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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
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/1462464744200323076/q_vE... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/mcbrideace-sorarescp-thedonofsorare/1654554022265/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/mcbrideace-sorarescp-thedonofsorare | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-06T21:17:18+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI CYBORG
The Don & URL & Sonhos\_10A
@mcbrideace-sorarescp-thedonofsorare
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... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
null | 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. -->
# rule_learning_test
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the en... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["enoriega/odinsynth_dataset"], "model-index": [{"name": "rule_learning_test", "results": []}]} | enoriega/rule_learning_test | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"generated_from_trainer",
"dataset:enoriega/odinsynth_dataset",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-06-06T21:29:06+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #generated_from_trainer #dataset-enoriega/odinsynth_dataset #license-apache-2.0 #endpoints_compatible #region-us
| rule\_learning\_test
====================
This model is a fine-tuned version of bert-base-uncased on the enoriega/odinsynth\_dataset dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1255
Model description
-----------------
More information needed
Intended uses & limitations
--------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 1000\n* total\\_train\\_batch\\_size: 8000\n* optimizer: Adam with betas=(0.9,0.999) and epsi... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #generated_from_trainer #dataset-enoriega/odinsynth_dataset #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: 5e-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. -->
# arabert2arabert-finetuned-ar-xlsum
This model is a fine-tuned version of [](https://huggingface.co/) on the xlsum dataset.
It ac... | {"tags": ["summarization", "ar", "encoder-decoder", "arabert", "Abstractive Summarization", "generated_from_trainer"], "datasets": ["xlsum"], "model-index": [{"name": "arabert2arabert-finetuned-ar-xlsum", "results": []}]} | eslamxm/arabert2arabert-finetuned-ar-xlsum | null | [
"transformers",
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"summarization",
"ar",
"arabert",
"Abstractive Summarization",
"generated_from_trainer",
"dataset:xlsum",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-06T21:31:07+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #encoder-decoder #text2text-generation #summarization #ar #arabert #Abstractive Summarization #generated_from_trainer #dataset-xlsum #autotrain_compatible #endpoints_compatible #region-us
|
# arabert2arabert-finetuned-ar-xlsum
This model is a fine-tuned version of [](URL on the xlsum dataset.
It achieves the following results on the evaluation set:
- Loss: 5.1557
- Rouge-1: 25.3
- Rouge-2: 10.46
- Rouge-l: 22.12
- Gen Len: 20.0
- Bertscore: 71.98
## Model description
More information needed
## Inte... | [
"# arabert2arabert-finetuned-ar-xlsum\n\nThis model is a fine-tuned version of [](URL on the xlsum dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 5.1557\n- Rouge-1: 25.3\n- Rouge-2: 10.46\n- Rouge-l: 22.12\n- Gen Len: 20.0\n- Bertscore: 71.98",
"## Model description\n\nMore informatio... | [
"TAGS\n#transformers #pytorch #tensorboard #encoder-decoder #text2text-generation #summarization #ar #arabert #Abstractive Summarization #generated_from_trainer #dataset-xlsum #autotrain_compatible #endpoints_compatible #region-us \n",
"# arabert2arabert-finetuned-ar-xlsum\n\nThis model is a fine-tuned version of... |
text-generation | transformers | # My Story model
{'top_p': 0.9, 'top_k': 50, 'temperature': 1, 'repetition_penalty': 1}
Arthur goes to the beach. Arthur and his friends go to the beach one day. They go swimming. Then they play volleyball. Arthur is so tired he falls asleep on the beach. Arthur wakes up later and they never go back.
Arthur goes to... | {} | jppaolim/v57_Large_3E | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-06T21:55:15+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # My Story model
{'top_p': 0.9, 'top_k': 50, 'temperature': 1, 'repetition_penalty': 1}
Arthur goes to the beach. Arthur and his friends go to the beach one day. They go swimming. Then they play volleyball. Arthur is so tired he falls asleep on the beach. Arthur wakes up later and they never go back.
Arthur goes to... | [
"# My Story model\n{'top_p': 0.9, 'top_k': 50, 'temperature': 1, 'repetition_penalty': 1} \nArthur goes to the beach. Arthur and his friends go to the beach one day. They go swimming. Then they play volleyball. Arthur is so tired he falls asleep on the beach. Arthur wakes up later and they never go back. \nArthur... | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# My Story model\n{'top_p': 0.9, 'top_k': 50, 'temperature': 1, 'repetition_penalty': 1} \nArthur goes to the beach. Arthur and his friends go to the beach one day. They go... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# nestoralvaro/mt5-small-finetuned-google_small_for_summarization_TF
This model is a fine-tuned version of [google/mt5-small](https://hu... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "nestoralvaro/mt5-small-finetuned-google_small_for_summarization_TF", "results": []}]} | nestoralvaro/mt5-small-finetuned-google_small_for_summarization_TF | null | [
"transformers",
"tf",
"mt5",
"text2text-generation",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-06T22:07:13+00:00 | [] | [] | TAGS
#transformers #tf #mt5 #text2text-generation #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| nestoralvaro/mt5-small-finetuned-google\_small\_for\_summarization\_TF
======================================================================
This model is a fine-tuned version of google/mt5-small on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 2.3123
* Validation Loss:... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 5.6e-05, 'decay\\_steps': 266360, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycl... | [
"TAGS\n#transformers #tf #mt5 #text2text-generation #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam... |
text2text-generation | transformers |
# Model Card of `lmqg/mt5-small-ruquad-qg`
This model is fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) for question generation task on the [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-ge... | {"language": "ru", "license": "cc-by-4.0", "tags": ["question generation"], "datasets": ["lmqg/qg_ruquad"], "metrics": ["bleu4", "meteor", "rouge-l", "bertscore", "moverscore"], "pipeline_tag": "text2text-generation", "widget": [{"text": "\u041d\u0435\u043b\u0438\u0448\u043d\u0438\u043c \u0431\u0443\u0434\u0435\u0442 \... | lmqg/mt5-small-ruquad-qg | null | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"question generation",
"ru",
"dataset:lmqg/qg_ruquad",
"arxiv:2210.03992",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-06T23:39:31+00:00 | [
"2210.03992"
] | [
"ru"
] | TAGS
#transformers #pytorch #mt5 #text2text-generation #question generation #ru #dataset-lmqg/qg_ruquad #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Model Card of 'lmqg/mt5-small-ruquad-qg'
========================================
This model is fine-tuned version of google/mt5-small for question generation task on the lmqg/qg\_ruquad (dataset\_name: default) via 'lmqg'.
### Overview
* Language model: google/mt5-small
* Language: ru
* Training data: lmqg/qg\_r... | [
"### Overview\n\n\n* Language model: google/mt5-small\n* Language: ru\n* Training data: lmqg/qg\\_ruquad (default)\n* Online Demo: URL\n* Repository: URL\n* Paper: URL",
"### Usage\n\n\n* With 'lmqg'\n* With 'transformers'\n\n\nEvaluation\n----------\n\n\n* *Metric (Question Generation)*: raw metric file\n\n\n\n*... | [
"TAGS\n#transformers #pytorch #mt5 #text2text-generation #question generation #ru #dataset-lmqg/qg_ruquad #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Overview\n\n\n* Language model: google/mt5-small\n* Language: ru\n... |
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/1420954294082326529/ZkxW... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/hopedavistweets/1654562883505/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/hopedavistweets | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-06T23:46:24+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Hope Davis
@hopedavistweets
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"
] |
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/1052029344254701568/2yAQ... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/heylookaturtle/1654563018664/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/heylookaturtle | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-06T23:48:04+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Adam Porter
@heylookaturtle
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"
] |
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/1511483454495637510/BWEF... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/sofiaazeman/1654563180290/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/sofiaazeman | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-06T23:51:46+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Sofi Zeman
@sofiaazeman
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"
] |
text-generation | transformers | ```
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("BigSalmon/InformalToFormalLincoln50")
model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincoln50")
```
```
How To Make Prompt:
informal english: i am very ready to do that just that.
Tra... | {} | BigSalmon/InformalToFormalLincoln50 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-06-06T23:53:36+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
Keywords to sentences or sentence.
| [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n"
] |
reinforcement-learning | null |
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="iambored1009/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional... | {"tags": ["FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-FrozenLake-v1-4x4-noSlippery", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "FrozenLake-v1-4x4-no_slippery", "type": ... | iambored1009/q-FrozenLake-v1-4x4-noSlippery | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-06-06T23:55:08+00:00 | [] | [] | TAGS
#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing FrozenLake-v1
This is a trained model of a Q-Learning agent playing FrozenLake-v1 .
## Usage
| [
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] | [
"TAGS\n#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] |
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/1475251222802309123/0V1B... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/sophiadonis10/1654563613795/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/sophiadonis10 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-06T23:57:21+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Sophia Donis
@sophiadonis10
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"
] |
reinforcement-learning | null |
# **Q-Learning** Agent playing **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="iambored1009/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False et... | {"tags": ["Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-Taxi-v3", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Taxi-v3", "type": "Taxi-v3"}, "metrics": [{"type": "mean_reward", "value": "7.56 +/... | iambored1009/q-Taxi-v3 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-06-06T23:59:19+00:00 | [] | [] | TAGS
#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing Taxi-v3
This is a trained model of a Q-Learning agent playing Taxi-v3 .
