a star of 5 in the [amazon_review dataset](https://huggingface.co/datasets/amazon_reviews_multi).
* Input text what you want to generate review.
* If the context is longer than 1200 characters, the context may be cut in the middle and the ... | {} | ehdwns1516/gpt2_review_star5 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # gpt2_review_star5
* This model has been trained as a review_body dataset with a star of 5 in the amazon_review dataset.
* Input text what you want to generate review.
* If the context is longer than 1200 characters, the context may be cut in the middle and the result may not come out well.
review generator DEMO: Ai... | [
"# gpt2_review_star5\n\n* This model has been trained as a review_body dataset with a star of 5 in the amazon_review dataset.\n* Input text what you want to generate review.\n* If the context is longer than 1200 characters, the context may be cut in the middle and the result may not come out well.\n\nreview generat... | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# gpt2_review_star5\n\n* This model has been trained as a review_body dataset with a star of 5 in the amazon_review dataset.\n* Input text what you want to generate review.\... |
text-generation | transformers | # ehdwns1516/gpt3-kor-based_gpt2_review_SR1
* This model has been trained Korean dataset as a star of 1 in the [naver shopping reivew dataset](https://github.com/bab2min/corpus/tree/master/sentiment).
* Input text what you want to generate review.
* If the context is longer than 1200 characters, the context may be cut... | {} | ehdwns1516/gpt3-kor-based_gpt2_review_SR1 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # ehdwns1516/gpt3-kor-based_gpt2_review_SR1
* This model has been trained Korean dataset as a star of 1 in the naver shopping reivew dataset.
* Input text what you want to generate review.
* If the context is longer than 1200 characters, the context may be cut in the middle and the result may not come out well.
revie... | [
"# ehdwns1516/gpt3-kor-based_gpt2_review_SR1\n\n* This model has been trained Korean dataset as a star of 1 in the naver shopping reivew dataset.\n* Input text what you want to generate review.\n* If the context is longer than 1200 characters, the context may be cut in the middle and the result may not come out wel... | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# ehdwns1516/gpt3-kor-based_gpt2_review_SR1\n\n* This model has been trained Korean dataset as a star of 1 in the naver shopping reivew dataset.\n* Input text what you want ... |
text-generation | transformers | # ehdwns1516/gpt3-kor-based_gpt2_review_SR2
* This model has been trained Korean dataset as a star of 2 in the [naver shopping reivew dataset](https://github.com/bab2min/corpus/tree/master/sentiment).
* Input text what you want to generate review.
* If the context is longer than 1200 characters, the context may be cut... | {} | ehdwns1516/gpt3-kor-based_gpt2_review_SR2 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # ehdwns1516/gpt3-kor-based_gpt2_review_SR2
* This model has been trained Korean dataset as a star of 2 in the naver shopping reivew dataset.
* Input text what you want to generate review.
* If the context is longer than 1200 characters, the context may be cut in the middle and the result may not come out well.
revie... | [
"# ehdwns1516/gpt3-kor-based_gpt2_review_SR2\n\n* This model has been trained Korean dataset as a star of 2 in the naver shopping reivew dataset.\n* Input text what you want to generate review.\n* If the context is longer than 1200 characters, the context may be cut in the middle and the result may not come out wel... | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# ehdwns1516/gpt3-kor-based_gpt2_review_SR2\n\n* This model has been trained Korean dataset as a star of 2 in the naver shopping reivew dataset.\n* Input text what you want ... |
text-generation | transformers | # ehdwns1516/gpt3-kor-based_gpt2_review_SR3
* This model has been trained Korean dataset as a star of 3 in the [naver shopping reivew dataset](https://github.com/bab2min/corpus/tree/master/sentiment).
* Input text what you want to generate review.
* If the context is longer than 1200 characters, the context may be cut... | {} | ehdwns1516/gpt3-kor-based_gpt2_review_SR3 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # ehdwns1516/gpt3-kor-based_gpt2_review_SR3
* This model has been trained Korean dataset as a star of 3 in the naver shopping reivew dataset.
* Input text what you want to generate review.
* If the context is longer than 1200 characters, the context may be cut in the middle and the result may not come out well.
revie... | [
"# ehdwns1516/gpt3-kor-based_gpt2_review_SR3\n\n* This model has been trained Korean dataset as a star of 3 in the naver shopping reivew dataset.\n* Input text what you want to generate review.\n* If the context is longer than 1200 characters, the context may be cut in the middle and the result may not come out wel... | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# ehdwns1516/gpt3-kor-based_gpt2_review_SR3\n\n* This model has been trained Korean dataset as a star of 3 in the naver shopping reivew dataset.\n* Input text what you want ... |
text-generation | transformers | # ehdwns1516/gpt3-kor-based_gpt2_review_SR4
* This model has been trained Korean dataset as a star of 4 in the [naver shopping reivew dataset](https://github.com/bab2min/corpus/tree/master/sentiment).
* Input text what you want to generate review.
* If the context is longer than 1200 characters, the context may be cut... | {} | ehdwns1516/gpt3-kor-based_gpt2_review_SR4 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # ehdwns1516/gpt3-kor-based_gpt2_review_SR4
* This model has been trained Korean dataset as a star of 4 in the naver shopping reivew dataset.
* Input text what you want to generate review.
* If the context is longer than 1200 characters, the context may be cut in the middle and the result may not come out well.
revie... | [
"# ehdwns1516/gpt3-kor-based_gpt2_review_SR4\n\n* This model has been trained Korean dataset as a star of 4 in the naver shopping reivew dataset.\n* Input text what you want to generate review.\n* If the context is longer than 1200 characters, the context may be cut in the middle and the result may not come out wel... | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# ehdwns1516/gpt3-kor-based_gpt2_review_SR4\n\n* This model has been trained Korean dataset as a star of 4 in the naver shopping reivew dataset.\n* Input text what you want ... |
text-generation | transformers | # ehdwns1516/gpt3-kor-based_gpt2_review_SR5
* This model has been trained Korean dataset as a star of 5 in the [naver shopping reivew dataset](https://github.com/bab2min/corpus/tree/master/sentiment).
* Input text what you want to generate review.
* If the context is longer than 1200 characters, the context may be cut... | {} | ehdwns1516/gpt3-kor-based_gpt2_review_SR5 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # ehdwns1516/gpt3-kor-based_gpt2_review_SR5
* This model has been trained Korean dataset as a star of 5 in the naver shopping reivew dataset.
* Input text what you want to generate review.
* If the context is longer than 1200 characters, the context may be cut in the middle and the result may not come out well.
revie... | [
"# ehdwns1516/gpt3-kor-based_gpt2_review_SR5\n\n* This model has been trained Korean dataset as a star of 5 in the naver shopping reivew dataset.\n* Input text what you want to generate review.\n* If the context is longer than 1200 characters, the context may be cut in the middle and the result may not come out wel... | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# ehdwns1516/gpt3-kor-based_gpt2_review_SR5\n\n* This model has been trained Korean dataset as a star of 5 in the naver shopping reivew dataset.\n* Input text what you want ... |
text-classification | transformers | # klue-roberta-base-kornli
* This model trained with Korean dataset.
* Input premise sentence and hypothesis sentence.
* You can use English, but don't expect accuracy.
* If the context is longer than 1200 characters, the context may be cut in the middle and the result may not come out well.
klue-roberta-base-kornli ... | {} | ehdwns1516/klue-roberta-base-kornli | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #text-classification #autotrain_compatible #endpoints_compatible #region-us
| # klue-roberta-base-kornli
* This model trained with Korean dataset.
* Input premise sentence and hypothesis sentence.
* You can use English, but don't expect accuracy.
