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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
sentence-similarity | sentence-transformers |
# Model description
The project aims to train sentence embedding models on very large sentence level datasets using a self-supervised
contrastive learning objective. We used the pretrained ['mpnet-base'](https://huggingface.co/microsoft/mpnet-base) model and fine-tuned in on a
700M sentence pairs dataset. We use a ... | {"language": "en", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity"], "pipeline_tag": "sentence-similarity"} | flax-sentence-embeddings/reddit_single-context_mpnet-base | null | [
"sentence-transformers",
"pytorch",
"mpnet",
"feature-extraction",
"sentence-similarity",
"en",
"arxiv:1904.06472",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1904.06472"
] | [
"en"
] | TAGS
#sentence-transformers #pytorch #mpnet #feature-extraction #sentence-similarity #en #arxiv-1904.06472 #endpoints_compatible #region-us
| Model description
=================
The project aims to train sentence embedding models on very large sentence level datasets using a self-supervised
contrastive learning objective. We used the pretrained 'mpnet-base' model and fine-tuned in on a
700M sentence pairs dataset. We use a contrastive learning objective: g... | [
"### Hyper parameters\n\n\nWe trained ou model on a TPU v3-8. We train the model during 540k steps using a batch size of 1024 (128 per TPU core).\nWe use a learning rate warm up of 500. The sequence length was limited to 128 tokens. We used the AdamW optimizer with\na 2e-5 learning rate. The full training script is... | [
"TAGS\n#sentence-transformers #pytorch #mpnet #feature-extraction #sentence-similarity #en #arxiv-1904.06472 #endpoints_compatible #region-us \n",
"### Hyper parameters\n\n\nWe trained ou model on a TPU v3-8. We train the model during 540k steps using a batch size of 1024 (128 per TPU core).\nWe use a learning ra... |
sentence-similarity | sentence-transformers |
# flax-sentence-embeddings/st-codesearch-distilroberta-base
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
It was trained on the [code_search_net](https://huggingface.... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity"], "datasets": ["code_search_net"], "pipeline_tag": "sentence-similarity"} | flax-sentence-embeddings/st-codesearch-distilroberta-base | null | [
"sentence-transformers",
"pytorch",
"roberta",
"feature-extraction",
"sentence-similarity",
"dataset:code_search_net",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #roberta #feature-extraction #sentence-similarity #dataset-code_search_net #endpoints_compatible #has_space #region-us
|
# flax-sentence-embeddings/st-codesearch-distilroberta-base
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
It was trained on the code_search_net dataset and can be used to search program code ... | [
"# flax-sentence-embeddings/st-codesearch-distilroberta-base\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.\n\nIt was trained on the code_search_net dataset and can be used to search progr... | [
"TAGS\n#sentence-transformers #pytorch #roberta #feature-extraction #sentence-similarity #dataset-code_search_net #endpoints_compatible #has_space #region-us \n",
"# flax-sentence-embeddings/st-codesearch-distilroberta-base\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensio... |
sentence-similarity | sentence-transformers |
# stackoverflow_mpnet-base
This is a microsoft/mpnet-base model trained on 18,562,443 (title, body) pairs from StackOverflow.
SentenceTransformers is a set of models and frameworks that enable training and generating sentence embeddings from given data. The generated sentence embeddings can be utilized for Clusteri... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity"], "pipeline_tag": "sentence-similarity"} | flax-sentence-embeddings/stackoverflow_mpnet-base | null | [
"sentence-transformers",
"pytorch",
"mpnet",
"feature-extraction",
"sentence-similarity",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #mpnet #feature-extraction #sentence-similarity #endpoints_compatible #region-us
| stackoverflow\_mpnet-base
=========================
This is a microsoft/mpnet-base model trained on 18,562,443 (title, body) pairs from StackOverflow.
SentenceTransformers is a set of models and frameworks that enable training and generating sentence embeddings from given data. The generated sentence embeddings can... | [
"### Hyper parameters\n\n\nWe trained on model on a TPU v3-8. We train the model during 80k steps using a batch size of 1024 (128 per TPU core).\nWe use a learning rate warm up of 500. The sequence length was limited to 128 tokens. We used the AdamW optimizer with\na 2e-5 learning rate. The full training script is ... | [
"TAGS\n#sentence-transformers #pytorch #mpnet #feature-extraction #sentence-similarity #endpoints_compatible #region-us \n",
"### Hyper parameters\n\n\nWe trained on model on a TPU v3-8. We train the model during 80k steps using a batch size of 1024 (128 per TPU core).\nWe use a learning rate warm up of 500. The ... |
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# reddit-bert-text2
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unkn... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "reddit-bert-text2", "results": []}]} | flboehm/reddit-bert-text2 | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"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 #bert #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| reddit-bert-text2
=================
This model is a fine-tuned version of bert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 2.4969
* Perplexity: 12.14
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: 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 #bert #fill-mask #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: 2e-05\n* train\\_batch\\_size: 8\n... |
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# reddit-bert-text3
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unkn... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "reddit-bert-text3", "results": []}]} | flboehm/reddit-bert-text3 | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"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 #bert #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| reddit-bert-text3
=================
This model is a fine-tuned version of bert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 2.5346
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #fill-mask #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: 2e-05\n* train\\_batch\\_size: 8\n... |
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# reddit-bert-text4
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unkn... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "reddit-bert-text4", "results": []}]} | flboehm/reddit-bert-text4 | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"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 #bert #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| reddit-bert-text4
=================
This model is a fine-tuned version of bert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 2.4763
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #fill-mask #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: 2e-05\n* train\\_batch\\_size: 8\n... |
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# reddit-bert-text_10
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an un... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "reddit-bert-text_10", "results": []}]} | flboehm/reddit-bert-text_10 | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"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 #bert #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| reddit-bert-text\_10
====================
This model is a fine-tuned version of bert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 2.5198
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: 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 #bert #fill-mask #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: 2e-05\n* train\\_batch\\_size: 8\n... |
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# reddit-bert-text_20
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an un... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "reddit-bert-text_20", "results": []}]} | flboehm/reddit-bert-text_20 | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"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 #bert #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| reddit-bert-text\_20
====================
This model is a fine-tuned version of bert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 2.4702
* Perplexity: 11.82
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: 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 #bert #fill-mask #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: 2e-05\n* train\\_batch\\_size: 8\n... |
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# reddit-bert-text5
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unkn... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "reddit-bert-text5", "results": []}]} | flboehm/reddit-bert-text_5 | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"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 #bert #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| reddit-bert-text5
=================
This model is a fine-tuned version of bert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 2.5749
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #fill-mask #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: 2e-05\n* train\\_batch\\_size: 8\n... |
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# youtube-bert
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown d... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "youtube-bert", "results": []}]} | flboehm/youtube-bert | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"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 #bert #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| youtube-bert
============
This model is a fine-tuned version of bert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 2.4771
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More informatio... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #fill-mask #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: 2e-05\n* train\\_batch\\_size: 8\n... |
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# youtube-bert_10
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknow... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "youtube-bert_10", "results": []}]} | flboehm/youtube-bert_10 | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"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 #bert #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| youtube-bert\_10
================
This model is a fine-tuned version of bert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 2.4456
* Perplexity: 11.54
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: 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 #bert #fill-mask #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: 2e-05\n* train\\_batch\\_size: 8\n... |
text2text-generation | transformers | # Cheapity3 🐷
GPT-like T5 model trained to generate text in multiple languages.
## Motivation
- GPT models are expensive to run.
- GPT models are monolingual.
## Solution
- Maybe, Small Models aren't Terrible (*SMarT*)
- Plus, they are cheaper to run.
I fine-tuned T5 on multiple languages (🇬🇧 English, 🇩🇪 Ger... | {} | flexudy/cheapity3 | 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
| # Cheapity3
GPT-like T5 model trained to generate text in multiple languages.
## Motivation
- GPT models are expensive to run.
- GPT models are monolingual.
## Solution
- Maybe, Small Models aren't Terrible (*SMarT*)
- Plus, they are cheaper to run.
I fine-tuned T5 on multiple languages (🇬🇧 English, 🇩🇪 Germa... | [
"# Cheapity3 \n\nGPT-like T5 model trained to generate text in multiple languages.",
"## Motivation\n\n- GPT models are expensive to run.\n- GPT models are monolingual.",
"## Solution\n\n- Maybe, Small Models aren't Terrible (*SMarT*)\n- Plus, they are cheaper to run.\n\nI fine-tuned T5 on multiple languages (�... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Cheapity3 \n\nGPT-like T5 model trained to generate text in multiple languages.",
"## Motivation\n\n- GPT models are expensive to run.\n- GPT models are monolingual."... |
text2text-generation | transformers | # Towards Neuro-Symbolic Language Understanding
At [Flexudy](https://flexudy.com), we look for ways to unify symbolic and sub-symbolic methods to improve model interpretation and inference.
## Problem
1. Word embeddi... | {} | flexudy/t5-base-conceptor | 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
| Towards Neuro-Symbolic Language Understanding
=============================================
!alt text
At Flexudy, we look for ways to unify symbolic and sub-symbolic methods to improve model interpretation and inference.
Problem
-------
1. Word embeddings are awesome . However, no one really knows what an array... | [
"### Usage\n\n\nNo library should anyone suffer. Especially not if it is built on top of HF Transformers.\n\n\nGo to the Github repo\n\n\n'pip install git+URL\n\n\nOutput:",
"### How was it trained?\n\n\n1. Using Google's T5-base and T5-small. Both models are released on the Hugging Face Hub.\n2. T5-base was trai... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Usage\n\n\nNo library should anyone suffer. Especially not if it is built on top of HF Transformers.\n\n\nGo to the Github repo\n\n\n'pip install git+URL\n\n\nOutput:... |
text2text-generation | transformers | 
# Sentence-Doctor
Sentence doctor is a T5 model that attempts to correct the errors or mistakes found in sentences. Model works on English, German and French text.
## 1. Problem:
Many NLP models depend on tasks like *Text Extraction Libraries, OCR, Speech to Text libraries* and **Sentence B... | {} | flexudy/t5-base-multi-sentence-doctor | null | [
"transformers",
"pytorch",
"tf",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| !avatar
# Sentence-Doctor
Sentence doctor is a T5 model that attempts to correct the errors or mistakes found in sentences. Model works on English, German and French text.
## 1. Problem:
Many NLP models depend on tasks like *Text Extraction Libraries, OCR, Speech to Text libraries* and Sentence Boundary Detection
As ... | [
"# Sentence-Doctor\nSentence doctor is a T5 model that attempts to correct the errors or mistakes found in sentences. Model works on English, German and French text.",
"## 1. Problem:\nMany NLP models depend on tasks like *Text Extraction Libraries, OCR, Speech to Text libraries* and Sentence Boundary Detection\n... | [
"TAGS\n#transformers #pytorch #tf #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Sentence-Doctor\nSentence doctor is a T5 model that attempts to correct the errors or mistakes found in sentences. Model works on English, German and French text.",... |
null | transformers | # flexudy-pipe-question-generation-v2
After transcribing your audio with Wav2Vec2, you might be interested in a post processor.
All paragraphs had at most 128 tokens (separated by white spaces)
```python
from transformers import T5Tokenizer, T5ForConditionalGeneration
model_name = "flexudy/t5-small-wav2vec2-grammar-... | {} | flexudy/t5-small-wav2vec2-grammar-fixer | null | [
"transformers",
"pytorch",
"tf",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #endpoints_compatible #has_space #region-us
| # flexudy-pipe-question-generation-v2
After transcribing your audio with Wav2Vec2, you might be interested in a post processor.
All paragraphs had at most 128 tokens (separated by white spaces)
INPUT 1:
OUTPUT 1:
INPUT 2:
OUTPUT 2:
I strongly recommend improving the performance via further fine-tuning or by t... | [
"# flexudy-pipe-question-generation-v2\nAfter transcribing your audio with Wav2Vec2, you might be interested in a post processor.\n\nAll paragraphs had at most 128 tokens (separated by white spaces)\n\n\n\nINPUT 1:\n\nOUTPUT 1:\n\n\nINPUT 2:\n\n\nOUTPUT 2:\n\nI strongly recommend improving the performance via furth... | [
"TAGS\n#transformers #pytorch #tf #endpoints_compatible #has_space #region-us \n",
"# flexudy-pipe-question-generation-v2\nAfter transcribing your audio with Wav2Vec2, you might be interested in a post processor.\n\nAll paragraphs had at most 128 tokens (separated by white spaces)\n\n\n\nINPUT 1:\n\nOUTPUT 1:\n\n... |
text-generation | transformers | @Rick from Rick and Morty GPT-2 Conversation Model
---
| {"tags": "conversational"} | flooptherocket/DialogGPT-small-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 from Rick and Morty GPT-2 Conversation Model
---
| [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text2text-generation | transformers |
example outputs:
input: ich liebe das leben --> output: Ich liebe das Leben.
input: es ist schön so viele tolle menschen um sich zu haben denn ohne sie wäre es nicht so schön --> output: Es ist schön, so viele tolle Menschen, um sich zu haben, denn ohne sie wäre es nicht so schön.
input: der kunde hat ausdrücklich ... | {"language": "de", "tags": ["grammar"], "widget": [{"text": "correct german grammar: es ist sch\u00f6n so viele tolle menschen um sich zu haben denn ohne sie w\u00e4re es nicht so sch\u00f6n"}]} | aware-ai/byt5-german-grammar | null | [
"transformers",
"pytorch",
"safetensors",
"t5",
"text2text-generation",
"grammar",
"de",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"de"
] | TAGS
#transformers #pytorch #safetensors #t5 #text2text-generation #grammar #de #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
example outputs:
input: ich liebe das leben --> output: Ich liebe das Leben.
input: es ist schön so viele tolle menschen um sich zu haben denn ohne sie wäre es nicht so schön --> output: Es ist schön, so viele tolle Menschen, um sich zu haben, denn ohne sie wäre es nicht so schön.
input: der kunde hat ausdrücklich ... | [] | [
"TAGS\n#transformers #pytorch #safetensors #t5 #text2text-generation #grammar #de #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
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-skills
This model is a fine-tuned version of [flozi00/t5-skills](https://huggingface.co/flozi00/t5-skills) on the None datase... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "t5-skills", "results": []}]} | aware-ai/t5-skills | null | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #safetensors #t5 #text2text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# t5-skills
This model is a fine-tuned version of flozi00/t5-skills on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The followi... | [
"# t5-skills\n\nThis model is a fine-tuned version of flozi00/t5-skills on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Trainin... | [
"TAGS\n#transformers #pytorch #tensorboard #safetensors #t5 #text2text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# t5-skills\n\nThis model is a fine-tuned version of flozi00/t5-skills on the None dataset.",
"## Model description\n\... |
automatic-speech-recognition | transformers |
**Test Result**
| Model | WER | CER |
| ------------- | ------------- | ------------- |
| flozi00/wav2vec2-large-xlsr-53-german-with-lm | **5.7467896819046755%** | **1.8980142607670552%** |
## Evaluation
The model can be evaluated as follows on the German test data of Common Voice.