## Usage
| [
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] | [
"TAGS\n#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] |
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/1120118423357464577/j4gz... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/ryang73/1654563663272/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/ryang73 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-06T23:59:56+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Ryan G
@ryang73
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
-------------
T... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wangchanberta-base-att-spm-uncased-finetuned-imdb
This model is a fine-tuned version of [airesearch/wangchanberta-base-att-spm-u... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "wangchanberta-base-att-spm-uncased-finetuned-imdb", "results": []}]} | Nithiwat/wangchanberta-base-att-spm-uncased-finetuned-imdb | null | [
"transformers",
"pytorch",
"tensorboard",
"camembert",
"fill-mask",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-07T00:04:12+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #camembert #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| wangchanberta-base-att-spm-uncased-finetuned-imdb
=================================================
This model is a fine-tuned version of airesearch/wangchanberta-base-att-spm-uncased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 2.5910
Model description
-----------------
... | [
"### 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: 3.0\n* mixed\\_pr... | [
"TAGS\n#transformers #pytorch #tensorboard #camembert #fill-mask #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: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch... |
reinforcement-learning | stable-baselines3 |
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 ... | {"library_name": "stable-baselines3", "tags": ["LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "LunarLander-v2", "type": "LunarL... | iambored1009/TEST2ppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-07T01:08:41+00:00 | [] | [] | TAGS
#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add you... |
fill-mask | 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. -->
# spencerkmarley/distilbert
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncas... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "spencerkmarley/distilbert", "results": []}]} | spencerkmarley/distilbert | null | [
"transformers",
"tf",
"distilbert",
"fill-mask",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-07T01:28:18+00:00 | [] | [] | TAGS
#transformers #tf #distilbert #fill-mask #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| spencerkmarley/distilbert
=========================
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 3.2904
* Validation Loss: 2.8356
* Epoch: 0
Model description
-----------------
More information needed
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'WarmUp', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_schedule\\_fn': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_le... | [
"TAGS\n#transformers #tf #distilbert #fill-mask #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': 'AdamWeightDecay', 'learning\\_rate'... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# VN_ja-en_mt5_small
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on an unkno... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "VN_ja-en_mt5_small", "results": []}]} | twieland/VN_ja-en_mt5_small | null | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-07T01:40:13+00:00 | [] | [] | TAGS
#transformers #pytorch #mt5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| VN\_ja-en\_mt5\_small
=====================
This model is a fine-tuned version of google/mt5-small on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 2.3148
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\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",
"### Train... | [
"TAGS\n#transformers #pytorch #mt5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* ... |
token-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. -->
# Cube/distilbert-base-uncased-finetuned-ner
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/dist... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "Cube/distilbert-base-uncased-finetuned-ner", "results": []}]} | Cube/distilbert-base-uncased-finetuned-ner | null | [
"transformers",
"tf",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-07T01:56:38+00:00 | [] | [] | TAGS
#transformers #tf #tensorboard #distilbert #token-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| Cube/distilbert-base-uncased-finetuned-ner
==========================================
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.0339
* Validation Loss: 0.0646
* Train Precision: 0.9217
* Train Recall:... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_steps': 2631, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': ... | [
"TAGS\n#transformers #tf #tensorboard #distilbert #token-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': 'AdamWeightD... |
fill-mask | 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. -->
# BobBraico/rlb-cyber-finetuned-cyber
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "BobBraico/rlb-cyber-finetuned-cyber", "results": []}]} | BobBraico/rlb-cyber-finetuned-cyber | null | [
"transformers",
"tf",
"distilbert",
"fill-mask",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-07T02:13:55+00:00 | [] | [] | TAGS
#transformers #tf #distilbert #fill-mask #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| BobBraico/rlb-cyber-finetuned-cyber
===================================
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 2.7822
* Validation Loss: 2.4283
* Epoch: 0
Model description
-----------------
More... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'WarmUp', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_schedule\\_fn': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_le... | [
"TAGS\n#transformers #tf #distilbert #fill-mask #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': 'AdamWeightDecay', 'learning\\_rate'... |
fill-mask | 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. -->
# distilbert-base-uncased-finetuned-cyber
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilb... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "distilbert-base-uncased-finetuned-cyber", "results": []}]} | BobBraico/distilbert-base-uncased-finetuned-cyber | null | [
"transformers",
"tf",
"distilbert",
"fill-mask",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-07T02:40:56+00:00 | [] | [] | TAGS
#transformers #tf #distilbert #fill-mask #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# distilbert-base-uncased-finetuned-cyber
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation... | [
"# distilbert-base-uncased-finetuned-cyber\n\nThis model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.\nIt achieves the following results on the evaluation set:",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Tra... | [
"TAGS\n#transformers #tf #distilbert #fill-mask #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# distilbert-base-uncased-finetuned-cyber\n\nThis model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.\nIt achieves the followi... |
text2text-generation | transformers |
# Indonesian Version of Multilingual T5 Transformer
Smaller version of the [Google's Multilingual T5-base](https://huggingface.co/google/mt5-base) model with only Indonesian and some English embeddings.
This model has to be fine-tuned before it is useable on a downstream task.\
Fine-tuned idT5 for the Question Gener... | {"language": ["id", "en", "multilingual"], "license": "apache-2.0", "tags": ["idt5"]} | muchad/idt5-base | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"idt5",
"id",
"en",
"multilingual",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-06-07T03:05:07+00:00 | [] | [
"id",
"en",
"multilingual"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #idt5 #id #en #multilingual #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
# Indonesian Version of Multilingual T5 Transformer
Smaller version of the Google's Multilingual T5-base model with only Indonesian and some English embeddings.
This model has to be fine-tuned before it is useable on a downstream task.\
Fine-tuned idT5 for the Question Generation and Question Answering tasks, availa... | [
"# Indonesian Version of Multilingual T5 Transformer\n\nSmaller version of the Google's Multilingual T5-base model with only Indonesian and some English embeddings.\n\nThis model has to be fine-tuned before it is useable on a downstream task.\\\nFine-tuned idT5 for the Question Generation and Question Answering tas... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #idt5 #id #en #multilingual #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# Indonesian Version of Multilingual T5 Transformer\n\nSmaller version of the Google's Multilingual T5-base mod... |
text-classification | transformers |
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 955631800
- CO2 Emissions (in grams): 0.08564281067919652
## Validation Metrics
- Loss: 0.34108611941337585
- Accuracy: 0.8671983356449375
- Precision: 0.7883283877349159
- Recall: 0.8250517598343685
- AUC: 0.9236450689447471
- F1: 0.... | {"language": "en", "tags": "autotrain", "datasets": ["BraveOni/autotrain-data-2ch-text-classification"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 0.08564281067919652} | BraveOni/2ch-text-classification | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain",
"en",
"dataset:BraveOni/autotrain-data-2ch-text-classification",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-07T03:08:45+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bert #text-classification #autotrain #en #dataset-BraveOni/autotrain-data-2ch-text-classification #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 955631800
- CO2 Emissions (in grams): 0.08564281067919652
## Validation Metrics
- Loss: 0.34108611941337585
- Accuracy: 0.8671983356449375
- Precision: 0.7883283877349159
- Recall: 0.8250517598343685
- AUC: 0.9236450689447471
- F1: 0.... | [
"# Model Trained Using AutoTrain\n\n- Problem type: Binary Classification\n- Model ID: 955631800\n- CO2 Emissions (in grams): 0.08564281067919652",
"## Validation Metrics\n\n- Loss: 0.34108611941337585\n- Accuracy: 0.8671983356449375\n- Precision: 0.7883283877349159\n- Recall: 0.8250517598343685\n- AUC: 0.9236450... | [
"TAGS\n#transformers #pytorch #bert #text-classification #autotrain #en #dataset-BraveOni/autotrain-data-2ch-text-classification #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Binary Classification\n- Model ID: 955631800\n- CO2 Emi... |
token-classification | transformers |
# LayoutLM-v3 model fine-tuned on invoice dataset
This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the invoice dataset.
We use Microsoft’s LayoutLMv3 trained on Invoice Dataset to predict the Biller Name, Biller Address, Biller post_code, Due_date... | {"tags": ["generated_from_trainer"], "datasets": ["invoice"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "layoutlmv3-finetuned-invoice", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "Invoice", "type": "invoice", "args": "i... | Theivaprakasham/layoutlmv3-finetuned-invoice | null | [
"transformers",
"pytorch",
"tensorboard",
"layoutlmv3",
"token-classification",
"generated_from_trainer",
"dataset:invoice",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-06-07T03:11:33+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #layoutlmv3 #token-classification #generated_from_trainer #dataset-invoice #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
| LayoutLM-v3 model fine-tuned on invoice dataset
===============================================
This model is a fine-tuned version of microsoft/layoutlmv3-base on the invoice dataset.
We use Microsoft’s LayoutLMv3 trained on Invoice Dataset to predict the Biller Name, Biller Address, Biller post\_code, Due\_date, G... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-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* training\\_steps: 2000",
"### T... | [
"TAGS\n#transformers #pytorch #tensorboard #layoutlmv3 #token-classification #generated_from_trainer #dataset-invoice #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_ra... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-clean-semaphore-prediction-w0
This model is a fine-tuned version of [dccuchile/bert-base-spanish-wwm-cased](https://hugging... | {"tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "bert-clean-semaphore-prediction-w0", "results": []}]} | bondi/bert-clean-semaphore-prediction-w0 | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-07T03:46:28+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
|
# bert-clean-semaphore-prediction-w0
This model is a fine-tuned version of dccuchile/bert-base-spanish-wwm-cased on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0680
- Accuracy: 0.9693
- F1: 0.9694
## Model description
More information needed
## Intended uses & limitations... | [
"# bert-clean-semaphore-prediction-w0\n\nThis model is a fine-tuned version of dccuchile/bert-base-spanish-wwm-cased on the None dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.0680\n- Accuracy: 0.9693\n- F1: 0.9694",
"## Model description\n\nMore information needed",
"## Intended ... | [
"TAGS\n#transformers #pytorch #bert #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"# bert-clean-semaphore-prediction-w0\n\nThis model is a fine-tuned version of dccuchile/bert-base-spanish-wwm-cased on the None dataset.\nIt achieves the following results ... |
text-generation | transformers | # My Story model
{'top_p': 0.9, 'top_k': 50, 'temperature': 1, 'repetition_penalty': 1}
Arthur goes to the beach. Arthur is in love with his girlfriend. They go to the beach together. Arthur falls asleep on the beach. He is found by his girlfriend. Arthur is very sad he went to the beach.
Arthur goes to the beach. ... | {} | jppaolim/v58_Large_2E | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-07T04:02:26+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # My Story model
{'top_p': 0.9, 'top_k': 50, 'temperature': 1, 'repetition_penalty': 1}
Arthur goes to the beach. Arthur is in love with his girlfriend. They go to the beach together. Arthur falls asleep on the beach. He is found by his girlfriend. Arthur is very sad he went to the beach.