* If the context is longer than 1200 characters, the context may be cut in the middle and the result may not come out well.
klue-roberta-base-kornli ... | [
"# klue-roberta-base-kornli\n\n* This model trained with Korean dataset.\n* Input premise sentence and hypothesis sentence.\n* You can use English, but don't expect accuracy.\n* If the context is longer than 1200 characters, the context may be cut in the middle and the result may not come out well.\n\nklue-roberta-... | [
"TAGS\n#transformers #pytorch #roberta #text-classification #autotrain_compatible #endpoints_compatible #region-us \n",
"# klue-roberta-base-kornli\n\n* This model trained with Korean dataset.\n* Input premise sentence and hypothesis sentence.\n* You can use English, but don't expect accuracy.\n* If the context i... |
text-classification | transformers | # klue-roberta-base-sae
* This model trained with Korean dataset.
* Input sentence what you want to grasp intent.
* You can use English, but don't expect accuracy.
klue-roberta-base-kornli DEMO: [Ainize DEMO](https://main-klue-roberta-base-kornli-ehdwns1516.endpoint.ainize.ai/)
klue-roberta-base-kornli API: [Ainize ... | {} | ehdwns1516/klue-roberta-base_sae | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #text-classification #autotrain_compatible #endpoints_compatible #region-us
| # klue-roberta-base-sae
* This model trained with Korean dataset.
* Input sentence what you want to grasp intent.
* You can use English, but don't expect accuracy.
klue-roberta-base-kornli DEMO: Ainize DEMO
klue-roberta-base-kornli API: Ainize API
## Overview
Language model: klue/roberta-base
Language: Korean
Tr... | [
"# klue-roberta-base-sae\n\n* This model trained with Korean dataset.\n* Input sentence what you want to grasp intent.\n* You can use English, but don't expect accuracy.\n\nklue-roberta-base-kornli DEMO: Ainize DEMO\n\nklue-roberta-base-kornli API: Ainize API",
"## Overview\n\nLanguage model: klue/roberta-base\n\... | [
"TAGS\n#transformers #pytorch #roberta #text-classification #autotrain_compatible #endpoints_compatible #region-us \n",
"# klue-roberta-base-sae\n\n* This model trained with Korean dataset.\n* Input sentence what you want to grasp intent.\n* You can use English, but don't expect accuracy.\n\nklue-roberta-base-kor... |
null | null | # Load the Model
```python
from transformers import GPT2Tokenizer, GPT2LMHeadModel
import torch
# start and end tokens for generation
START_TKN = "<|startoftext|>"
END_TKN = "<|endoftext|>"
# fine tuned on onion dataset w/ distilgpt2
tokenizer = GPT2Tokenizer.from_pretrained("distilgpt2")
model = GPT2LMHeadModel.fro... | {} | ejjaffe/distilgpt2-onion | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| # Load the Model
| [
"# Load the Model"
] | [
"TAGS\n#region-us \n",
"# Load the Model"
] |
text-generation | transformers |
# Harry Potter DialoGPT Model | {"tags": ["conversational"]} | eklrivera/DialoGPT-small-harrypotter | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Harry Potter DialoGPT Model | [
"# Harry Potter DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Harry Potter DialoGPT Model"
] |
token-classification | transformers |
[DistilBERT base cased](https://huggingface.co/distilbert-base-cased), fine-tuned for NER using the [conll03 english dataset](https://huggingface.co/datasets/conll2003). Note that this model is sensitive to capital letters — "english" is different than "English". For the case insensitive version, please use [elastic/d... | {"language": "en", "license": "apache-2.0", "datasets": ["conll2003"], "model-index": [{"name": "elastic/distilbert-base-cased-finetuned-conll03-english", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "conll2003", "type": "conll2003", "config": "conll2003", "... | elastic/distilbert-base-cased-finetuned-conll03-english | null | [
"transformers",
"pytorch",
"safetensors",
"distilbert",
"token-classification",
"en",
"dataset:conll2003",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #safetensors #distilbert #token-classification #en #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
|
DistilBERT base cased, fine-tuned for NER using the conll03 english dataset. Note that this model is sensitive to capital letters — "english" is different than "English". For the case insensitive version, please use elastic/distilbert-base-uncased-finetuned-conll03-english.
## Versions
- Transformers version: 4.3.1
... | [
"## Versions\n\n- Transformers version: 4.3.1\n- Datasets version: 1.3.0",
"## Training\n\n\n\nAfter training, we update the labels to match the NER specific labels from the\ndataset conll2003"
] | [
"TAGS\n#transformers #pytorch #safetensors #distilbert #token-classification #en #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"## Versions\n\n- Transformers version: 4.3.1\n- Datasets version: 1.3.0",
"## Training\n\n\n\nAfter trainin... |
token-classification | transformers |
[DistilBERT base uncased](https://huggingface.co/distilbert-base-uncased), fine-tuned for NER using the [conll03 english dataset](https://huggingface.co/datasets/conll2003). Note that this model is **not** sensitive to capital letters — "english" is the same as "English". For the case sensitive version, please use [el... | {"language": "en", "license": "apache-2.0", "datasets": ["conll2003"], "model-index": [{"name": "elastic/distilbert-base-uncased-finetuned-conll03-english", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "conll2003", "type": "conll2003", "config": "conll2003",... | elastic/distilbert-base-uncased-finetuned-conll03-english | null | [
"transformers",
"pytorch",
"safetensors",
"distilbert",
"token-classification",
"en",
"dataset:conll2003",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #safetensors #distilbert #token-classification #en #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
|
DistilBERT base uncased, fine-tuned for NER using the conll03 english dataset. Note that this model is not sensitive to capital letters — "english" is the same as "English". For the case sensitive version, please use elastic/distilbert-base-cased-finetuned-conll03-english.
## Versions
- Transformers version: 4.3.1
-... | [
"## Versions\n\n- Transformers version: 4.3.1\n- Datasets version: 1.3.0",
"## Training\n\n\n\nAfter training, we update the labels to match the NER specific labels from the\ndataset conll2003"
] | [
"TAGS\n#transformers #pytorch #safetensors #distilbert #token-classification #en #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"## Versions\n\n- Transformers version: 4.3.1\n- Datasets version: 1.3.0",
"## Training\n\n\n\nAfter trainin... |
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. -->
# MarianMix_en-10
This model is a fine-tuned version of [Helsinki-NLP/opus-tatoeba-en-ja](https://huggingface.co/Helsinki-NLP/opus... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["bleu"], "model-index": [{"name": "MarianMix_en-10", "results": []}]} | eldor-97/MarianMix_en-10 | null | [
"transformers",
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #marian #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| MarianMix\_en-10
================
This model is a fine-tuned version of Helsinki-NLP/opus-tatoeba-en-ja on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.0752
* Bleu: 14.601
* Gen Len: 45.8087
Model description
-----------------
More information needed
Intended uses & lim... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 99\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_step... | [
"TAGS\n#transformers #pytorch #tensorboard #marian #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_bat... |
text-generation | transformers |
#Rick DialoGPT model | {"tags": ["conversational"]} | eldritch-axolotl/Rick | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
#Rick DialoGPT model | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text2text-generation | transformers | T5 pre-trained on e-commerce data | {} | elena-soare/t5-base-ecommerce | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| T5 pre-trained on e-commerce data | [] | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
null | null | Datasaur project | {} | elena-soare/t5-small-datasaur | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| Datasaur project | [] | [
"TAGS\n#region-us \n"
] |
question-answering | transformers |
## CS224n SQuAD2.0 Project Dataset
The goal of this model is to save CS224n students GPU time when establishing
baselines to beat for the [Default Final Project](http://web.stanford.edu/class/cs224n/project/default-final-project-handout.pdf).
The training set used to fine-tune this model is the same as
the [official o... | {"tags": ["exbert"]} | elgeish/cs224n-squad2.0-albert-base-v2 | null | [
"transformers",
"pytorch",
"albert",
"question-answering",
"exbert",
"arxiv:2004.07067",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2004.07067"
] | [] | TAGS
#transformers #pytorch #albert #question-answering #exbert #arxiv-2004.07067 #endpoints_compatible #region-us
|
## CS224n SQuAD2.0 Project Dataset
The goal of this model is to save CS224n students GPU time when establishing
baselines to beat for the Default Final Project.
