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text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# predict-perception-bert-cause-concept
This model is a fine-tuned version of [dbmdz/bert-base-italian-xxl-cased](https://huggingf... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "predict-perception-bert-cause-concept", "results": []}]} | responsibility-framing/predict-perception-bert-cause-concept | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-10T16:04:43+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| predict-perception-bert-cause-concept
=====================================
This model is a fine-tuned version of dbmdz/bert-base-italian-xxl-cased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4044
* Rmse: 0.6076
* Rmse Cause::a Causata da un concetto astratto (es. gelos... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 20\n* eval\\_batch\\_size: 8\n* seed: 1996\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 30",
"### Trai... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: ... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# predict-perception-bert-cause-none
This model is a fine-tuned version of [dbmdz/bert-base-italian-xxl-cased](https://huggingface... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "predict-perception-bert-cause-none", "results": []}]} | responsibility-framing/predict-perception-bert-cause-none | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-10T16:08:40+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| predict-perception-bert-cause-none
==================================
This model is a fine-tuned version of dbmdz/bert-base-italian-xxl-cased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 1.6269
* Rmse: 1.2763
* Rmse Cause::a Spontanea, priva di un agente scatenante: 1.2763... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 20\n* eval\\_batch\\_size: 8\n* seed: 1996\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 30",
"### Trai... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: ... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# pegasus-arxiv-finetuned-pubmed
This model is a fine-tuned version of [google/pegasus-arxiv](https://huggingface.co/google/pegasu... | {"tags": ["generated_from_trainer"], "datasets": ["pub_med_summarization_dataset"], "metrics": ["rouge"], "model-index": [{"name": "pegasus-arxiv-finetuned-pubmed", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "pub_med_summarization_dataset... | Kevincp560/pegasus-arxiv-finetuned-pubmed | null | [
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"generated_from_trainer",
"dataset:pub_med_summarization_dataset",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-10T16:09:00+00:00 | [] | [] | TAGS
#transformers #pytorch #pegasus #text2text-generation #generated_from_trainer #dataset-pub_med_summarization_dataset #model-index #autotrain_compatible #endpoints_compatible #region-us
| pegasus-arxiv-finetuned-pubmed
==============================
This model is a fine-tuned version of google/pegasus-arxiv on the pub\_med\_summarization\_dataset dataset.
It achieves the following results on the evaluation set:
* Loss: 1.8118
* Rouge1: 44.286
* Rouge2: 19.0477
* Rougel: 27.1122
* Rougelsum: 40.2609
... | [
"### 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: 5",
"### Training... | [
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #generated_from_trainer #dataset-pub_med_summarization_dataset #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: 2... |
text-generation | transformers |
# Handsome Jack DialoGPT Model | {"tags": ["conversational"]} | Prime2911/DialoGPT-small-handsomejack | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-10T16:09:14+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Handsome Jack DialoGPT Model | [
"# Handsome Jack DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Handsome Jack DialoGPT Model"
] |
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. -->
# predict-perception-bert-focus-assassin
This model is a fine-tuned version of [dbmdz/bert-base-italian-xxl-cased](https://hugging... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "predict-perception-bert-focus-assassin", "results": []}]} | responsibility-framing/predict-perception-bert-focus-assassin | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-10T16:11:08+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| predict-perception-bert-focus-assassin
======================================
This model is a fine-tuned version of dbmdz/bert-base-italian-xxl-cased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2964
* Rmse: 0.8992
* Rmse Focus::a Sull'assassino: 0.8992
* Mae: 0.7331
* M... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 20\n* eval\\_batch\\_size: 8\n* seed: 1996\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 30",
"### Trai... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: ... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# predict-perception-bert-focus-victim
This model is a fine-tuned version of [dbmdz/bert-base-italian-xxl-cased](https://huggingfa... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "predict-perception-bert-focus-victim", "results": []}]} | responsibility-framing/predict-perception-bert-focus-victim | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-10T16:13:32+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| predict-perception-bert-focus-victim
====================================
This model is a fine-tuned version of dbmdz/bert-base-italian-xxl-cased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2466
* Rmse: 0.6201
* Rmse Focus::a Sulla vittima: 0.6201
* Mae: 0.4936
* Mae Fo... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 20\n* eval\\_batch\\_size: 8\n* seed: 1996\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 30",
"### Trai... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: ... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# predict-perception-bert-focus-object
This model is a fine-tuned version of [dbmdz/bert-base-italian-xxl-cased](https://huggingfa... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "predict-perception-bert-focus-object", "results": []}]} | responsibility-framing/predict-perception-bert-focus-object | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-10T16:18:25+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| predict-perception-bert-focus-object
====================================
This model is a fine-tuned version of dbmdz/bert-base-italian-xxl-cased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2271
* Rmse: 0.5965
* Rmse Focus::a Su un oggetto: 0.5965
* Mae: 0.4372
* Mae Fo... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 20\n* eval\\_batch\\_size: 8\n* seed: 1996\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 30",
"### Trai... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: ... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# 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... | farrokhguiahi/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 | 2022-03-10T16:20:12+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: 0.2188
* Accuracy: 0.923
* F1: 0.9231
Model description
-----------------
Mor... | [
"### 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... |
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. -->
# predict-perception-bert-focus-concept
This model is a fine-tuned version of [dbmdz/bert-base-italian-xxl-cased](https://huggingf... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "predict-perception-bert-focus-concept", "results": []}]} | responsibility-framing/predict-perception-bert-focus-concept | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-10T16:21:24+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| predict-perception-bert-focus-concept
=====================================
This model is a fine-tuned version of dbmdz/bert-base-italian-xxl-cased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.8129
* Rmse: 1.0197
* Rmse Focus::a Su un concetto astratto o un'emozione: 1.0... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 20\n* eval\\_batch\\_size: 8\n* seed: 1996\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 30",
"### Trai... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: ... |
summarization | 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. -->
# bart-large-cnn-samsum-rescom-finetuned-resume-summarizer-9-epoch-tweak
This model is a fine-tuned version of [Ameer05/model-toke... | {"tags": ["summarization", "generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "bart-large-cnn-samsum-rescom-finetuned-resume-summarizer-9-epoch-tweak", "results": []}]} | Ameer05/bart-large-cnn-samsum-rescom-finetuned-resume-summarizer-9-epoch-tweak | null | [
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"summarization",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-10T16:32:19+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bart #text2text-generation #summarization #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| bart-large-cnn-samsum-rescom-finetuned-resume-summarizer-9-epoch-tweak
======================================================================
This model is a fine-tuned version of Ameer05/model-token-repo on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 1.4511
* Rouge1: 59.76
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1... | [
"TAGS\n#transformers #pytorch #tensorboard #bart #text2text-generation #summarization #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_siz... |
automatic-speech-recognition | transformers |
# wav2vec2-large-xls-r-300m-german-with-lm
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the German set of the Common Voice dataset.
It achieves a Word Error Rate of 8,8 percent on the evaluation set
## Model description
German wav2vec2-... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-large-xls-r-300m-german-with-lm", "results": []}]} | mfleck/wav2vec2-large-xls-r-300m-german-with-lm | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-10T16:46:25+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-large-xls-r-300m-german-with-lm
========================================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the German set of the Common Voice dataset.
It achieves a Word Error Rate of 8,8 percent on the evaluation set
Model description
-----------------
German wav2vec2... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 64\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #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: 0.0001\n* train\\_batch\\_size: 32\n* eval\\_b... |
question-answering | transformers | { 'max_seq_length': 384,
'batch_size': 24,
'learning_rate': {'val': 3e-5, 'schelduler': 'Linear'},
'max_clip_norm': None,
'epochs': 2
} | {} | OrfeasTsk/bert-base-uncased-finetuned-quac-large-batch | null | [
"transformers",
"pytorch",
"bert",
"question-answering",
"endpoints_compatible",
"region:us"
] | null | 2022-03-10T16:52:20+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #question-answering #endpoints_compatible #region-us
| { 'max_seq_length': 384,
'batch_size': 24,
'learning_rate': {'val': 3e-5, 'schelduler': 'Linear'},
'max_clip_norm': None,
'epochs': 2
} | [] | [
"TAGS\n#transformers #pytorch #bert #question-answering #endpoints_compatible #region-us \n"
] |
token-classification | transformers |
## MahaNER-BERT
MahaNER-BERT is a MahaBERT(<a href="https://huggingface.co/l3cube-pune/marathi-bert-v2">l3cube-pune/marathi-bert</a>) model fine-tuned on L3Cube-MahaNER - a Marathi named entity recognition dataset.
[dataset link] (https://github.com/l3cube-pune/MarathiNLP)
More details on the dataset, models, and... | {"language": "mr", "license": "cc-by-4.0", "datasets": ["L3Cube-MahaNER"], "widget": [{"text": "\u092a\u0941\u0923\u0947 \u0935\u093f\u0926\u094d\u092f\u093e\u092a\u0940\u0920\u093e\u091a\u0940 \u092e\u0945\u0928\u0947\u091c\u092e\u0947\u0902\u091f \u0915\u094c\u0928\u094d\u0938\u093f\u0932\u091a\u0940 \u092c\u0948\u09... | l3cube-pune/marathi-ner | null | [
"transformers",
"pytorch",
"safetensors",
"bert",
"token-classification",
"mr",
"dataset:L3Cube-MahaNER",
"arxiv:2204.06029",
"license:cc-by-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-10T17:29:23+00:00 | [
"2204.06029"
] | [
"mr"
] | TAGS
#transformers #pytorch #safetensors #bert #token-classification #mr #dataset-L3Cube-MahaNER #arxiv-2204.06029 #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #region-us
|
## MahaNER-BERT
MahaNER-BERT is a MahaBERT(<a href="URL model fine-tuned on L3Cube-MahaNER - a Marathi named entity recognition dataset.
[dataset link] (URL
More details on the dataset, models, and baseline results can be found in our [paper] (URL
IOB Model : <a href="URL marathi-ner-iob </a>
Other models fro... | [
"## MahaNER-BERT\n\nMahaNER-BERT is a MahaBERT(<a href=\"URL model fine-tuned on L3Cube-MahaNER - a Marathi named entity recognition dataset. \n\n[dataset link] (URL\n\nMore details on the dataset, models, and baseline results can be found in our [paper] (URL\n\nIOB Model : <a href=\"URL marathi-ner-iob </a>\n\n\n\... | [
"TAGS\n#transformers #pytorch #safetensors #bert #token-classification #mr #dataset-L3Cube-MahaNER #arxiv-2204.06029 #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"## MahaNER-BERT\n\nMahaNER-BERT is a MahaBERT(<a href=\"URL model fine-tuned on L3Cube-MahaNER - a Marathi named enti... |
text-generation | transformers |
# DialoGPT model | {"tags": ["conversational"]} | Starry/KARENTRIES | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-10T17:36:48+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# DialoGPT model | [
"# DialoGPT model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# DialoGPT model"
] |
text-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1507523916981583875/6n7n... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/atarifounders/1648266306699/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/atarifounders | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-10T18:31:26+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
koala/claw/soppy
@atarifounders
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #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. -->
# pegasus-cnn_dailymail-finetuned-pubmed
This model is a fine-tuned version of [google/pegasus-cnn_dailymail](https://huggingface.... | {"tags": ["generated_from_trainer"], "datasets": ["pub_med_summarization_dataset"], "metrics": ["rouge"], "model-index": [{"name": "pegasus-cnn_dailymail-finetuned-pubmed", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "pub_med_summarization... | Kevincp560/pegasus-cnn_dailymail-finetuned-pubmed | null | [
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"generated_from_trainer",
"dataset:pub_med_summarization_dataset",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-10T18:42:22+00:00 | [] | [] | TAGS
#transformers #pytorch #pegasus #text2text-generation #generated_from_trainer #dataset-pub_med_summarization_dataset #model-index #autotrain_compatible #endpoints_compatible #region-us
| pegasus-cnn\_dailymail-finetuned-pubmed
=======================================
This model is a fine-tuned version of google/pegasus-cnn\_dailymail on the pub\_med\_summarization\_dataset dataset.