Arthur goes to the beach. ... | [
"# My Story model\n{'top_p': 0.9, 'top_k': 50, 'temperature': 1, 'repetition_penalty': 1} \nArthur goes to the beach. Arthur is in love with his girlfriend. They go to the beach together. Arthur falls asleep on the beach. He is found by his girlfriend. Arthur is very sad he went to the beach. \nArthur goes to the... | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# My Story model\n{'top_p': 0.9, 'top_k': 50, 'temperature': 1, 'repetition_penalty': 1} \nArthur goes to the beach. Arthur is in love with his girlfriend. They go to the b... |
reinforcement-learning | null |
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="SuperSecureHuman/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additi... | {"tags": ["FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-FrozenLake-v1-4x4-noSlippery", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "FrozenLake-v1-4x4-no_slippery", "type": ... | SuperSecureHuman/q-FrozenLake-v1-4x4-noSlippery | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-06-07T04:34:48+00:00 | [] | [] | TAGS
#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing FrozenLake-v1
This is a trained model of a Q-Learning agent playing FrozenLake-v1 .
## Usage
| [
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] | [
"TAGS\n#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] |
reinforcement-learning | null |
# **Q-Learning** Agent playing **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="SuperSecureHuman/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=Fals... | {"tags": ["Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-Taxi-v3", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Taxi-v3", "type": "Taxi-v3"}, "metrics": [{"type": "mean_reward", "value": "7.56 +/... | SuperSecureHuman/q-Taxi-v3 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-06-07T04:36:40+00:00 | [] | [] | TAGS
#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing Taxi-v3
This is a trained model of a Q-Learning agent playing Taxi-v3 .
## Usage
| [
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] | [
"TAGS\n#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] |
reinforcement-learning | null |
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="QuickSilver007/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need t... | {"tags": ["FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-FrozenLake-v1-4x4-noSlippery", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "FrozenLake-v1-4x4-no_slippery", "type": ... | QuickSilver007/q-FrozenLake-v1-4x4-noSlippery | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-06-07T04:44:44+00:00 | [] | [] | TAGS
#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing FrozenLake-v1
This is a trained model of a Q-Learning agent playing FrozenLake-v1 .
## Usage
| [
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] | [
"TAGS\n#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] |
reinforcement-learning | null |
# **Q-Learning** Agent playing **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="QuickSilver007/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_s... | {"tags": ["Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-Taxi-v3", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Taxi-v3", "type": "Taxi-v3"}, "metrics": [{"type": "mean_reward", "value": "7.50 +/... | QuickSilver007/q-Taxi-v3 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-06-07T04:50:07+00:00 | [] | [] | TAGS
#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing Taxi-v3
This is a trained model of a Q-Learning agent playing Taxi-v3 .
## Usage
| [
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] | [
"TAGS\n#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-clean-semaphore-prediction-w2
This model is a fine-tuned version of [dccuchile/bert-base-spanish-wwm-cased](https://hugging... | {"tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "bert-clean-semaphore-prediction-w2", "results": []}]} | bondi/bert-clean-semaphore-prediction-w2 | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-07T04:55:06+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
|
# bert-clean-semaphore-prediction-w2
This model is a fine-tuned version of dccuchile/bert-base-spanish-wwm-cased on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0685
- Accuracy: 0.9716
- F1: 0.9715
## Model description
More information needed
## Intended uses & limitations... | [
"# bert-clean-semaphore-prediction-w2\n\nThis model is a fine-tuned version of dccuchile/bert-base-spanish-wwm-cased on the None dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.0685\n- Accuracy: 0.9716\n- F1: 0.9715",
"## Model description\n\nMore information needed",
"## Intended ... | [
"TAGS\n#transformers #pytorch #bert #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"# bert-clean-semaphore-prediction-w2\n\nThis model is a fine-tuned version of dccuchile/bert-base-spanish-wwm-cased on the None dataset.\nIt achieves the following results ... |
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. -->
# mt5-base-finetuned-xsum-mlsum___topic_text_google_mt5_base
This model is a fine-tuned version of [google/mt5-base](https://huggi... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["mlsum"], "metrics": ["rouge"], "model-index": [{"name": "mt5-base-finetuned-xsum-mlsum___topic_text_google_mt5_base", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "... | nestoralvaro/mt5-base-finetuned-xsum-mlsum___topic_text_google_mt5_base | null | [
"transformers",
"pytorch",
"tensorboard",
"mt5",
"text2text-generation",
"generated_from_trainer",
"dataset:mlsum",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-07T04:56:41+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #mt5 #text2text-generation #generated_from_trainer #dataset-mlsum #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| mt5-base-finetuned-xsum-mlsum\_\_\_topic\_text\_google\_mt5\_base
=================================================================
This model is a fine-tuned version of google/mt5-base on the mlsum dataset.
It achieves the following results on the evaluation set:
* Loss: nan
* Rouge1: 0.1582
* Rouge2: 0.0133
* Rou... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1\n* mixed\\_precis... | [
"TAGS\n#transformers #pytorch #tensorboard #mt5 #text2text-generation #generated_from_trainer #dataset-mlsum #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during tra... |
null | null | # Load the checkpoint
```python
checkpoint = torch.load(SAVE_PATH)
```
# Reload the model
```python
model = BertForSequenceClassification.from_pretrained(
'bert-base-chinese',
num_labels=2,
problem_type='single_label_classification'
).to(device)
model.load_state_dict(checkpoint['model_state_dict'])
... | {} | dyyyyyyyy/LCQMC_BERT-base-Chinese | null | [
"region:us"
] | null | 2022-06-07T05:17:51+00:00 | [] | [] | TAGS
#region-us
| # Load the checkpoint
# Reload the model
# Reload the optimizer
# Other Info
| [
"# Load the checkpoint",
"# Reload the model",
"# Reload the optimizer",
"# Other Info"
] | [
"TAGS\n#region-us \n",
"# Load the checkpoint",
"# Reload the model",
"# Reload the optimizer",
"# Other Info"
] |
null | null | All models are moved / redirected to [KBLab](https://huggingface.co/KBLab) | {} | KB/ALL-MODELS-MOVED-TO-KBLAB | null | [
"region:us"
] | null | 2022-06-07T05:33:24+00:00 | [] | [] | TAGS
#region-us
| All models are moved / redirected to KBLab | [] | [
"TAGS\n#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. -->
# bert-finetuned-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["conll2003"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "bert-finetuned-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "conll2003", "type": "c... | suonbo/bert-finetuned-ner | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-07T05:43:31+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| bert-finetuned-ner
==================
This model is a fine-tuned version of bert-base-cased on the conll2003 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0637
* Precision: 0.9336
* Recall: 0.9488
* F1: 0.9412
* Accuracy: 0.9854
Model description
-----------------
More information ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-conll2003 #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 |
<!-- 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. -->
# depression_suggestion
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on an unknown datase... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "depression_suggestion", "results": []}]} | ziq/depression_suggestion | null | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-07T05:49:55+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
| depression\_suggestion
======================
This model is a fine-tuned version of distilgpt2 on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 3.3740
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
Mo... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 64\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 #tensorboard #gpt2 #text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-clean-semaphore-prediction-w4
This model is a fine-tuned version of [dccuchile/bert-base-spanish-wwm-cased](https://hugging... | {"tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "bert-clean-semaphore-prediction-w4", "results": []}]} | bondi/bert-clean-semaphore-prediction-w4 | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-07T05:55:22+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
|
# bert-clean-semaphore-prediction-w4
This model is a fine-tuned version of dccuchile/bert-base-spanish-wwm-cased on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0747
- Accuracy: 0.9652
- F1: 0.9651
## Model description
More information needed
## Intended uses & limitations... | [
"# bert-clean-semaphore-prediction-w4\n\nThis model is a fine-tuned version of dccuchile/bert-base-spanish-wwm-cased on the None dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.0747\n- Accuracy: 0.9652\n- F1: 0.9651",
"## Model description\n\nMore information needed",
"## Intended ... | [
"TAGS\n#transformers #pytorch #bert #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"# bert-clean-semaphore-prediction-w4\n\nThis model is a fine-tuned version of dccuchile/bert-base-spanish-wwm-cased on the None dataset.\nIt achieves the following results ... |
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. -->
# mbart-large-50-finetuned-en-to-te
This model is a fine-tuned version of [facebook/mbart-large-50](https://huggingface.co/faceboo... | {"tags": ["generated_from_trainer"], "datasets": ["kde4"], "metrics": ["bleu"], "model-index": [{"name": "mbart-large-50-finetuned-en-to-te", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "kde4", "type": "kde4", "args": "en-te"}, "metrics": ... | anjankumar/mbart-large-50-finetuned-en-to-te | null | [
"transformers",
"pytorch",
"tensorboard",
"mbart",
"text2text-generation",
"generated_from_trainer",
"dataset:kde4",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-07T06:02:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #mbart #text2text-generation #generated_from_trainer #dataset-kde4 #model-index #autotrain_compatible #endpoints_compatible #region-us
| mbart-large-50-finetuned-en-to-te
=================================
This model is a fine-tuned version of facebook/mbart-large-50 on the kde4 dataset.
It achieves the following results on the evaluation set:
* Loss: 13.8521
* Bleu: 0.7152
* Gen Len: 20.5
Model description
-----------------
More information need... | [
"### 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: 1\n* mixed\\_precis... | [
"TAGS\n#transformers #pytorch #tensorboard #mbart #text2text-generation #generated_from_trainer #dataset-kde4 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\... |
text2text-generation | transformers |
# GENRE
The GENRE (Generative ENtity REtrieval) system as presented in [Autoregressive Entity Retrieval](https://arxiv.org/abs/2010.00904) implemented in pytorch.