The training set used to fine-tune this model is the same as
the official one; however,
evaluation and model selection were performed using roughly half of th... | [
"## CS224n SQuAD2.0 Project Dataset\nThe goal of this model is to save CS224n students GPU time when establishing\nbaselines to beat for the Default Final Project.\nThe training set used to fine-tune this model is the same as\nthe official one; however,\nevaluation and model selection were performed using roughly h... | [
"TAGS\n#transformers #pytorch #albert #question-answering #exbert #arxiv-2004.07067 #endpoints_compatible #region-us \n",
"## CS224n SQuAD2.0 Project Dataset\nThe goal of this model is to save CS224n students GPU time when establishing\nbaselines to beat for the Default Final Project.\nThe training set used to fi... |
question-answering | transformers |
## CS224n SQuAD2.0 Project Dataset
The goal of this model is to save CS224n students GPU time when establishing
baselines to beat for the [Default Final Project](http://web.stanford.edu/class/cs224n/project/default-final-project-handout.pdf).
The training set used to fine-tune this model is the same as
the [official o... | {"tags": ["exbert"]} | elgeish/cs224n-squad2.0-albert-large-v2 | null | [
"transformers",
"pytorch",
"albert",
"question-answering",
"exbert",
"arxiv:2004.07067",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2004.07067"
] | [] | TAGS
#transformers #pytorch #albert #question-answering #exbert #arxiv-2004.07067 #endpoints_compatible #region-us
|
## CS224n SQuAD2.0 Project Dataset
The goal of this model is to save CS224n students GPU time when establishing
baselines to beat for the Default Final Project.
The training set used to fine-tune this model is the same as
the official one; however,
evaluation and model selection were performed using roughly half of th... | [
"## CS224n SQuAD2.0 Project Dataset\nThe goal of this model is to save CS224n students GPU time when establishing\nbaselines to beat for the Default Final Project.\nThe training set used to fine-tune this model is the same as\nthe official one; however,\nevaluation and model selection were performed using roughly h... | [
"TAGS\n#transformers #pytorch #albert #question-answering #exbert #arxiv-2004.07067 #endpoints_compatible #region-us \n",
"## CS224n SQuAD2.0 Project Dataset\nThe goal of this model is to save CS224n students GPU time when establishing\nbaselines to beat for the Default Final Project.\nThe training set used to fi... |
question-answering | transformers |
## CS224n SQuAD2.0 Project Dataset
The goal of this model is to save CS224n students GPU time when establishing
baselines to beat for the [Default Final Project](http://web.stanford.edu/class/cs224n/project/default-final-project-handout.pdf).
The training set used to fine-tune this model is the same as
the [official o... | {"tags": ["exbert"]} | elgeish/cs224n-squad2.0-albert-xxlarge-v1 | null | [
"transformers",
"pytorch",
"albert",
"question-answering",
"exbert",
"arxiv:2004.07067",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2004.07067"
] | [] | TAGS
#transformers #pytorch #albert #question-answering #exbert #arxiv-2004.07067 #endpoints_compatible #region-us
|
## CS224n SQuAD2.0 Project Dataset
The goal of this model is to save CS224n students GPU time when establishing
baselines to beat for the Default Final Project.
The training set used to fine-tune this model is the same as
the official one; however,
evaluation and model selection were performed using roughly half of th... | [
"## CS224n SQuAD2.0 Project Dataset\nThe goal of this model is to save CS224n students GPU time when establishing\nbaselines to beat for the Default Final Project.\nThe training set used to fine-tune this model is the same as\nthe official one; however,\nevaluation and model selection were performed using roughly h... | [
"TAGS\n#transformers #pytorch #albert #question-answering #exbert #arxiv-2004.07067 #endpoints_compatible #region-us \n",
"## CS224n SQuAD2.0 Project Dataset\nThe goal of this model is to save CS224n students GPU time when establishing\nbaselines to beat for the Default Final Project.\nThe training set used to fi... |
question-answering | transformers | ## CS224n SQuAD2.0 Project Dataset
The goal of this model is to save CS224n students GPU time when establishing
baselines to beat for the [Default Final Project](http://web.stanford.edu/class/cs224n/project/default-final-project-handout.pdf).
The training set used to fine-tune this model is the same as
the [official on... | {} | elgeish/cs224n-squad2.0-distilbert-base-uncased | null | [
"transformers",
"pytorch",
"distilbert",
"question-answering",
"arxiv:2004.07067",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2004.07067"
] | [] | TAGS
#transformers #pytorch #distilbert #question-answering #arxiv-2004.07067 #endpoints_compatible #region-us
| ## CS224n SQuAD2.0 Project Dataset
The goal of this model is to save CS224n students GPU time when establishing
baselines to beat for the Default Final Project.
The training set used to fine-tune this model is the same as
the official one; however,
evaluation and model selection were performed using roughly half of the... | [
"## CS224n SQuAD2.0 Project Dataset\nThe goal of this model is to save CS224n students GPU time when establishing\nbaselines to beat for the Default Final Project.\nThe training set used to fine-tune this model is the same as\nthe official one; however,\nevaluation and model selection were performed using roughly h... | [
"TAGS\n#transformers #pytorch #distilbert #question-answering #arxiv-2004.07067 #endpoints_compatible #region-us \n",
"## CS224n SQuAD2.0 Project Dataset\nThe goal of this model is to save CS224n students GPU time when establishing\nbaselines to beat for the Default Final Project.\nThe training set used to fine-t... |
question-answering | transformers | ## CS224n SQuAD2.0 Project Dataset
The goal of this model is to save CS224n students GPU time when establishing
baselines to beat for the [Default Final Project](http://web.stanford.edu/class/cs224n/project/default-final-project-handout.pdf).
The training set used to fine-tune this model is the same as
the [official on... | {} | elgeish/cs224n-squad2.0-roberta-base | null | [
"transformers",
"pytorch",
"jax",
"roberta",
"question-answering",
"arxiv:2004.07067",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2004.07067"
] | [] | TAGS
#transformers #pytorch #jax #roberta #question-answering #arxiv-2004.07067 #endpoints_compatible #region-us
| ## CS224n SQuAD2.0 Project Dataset
The goal of this model is to save CS224n students GPU time when establishing
baselines to beat for the Default Final Project.
The training set used to fine-tune this model is the same as
the official one; however,
evaluation and model selection were performed using roughly half of the... | [
"## CS224n SQuAD2.0 Project Dataset\nThe goal of this model is to save CS224n students GPU time when establishing\nbaselines to beat for the Default Final Project.\nThe training set used to fine-tune this model is the same as\nthe official one; however,\nevaluation and model selection were performed using roughly h... | [
"TAGS\n#transformers #pytorch #jax #roberta #question-answering #arxiv-2004.07067 #endpoints_compatible #region-us \n",
"## CS224n SQuAD2.0 Project Dataset\nThe goal of this model is to save CS224n students GPU time when establishing\nbaselines to beat for the Default Final Project.\nThe training set used to fine... |
text-generation | transformers |
# GPT2-Medium-Arabic-Poetry
Fine-tuned [aubmindlab/aragpt2-medium](https://huggingface.co/aubmindlab/aragpt2-medium) on
the [Arabic Poetry Dataset (6th - 21st century)](https://www.kaggle.com/fahd09/arabic-poetry-dataset-478-2017)
using 41,922 lines of poetry as the train split and 9,007 (by poets not in the train sp... | {"language": "ar", "license": "apache-2.0", "tags": ["text-generation", "poetry"], "datasets": ["Arabic Poetry Dataset (6th - 21st century)"], "metrics": ["perplexity"], "widget": [{"text": "\u0644\u0644\u0648\u0647\u0644\u0629 \u0627\u0644\u0623\u0648\u0644\u0649 \u0642\u0631\u0623\u062a \u0641\u064a \u0639\u064a\u064... | elgeish/gpt2-medium-arabic-poetry | null | [
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"poetry",
"ar",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ar"
] | TAGS
#transformers #pytorch #jax #gpt2 #text-generation #poetry #ar #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# GPT2-Medium-Arabic-Poetry
Fine-tuned aubmindlab/aragpt2-medium on
the Arabic Poetry Dataset (6th - 21st century)
using 41,922 lines of poetry as the train split and 9,007 (by poets not in the train split) for validation.