It achieves the following results on the evaluation set:
* Loss: 1.8050
* Rouge1: 37.2569
* Rouge2: 15.8205
* Rougel: 2... | [
"### 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: 5",
"### Training... | [
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #generated_from_trainer #dataset-pub_med_summarization_dataset #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: 2... |
text-classification | transformers | ## bert-base-uncased finetuned on IMDB dataset
Evaluation set was created by taking 1000 samples from test set
```
DatasetDict({
train: Dataset({
features: ['text', 'label'],
num_rows: 25000
})
dev: Dataset({
features: ['text', 'label'],
num_rows: 1000
})
test: Data... | {} | artemis13fowl/bert-base-uncased-imdb | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-10T18:53:49+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us
| ## bert-base-uncased finetuned on IMDB dataset
Evaluation set was created by taking 1000 samples from test set
## Parameters
## Training Run
## Classification Report
| [
"## bert-base-uncased finetuned on IMDB dataset\n\nEvaluation set was created by taking 1000 samples from test set",
"## Parameters",
"## Training Run",
"## Classification Report"
] | [
"TAGS\n#transformers #pytorch #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us \n",
"## bert-base-uncased finetuned on IMDB dataset\n\nEvaluation set was created by taking 1000 samples from test set",
"## Parameters",
"## Training Run",
"## Classification Report"
] |
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... | Sarahliu186/distilbert-base-uncased-finetuned-cola | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-10T20:02:25+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: 0.7415
* Matthews Correlation: 0.5488
Model description
-----------------
More informa... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning... |
text-generation | transformers |
# My Awesome Model
| {"tags": ["conversational"]} | dietconk/DialogGPT-small-Orange | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-10T20:03:41+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# My Awesome Model
| [
"# My Awesome Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# My Awesome Model"
] |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
#
This model was trained from scratch on the librispeech_asr dataset.
It achieves the following results on the evaluation set:
- ... | {"tags": ["generated_from_trainer"], "datasets": ["librispeech_asr"], "model-index": [{"name": "", "results": []}]} | sanchit-gandhi/wav2vec2-2-bert-large-long-run | null | [
"transformers",
"pytorch",
"tensorboard",
"speech-encoder-decoder",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:librispeech_asr",
"endpoints_compatible",
"region:us"
] | null | 2022-03-10T20:49:38+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #speech-encoder-decoder #automatic-speech-recognition #generated_from_trainer #dataset-librispeech_asr #endpoints_compatible #region-us
|
This model was trained from scratch on the librispeech\_asr dataset.
It achieves the following results on the evaluation set:
* Loss: 12.7395
* Wer: 2.0272
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training an... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1... | [
"TAGS\n#transformers #pytorch #tensorboard #speech-encoder-decoder #automatic-speech-recognition #generated_from_trainer #dataset-librispeech_asr #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
#
This model was trained from scratch on the librispeech_asr dataset.
It achieves the following results on the evaluation set:
- ... | {"tags": ["generated_from_trainer"], "datasets": ["librispeech_asr"], "model-index": [{"name": "", "results": []}]} | sanchit-gandhi/wav2vec2-2-rnd-2-layer-bart | null | [
"transformers",
"pytorch",
"tensorboard",
"speech-encoder-decoder",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:librispeech_asr",
"endpoints_compatible",
"region:us"
] | null | 2022-03-10T20:56:10+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #speech-encoder-decoder #automatic-speech-recognition #generated_from_trainer #dataset-librispeech_asr #endpoints_compatible #region-us
|
This model was trained from scratch on the librispeech\_asr dataset.
It achieves the following results on the evaluation set:
* Loss: 4.6263
* Wer: 0.8568
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1... | [
"TAGS\n#transformers #pytorch #tensorboard #speech-encoder-decoder #automatic-speech-recognition #generated_from_trainer #dataset-librispeech_asr #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train... |
question-answering | transformers | { 'max_seq_length': 384,
'batch_size': 24,
'learning_rate': {'val': 3e-5, 'schelduler': 'Linear'},
'max_clip_norm': None,
'epochs': 2
} | {} | OrfeasTsk/bert-base-uncased-finetuned-newsqa-large-batch | null | [
"transformers",
"pytorch",
"bert",
"question-answering",
"endpoints_compatible",
"region:us"
] | null | 2022-03-10T21:18:30+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #question-answering #endpoints_compatible #region-us
| { 'max_seq_length': 384,
'batch_size': 24,
'learning_rate': {'val': 3e-5, 'schelduler': 'Linear'},
'max_clip_norm': None,
'epochs': 2
} | [] | [
"TAGS\n#transformers #pytorch #bert #question-answering #endpoints_compatible #region-us \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_accents
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebo... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2_common_voice_accents", "results": []}]} | willcai/wav2vec2_common_voice_accents | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-10T21:28:18+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2\_common\_voice\_accents
================================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common\_voice dataset.
It achieves the following results on the evaluation set:
* Loss: 0.9095
* Wer: 0.4269
Model description
-----------------
More information needed
I... | [
"### 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: 4\n* seed: 42\n* distributed\\_type: multi-GPU\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\... |
text2text-generation | transformers | ---
language: en
license: apache-2.0
---
HF-version model for PRIMERA: Pyramid-based Masked Sentence Pre-training for Multi-document Summarization (ACL 2022).
The original code can be found [here](https://github.com/allenai/PRIMER). You can find the script and notebook to train/evaluate the model in ... | {} | allenai/PRIMERA | null | [
"transformers",
"pytorch",
"tf",
"led",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-10T23:37:17+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #led #text2text-generation #autotrain_compatible #endpoints_compatible #has_space #region-us
| ---
language: en
license: apache-2.0
---
HF-version model for PRIMERA: Pyramid-based Masked Sentence Pre-training for Multi-document Summarization (ACL 2022).
The original code can be found here. You can find the script and notebook to train/evaluate the model in the original github repo.
* Note... | [] | [
"TAGS\n#transformers #pytorch #tf #led #text2text-generation #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
text2text-generation | transformers | ---
language: en
license: apache-2.0
---
HF-version model for PRIMERA: Pyramid-based Masked Sentence Pre-training for Multi-document Summarization (ACL 2022).
The original code can be found [here](https://github.com/allenai/PRIMER). You can find the script and notebook to train/evaluate the model in ... | {} | allenai/PRIMERA-multinews | null | [
"transformers",
"pytorch",
"tf",
"led",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-11T00:09:35+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #led #text2text-generation #autotrain_compatible #endpoints_compatible #has_space #region-us
| ---
language: en
license: apache-2.0
---
HF-version model for PRIMERA: Pyramid-based Masked Sentence Pre-training for Multi-document Summarization (ACL 2022).
The original code can be found here. You can find the script and notebook to train/evaluate the model in the original github repo.
* Note... | [] | [
"TAGS\n#transformers #pytorch #tf #led #text2text-generation #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
text2text-generation | transformers | ---
language: en
license: apache-2.0
---
HF-version model for PRIMERA: Pyramid-based Masked Sentence Pre-training for Multi-document Summarization (ACL 2022).
The original code can be found [here](https://github.com/allenai/PRIMER). You can find the script and notebook to train/evaluate the model in ... | {} | allenai/PRIMERA-multixscience | null | [
"transformers",
"pytorch",
"led",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-11T00:27:19+00:00 | [] | [] | TAGS
#transformers #pytorch #led #text2text-generation #autotrain_compatible #endpoints_compatible #has_space #region-us
| ---
language: en
license: apache-2.0
---
HF-version model for PRIMERA: Pyramid-based Masked Sentence Pre-training for Multi-document Summarization (ACL 2022).
The original code can be found here. You can find the script and notebook to train/evaluate the model in the original github repo.
* Note... | [] | [
"TAGS\n#transformers #pytorch #led #text2text-generation #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
text2text-generation | transformers | ---
language: en
license: apache-2.0
---
HF-version model for PRIMERA: Pyramid-based Masked Sentence Pre-training for Multi-document Summarization (ACL 2022).
The original code can be found [here](https://github.com/allenai/PRIMER). You can find the script and notebook to train/evaluate the model in ... | {} | allenai/PRIMERA-wcep | null | [
"transformers",
"pytorch",
"tf",
"led",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-11T00:34:35+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #led #text2text-generation #autotrain_compatible #endpoints_compatible #has_space #region-us
| ---
language: en
license: apache-2.0
---
HF-version model for PRIMERA: Pyramid-based Masked Sentence Pre-training for Multi-document Summarization (ACL 2022).
The original code can be found here. You can find the script and notebook to train/evaluate the model in the original github repo.
* Note... | [] | [
"TAGS\n#transformers #pytorch #tf #led #text2text-generation #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
text2text-generation | transformers |
HF-version model for PRIMERA: Pyramid-based Masked Sentence Pre-training for Multi-document Summarization (ACL 2022).
The original code can be found [here](https://github.com/allenai/PRIMER). You can find the script and notebook to train/evaluate the model in the original github repo.
* Note: due to the differ... | {"license": "apache-2.0"} | allenai/PRIMERA-arxiv | null | [
"transformers",
"pytorch",
"led",
"text2text-generation",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-11T00:49:18+00:00 | [] | [] | TAGS
#transformers #pytorch #led #text2text-generation #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| HF-version model for PRIMERA: Pyramid-based Masked Sentence Pre-training for Multi-document Summarization (ACL 2022).
The original code can be found here. You can find the script and notebook to train/evaluate the model in the original github repo.
* Note: due to the difference between the implementations of the or... | [] | [
"TAGS\n#transformers #pytorch #led #text2text-generation #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
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-xl-fine-tuned-debiased
This model is a fine-tuned version of [gpt2-xl](https://huggingface.co/gpt2-xl) on an unknown datase... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "gpt2-xl-fine-tuned-debiased", "results": []}]} | newtonkwan/gpt2-xl-fine-tuned-debiased | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-11T00:49:48+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| gpt2-xl-fine-tuned-debiased
===========================
This model is a fine-tuned version of gpt2-xl on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 2.1714
Model description
-----------------
More information needed
Intended uses & limitations
-------------------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 2\n* seed: 42\n* gradient\\_accumulation\\_steps: 32\n* total\\_train\\_batch\\_size: 64\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #generated_from_trainer #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: 0.0005\n* train\\_batch\\_size: 2\... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-de-fr
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-robert... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["f1"], "model-index": [{"name": "xlm-roberta-base-finetuned-panx-de-fr", "results": []}]} | lijingxin/xlm-roberta-base-finetuned-panx-de-fr | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-11T01:51:38+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-panx-de-fr
=====================================
This model is a fine-tuned version of xlm-roberta-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1664
* F1: 0.8556
Model description
-----------------
More information needed
Intended uses... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 24\n* eval\\_batch\\_size: 24\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 24\n*... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-fr
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-b... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["xtreme"], "metrics": ["f1"], "model-index": [{"name": "xlm-roberta-base-finetuned-panx-fr", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "xtreme", "type": "xtreme", "args": "PAN-X.fr"}, "me... | lijingxin/xlm-roberta-base-finetuned-panx-fr | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-11T02:15:53+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-panx-fr
==================================
This model is a fine-tuned version of xlm-roberta-base on the xtreme dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2691
* F1: 0.8383
Model description
-----------------
More information needed
Intended uses & l... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 24\n* eval\\_batch\\_size: 24\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-it
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-b... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["xtreme"], "metrics": ["f1"], "model-index": [{"name": "xlm-roberta-base-finetuned-panx-it", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "xtreme", "type": "xtreme", "args": "PAN-X.it"}, "me... | lijingxin/xlm-roberta-base-finetuned-panx-it | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-11T02:19:59+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-panx-it
==================================
This model is a fine-tuned version of xlm-roberta-base on the xtreme dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2400
* F1: 0.8306
Model description
-----------------
More information needed
Intended uses & l... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 24\n* eval\\_batch\\_size: 24\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-en
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-b... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["xtreme"], "metrics": ["f1"], "model-index": [{"name": "xlm-roberta-base-finetuned-panx-en", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "xtreme", "type": "xtreme", "args": "PAN-X.en"}, "me... | lijingxin/xlm-roberta-base-finetuned-panx-en | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-11T02:22:57+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-panx-en
==================================
This model is a fine-tuned version of xlm-roberta-base on the xtreme dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3814
* F1: 0.7043
Model description
-----------------
More information needed
Intended uses & l... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 24\n* eval\\_batch\\_size: 24\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n... |
token-classification | transformers |
# bert-large-german-upos
## Model Description
This is a BERT model pre-trained with [UD_German-HDT](https://github.com/UniversalDependencies/UD_German-HDT) for POS-tagging and dependency-parsing, derived from [gbert-large](https://huggingface.co/deepset/gbert-large). Every word is tagged by [UPOS](https://universald... | {"language": ["de"], "license": "mit", "tags": ["german", "token-classification", "pos", "dependency-parsing"], "datasets": ["universal_dependencies"], "pipeline_tag": "token-classification"} | KoichiYasuoka/bert-large-german-upos | null | [
"transformers",
"pytorch",
"bert",
"token-classification",
"german",
"pos",
"dependency-parsing",
"de",
"dataset:universal_dependencies",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-11T02:29:01+00:00 | [] | [
"de"
] | TAGS
#transformers #pytorch #bert #token-classification #german #pos #dependency-parsing #de #dataset-universal_dependencies #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# bert-large-german-upos
## Model Description
This is a BERT model pre-trained with UD_German-HDT for POS-tagging and dependency-parsing, derived from gbert-large. Every word is tagged by UPOS (Universal Part-Of-Speech).