```python
import torchaudio.funct... | {"language": "de", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week", "hf-asr-leaderboard"], "datasets": ["common_voice"], "metrics": ["wer", "cer"], "model-index": [{"name": "XLSR Wav2Vec2 German with LM by Florian Zimmermeister @A\\\\Ware", "results": [{"task... | aware-ai/wav2vec2-large-xlsr-53-german-with-lm | null | [
"transformers",
"pytorch",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"hf-asr-leaderboard",
"de",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"de"
] | TAGS
#transformers #pytorch #safetensors #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #hf-asr-leaderboard #de #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us
| Test Result
Model: flozi00/wav2vec2-large-xlsr-53-german-with-lm, WER: 5.7467896819046755%, CER: 1.8980142607670552%
Evaluation
----------
The model can be evaluated as follows on the German test data of Common Voice.
Credits:
The Acoustic model is an copy of jonatasgrosman's model I used to train an matching... | [] | [
"TAGS\n#transformers #pytorch #safetensors #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #hf-asr-leaderboard #de #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us \n"
] |
text-generation | transformers | ### Model Description
GPT-J 6B is a transformer model designed using EleutherAI's replication of the GPT-3 architecture. GPT-J refers to the class of models, while 6B represents the number of parameters of this particular pre-trained model.
The original GPT-J-6B model is trained with TPUs, which is not easy to use for... | {} | flyhero/gpt-j-6B | null | [
"transformers",
"pytorch",
"gpt_neo",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt_neo #text-generation #autotrain_compatible #endpoints_compatible #has_space #region-us
| ### Model Description
GPT-J 6B is a transformer model designed using EleutherAI's replication of the GPT-3 architecture. GPT-J refers to the class of models, while 6B represents the number of parameters of this particular pre-trained model.
The original GPT-J-6B model is trained with TPUs, which is not easy to use for... | [
"### Model Description\nGPT-J 6B is a transformer model designed using EleutherAI's replication of the GPT-3 architecture. GPT-J refers to the class of models, while 6B represents the number of parameters of this particular pre-trained model.\n\nThe original GPT-J-6B model is trained with TPUs, which is not easy to... | [
"TAGS\n#transformers #pytorch #gpt_neo #text-generation #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### Model Description\nGPT-J 6B is a transformer model designed using EleutherAI's replication of the GPT-3 architecture. GPT-J refers to the class of models, while 6B represents the num... |
text2text-generation | transformers | # Chinese BART-Base
### News
**12/30/2022**
An updated version of CPT & Chinese BART are released. In the new version, we changed the following parts:
- **Vocabulary** We replace the old BERT vocabulary with a larger one of size 51271 built from the training data, in which we 1) add missing 6800+ Chinese characters... | {"language": "zh", "tags": ["text2text-generation", "Chinese", "seq2seq", "BART"]} | fnlp/bart-base-chinese | null | [
"transformers",
"pytorch",
"safetensors",
"bart",
"text2text-generation",
"Chinese",
"seq2seq",
"BART",
"zh",
"arxiv:2109.05729",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2109.05729"
] | [
"zh"
] | TAGS
#transformers #pytorch #safetensors #bart #text2text-generation #Chinese #seq2seq #BART #zh #arxiv-2109.05729 #autotrain_compatible #endpoints_compatible #has_space #region-us
| Chinese BART-Base
=================
### News
12/30/2022
An updated version of CPT & Chinese BART are released. In the new version, we changed the following parts:
* Vocabulary We replace the old BERT vocabulary with a larger one of size 51271 built from the training data, in which we 1) add missing 6800+ Chines... | [
"### News\n\n\n12/30/2022\n\n\nAn updated version of CPT & Chinese BART are released. In the new version, we changed the following parts:\n\n\n* Vocabulary We replace the old BERT vocabulary with a larger one of size 51271 built from the training data, in which we 1) add missing 6800+ Chinese characters (most of th... | [
"TAGS\n#transformers #pytorch #safetensors #bart #text2text-generation #Chinese #seq2seq #BART #zh #arxiv-2109.05729 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### News\n\n\n12/30/2022\n\n\nAn updated version of CPT & Chinese BART are released. In the new version, we changed the follo... |
text2text-generation | transformers | # Chinese BART-Large
### News
**12/30/2022**
An updated version of CPT & Chinese BART are released. In the new version, we changed the following parts:
- **Vocabulary** We replace the old BERT vocabulary with a larger one of size 51271 built from the training data, in which we 1) add missing 6800+ Chinese character... | {"language": "zh", "tags": ["text2text-generation", "Chinese", "seq2seq"]} | fnlp/bart-large-chinese | null | [
"transformers",
"pytorch",
"safetensors",
"bart",
"text2text-generation",
"Chinese",
"seq2seq",
"zh",
"arxiv:2109.05729",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2109.05729"
] | [
"zh"
] | TAGS
#transformers #pytorch #safetensors #bart #text2text-generation #Chinese #seq2seq #zh #arxiv-2109.05729 #autotrain_compatible #endpoints_compatible #has_space #region-us
| Chinese BART-Large
==================
### News
12/30/2022
An updated version of CPT & Chinese BART are released. In the new version, we changed the following parts:
* Vocabulary We replace the old BERT vocabulary with a larger one of size 51271 built from the training data, in which we 1) add missing 6800+ Chin... | [
"### News\n\n\n12/30/2022\n\n\nAn updated version of CPT & Chinese BART are released. In the new version, we changed the following parts:\n\n\n* Vocabulary We replace the old BERT vocabulary with a larger one of size 51271 built from the training data, in which we 1) add missing 6800+ Chinese characters (most of th... | [
"TAGS\n#transformers #pytorch #safetensors #bart #text2text-generation #Chinese #seq2seq #zh #arxiv-2109.05729 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### News\n\n\n12/30/2022\n\n\nAn updated version of CPT & Chinese BART are released. In the new version, we changed the following p... |
text2text-generation | transformers | # Chinese CPT-Base
### News
**12/30/2022**
An updated version of CPT & Chinese BART are released. In the new version, we changed the following parts:
- **Vocabulary** We replace the old BERT vocabulary with a larger one of size 51271 built from the training data, in which we 1) add missing 6800+ Chinese characters ... | {"language": "zh", "initializedtags": ["fill-mask", "text2text-generation", "fill-mask", "text-classification", "Summarization", "Chinese", "CPT", "BART", "BERT", "seq2seq"]} | fnlp/cpt-base | null | [
"transformers",
"pytorch",
"safetensors",
"bart",
"text2text-generation",
"zh",
"arxiv:2109.05729",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2109.05729"
] | [
"zh"
] | TAGS
#transformers #pytorch #safetensors #bart #text2text-generation #zh #arxiv-2109.05729 #autotrain_compatible #endpoints_compatible #region-us
| Chinese CPT-Base
================
### News
12/30/2022
An updated version of CPT & Chinese BART are released. In the new version, we changed the following parts:
* Vocabulary We replace the old BERT vocabulary with a larger one of size 51271 built from the training data, in which we 1) add missing 6800+ Chinese ... | [
"### News\n\n\n12/30/2022\n\n\nAn updated version of CPT & Chinese BART are released. In the new version, we changed the following parts:\n\n\n* Vocabulary We replace the old BERT vocabulary with a larger one of size 51271 built from the training data, in which we 1) add missing 6800+ Chinese characters (most of th... | [
"TAGS\n#transformers #pytorch #safetensors #bart #text2text-generation #zh #arxiv-2109.05729 #autotrain_compatible #endpoints_compatible #region-us \n",
"### News\n\n\n12/30/2022\n\n\nAn updated version of CPT & Chinese BART are released. In the new version, we changed the following parts:\n\n\n* Vocabulary We re... |
text-classification | transformers | # Chinese CPT-Large
### News
**12/30/2022**
An updated version of CPT & Chinese BART are released. In the new version, we changed the following parts:
- **Vocabulary** We replace the old BERT vocabulary with a larger one of size 51271 built from the training data, in which we 1) add missing 6800+ Chinese characters... | {"language": "zh", "tags": ["fill-mask", "text2text-generation", "fill-mask", "text-classification", "Summarization", "Chinese", "CPT", "BART", "BERT", "seq2seq"]} | fnlp/cpt-large | null | [
"transformers",
"pytorch",
"safetensors",
"bart",
"text2text-generation",
"fill-mask",
"text-classification",
"Summarization",
"Chinese",
"CPT",
"BART",
"BERT",
"seq2seq",
"zh",
"arxiv:2109.05729",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2109.05729"
] | [
"zh"
] | TAGS
#transformers #pytorch #safetensors #bart #text2text-generation #fill-mask #text-classification #Summarization #Chinese #CPT #BART #BERT #seq2seq #zh #arxiv-2109.05729 #autotrain_compatible #endpoints_compatible #region-us
| Chinese CPT-Large
=================
### News
12/30/2022
An updated version of CPT & Chinese BART are released. In the new version, we changed the following parts:
* Vocabulary We replace the old BERT vocabulary with a larger one of size 51271 built from the training data, in which we 1) add missing 6800+ Chines... | [
"### News\n\n\n12/30/2022\n\n\nAn updated version of CPT & Chinese BART are released. In the new version, we changed the following parts:\n\n\n* Vocabulary We replace the old BERT vocabulary with a larger one of size 51271 built from the training data, in which we 1) add missing 6800+ Chinese characters (most of th... | [
"TAGS\n#transformers #pytorch #safetensors #bart #text2text-generation #fill-mask #text-classification #Summarization #Chinese #CPT #BART #BERT #seq2seq #zh #arxiv-2109.05729 #autotrain_compatible #endpoints_compatible #region-us \n",
"### News\n\n\n12/30/2022\n\n\nAn updated version of CPT & Chinese BART are rel... |
fill-mask | transformers |
# ElasticBERT-BASE
## Model description
This is an implementation of the `base` version of ElasticBERT.
[**Towards Efficient NLP: A Standard Evaluation and A Strong Baseline**](https://arxiv.org/pdf/2110.07038.pdf)
Xiangyang Liu, Tianxiang Sun, Junliang He, Lingling Wu, Xinyu Zhang, Hao Jiang, Zhao Cao, Xuanjing H... | {"language": "en", "tags": ["Multi-exit-BERT"], "datasets": ["wikipedia", "bookcorpus", "c4"]} | fnlp/elasticbert-base | null | [
"transformers",
"pytorch",
"elasticbert",
"fill-mask",
"Multi-exit-BERT",
"en",
"dataset:wikipedia",
"dataset:bookcorpus",
"dataset:c4",
"arxiv:2110.07038",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2110.07038"
] | [
"en"
] | TAGS
#transformers #pytorch #elasticbert #fill-mask #Multi-exit-BERT #en #dataset-wikipedia #dataset-bookcorpus #dataset-c4 #arxiv-2110.07038 #autotrain_compatible #endpoints_compatible #region-us
|
# ElasticBERT-BASE
## Model description
This is an implementation of the 'base' version of ElasticBERT.
Towards Efficient NLP: A Standard Evaluation and A Strong Baseline
Xiangyang Liu, Tianxiang Sun, Junliang He, Lingling Wu, Xinyu Zhang, Hao Jiang, Zhao Cao, Xuanjing Huang, Xipeng Qiu
## Code link
fastnlp/elas... | [
"# ElasticBERT-BASE",
"## Model description\n\nThis is an implementation of the 'base' version of ElasticBERT.\n\nTowards Efficient NLP: A Standard Evaluation and A Strong Baseline\n\nXiangyang Liu, Tianxiang Sun, Junliang He, Lingling Wu, Xinyu Zhang, Hao Jiang, Zhao Cao, Xuanjing Huang, Xipeng Qiu",
"## Code ... | [
"TAGS\n#transformers #pytorch #elasticbert #fill-mask #Multi-exit-BERT #en #dataset-wikipedia #dataset-bookcorpus #dataset-c4 #arxiv-2110.07038 #autotrain_compatible #endpoints_compatible #region-us \n",
"# ElasticBERT-BASE",
"## Model description\n\nThis is an implementation of the 'base' version of ElasticBER... |
fill-mask | transformers |
# ElasticBERT-LARGE
## Model description
This is an implementation of the `large` version of ElasticBERT.
[**Towards Efficient NLP: A Standard Evaluation and A Strong Baseline**](https://arxiv.org/pdf/2110.07038.pdf)
Xiangyang Liu, Tianxiang Sun, Junliang He, Lingling Wu, Xinyu Zhang, Hao Jiang, Zhao Cao, Xuanjing... | {"language": "en", "tags": ["Multi-exit-BERT"], "datasets": ["wikipedia", "bookcorpus", "c4"]} | fnlp/elasticbert-large | null | [
"transformers",
"pytorch",
"elasticbert",
"fill-mask",
"Multi-exit-BERT",
"en",
"dataset:wikipedia",
"dataset:bookcorpus",
"dataset:c4",
"arxiv:2110.07038",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2110.07038"
] | [
"en"
] | TAGS
#transformers #pytorch #elasticbert #fill-mask #Multi-exit-BERT #en #dataset-wikipedia #dataset-bookcorpus #dataset-c4 #arxiv-2110.07038 #autotrain_compatible #endpoints_compatible #region-us
|
# ElasticBERT-LARGE
## Model description
This is an implementation of the 'large' version of ElasticBERT.