## Usage
Here's the output:
| [
"# GPT2-Medium-Arabic-Poetry\n\nFine-tuned aubmindlab/aragpt2-medium on\nthe Arabic Poetry Dataset (6th - 21st century)\nusing 41,922 lines of poetry as the train split and 9,007 (by poets not in the train split) for validation.",
"## Usage\n\n\n\nHere's the output:"
] | [
"TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #poetry #ar #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# GPT2-Medium-Arabic-Poetry\n\nFine-tuned aubmindlab/aragpt2-medium on\nthe Arabic Poetry Dataset (6th - 21st century)\nusi... |
automatic-speech-recognition | transformers |
# Wav2Vec2-Base-TIMIT
Fine-tuned [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base)
on the [timit_asr dataset](https://huggingface.co/datasets/timit_asr).
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (without a language mod... | {"language": "en", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech"], "datasets": ["timit_asr"]} | elgeish/wav2vec2-base-timit-asr | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"en",
"dataset:timit_asr",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #speech #en #dataset-timit_asr #license-apache-2.0 #endpoints_compatible #region-us
|
# Wav2Vec2-Base-TIMIT
Fine-tuned facebook/wav2vec2-base
on the timit_asr dataset.
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (without a language model) as follows:
Here's the output:
## Fine-Tuning Script
You can find the script used t... | [
"# Wav2Vec2-Base-TIMIT\n\nFine-tuned facebook/wav2vec2-base\non the timit_asr dataset.\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:\n\n\n\nHere's the output:",
"## Fine-Tuning Script\n\nYou can ... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #speech #en #dataset-timit_asr #license-apache-2.0 #endpoints_compatible #region-us \n",
"# Wav2Vec2-Base-TIMIT\n\nFine-tuned facebook/wav2vec2-base\non the timit_asr dataset.\nWhen using this model, make sure that your speech input is s... |
automatic-speech-recognition | transformers |
# Wav2Vec2-Large-LV60-TIMIT
Fine-tuned [facebook/wav2vec2-large-lv60](https://huggingface.co/facebook/wav2vec2-large-lv60)
on the [timit_asr dataset](https://huggingface.co/datasets/timit_asr).
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (with... | {"language": "en", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech"], "datasets": ["timit_asr"]} | elgeish/wav2vec2-large-lv60-timit-asr | null | [
"transformers",
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"en",
"dataset:timit_asr",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #en #dataset-timit_asr #license-apache-2.0 #endpoints_compatible #region-us
|
# Wav2Vec2-Large-LV60-TIMIT
Fine-tuned facebook/wav2vec2-large-lv60
on the timit_asr dataset.
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (without a language model) as follows:
Here's the output:
## Fine-Tuning Script
You can find the s... | [
"# Wav2Vec2-Large-LV60-TIMIT\n\nFine-tuned facebook/wav2vec2-large-lv60\non the timit_asr dataset.\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:\n\n\n\nHere's the output:",
"## Fine-Tuning Script... | [
"TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #en #dataset-timit_asr #license-apache-2.0 #endpoints_compatible #region-us \n",
"# Wav2Vec2-Large-LV60-TIMIT\n\nFine-tuned facebook/wav2vec2-large-lv60\non the timit_asr dataset.\nWhen using this model, make sure that your ... |
automatic-speech-recognition | transformers |
# Wav2Vec2-Large-XLSR-53-Arabic
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53)
on Arabic using the `train` splits of [Common Voice](https://huggingface.co/datasets/common_voice)
and [Arabic Speech Corpus](https://huggingface.co/datasets/arabic_speech_corpus).
When... | {"language": "ar", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week", "hf-asr-leaderboard"], "datasets": ["arabic_speech_corpus", "mozilla-foundation/common_voice_6_1"], "metrics": ["wer"], "model-index": [{"name": "elgeish-wav2vec2-large-xlsr-53-arabic", "resu... | elgeish/wav2vec2-large-xlsr-53-arabic | null | [
"transformers",
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"hf-asr-leaderboard",
"ar",
"dataset:arabic_speech_corpus",
"dataset:mozilla-foundation/common_voice_6_1",
"license:apache-2.0",
"model-index",
"endpoints_compatible"... | null | 2022-03-02T23:29:05+00:00 | [] | [
"ar"
] | TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #hf-asr-leaderboard #ar #dataset-arabic_speech_corpus #dataset-mozilla-foundation/common_voice_6_1 #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us
|
# Wav2Vec2-Large-XLSR-53-Arabic
Fine-tuned facebook/wav2vec2-large-xlsr-53
on Arabic using the 'train' splits of Common Voice
and Arabic Speech Corpus.
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:
Here'... | [
"# Wav2Vec2-Large-XLSR-53-Arabic\n\nFine-tuned facebook/wav2vec2-large-xlsr-53\non Arabic using the 'train' splits of Common Voice\nand Arabic Speech Corpus.\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 fo... | [
"TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #hf-asr-leaderboard #ar #dataset-arabic_speech_corpus #dataset-mozilla-foundation/common_voice_6_1 #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us \n",
"# Wav2Vec2-Large-XL... |
automatic-speech-recognition | transformers |
# Wav2Vec2-Large-XLSR-53-Arabic
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53)
on the [Arabic Speech Corpus dataset](https://huggingface.co/datasets/arabic_speech_corpus).
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The ... | {"language": "ar", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech"], "datasets": ["arabic_speech_corpus"]} | elgeish/wav2vec2-large-xlsr-53-levantine-arabic | null | [
"transformers",
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"ar",
"dataset:arabic_speech_corpus",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ar"
] | TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #ar #dataset-arabic_speech_corpus #license-apache-2.0 #endpoints_compatible #region-us
|
# Wav2Vec2-Large-XLSR-53-Arabic
Fine-tuned facebook/wav2vec2-large-xlsr-53
on the Arabic Speech Corpus dataset.
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (without a language model) as follows:
Here's the output:
## Fine-Tuning Script
... | [
"# Wav2Vec2-Large-XLSR-53-Arabic\n\nFine-tuned facebook/wav2vec2-large-xlsr-53\non the Arabic Speech Corpus dataset.\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:\n\n\n\nHere's the output:",
"## ... | [
"TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #ar #dataset-arabic_speech_corpus #license-apache-2.0 #endpoints_compatible #region-us \n",
"# Wav2Vec2-Large-XLSR-53-Arabic\n\nFine-tuned facebook/wav2vec2-large-xlsr-53\non the Arabic Speech Corpus dataset.\nWhen using thi... |
null | null | # zero-shot-absa
## About
The goal of this project is to accomplish aspect-based sentiment analysis without dependence on the severely limited training data available - that is, the task of aspect-based sentiment analysis is not explicitly supervised, an approach known as “zero-shot learning”. Sentiment analysis has a... | {} | eli/zero-shot-absa | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| # zero-shot-absa
## About
The goal of this project is to accomplish aspect-based sentiment analysis without dependence on the severely limited training data available - that is, the task of aspect-based sentiment analysis is not explicitly supervised, an approach known as “zero-shot learning”. Sentiment analysis has a... | [
"# zero-shot-absa",
"## About\nThe goal of this project is to accomplish aspect-based sentiment analysis without dependence on the severely limited training data available - that is, the task of aspect-based sentiment analysis is not explicitly supervised, an approach known as “zero-shot learning”. Sentiment anal... | [
"TAGS\n#region-us \n",
"# zero-shot-absa",
"## About\nThe goal of this project is to accomplish aspect-based sentiment analysis without dependence on the severely limited training data available - that is, the task of aspect-based sentiment analysis is not explicitly supervised, an approach known as “zero-shot ... |
fill-mask | transformers | This model was pretrained on the bookcorpus dataset using knowledge distillation.