## How to Use
or
## See Also
esupar: Tokenizer POS-tagger and Dependency-parser with BER... | [
"# bert-large-german-upos",
"## Model Description\n\nThis is a BERT model pre-trained with UD_German-HDT for POS-tagging and dependency-parsing, derived from gbert-large. Every word is tagged by UPOS (Universal Part-Of-Speech).",
"## How to Use\n\n\n\nor",
"## See Also\n\nesupar: Tokenizer POS-tagger and Depe... | [
"TAGS\n#transformers #pytorch #bert #token-classification #german #pos #dependency-parsing #de #dataset-universal_dependencies #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# bert-large-german-upos",
"## Model Description\n\nThis is a BERT model pre-trained with UD_German-HDT for POS... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-all
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["f1"], "model-index": [{"name": "xlm-roberta-base-finetuned-panx-all", "results": []}]} | lijingxin/xlm-roberta-base-finetuned-panx-all | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-11T02:37:03+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-panx-all
===================================
This model is a fine-tuned version of xlm-roberta-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1748
* F1: 0.8555
Model description
-----------------
More information needed
Intended uses & l... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 24\n* eval\\_batch\\_size: 24\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 24\n*... |
question-answering | transformers |
# Model Description
This model is for Thai extractive question answering. It is based on the multilingual BERT [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) model, and it is case-sensitive: it makes a difference between english and English
# Training data
We split the original... | {"language": "Thai", "tags": ["bert-base"], "datasets": "xquad.th", "task": "extractive question answering"} | zhufy/xquad-th-mbert-base | null | [
"transformers",
"pytorch",
"bert",
"question-answering",
"bert-base",
"dataset:xquad.th",
"endpoints_compatible",
"region:us"
] | null | 2022-03-11T02:51:48+00:00 | [] | [
"Thai"
] | TAGS
#transformers #pytorch #bert #question-answering #bert-base #dataset-xquad.th #endpoints_compatible #region-us
|
# Model Description
This model is for Thai extractive question answering. It is based on the multilingual BERT bert-base-multilingual-cased model, and it is case-sensitive: it makes a difference between english and English
# Training data
We split the original xquad dataset into the training/validation/testing set... | [
"# Model Description\n\nThis model is for Thai extractive question answering. It is based on the multilingual BERT bert-base-multilingual-cased model, and it is case-sensitive: it makes a difference between english and English",
"# Training data\n\nWe split the original xquad dataset into the training/validation/... | [
"TAGS\n#transformers #pytorch #bert #question-answering #bert-base #dataset-xquad.th #endpoints_compatible #region-us \n",
"# Model Description\n\nThis model is for Thai extractive question answering. It is based on the multilingual BERT bert-base-multilingual-cased model, and it is case-sensitive: it makes a dif... |
question-answering | transformers |
# Model Description
This model is for Malay extractive question answering. It is based on the [malay-huggingface/bert-base-bahasa-cased](https://huggingface.co/malay-huggingface/bert-base-bahasa-cased/tree/main) model, and it is case-sensitive: it makes a difference between english and English.
# Training data
[Mal... | {"language": "Malay", "tags": ["bert-base"], "datasets": "Malay SQuAD", "task": "extractive question answering"} | zhufy/squad-ms-bert-base | null | [
"transformers",
"pytorch",
"bert",
"question-answering",
"bert-base",
"endpoints_compatible",
"region:us"
] | null | 2022-03-11T04:46:48+00:00 | [] | [
"Malay"
] | TAGS
#transformers #pytorch #bert #question-answering #bert-base #endpoints_compatible #region-us
|
# Model Description
This model is for Malay extractive question answering. It is based on the malay-huggingface/bert-base-bahasa-cased model, and it is case-sensitive: it makes a difference between english and English.
# Training data
Malay SQuAD v2.0
# How to use
You can use it directly from the Transformers l... | [
"# Model Description\n\nThis model is for Malay extractive question answering. It is based on the malay-huggingface/bert-base-bahasa-cased model, and it is case-sensitive: it makes a difference between english and English.",
"# Training data\n\nMalay SQuAD v2.0",
"# How to use\n\nYou can use it directly from th... | [
"TAGS\n#transformers #pytorch #bert #question-answering #bert-base #endpoints_compatible #region-us \n",
"# Model Description\n\nThis model is for Malay extractive question answering. It is based on the malay-huggingface/bert-base-bahasa-cased model, and it is case-sensitive: it makes a difference between english... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base-timit-demo-colab
This model is a fine-tuned version of [facebook/wav2vec2-large](https://huggingface.co/facebook/w... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-base-timit-demo-colab", "results": []}]} | tiot07/wav2vec2-base-timit-demo-colab | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-11T04:56:00+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-base-timit-demo-colab
==============================
This model is a fine-tuned version of facebook/wav2vec2-large on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4612
* Wer: 0.2963
Model description
-----------------
More information needed
Intended uses & lim... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #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: 0.0001\n* train\\_batch\\_size: 32\n* eval\\_b... |
text-generation | transformers |
# willem DialoGPT Model | {"tags": ["conversational"]} | mafeu/DialoGPT-medium-willem | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-11T05:10:45+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# willem DialoGPT Model | [
"# willem DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# willem DialoGPT Model"
] |
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-SARC_withcontext
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-ba... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-SARC_withcontext", "results": []}]} | ScandinavianMrT/distilbert-SARC_withcontext | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-11T06:50:56+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-SARC\_withcontext
============================
This model is a fine-tuned version of distilbert-base-uncased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4736
* Accuracy: 0.7732
Model description
-----------------
More information needed
Intended uses & li... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #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: 5e-05\n* train\\_b... |
text-generation | transformers |
# Handsome Jack DialoGPT Model | {"tags": ["conversational"]} | Prime2911/DialoGPT-medium-handsomejack | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-11T07:46:00+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Handsome Jack DialoGPT Model | [
"# Handsome Jack DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Handsome Jack DialoGPT Model"
] |
text2text-generation | transformers | Hi | {} | ChanP/finetuned-th-to-en | null | [
"transformers",
"pytorch",
"marian",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-11T07:51:32+00:00 | [] | [] | TAGS
#transformers #pytorch #marian #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
| Hi | [] | [
"TAGS\n#transformers #pytorch #marian #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-classification | transformers |
## Eval results
We obtain the following results on ```validation``` and ```test``` sets:
| Set | F1<sub>micro</sub> | F1<sub>macro</sub> |
|------------|--------------------|--------------------|
| validation | 89.2 | 87.6 |
| test | 88.9 | 87.4 |
| {"language": ["fr"], "tags": ["nli"], "metrics": ["f1"]} | waboucay/camembert-base-finetuned-xnli_fr | null | [
"transformers",
"pytorch",
"camembert",
"text-classification",
"nli",
"fr",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-11T08:54:07+00:00 | [] | [
"fr"
] | TAGS
#transformers #pytorch #camembert #text-classification #nli #fr #autotrain_compatible #endpoints_compatible #region-us
| Eval results
------------
We obtain the following results on and sets:
Set: validation, F1micro: 89.2, F1macro: 87.6
Set: test, F1micro: 88.9, F1macro: 87.4
| [] | [
"TAGS\n#transformers #pytorch #camembert #text-classification #nli #fr #autotrain_compatible #endpoints_compatible #region-us \n"
] |
fill-mask | transformers | This is a smaller version of the [XLM-RoBERTa](https://huggingface.co/xlm-roberta-base) model with only Ukrainian and some English embeddings left.
* The original model has 470M parameters, with 384M of them being input and output embeddings.
* After shrinking the `sentencepiece` vocabulary from 250K to 31K (top 25K... | {"language": ["uk"], "license": "mit", "tags": ["ukrainian"], "widget": [{"text": "\u0422\u0430\u0440\u0430\u0441 \u0428\u0435\u0432\u0447\u0435\u043d\u043a\u043e \u2013 \u0432\u0435\u043b\u0438\u043a\u0438\u0439 \u0443\u043a\u0440\u0430\u0457\u043d\u0441\u044c\u043a\u0438\u0439 <mask>."}]} | ukr-models/xlm-roberta-base-uk | null | [
"transformers",
"pytorch",
"safetensors",
"xlm-roberta",
"fill-mask",
"ukrainian",
"uk",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-11T10:53:02+00:00 | [] | [
"uk"
] | TAGS
#transformers #pytorch #safetensors #xlm-roberta #fill-mask #ukrainian #uk #license-mit #autotrain_compatible #endpoints_compatible #region-us
| This is a smaller version of the XLM-RoBERTa model with only Ukrainian and some English embeddings left.
* The original model has 470M parameters, with 384M of them being input and output embeddings.
* After shrinking the 'sentencepiece' vocabulary from 250K to 31K (top 25K Ukrainian tokens and top English tokens) t... | [] | [
"TAGS\n#transformers #pytorch #safetensors #xlm-roberta #fill-mask #ukrainian #uk #license-mit #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-classification | transformers |
#meme description classification | {"tags": ["text-classification"]} | AmrSheta/Meme | null | [
"transformers",
"pytorch",
"bert",
"feature-extraction",
"text-classification",
"endpoints_compatible",
"region:us"
] | null | 2022-03-11T11:45:28+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #feature-extraction #text-classification #endpoints_compatible #region-us
|
#meme description classification | [] | [
"TAGS\n#transformers #pytorch #bert #feature-extraction #text-classification #endpoints_compatible #region-us \n"
] |
null | null |
This repo contains model for [Data-to-text Generation with Variational Sequential Planning](https://arxiv.org/abs/2202.13756) (Ratish Puduppully and Yao Fu and Mirella Lapata; In Transactions of the Association for Computational Linguistics (TACL)). This model is trained on the [MLB dataset](https://huggingface.co/d... | {} | ratishsp/SeqPlan-MLB | null | [
"arxiv:2202.13756",
"region:us"
] | null | 2022-03-11T11:54:01+00:00 | [
"2202.13756"
] | [] | TAGS
#arxiv-2202.13756 #region-us
|
This repo contains model for Data-to-text Generation with Variational Sequential Planning (Ratish Puduppully and Yao Fu and Mirella Lapata; In Transactions of the Association for Computational Linguistics (TACL)). This model is trained on the MLB dataset. The code is available on github repo.
## License
The mod... | [
"## License\r\nThe model is available under the MIT License."
] | [
"TAGS\n#arxiv-2202.13756 #region-us \n",
"## License\r\nThe model is available under the MIT License."
] |
null | transformers |
CER: 0.0019
training code
https://colab.research.google.com/drive/14MfFkhgPS63RJcP7rpBOK6OII_y34jx_?usp=sharing | {"license": "mit"} | tomofi/trocr-captcha | null | [
"transformers",
"pytorch",
"vision-encoder-decoder",
"license:mit",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-11T12:04:59+00:00 | [] | [] | TAGS
#transformers #pytorch #vision-encoder-decoder #license-mit #endpoints_compatible #has_space #region-us
|
CER: 0.0019
training code
URL | [] | [
"TAGS\n#transformers #pytorch #vision-encoder-decoder #license-mit #endpoints_compatible #has_space #region-us \n"
] |
null | null |
This repo contains model for [Data-to-text Generation with Variational Sequential Planning](https://arxiv.org/abs/2202.13756) (Ratish Puduppully and Yao Fu and Mirella Lapata; In Transactions of the Association for Computational Linguistics (TACL)). This model is trained on the [RotoWire dataset](https://github.com/... | {} | ratishsp/SeqPlan-RotoWire | null | [
"arxiv:2202.13756",
"region:us"
] | null | 2022-03-11T12:20:13+00:00 | [
"2202.13756"
] | [] | TAGS
#arxiv-2202.13756 #region-us
|
This repo contains model for Data-to-text Generation with Variational Sequential Planning (Ratish Puduppully and Yao Fu and Mirella Lapata; In Transactions of the Association for Computational Linguistics (TACL)). This model is trained on the RotoWire dataset. The code is available on github repo.
## License
Th... | [
"## License\r\nThe model is available under the MIT License."
] | [
"TAGS\n#arxiv-2202.13756 #region-us \n",
"## License\r\nThe model is available under the MIT License."