Towards Efficient NLP: A Standard Evaluation and A Strong Baseline
Xiangyang Liu, Tianxiang Sun, Junliang He, Lingling Wu, Xinyu Zhang, Hao Jiang, Zhao Cao, Xuanjing Huang, Xipeng Qiu
## Code link
fastnlp/el... | [
"# ElasticBERT-LARGE",
"## Model description\n\nThis is an implementation of the 'large' version of ElasticBERT.\n\nTowards Efficient NLP: A Standard Evaluation and A Strong Baseline\n\nXiangyang Liu, Tianxiang Sun, Junliang He, Lingling Wu, Xinyu Zhang, Hao Jiang, Zhao Cao, Xuanjing Huang, Xipeng Qiu",
"## Cod... | [
"TAGS\n#transformers #pytorch #elasticbert #fill-mask #Multi-exit-BERT #en #dataset-wikipedia #dataset-bookcorpus #dataset-c4 #arxiv-2110.07038 #autotrain_compatible #endpoints_compatible #region-us \n",
"# ElasticBERT-LARGE",
"## Model description\n\nThis is an implementation of the 'large' version of ElasticB... |
text2text-generation | transformers |
# bart-base-python-1m | {"language": "py", "license": "mit", "tags": ["bart", "pytorch"], "thumbnail": "https://avatars.githubusercontent.com/u/70610668?s=400&u=f0699303289113c125e8686338739d9a63d5826c&v=4"} | formermagic/bart-base-python-1m | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"py",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"py"
] | TAGS
#transformers #pytorch #bart #text2text-generation #py #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# bart-base-python-1m | [
"# bart-base-python-1m"
] | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #py #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# bart-base-python-1m"
] |
text2text-generation | transformers | # Python T5 base model
Pre-trained model on CodeSearchNet Python dataset using a span-masking objective. The training objective and model were introduced in [this paper](https://arxiv.org/pdf/1910.10683.pdf) and first released in [this repository](https://github.com/google-research/text-to-text-transfer-transformer). ... | {} | formermagic/pyt5-base | null | [
"transformers",
"pytorch",
"jax",
"tensorboard",
"t5",
"text2text-generation",
"arxiv:1910.10683",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1910.10683"
] | [] | TAGS
#transformers #pytorch #jax #tensorboard #t5 #text2text-generation #arxiv-1910.10683 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # Python T5 base model
Pre-trained model on CodeSearchNet Python dataset using a span-masking objective. The training objective and model were introduced in this paper and first released in this repository. PyT5 model used git-t5 framework built on top of JAX/Flax to pre-train the model on a TPU v3-8 node.
# How to u... | [
"# Python T5 base model\n\nPre-trained model on CodeSearchNet Python dataset using a span-masking objective. The training objective and model were introduced in this paper and first released in this repository. PyT5 model used git-t5 framework built on top of JAX/Flax to pre-train the model on a TPU v3-8 node.",
... | [
"TAGS\n#transformers #pytorch #jax #tensorboard #t5 #text2text-generation #arxiv-1910.10683 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Python T5 base model\n\nPre-trained model on CodeSearchNet Python dataset using a span-masking objective. The training objective and... |
fill-mask | transformers |
# roberta-base-python-1m | {"language": "py", "license": "mit", "tags": ["roberta", "pytorch"], "thumbnail": "https://avatars.githubusercontent.com/u/70610668?s=400&u=f0699303289113c125e8686338739d9a63d5826c&v=4"} | formermagic/roberta-base-python-1m | null | [
"transformers",
"pytorch",
"jax",
"roberta",
"fill-mask",
"py",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"py"
] | TAGS
#transformers #pytorch #jax #roberta #fill-mask #py #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# roberta-base-python-1m | [
"# roberta-base-python-1m"
] | [
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"# roberta-base-python-1m"
] |
null | null | https://www.geogebra.org/m/w8uzjttg
https://www.geogebra.org/m/gvn7m78g
https://www.geogebra.org/m/arxecanq
https://www.geogebra.org/m/xb69bvww
https://www.geogebra.org/m/apvepfnd
https://www.geogebra.org/m/evmj8ckk
https://www.geogebra.org/m/qxcxwmhp
https://www.geogebra.org/m/p3cxqh6c
https://www.geogebra.org/m/ggrah... | {} | formu/DR-Site | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| URL
URL
URL
URL
URL
URL
URL
URL
URL
URL
URL
URL
URL
URL
URL | [] | [
"TAGS\n#region-us \n"
] |
text-generation | transformers | tags:
- Text2text Generation
- Conversational
- Text generation
model:
- "355M"
model-type:
- gpt2
widgets:
text_example_1:
- "One would be forgiven if one was not aware that Julian Assange is being"
title_example_1:
- "David North wsws"
text_example_2:
- "I would like to extend my sincerest greetings to the people ... | {} | fractaldna22/GPT_2_Marxism | null | [
"transformers",
"pytorch",
"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 #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| tags:
- Text2text Generation
- Conversational
- Text generation
model:
- "355M"
model-type:
- gpt2
widgets:
text_example_1:
- "One would be forgiven if one was not aware that Julian Assange is being"
title_example_1:
- "David North wsws"
text_example_2:
- "I would like to extend my sincerest greetings to the people ... | [
"# GPT_2_Marxism is based on the gpt-2 355M model finetuned on a large corpus of Marxist documents, polemics and literature from historical and contemporary writers",
"# in the international socialist movement and the ICFI (fourth international) which upholds the principles which characterize genuine revolutionar... | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# GPT_2_Marxism is based on the gpt-2 355M model finetuned on a large corpus of Marxist documents, polemics and literature from historical and contemporary writer... |
text-generation | transformers | ## Fact checking
This generative model - trained on FEVER - aims to predict whether a claim is consistent with the provided evidence.
### Installation and simple usage
One quick way to install it is to type
```bash
pip install fact_checking
```
and then use the following code:
```python
from transformers import (... | {} | fractalego/fact-checking | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"doi:10.57967/hf/0009",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #doi-10.57967/hf/0009 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Fact checking
-------------
This generative model - trained on FEVER - aims to predict whether a claim is consistent with the provided evidence.
### Installation and simple usage
One quick way to install it is to type
and then use the following code:
which gives the output
### Probabilistic output with repl... | [
"### Installation and simple usage\n\n\nOne quick way to install it is to type\n\n\nand then use the following code:\n\n\nwhich gives the output",
"### Probabilistic output with replicas\n\n\nThe output can include a probabilistic component, obtained by iterating a number of times the output generation.\nThe syst... | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #doi-10.57967/hf/0009 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Installation and simple usage\n\n\nOne quick way to install it is to type\n\n\nand then use the following code:\n\n\nwhich gives the output",
"##... |
question-answering | transformers | ## Introduction
This is a zero-shot relation extractor based on the paper [Exploring the zero-shot limit of FewRel](https://www.aclweb.org/anthology/2020.coling-main.124).
## Installation
```bash
$ pip install zero-shot-re
```
## Run the Extractor
```python
from transformers import AutoTokenizer
from zero_shot_re im... | {} | fractalego/fewrel-zero-shot | 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
| Introduction
------------
This is a zero-shot relation extractor based on the paper Exploring the zero-shot limit of FewRel.
Installation
------------
Run the Extractor
-----------------
with results
Accuracy
--------
The results as in the paper are
Model: (1) Distillbert, 0-shot 5-ways: 70.1±0.5, 0-shot ... | [] | [
"TAGS\n#transformers #pytorch #bert #question-answering #endpoints_compatible #region-us \n"
] |
automatic-speech-recognition | transformers | # Personal speech to text model
s2t models often do not understand my accent, so I fine tuned this one from "facebook/wav2vec2-large-robust-ft-swbd-300h" using about 1000 recordings of my voice.
Do not download unless you have exactly my accent. | {} | fractalego/personal-speech-to-text-model | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #endpoints_compatible #has_space #region-us
| # Personal speech to text model
s2t models often do not understand my accent, so I fine tuned this one from "facebook/wav2vec2-large-robust-ft-swbd-300h" using about 1000 recordings of my voice.
Do not download unless you have exactly my accent. | [
"# Personal speech to text model\ns2t models often do not understand my accent, so I fine tuned this one from \"facebook/wav2vec2-large-robust-ft-swbd-300h\" using about 1000 recordings of my voice.\n\nDo not download unless you have exactly my accent."
] | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #endpoints_compatible #has_space #region-us \n",
"# Personal speech to text model\ns2t models often do not understand my accent, so I fine tuned this one from \"facebook/wav2vec2-large-robust-ft-swbd-300h\" using about 1000 recordings of my voi... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-distilled-clinc
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/d... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["clinc_oos"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-base-uncased-distilled-clinc", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "clinc_oos", "type": "clinc_oos",... | frahman/distilbert-base-uncased-distilled-clinc | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:clinc_oos",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-clinc_oos #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-distilled-clinc
=======================================
This model is a fine-tuned version of distilbert-base-uncased on the clinc\_oos dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1002
* Accuracy: 0.9406
Model description
-----------------
More information... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 48\n* eval\\_batch\\_size: 48\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10",
"### Train... | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-clinc_oos #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate:... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-clinc
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/d... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["clinc_oos"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-base-uncased-finetuned-clinc", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "clinc_oos", "type": "clinc_oos",... | frahman/distilbert-base-uncased-finetuned-clinc | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:clinc_oos",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-clinc_oos #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-clinc
=======================================
This model is a fine-tuned version of distilbert-base-uncased on the clinc\_oos dataset.
It achieves the following results on the evaluation set:
* Loss: 0.7703
* Accuracy: 0.9187
Model description
-----------------
More information... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 48\n* eval\\_batch\\_size: 48\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-clinc_oos #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* lea... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["emotion"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "distilbert-base-uncased-finetuned-emotion", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "emotion", "type": "emotion... | frahman/distilbert-base-uncased-finetuned-emotion | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-emotion
=========================================
This model is a fine-tuned version of distilbert-base-uncased on the emotion dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2202
* Accuracy: 0.9205
* F1: 0.9207
Model description
-----------------
Mo... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learn... |
token-classification | transformers |
# SciBERT finetuned on JNLPA for NER downstream task
## Language Model
[SciBERT](https://arxiv.org/pdf/1903.10676.pdf) is a pretrained language model based on BERT and trained by the
[Allen Institute for AI](https://allenai.org/) on papers from the corpus of
[Semantic Scholar](https://www.semanticscholar.org/).
... | {"language": "scientific english"} | fran-martinez/scibert_scivocab_cased_ner_jnlpba | null | [
"transformers",
"pytorch",
"jax",
"bert",
"token-classification",
"arxiv:1903.10676",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1903.10676"
] | [
"scientific english"
] | TAGS
#transformers #pytorch #jax #bert #token-classification #arxiv-1903.10676 #autotrain_compatible #endpoints_compatible #region-us
| SciBERT finetuned on JNLPA for NER downstream task
==================================================
Language Model
--------------
SciBERT is a pretrained language model based on BERT and trained by the
Allen Institute for AI on papers from the corpus of
Semantic Scholar.
Corpus size is 1.14M papers, 3.1B tokens. ... | [
"### Data\n\n\nThe corpus used to fine-tune the NER is BioNLP / JNLPBA shared task.\n\n\n* Training data consist of 2,000 PubMed abstracts with term/word annotation. This corresponds to 18,546 samples (senteces).\n* Evaluation data consist of 404 PubMed abstracts with term/word annotation. This corresponds to 3,856... | [
"TAGS\n#transformers #pytorch #jax #bert #token-classification #arxiv-1903.10676 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Data\n\n\nThe corpus used to fine-tune the NER is BioNLP / JNLPBA shared task.\n\n\n* Training data consist of 2,000 PubMed abstracts with term/word annotation. This co... |
question-answering | transformers | **[`microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext`](https://huggingface.co/microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext)** fine-tuned on **[`SQuAD V2`](https://rajpurkar.github.io/SQuAD-explorer/)** using **[`run_qa.py`](https://github.com/huggingface/transformers/blob/master/examples/p... | {} | franklu/pubmed_bert_squadv2 | null | [
"transformers",
"pytorch",
"bert",
"question-answering",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #question-answering #endpoints_compatible #has_space #region-us
| 'microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext' fine-tuned on 'SQuAD V2' using 'run_qa.py'
Tunning script:
| [] | [
"TAGS\n#transformers #pytorch #bert #question-answering #endpoints_compatible #has_space #region-us \n"
] |
image-classification | transformers |
# CSP-Darknet-53 model
Pretrained on [ImageNette](https://github.com/fastai/imagenette). The CSP-Darknet-53 architecture was introduced in [this paper](https://arxiv.org/pdf/1911.11929.pdf).
## Model description
The core idea of the author is to change the convolutional stage by adding cross stage partial blocks ... | {"license": "apache-2.0", "tags": ["image-classification", "pytorch"], "datasets": ["frgfm/imagenette"]} | frgfm/cspdarknet53 | null | [
"transformers",
"pytorch",
"image-classification",
"dataset:frgfm/imagenette",
"arxiv:1911.11929",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1911.11929"
] | [] | TAGS
#transformers #pytorch #image-classification #dataset-frgfm/imagenette #arxiv-1911.11929 #license-apache-2.0 #endpoints_compatible #region-us
|
# CSP-Darknet-53 model
Pretrained on ImageNette. The CSP-Darknet-53 architecture was introduced in this paper.
## Model description
The core idea of the author is to change the convolutional stage by adding cross stage partial blocks in the architecture.