In a nutshell, GENRE uses a sequence-to-sequence approach to entity retrieval (e.g., linking), based on fine-tuned [BART](https://arxiv.org/abs/1910.134... | {"language": ["en"], "tags": ["retrieval", "entity-retrieval", "named-entity-disambiguation", "entity-disambiguation", "named-entity-linking", "entity-linking", "text2text-generation"]} | facebook/genre-kilt | null | [
"transformers",
"pytorch",
"tf",
"jax",
"bart",
"text2text-generation",
"retrieval",
"entity-retrieval",
"named-entity-disambiguation",
"entity-disambiguation",
"named-entity-linking",
"entity-linking",
"en",
"arxiv:2010.00904",
"arxiv:1910.13461",
"arxiv:2009.02252",
"autotrain_comp... | null | 2022-06-07T06:05:58+00:00 | [
"2010.00904",
"1910.13461",
"2009.02252"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #jax #bart #text2text-generation #retrieval #entity-retrieval #named-entity-disambiguation #entity-disambiguation #named-entity-linking #entity-linking #en #arxiv-2010.00904 #arxiv-1910.13461 #arxiv-2009.02252 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# GENRE
The GENRE (Generative ENtity REtrieval) system as presented in Autoregressive Entity Retrieval implemented in pytorch.
In a nutshell, GENRE uses a sequence-to-sequence approach to entity retrieval (e.g., linking), based on fine-tuned BART architecture. GENRE performs retrieval generating the unique entity ... | [
"# GENRE\n\n\nThe GENRE (Generative ENtity REtrieval) system as presented in Autoregressive Entity Retrieval implemented in pytorch.\n\nIn a nutshell, GENRE uses a sequence-to-sequence approach to entity retrieval (e.g., linking), based on fine-tuned BART architecture. GENRE performs retrieval generating the unique... | [
"TAGS\n#transformers #pytorch #tf #jax #bart #text2text-generation #retrieval #entity-retrieval #named-entity-disambiguation #entity-disambiguation #named-entity-linking #entity-linking #en #arxiv-2010.00904 #arxiv-1910.13461 #arxiv-2009.02252 #autotrain_compatible #endpoints_compatible #has_space #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. -->
# VN_ja-en_helsinki
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-ja-en](https://huggingface.co/Helsinki-NLP/opus-mt... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "VN_ja-en_helsinki", "results": []}]} | twieland/VN_ja-en_helsinki | null | [
"transformers",
"pytorch",
"marian",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-07T06:31:58+00:00 | [] | [] | TAGS
#transformers #pytorch #marian #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| VN\_ja-en\_helsinki
===================
This model is a fine-tuned version of Helsinki-NLP/opus-mt-ja-en on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 2.2409
* BLEU: 15.28
Model description
-----------------
More information needed
Intended uses & limitations
--------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\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: 3\n* mixed\\_pre... | [
"TAGS\n#transformers #pytorch #marian #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 64... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-finetuned-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["conll2003"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "bert-finetuned-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "conll2003", "type": "c... | tolgahanturker/bert-finetuned-ner | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-07T06:55:23+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| bert-finetuned-ner
==================
This model is a fine-tuned version of bert-base-cased on the conll2003 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0636
* Precision: 0.9316
* Recall: 0.9483
* F1: 0.9399
* Accuracy: 0.9860
Model description
-----------------
More information ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-conll2003 #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-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-clean-semaphore-prediction-w8
This model is a fine-tuned version of [dccuchile/bert-base-spanish-wwm-cased](https://hugging... | {"tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "bert-clean-semaphore-prediction-w8", "results": []}]} | bondi/bert-clean-semaphore-prediction-w8 | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-07T06:55:38+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
|
# bert-clean-semaphore-prediction-w8
This model is a fine-tuned version of dccuchile/bert-base-spanish-wwm-cased on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0669
- Accuracy: 0.9671
- F1: 0.9672
## Model description
More information needed
## Intended uses & limitations... | [
"# bert-clean-semaphore-prediction-w8\n\nThis model is a fine-tuned version of dccuchile/bert-base-spanish-wwm-cased on the None dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.0669\n- Accuracy: 0.9671\n- F1: 0.9672",
"## Model description\n\nMore information needed",
"## Intended ... | [
"TAGS\n#transformers #pytorch #bert #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"# bert-clean-semaphore-prediction-w8\n\nThis model is a fine-tuned version of dccuchile/bert-base-spanish-wwm-cased on the None dataset.\nIt achieves the following results ... |
text2text-generation | transformers |
# Model Trained Using AutoTrain
- Problem type: Summarization
- Model ID: 956131825
- CO2 Emissions (in grams): 0.647019768976749
## Validation Metrics
- Loss: 2.330639123916626
- Rouge1: 53.3589
- Rouge2: 40.4273
- RougeL: 48.4928
- RougeLsum: 49.4952
- Gen Len: 18.8741
## Usage
You can use cURL to access this m... | {"language": "unk", "tags": "autotrain", "datasets": ["spy24/autotrain-data-expand-parrot"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 0.647019768976749} | spy24/autotrain-expand-parrot-956131825 | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autotrain",
"unk",
"dataset:spy24/autotrain-data-expand-parrot",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-07T06:59:01+00:00 | [] | [
"unk"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #autotrain #unk #dataset-spy24/autotrain-data-expand-parrot #co2_eq_emissions #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Model Trained Using AutoTrain
- Problem type: Summarization
- Model ID: 956131825
- CO2 Emissions (in grams): 0.647019768976749
## Validation Metrics
- Loss: 2.330639123916626
- Rouge1: 53.3589
- Rouge2: 40.4273
- RougeL: 48.4928
- RougeLsum: 49.4952
- Gen Len: 18.8741
## Usage
You can use cURL to access this m... | [
"# Model Trained Using AutoTrain\n\n- Problem type: Summarization\n- Model ID: 956131825\n- CO2 Emissions (in grams): 0.647019768976749",
"## Validation Metrics\n\n- Loss: 2.330639123916626\n- Rouge1: 53.3589\n- Rouge2: 40.4273\n- RougeL: 48.4928\n- RougeLsum: 49.4952\n- Gen Len: 18.8741",
"## Usage\n\nYou can ... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #autotrain #unk #dataset-spy24/autotrain-data-expand-parrot #co2_eq_emissions #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Summarization\n- Model ID: 956131825\n- C... |
automatic-speech-recognition | transformers |
# Thai Wav2Vec2 with CommonVoice V8 (deepcut tokenizer) + language model
This model trained with CommonVoice V8 dataset by increase data from CommonVoice V7 dataset that It was use in [airesearch/wav2vec2-large-xlsr-53-th](https://huggingface.co/airesearch/wav2vec2-large-xlsr-53-th). It was finetune [wav2vec2-large-x... | {"language": ["th"], "license": "apache-2.0", "tags": ["automatic-speech-recognition"], "datasets": ["common_voice"], "metrics": ["wer", "cer"]} | wannaphong/wav2vec2-large-xlsr-53-th-cv8-deepcut | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"th",
"dataset:common_voice",
"arxiv:2208.04799",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-06-07T07:11:41+00:00 | [
"2208.04799"
] | [
"th"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #th #dataset-common_voice #arxiv-2208.04799 #license-apache-2.0 #endpoints_compatible #has_space #region-us
| Thai Wav2Vec2 with CommonVoice V8 (deepcut tokenizer) + language model
======================================================================
This model trained with CommonVoice V8 dataset by increase data from CommonVoice V7 dataset that It was use in airesearch/wav2vec2-large-xlsr-53-th. It was finetune wav2vec2-la... | [] | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #th #dataset-common_voice #arxiv-2208.04799 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n"
] |
text-classification | transformers | # Traditional Chinese news classification
繁體中文新聞分類任務，使用ckiplab/albert-base-chinese預訓練模型，資料集只有2.6萬筆，做為課程的範例模型。
from transformers import BertTokenizer, AlbertForSequenceClassification
model_path = "clhuang/albert-news-classification"
model = AlbertForSequenceClassification.from_pretrained(model_path)
to... | {"language": ["tw"], "license": "afl-3.0", "tags": ["albert", "classification"], "metrics": ["Accuracy"]} | clhuang/albert-news-classification | null | [
"transformers",
"pytorch",
"albert",
"text-classification",
"classification",
"tw",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-07T07:13:42+00:00 | [] | [
"tw"
] | TAGS
#transformers #pytorch #albert #text-classification #classification #tw #license-afl-3.0 #autotrain_compatible #endpoints_compatible #region-us
| # Traditional Chinese news classification
繁體中文新聞分類任務，使用ckiplab/albert-base-chinese預訓練模型，資料集只有2.6萬筆，做為課程的範例模型。
from transformers import BertTokenizer, AlbertForSequenceClassification
model_path = "clhuang/albert-news-classification"
model = AlbertForSequenceClassification.from_pretrained(model_path)
to... | [
"# Traditional Chinese news classification\n\n繁體中文新聞分類任務，使用ckiplab/albert-base-chinese預訓練模型，資料集只有2.6萬筆，做為課程的範例模型。\n\n from transformers import BertTokenizer, AlbertForSequenceClassification\n model_path = \"clhuang/albert-news-classification\"\n model = AlbertForSequenceClassification.from_pretrained(model... | [
"TAGS\n#transformers #pytorch #albert #text-classification #classification #tw #license-afl-3.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Traditional Chinese news classification\n\n繁體中文新聞分類任務，使用ckiplab/albert-base-chinese預訓練模型，資料集只有2.6萬筆，做為課程的範例模型。\n\n from transformers import BertTokenize... |
null | null | A model trained to classify the material of European plates, as found in the British Museum collection.
Initial model was trained using basic fastai workflow with timm integration.
Architecture: "vit_base_patch16_224_in21k"
Should be able to predict the Material used (as defined by the British Museum) if that materia... | {} | Kieranm/brtisih_must_plates_old | null | [
"region:us"
] | null | 2022-06-07T07:32:30+00:00 | [] | [] | TAGS
#region-us
| A model trained to classify the material of European plates, as found in the British Museum collection.
Initial model was trained using basic fastai workflow with timm integration.
Architecture: "vit_base_patch16_224_in21k"
Should be able to predict the Material used (as defined by the British Museum) if that materia... | [] | [
"TAGS\n#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. -->
# LN_ja-en_helsinki
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-ja-en](https://huggingface.co/Helsinki-NLP/opus-mt... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "LN_ja-en_helsinki", "results": []}]} | twieland/LN_ja-en_helsinki | null | [
"transformers",
"pytorch",
"marian",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-07T08:12:27+00:00 | [] | [] | TAGS
#transformers #pytorch #marian #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| LN\_ja-en\_helsinki
===================
This model is a fine-tuned version of Helsinki-NLP/opus-mt-ja-en on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 2.5382
Model description
-----------------
More information needed
Intended uses & limitations
----------------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\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: 3\n* mixed\\_pre... | [
"TAGS\n#transformers #pytorch #marian #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 64... |
text2text-generation | transformers |
# GENRE
The GENRE (Generative ENtity REtrieval) system as presented in [Autoregressive Entity Retrieval](https://arxiv.org/abs/2010.00904) implemented in pytorch.