] |
null | null |
This repo contains model for [Data-to-text Generation with Variational Sequential Planning](https://arxiv.org/abs/2202.13756) (Ratish Puduppully and Yao Fu and Mirella Lapata; In Transactions of the Association for Computational Linguistics (TACL)). This model is trained on the [German RotoWire dataset](https://hugg... | {} | ratishsp/SeqPlan-GermanRotoWire | null | [
"arxiv:2202.13756",
"region:us"
] | null | 2022-03-11T12:27:42+00:00 | [
"2202.13756"
] | [] | TAGS
#arxiv-2202.13756 #region-us
|
This repo contains model for Data-to-text Generation with Variational Sequential Planning (Ratish Puduppully and Yao Fu and Mirella Lapata; In Transactions of the Association for Computational Linguistics (TACL)). This model is trained on the German RotoWire dataset. The code is available on github repo.
## Lice... | [
"## License\r\nThe model is available under the MIT License."
] | [
"TAGS\n#arxiv-2202.13756 #region-us \n",
"## License\r\nThe model is available under the MIT License."
] |
null | transformers | This model generate the math expression LATEX sequence according to the handwritten math expression image.
in CROHME 2014 test dataset CER=0.507772718700326 | {} | Azu/trocr-handwritten-math | null | [
"transformers",
"pytorch",
"vision-encoder-decoder",
"endpoints_compatible",
"region:us"
] | null | 2022-03-11T12:51:19+00:00 | [] | [] | TAGS
#transformers #pytorch #vision-encoder-decoder #endpoints_compatible #region-us
| This model generate the math expression LATEX sequence according to the handwritten math expression image.
in CROHME 2014 test dataset CER=0.507772718700326 | [] | [
"TAGS\n#transformers #pytorch #vision-encoder-decoder #endpoints_compatible #region-us \n"
] |
text-generation | transformers |
# An Arabic abstractive text summarization model
A fine-tuned AraGPT2 model on a dataset of 84,764 paragraph-summary pairs.
Paper: [Arabic abstractive text summarization using RNN-based and transformer-based architectures](https://www.sciencedirect.com/science/article/abs/pii/S0306457322003284).
Dataset: [link](http... | {"language": ["ar"], "tags": ["AraGPT2", "GPT-2", "MSA", "Arabic Text Summarization", "Arabic News Title Generation", "Arabic Paraphrasing"], "widget": [{"text": ""}]} | malmarjeh/gpt2 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"AraGPT2",
"GPT-2",
"MSA",
"Arabic Text Summarization",
"Arabic News Title Generation",
"Arabic Paraphrasing",
"ar",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-11T13:07:31+00:00 | [] | [
"ar"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #AraGPT2 #GPT-2 #MSA #Arabic Text Summarization #Arabic News Title Generation #Arabic Paraphrasing #ar #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
# An Arabic abstractive text summarization model
A fine-tuned AraGPT2 model on a dataset of 84,764 paragraph-summary pairs.
Paper: Arabic abstractive text summarization using RNN-based and transformer-based architectures.
Dataset: link.
The model can be used as follows:
## Contact:
<banimarje@URL>
| [
"# An Arabic abstractive text summarization model\nA fine-tuned AraGPT2 model on a dataset of 84,764 paragraph-summary pairs.\n\nPaper: Arabic abstractive text summarization using RNN-based and transformer-based architectures.\n\nDataset: link.\n\nThe model can be used as follows:",
"## Contact:\n<banimarje@URL>"... | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #AraGPT2 #GPT-2 #MSA #Arabic Text Summarization #Arabic News Title Generation #Arabic Paraphrasing #ar #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# An Arabic abstractive text summarization model\nA fine-tu... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-base-biomedical-clinical-es-finetuned-ner-Concat_CRAFT_es
This model is a fine-tuned version of [PlanTL-GOB-ES/roberta-b... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "roberta-base-biomedical-clinical-es-finetuned-ner-Concat_CRAFT_es", "results": []}]} | StivenLancheros/roberta-base-biomedical-clinical-es-finetuned-ner-Concat_CRAFT_es | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-11T13:41:44+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| roberta-base-biomedical-clinical-es-finetuned-ner-Concat\_CRAFT\_es
===================================================================
This model is a fine-tuned version of PlanTL-GOB-ES/roberta-base-biomedical-clinical-es on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1874... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 4",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #token-classification #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: 3e-05\n* train\\_bat... |
text2text-generation | transformers |
# An Arabic abstractive text summarization model
A Transformer-based encoder-decoder model which has been trained on a dataset of 384,764 paragraph-summary pairs.
Paper: [Arabic abstractive text summarization using RNN-based and transformer-based architectures](https://www.sciencedirect.com/science/article/abs/pii/S0... | {"language": ["ar"], "tags": ["Transformer", "MSA", "Arabic Text Summarization", "Arabic News Title Generation", "Arabic Paraphrasing"], "widget": [{"text": "\u0634\u0647\u062f\u062a \u0645\u062f\u064a\u0646\u0629 \u0637\u0631\u0627\u0628\u0644\u0633\u060c \u0645\u0633\u0627\u0621 \u0623\u0645\u0633 \u0627\u0644\u0623\... | malmarjeh/transformer | null | [
"transformers",
"pytorch",
"encoder-decoder",
"text2text-generation",
"Transformer",
"MSA",
"Arabic Text Summarization",
"Arabic News Title Generation",
"Arabic Paraphrasing",
"ar",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-11T13:45:32+00:00 | [] | [
"ar"
] | TAGS
#transformers #pytorch #encoder-decoder #text2text-generation #Transformer #MSA #Arabic Text Summarization #Arabic News Title Generation #Arabic Paraphrasing #ar #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# An Arabic abstractive text summarization model
A Transformer-based encoder-decoder model which has been trained on a dataset of 384,764 paragraph-summary pairs.
Paper: Arabic abstractive text summarization using RNN-based and transformer-based architectures.
Dataset: link.
The model can be used as follows:
## C... | [
"# An Arabic abstractive text summarization model\nA Transformer-based encoder-decoder model which has been trained on a dataset of 384,764 paragraph-summary pairs.\n\nPaper: Arabic abstractive text summarization using RNN-based and transformer-based architectures.\n\nDataset: link.\n\nThe model can be used as foll... | [
"TAGS\n#transformers #pytorch #encoder-decoder #text2text-generation #Transformer #MSA #Arabic Text Summarization #Arabic News Title Generation #Arabic Paraphrasing #ar #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# An Arabic abstractive text summarization model\nA Transformer-based enc... |
text-classification | transformers |
## MahaHate-multi-RoBERTa
MahaHate-multi-RoBERTa (Marathi Hate speech identification) is a MahaRoBERTa(l3cube-pune/marathi-roberta) model fine-tuned on L3Cube-MahaHate - a Marathi tweet-based hate speech detection dataset. This is a four-class model with labels as hate, offensive, profane, and not. The 2-class mode... | {"language": "mr", "license": "cc-by-4.0", "datasets": ["L3Cube-MahaHate"], "widget": [{"text": "I like you. </s></s> I love you."}]} | l3cube-pune/mahahate-multi-roberta | null | [
"transformers",
"pytorch",
"safetensors",
"xlm-roberta",
"text-classification",
"mr",
"dataset:L3Cube-MahaHate",
"arxiv:2203.13778",
"license:cc-by-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-11T14:22:46+00:00 | [
"2203.13778"
] | [
"mr"
] | TAGS
#transformers #pytorch #safetensors #xlm-roberta #text-classification #mr #dataset-L3Cube-MahaHate #arxiv-2203.13778 #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #region-us
|
## MahaHate-multi-RoBERTa
MahaHate-multi-RoBERTa (Marathi Hate speech identification) is a MahaRoBERTa(l3cube-pune/marathi-roberta) model fine-tuned on L3Cube-MahaHate - a Marathi tweet-based hate speech detection dataset. This is a four-class model with labels as hate, offensive, profane, and not. The 2-class mode... | [
"## MahaHate-multi-RoBERTa\n\nMahaHate-multi-RoBERTa (Marathi Hate speech identification) is a MahaRoBERTa(l3cube-pune/marathi-roberta) model fine-tuned on L3Cube-MahaHate - a Marathi tweet-based hate speech detection dataset. This is a four-class model with labels as hate, offensive, profane, and not. The 2-class... | [
"TAGS\n#transformers #pytorch #safetensors #xlm-roberta #text-classification #mr #dataset-L3Cube-MahaHate #arxiv-2203.13778 #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"## MahaHate-multi-RoBERTa\n\nMahaHate-multi-RoBERTa (Marathi Hate speech identification) is a MahaRoBERTa(l3c... |
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. -->
# albert_ernie_summarization_cnn_dailymail
This model is a fine-tuned version of [](https://huggingface.co/) on the cnn_dailymail ... | {"tags": ["generated_from_trainer"], "datasets": ["cnn_dailymail"], "model-index": [{"name": "albert_ernie_summarization_cnn_dailymail", "results": []}]} | Ayham/albert_ernie_50beam_summarization_cnn_dailymail | null | [
"transformers",
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"generated_from_trainer",
"dataset:cnn_dailymail",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-11T14:33:54+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #encoder-decoder #text2text-generation #generated_from_trainer #dataset-cnn_dailymail #autotrain_compatible #endpoints_compatible #region-us
|
# albert_ernie_summarization_cnn_dailymail
This model is a fine-tuned version of [](URL on the cnn_dailymail dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training... | [
"# albert_ernie_summarization_cnn_dailymail\n\nThis model is a fine-tuned version of [](URL on the cnn_dailymail dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #encoder-decoder #text2text-generation #generated_from_trainer #dataset-cnn_dailymail #autotrain_compatible #endpoints_compatible #region-us \n",
"# albert_ernie_summarization_cnn_dailymail\n\nThis model is a fine-tuned version of [](URL on the cnn_dailymail dataset.",
... |
null | null | AI4Bharat's IndicBERT finetuned for few-shot transfer learning by fine-tuning on Hindi training data with Urdu validation and test sets. Expected low accuracy. Leverages mbert's tokenizer in this implementation.
---
language:
- ur
tags:
- named entity recognition
- ner
license: apache-2.0
datasets:
- wikiann
metrics:... | {} | anwesham/indic_with_mbert_tokens | null | [
"region:us"
] | null | 2022-03-11T14:34:01+00:00 | [] | [] | TAGS
#region-us
| AI4Bharat's IndicBERT finetuned for few-shot transfer learning by fine-tuning on Hindi training data with Urdu validation and test sets. Expected low accuracy. Leverages mbert's tokenizer in this implementation.
---
language:
- ur
tags:
- named entity recognition
- ner
license: apache-2.0
datasets:
- wikiann
metrics:... | [] | [
"TAGS\n#region-us \n"
] |
text-classification | transformers |
## MahaHate-BERT
MahaHate-BERT (Marathi Hate speech identification) is a MahaBERT(l3cube-pune/marathi-bert) model fine-tuned on L3Cube-MahaHate - a Marathi tweet-based hate speech detection dataset. This is a two-class model with labels as hate (LABEL_1) and not (LABEL_0). The 4-class model can be found <a href='htt... | {"language": "mr", "license": "cc-by-4.0", "datasets": ["L3Cube-MahaHate"], "widget": [{"text": "I like you. </s></s> I love you."}]} | l3cube-pune/mahahate-bert | null | [
"transformers",
"pytorch",
"safetensors",
"bert",
"text-classification",
"mr",
"dataset:L3Cube-MahaHate",
"arxiv:2203.13778",
"license:cc-by-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-11T15:12:47+00:00 | [
"2203.13778"
] | [
"mr"
] | TAGS
#transformers #pytorch #safetensors #bert #text-classification #mr #dataset-L3Cube-MahaHate #arxiv-2203.13778 #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #region-us
|
## MahaHate-BERT
MahaHate-BERT (Marathi Hate speech identification) is a MahaBERT(l3cube-pune/marathi-bert) model fine-tuned on L3Cube-MahaHate - a Marathi tweet-based hate speech detection dataset. This is a two-class model with labels as hate (LABEL_1) and not (LABEL_0). The 4-class model can be found <a href='URL... | [
"## MahaHate-BERT\n\nMahaHate-BERT (Marathi Hate speech identification) is a MahaBERT(l3cube-pune/marathi-bert) model fine-tuned on L3Cube-MahaHate - a Marathi tweet-based hate speech detection dataset. This is a two-class model with labels as hate (LABEL_1) and not (LABEL_0). The 4-class model can be found <a href... | [
"TAGS\n#transformers #pytorch #safetensors #bert #text-classification #mr #dataset-L3Cube-MahaHate #arxiv-2203.13778 #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"## MahaHate-BERT\n\nMahaHate-BERT (Marathi Hate speech identification) is a MahaBERT(l3cube-pune/marathi-bert) model ... |
feature-extraction | transformers |
## ELECTRA for IF
**ELECTRA** is a method for self-supervised language representation learning. They are trained to distinguish "real" input tokens vs "fake" input tokens generated by another neural network, similar to the discriminator of a [GAN](https://arxiv.org/pdf/1406.2661.pdf).