The particularity of this model is that even though it shares the same architecture as BERT, it has a hidden size of 240. Since it has 12 attention heads, the head size (20) is different from the one of the BERT base model (64).
The kno... | {} | eli4s/Bert-L12-h240-A12 | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| This model was pretrained on the bookcorpus dataset using knowledge distillation.
The particularity of this model is that even though it shares the same architecture as BERT, it has a hidden size of 240. Since it has 12 attention heads, the head size (20) is different from the one of the BERT base model (64).
The kno... | [] | [
"TAGS\n#transformers #pytorch #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n"
] |
fill-mask | transformers | This model was pretrained on the bookcorpus dataset using knowledge distillation.
The particularity of this model is that even though it shares the same architecture as BERT, it has a hidden size of 256. Since it has 4 attention heads, the head size is 64 just as for the BERT base model.
The knowledge distillation wa... | {} | eli4s/Bert-L12-h256-A4 | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| This model was pretrained on the bookcorpus dataset using knowledge distillation.
The particularity of this model is that even though it shares the same architecture as BERT, it has a hidden size of 256. Since it has 4 attention heads, the head size is 64 just as for the BERT base model.
The knowledge distillation wa... | [] | [
"TAGS\n#transformers #pytorch #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n"
] |
fill-mask | transformers | This model was pretrained on the bookcorpus dataset using knowledge distillation.
The particularity of this model is that even though it shares the same architecture as BERT, it has a hidden size of 384 (half the hidden size of BERT) and 6 attention heads (hence the same head size of BERT).
The knowledge distillation... | {} | eli4s/Bert-L12-h384-A6 | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| This model was pretrained on the bookcorpus dataset using knowledge distillation.
The particularity of this model is that even though it shares the same architecture as BERT, it has a hidden size of 384 (half the hidden size of BERT) and 6 attention heads (hence the same head size of BERT).
The knowledge distillation... | [] | [
"TAGS\n#transformers #pytorch #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n"
] |
fill-mask | transformers | This model was pretrained on the bookcorpus dataset using knowledge distillation.
The particularity of this model is that even though it shares the same architecture as BERT, it has a hidden size of 256 (a third of the hidden size of BERT) and 4 attention heads (hence the same head size of BERT).
The weights of the m... | {} | eli4s/prunedBert-L12-h256-A4-finetuned | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| This model was pretrained on the bookcorpus dataset using knowledge distillation.
The particularity of this model is that even though it shares the same architecture as BERT, it has a hidden size of 256 (a third of the hidden size of BERT) and 4 attention heads (hence the same head size of BERT).
The weights of the m... | [] | [
"TAGS\n#transformers #pytorch #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n"
] |
fill-mask | transformers | This model was pretrained on the bookcorpus dataset using knowledge distillation.
The particularity of this model is that even though it shares the same architecture as BERT, it has a hidden size of 384 (half the hidden size of BERT) and 6 attention heads (hence the same head size of BERT).
The weights of the model w... | {} | eli4s/prunedBert-L12-h384-A6-finetuned | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| This model was pretrained on the bookcorpus dataset using knowledge distillation.
The particularity of this model is that even though it shares the same architecture as BERT, it has a hidden size of 384 (half the hidden size of BERT) and 6 attention heads (hence the same head size of BERT).
The weights of the model w... | [] | [
"TAGS\n#transformers #pytorch #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n"
] |
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. -->
# IceBERT-finetuned-ner
This model is a fine-tuned version of [eliasbe/IceBERT-finetuned-ner](https://huggingface.co/eliasbe/IceBE... | {"license": "gpl-3.0", "tags": ["generated_from_trainer"], "datasets": ["mim_gold_ner"], "widget": [{"text": "systurnar gu\u00f0r\u00fan og monique voru einar \u00ed sk\u00f3ginum umkringdar v\u00ed\u00f0i, eik og reyni me\u00f0 \u00fe\u00e1 \u00f3sk a\u00f0 sameinast fj\u00f6lskyldu sinni sem f\u00f3r \u00e1 mai thai ... | eliasbe/IceBERT-finetuned-ner | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"token-classification",
"generated_from_trainer",
"dataset:mim_gold_ner",
"license:gpl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #token-classification #generated_from_trainer #dataset-mim_gold_ner #license-gpl-3.0 #autotrain_compatible #endpoints_compatible #region-us
|
# IceBERT-finetuned-ner
This model is a fine-tuned version of eliasbe/IceBERT-finetuned-ner on the mim_gold_ner dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Train... | [
"# IceBERT-finetuned-ner\n\nThis model is a fine-tuned version of eliasbe/IceBERT-finetuned-ner on the mim_gold_ner dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Tra... | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #token-classification #generated_from_trainer #dataset-mim_gold_ner #license-gpl-3.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# IceBERT-finetuned-ner\n\nThis model is a fine-tuned version of eliasbe/IceBERT-finetuned-ner on the mim_gold_ner d... |
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. -->
# XLMR-ENIS-finetuned-ner
This model is a fine-tuned version of [vesteinn/XLMR-ENIS](https://huggingface.co/vesteinn/XLMR-ENIS) on... | {"license": "agpl-3.0", "tags": ["generated_from_trainer"], "datasets": ["mim_gold_ner"], "metrics": ["precision", "recall", "f1", "accuracy"], "widget": [{"text": "systurnar gu\u00f0r\u00fan og monique voru einar \u00ed sk\u00f3ginum umkringdar v\u00ed\u00f0i, eik og reyni me\u00f0 \u00fe\u00e1 \u00f3sk a\u00f0 samein... | eliasbe/XLMR-ENIS-finetuned-ner | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"token-classification",
"generated_from_trainer",
"dataset:mim_gold_ner",
"license:agpl-3.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #token-classification #generated_from_trainer #dataset-mim_gold_ner #license-agpl-3.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| XLMR-ENIS-finetuned-ner
=======================
This model is a fine-tuned version of vesteinn/XLMR-ENIS on the mim\_gold\_ner dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0827
* Precision: 0.9002
* Recall: 0.896
* F1: 0.8981
* Accuracy: 0.9844
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: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #token-classification #generated_from_trainer #dataset-mim_gold_ner #license-agpl-3.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* lear... |
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-en-to-ro-LR_1e-3
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the wmt... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["wmt16"], "metrics": ["bleu"], "model-index": [{"name": "t5-small-finetuned-en-to-ro-LR_1e-3", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "wmt16", "type": "wmt16",... | eliotm/t5-small-finetuned-en-to-ro-LR_1e-3 | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:wmt16",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-wmt16 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| t5-small-finetuned-en-to-ro-LR\_1e-3
====================================
This model is a fine-tuned version of t5-small on the wmt16 dataset.
It achieves the following results on the evaluation set:
* Loss: 1.5215
* Bleu: 7.1606
* Gen Len: 18.2451
Model description
-----------------
More information needed
I... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.001\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... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-wmt16 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during trai... |
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-en-to-ro-fp16_off
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the wm... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["wmt16"], "metrics": ["bleu"], "model-index": [{"name": "t5-small-finetuned-en-to-ro-fp16_off", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "wmt16", "type": "wmt16"... | eliotm/t5-small-finetuned-en-to-ro-fp16_off | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:wmt16",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-wmt16 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| t5-small-finetuned-en-to-ro-fp16\_off
=====================================
This model is a fine-tuned version of t5-small on the wmt16 dataset.