In a nutshell, GENRE uses a sequence-to-sequence approach to entity retrieval (e.g., linking), based on fine-tuned [BART](https://arxiv.org/abs/1910.134... | {"language": ["en"], "tags": ["retrieval", "entity-retrieval", "named-entity-disambiguation", "entity-disambiguation", "named-entity-linking", "entity-linking", "text2text-generation"]} | facebook/genre-linking-blink | null | [
"transformers",
"pytorch",
"tf",
"jax",
"bart",
"text2text-generation",
"retrieval",
"entity-retrieval",
"named-entity-disambiguation",
"entity-disambiguation",
"named-entity-linking",
"entity-linking",
"en",
"arxiv:2010.00904",
"arxiv:1910.13461",
"arxiv:1911.03814",
"autotrain_comp... | null | 2022-06-07T08:15:29+00:00 | [
"2010.00904",
"1910.13461",
"1911.03814"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #jax #bart #text2text-generation #retrieval #entity-retrieval #named-entity-disambiguation #entity-disambiguation #named-entity-linking #entity-linking #en #arxiv-2010.00904 #arxiv-1910.13461 #arxiv-1911.03814 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# GENRE
The GENRE (Generative ENtity REtrieval) system as presented in Autoregressive Entity Retrieval implemented in pytorch.
In a nutshell, GENRE uses a sequence-to-sequence approach to entity retrieval (e.g., linking), based on fine-tuned BART architecture. GENRE performs retrieval generating the unique entity ... | [
"# GENRE\n\n\nThe GENRE (Generative ENtity REtrieval) system as presented in Autoregressive Entity Retrieval implemented in pytorch.\n\nIn a nutshell, GENRE uses a sequence-to-sequence approach to entity retrieval (e.g., linking), based on fine-tuned BART architecture. GENRE performs retrieval generating the unique... | [
"TAGS\n#transformers #pytorch #tf #jax #bart #text2text-generation #retrieval #entity-retrieval #named-entity-disambiguation #entity-disambiguation #named-entity-linking #entity-linking #en #arxiv-2010.00904 #arxiv-1910.13461 #arxiv-1911.03814 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
... |
null | null |
# Configuration
`title`: _string_
Display title for the Space
`emoji`: _string_
Space emoji (emoji-only character allowed)
`colorFrom`: _string_
Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
`colorTo`: _string_
Color for Thumbnail gradient (red, yellow, green, blue, in... | {"title": "Wikipedia Assistant", "emoji": "\ud83c\udf16", "colorFrom": "green", "colorTo": "yellow", "sdk": "streamlit", "app_file": "app.py", "pinned": false} | theachyuttiwari/lfqa | null | [
"region:us"
] | null | 2022-06-07T08:26:20+00:00 | [] | [] | TAGS
#region-us
|
# Configuration
'title': _string_
Display title for the Space
'emoji': _string_
Space emoji (emoji-only character allowed)
'colorFrom': _string_
Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
'colorTo': _string_
Color for Thumbnail gradient (red, yellow, green, blue, in... | [
"# Configuration\n\n'title': _string_ \nDisplay title for the Space\n\n'emoji': _string_ \nSpace emoji (emoji-only character allowed)\n\n'colorFrom': _string_ \nColor for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)\n\n'colorTo': _string_ \nColor for Thumbnail gradient (red, yellow,... | [
"TAGS\n#region-us \n",
"# Configuration\n\n'title': _string_ \nDisplay title for the Space\n\n'emoji': _string_ \nSpace emoji (emoji-only character allowed)\n\n'colorFrom': _string_ \nColor for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)\n\n'colorTo': _string_ \nColor for Thumbna... |
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. -->
# IndicBART-ibart-hi-to-en
This model is a fine-tuned version of [ai4bharat/IndicBART](https://huggingface.co/ai4bharat/IndicBART)... | {"tags": ["generated_from_trainer"], "datasets": ["hindi_english_machine_translation"], "model-index": [{"name": "IndicBART-ibart-hi-to-en", "results": []}]} | prashanth/IndicBART-ibart-hi-to-en | null | [
"transformers",
"pytorch",
"tensorboard",
"mbart",
"text2text-generation",
"generated_from_trainer",
"dataset:hindi_english_machine_translation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-07T08:30:43+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #mbart #text2text-generation #generated_from_trainer #dataset-hindi_english_machine_translation #autotrain_compatible #endpoints_compatible #region-us
| IndicBART-ibart-hi-to-en
========================
This model is a fine-tuned version of ai4bharat/IndicBART on the hindi\_english\_machine\_translation dataset.
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Trainin... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\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 #mbart #text2text-generation #generated_from_trainer #dataset-hindi_english_machine_translation #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate:... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-finetuned-hindi-common-voice-9-0
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-large-xls-r-300m-finetuned-hindi-common-voice-9-0", "results": []}]} | ishansharma1320/wav2vec2-large-xls-r-300m-finetuned-hindi-common-voice-9-0 | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-06-07T08:32:27+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-large-xls-r-300m-finetuned-hindi-common-voice-9-0
==========================================================
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: 0.7392
* Wer: 1.0141
Model descrip... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 4.42184e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and ep... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 4.42184e-05... |
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. -->
# IndicBART-ibart-en-to-hi
This model is a fine-tuned version of [ai4bharat/IndicBART](https://huggingface.co/ai4bharat/IndicBART)... | {"tags": ["generated_from_trainer"], "datasets": ["hindi_english_machine_translation"], "model-index": [{"name": "IndicBART-ibart-en-to-hi", "results": []}]} | prashanth/IndicBART-ibart-en-to-hi | null | [
"transformers",
"pytorch",
"tensorboard",
"mbart",
"text2text-generation",
"generated_from_trainer",
"dataset:hindi_english_machine_translation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-07T08:41:24+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #mbart #text2text-generation #generated_from_trainer #dataset-hindi_english_machine_translation #autotrain_compatible #endpoints_compatible #region-us
| IndicBART-ibart-en-to-hi
========================
This model is a fine-tuned version of ai4bharat/IndicBART on the hindi\_english\_machine\_translation dataset.
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Trainin... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\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 #mbart #text2text-generation #generated_from_trainer #dataset-hindi_english_machine_translation #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate:... |
text2text-generation | transformers |
# GENRE
The GENRE (Generative ENtity REtrieval) system as presented in [Autoregressive Entity Retrieval](https://arxiv.org/abs/2010.00904) implemented in pytorch.
In a nutshell, GENRE uses a sequence-to-sequence approach to entity retrieval (e.g., linking), based on fine-tuned [BART](https://arxiv.org/abs/1910.134... | {"language": ["en"], "tags": ["retrieval", "entity-retrieval", "named-entity-disambiguation", "entity-disambiguation", "named-entity-linking", "entity-linking", "text2text-generation"]} | facebook/genre-linking-aidayago2 | null | [
"transformers",
"pytorch",
"tf",
"jax",
"bart",
"text2text-generation",
"retrieval",
"entity-retrieval",
"named-entity-disambiguation",
"entity-disambiguation",
"named-entity-linking",
"entity-linking",
"en",
"arxiv:2010.00904",
"arxiv:1910.13461",
"arxiv:1911.03814",
"autotrain_comp... | null | 2022-06-07T09:03:35+00:00 | [
"2010.00904",
"1910.13461",
"1911.03814"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #jax #bart #text2text-generation #retrieval #entity-retrieval #named-entity-disambiguation #entity-disambiguation #named-entity-linking #entity-linking #en #arxiv-2010.00904 #arxiv-1910.13461 #arxiv-1911.03814 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# GENRE
The GENRE (Generative ENtity REtrieval) system as presented in Autoregressive Entity Retrieval implemented in pytorch.
In a nutshell, GENRE uses a sequence-to-sequence approach to entity retrieval (e.g., linking), based on fine-tuned BART architecture. GENRE performs retrieval generating the unique entity ... | [
"# GENRE\n\n\nThe GENRE (Generative ENtity REtrieval) system as presented in Autoregressive Entity Retrieval implemented in pytorch.\n\nIn a nutshell, GENRE uses a sequence-to-sequence approach to entity retrieval (e.g., linking), based on fine-tuned BART architecture. GENRE performs retrieval generating the unique... | [
"TAGS\n#transformers #pytorch #tf #jax #bart #text2text-generation #retrieval #entity-retrieval #named-entity-disambiguation #entity-disambiguation #named-entity-linking #entity-linking #en #arxiv-2010.00904 #arxiv-1910.13461 #arxiv-1911.03814 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
... |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-fira
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/di... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "distilbert-base-uncased-finetuned-fira", "results": []}]} | ThaisBeham/distilbert-base-uncased-finetuned-fira | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-06-07T09:04:46+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-fira
======================================
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: 2.7687
Model description
-----------------
More information needed
Intended uses & l... | [
"### 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: 3",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #question-answering #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: 2e-05\n* train\\_batch\\_size: 2\n* eval\... |
null | null |
## Stable Diffusion | {"extra_gated_prompt": "One more step before getting this model\nThis model is open access and available to all, but it has the CreativeML OpenRAIL-M license you have to be aware of before using it - don't worry you are just one click away! \nBy clicking on \"Access repository\" below, you accept that your *contact inf... | patrickvonplaten/diffusion_model_very_cool | null | [
"region:us"
] | null | 2022-06-07T09:25:21+00:00 | [] | [] | TAGS
#region-us
|
## Stable Diffusion | [
"## Stable Diffusion"
] | [
"TAGS\n#region-us \n",
"## Stable Diffusion"
] |
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. -->
# layoutlmv3-finetuned-sroie
This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/la... | {"tags": ["generated_from_trainer"], "datasets": ["sroie"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "layoutlmv3-finetuned-sroie", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "sroie", "type": "sroie", "args": "sroie"}, ... | Theivaprakasham/layoutlmv3-finetuned-sroie | null | [
"transformers",
"pytorch",
"tensorboard",
"layoutlmv3",
"token-classification",
"generated_from_trainer",
"dataset:sroie",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-06-07T09:26:57+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #layoutlmv3 #token-classification #generated_from_trainer #dataset-sroie #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
| layoutlmv3-finetuned-sroie
==========================
This model is a fine-tuned version of microsoft/layoutlmv3-base on the sroie dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0426
* Precision: 0.9371
* Recall: 0.9438
* F1: 0.9404
* Accuracy: 0.9945
Model description
---------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-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* training\\_steps: 5000",
"### T... | [
"TAGS\n#transformers #pytorch #tensorboard #layoutlmv3 #token-classification #generated_from_trainer #dataset-sroie #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mt5-base-finetuned-xsum-data_prep_2021_12_26___t55_403.csv___topic_text_google_mt5_base
This model is a fine-tuned version of [g... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "mt5-base-finetuned-xsum-data_prep_2021_12_26___t55_403.csv___topic_text_google_mt5_base", "results": []}]} | nestoralvaro/mt5-base-finetuned-xsum-data_prep_2021_12_26___t55_403.csv___topic_text_google_mt5_base | null | [
"transformers",
"pytorch",
"tensorboard",
"mt5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-07T09:31:03+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #mt5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| mt5-base-finetuned-xsum-data\_prep\_2021\_12\_26\_\_\_t55\_403.csv\_\_\_topic\_text\_google\_mt5\_base
======================================================================================================
This model is a fine-tuned version of google/mt5-base on an unknown dataset.