## Installation
### Prerequisites
Python 3.6 (or higher... | [
"# CSP-Darknet-53 model\n\nPretrained on ImageNette. The CSP-Darknet-53 architecture was introduced in this paper.",
"## Model description\n\nThe core idea of the author is to change the convolutional stage by adding cross stage partial blocks in the architecture.",
"## Installation",
"### Prerequisites\n\nPy... | [
"TAGS\n#transformers #pytorch #image-classification #dataset-frgfm/imagenette #arxiv-1911.11929 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# CSP-Darknet-53 model\n\nPretrained on ImageNette. The CSP-Darknet-53 architecture was introduced in this paper.",
"## Model description\n\nThe core idea of... |
image-classification | transformers |
# CSP-Darknet-53 Mish model
Pretrained on [ImageNette](https://github.com/fastai/imagenette). The CSP-Darknet-53 Mish architecture was introduced in [this paper](https://arxiv.org/pdf/1911.11929.pdf).
## Model description
The core idea of the author is to change the convolutional stage by adding cross stage parti... | {"license": "apache-2.0", "tags": ["image-classification", "pytorch"], "datasets": ["frgfm/imagenette"]} | frgfm/cspdarknet53_mish | null | [
"transformers",
"pytorch",
"image-classification",
"dataset:frgfm/imagenette",
"arxiv:1911.11929",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1911.11929"
] | [] | TAGS
#transformers #pytorch #image-classification #dataset-frgfm/imagenette #arxiv-1911.11929 #license-apache-2.0 #endpoints_compatible #region-us
|
# CSP-Darknet-53 Mish model
Pretrained on ImageNette. The CSP-Darknet-53 Mish architecture was introduced in this paper.
## Model description
The core idea of the author is to change the convolutional stage by adding cross stage partial blocks in the architecture and replace activations with Mish.
## Installati... | [
"# CSP-Darknet-53 Mish model\n\nPretrained on ImageNette. The CSP-Darknet-53 Mish architecture was introduced in this paper.",
"## Model description\n\nThe core idea of the author is to change the convolutional stage by adding cross stage partial blocks in the architecture and replace activations with Mish.",
"... | [
"TAGS\n#transformers #pytorch #image-classification #dataset-frgfm/imagenette #arxiv-1911.11929 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# CSP-Darknet-53 Mish model\n\nPretrained on ImageNette. The CSP-Darknet-53 Mish architecture was introduced in this paper.",
"## Model description\n\nThe co... |
image-classification | transformers |
# Darknet-19 model
Pretrained on [ImageNette](https://github.com/fastai/imagenette). The Darknet-19 architecture was introduced in [this paper](https://pjreddie.com/media/files/papers/YOLO9000.pdf).
## Model description
The core idea of the author is to combine high throughput of a highway net with performance ga... | {"license": "apache-2.0", "tags": ["image-classification", "pytorch"], "datasets": ["frgfm/imagenette"]} | frgfm/darknet19 | null | [
"transformers",
"pytorch",
"image-classification",
"dataset:frgfm/imagenette",
"arxiv:1612.08242",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1612.08242"
] | [] | TAGS
#transformers #pytorch #image-classification #dataset-frgfm/imagenette #arxiv-1612.08242 #license-apache-2.0 #endpoints_compatible #has_space #region-us
|
# Darknet-19 model
Pretrained on ImageNette. The Darknet-19 architecture was introduced in this paper.
## Model description
The core idea of the author is to combine high throughput of a highway net with performance gains using better activations (Leaky ReLU) and batch normalization. This architecture is used as ... | [
"# Darknet-19 model\n\nPretrained on ImageNette. The Darknet-19 architecture was introduced in this paper.",
"## Model description\n\nThe core idea of the author is to combine high throughput of a highway net with performance gains using better activations (Leaky ReLU) and batch normalization. This architecture i... | [
"TAGS\n#transformers #pytorch #image-classification #dataset-frgfm/imagenette #arxiv-1612.08242 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"# Darknet-19 model\n\nPretrained on ImageNette. The Darknet-19 architecture was introduced in this paper.",
"## Model description\n\nThe core idea... |
image-classification | transformers |
# Darknet-53 model
Pretrained on [ImageNette](https://github.com/fastai/imagenette). The Darknet-53 architecture was introduced in [this paper](https://pjreddie.com/media/files/papers/YOLOv3.pdf).
## Model description
The core idea of the author is to increase the depth of the Darknet-19 architecture, and adding ... | {"license": "apache-2.0", "tags": ["image-classification", "pytorch"], "datasets": ["frgfm/imagenette"]} | frgfm/darknet53 | null | [
"transformers",
"pytorch",
"image-classification",
"dataset:frgfm/imagenette",
"arxiv:1804.02767",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1804.02767"
] | [] | TAGS
#transformers #pytorch #image-classification #dataset-frgfm/imagenette #arxiv-1804.02767 #license-apache-2.0 #endpoints_compatible #region-us
|
# Darknet-53 model
Pretrained on ImageNette. The Darknet-53 architecture was introduced in this paper.
## Model description
The core idea of the author is to increase the depth of the Darknet-19 architecture, and adding shortcut connections to ease the gradient propagation.
## Installation
### Prerequisites
P... | [
"# Darknet-53 model\n\nPretrained on ImageNette. The Darknet-53 architecture was introduced in this paper.",
"## Model description\n\nThe core idea of the author is to increase the depth of the Darknet-19 architecture, and adding shortcut connections to ease the gradient propagation.",
"## Installation",
"###... | [
"TAGS\n#transformers #pytorch #image-classification #dataset-frgfm/imagenette #arxiv-1804.02767 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# Darknet-53 model\n\nPretrained on ImageNette. The Darknet-53 architecture was introduced in this paper.",
"## Model description\n\nThe core idea of the aut... |
image-classification | transformers |
# RepVGG-A0 model
Pretrained on [ImageNette](https://github.com/fastai/imagenette). The RepVGG architecture was introduced in [this paper](https://arxiv.org/pdf/2101.03697.pdf).
## Model description
The core idea of the author is to distinguish the training architecture (with shortcut connections), from the infer... | {"license": "apache-2.0", "tags": ["image-classification", "pytorch", "onnx"], "datasets": ["frgfm/imagenette"]} | frgfm/repvgg_a0 | null | [
"transformers",
"pytorch",
"onnx",
"image-classification",
"dataset:frgfm/imagenette",
"arxiv:2101.03697",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2101.03697"
] | [] | TAGS
#transformers #pytorch #onnx #image-classification #dataset-frgfm/imagenette #arxiv-2101.03697 #license-apache-2.0 #endpoints_compatible #region-us
|
# RepVGG-A0 model
Pretrained on ImageNette. The RepVGG architecture was introduced in this paper.
## Model description
The core idea of the author is to distinguish the training architecture (with shortcut connections), from the inference one (a pure highway network). By designing the residual block, the training... | [
"# RepVGG-A0 model\n\nPretrained on ImageNette. The RepVGG architecture was introduced in this paper.",
"## Model description\n\nThe core idea of the author is to distinguish the training architecture (with shortcut connections), from the inference one (a pure highway network). By designing the residual block, th... | [
"TAGS\n#transformers #pytorch #onnx #image-classification #dataset-frgfm/imagenette #arxiv-2101.03697 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# RepVGG-A0 model\n\nPretrained on ImageNette. The RepVGG architecture was introduced in this paper.",
"## Model description\n\nThe core idea of the au... |
image-classification | transformers |
# RepVGG-A1 model
Pretrained on [ImageNette](https://github.com/fastai/imagenette). The RepVGG architecture was introduced in [this paper](https://arxiv.org/pdf/2101.03697.pdf).
## Model description
The core idea of the author is to distinguish the training architecture (with shortcut connections), from the infer... | {"license": "apache-2.0", "tags": ["image-classification", "pytorch", "onnx"], "datasets": ["frgfm/imagenette"]} | frgfm/repvgg_a1 | null | [
"transformers",
"pytorch",
"onnx",
"image-classification",
"dataset:frgfm/imagenette",
"arxiv:2101.03697",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2101.03697"
] | [] | TAGS
#transformers #pytorch #onnx #image-classification #dataset-frgfm/imagenette #arxiv-2101.03697 #license-apache-2.0 #endpoints_compatible #region-us
|
# RepVGG-A1 model
Pretrained on ImageNette. The RepVGG architecture was introduced in this paper.
## Model description
The core idea of the author is to distinguish the training architecture (with shortcut connections), from the inference one (a pure highway network). By designing the residual block, the training... | [
"# RepVGG-A1 model\n\nPretrained on ImageNette. The RepVGG architecture was introduced in this paper.",
"## Model description\n\nThe core idea of the author is to distinguish the training architecture (with shortcut connections), from the inference one (a pure highway network). By designing the residual block, th... | [
"TAGS\n#transformers #pytorch #onnx #image-classification #dataset-frgfm/imagenette #arxiv-2101.03697 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# RepVGG-A1 model\n\nPretrained on ImageNette. The RepVGG architecture was introduced in this paper.",
"## Model description\n\nThe core idea of the au... |
image-classification | transformers |
# RepVGG-A2 model
Pretrained on [ImageNette](https://github.com/fastai/imagenette). The RepVGG architecture was introduced in [this paper](https://arxiv.org/pdf/2101.03697.pdf).
## Model description
The core idea of the author is to distinguish the training architecture (with shortcut connections), from the infer... | {"license": "apache-2.0", "tags": ["image-classification", "pytorch", "onnx"], "datasets": ["frgfm/imagenette"]} | frgfm/repvgg_a2 | null | [
"transformers",
"pytorch",
"onnx",
"image-classification",
"dataset:frgfm/imagenette",
"arxiv:2101.03697",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2101.03697"
] | [] | TAGS
#transformers #pytorch #onnx #image-classification #dataset-frgfm/imagenette #arxiv-2101.03697 #license-apache-2.0 #endpoints_compatible #region-us
|
# RepVGG-A2 model
Pretrained on ImageNette. The RepVGG architecture was introduced in this paper.
## Model description
The core idea of the author is to distinguish the training architecture (with shortcut connections), from the inference one (a pure highway network). By designing the residual block, the training... | [
"# RepVGG-A2 model\n\nPretrained on ImageNette. The RepVGG architecture was introduced in this paper.",
"## Model description\n\nThe core idea of the author is to distinguish the training architecture (with shortcut connections), from the inference one (a pure highway network). By designing the residual block, th... | [
"TAGS\n#transformers #pytorch #onnx #image-classification #dataset-frgfm/imagenette #arxiv-2101.03697 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# RepVGG-A2 model\n\nPretrained on ImageNette. The RepVGG architecture was introduced in this paper.",
"## Model description\n\nThe core idea of the au... |
image-classification | transformers |
# ResNet-18 model
Pretrained on [ImageNette](https://github.com/fastai/imagenette). The ResNet architecture was introduced in [this paper](https://arxiv.org/pdf/1512.03385.pdf).
## Model description
The core idea of the author is to help the gradient propagation through numerous layers by adding a skip connection... | {"license": "apache-2.0", "tags": ["image-classification", "pytorch", "onnx"], "datasets": ["frgfm/imagenette"]} | frgfm/resnet18 | null | [
"transformers",
"pytorch",
"onnx",
"image-classification",
"dataset:frgfm/imagenette",
"arxiv:1512.03385",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1512.03385"
] | [] | TAGS
#transformers #pytorch #onnx #image-classification #dataset-frgfm/imagenette #arxiv-1512.03385 #license-apache-2.0 #endpoints_compatible #region-us
|
# ResNet-18 model
Pretrained on ImageNette. The ResNet architecture was introduced in this paper.
## Model description
The core idea of the author is to help the gradient propagation through numerous layers by adding a skip connection.
## Installation
### Prerequisites
Python 3.6 (or higher) and pip/conda are... | [
"# ResNet-18 model\n\nPretrained on ImageNette. The ResNet architecture was introduced in this paper.",
"## Model description\n\nThe core idea of the author is to help the gradient propagation through numerous layers by adding a skip connection.",
"## Installation",
"### Prerequisites\n\nPython 3.6 (or higher... | [
"TAGS\n#transformers #pytorch #onnx #image-classification #dataset-frgfm/imagenette #arxiv-1512.03385 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# ResNet-18 model\n\nPretrained on ImageNette. The ResNet architecture was introduced in this paper.",
"## Model description\n\nThe core idea of the au... |
image-classification | transformers |
# ResNet-34 model
Pretrained on [ImageNette](https://github.com/fastai/imagenette). The ResNet architecture was introduced in [this paper](https://arxiv.org/pdf/1512.03385.pdf).
## Model description
The core idea of the author is to help the gradient propagation through numerous layers by adding a skip connection... | {"license": "apache-2.0", "tags": ["image-classification", "pytorch", "onnx"], "datasets": ["frgfm/imagenette"]} | frgfm/resnet34 | null | [
"transformers",
"pytorch",
"onnx",
"image-classification",
"dataset:frgfm/imagenette",
"arxiv:1512.03385",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1512.03385"
] | [] | TAGS
#transformers #pytorch #onnx #image-classification #dataset-frgfm/imagenette #arxiv-1512.03385 #license-apache-2.0 #endpoints_compatible #region-us
|
# ResNet-34 model
Pretrained on ImageNette. The ResNet architecture was introduced in this paper.
## Model description
The core idea of the author is to help the gradient propagation through numerous layers by adding a skip connection.
## Installation
### Prerequisites
Python 3.6 (or higher) and pip/conda are... | [
"# ResNet-34 model\n\nPretrained on ImageNette. The ResNet architecture was introduced in this paper.",
"## Model description\n\nThe core idea of the author is to help the gradient propagation through numerous layers by adding a skip connection.",
"## Installation",
"### Prerequisites\n\nPython 3.6 (or higher... | [
"TAGS\n#transformers #pytorch #onnx #image-classification #dataset-frgfm/imagenette #arxiv-1512.03385 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# ResNet-34 model\n\nPretrained on ImageNette. The ResNet architecture was introduced in this paper.",
"## Model description\n\nThe core idea of the au... |
image-classification | transformers |
# ReXNet-1.0x model
Pretrained on [ImageNette](https://github.com/fastai/imagenette). The ReXNet architecture was introduced in [this paper](https://arxiv.org/pdf/2007.00992.pdf).