It achieves the following results on the evaluation set:
* Loss: 1.8351
* Bleu: 5.9132
* Gen Len: 18.2656
Model description
-----------------
More information needed
... | [
"### 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",
"### Traini... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during trai... |
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-en-to-ro-lr0.001
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the wmt... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["wmt16"], "metrics": ["bleu"], "model-index": [{"name": "t5-small-finetuned-en-to-ro-lr0.001", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "wmt16", "type": "wmt16",... | eliotm/t5-small-finetuned-en-to-ro-lr0.001 | null | [
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"tensorboard",
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"dataset:wmt16",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-wmt16 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| t5-small-finetuned-en-to-ro-lr0.001
===================================
This model is a fine-tuned version of t5-small on the wmt16 dataset.
It achieves the following results on the evaluation set:
* Loss: 1.8309
* Bleu: 5.8837
* Gen Len: 18.2656
Model description
-----------------
More information needed
Int... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.01\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\\_preci... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during trai... |
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-en-to-ro-lr_2e-6
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the wmt... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["wmt16"], "metrics": ["bleu"], "model-index": [{"name": "t5-small-finetuned-en-to-ro-lr_2e-6", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "wmt16", "type": "wmt16",... | eliotm/t5-small-finetuned-en-to-ro-lr_2e-6 | null | [
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"dataset:wmt16",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
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| t5-small-finetuned-en-to-ro-lr\_2e-6
====================================
This model is a fine-tuned version of t5-small on the wmt16 dataset.
It achieves the following results on the evaluation set:
* Loss: 1.4232
* Bleu: 7.2935
* Gen Len: 18.2521
Model description
-----------------
More information needed
I... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-06\n* train\\_batch\\_size: 10\n* eval\\_batch\\_size: 10\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 0.04375\n* mixed\... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during trai... |
null | null |
# Test | {"language": "eo", "license": "apache-2.0", "thumbnail": "https://huggingface.co/blog/assets/01_how-to-train/EsperBERTo-thumbnail-v2.png", "widget": [{"text": "Jen la komenco de bela <mask>."}, {"text": "Uno du <mask> top"}, {"text": "Jen fini\u011das bela <mask>."}]} | elishowk/EsperBERTo-small | null | [
"eo",
"license:apache-2.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"eo"
] | TAGS
#eo #license-apache-2.0 #region-us
|
# Test | [
"# Test"
] | [
"TAGS\n#eo #license-apache-2.0 #region-us \n",
"# Test"
] |
feature-extraction | generic |
# Pretrained FastText word vector for English
https://github.com/facebookresearch/fastText
Usage
```
import fasttext.util
ft = fasttext.load_model('cc.en.300.bin')
ft.get_word_vector('hello')
``` | {"library_name": "generic", "tags": ["feature-extraction"]} | elishowk/fasttext_test2 | null | [
"generic",
"feature-extraction",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#generic #feature-extraction #region-us
|
# Pretrained FastText word vector for English
URL
Usage
| [
"# Pretrained FastText word vector for English\n\nURL\n\nUsage"
] | [
"TAGS\n#generic #feature-extraction #region-us \n",
"# Pretrained FastText word vector for English\n\nURL\n\nUsage"
] |
token-classification | spacy | | Feature | Description |
| --- | --- |
| **Name** | `is_core_web_trf` |
| **Version** | `0.0.0` |
| **spaCy** | `>=3.1.1,<3.2.0` |
| **Default Pipeline** | `transformer`, `ner`, `tagger`, `parser` |
| **Components** | `transformer`, `ner`, `tagger`, `parser` |
| **Vectors** | 0 keys, 0 unique vectors (0 dimensions) |
... | {"language": ["is"], "tags": ["spacy", "token-classification"]} | elisno/is_core_web_trf | null | [
"spacy",
"token-classification",
"is",
"model-index",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"is"
] | TAGS
#spacy #token-classification #is #model-index #region-us
|
### Label Scheme
View label scheme (591 labels for 3 components)
### Accuracy
| [
"### Label Scheme\n\n\n\nView label scheme (591 labels for 3 components)",
"### Accuracy"
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"TAGS\n#spacy #token-classification #is #model-index #region-us \n",
"### Label Scheme\n\n\n\nView label scheme (591 labels for 3 components)",
"### Accuracy"
] |
token-classification | spacy | | Feature | Description |
| --- | --- |
| **Name** | `is_ner_mim_sm` |
| **Version** | `0.0.0` |
| **spaCy** | `>=3.1.1,<3.2.0` |
| **Default Pipeline** | `tok2vec`, `ner` |
| **Components** | `tok2vec`, `ner` |
| **Vectors** | 0 keys, 0 unique vectors (0 dimensions) |
| **Sources** | n/a |
| **License** | n/a |
| **Au... | {"language": ["is"], "tags": ["spacy", "token-classification"]} | elisno/is_ner_mim_sm | null | [
"spacy",
"token-classification",
"is",
"model-index",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"is"
] | TAGS
#spacy #token-classification #is #model-index #region-us
|
### Label Scheme
View label scheme (8 labels for 1 components)
### Accuracy
| [
"### Label Scheme\n\n\n\nView label scheme (8 labels for 1 components)",
"### Accuracy"
] | [
"TAGS\n#spacy #token-classification #is #model-index #region-us \n",
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"### Accuracy"
] |
token-classification | spacy | | Feature | Description |
| --- | --- |
| **Name** | `is_ner_mim_trf` |
| **Version** | `0.0.1` |
| **spaCy** | `>=3.1.1,<3.2.0` |
| **Default Pipeline** | `transformer`, `ner` |
| **Components** | `transformer`, `ner` |
| **Vectors** | 0 keys, 0 unique vectors (0 dimensions) |
| **Sources** | n/a |
| **License** | n/a... | {"language": ["is"], "tags": ["spacy", "token-classification"]} | elisno/is_ner_mim_trf | null | [
"spacy",
"token-classification",
"is",
"model-index",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"is"
] | TAGS
#spacy #token-classification #is #model-index #region-us
|
### Label Scheme
View label scheme (8 labels for 1 components)
### Accuracy
| [
"### Label Scheme\n\n\n\nView label scheme (8 labels for 1 components)",
"### Accuracy"
] | [
"TAGS\n#spacy #token-classification #is #model-index #region-us \n",
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"### Accuracy"
] |
image-classification | transformers |
# rare-puppers
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/nateraw/hugging... | {"tags": ["image-classification", "pytorch", "huggingpics"], "metrics": ["accuracy"]} | eliwill/rare-puppers | null | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# rare-puppers
Autogenerated by HuggingPics️
Create your own image classifier for anything by running the demo on Google Colab.
Report any issues with the demo at the github repo.