It achieves the following results o... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1\n* mixed\\_precis... | [
"TAGS\n#transformers #pytorch #tensorboard #mt5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rat... |
reinforcement-learning | null |
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="DenisKochetov/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additiona... | {"tags": ["FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-FrozenLake-v1-4x4-noSlippery", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "FrozenLake-v1-4x4-no_slippery", "type": ... | DenisKochetov/q-FrozenLake-v1-4x4-noSlippery | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-06-07T09:37:56+00:00 | [] | [] | TAGS
#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing FrozenLake-v1
This is a trained model of a Q-Learning agent playing FrozenLake-v1 .
## Usage
| [
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] | [
"TAGS\n#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] |
null | transformers |
# Denoising Diffusion Probabilistic Models (DDPM)
**Paper**: [Denoising Diffusion Probabilistic Models](https://arxiv.org/abs/2006.11239)
**Abstract**:
*We present high quality image synthesis results using diffusion probabilistic models, a class of latent variable models inspired by considerations from nonequilibr... | {"tags": ["ddpm_diffusion"]} | fusing/ddpm-lsun-bedroom-ema | null | [
"transformers",
"ddpm_diffusion",
"arxiv:2006.11239",
"endpoints_compatible",
"region:us"
] | null | 2022-06-07T09:37:58+00:00 | [
"2006.11239"
] | [] | TAGS
#transformers #ddpm_diffusion #arxiv-2006.11239 #endpoints_compatible #region-us
|
# Denoising Diffusion Probabilistic Models (DDPM)
Paper: Denoising Diffusion Probabilistic Models
Abstract:
*We present high quality image synthesis results using diffusion probabilistic models, a class of latent variable models inspired by considerations from nonequilibrium thermodynamics. Our best results are obt... | [
"# Denoising Diffusion Probabilistic Models (DDPM)\n\nPaper: Denoising Diffusion Probabilistic Models\n\nAbstract:\n\n*We present high quality image synthesis results using diffusion probabilistic models, a class of latent variable models inspired by considerations from nonequilibrium thermodynamics. Our best resul... | [
"TAGS\n#transformers #ddpm_diffusion #arxiv-2006.11239 #endpoints_compatible #region-us \n",
"# Denoising Diffusion Probabilistic Models (DDPM)\n\nPaper: Denoising Diffusion Probabilistic Models\n\nAbstract:\n\n*We present high quality image synthesis results using diffusion probabilistic models, a class of laten... |
null | transformers |
# Denoising Diffusion Probabilistic Models (DDPM)
**Paper**: [Denoising Diffusion Probabilistic Models](https://arxiv.org/abs/2006.11239)
**Abstract**:
*We present high quality image synthesis results using diffusion probabilistic models, a class of latent variable models inspired by considerations from nonequilibr... | {"tags": ["ddpm_diffusion"]} | fusing/ddpm-lsun-cat-ema | null | [
"transformers",
"ddpm_diffusion",
"arxiv:2006.11239",
"endpoints_compatible",
"region:us"
] | null | 2022-06-07T09:38:07+00:00 | [
"2006.11239"
] | [] | TAGS
#transformers #ddpm_diffusion #arxiv-2006.11239 #endpoints_compatible #region-us
|
# Denoising Diffusion Probabilistic Models (DDPM)
Paper: Denoising Diffusion Probabilistic Models
Abstract:
*We present high quality image synthesis results using diffusion probabilistic models, a class of latent variable models inspired by considerations from nonequilibrium thermodynamics. Our best results are obt... | [
"# Denoising Diffusion Probabilistic Models (DDPM)\n\nPaper: Denoising Diffusion Probabilistic Models\n\nAbstract:\n\n*We present high quality image synthesis results using diffusion probabilistic models, a class of latent variable models inspired by considerations from nonequilibrium thermodynamics. Our best resul... | [
"TAGS\n#transformers #ddpm_diffusion #arxiv-2006.11239 #endpoints_compatible #region-us \n",
"# Denoising Diffusion Probabilistic Models (DDPM)\n\nPaper: Denoising Diffusion Probabilistic Models\n\nAbstract:\n\n*We present high quality image synthesis results using diffusion probabilistic models, a class of laten... |
null | transformers |
# Denoising Diffusion Probabilistic Models (DDPM)
**Paper**: [Denoising Diffusion Probabilistic Models](https://arxiv.org/abs/2006.11239)
**Abstract**:
*We present high quality image synthesis results using diffusion probabilistic models, a class of latent variable models inspired by considerations from nonequilibr... | {"tags": ["ddpm_diffusion"]} | fusing/ddpm-lsun-church-ema | null | [
"transformers",
"ddpm_diffusion",
"arxiv:2006.11239",
"endpoints_compatible",
"region:us"
] | null | 2022-06-07T09:38:18+00:00 | [
"2006.11239"
] | [] | TAGS
#transformers #ddpm_diffusion #arxiv-2006.11239 #endpoints_compatible #region-us
|
# Denoising Diffusion Probabilistic Models (DDPM)
Paper: Denoising Diffusion Probabilistic Models
Abstract:
*We present high quality image synthesis results using diffusion probabilistic models, a class of latent variable models inspired by considerations from nonequilibrium thermodynamics. Our best results are obt... | [
"# Denoising Diffusion Probabilistic Models (DDPM)\n\nPaper: Denoising Diffusion Probabilistic Models\n\nAbstract:\n\n*We present high quality image synthesis results using diffusion probabilistic models, a class of latent variable models inspired by considerations from nonequilibrium thermodynamics. Our best resul... | [
"TAGS\n#transformers #ddpm_diffusion #arxiv-2006.11239 #endpoints_compatible #region-us \n",
"# Denoising Diffusion Probabilistic Models (DDPM)\n\nPaper: Denoising Diffusion Probabilistic Models\n\nAbstract:\n\n*We present high quality image synthesis results using diffusion probabilistic models, a class of laten... |
null | transformers |
# Denoising Diffusion Probabilistic Models (DDPM)
**Paper**: [Denoising Diffusion Probabilistic Models](https://arxiv.org/abs/2006.11239)
**Abstract**:
*We present high quality image synthesis results using diffusion probabilistic models, a class of latent variable models inspired by considerations from nonequilibr... | {"tags": ["ddpm_diffusion"]} | fusing/ddpm-cifar10-ema | null | [
"transformers",
"ddpm_diffusion",
"arxiv:2006.11239",
"endpoints_compatible",
"region:us"
] | null | 2022-06-07T09:38:31+00:00 | [
"2006.11239"
] | [] | TAGS
#transformers #ddpm_diffusion #arxiv-2006.11239 #endpoints_compatible #region-us
|
# Denoising Diffusion Probabilistic Models (DDPM)
Paper: Denoising Diffusion Probabilistic Models
Abstract:
*We present high quality image synthesis results using diffusion probabilistic models, a class of latent variable models inspired by considerations from nonequilibrium thermodynamics. Our best results are obt... | [
"# Denoising Diffusion Probabilistic Models (DDPM)\n\nPaper: Denoising Diffusion Probabilistic Models\n\nAbstract:\n\n*We present high quality image synthesis results using diffusion probabilistic models, a class of latent variable models inspired by considerations from nonequilibrium thermodynamics. Our best resul... | [
"TAGS\n#transformers #ddpm_diffusion #arxiv-2006.11239 #endpoints_compatible #region-us \n",
"# Denoising Diffusion Probabilistic Models (DDPM)\n\nPaper: Denoising Diffusion Probabilistic Models\n\nAbstract:\n\n*We present high quality image synthesis results using diffusion probabilistic models, a class of laten... |
null | transformers |
# Denoising Diffusion Probabilistic Models (DDPM)
**Paper**: [Denoising Diffusion Probabilistic Models](https://arxiv.org/abs/2006.11239)
**Abstract**:
*We present high quality image synthesis results using diffusion probabilistic models, a class of latent variable models inspired by considerations from nonequilibr... | {"tags": ["ddpm_diffusion"]} | fusing/ddpm-celeba-hq-ema | null | [
"transformers",
"ddpm_diffusion",
"arxiv:2006.11239",
"endpoints_compatible",
"region:us"
] | null | 2022-06-07T09:39:30+00:00 | [
"2006.11239"
] | [] | TAGS
#transformers #ddpm_diffusion #arxiv-2006.11239 #endpoints_compatible #region-us
|
# Denoising Diffusion Probabilistic Models (DDPM)
Paper: Denoising Diffusion Probabilistic Models
Abstract:
*We present high quality image synthesis results using diffusion probabilistic models, a class of latent variable models inspired by considerations from nonequilibrium thermodynamics. Our best results are obt... | [
"# Denoising Diffusion Probabilistic Models (DDPM)\n\nPaper: Denoising Diffusion Probabilistic Models\n\nAbstract:\n\n*We present high quality image synthesis results using diffusion probabilistic models, a class of latent variable models inspired by considerations from nonequilibrium thermodynamics. Our best resul... | [
"TAGS\n#transformers #ddpm_diffusion #arxiv-2006.11239 #endpoints_compatible #region-us \n",
"# Denoising Diffusion Probabilistic Models (DDPM)\n\nPaper: Denoising Diffusion Probabilistic Models\n\nAbstract:\n\n*We present high quality image synthesis results using diffusion probabilistic models, a class of laten... |
null | transformers |
# Denoising Diffusion Probabilistic Models (DDPM)
**Paper**: [Denoising Diffusion Probabilistic Models](https://arxiv.org/abs/2006.11239)
**Abstract**:
*We present high quality image synthesis results using diffusion probabilistic models, a class of latent variable models inspired by considerations from nonequilibr... | {"tags": ["ddpm_diffusion"]} | fusing/ddpm-celeba-hq | null | [
"transformers",
"ddpm_diffusion",
"arxiv:2006.11239",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-06-07T09:39:38+00:00 | [
"2006.11239"
] | [] | TAGS
#transformers #ddpm_diffusion #arxiv-2006.11239 #endpoints_compatible #has_space #region-us
|
# Denoising Diffusion Probabilistic Models (DDPM)
Paper: Denoising Diffusion Probabilistic Models
Abstract:
*We present high quality image synthesis results using diffusion probabilistic models, a class of latent variable models inspired by considerations from nonequilibrium thermodynamics. Our best results are obt... | [
"# Denoising Diffusion Probabilistic Models (DDPM)\n\nPaper: Denoising Diffusion Probabilistic Models\n\nAbstract:\n\n*We present high quality image synthesis results using diffusion probabilistic models, a class of latent variable models inspired by considerations from nonequilibrium thermodynamics. Our best resul... | [
"TAGS\n#transformers #ddpm_diffusion #arxiv-2006.11239 #endpoints_compatible #has_space #region-us \n",
"# Denoising Diffusion Probabilistic Models (DDPM)\n\nPaper: Denoising Diffusion Probabilistic Models\n\nAbstract:\n\n*We present high quality image synthesis results using diffusion probabilistic models, a cla... |
reinforcement-learning | null |
# **Q-Learning** Agent playing **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="DenisKochetov/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False e... | {"tags": ["Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-Taxi-v3", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Taxi-v3", "type": "Taxi-v3"}, "metrics": [{"type": "mean_reward", "value": "7.56 +/... | DenisKochetov/q-Taxi-v3 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-06-07T09:40:09+00:00 | [] | [] | TAGS
#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing Taxi-v3
This is a trained model of a Q-Learning agent playing Taxi-v3 .