## Model description
The core idea of the author is to add a customized Squeeze-Excitation layer in the residual blocks that will prev... | {"license": "apache-2.0", "tags": ["image-classification", "pytorch", "onnx"], "datasets": ["frgfm/imagenette"]} | frgfm/rexnet1_0x | null | [
"transformers",
"pytorch",
"onnx",
"image-classification",
"dataset:frgfm/imagenette",
"arxiv:2007.00992",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2007.00992"
] | [] | TAGS
#transformers #pytorch #onnx #image-classification #dataset-frgfm/imagenette #arxiv-2007.00992 #license-apache-2.0 #endpoints_compatible #has_space #region-us
|
# ReXNet-1.0x model
Pretrained on ImageNette. The ReXNet architecture was introduced in this paper.
## Model description
The core idea of the author is to add a customized Squeeze-Excitation layer in the residual blocks that will prevent channel redundancy.
## Installation
### Prerequisites
Python 3.6 (or hig... | [
"# ReXNet-1.0x model\n\nPretrained on ImageNette. The ReXNet architecture was introduced in this paper.",
"## Model description\n\nThe core idea of the author is to add a customized Squeeze-Excitation layer in the residual blocks that will prevent channel redundancy.",
"## Installation",
"### Prerequisites\n\... | [
"TAGS\n#transformers #pytorch #onnx #image-classification #dataset-frgfm/imagenette #arxiv-2007.00992 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"# ReXNet-1.0x model\n\nPretrained on ImageNette. The ReXNet architecture was introduced in this paper.",
"## Model description\n\nThe core i... |
image-classification | transformers |
# ReXNet-1.3x model
Pretrained on [ImageNette](https://github.com/fastai/imagenette). The ReXNet architecture was introduced in [this paper](https://arxiv.org/pdf/2007.00992.pdf).
## Model description
The core idea of the author is to add a customized Squeeze-Excitation layer in the residual blocks that will prev... | {"license": "apache-2.0", "tags": ["image-classification", "pytorch", "onnx"], "datasets": ["frgfm/imagenette"]} | frgfm/rexnet1_3x | null | [
"transformers",
"pytorch",
"onnx",
"image-classification",
"dataset:frgfm/imagenette",
"arxiv:2007.00992",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2007.00992"
] | [] | TAGS
#transformers #pytorch #onnx #image-classification #dataset-frgfm/imagenette #arxiv-2007.00992 #license-apache-2.0 #endpoints_compatible #region-us
|
# ReXNet-1.3x model
Pretrained on ImageNette. The ReXNet architecture was introduced in this paper.
## Model description
The core idea of the author is to add a customized Squeeze-Excitation layer in the residual blocks that will prevent channel redundancy.
## Installation
### Prerequisites
Python 3.6 (or hig... | [
"# ReXNet-1.3x model\n\nPretrained on ImageNette. The ReXNet architecture was introduced in this paper.",
"## Model description\n\nThe core idea of the author is to add a customized Squeeze-Excitation layer in the residual blocks that will prevent channel redundancy.",
"## Installation",
"### Prerequisites\n\... | [
"TAGS\n#transformers #pytorch #onnx #image-classification #dataset-frgfm/imagenette #arxiv-2007.00992 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# ReXNet-1.3x model\n\nPretrained on ImageNette. The ReXNet architecture was introduced in this paper.",
"## Model description\n\nThe core idea of the ... |
image-classification | transformers |
# ReXNet-1.5x model
Pretrained on [ImageNette](https://github.com/fastai/imagenette). The ReXNet architecture was introduced in [this paper](https://arxiv.org/pdf/2007.00992.pdf).
## Model description
The core idea of the author is to add a customized Squeeze-Excitation layer in the residual blocks that will prev... | {"license": "apache-2.0", "tags": ["image-classification", "pytorch", "onnx"], "datasets": ["frgfm/imagenette"]} | frgfm/rexnet1_5x | null | [
"transformers",
"pytorch",
"onnx",
"image-classification",
"dataset:frgfm/imagenette",
"arxiv:2007.00992",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2007.00992"
] | [] | TAGS
#transformers #pytorch #onnx #image-classification #dataset-frgfm/imagenette #arxiv-2007.00992 #license-apache-2.0 #endpoints_compatible #region-us
|
# ReXNet-1.5x model
Pretrained on ImageNette. The ReXNet architecture was introduced in this paper.
## Model description
The core idea of the author is to add a customized Squeeze-Excitation layer in the residual blocks that will prevent channel redundancy.
## Installation
### Prerequisites
Python 3.6 (or hig... | [
"# ReXNet-1.5x model\n\nPretrained on ImageNette. The ReXNet architecture was introduced in this paper.",
"## Model description\n\nThe core idea of the author is to add a customized Squeeze-Excitation layer in the residual blocks that will prevent channel redundancy.",
"## Installation",
"### Prerequisites\n\... | [
"TAGS\n#transformers #pytorch #onnx #image-classification #dataset-frgfm/imagenette #arxiv-2007.00992 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# ReXNet-1.5x model\n\nPretrained on ImageNette. The ReXNet architecture was introduced in this paper.",
"## Model description\n\nThe core idea of the ... |
image-classification | transformers |
# ReXNet-2.0x model
Pretrained on [ImageNette](https://github.com/fastai/imagenette). The ReXNet architecture was introduced in [this paper](https://arxiv.org/pdf/2007.00992.pdf).
## Model description
The core idea of the author is to add a customized Squeeze-Excitation layer in the residual blocks that will prev... | {"license": "apache-2.0", "tags": ["image-classification", "pytorch", "onnx"], "datasets": ["frgfm/imagenette"]} | frgfm/rexnet2_0x | null | [
"transformers",
"pytorch",
"onnx",
"image-classification",
"dataset:frgfm/imagenette",
"arxiv:2007.00992",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2007.00992"
] | [] | TAGS
#transformers #pytorch #onnx #image-classification #dataset-frgfm/imagenette #arxiv-2007.00992 #license-apache-2.0 #endpoints_compatible #region-us
|
# ReXNet-2.0x model
Pretrained on ImageNette. The ReXNet architecture was introduced in this paper.
## Model description
The core idea of the author is to add a customized Squeeze-Excitation layer in the residual blocks that will prevent channel redundancy.
## Installation
### Prerequisites
Python 3.6 (or hig... | [
"# ReXNet-2.0x model\n\nPretrained on ImageNette. The ReXNet architecture was introduced in this paper.",
"## Model description\n\nThe core idea of the author is to add a customized Squeeze-Excitation layer in the residual blocks that will prevent channel redundancy.",
"## Installation",
"### Prerequisites\n\... | [
"TAGS\n#transformers #pytorch #onnx #image-classification #dataset-frgfm/imagenette #arxiv-2007.00992 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# ReXNet-2.0x model\n\nPretrained on ImageNette. The ReXNet architecture was introduced in this paper.",
"## Model description\n\nThe core idea of the ... |
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. -->
# ted_mt-Spanish-to-Italian
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-es-it](https://huggingface.co/Helsinki-NLP... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["new_dataset"], "model-index": [{"name": "ted_mt-Spanish-to-Italian", "results": []}]} | frtna/ted_mt-Spanish-to-Italian | null | [
"transformers",
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"generated_from_trainer",
"dataset:new_dataset",
"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 #dataset-new_dataset #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| ted\_mt-Spanish-to-Italian
==========================
This model is a fine-tuned version of Helsinki-NLP/opus-mt-es-it on the new\_dataset dataset.
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluat... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1\n* mixed\\_prec... | [
"TAGS\n#transformers #pytorch #tensorboard #marian #text2text-generation #generated_from_trainer #dataset-new_dataset #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: ... |
null | null | # Fasttext
2 million word vectors trained with subword information on Common Crawl (600B tokens).
Read more:
* https://fasttext.cc/docs/en/english-vectors.html
| {"tags": ["glove", "gensim", "fse"]} | fse/fasttext-crawl-subwords-300 | null | [
"glove",
"gensim",
"fse",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#glove #gensim #fse #region-us
| # Fasttext
2 million word vectors trained with subword information on Common Crawl (600B tokens).
Read more:
* URL
| [
"# Fasttext\n\n2 million word vectors trained with subword information on Common Crawl (600B tokens).\n\nRead more:\n* URL"
] | [
"TAGS\n#glove #gensim #fse #region-us \n",
"# Fasttext\n\n2 million word vectors trained with subword information on Common Crawl (600B tokens).\n\nRead more:\n* URL"
] |
null | null | # Fasttext
1 million word vectors trained on Wikipedia 2017, UMBC webbase corpus and statmt.org news dataset (16B tokens).
Read more:
* https://fasttext.cc/docs/en/english-vectors.html
| {"tags": ["glove", "gensim", "fse"]} | fse/fasttext-wiki-news-subwords-300 | null | [
"glove",
"gensim",
"fse",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#glove #gensim #fse #region-us
| # Fasttext
1 million word vectors trained on Wikipedia 2017, UMBC webbase corpus and URL news dataset (16B tokens).
Read more:
* URL
| [
"# Fasttext\n\n1 million word vectors trained on Wikipedia 2017, UMBC webbase corpus and URL news dataset (16B tokens).\n\nRead more:\n* URL"
] | [
"TAGS\n#glove #gensim #fse #region-us \n",
"# Fasttext\n\n1 million word vectors trained on Wikipedia 2017, UMBC webbase corpus and URL news dataset (16B tokens).\n\nRead more:\n* URL"
] |
null | null | # Glove Twitter
Pre-trained glove vectors based on 2B tweets, 27B tokens, 1.2M vocab, uncased.
Read more:
* https://nlp.stanford.edu/projects/glove/
* https://nlp.stanford.edu/pubs/glove.pdf
| {"tags": ["glove", "gensim", "fse"]} | fse/glove-twitter-100 | null | [
"glove",
"gensim",
"fse",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#glove #gensim #fse #region-us
| # Glove Twitter
Pre-trained glove vectors based on 2B tweets, 27B tokens, 1.2M vocab, uncased.
Read more:
* URL
* URL
| [
"# Glove Twitter \n\nPre-trained glove vectors based on 2B tweets, 27B tokens, 1.2M vocab, uncased.\n\nRead more:\n* URL\n* URL"
] | [
"TAGS\n#glove #gensim #fse #region-us \n",
"# Glove Twitter \n\nPre-trained glove vectors based on 2B tweets, 27B tokens, 1.2M vocab, uncased.\n\nRead more:\n* URL\n* URL"
] |
null | null | # Glove Twitter
Pre-trained glove vectors based on 2B tweets, 27B tokens, 1.2M vocab, uncased.
Read more:
* https://nlp.stanford.edu/projects/glove/
* https://nlp.stanford.edu/pubs/glove.pdf
| {"tags": ["glove", "gensim", "fse"]} | fse/glove-twitter-200 | null | [
"glove",
"gensim",
"fse",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#glove #gensim #fse #region-us
| # Glove Twitter
Pre-trained glove vectors based on 2B tweets, 27B tokens, 1.2M vocab, uncased.
Read more:
* URL
* URL
| [
"# Glove Twitter \n\nPre-trained glove vectors based on 2B tweets, 27B tokens, 1.2M vocab, uncased.\n\nRead more:\n* URL\n* URL"
] | [
"TAGS\n#glove #gensim #fse #region-us \n",
"# Glove Twitter \n\nPre-trained glove vectors based on 2B tweets, 27B tokens, 1.2M vocab, uncased.\n\nRead more:\n* URL\n* URL"
] |
null | null | # Glove Twitter
Pre-trained glove vectors based on 2B tweets, 27B tokens, 1.2M vocab, uncased.
Read more:
* https://nlp.stanford.edu/projects/glove/
* https://nlp.stanford.edu/pubs/glove.pdf
| {"tags": ["glove", "gensim", "fse"]} | fse/glove-twitter-25 | null | [
"glove",
"gensim",
"fse",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#glove #gensim #fse #region-us
| # Glove Twitter
Pre-trained glove vectors based on 2B tweets, 27B tokens, 1.2M vocab, uncased.
Read more:
* URL
* URL
| [
"# Glove Twitter \n\nPre-trained glove vectors based on 2B tweets, 27B tokens, 1.2M vocab, uncased.\n\nRead more:\n* URL\n* URL"
] | [
"TAGS\n#glove #gensim #fse #region-us \n",
"# Glove Twitter \n\nPre-trained glove vectors based on 2B tweets, 27B tokens, 1.2M vocab, uncased.\n\nRead more:\n* URL\n* URL"
] |
null | null | # Glove Twitter
Pre-trained glove vectors based on 2B tweets, 27B tokens, 1.2M vocab, uncased.
Read more:
* https://nlp.stanford.edu/projects/glove/
* https://nlp.stanford.edu/pubs/glove.pdf
| {"tags": ["glove", "gensim", "fse"]} | fse/glove-twitter-50 | null | [
"glove",
"gensim",
"fse",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#glove #gensim #fse #region-us
| # Glove Twitter
Pre-trained glove vectors based on 2B tweets, 27B tokens, 1.2M vocab, uncased.
Read more:
* URL
* URL
| [
"# Glove Twitter \n\nPre-trained glove vectors based on 2B tweets, 27B tokens, 1.2M vocab, uncased.\n\nRead more:\n* URL\n* URL"
] | [
"TAGS\n#glove #gensim #fse #region-us \n",
"# Glove Twitter \n\nPre-trained glove vectors based on 2B tweets, 27B tokens, 1.2M vocab, uncased.\n\nRead more:\n* URL\n* URL"
] |
null | null | # Glove Twitter
Pre-trained glove vectors based on 2B tweets, 27B tokens, 1.2M vocab, uncased.