## Example Images
#### algebra
!algebra
#### arithmetic
!arithmetic
#### calculus
!calculus
#### geometry
!geometry
#### tr... | [
"# rare-puppers\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nReport any issues with the demo at the github repo.",
"## Example Images",
"#### algebra\n\n!algebra",
"#### arithmetic\n\n!arithmetic",
"#### calculus\n\n!calculus",
... | [
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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. -->
# bert-base-finetuned-sts
This model is a fine-tuned version of [klue/bert-base](https://huggingface.co/klue/bert-base) on the klu... | {"tags": ["generated_from_trainer"], "datasets": ["klue"], "metrics": ["pearsonr"], "model_index": [{"name": "bert-base-finetuned-sts", "results": [{"task": {"name": "Text Classification", "type": "text-classification"}, "dataset": {"name": "klue", "type": "klue", "args": "sts"}, "metric": {"name": "Pearsonr", "type": ... | eliza-dukim/bert-base-finetuned-sts-deprecated | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:klue",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #text-classification #generated_from_trainer #dataset-klue #autotrain_compatible #endpoints_compatible #region-us
| bert-base-finetuned-sts
=======================
This model is a fine-tuned version of klue/bert-base on the klue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5657
* Pearsonr: 0.8375
Model description
-----------------
More information needed
Intended uses & limitations
---------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\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",
"### Trai... | [
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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. -->
# bert-base-finetuned-sts
This model is a fine-tuned version of [klue/bert-base](https://huggingface.co/klue/bert-base) on the klu... | {"tags": ["generated_from_trainer"], "datasets": ["klue"], "metrics": ["pearsonr", "f1"], "model-index": [{"name": "bert-base-finetuned-sts", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "klue", "type": "klue", "args": "sts"}, "metrics": [{"type": "pearsonr", ... | eliza-dukim/bert-base-finetuned-sts | null | [
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"bert",
"text-classification",
"generated_from_trainer",
"dataset:klue",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #text-classification #generated_from_trainer #dataset-klue #model-index #autotrain_compatible #endpoints_compatible #region-us
| bert-base-finetuned-sts
=======================
This model is a fine-tuned version of klue/bert-base on the klue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4115
* Pearsonr: 0.8756
* F1: 0.8417
Model description
-----------------
More information needed
Intended uses & limitati... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 128\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_rati... | [
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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. -->
# bert-base-finetuned-ynat
This model is a fine-tuned version of [klue/bert-base](https://huggingface.co/klue/bert-base) on the kl... | {"tags": ["generated_from_trainer"], "datasets": ["klue"], "metrics": ["f1"], "model_index": [{"name": "bert-base-finetuned-ynat", "results": [{"task": {"name": "Text Classification", "type": "text-classification"}, "dataset": {"name": "klue", "type": "klue", "args": "ynat"}, "metric": {"name": "F1", "type": "f1", "val... | eliza-dukim/bert-base-finetuned-ynat | null | [
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"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:klue",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #text-classification #generated_from_trainer #dataset-klue #autotrain_compatible #endpoints_compatible #region-us
| bert-base-finetuned-ynat
========================
This model is a fine-tuned version of klue/bert-base on the klue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3741
* F1: 0.8700
Model description
-----------------
More information needed
Intended uses & limitations
-------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 256\n* eval\\_batch\\_size: 256\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",
"### Trai... | [
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question-answering | transformers | ## Boostcamp AI Tech Special Mission 01, Multi-lingual BERT for KorQuAD v1
{'exact_match': 69.89954970557672, 'f1': 77.40349093437989, 'epoch': 15.0} | {} | eliza-dukim/bert-base-multilingual-cased_korquad-v1 | null | [
"transformers",
"pytorch",
"bert",
"question-answering",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #question-answering #endpoints_compatible #region-us
| ## Boostcamp AI Tech Special Mission 01, Multi-lingual BERT for KorQuAD v1
{'exact_match': 69.89954970557672, 'f1': 77.40349093437989, 'epoch': 15.0} | [
"## Boostcamp AI Tech Special Mission 01, Multi-lingual BERT for KorQuAD v1\n{'exact_match': 69.89954970557672, 'f1': 77.40349093437989, 'epoch': 15.0}"
] | [
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"## Boostcamp AI Tech Special Mission 01, Multi-lingual BERT for KorQuAD v1\n{'exact_match': 69.89954970557672, 'f1': 77.40349093437989, 'epoch': 15.0}"
] |
fill-mask | transformers | Test model to get an idea how this thing works | {} | elliotsmith/dummy-model | null | [
"transformers",
"pytorch",
"camembert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #camembert #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| Test model to get an idea how this thing works | [] | [
"TAGS\n#transformers #pytorch #camembert #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n"
] |
null | transformers | # MacBERTh
This model is a Historical Language Model for English coming from the [MacBERTh project](https://macberth.netlify.app/).
The architecture is based on BERT base uncased from the original BERT pre-training codebase.
The training material comes from different sources including:
- EEBO
- ECCO
- COHA
- CLMET3... | {"language": ["en"], "license": "mit"} | emanjavacas/MacBERTh | null | [
"transformers",
"pytorch",
"bert",
"en",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bert #en #license-mit #endpoints_compatible #region-us
| # MacBERTh
This model is a Historical Language Model for English coming from the MacBERTh project.
The architecture is based on BERT base uncased from the original BERT pre-training codebase.
The training material comes from different sources including:
- EEBO
- ECCO
- COHA
- CLMET3.1
- EVANS
- Hansard Corpus
with... | [
"# MacBERTh\n\nThis model is a Historical Language Model for English coming from the MacBERTh project.\n\nThe architecture is based on BERT base uncased from the original BERT pre-training codebase. \nThe training material comes from different sources including:\n\n- EEBO\n- ECCO\n- COHA\n- CLMET3.1\n- EVANS\n- Han... | [
"TAGS\n#transformers #pytorch #bert #en #license-mit #endpoints_compatible #region-us \n",
"# MacBERTh\n\nThis model is a Historical Language Model for English coming from the MacBERTh project.\n\nThe architecture is based on BERT base uncased from the original BERT pre-training codebase. \nThe training material ... |
text-classification | transformers |
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 607517182
- CO2 Emissions (in grams): 3.842950628218143
## Validation Metrics
- Loss: 0.4033123552799225
- Accuracy: 0.8679706601466992
- Macro F1: 0.719846919916469
- Micro F1: 0.8679706601466993
- Weighted F1: 0.8622411469250695
... | {"language": "unk", "tags": "autonlp", "datasets": ["emekaboris/autonlp-data-new_tx"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}], "co2_eq_emissions": 3.842950628218143} | emekaboris/autonlp-new_tx-607517182 | null | [
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"unk",
"dataset:emekaboris/autonlp-data-new_tx",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"unk"
] | TAGS
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|
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 607517182
- CO2 Emissions (in grams): 3.842950628218143
## Validation Metrics
- Loss: 0.4033123552799225
- Accuracy: 0.8679706601466992
- Macro F1: 0.719846919916469
- Micro F1: 0.8679706601466993
- Weighted F1: 0.8622411469250695
... | [
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"## Validation Metrics\n\n- Loss: 0.4033123552799225\n- Accuracy: 0.8679706601466992\n- Macro F1: 0.719846919916469\n- Micro F1: 0.8679706601466993\n- Weighted F1: 0... | [
"TAGS\n#transformers #pytorch #roberta #text-classification #autonlp #unk #dataset-emekaboris/autonlp-data-new_tx #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoNLP\n\n- Problem type: Multi-class Classification\n- Model ID: 607517182\n- CO2 Emissions (in g... |
text-classification | transformers |
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 17923124
- CO2 Emissions (in grams): 133.57087522185148
## Validation Metrics
- Loss: 0.2080804407596588
- Accuracy: 0.9325402190077058
- Macro F1: 0.7283811287183823
- Micro F1: 0.9325402190077058
- Weighted F1: 0.9315711955594153... | {"language": "en", "tags": "autonlp", "datasets": ["emekaboris/autonlp-data-txc"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}], "co2_eq_emissions": 133.57087522185148} | emekaboris/autonlp-txc-17923124 | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"autonlp",
"en",
"dataset:emekaboris/autonlp-data-txc",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #roberta #text-classification #autonlp #en #dataset-emekaboris/autonlp-data-txc #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 17923124
- CO2 Emissions (in grams): 133.57087522185148
## Validation Metrics
- Loss: 0.2080804407596588
- Accuracy: 0.9325402190077058
- Macro F1: 0.7283811287183823
- Micro F1: 0.9325402190077058
- Weighted F1: 0.9315711955594153... | [
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"# Model Trained Using AutoNLP\n\n- Problem type: Multi-class Classification\n- Model ID: 17923124\n- CO2 Emissions (in grams)... |
text-classification | transformers |
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 17923129
- CO2 Emissions (in grams): 610.861733873082
## Validation Metrics
- Loss: 0.2319454699754715
- Accuracy: 0.9264228741381642
- Macro F1: 0.6730537318152493
- Micro F1: 0.9264228741381642
- Weighted F1: 0.9251493598895151
-... | {"language": "en", "tags": "autonlp", "datasets": ["emekaboris/autonlp-data-txc"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}], "co2_eq_emissions": 610.861733873082} | emekaboris/autonlp-txc-17923129 | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autonlp",
"en",
"dataset:emekaboris/autonlp-data-txc",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bert #text-classification #autonlp #en #dataset-emekaboris/autonlp-data-txc #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 17923129
- CO2 Emissions (in grams): 610.861733873082
## Validation Metrics
- Loss: 0.2319454699754715
- Accuracy: 0.9264228741381642
- Macro F1: 0.6730537318152493
- Micro F1: 0.9264228741381642
- Weighted F1: 0.9251493598895151
-... | [
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"## Validation Metrics\n\n- Loss: 0.2319454699754715\n- Accuracy: 0.9264228741381642\n- Macro F1: 0.6730537318152493\n- Micro F1: 0.9264228741381642\n- Weighted F1: 0.... | [
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"# Model Trained Using AutoNLP\n\n- Problem type: Multi-class Classification\n- Model ID: 17923129\n- CO2 Emissions (in grams): 6... |
null | transformers | KcELECTRA([https://github.com/Beomi/KcELECTRA](https://github.com/Beomi/KcELECTRA))의 Tokenizer에서 [UNK]로 대체되는 토큰들을 추가했습니다.