## Usage
| [
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] | [
"TAGS\n#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] |
reinforcement-learning | null |
# **Q-Learning** Agent playing **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="DenisKochetov/q-Taxi-v3_2", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False... | {"tags": ["Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-Taxi-v3_2", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Taxi-v3", "type": "Taxi-v3"}, "metrics": [{"type": "mean_reward", "value": "7.56 ... | DenisKochetov/q-Taxi-v3_2 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-06-07T09:45:23+00:00 | [] | [] | TAGS
#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing Taxi-v3
This is a trained model of a Q-Learning agent playing Taxi-v3 .
## Usage
| [
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] | [
"TAGS\n#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] |
reinforcement-learning | null |
# **Q-Learning** Agent playing **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="DenisKochetov/q-Taxi-v3_3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False... | {"tags": ["Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-Taxi-v3_3", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Taxi-v3", "type": "Taxi-v3"}, "metrics": [{"type": "mean_reward", "value": "-2.00... | DenisKochetov/q-Taxi-v3_3 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-06-07T09:49:20+00:00 | [] | [] | TAGS
#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing Taxi-v3
This is a trained model of a Q-Learning agent playing Taxi-v3 .
## Usage
| [
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] | [
"TAGS\n#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] |
null | null |
# DistilGPT2
DistilGPT2 (short for Distilled-GPT2) is an English-language model pre-trained with the supervision of the smallest version of Generative Pre-trained Transformer 2 (GPT-2). Like GPT-2, DistilGPT2 can be used to generate text. Users of this model card should also consider information about the design, tra... | {"language": "en", "license": "apache-2.0", "tags": ["exbert"], "datasets": ["openwebtext"], "co2_eq_emissions": "149200 g", "model-index": [{"name": "distilgpt2", "results": [{"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "WikiText-103", "type": "wikitext"}, "metrics": [{"type": "... | Sussybaka/gpt2wilkinscoffee | null | [
"exbert",
"en",
"dataset:openwebtext",
"arxiv:1910.01108",
"arxiv:2201.08542",
"arxiv:2203.12574",
"arxiv:1910.09700",
"arxiv:1503.02531",
"license:apache-2.0",
"model-index",
"region:us"
] | null | 2022-06-07T09:58:10+00:00 | [
"1910.01108",
"2201.08542",
"2203.12574",
"1910.09700",
"1503.02531"
] | [
"en"
] | TAGS
#exbert #en #dataset-openwebtext #arxiv-1910.01108 #arxiv-2201.08542 #arxiv-2203.12574 #arxiv-1910.09700 #arxiv-1503.02531 #license-apache-2.0 #model-index #region-us
|
# DistilGPT2
DistilGPT2 (short for Distilled-GPT2) is an English-language model pre-trained with the supervision of the smallest version of Generative Pre-trained Transformer 2 (GPT-2). Like GPT-2, DistilGPT2 can be used to generate text. Users of this model card should also consider information about the design, tra... | [
"# DistilGPT2\n\nDistilGPT2 (short for Distilled-GPT2) is an English-language model pre-trained with the supervision of the smallest version of Generative Pre-trained Transformer 2 (GPT-2). Like GPT-2, DistilGPT2 can be used to generate text. Users of this model card should also consider information about the desig... | [
"TAGS\n#exbert #en #dataset-openwebtext #arxiv-1910.01108 #arxiv-2201.08542 #arxiv-2203.12574 #arxiv-1910.09700 #arxiv-1503.02531 #license-apache-2.0 #model-index #region-us \n",
"# DistilGPT2\n\nDistilGPT2 (short for Distilled-GPT2) is an English-language model pre-trained with the supervision of the smallest ve... |
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. -->
# roberta_fine_tuned_sentiment_financial_news
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "roberta_fine_tuned_sentiment_financial_news", "results": []}]} | RogerKam/roberta_fine_tuned_sentiment_financial_news | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-07T10:08:02+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# roberta_fine_tuned_sentiment_financial_news
This model is a fine-tuned version of roberta-base on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6362
- Accuracy: 0.8826
- F1 Score: 0.8865
## Model description
More information needed
## Intended uses & limitations
More inf... | [
"# roberta_fine_tuned_sentiment_financial_news\n\nThis model is a fine-tuned version of roberta-base on the None dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.6362\n- Accuracy: 0.8826\n- F1 Score: 0.8865",
"## Model description\n\nMore information needed",
"## Intended uses & lim... | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# roberta_fine_tuned_sentiment_financial_news\n\nThis model is a fine-tuned version of roberta-base on the None dataset.\nIt achieves the follo... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-finetuned-en-to-ro
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the wmt16 datas... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["wmt16"], "model-index": [{"name": "t5-small-finetuned-en-to-ro", "results": []}]} | giolisandro/t5-small-finetuned-en-to-ro | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:wmt16",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-07T10:19:25+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-wmt16 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| t5-small-finetuned-en-to-ro
===========================
This model is a fine-tuned version of t5-small on the wmt16 dataset.
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
--------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1\n* mixed\\_prec... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-wmt16 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* ... |
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/1507627313604743171/T8ks... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/aoc-itsjefftiedrich-shaun_vids/1654603284413/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/aoc-itsjefftiedrich-shaun_vids | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-07T10:43:07+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI CYBORG
Shaun & Jeff Tiedrich & Alexandria Ocasio-Cortez
@aoc-itsjefftiedrich-shaun\_vids
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 d... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
fill-mask | 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. -->
# forcorpus/bert-finetuned-imdb
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-u... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "forcorpus/bert-finetuned-imdb", "results": []}]} | forcorpus/bert-finetuned-imdb | null | [
"transformers",
"tf",
"distilbert",
"fill-mask",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-07T11:01:35+00:00 | [] | [] | TAGS
#transformers #tf #distilbert #fill-mask #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| forcorpus/bert-finetuned-imdb
=============================
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 2.8451
* Validation Loss: 2.6283
* Epoch: 0
Model description
-----------------
More information... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'WarmUp', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_schedule\\_fn': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_le... | [
"TAGS\n#transformers #tf #distilbert #fill-mask #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': 'AdamWeightDecay', 'learning\\_rate'... |
text-generation | transformers | # My Story model
{'top_p': 0.9, 'top_k': 50, 'temperature': 1, 'repetition_penalty': 1}
Arthur goes to the beach. Arthur is in love with his girlfriend. They go to the beach together. Arthur falls off the beach. Arthur needs medical attention. Arthur gets a broken leg from the fall.
Arthur goes to the beach. Arthur... | {} | jppaolim/v59_Large_2E | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-07T11:11:41+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # My Story model
{'top_p': 0.9, 'top_k': 50, 'temperature': 1, 'repetition_penalty': 1}
Arthur goes to the beach. Arthur is in love with his girlfriend. They go to the beach together. Arthur falls off the beach. Arthur needs medical attention. Arthur gets a broken leg from the fall.
Arthur goes to the beach. Arthur... | [
"# My Story model\n{'top_p': 0.9, 'top_k': 50, 'temperature': 1, 'repetition_penalty': 1} \nArthur goes to the beach. Arthur is in love with his girlfriend. They go to the beach together. Arthur falls off the beach. Arthur needs medical attention. Arthur gets a broken leg from the fall. \nArthur goes to the beach... | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# My Story model\n{'top_p': 0.9, 'top_k': 50, 'temperature': 1, 'repetition_penalty': 1} \nArthur goes to the beach. Arthur is in love with his girlfriend. They go to the b... |
reinforcement-learning | stable-baselines3 |
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framewor... | {"library_name": "stable-baselines3", "tags": ["SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "DQN", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "SpaceInvadersNoFram... | ThomasSimonini/dqn-SpaceInvadersNoFrameskip-v4 | null | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-07T11:25:18+00:00 | [] | [] | TAGS
#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# DQN Agent playing SpaceInvadersNoFrameskip-v4
This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4
using the stable-baselines3 library
and the RL Zoo.