For a detailed description... | {"language": "en", "license": "apache-2.0"} | Aureliano/electra-if | null | [
"transformers",
"pytorch",
"tf",
"electra",
"feature-extraction",
"en",
"arxiv:1406.2661",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-11T15:40:21+00:00 | [
"1406.2661"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #electra #feature-extraction #en #arxiv-1406.2661 #license-apache-2.0 #endpoints_compatible #region-us
|
## ELECTRA for IF
ELECTRA is a method for self-supervised language representation learning. They are trained to distinguish "real" input tokens vs "fake" input tokens generated by another neural network, similar to the discriminator of a GAN.
For a detailed description and experimental results, please refer to ... | [
"## ELECTRA for IF\r\n\r\nELECTRA is a method for self-supervised language representation learning. They are trained to distinguish \"real\" input tokens vs \"fake\" input tokens generated by another neural network, similar to the discriminator of a GAN.\r\n\r\nFor a detailed description and experimental results, p... | [
"TAGS\n#transformers #pytorch #tf #electra #feature-extraction #en #arxiv-1406.2661 #license-apache-2.0 #endpoints_compatible #region-us \n",
"## ELECTRA for IF\r\n\r\nELECTRA is a method for self-supervised language representation learning. They are trained to distinguish \"real\" input tokens vs \"fake\" input ... |
null | transformers |
# Wav2Vec2-Dutch-Large
A Dutch Wav2Vec2 model. This model is created by further pre-training the original English [`facebook/wav2vec2-large`](https://huggingface.co/facebook/wav2vec2-large) model on Dutch speech from [Het Corpus Gesproken Nederlands](https://taalmaterialen.ivdnt.org/download/tstc-corpus-gesproken-ned... | {"language": "nl", "tags": ["speech"]} | GroNLP/wav2vec2-dutch-large | null | [
"transformers",
"pytorch",
"wav2vec2",
"pretraining",
"speech",
"nl",
"endpoints_compatible",
"region:us"
] | null | 2022-03-11T15:41:51+00:00 | [] | [
"nl"
] | TAGS
#transformers #pytorch #wav2vec2 #pretraining #speech #nl #endpoints_compatible #region-us
|
# Wav2Vec2-Dutch-Large
A Dutch Wav2Vec2 model. This model is created by further pre-training the original English 'facebook/wav2vec2-large' model on Dutch speech from Het Corpus Gesproken Nederlands.
This model is one of two Dutch Wav2Vec2 models:
- 'GroNLP/wav2vec2-dutch-base'
- 'GroNLP/wav2vec2-dutch-large' (th... | [
"# Wav2Vec2-Dutch-Large\n\nA Dutch Wav2Vec2 model. This model is created by further pre-training the original English 'facebook/wav2vec2-large' model on Dutch speech from Het Corpus Gesproken Nederlands.\n\nThis model is one of two Dutch Wav2Vec2 models:\n\n - 'GroNLP/wav2vec2-dutch-base'\n - 'GroNLP/wav2vec2-dutch... | [
"TAGS\n#transformers #pytorch #wav2vec2 #pretraining #speech #nl #endpoints_compatible #region-us \n",
"# Wav2Vec2-Dutch-Large\n\nA Dutch Wav2Vec2 model. This model is created by further pre-training the original English 'facebook/wav2vec2-large' model on Dutch speech from Het Corpus Gesproken Nederlands.\n\nThis... |
null | transformers |
# Wav2Vec2-Dutch-Base
A Dutch Wav2Vec2 model. This model is created by further pre-training the original English [`facebook/wav2vec2-base`](https://huggingface.co/facebook/wav2vec2-base) model on Dutch speech from [Het Corpus Gesproken Nederlands](https://taalmaterialen.ivdnt.org/download/tstc-corpus-gesproken-nederl... | {"language": "nl", "tags": ["speech"]} | GroNLP/wav2vec2-dutch-base | null | [
"transformers",
"pytorch",
"wav2vec2",
"pretraining",
"speech",
"nl",
"endpoints_compatible",
"region:us"
] | null | 2022-03-11T15:43:01+00:00 | [] | [
"nl"
] | TAGS
#transformers #pytorch #wav2vec2 #pretraining #speech #nl #endpoints_compatible #region-us
|
# Wav2Vec2-Dutch-Base
A Dutch Wav2Vec2 model. This model is created by further pre-training the original English 'facebook/wav2vec2-base' model on Dutch speech from Het Corpus Gesproken Nederlands.
This model is one of two Dutch Wav2Vec2 models:
- 'GroNLP/wav2vec2-dutch-base' (this model)
- 'GroNLP/wav2vec2-dutch... | [
"# Wav2Vec2-Dutch-Base\n\nA Dutch Wav2Vec2 model. This model is created by further pre-training the original English 'facebook/wav2vec2-base' model on Dutch speech from Het Corpus Gesproken Nederlands.\n\nThis model is one of two Dutch Wav2Vec2 models:\n\n - 'GroNLP/wav2vec2-dutch-base' (this model)\n - 'GroNLP/wav... | [
"TAGS\n#transformers #pytorch #wav2vec2 #pretraining #speech #nl #endpoints_compatible #region-us \n",
"# Wav2Vec2-Dutch-Base\n\nA Dutch Wav2Vec2 model. This model is created by further pre-training the original English 'facebook/wav2vec2-base' model on Dutch speech from Het Corpus Gesproken Nederlands.\n\nThis m... |
object-detection | transformers |
# Model Card for detr-doc-table-detection
# Model Details
detr-doc-table-detection is a model trained to detect both **Bordered** and **Borderless** tables in documents, based on [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50).
- **Developed by:** Taha Douaji
- **Shared by [Optional]:**... | {"tags": ["object-detection"]} | TahaDouaji/detr-doc-table-detection | null | [
"transformers",
"pytorch",
"detr",
"object-detection",
"arxiv:2005.12872",
"arxiv:1910.09700",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-11T15:55:14+00:00 | [
"2005.12872",
"1910.09700"
] | [] | TAGS
#transformers #pytorch #detr #object-detection #arxiv-2005.12872 #arxiv-1910.09700 #endpoints_compatible #has_space #region-us
|
# Model Card for detr-doc-table-detection
# Model Details
detr-doc-table-detection is a model trained to detect both Bordered and Borderless tables in documents, based on facebook/detr-resnet-50.
- Developed by: Taha Douaji
- Shared by [Optional]: Taha Douaji
- Model type: Object Detection
- Language(s) (NLP): ... | [
"# Model Card for detr-doc-table-detection",
"# Model Details\n \ndetr-doc-table-detection is a model trained to detect both Bordered and Borderless tables in documents, based on facebook/detr-resnet-50.\n \n- Developed by: Taha Douaji\n- Shared by [Optional]: Taha Douaji\n- Model type: Object Detection \n- Langu... | [
"TAGS\n#transformers #pytorch #detr #object-detection #arxiv-2005.12872 #arxiv-1910.09700 #endpoints_compatible #has_space #region-us \n",
"# Model Card for detr-doc-table-detection",
"# Model Details\n \ndetr-doc-table-detection is a model trained to detect both Bordered and Borderless tables in documents, bas... |
sentence-similarity | transformers |
## Pre-trained sentence embedding models are the state-of-the-art of Sentence Embeddings for French.
Model is Fine-tuned using pre-trained [facebook/camembert-base](https://huggingface.co/camembert/camembert-base) and
[Siamese BERT-Networks with 'sentences-transformers'](https://www.sbert.net/) on dataset [stsb](https... | {"language": "fr", "license": "apache-2.0", "tags": ["Text", "Sentence Similarity", "Sentence-Embedding", "camembert-base"], "datasets": ["stsb_multi_mt"], "pipeline_tag": "sentence-similarity", "model-index": [{"name": "sentence-camembert-base by Van Tuan DANG", "results": [{"task": {"type": "Text Similarity", "name":... | dangvantuan/sentence-camembert-base | null | [
"transformers",
"pytorch",
"camembert",
"feature-extraction",
"Text",
"Sentence Similarity",
"Sentence-Embedding",
"camembert-base",
"sentence-similarity",
"fr",
"dataset:stsb_multi_mt",
"arxiv:1908.10084",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"has_space",
"re... | null | 2022-03-11T16:16:15+00:00 | [
"1908.10084"
] | [
"fr"
] | TAGS
#transformers #pytorch #camembert #feature-extraction #Text #Sentence Similarity #Sentence-Embedding #camembert-base #sentence-similarity #fr #dataset-stsb_multi_mt #arxiv-1908.10084 #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us
| Pre-trained sentence embedding models are the state-of-the-art of Sentence Embeddings for French.
-------------------------------------------------------------------------------------------------
Model is Fine-tuned using pre-trained facebook/camembert-base and
Siamese BERT-Networks with 'sentences-transformers' on d... | [] | [
"TAGS\n#transformers #pytorch #camembert #feature-extraction #Text #Sentence Similarity #Sentence-Embedding #camembert-base #sentence-similarity #fr #dataset-stsb_multi_mt #arxiv-1908.10084 #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us \n"
] |
null | transformers |
# Additional pretrained BERT base Japanese finance
This is a [BERT](https://github.com/google-research/bert) model pretrained on texts in the Japanese language.
The codes for the pretraining are available at [retarfi/language-pretraining](https://github.com/retarfi/language-pretraining/tree/v1.0).
## Model archite... | {"language": "ja", "license": "cc-by-sa-4.0", "tags": ["finance"], "widget": [{"text": "\u6d41\u52d5[MASK]\u306f\u30011\u5104\u5186\u3068\u306a\u308a\u307e\u3057\u305f\u3002"}]} | izumi-lab/bert-base-japanese-fin-additional | null | [
"transformers",
"pytorch",
"bert",
"pretraining",
"finance",
"ja",
"arxiv:1810.04805",
"license:cc-by-sa-4.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-11T17:41:11+00:00 | [
"1810.04805"
] | [
"ja"
] | TAGS
#transformers #pytorch #bert #pretraining #finance #ja #arxiv-1810.04805 #license-cc-by-sa-4.0 #endpoints_compatible #region-us
|
# Additional pretrained BERT base Japanese finance
This is a BERT model pretrained on texts in the Japanese language.
The codes for the pretraining are available at retarfi/language-pretraining.
## Model architecture
The model architecture is the same as BERT small in the original BERT paper; 12 layers, 768 dimen... | [
"# Additional pretrained BERT base Japanese finance \n\nThis is a BERT model pretrained on texts in the Japanese language.\n\nThe codes for the pretraining are available at retarfi/language-pretraining.",
"## Model architecture\n\nThe model architecture is the same as BERT small in the original BERT paper; 12 lay... | [
"TAGS\n#transformers #pytorch #bert #pretraining #finance #ja #arxiv-1810.04805 #license-cc-by-sa-4.0 #endpoints_compatible #region-us \n",
"# Additional pretrained BERT base Japanese finance \n\nThis is a BERT model pretrained on texts in the Japanese language.\n\nThe codes for the pretraining are available at r... |
null | null | # Graphcore/lxmert-base-ipu
Optimum Graphcore is a new open-source library and toolkit that enables developers to access IPU-optimized models certified by Hugging Face. It is an extension of Transformers, providing a set of performance optimization tools enabling maximum efficiency to train and run models on Graphcore... | {} | Graphcore/lxmert-base-ipu | null | [
"optimum_graphcore",
"arxiv:1908.07490",
"region:us"
] | null | 2022-03-11T17:45:10+00:00 | [
"1908.07490"
] | [] | TAGS
#optimum_graphcore #arxiv-1908.07490 #region-us
| # Graphcore/lxmert-base-ipu
Optimum Graphcore is a new open-source library and toolkit that enables developers to access IPU-optimized models certified by Hugging Face. It is an extension of Transformers, providing a set of performance optimization tools enabling maximum efficiency to train and run models on Graphcore... | [
"# Graphcore/lxmert-base-ipu\n\nOptimum Graphcore is a new open-source library and toolkit that enables developers to access IPU-optimized models certified by Hugging Face. It is an extension of Transformers, providing a set of performance optimization tools enabling maximum efficiency to train and run models on Gr... | [
"TAGS\n#optimum_graphcore #arxiv-1908.07490 #region-us \n",
"# Graphcore/lxmert-base-ipu\n\nOptimum Graphcore is a new open-source library and toolkit that enables developers to access IPU-optimized models certified by Hugging Face. It is an extension of Transformers, providing a set of performance optimization t... |
null | transformers |
# led-base for QA with qasper
A 10 epochs train of [Longformer Encoder Decoder Baselines for Qasper](https://github.com/allenai/qasper-led-baseline).