| {} | emeraldgoose/bad-korean-tokenizer | null | [
"transformers",
"electra",
"pretraining",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #electra #pretraining #endpoints_compatible #region-us
| KcELECTRA(URL)의 Tokenizer에서 [UNK]로 대체되는 토큰들을 추가했습니다.
| [] | [
"TAGS\n#transformers #electra #pretraining #endpoints_compatible #region-us \n"
] |
fill-mask | transformers |
## Data-annotation-nlp-10 (BoostCamp AI)
위키피디아(스포츠) dataset 구축을 진행하면서 얻은 문장을 통해 bert 사전학습을 진행
## How to use
```python
from transformers import AutoTokenizer, BertForMaskedLM
model = BertForMaskedLM.from_pretrained("emeraldgoose/bert-base-v1-sports")
tokenizer = AutoTokenizer.from_pretrained("emeraldgoose/bert-base-... | {"language": "ko", "mask_token": "[MASK]", "widget": [{"text": "\uc0b0\uc545 \uc790\uc804\uac70 \uacbd\uae30\ub294 \uc0c1\ub300\uc801\uc73c\ub85c \uc0c8\ub85c\uc6b4 [MASK] 1990\ub144\ub300\uc5d0 \ud65c\uc131\ud654 \ub418\uc5c8\ub2e4."}]} | emeraldgoose/bert-base-v1-sports | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"ko",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ko"
] | TAGS
#transformers #pytorch #bert #fill-mask #ko #autotrain_compatible #endpoints_compatible #region-us
|
## Data-annotation-nlp-10 (BoostCamp AI)
위키피디아(스포츠) dataset 구축을 진행하면서 얻은 문장을 통해 bert 사전학습을 진행
## How to use
| [
"## Data-annotation-nlp-10 (BoostCamp AI)\n위키피디아(스포츠) dataset 구축을 진행하면서 얻은 문장을 통해 bert 사전학습을 진행",
"## How to use"
] | [
"TAGS\n#transformers #pytorch #bert #fill-mask #ko #autotrain_compatible #endpoints_compatible #region-us \n",
"## Data-annotation-nlp-10 (BoostCamp AI)\n위키피디아(스포츠) dataset 구축을 진행하면서 얻은 문장을 통해 bert 사전학습을 진행",
"## How to use"
] |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-turkish-colab
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-large-xls-r-300m-turkish-colab", "results": []}]} | emeson77/wav2vec2-large-xls-r-300m-turkish-colab | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-large-xls-r-300m-turkish-colab
=======================================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common\_voice dataset.
It achieves the following results on the evaluation set:
* Loss: 0.7214
* Wer: 0.5555
Model description
-----------------
More informat... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* t... |
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. -->
# danish-bert-botxo-danish-finetuned-hatespeech
This model is for a university project and is uploaded for sharing between students... | {"license": "cc-by-4.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "danish-bert-botxo-danish-finetuned-hatespeech", "results": []}]} | emfa/danish-bert-botxo-danish-finetuned-hatespeech | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:cc-by-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #region-us
| danish-bert-botxo-danish-finetuned-hatespeech
=============================================
This model is for a university project and is uploaded for sharing between students. It is training on a danish hate speech labeled training set. Feel free to use it, but as of now, we don't promise any good results ;-)
This... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-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: 4.0",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-cc-by-4.0 #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\\_... |
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. -->
# danish-roberta-botxo-danish-finetuned-hatespeech
This model is for a university project and is uploaded for sharing between studen... | {"license": "cc-by-4.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "danish-roberta-botxo-danish-finetuned-hatespeech", "results": []}]} | emfa/danish-roberta-botxo-danish-finetuned-hatespeech | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"license:cc-by-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #region-us
| danish-roberta-botxo-danish-finetuned-hatespeech
================================================
This model is for a university project and is uploaded for sharing between students. It is training on a danish hate speech labeled training set. Feel free to use it, but as of now, we don't promise any good results ;-)
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-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",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #license-cc-by-4.0 #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... |
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. -->
# l-lectra-danish-finetuned-hatespeech
This model is for a university project and is uploaded for sharing between students. It is t... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "l-lectra-danish-finetuned-hatespeech", "results": []}]} | emfa/l-lectra-danish-finetuned-hatespeech | null | [
"transformers",
"pytorch",
"tensorboard",
"electra",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #electra #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| l-lectra-danish-finetuned-hatespeech
====================================
This model is for a university project and is uploaded for sharing between students. It is training on a danish hate speech labeled training set. Feel free to use it, but as of now, we don't promise any good results ;-)
This model is a fine-t... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-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: 4.0",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #electra #text-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\\_siz... |
text-generation | transformers | This model aims at being a french conversational agent. This consists of a fine-tuning of Dialo-GPT for french language. The dataset used gathers 36k conversations extracted from books, movies, interviews and dialogues for learning french.
More details about the model can be found [there](https://github.com/emil2000d... | {"language": ["fr"], "tags": [{}, {}]} | emil2000/dialogpt-for-french-language | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"fr",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"fr"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #fr #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| This model aims at being a french conversational agent. This consists of a fine-tuning of Dialo-GPT for french language. The dataset used gathers 36k conversations extracted from books, movies, interviews and dialogues for learning french.
More details about the model can be found there
| [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #fr #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n"
] |
text2text-generation | transformers | ## daT5-base
A smaller version of [Google's mt5-base](https://huggingface.co/google/mt5-base) model, where the original model is reduced to only include Danish embeddings.
## How to use
```python
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("emillykkejensen/daT5-base")
mo... | {"language": ["da"], "license": "apache-2.0"} | emillykkejensen/daT5-base | null | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"da",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"da"
] | TAGS
#transformers #pytorch #mt5 #text2text-generation #da #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| ## daT5-base
A smaller version of Google's mt5-base model, where the original model is reduced to only include Danish embeddings.
## How to use
## Further reading
Gist showing (in Danish) how the embeddings are extracted
Article explaining how to do it by David Dale
## Also check out
daT5-large | [
"## daT5-base\nA smaller version of Google's mt5-base model, where the original model is reduced to only include Danish embeddings.",
"## How to use",
"## Further reading\n\nGist showing (in Danish) how the embeddings are extracted\n\nArticle explaining how to do it by David Dale",
"## Also check out\ndaT5-la... | [
"TAGS\n#transformers #pytorch #mt5 #text2text-generation #da #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"## daT5-base\nA smaller version of Google's mt5-base model, where the original model is reduced to only include Danish embeddings.",
"## How to... |
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