Read more:
* https://nlp.stanford.edu/projects/glove/
* https://nlp.stanford.edu/pubs/glove.pdf
| {"tags": ["glove", "gensim", "fse"]} | fse/glove-wiki-gigaword-100 | null | [
"glove",
"gensim",
"fse",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#glove #gensim #fse #region-us
| # Glove Twitter
Pre-trained glove vectors based on 2B tweets, 27B tokens, 1.2M vocab, uncased.
Read more:
* URL
* URL
| [
"# Glove Twitter \n\nPre-trained glove vectors based on 2B tweets, 27B tokens, 1.2M vocab, uncased.\n\nRead more:\n* URL\n* URL"
] | [
"TAGS\n#glove #gensim #fse #region-us \n",
"# Glove Twitter \n\nPre-trained glove vectors based on 2B tweets, 27B tokens, 1.2M vocab, uncased.\n\nRead more:\n* URL\n* URL"
] |
null | null | # Glove Twitter
Pre-trained glove vectors based on 2B tweets, 27B tokens, 1.2M vocab, uncased.
Read more:
* https://nlp.stanford.edu/projects/glove/
* https://nlp.stanford.edu/pubs/glove.pdf
| {"tags": ["glove", "gensim", "fse"]} | fse/glove-wiki-gigaword-200 | null | [
"glove",
"gensim",
"fse",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#glove #gensim #fse #region-us
| # Glove Twitter
Pre-trained glove vectors based on 2B tweets, 27B tokens, 1.2M vocab, uncased.
Read more:
* URL
* URL
| [
"# Glove Twitter \n\nPre-trained glove vectors based on 2B tweets, 27B tokens, 1.2M vocab, uncased.\n\nRead more:\n* URL\n* URL"
] | [
"TAGS\n#glove #gensim #fse #region-us \n",
"# Glove Twitter \n\nPre-trained glove vectors based on 2B tweets, 27B tokens, 1.2M vocab, uncased.\n\nRead more:\n* URL\n* URL"
] |
null | null | # Glove Twitter
Pre-trained glove vectors based on 2B tweets, 27B tokens, 1.2M vocab, uncased.
Read more:
* https://nlp.stanford.edu/projects/glove/
* https://nlp.stanford.edu/pubs/glove.pdf
| {"tags": ["glove", "gensim", "fse"]} | fse/glove-wiki-gigaword-300 | null | [
"glove",
"gensim",
"fse",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#glove #gensim #fse #region-us
| # Glove Twitter
Pre-trained glove vectors based on 2B tweets, 27B tokens, 1.2M vocab, uncased.
Read more:
* URL
* URL
| [
"# Glove Twitter \n\nPre-trained glove vectors based on 2B tweets, 27B tokens, 1.2M vocab, uncased.\n\nRead more:\n* URL\n* URL"
] | [
"TAGS\n#glove #gensim #fse #region-us \n",
"# Glove Twitter \n\nPre-trained glove vectors based on 2B tweets, 27B tokens, 1.2M vocab, uncased.\n\nRead more:\n* URL\n* URL"
] |
null | null | # Glove Twitter
Pre-trained glove vectors based on 2B tweets, 27B tokens, 1.2M vocab, uncased.
Read more:
* https://nlp.stanford.edu/projects/glove/
* https://nlp.stanford.edu/pubs/glove.pdf
| {"tags": ["glove", "gensim", "fse"]} | fse/glove-wiki-gigaword-50 | null | [
"glove",
"gensim",
"fse",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#glove #gensim #fse #region-us
| # Glove Twitter
Pre-trained glove vectors based on 2B tweets, 27B tokens, 1.2M vocab, uncased.
Read more:
* URL
* URL
| [
"# Glove Twitter \n\nPre-trained glove vectors based on 2B tweets, 27B tokens, 1.2M vocab, uncased.\n\nRead more:\n* URL\n* URL"
] | [
"TAGS\n#glove #gensim #fse #region-us \n",
"# Glove Twitter \n\nPre-trained glove vectors based on 2B tweets, 27B tokens, 1.2M vocab, uncased.\n\nRead more:\n* URL\n* URL"
] |
null | null | # Paragram Embeddings
Towards Universal Paraphrastic Sentence Embeddings (25 dimensions)
Read more:
* https://www.cs.cmu.edu/~jwieting/
* https://www.cs.cmu.edu/~jwieting/wieting2016ICLR.pdf
| {"tags": ["glove", "gensim", "fse"]} | fse/paragram-25 | null | [
"glove",
"gensim",
"fse",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#glove #gensim #fse #region-us
| # Paragram Embeddings
Towards Universal Paraphrastic Sentence Embeddings (25 dimensions)
Read more:
* URL
* URL
| [
"# Paragram Embeddings \n\nTowards Universal Paraphrastic Sentence Embeddings (25 dimensions)\n\nRead more:\n* URL\n* URL"
] | [
"TAGS\n#glove #gensim #fse #region-us \n",
"# Paragram Embeddings \n\nTowards Universal Paraphrastic Sentence Embeddings (25 dimensions)\n\nRead more:\n* URL\n* URL"
] |
null | null | # Paragram Embeddings
300 dimensional Paragram embeddings tuned on SimLex999 dataset
Read more:
* https://www.cs.cmu.edu/~jwieting/
| {"tags": ["glove", "gensim", "fse"]} | fse/paragram-300-sl999 | null | [
"glove",
"gensim",
"fse",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#glove #gensim #fse #region-us
| # Paragram Embeddings
300 dimensional Paragram embeddings tuned on SimLex999 dataset
Read more:
* URL
| [
"# Paragram Embeddings \n\n300 dimensional Paragram embeddings tuned on SimLex999 dataset\n\nRead more:\n* URL"
] | [
"TAGS\n#glove #gensim #fse #region-us \n",
"# Paragram Embeddings \n\n300 dimensional Paragram embeddings tuned on SimLex999 dataset\n\nRead more:\n* URL"
] |
null | null | # Paragram Embeddings
300 dimensional Paragram embeddings tuned on WordSim353 dataset
Read more:
* https://www.cs.cmu.edu/~jwieting/
| {"tags": ["glove", "gensim", "fse"]} | fse/paragram-300-ws353 | null | [
"glove",
"gensim",
"fse",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#glove #gensim #fse #region-us
| # Paragram Embeddings
300 dimensional Paragram embeddings tuned on WordSim353 dataset
Read more:
* URL
| [
"# Paragram Embeddings \n\n300 dimensional Paragram embeddings tuned on WordSim353 dataset\n\nRead more:\n* URL"
] | [
"TAGS\n#glove #gensim #fse #region-us \n",
"# Paragram Embeddings \n\n300 dimensional Paragram embeddings tuned on WordSim353 dataset\n\nRead more:\n* URL"
] |
null | null | # Paragram Embeddings
Pushing the Limits of Paraphrastic Sentence Embeddings with Millions of Machine Translations (300 dimensions)
Read more:
* https://www.cs.cmu.edu/~jwieting/
* https://www.cs.cmu.edu/~jwieting/wieting2017Millions.pdf
| {"tags": ["glove", "gensim", "fse"]} | fse/paranmt-300 | null | [
"glove",
"gensim",
"fse",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#glove #gensim #fse #region-us
| # Paragram Embeddings
Pushing the Limits of Paraphrastic Sentence Embeddings with Millions of Machine Translations (300 dimensions)
Read more:
* URL
* URL
| [
"# Paragram Embeddings \n\nPushing the Limits of Paraphrastic Sentence Embeddings with Millions of Machine Translations (300 dimensions)\n\nRead more:\n* URL\n* URL"
] | [
"TAGS\n#glove #gensim #fse #region-us \n",
"# Paragram Embeddings \n\nPushing the Limits of Paraphrastic Sentence Embeddings with Millions of Machine Translations (300 dimensions)\n\nRead more:\n* URL\n* URL"
] |
null | null | # Word2Vec
Pre-trained vectors trained on a part of the Google News dataset (about 100 billion words). The model contains 300-dimensional vectors for 3 million words and phrases. The phrases were obtained using a simple data-driven approach described in 'Distributed Representations of Words and Phrases and their Comp... | {"tags": ["glove", "gensim", "fse"]} | fse/word2vec-google-news-300 | null | [
"glove",
"gensim",
"fse",
"arxiv:1301.3781",
"arxiv:1310.4546",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1301.3781",
"1310.4546"
] | [] | TAGS
#glove #gensim #fse #arxiv-1301.3781 #arxiv-1310.4546 #has_space #region-us
| # Word2Vec
Pre-trained vectors trained on a part of the Google News dataset (about 100 billion words). The model contains 300-dimensional vectors for 3 million words and phrases. The phrases were obtained using a simple data-driven approach described in 'Distributed Representations of Words and Phrases and their Comp... | [
"# Word2Vec \n\nPre-trained vectors trained on a part of the Google News dataset (about 100 billion words). The model contains 300-dimensional vectors for 3 million words and phrases. The phrases were obtained using a simple data-driven approach described in 'Distributed Representations of Words and Phrases and the... | [
"TAGS\n#glove #gensim #fse #arxiv-1301.3781 #arxiv-1310.4546 #has_space #region-us \n",
"# Word2Vec \n\nPre-trained vectors trained on a part of the Google News dataset (about 100 billion words). The model contains 300-dimensional vectors for 3 million words and phrases. The phrases were obtained using a simple d... |
text-generation | transformers |
#Bully Maguire demo bot | {"tags": ["conversational"]} | ftnvir/DialoGPT-medium-bullyMaguire | 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
|
#Bully Maguire demo bot | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-to-speech | espnet |
This model was trained by ftshijt using aishell3/tts1 recipe in <a href="https://github.com/espnet/espnet/">espnet</a>.
<p> </p>
<ul>
<li><strong>Python API</strong><pre><code class="language-python">See https://github.com/espnet/espnet_model_zoo</code></pre></li>
<li><strong>Evaluate in the recipe</strong><pre>
... | {"language": "zh", "license": "cc-by-4.0", "tags": ["espnet", "audio", "text-to-speech"], "datasets": ["aishell3"], "inference": false} | ftshijt/ESPnet2_pretrained_model_ftshijt_aishell3_tts_train_raw_phn_pypinyin_g2p_phone_train.loss.best | null | [
"espnet",
"audio",
"text-to-speech",
"zh",
"dataset:aishell3",
"license:cc-by-4.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"zh"
] | TAGS
#espnet #audio #text-to-speech #zh #dataset-aishell3 #license-cc-by-4.0 #region-us
|
This model was trained by ftshijt using aishell3/tts1 recipe in <a href="URL
<p> </p>
<ul>
<li><strong>Python API</strong><pre><code class="language-python">See URL
<li><strong>Evaluate in the recipe</strong><pre>
<code class="language-bash">
See ESPNet repo for how to use pre-trained models
</pre></li>
<li><stro... | [] | [
"TAGS\n#espnet #audio #text-to-speech #zh #dataset-aishell3 #license-cc-by-4.0 #region-us \n"
] |
text-to-speech | espnet |
This model was trained by ftshijt using thchs30/tts1 recipe in <a href="https://github.com/espnet/espnet/">espnet</a>.
<p> </p>
<ul>
<li><strong>Python API</strong><pre><code class="language-python">See https://github.com/espnet/espnet_model_zoo</code></pre></li>
<li><strong>Evaluate in the recipe</strong><pre>
... | {"language": "zh", "license": "cc-by-4.0", "tags": ["espnet", "audio", "text-to-speech"], "datasets": ["thchs30"], "inference": false} | ftshijt/ESPnet2_pretrained_model_ftshijt_thchs30_tts_train_raw_phn_pypinyin_g2p_phone_train.loss.best | null | [
"espnet",
"audio",
"text-to-speech",
"zh",
"dataset:thchs30",
"license:cc-by-4.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"zh"
] | TAGS
#espnet #audio #text-to-speech #zh #dataset-thchs30 #license-cc-by-4.0 #region-us
|
This model was trained by ftshijt using thchs30/tts1 recipe in <a href="URL
<p> </p>
<ul>
<li><strong>Python API</strong><pre><code class="language-python">See URL
<li><strong>Evaluate in the recipe</strong><pre>
<code class="language-bash">Please see ESPNet for how to use pre-trained model
</pre></li>
<li><stro... | [] | [
"TAGS\n#espnet #audio #text-to-speech #zh #dataset-thchs30 #license-cc-by-4.0 #region-us \n"
] |
null | null | https://vrip.unmsm.edu.pe/forum/profile/liexylezzy/
https://vrip.unmsm.edu.pe/forum/profile/ellindanatasya/
https://vrip.unmsm.edu.pe/forum/profile/oploscgv/
https://vrip.unmsm.edu.pe/forum/profile/Zackoplos/
https://vrip.unmsm.edu.pe/forum/profile/unholyzulk/
https://vrip.unmsm.edu.pe/forum/profile/aurorarezash/ | {} | fullshowbox/DSADAWF | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| URL
URL
URL
URL
URL
URL | [] | [
"TAGS\n#region-us \n"
] |
null | null | https://community.afpglobal.org/network/members/profile?UserKey=fb4fdcef-dde4-4258-a423-2159545d84c1
https://community.afpglobal.org/network/members/profile?UserKey=e6ccc088-b709-45ec-b61e-4d56088acbda
https://community.afpglobal.org/network/members/profile?UserKey=ba280059-0890-4510-81d0-a79522b75ac8
https://community... | {} | fullshowbox/full-tv-free | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| URL
URL
URL
URL
URL
URL
URL
URL
URL
URL
URL
URL
URL
URL
URL
URL
URL
URL
URL | [] | [
"TAGS\n#region-us \n"
] |
null | null | https://volunteer.alz.org/network/members/profile?UserKey=f4774542-39b3-4cfd-8c21-7b834795f7d7
https://volunteer.alz.org/network/members/profile?UserKey=05a00b90-f854-45fb-9a3a-7420144d290c
https://volunteer.alz.org/network/members/profile?UserKey=45cceddd-29b9-4c6c-8612-e2a16aaa391a
https://volunteer.alz.org/network/m... | {} | fullshowbox/nacenetwork21 | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| URL
URL
URL
URL
URL
URL
URL
123movies-watch-online-movie-full-free-2021
URL
URL
URL | [] | [
"TAGS\n#region-us \n"
] |
null | null | https://www.nace.org/network/members/profile?UserKey=461a690a-bff6-4e4c-be63-ea8e39264459
https://www.nace.org/network/members/profile?UserKey=b4a6a66a-fb8a-4f2b-8af9-04f003ad9d46
https://www.nace.org/network/members/profile?UserKey=24544ab2-551d-42aa-adbe-7a1c1d68fd9c
https://www.nace.org/network/members/profile?UserK... | {} | fullshowbox/networkprofile | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| URL
URL
URL
URL
URL
URL
URL
URL | [] | [
"TAGS\n#region-us \n"
] |
null | null | https://ragbrai.com/groups/hd-movie-watch-french-exit-2021-full-movie-online-for-free/
https://ragbrai.com/groups/hd-movie-watch-nobody-2021-full-movie-online-for-free/
https://ragbrai.com/groups/hd-movie-watch-voyagers-2021-full-movie-online-for-free/
https://ragbrai.com/groups/hd-movie-watch-godzilla-vs-kong-2021-ful... | {} | fullshowbox/ragbrai | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| URL
URL
URL
URL
URL
URL
URL | [] | [
"TAGS\n#region-us \n"
] |
feature-extraction | transformers |
# Funnel Transformer intermediate model (B6-6-6 without decoder)
Pretrained model on English language using a similar objective objective as [ELECTRA](https://huggingface.co/transformers/model_doc/electra.html). It was introduced in
[this paper](https://arxiv.org/pdf/2006.03236.pdf) and first released in
[this reposi... | {"language": "en", "license": "apache-2.0", "datasets": ["bookcorpus", "wikipedia", "gigaword"]} | funnel-transformer/intermediate-base | null | [
"transformers",
"pytorch",
"tf",
"funnel",
"feature-extraction",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"dataset:gigaword",
"arxiv:2006.03236",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2006.03236"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #funnel #feature-extraction #en #dataset-bookcorpus #dataset-wikipedia #dataset-gigaword #arxiv-2006.03236 #license-apache-2.0 #endpoints_compatible #region-us
|
# Funnel Transformer intermediate model (B6-6-6 without decoder)
Pretrained model on English language using a similar objective objective as ELECTRA. It was introduced in
this paper and first released in
this repository. This model is uncased: it does not make a difference
between english and English.