## How to use
```
git clone https://github.com/allenai/qasper-led-baseline.git
cd qasper-led-baseline
git clone https://huggingface.co/z-uo/led-base-qasper
pip instal... | {"language": "en", "tags": ["question_answering"], "datasets": ["qasper"]} | z-uo/led-base-qasper | null | [
"transformers",
"tensorboard",
"question_answering",
"en",
"dataset:qasper",
"endpoints_compatible",
"region:us"
] | null | 2022-03-11T18:27:48+00:00 | [] | [
"en"
] | TAGS
#transformers #tensorboard #question_answering #en #dataset-qasper #endpoints_compatible #region-us
|
# led-base for QA with qasper
A 10 epochs train of Longformer Encoder Decoder Baselines for Qasper.
## How to use
| [
"# led-base for QA with qasper\n\nA 10 epochs train of Longformer Encoder Decoder Baselines for Qasper.",
"## How to use"
] | [
"TAGS\n#transformers #tensorboard #question_answering #en #dataset-qasper #endpoints_compatible #region-us \n",
"# led-base for QA with qasper\n\nA 10 epochs train of Longformer Encoder Decoder Baselines for Qasper.",
"## How to use"
] |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Roberta-base-biomedical-clinical-es-finetuned-ner-CRAFT_en_es
This model is a fine-tuned version of [PlanTL-GOB-ES/roberta-base-... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "Roberta-base-biomedical-clinical-es-finetuned-ner-CRAFT_en_es", "results": []}]} | StivenLancheros/Roberta-base-biomedical-clinical-es-finetuned-ner-CRAFT_en_es | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-11T19:08:07+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| Roberta-base-biomedical-clinical-es-finetuned-ner-CRAFT\_en\_es
===============================================================
This model is a fine-tuned version of PlanTL-GOB-ES/roberta-base-biomedical-clinical-es on the CRAFT dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1750
* Prec... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 4",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #token-classification #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: 3e-05\n* train\\_bat... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Biobert-base-cased-v1.2-finetuned-ner-CRAFT
This model is a fine-tuned version of [dmis-lab/biobert-base-cased-v1.2](https://hug... | {"tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "Biobert-base-cased-v1.2-finetuned-ner-CRAFT", "results": []}]} | StivenLancheros/Biobert-base-cased-v1.2-finetuned-ner-CRAFT | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-11T19:17:16+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| Biobert-base-cased-v1.2-finetuned-ner-CRAFT
===========================================
This model is a fine-tuned version of dmis-lab/biobert-base-cased-v1.2 on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1878
* Precision: 0.8397
* Recall: 0.8366
* F1: 0.8382
* Accuracy: 0.... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 4",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 8\n* eval\\_... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Biobert-base-cased-v1.2-finetuned-ner-CRAFT_es_en
This model is a fine-tuned version of [dmis-lab/biobert-base-cased-v1.2](https... | {"tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "Biobert-base-cased-v1.2-finetuned-ner-CRAFT_es_en", "results": []}]} | StivenLancheros/Biobert-base-cased-v1.2-finetuned-ner-CRAFT_es_en | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-11T20:09:49+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| Biobert-base-cased-v1.2-finetuned-ner-CRAFT\_es\_en
===================================================
This model is a fine-tuned version of dmis-lab/biobert-base-cased-v1.2 on the CRAFT dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1811
* Precision: 0.8555
* Recall: 0.8539
* F1: 0.85... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 4",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 8\n* eval\\_... |
text-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1502166273064517632/RdLw... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true", "widget": [{"text": "My dream is"}]} | huggingtweets/thed3linquent_ | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-11T22:57:19+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('URL
</div>
<div
style="display:no... | [
"## How does it work?\n\nThe model uses the following pipeline.\n\n!pipeline\n\nTo understand how the model was developed, check the W&B report.",
"## Training data\n\nThe model was trained on tweets from rogue|| BIRFDAY BOY.\n\n| Data | rogue|| BIRFDAY BOY |\n| --- | --- |\n| Tweets downloaded | 3246 |\n| Retwee... | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"## How does it work?\n\nThe model uses the following pipeline.\n\n!pipeline\n\nTo understand how the model was developed, check the W&B report.",
"## Tr... |
text2text-generation | transformers |
# M2M100 12B
M2M100 is a multilingual encoder-decoder (seq-to-seq) model trained for Many-to-Many multilingual translation.
It was introduced in this [paper](https://arxiv.org/abs/2010.11125) and first released in [this](https://github.com/pytorch/fairseq/tree/master/examples/m2m_100) repository.
The model that can ... | {"language": ["multilingual", "af", "am", "ar", "ast", "az", "ba", "be", "bg", "bn", "br", "bs", "ca", "ceb", "cs", "cy", "da", "de", "el", "en", "es", "et", "fa", "ff", "fi", "fr", "fy", "ga", "gd", "gl", "gu", "ha", "he", "hi", "hr", "ht", "hu", "hy", "id", "ig", "ilo", "is", "it", "ja", "jv", "ka", "kk", "km", "kn",... | facebook/m2m100-12B-last-ckpt | null | [
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"fr",
"fy",
"g... | null | 2022-03-12T00:28:28+00:00 | [
"2010.11125"
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... | TAGS
#transformers #pytorch #m2m_100 #text2text-generation #m2m100-12B #multilingual #af #am #ar #ast #az #ba #be #bg #bn #br #bs #ca #ceb #cs #cy #da #de #el #en #es #et #fa #ff #fi #fr #fy #ga #gd #gl #gu #ha #he #hi #hr #ht #hu #hy #id #ig #ilo #is #it #ja #jv #ka #kk #km #kn #ko #lb #lg #ln #lo #lt #lv #mg #mk #ml ... |
# M2M100 12B
M2M100 is a multilingual encoder-decoder (seq-to-seq) model trained for Many-to-Many multilingual translation.
It was introduced in this paper and first released in this repository.
The model that can directly translate between the 9,900 directions of 100 languages.
To translate into a target language, ... | [
"# M2M100 12B\n\nM2M100 is a multilingual encoder-decoder (seq-to-seq) model trained for Many-to-Many multilingual translation.\nIt was introduced in this paper and first released in this repository.\n\nThe model that can directly translate between the 9,900 directions of 100 languages.\nTo translate into a target ... | [
"TAGS\n#transformers #pytorch #m2m_100 #text2text-generation #m2m100-12B #multilingual #af #am #ar #ast #az #ba #be #bg #bn #br #bs #ca #ceb #cs #cy #da #de #el #en #es #et #fa #ff #fi #fr #fy #ga #gd #gl #gu #ha #he #hi #hr #ht #hu #hy #id #ig #ilo #is #it #ja #jv #ka #kk #km #kn #ko #lb #lg #ln #lo #lt #lv #mg #m... |
text-generation | transformers |
- [✨Version v1✨](https://huggingface.co/bertin-project/bertin-gpt-j-6B/tree/v1): August 25th, 2022 (*[full](https://huggingface.co/bertin-project/bertin-gpt-j-6B/tree/v1) and [half-precision weights](https://huggingface.co/bertin-project/bertin-gpt-j-6B/tree/v1-half)*, at step 1M)
- [Version v1beta3](https://huggingfa... | {"language": ["es"], "license": "apache-2.0", "tags": ["pytorch", "causal-lm"], "datasets": ["bertin-project/mc4-es-sampled"]} | bertin-project/bertin-gpt-j-6B | null | [
"transformers",
"pytorch",
"gptj",
"text-generation",
"causal-lm",
"es",
"dataset:bertin-project/mc4-es-sampled",
"arxiv:2104.09864",
"arxiv:2101.00027",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-12T00:46:20+00:00 | [
"2104.09864",
"2101.00027"
] | [
"es"
] | TAGS
#transformers #pytorch #gptj #text-generation #causal-lm #es #dataset-bertin-project/mc4-es-sampled #arxiv-2104.09864 #arxiv-2101.00027 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| * Version v1: August 25th, 2022 (*full and half-precision weights*, at step 1M)
* Version v1beta3: July 22nd, 2022 (*full and half-precision weights*, at step 850k)
* Version v1beta2: June 6th, 2022 (*full and half-precision weights*, at step 616k)
* Version v1beta1: April 28th, 2022 (*half-precision weights only*, at ... | [
"### How to use\n\n\nThis model can be easily loaded using the 'AutoModelForCausalLM' functionality:",
"### Limitations and Biases\n\n\nAs the original GPT-J model, the core functionality of BERTIN-GPT-J-6B is taking a string of text and predicting the next token. While language models are widely used for tasks o... | [
"TAGS\n#transformers #pytorch #gptj #text-generation #causal-lm #es #dataset-bertin-project/mc4-es-sampled #arxiv-2104.09864 #arxiv-2101.00027 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### How to use\n\n\nThis model can be easily loaded using the 'AutoModelForCaus... |
null | transformers | Deberta large trained on slue transcriptions for 50 epochs, lr = 5e-6
| {} | Splend1dchan/deberta-large-slue-goldtrascription-e50 | null | [
"transformers",
"pytorch",
"deberta",
"endpoints_compatible",
"region:us"
] | null | 2022-03-12T03:52:10+00:00 | [] | [] | TAGS
#transformers #pytorch #deberta #endpoints_compatible #region-us
| Deberta large trained on slue transcriptions for 50 epochs, lr = 5e-6
| [] | [
"TAGS\n#transformers #pytorch #deberta #endpoints_compatible #region-us \n"
] |
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. -->
# reverse_text_generation_HarryPotter
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "reverse_text_generation_HarryPotter", "results": []}]} | calebcsjm/reverse_text_generation_HarryPotter | null | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-12T06:07:13+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# reverse_text_generation_HarryPotter
This model is a fine-tuned version of distilgpt2 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 hyperpara... | [
"# reverse_text_generation_HarryPotter\n\nThis model is a fine-tuned version of distilgpt2 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 procedu... | [
"TAGS\n#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# reverse_text_generation_HarryPotter\n\nThis model is a fine-tuned version of distilgpt2 on the None dataset.",
"... |
null | k2 |
# Introduction
This repo contains pre-trained model using
<https://github.com/k2-fsa/icefall/pull/248>.
It is trained on full LibriSpeech dataset using pruned RNN-T loss from [k2](https://github.com/k2-fsa/k2).
## How to clone this repo
```
sudo apt-get install git-lfs
git clone https://huggingface.co/csukuangfj/i... | {"language": "en", "license": "apache-2.0", "tags": ["icefall", "k2", "transducer", "librispeech", "ASR", "stateless transducer", "PyTorch", "RNN-T", "pruned RNN-T", "speech recognition"], "datasets": ["librispeech"], "metrics": ["WER"]} | csukuangfj/icefall-asr-librispeech-pruned-transducer-stateless-2022-03-12 | null | [
"k2",
"icefall",
"transducer",
"librispeech",
"ASR",
"stateless transducer",
"PyTorch",
"RNN-T",
"pruned RNN-T",
"speech recognition",
"en",
"dataset:librispeech",
"license:apache-2.0",
"region:us"
] | null | 2022-03-12T06:31:36+00:00 | [] | [
"en"
] | TAGS
#k2 #icefall #transducer #librispeech #ASR #stateless transducer #PyTorch #RNN-T #pruned RNN-T #speech recognition #en #dataset-librispeech #license-apache-2.0 #region-us
| Introduction
============
This repo contains pre-trained model using
<URL
It is trained on full LibriSpeech dataset using pruned RNN-T loss from k2.
How to clone this repo
----------------------
Caution: You have to run 'git lfs pull'. Otherwise, you will be SAD later.
The model in this repo is trained using ... | [] | [
"TAGS\n#k2 #icefall #transducer #librispeech #ASR #stateless transducer #PyTorch #RNN-T #pruned RNN-T #speech recognition #en #dataset-librispeech #license-apache-2.0 #region-us \n"
] |
text2text-generation | transformers |
Paper: [BioBART: Pretraining and Evaluation of A Biomedical Generative Language Model](https://arxiv.org/pdf/2204.03905.pdf)
```
@misc{BioBART,
title={BioBART: Pretraining and Evaluation of A Biomedical Generative Language Model},
author={Hongyi Yuan and Zheng Yuan and Ruyi Gan and Jiaxing Zhang and Yutao Xie and... | {"language": ["en"], "license": "apache-2.0", "tags": ["bart", "biobart", "biomedical"], "inference": true, "widget": [{"text": "Influenza is a <mask> disease."}, {"type": "text-generation"}]} | GanjinZero/biobart-base | null | [
"transformers",
"pytorch",
"safetensors",
"bart",
"text2text-generation",
"biobart",
"biomedical",
"en",
"arxiv:2204.03905",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-12T07:00:32+00:00 | [
"2204.03905"
] | [
"en"
] | TAGS
#transformers #pytorch #safetensors #bart #text2text-generation #biobart #biomedical #en #arxiv-2204.03905 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
Paper: BioBART: Pretraining and Evaluation of A Biomedical Generative Language Model
| [] | [
"TAGS\n#transformers #pytorch #safetensors #bart #text2text-generation #biobart #biomedical #en #arxiv-2204.03905 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text2text-generation | transformers |
Paper: [BioBART: Pretraining and Evaluation of A Biomedical Generative Language Model](https://arxiv.org/pdf/2204.03905.pdf)
```
@misc{BioBART,
title={BioBART: Pretraining and Evaluation of A Biomedical Generative Language Model},
author={Hongyi Yuan and Zheng Yuan and Ruyi Gan and Jiaxing Zhang and Yutao Xie and... | {"language": ["en"], "license": "apache-2.0", "tags": ["bart", "biobart", "biomedical"], "inference": true, "widget": [{"text": "Influenza is a <mask> disease."}, {"type": "text-generation"}]} | GanjinZero/biobart-large | null | [
"transformers",
"pytorch",
"safetensors",
"bart",
"text2text-generation",
"biobart",
"biomedical",
"en",
"arxiv:2204.03905",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-12T07:01:05+00:00 | [
"2204.03905"
] | [
"en"
] | TAGS
#transformers #pytorch #safetensors #bart #text2text-generation #biobart #biomedical #en #arxiv-2204.03905 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
Paper: BioBART: Pretraining and Evaluation of A Biomedical Generative Language Model
| [] | [
"TAGS\n#transformers #pytorch #safetensors #bart #text2text-generation #biobart #biomedical #en #arxiv-2204.03905 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuning-sentiment-model-3000-samples
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/d... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imdb"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "finetuning-sentiment-model-3000-samples", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "imdb", "type": "imdb", "args": ... | markt23917/finetuning-sentiment-model-3000-samples | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-12T07:44:57+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-imdb #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# finetuning-sentiment-model-3000-samples
This model is a fine-tuned version of distilbert-base-uncased on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3351
- Accuracy: 0.8767
- F1: 0.8825
## Model description
More information needed
## Intended uses & limitations
More in... | [
"# finetuning-sentiment-model-3000-samples\n\nThis model is a fine-tuned version of distilbert-base-uncased on the imdb dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.3351\n- Accuracy: 0.8767\n- F1: 0.8825",
"## Model description\n\nMore information needed",
"## Intended uses & li... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-imdb #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# finetuning-sentiment-model-3000-samples\n\nThis model is a fine-tuned version of distilbert-base-uncased... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-de
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-b... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["xtreme"], "metrics": ["f1"], "model-index": [{"name": "xlm-roberta-base-finetuned-panx-de", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "xtreme", "type": "xtreme", "args": "PAN-X.de"}, "me... | wooihen/xlm-roberta-base-finetuned-panx-de | null | [
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-12T07:47:47+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-panx-de
==================================
This model is a fine-tuned version of xlm-roberta-base on the xtreme dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1365
* F1: 0.8649
Model description
-----------------
More information needed
Intended uses & l... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 24\n* eval\\_batch\\_size: 24\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_... |
audio-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base-finetuned-ks
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2ve... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "wav2vec2-base-finetuned-ks", "results": []}]} | Mnauel/wav2vec2-base-finetuned-ks | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"audio-classification",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-12T10:51:33+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #audio-classification #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-base-finetuned-ks
==========================
This model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5766
* Accuracy: 0.8308
Model description
-----------------
More information needed
Intended uses & limitat... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1.5e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsi... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #audio-classification #generated_from_trainer #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: 1.5e-05\n* train\\_batch\\_size: 32\n* ev... |
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. -->
# bart-large-cnn-weaksup-original-100k
This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/face... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "bart-large-cnn-weaksup-original-100k", "results": []}]} | cammy/bart-large-cnn-weaksup-original-100k | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-12T12:19:39+00:00 | [] | [] | TAGS
#transformers #pytorch #bart #text2text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| bart-large-cnn-weaksup-original-100k
====================================
This model is a fine-tuned version of facebook/bart-large-cnn on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 1.5931
* Rouge1: 30.4429
* Rouge2: 15.6691
* Rougel: 24.1975
* Rougelsum: 27.4761
* Gen Len:... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\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 #bart #text2text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 1\n* eval\\_... |
text-classification | transformers | # Fine-tuned version of XLM-RoBERTa (large-sized model)
fine tune by Ryan Abdurohman
# XLM-RoBERTa (large-sized model)
XLM-RoBERTa model pre-trained on 2.5TB of filtered CommonCrawl data containing 100 languages. It was introduced in the paper [Unsupervised Cross-lingual Representation Learning at Scale](https://ar... | {"language": "multilingual", "license": "mit", "tags": ["exbert"], "inference": true} | khavitidala/xlmroberta-large-fine-tuned-indo-hoax-classification | null | [
"transformers",
"pytorch",
"xlm-roberta",
"text-classification",
"exbert",
"multilingual",
"arxiv:1911.02116",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-12T12:40:20+00:00 | [
"1911.02116"
] | [
"multilingual"
] | TAGS
#transformers #pytorch #xlm-roberta #text-classification #exbert #multilingual #arxiv-1911.02116 #license-mit #autotrain_compatible #endpoints_compatible #region-us
| # Fine-tuned version of XLM-RoBERTa (large-sized model)
fine tune by Ryan Abdurohman
# XLM-RoBERTa (large-sized model)
XLM-RoBERTa model pre-trained on 2.5TB of filtered CommonCrawl data containing 100 languages. It was introduced in the paper Unsupervised Cross-lingual Representation Learning at Scale by Conneau e... | [
"# Fine-tuned version of XLM-RoBERTa (large-sized model) \nfine tune by Ryan Abdurohman",
"# XLM-RoBERTa (large-sized model) \n\nXLM-RoBERTa model pre-trained on 2.5TB of filtered CommonCrawl data containing 100 languages. It was introduced in the paper Unsupervised Cross-lingual Representation Learning at Scale ... | [
"TAGS\n#transformers #pytorch #xlm-roberta #text-classification #exbert #multilingual #arxiv-1911.02116 #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# Fine-tuned version of XLM-RoBERTa (large-sized model) \nfine tune by Ryan Abdurohman",
"# XLM-RoBERTa (large-sized model) \n\nXLM-Ro... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# pegasus-samsum
This model is a fine-tuned version of [google/pegasus-cnn_dailymail](https://huggingface.co/google/pegasus-cnn_da... | {"tags": ["generated_from_trainer"], "datasets": ["samsum"], "model-index": [{"name": "pegasus-samsum", "results": []}]} | lijingxin/pegasus-samsum | null | [
"transformers",
"pytorch",
"tensorboard",
"pegasus",
"text2text-generation",
"generated_from_trainer",
"dataset:samsum",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-12T14:40:20+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #pegasus #text2text-generation #generated_from_trainer #dataset-samsum #autotrain_compatible #endpoints_compatible #region-us
| pegasus-samsum
==============
This model is a fine-tuned version of google/pegasus-cnn\_dailymail on the samsum dataset.
It achieves the following results on the evaluation set:
* Loss: 1.4874
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\n* seed: 42\n* gradient\\_accumulation\\_steps: 16\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=... | [
"TAGS\n#transformers #pytorch #tensorboard #pegasus #text2text-generation #generated_from_trainer #dataset-samsum #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\... |
text2text-generation | transformers |
# ByT5-Korean - small
ByT5-Korean is a Korean specific extension of Google's [ByT5](https://github.com/google-research/byt5).
A Korean syllable has three components (called Jamo): a beginning consonant, a middle vowel, and an optional final consonant; they are like individual characters of alphabet.
While the ... | {"license": "apache-2.0", "datasets": ["mc4"]} | everdoubling/byt5-Korean-small | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"dataset:mc4",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-12T15:21:45+00:00 | [] | [] | TAGS
#transformers #pytorch #t5 #text2text-generation #dataset-mc4 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# ByT5-Korean - small
ByT5-Korean is a Korean specific extension of Google's ByT5.
A Korean syllable has three components (called Jamo): a beginning consonant, a middle vowel, and an optional final consonant; they are like individual characters of alphabet.
While the ByT5's utf-8 encoding allows generic encodi... | [
"# ByT5-Korean - small\r\n\r\nByT5-Korean is a Korean specific extension of Google's ByT5.\r\n\r\nA Korean syllable has three components (called Jamo): a beginning consonant, a middle vowel, and an optional final consonant; they are like individual characters of alphabet.\r\nWhile the ByT5's utf-8 encoding allows g... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #dataset-mc4 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# ByT5-Korean - small\r\n\r\nByT5-Korean is a Korean specific extension of Google's ByT5.\r\n\r\nA Korean syllable has three components (... |
null | transformers | # Note
This model is training with 180k+ ABSA samples, see [ABSADatasets](https://github.com/yangheng95/ABSADatasets). Yet the test sets are not included in pre-training, so you can use this model for training and benchmarking on common ABSA datasets, e.g., Laptop14, Rest14 datasets. (Except for the Rest15 dataset!)
... | {"language": ["en"], "license": "mit", "tags": ["aspect-based-sentiment-analysis", "lcf-bert"], "datasets": ["laptop14 (w/ augmentation)", "restaurant14 (w/ augmentation)", "restaurant16 (w/ augmentation)", "ACL-Twitter (w/ augmentation)", "MAMS (w/ augmentation)", "Television (w/ augmentation)", "TShirt (w/ augmentati... | yangheng/deberta-v3-base-absa | null | [
"transformers",
"pytorch",
"deberta-v2",
"aspect-based-sentiment-analysis",
"lcf-bert",
"en",
"arxiv:2110.08604",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-03-12T16:46:41+00:00 | [
"2110.08604"
] | [
"en"
] | TAGS
#transformers #pytorch #deberta-v2 #aspect-based-sentiment-analysis #lcf-bert #en #arxiv-2110.08604 #license-mit #endpoints_compatible #region-us
| # Note
This model is training with 180k+ ABSA samples, see ABSADatasets. Yet the test sets are not included in pre-training, so you can use this model for training and benchmarking on common ABSA datasets, e.g., Laptop14, Rest14 datasets. (Except for the Rest15 dataset!)