Disclaimer: Th... | [
"# Funnel Transformer intermediate model (B6-6-6 without decoder)\n\nPretrained model on English language using a similar objective objective as ELECTRA. It was introduced in\nthis paper and first released in\nthis repository. This model is uncased: it does not make a difference\nbetween english and English.\n\nDis... | [
"TAGS\n#transformers #pytorch #tf #funnel #feature-extraction #en #dataset-bookcorpus #dataset-wikipedia #dataset-gigaword #arxiv-2006.03236 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# Funnel Transformer intermediate model (B6-6-6 without decoder)\n\nPretrained model on English language using a s... |
feature-extraction | transformers |
# Funnel Transformer intermediate model (B6-6-6 with decoder)
Pretrained model on English language using a similar objective objective as [ELECTRA](https://huggingface.co/transformers/model_doc/electra.html). It was introduced in
[this paper](https://arxiv.org/pdf/2006.03236.pdf) and first released in
[this repositor... | {"language": "en", "license": "apache-2.0", "datasets": ["bookcorpus", "wikipedia", "gigaword"]} | funnel-transformer/intermediate | null | [
"transformers",
"pytorch",
"tf",
"safetensors",
"funnel",
"feature-extraction",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"dataset:gigaword",
"arxiv:2006.03236",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2006.03236"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #safetensors #funnel #feature-extraction #en #dataset-bookcorpus #dataset-wikipedia #dataset-gigaword #arxiv-2006.03236 #license-apache-2.0 #endpoints_compatible #region-us
|
# Funnel Transformer intermediate model (B6-6-6 with decoder)
Pretrained model on English language using a similar objective objective as ELECTRA. It was introduced in
this paper and first released in
this repository. This model is uncased: it does not make a difference
between english and English.
Disclaimer: The t... | [
"# Funnel Transformer intermediate model (B6-6-6 with decoder)\n\nPretrained model on English language using a similar objective objective as ELECTRA. It was introduced in\nthis paper and first released in\nthis repository. This model is uncased: it does not make a difference\nbetween english and English.\n\nDiscla... | [
"TAGS\n#transformers #pytorch #tf #safetensors #funnel #feature-extraction #en #dataset-bookcorpus #dataset-wikipedia #dataset-gigaword #arxiv-2006.03236 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# Funnel Transformer intermediate model (B6-6-6 with decoder)\n\nPretrained model on English language... |
feature-extraction | transformers |
# Funnel Transformer large model (B8-8-8 without decoder)
Pretrained model on English language using a similar objective objective as [ELECTRA](https://huggingface.co/transformers/model_doc/electra.html). It was introduced in
[this paper](https://arxiv.org/pdf/2006.03236.pdf) and first released in
[this repository](h... | {"language": "en", "license": "apache-2.0", "datasets": ["bookcorpus", "wikipedia", "gigaword"]} | funnel-transformer/large-base | null | [
"transformers",
"pytorch",
"tf",
"funnel",
"feature-extraction",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"dataset:gigaword",
"arxiv:2006.03236",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2006.03236"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #funnel #feature-extraction #en #dataset-bookcorpus #dataset-wikipedia #dataset-gigaword #arxiv-2006.03236 #license-apache-2.0 #endpoints_compatible #region-us
|
# Funnel Transformer large model (B8-8-8 without decoder)
Pretrained model on English language using a similar objective objective as ELECTRA. It was introduced in
this paper and first released in
this repository. This model is uncased: it does not make a difference
between english and English.
Disclaimer: The team ... | [
"# Funnel Transformer large model (B8-8-8 without decoder)\n\nPretrained model on English language using a similar objective objective as ELECTRA. It was introduced in\nthis paper and first released in\nthis repository. This model is uncased: it does not make a difference\nbetween english and English.\n\nDisclaimer... | [
"TAGS\n#transformers #pytorch #tf #funnel #feature-extraction #en #dataset-bookcorpus #dataset-wikipedia #dataset-gigaword #arxiv-2006.03236 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# Funnel Transformer large model (B8-8-8 without decoder)\n\nPretrained model on English language using a similar ... |
feature-extraction | transformers |
# Funnel Transformer large model (B8-8-8 with decoder)
Pretrained model on English language using a similar objective as [ELECTRA](https://huggingface.co/transformers/model_doc/electra.html). It was introduced in
[this paper](https://arxiv.org/pdf/2006.03236.pdf) and first released in
[this repository](https://github... | {"language": "en", "license": "apache-2.0", "datasets": ["bookcorpus", "wikipedia", "gigaword"]} | funnel-transformer/large | null | [
"transformers",
"pytorch",
"tf",
"safetensors",
"funnel",
"feature-extraction",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"dataset:gigaword",
"arxiv:2006.03236",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2006.03236"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #safetensors #funnel #feature-extraction #en #dataset-bookcorpus #dataset-wikipedia #dataset-gigaword #arxiv-2006.03236 #license-apache-2.0 #endpoints_compatible #region-us
|
# Funnel Transformer large model (B8-8-8 with decoder)
Pretrained model on English language using a similar objective as ELECTRA. It was introduced in
this paper and first released in
this repository. This model is uncased: it does not make a difference
between english and English.
Disclaimer: The team releasing Fun... | [
"# Funnel Transformer large model (B8-8-8 with decoder)\n\nPretrained model on English language using a similar objective as ELECTRA. It was introduced in\nthis paper and first released in\nthis repository. This model is uncased: it does not make a difference\nbetween english and English.\n\nDisclaimer: The team re... | [
"TAGS\n#transformers #pytorch #tf #safetensors #funnel #feature-extraction #en #dataset-bookcorpus #dataset-wikipedia #dataset-gigaword #arxiv-2006.03236 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# Funnel Transformer large model (B8-8-8 with decoder)\n\nPretrained model on English language using ... |
feature-extraction | transformers |
# Funnel Transformer medium model (B6-3x2-3x2 without decoder)
Pretrained model on English language using a similar objective objective as [ELECTRA](https://huggingface.co/transformers/model_doc/electra.html). It was introduced in
[this paper](https://arxiv.org/pdf/2006.03236.pdf) and first released in
[this reposito... | {"language": "en", "license": "apache-2.0", "datasets": ["bookcorpus", "wikipedia", "gigaword"]} | funnel-transformer/medium-base | null | [
"transformers",
"pytorch",
"tf",
"funnel",
"feature-extraction",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"dataset:gigaword",
"arxiv:2006.03236",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2006.03236"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #funnel #feature-extraction #en #dataset-bookcorpus #dataset-wikipedia #dataset-gigaword #arxiv-2006.03236 #license-apache-2.0 #endpoints_compatible #region-us
|
# Funnel Transformer medium model (B6-3x2-3x2 without decoder)
Pretrained model on English language using a similar objective objective as ELECTRA. It was introduced in
this paper and first released in
this repository. This model is uncased: it does not make a difference
between english and English.
Disclaimer: The ... | [
"# Funnel Transformer medium model (B6-3x2-3x2 without decoder)\n\nPretrained model on English language using a similar objective objective as ELECTRA. It was introduced in\nthis paper and first released in\nthis repository. This model is uncased: it does not make a difference\nbetween english and English.\n\nDiscl... | [
"TAGS\n#transformers #pytorch #tf #funnel #feature-extraction #en #dataset-bookcorpus #dataset-wikipedia #dataset-gigaword #arxiv-2006.03236 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# Funnel Transformer medium model (B6-3x2-3x2 without decoder)\n\nPretrained model on English language using a sim... |
feature-extraction | transformers |
# Funnel Transformer medium model (B6-3x2-3x2 with decoder)
Pretrained model on English language using a similar objective objective as [ELECTRA](https://huggingface.co/transformers/model_doc/electra.html). It was introduced in
[this paper](https://arxiv.org/pdf/2006.03236.pdf) and first released in
[this repository]... | {"language": "en", "license": "apache-2.0", "datasets": ["bookcorpus", "wikipedia", "gigaword"]} | funnel-transformer/medium | null | [
"transformers",
"pytorch",
"tf",
"funnel",
"feature-extraction",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"dataset:gigaword",
"arxiv:2006.03236",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2006.03236"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #funnel #feature-extraction #en #dataset-bookcorpus #dataset-wikipedia #dataset-gigaword #arxiv-2006.03236 #license-apache-2.0 #endpoints_compatible #region-us
|
# Funnel Transformer medium model (B6-3x2-3x2 with decoder)
Pretrained model on English language using a similar objective objective as ELECTRA. It was introduced in
this paper and first released in
this repository. This model is uncased: it does not make a difference
between english and English.
Disclaimer: The tea... | [
"# Funnel Transformer medium model (B6-3x2-3x2 with decoder)\n\nPretrained model on English language using a similar objective objective as ELECTRA. It was introduced in\nthis paper and first released in\nthis repository. This model is uncased: it does not make a difference\nbetween english and English.\n\nDisclaim... | [
"TAGS\n#transformers #pytorch #tf #funnel #feature-extraction #en #dataset-bookcorpus #dataset-wikipedia #dataset-gigaword #arxiv-2006.03236 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# Funnel Transformer medium model (B6-3x2-3x2 with decoder)\n\nPretrained model on English language using a simila... |
feature-extraction | transformers |
# Funnel Transformer small model (B4-4-4 without decoder)
Pretrained model on English language using a similar objective objective as [ELECTRA](https://huggingface.co/transformers/model_doc/electra.html). It was introduced in
[this paper](https://arxiv.org/pdf/2006.03236.pdf) and first released in
[this repository](h... | {"language": "en", "license": "apache-2.0", "datasets": ["bookcorpus", "wikipedia", "gigaword"]} | funnel-transformer/small-base | null | [
"transformers",
"pytorch",
"tf",
"safetensors",
"funnel",
"feature-extraction",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"dataset:gigaword",
"arxiv:2006.03236",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2006.03236"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #safetensors #funnel #feature-extraction #en #dataset-bookcorpus #dataset-wikipedia #dataset-gigaword #arxiv-2006.03236 #license-apache-2.0 #endpoints_compatible #region-us
|
# Funnel Transformer small model (B4-4-4 without decoder)
Pretrained model on English language using a similar objective objective as ELECTRA. It was introduced in
this paper and first released in
this repository. This model is uncased: it does not make a difference
between english and English.
Disclaimer: The team ... | [
"# Funnel Transformer small model (B4-4-4 without decoder)\n\nPretrained model on English language using a similar objective objective as ELECTRA. It was introduced in\nthis paper and first released in\nthis repository. This model is uncased: it does not make a difference\nbetween english and English.\n\nDisclaimer... | [
"TAGS\n#transformers #pytorch #tf #safetensors #funnel #feature-extraction #en #dataset-bookcorpus #dataset-wikipedia #dataset-gigaword #arxiv-2006.03236 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# Funnel Transformer small model (B4-4-4 without decoder)\n\nPretrained model on English language usi... |
feature-extraction | transformers |
# Funnel Transformer small model (B4-4-4 with decoder)
Pretrained model on English language using a similar objective objective as [ELECTRA](https://huggingface.co/transformers/model_doc/electra.html). It was introduced in
[this paper](https://arxiv.org/pdf/2006.03236.pdf) and first released in
[this repository](http... | {"language": "en", "license": "apache-2.0", "datasets": ["bookcorpus", "wikipedia", "gigaword"]} | funnel-transformer/small | null | [
"transformers",
"pytorch",
"tf",
"funnel",
"feature-extraction",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"dataset:gigaword",
"arxiv:2006.03236",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2006.03236"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #funnel #feature-extraction #en #dataset-bookcorpus #dataset-wikipedia #dataset-gigaword #arxiv-2006.03236 #license-apache-2.0 #endpoints_compatible #has_space #region-us
|
# Funnel Transformer small model (B4-4-4 with decoder)
Pretrained model on English language using a similar objective objective as ELECTRA. It was introduced in
this paper and first released in
this repository. This model is uncased: it does not make a difference
between english and English.