# DeBERTa for aspect-based sentiment analysi... | [
"# Note\r\nThis model is training with 180k+ ABSA samples, see ABSADatasets. Yet the test sets are not included in pre-training, so you can use this model for training and benchmarking on common ABSA datasets, e.g., Laptop14, Rest14 datasets. (Except for the Rest15 dataset!)",
"# DeBERTa for aspect-based sentimen... | [
"TAGS\n#transformers #pytorch #deberta-v2 #aspect-based-sentiment-analysis #lcf-bert #en #arxiv-2110.08604 #license-mit #endpoints_compatible #region-us \n",
"# Note\r\nThis model is training with 180k+ ABSA samples, see ABSADatasets. Yet the test sets are not included in pre-training, so you can use this model f... |
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... | RobertoMCA97/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 | 2022-03-12T17:02:21+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: 0.2157
* Accuracy: 0.9255
* F1: 0.9258
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... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# cptanalatriste/request-for-help
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "cptanalatriste/request-for-help", "results": []}]} | cptanalatriste/request-for-help | null | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-12T17:19:43+00:00 | [] | [] | TAGS
#transformers #tf #bert #text-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| cptanalatriste/request-for-help
===============================
This model is a fine-tuned version of bert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.1342
* Train Sparse Categorical Accuracy: 1.0
* Validation Loss: 0.1514
* Validation Sparse Categori... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'learning\\_rate': 3e-05, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}\n* training\\_precision: float32",
"### Training results",
"### Framework... | [
"TAGS\n#transformers #tf #bert #text-classification #generated_from_keras_callback #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* optimizer: {'name': 'Adam', 'learning\\_rate': 3e-05... |
null | null | All credit to this repo: https://huggingface.co/spaces/carolineec/informativedrawings
JavaScript/browser demo here: https://github.com/josephrocca/image-to-line-art-js | {} | rocca/informative-drawings-line-art-onnx | null | [
"onnx",
"region:us"
] | null | 2022-03-12T17:52:02+00:00 | [] | [] | TAGS
#onnx #region-us
| All credit to this repo: URL
JavaScript/browser demo here: URL | [] | [
"TAGS\n#onnx #region-us \n"
] |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# small-e-czech-finetuned-ner-wikiann
This model is a fine-tuned version of [Seznam/small-e-czech](https://huggingface.co/Seznam/s... | {"license": "cc-by-4.0", "tags": ["generated_from_trainer"], "datasets": ["wikiann"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "small-e-czech-finetuned-ner-wikiann", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "wikiann"... | richielo/small-e-czech-finetuned-ner-wikiann | null | [
"transformers",
"pytorch",
"tensorboard",
"electra",
"token-classification",
"generated_from_trainer",
"dataset:wikiann",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-12T17:57:32+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #electra #token-classification #generated_from_trainer #dataset-wikiann #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| small-e-czech-finetuned-ner-wikiann
===================================
This model is a fine-tuned version of Seznam/small-e-czech on the wikiann dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2547
* Precision: 0.8713
* Recall: 0.8970
* F1: 0.8840
* Accuracy: 0.9557
Model description
... | [
"### 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: 20",
"### Trainin... | [
"TAGS\n#transformers #pytorch #tensorboard #electra #token-classification #generated_from_trainer #dataset-wikiann #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-de
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-b... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["xtreme"], "metrics": ["f1"], "model-index": [{"name": "xlm-roberta-base-finetuned-panx-de", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "xtreme", "type": "xtreme", "args": "PAN-X.de"}, "me... | zdepablo/xlm-roberta-base-finetuned-panx-de | null | [
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-12T18:16:44+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-panx-de
==================================
This model is a fine-tuned version of xlm-roberta-base on the xtreme dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1348
* F1: 0.8595
Model description
-----------------
More information needed
Intended uses & l... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 24\n* eval\\_batch\\_size: 24\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-de-fr
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-robert... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["f1"], "model-index": [{"name": "xlm-roberta-base-finetuned-panx-de-fr", "results": []}]} | zdepablo/xlm-roberta-base-finetuned-panx-de-fr | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-12T18:44:26+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-panx-de-fr
=====================================
This model is a fine-tuned version of xlm-roberta-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1664
* F1: 0.8556
Model description
-----------------
More information needed
Intended uses... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 24\n* eval\\_batch\\_size: 24\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 24\n*... |
text-classification | transformers |
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 631818261
- CO2 Emissions (in grams): 5.714250590300453
## Validation Metrics
- Loss: 0.44651690125465393
- Accuracy: 0.8792873051224944
- Macro F1: 0.839261602941426
- Micro F1: 0.8792873051224943
- Weighted F1: 0.8790427387522044... | {"language": "unk", "tags": "autonlp", "datasets": ["test1345/autonlp-data-savesome"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}], "co2_eq_emissions": 5.714250590300453} | test1345/autonlp-savesome-631818261 | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"autonlp",
"unk",
"dataset:test1345/autonlp-data-savesome",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-12T18:55:14+00:00 | [] | [
"unk"
] | TAGS
#transformers #pytorch #roberta #text-classification #autonlp #unk #dataset-test1345/autonlp-data-savesome #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 631818261
- CO2 Emissions (in grams): 5.714250590300453
## Validation Metrics
- Loss: 0.44651690125465393
- Accuracy: 0.8792873051224944
- Macro F1: 0.839261602941426
- Micro F1: 0.8792873051224943
- Weighted F1: 0.8790427387522044... | [
"# Model Trained Using AutoNLP\n\n- Problem type: Multi-class Classification\n- Model ID: 631818261\n- CO2 Emissions (in grams): 5.714250590300453",
"## Validation Metrics\n\n- Loss: 0.44651690125465393\n- Accuracy: 0.8792873051224944\n- Macro F1: 0.839261602941426\n- Micro F1: 0.8792873051224943\n- Weighted F1: ... | [
"TAGS\n#transformers #pytorch #roberta #text-classification #autonlp #unk #dataset-test1345/autonlp-data-savesome #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoNLP\n\n- Problem type: Multi-class Classification\n- Model ID: 631818261\n- CO2 Emissions (in g... |
text-generation | null |
# My Awesome Model | {"tags": ["conversational"]} | Meowren/DialoGPT-small-Rick-Bot | null | [
"conversational",
"region:us"
] | null | 2022-03-12T20:16:22+00:00 | [] | [] | TAGS
#conversational #region-us
|
# My Awesome Model | [
"# My Awesome Model"
] | [
"TAGS\n#conversational #region-us \n",
"# My Awesome Model"
] |
fill-mask | transformers | # Geneformer
Geneformer is a foundation transformer model pretrained on a large-scale corpus of ~30 million single cell transcriptomes to enable context-aware predictions in settings with limited data in network biology.
- See [our manuscript](https://rdcu.be/ddrx0) for details.
- See [geneformer.readthedocs.io](https... | {"license": "apache-2.0", "datasets": "ctheodoris/Genecorpus-30M"} | ctheodoris/Geneformer | null | [
"transformers",
"pytorch",
"safetensors",
"bert",
"fill-mask",
"dataset:ctheodoris/Genecorpus-30M",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-12T20:55:42+00:00 | [] | [] | TAGS
#transformers #pytorch #safetensors #bert #fill-mask #dataset-ctheodoris/Genecorpus-30M #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| # Geneformer
Geneformer is a foundation transformer model pretrained on a large-scale corpus of ~30 million single cell transcriptomes to enable context-aware predictions in settings with limited data in network biology.
- See our manuscript for details.
- See URL for documentation.
# Model Description
Geneformer is ... | [
"# Geneformer\nGeneformer is a foundation transformer model pretrained on a large-scale corpus of ~30 million single cell transcriptomes to enable context-aware predictions in settings with limited data in network biology.\n\n- See our manuscript for details.\n- See URL for documentation.",
"# Model Description\n... | [
"TAGS\n#transformers #pytorch #safetensors #bert #fill-mask #dataset-ctheodoris/Genecorpus-30M #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# Geneformer\nGeneformer is a foundation transformer model pretrained on a large-scale corpus of ~30 million single cell transc... |
text-classification | transformers |
# TransformationTransformer
**TransformationTransformer** is a fine-tuned [distilroberta](https://huggingface.co/distilroberta-base) model. It is trained and evaluated on 10,000 manually annotated sentences gleaned from the Q&A-section of quarterly earnings conference calls. In particular, it was trained on sentences... | {"language": ["en"], "pipeline_tag": "text-classification", "widget": [{"text": "And it was great to see how our Chinese team very much aware of that and of shifting all the resourcing to really tap into these opportunities.", "example_title": "Examplary Transformation Sentence"}, {"text": "But we will continue to recr... | simonschoe/TransformationTransformer | null | [
"transformers",
"pytorch",
"safetensors",
"roberta",
"text-classification",
"en",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-12T21:22:16+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #safetensors #roberta #text-classification #en #autotrain_compatible #endpoints_compatible #region-us
|
# TransformationTransformer
TransformationTransformer is a fine-tuned distilroberta model. It is trained and evaluated on 10,000 manually annotated sentences gleaned from the Q&A-section of quarterly earnings conference calls. In particular, it was trained on sentences issued by firm executives to discriminate betwee... | [
"# TransformationTransformer\n\nTransformationTransformer is a fine-tuned distilroberta model. It is trained and evaluated on 10,000 manually annotated sentences gleaned from the Q&A-section of quarterly earnings conference calls. In particular, it was trained on sentences issued by firm executives to discriminate ... | [
"TAGS\n#transformers #pytorch #safetensors #roberta #text-classification #en #autotrain_compatible #endpoints_compatible #region-us \n",
"# TransformationTransformer\n\nTransformationTransformer is a fine-tuned distilroberta model. It is trained and evaluated on 10,000 manually annotated sentences gleaned from th... |
audio-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xtreme_s_xlsr_minds14
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2v... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["f1"], "model-index": [{"name": "xtreme_s_xlsr_minds14", "results": []}]} | anton-l/xtreme_s_xlsr_minds14 | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"audio-classification",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-12T22:59:13+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #audio-classification #generated_from_trainer #license-apache-2.0 #endpoints_compatible #has_space #region-us
| xtreme\_s\_xlsr\_minds14
========================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2566
* F1: {'f1': 0.9460569664921582, 'accuracy': 0.9468540012217471}
Model description
-----------------
M... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 8\n* seed: 42\n* distributed\\_type: multi-GPU\n* num\\_devices: 2\n* total\\_train\\_batch\\_size: 64\n* total\\_eval\\_batch\\_size: 16\n* ... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #audio-classification #generated_from_trainer #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# 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... | Ramu/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 | 2022-03-13T01:55:37+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: 0.2167
* Accuracy: 0.926
* F1: 0.9262
Model description
-----------------
Mor... | [
"### 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... |
fill-mask | transformers |
# What is this model?
- 東北大学のBERT large JapaneseをRustで使える様に変換
- [cl-tohoku/bert-large-japanese](https://huggingface.co/cl-tohoku/bert-large-japanese)
# How to Try
### 1. Clone
```
git clone https://huggingface.co/Yokohide031/rust_cl-tohoku_bert-large-japanese
```
### 2. Create Project
```
cargo new <projectName>... | {"language": "ja", "license": "cc-by-sa-4.0", "datasets": ["wikipedia"], "widget": [{"text": "Rust\u3067[MASK]\u3092\u4f7f\u3046\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002\u3002"}]} | Yokohide031/rust_cl-tohoku_bert-large-japanese | null | [
"transformers",
"rust",
"bert",
"fill-mask",
"ja",
"dataset:wikipedia",
"license:cc-by-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-13T02:12:20+00:00 | [] | [
"ja"
] | TAGS
#transformers #rust #bert #fill-mask #ja #dataset-wikipedia #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us
|
# What is this model?
- 東北大学のBERT large JapaneseをRustで使える様に変換
- cl-tohoku/bert-large-japanese
# How to Try
### 1. Clone
### 2. Create Project
### 3. Edit URL
※ 上のコードでは、[MASK]の代わりに "*" を使うことになってます。
## Licenses
The pretrained models are distributed under the terms of the Creative Commons Attribution-ShareAli... | [
"# What is this model?\n- 東北大学のBERT large JapaneseをRustで使える様に変換\n- cl-tohoku/bert-large-japanese",
"# How to Try",
"### 1. Clone",
"### 2. Create Project",
"### 3. Edit URL\n\n\n\n※ 上のコードでは、[MASK]の代わりに \"*\" を使うことになってます。",
"## Licenses\nThe pretrained models are distributed under the terms of the Creative... | [
"TAGS\n#transformers #rust #bert #fill-mask #ja #dataset-wikipedia #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# What is this model?\n- 東北大学のBERT large JapaneseをRustで使える様に変換\n- cl-tohoku/bert-large-japanese",
"# How to Try",
"### 1. Clone",
"### 2. Create Project",
"... |
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... | aGabillon/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 | 2022-03-13T03:42:26+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: 0.2294
* Accuracy: 0.9215
* F1: 0.9219
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... |
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. -->
# bart-large-cnn-weaksup-100-NOpad-early
This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/fa... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "bart-large-cnn-weaksup-100-NOpad-early", "results": []}]} | cammy/bart-large-cnn-weaksup-100-NOpad-early | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-13T05:23:53+00:00 | [] | [] | TAGS
#transformers #pytorch #bart #text2text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| bart-large-cnn-weaksup-100-NOpad-early
======================================
This model is a fine-tuned version of facebook/bart-large-cnn on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 2.0768
* Rouge1: 28.7908
* Rouge2: 10.6989
* Rougel: 20.534
* Rougelsum: 24.1294
* Gen L... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\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\n* mixed\\_precis... | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 1\n* eval\\_... |
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. -->
# bart-large-cnn-weaksup-1000-NOpad-early
This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/f... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "bart-large-cnn-weaksup-1000-NOpad-early", "results": []}]} | cammy/bart-large-cnn-weaksup-1000-NOpad-early | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-13T05:36:31+00:00 | [] | [] | TAGS
#transformers #pytorch #bart #text2text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| bart-large-cnn-weaksup-1000-NOpad-early
=======================================
This model is a fine-tuned version of facebook/bart-large-cnn on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 1.9082
* Rouge1: 26.9663
* Rouge2: 11.3027
* Rougel: 20.7327
* Rougelsum: 23.5965
* Ge... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\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\n* mixed\\_precis... | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 1\n* eval\\_... |
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