Disclaimer: The team rel... | [
"# Funnel Transformer small model (B4-4-4 with decoder)\n\nPretrained model on English language using a similar objective objective as ELECTRA. It was introduced in\nthis paper and first released in\nthis repository. This model is uncased: it does not make a difference\nbetween english and English.\n\nDisclaimer: T... | [
"TAGS\n#transformers #pytorch #tf #funnel #feature-extraction #en #dataset-bookcorpus #dataset-wikipedia #dataset-gigaword #arxiv-2006.03236 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"# Funnel Transformer small model (B4-4-4 with decoder)\n\nPretrained model on English language using a ... |
feature-extraction | transformers |
# Funnel Transformer xlarge model (B10-10-10 without decoder)
Pretrained model on English language using a similar objective objective as [ELECTRA](https://huggingface.co/transformers/model_doc/electra.html). It was introduced in
[this paper](https://arxiv.org/pdf/2006.03236.pdf) and first released in
[this repositor... | {"language": "en", "license": "apache-2.0", "datasets": ["bookcorpus", "wikipedia", "gigaword"]} | funnel-transformer/xlarge-base | null | [
"transformers",
"pytorch",
"tf",
"safetensors",
"funnel",
"feature-extraction",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"dataset:gigaword",
"arxiv:2006.03236",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2006.03236"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #safetensors #funnel #feature-extraction #en #dataset-bookcorpus #dataset-wikipedia #dataset-gigaword #arxiv-2006.03236 #license-apache-2.0 #endpoints_compatible #region-us
|
# Funnel Transformer xlarge model (B10-10-10 without decoder)
Pretrained model on English language using a similar objective objective as ELECTRA. It was introduced in
this paper and first released in
this repository. This model is uncased: it does not make a difference
between english and English.
Disclaimer: The t... | [
"# Funnel Transformer xlarge model (B10-10-10 without decoder)\n\nPretrained model on English language using a similar objective objective as ELECTRA. It was introduced in\nthis paper and first released in\nthis repository. This model is uncased: it does not make a difference\nbetween english and English.\n\nDiscla... | [
"TAGS\n#transformers #pytorch #tf #safetensors #funnel #feature-extraction #en #dataset-bookcorpus #dataset-wikipedia #dataset-gigaword #arxiv-2006.03236 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# Funnel Transformer xlarge model (B10-10-10 without decoder)\n\nPretrained model on English language... |
feature-extraction | transformers |
# Funnel Transformer xlarge model (B10-10-10 with decoder)
Pretrained model on English language using a similar objective objective as [ELECTRA](https://huggingface.co/transformers/model_doc/electra.html). It was introduced in
[this paper](https://arxiv.org/pdf/2006.03236.pdf) and first released in
[this repository](... | {"language": "en", "license": "apache-2.0", "datasets": ["bookcorpus", "wikipedia", "gigaword"]} | funnel-transformer/xlarge | null | [
"transformers",
"pytorch",
"tf",
"funnel",
"feature-extraction",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"dataset:gigaword",
"arxiv:2006.03236",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2006.03236"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #funnel #feature-extraction #en #dataset-bookcorpus #dataset-wikipedia #dataset-gigaword #arxiv-2006.03236 #license-apache-2.0 #endpoints_compatible #has_space #region-us
|
# Funnel Transformer xlarge model (B10-10-10 with decoder)
Pretrained model on English language using a similar objective objective as ELECTRA. It was introduced in
this paper and first released in
this repository. This model is uncased: it does not make a difference
between english and English.
Disclaimer: The team... | [
"# Funnel Transformer xlarge model (B10-10-10 with decoder)\n\nPretrained model on English language using a similar objective objective as ELECTRA. It was introduced in\nthis paper and first released in\nthis repository. This model is uncased: it does not make a difference\nbetween english and English.\n\nDisclaime... | [
"TAGS\n#transformers #pytorch #tf #funnel #feature-extraction #en #dataset-bookcorpus #dataset-wikipedia #dataset-gigaword #arxiv-2006.03236 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"# Funnel Transformer xlarge model (B10-10-10 with decoder)\n\nPretrained model on English language usin... |
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-base-finetuned-bbc-headline
This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the None datas... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "t5-base-finetuned-bbc-headline", "results": []}]} | furyhawk/t5-base-finetuned-bbc-headline | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| t5-base-finetuned-bbc-headline
==============================
This model is a fine-tuned version of t5-base on the None dataset.
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 12\n* eval\\_batch\\_size: 12\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... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* tr... |
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-base-finetuned-bbc
This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the None dataset.
## M... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "t5-base-finetuned-bbc", "results": []}]} | furyhawk/t5-base-finetuned-bbc | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"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 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| t5-base-finetuned-bbc
=====================
This model is a fine-tuned version of t5-base on the None dataset.
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 6\n* eval\\_batch\\_size: 6\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",
"### Trainin... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate... |
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-bbc-headline
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the None da... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "t5-small-finetuned-bbc-headline", "results": []}]} | furyhawk/t5-small-finetuned-bbc-headline | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| t5-small-finetuned-bbc-headline
===============================
This model is a fine-tuned version of t5-small on the None dataset.
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
-------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 12\n* eval\\_batch\\_size: 12\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... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* tr... |
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-bbc
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the None dataset.
It... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "t5-small-finetuned-bbc", "results": []}]} | furyhawk/t5-small-finetuned-bbc | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"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 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| t5-small-finetuned-bbc
======================
This model is a fine-tuned version of t5-small on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3238
* Rouge1: 21.2266
* Rouge2: 16.0927
* Rougel: 19.6785
* Rougelsum: 19.8849
* Gen Len: 19.0
Model description
-----------------
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 2\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\\_precis... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate... |
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-xsum
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the xsum dataset.
... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["xsum"], "model-index": [{"name": "t5-small-finetuned-xsum", "results": []}]} | furyhawk/t5-small-finetuned-xsum | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:xsum",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #t5 #text2text-generation #generated_from_trainer #dataset-xsum #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| t5-small-finetuned-xsum
=======================
This model is a fine-tuned version of t5-small on the xsum dataset.
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
-----------------------... | [
"### 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 training:\n\n\n* learning\\_rat... |
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-cased-wikitext2
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "bert-base-cased-wikitext2", "results": []}]} | fznmhmmd/bert-base-cased-wikitext2 | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"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 #bert #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| bert-base-cased-wikitext2
=========================
This model is a fine-tuned version of bert-base-cased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 6.8575
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: 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\\_rate: 2e-05\n* train\\_batch\\_size: 8\n... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-cola
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/di... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["matthews_correlation"], "model-index": [{"name": "distilbert-base-uncased-finetuned-cola", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "ar... | fznmhmmd/distilbert-base-uncased-finetuned-cola | null | [
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"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-cola
======================================
This model is a fine-tuned version of distilbert-base-uncased on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.8273
* Matthews Correlation: 0.5544
Model description
-----------------
More informa... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Traini... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning... |
text-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. -->
# gpt2-wikitext2
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the None dataset.
It achieves the fo... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "gpt2-wikitext2", "results": []}]} | fznmhmmd/gpt2-wikitext2 | null | [
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"text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| gpt2-wikitext2
==============
This model is a fine-tuned version of gpt2 on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 6.1112
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
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: 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 #gpt2 #text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n*... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-common_voice-es-demo
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/fac... | {"language": ["es"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "common_voice", "generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-common_voice-es-demo", "results": []}]} | gabrieljg/wav2vec2-common_voice-es-demo | null | [
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"wav2vec2",
"automatic-speech-recognition",
"common_voice",
"generated_from_trainer",
"es",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"es"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #common_voice #generated_from_trainer #es #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-common\_voice-es-demo
==============================
This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the COMMON\_VOICE - ES dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1788
* Wer: 1.0239
Model description
-----------------
More information needed... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 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 #wav2vec2 #automatic-speech-recognition #common_voice #generated_from_trainer #es #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... |
text-generation | transformers |
# Tagalog DialoGPT
This is an extension of the base Tagalog DialoGPT model (https://huggingface.co/gabtan99/dialogpt-tagalog-medium).
This model is trained on 52K original conversations and 52K synthetic conversations, where 10% of tokens in each utterance in the synthetic conversation are machine-generated tokens.... | {"language": ["tl"], "tags": ["conversational", "tagalog", "filipino"]} | gabtan99/dialogpt-tagalog-medium-10 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"tagalog",
"filipino",
"tl",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"tl"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #tagalog #filipino #tl #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Tagalog DialoGPT
This is an extension of the base Tagalog DialoGPT model (URL
This model is trained on 52K original conversations and 52K synthetic conversations, where 10% of tokens in each utterance in the synthetic conversation are machine-generated tokens.
| [
"# Tagalog DialoGPT\n\nThis is an extension of the base Tagalog DialoGPT model (URL \n\nThis model is trained on 52K original conversations and 52K synthetic conversations, where 10% of tokens in each utterance in the synthetic conversation are machine-generated tokens."
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #tagalog #filipino #tl #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Tagalog DialoGPT\n\nThis is an extension of the base Tagalog DialoGPT model (URL \n\nThis model is trained on 52K original conversat... |
text-generation | transformers |
# Tagalog DialoGPT
This is an extension of the base Tagalog DialoGPT model (https://huggingface.co/gabtan99/dialogpt-tagalog-medium).
This model is trained on 52K original conversations and 52K synthetic conversations, where 20% of tokens in each utterance in the synthetic conversation are machine-generated tokens.... | {"language": ["tl"], "tags": ["conversational", "tagalog", "filipino"], "inference": false} | gabtan99/dialogpt-tagalog-medium-20 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"tagalog",
"filipino",
"tl",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"tl"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #tagalog #filipino #tl #autotrain_compatible #text-generation-inference #region-us
|
# Tagalog DialoGPT
This is an extension of the base Tagalog DialoGPT model (URL
This model is trained on 52K original conversations and 52K synthetic conversations, where 20% of tokens in each utterance in the synthetic conversation are machine-generated tokens.
| [
"# Tagalog DialoGPT\n\nThis is an extension of the base Tagalog DialoGPT model (URL \n\nThis model is trained on 52K original conversations and 52K synthetic conversations, where 20% of tokens in each utterance in the synthetic conversation are machine-generated tokens."
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #tagalog #filipino #tl #autotrain_compatible #text-generation-inference #region-us \n",
"# Tagalog DialoGPT\n\nThis is an extension of the base Tagalog DialoGPT model (URL \n\nThis model is trained on 52K original conversations and 52K synthetic... |
text-generation | transformers |
# Tagalog DialoGPT
This is an extension of the base Tagalog DialoGPT model (https://huggingface.co/gabtan99/dialogpt-tagalog-medium).
This model is trained on 52K original conversations and 52K synthetic conversations, where 30% of tokens in each utterance in the synthetic conversation are machine-generated tokens.... | {"language": ["tl"], "tags": ["conversational", "tagalog", "filipino"], "inference": false} | gabtan99/dialogpt-tagalog-medium-30 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"tagalog",
"filipino",
"tl",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"tl"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #tagalog #filipino #tl #autotrain_compatible #text-generation-inference #region-us
|
# Tagalog DialoGPT
This is an extension of the base Tagalog DialoGPT model (URL
This model is trained on 52K original conversations and 52K synthetic conversations, where 30% of tokens in each utterance in the synthetic conversation are machine-generated tokens.
| [
"# Tagalog DialoGPT\n\nThis is an extension of the base Tagalog DialoGPT model (URL \n\nThis model is trained on 52K original conversations and 52K synthetic conversations, where 30% of tokens in each utterance in the synthetic conversation are machine-generated tokens."
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #tagalog #filipino #tl #autotrain_compatible #text-generation-inference #region-us \n",
"# Tagalog DialoGPT\n\nThis is an extension of the base Tagalog DialoGPT model (URL \n\nThis model is trained on 52K original conversations and 52K synthetic... |
text-generation | transformers |
# Tagalog DialoGPT
A DialoGPT-medium model fine-tuned on Tagalog conversational data scraped from the web. This model is an output of a research on RoBERTa-based data augmentation for low resource languages. This is the baseline model which did not use any synthetic data in training.
# Latest release: July 25, 2021... | {"language": ["tl"], "tags": ["conversational", "tagalog", "filipino"], "datasets": ["gabtan99/pex-conversations"], "inference": false} | gabtan99/dialogpt-tagalog-medium | null | [
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"conversational",
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"filipino",
"tl",
"dataset:gabtan99/pex-conversations",
"autotrain_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"tl"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #tagalog #filipino #tl #dataset-gabtan99/pex-conversations #autotrain_compatible #has_space #text-generation-inference #region-us
|
# Tagalog DialoGPT
A DialoGPT-medium model fine-tuned on Tagalog conversational data scraped from the web. This model is an output of a research on RoBERTa-based data augmentation for low resource languages. This is the baseline model which did not use any synthetic data in training.
# Latest release: July 25, 2021... | [
"# Tagalog DialoGPT\nA DialoGPT-medium model fine-tuned on Tagalog conversational data scraped from the web. This model is an output of a research on RoBERTa-based data augmentation for low resource languages. This is the baseline model which did not use any synthetic data in training.",
"# Latest release: July ... | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #tagalog #filipino #tl #dataset-gabtan99/pex-conversations #autotrain_compatible #has_space #text-generation-inference #region-us \n",
"# Tagalog DialoGPT\nA DialoGPT-medium model fine-tuned on Tagalog conversational data scraped from the web. T... |
null | null | I am adding my first README in order to test the interface. How good is it really? | {} | gael1130/gael_first_model | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| I am adding my first README in order to test the interface. How good is it really? | [] | [
"TAGS\n#region-us \n"
] |
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