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents... | [
"# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for Stable Baselines3\nreinforcement learning agents,\nwith hyperparameter optimization and pre-tra... | [
"TAGS\n#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo... |
text-generation | transformers |
# Easy German GPT2 Model
A language model for german easy language ("leichte Sprache") based on [German GPT-2 model](https://huggingface.co/dbmdz/german-gpt2)
## Model Details
Initialized using the weights of [German GPT-2 model](https://huggingface.co/dbmdz/german-gpt2).
Then fine-tuned for one epoch on "leichte... | {"language": ["de"], "widget": [{"text": "Der Sinn des Lebens ist", "example_title": "Sinn des Lebens"}], "inference": {"parameters": {"temperature": 0.7, "repetition_penalty": 1.4}}} | josh-oo/german-gpt2-easy | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"de",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-07T11:29:58+00:00 | [] | [
"de"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #de #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Easy German GPT2 Model
======================
A language model for german easy language ("leichte Sprache") based on German GPT-2 model
Model Details
-------------
Initialized using the weights of German GPT-2 model.
Then fine-tuned for one epoch on "leichte Sprache" corpora consisting of:
* encyclopedia lik... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #de #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
audio-classification | keras |
## Model description
This model classifies UK & Ireland accents using feature extraction from [Yamnet](https://tfhub.dev/google/yamnet/1).
### Yamnet Model
Yamnet is an audio event classifier trained on the AudioSet dataset to predict audio events from the AudioSet ontology. It is available on TensorFlow Hub.
Yamnet... | {"library_name": "keras", "tags": ["keras", "audio-classification", "accent-classification"]} | keras-io/english-speaker-accent-recognition-using-transfer-learning | null | [
"keras",
"tensorboard",
"audio-classification",
"accent-classification",
"has_space",
"region:us"
] | null | 2022-06-07T11:31:02+00:00 | [] | [] | TAGS
#keras #tensorboard #audio-classification #accent-classification #has_space #region-us
| Model description
-----------------
This model classifies UK & Ireland accents using feature extraction from Yamnet.
### Yamnet Model
Yamnet is an audio event classifier trained on the AudioSet dataset to predict audio events from the AudioSet ontology. It is available on TensorFlow Hub.
Yamnet accepts a 1-D tens... | [
"### Yamnet Model\n\n\nYamnet is an audio event classifier trained on the AudioSet dataset to predict audio events from the AudioSet ontology. It is available on TensorFlow Hub.\nYamnet accepts a 1-D tensor of audio samples with a sample rate of 16 kHz. \n\nAs output, the model returns a 3-tuple:\n\n\n* Scores of ... | [
"TAGS\n#keras #tensorboard #audio-classification #accent-classification #has_space #region-us \n",
"### Yamnet Model\n\n\nYamnet is an audio event classifier trained on the AudioSet dataset to predict audio events from the AudioSet ontology. It is available on TensorFlow Hub.\nYamnet accepts a 1-D tensor of audio... |
question-answering | 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. -->
# ksabeh/bert-base-uncased-attribute-correction
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-ba... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "ksabeh/bert-base-uncased-attribute-correction", "results": []}]} | ksabeh/bert-base-uncased-attribute-correction | null | [
"transformers",
"tf",
"bert",
"question-answering",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-06-07T11:44:48+00:00 | [] | [] | TAGS
#transformers #tf #bert #question-answering #generated_from_keras_callback #license-apache-2.0 #endpoints_compatible #region-us
| ksabeh/bert-base-uncased-attribute-correction
=============================================
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: 0.0541
* Validation Loss: 0.0579
* Epoch: 1
Model description
----------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_steps': 36848, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': False, 'na... | [
"TAGS\n#transformers #tf #bert #question-answering #generated_from_keras_callback #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'learning\\_rate': {'class\\_name': 'Polynomial... |
reinforcement-learning | stable-baselines3 |
# **PPO** Agent playing **FrozenLake-v1**
This is a trained model of a **PPO** agent playing **FrozenLake-v1**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 im... | {"library_name": "stable-baselines3", "tags": ["FrozenLake-v1", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "FrozenLake-v1", "type": "FrozenLa... | clement-w/PPO-FrozenLakeV1-rlclass | null | [
"stable-baselines3",
"FrozenLake-v1",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-07T11:45:23+00:00 | [] | [] | TAGS
#stable-baselines3 #FrozenLake-v1 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing FrozenLake-v1
This is a trained model of a PPO agent playing FrozenLake-v1
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing FrozenLake-v1\nThis is a trained model of a PPO agent playing FrozenLake-v1\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #FrozenLake-v1 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing FrozenLake-v1\nThis is a trained model of a PPO agent playing FrozenLake-v1\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your c... |
null | null |
GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models
**Paper**: [GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models](https://arxiv.org/abs/2112.10741)
**Abstract**:
*Diffusion models have recently been shown to generate high-quality ... | {"license": "apache-2.0"} | fusing/glide-base | null | [
"arxiv:2112.10741",
"license:apache-2.0",
"region:us"
] | null | 2022-06-07T11:52:41+00:00 | [
"2112.10741"
] | [] | TAGS
#arxiv-2112.10741 #license-apache-2.0 #region-us
|
GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models
Paper: GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models
Abstract:
*Diffusion models have recently been shown to generate high-quality synthetic images, especially when paired wit... | [
"## Usage",
"## Samples\n\n1. !sample_1\n2. !sample_2\n3. !sample_3"
] | [
"TAGS\n#arxiv-2112.10741 #license-apache-2.0 #region-us \n",
"## Usage",
"## Samples\n\n1. !sample_1\n2. !sample_2\n3. !sample_3"
] |
text-generation | transformers |
# Rafa Dialog GPT Model Medium 10
# Trained on discord channels:
# half of Dragalia chat | {"tags": ["conversational"]} | lucataco/DialoGPT-medium-rafa | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-07T12:11:12+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Rafa Dialog GPT Model Medium 10
# Trained on discord channels:
# half of Dragalia chat | [
"# Rafa Dialog GPT Model Medium 10",
"# Trained on discord channels:",
"# half of Dragalia chat"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Rafa Dialog GPT Model Medium 10",
"# Trained on discord channels:",
"# half of Dragalia chat"
] |
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. -->
# mbert2mbert-finetuned-ar-xlsum
This model is a fine-tuned version of [](https://huggingface.co/) on the xlsum dataset.
## Model... | {"tags": ["summarization", "ar", "encoder-decoder", "mbert", "Abstractive Summarization", "generated_from_trainer"], "datasets": ["xlsum"], "model-index": [{"name": "mbert2mbert-finetuned-ar-xlsum", "results": []}]} | eslamxm/mbert2mbert-finetuned-ar-xlsum | null | [
"transformers",
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"summarization",
"ar",
"mbert",
"Abstractive Summarization",
"generated_from_trainer",
"dataset:xlsum",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-07T12:32:31+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #encoder-decoder #text2text-generation #summarization #ar #mbert #Abstractive Summarization #generated_from_trainer #dataset-xlsum #autotrain_compatible #endpoints_compatible #region-us
|
# mbert2mbert-finetuned-ar-xlsum
This model is a fine-tuned version of [](URL on the xlsum dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
... | [
"# mbert2mbert-finetuned-ar-xlsum\n\nThis model is a fine-tuned version of [](URL on the xlsum dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"... | [
"TAGS\n#transformers #pytorch #tensorboard #encoder-decoder #text2text-generation #summarization #ar #mbert #Abstractive Summarization #generated_from_trainer #dataset-xlsum #autotrain_compatible #endpoints_compatible #region-us \n",
"# mbert2mbert-finetuned-ar-xlsum\n\nThis model is a fine-tuned version of [](UR... |
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-amazon-shoe-reviews
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1", "precision", "recall"], "model-index": [{"name": "distilbert-amazon-shoe-reviews", "results": []}]} | juliensimon/distilbert-amazon-shoe-reviews | null | [
"transformers",
"pytorch",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-06-07T12:42:24+00:00 | [] | [] | TAGS
#transformers #pytorch #safetensors #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# distilbert-amazon-shoe-reviews
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: 0.9524
- Accuracy: 0.579
- F1: [0.62880121 0.47009599 0.50419753 0.55847134 0.73663068]
- Precision: [0.63086233 0.46744983 0.4887506 ... | [
"# distilbert-amazon-shoe-reviews\n\nThis model is a fine-tuned version of distilbert-base-uncased on the None dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.9524\n- Accuracy: 0.579\n- F1: [0.62880121 0.47009599 0.50419753 0.55847134 0.73663068]\n- Precision: [0.63086233 0.46744983 0.... | [
"TAGS\n#transformers #pytorch #safetensors #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# distilbert-amazon-shoe-reviews\n\nThis model is a fine-tuned version of distilbert-base-uncased on the None dataset.\nIt... |
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/3077349437/46e19fdb6614f... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true", "widget": [{"text": "My dream is"}]} | huggingtweets/arthur_rimbaud | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-07T12:46:29+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Arthur Rimbaud
@arthur\_rimbaud
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"
] |
text-generation | transformers |
# Ortho DialoGPT Model | {"tags": ["conversational"]} | gloomyworm/DialoGPT-small-ortho | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-07T12:48:08+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Ortho DialoGPT Model | [
"# Ortho DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Ortho DialoGPT Model"
] |
reinforcement-learning | stable-baselines3 |
# **PPO** Agent playing **Pendulum-v1**
This is a trained model of a **PPO** agent playing **Pendulum-v1**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforc... | {"library_name": "stable-baselines3", "tags": ["Pendulum-v1", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Pendulum-v1", "type": "Pendulum-v1"... | ernestumorga/ppo-Pendulum-v1 | null | [
"stable-baselines3",
"Pendulum-v1",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-07T13:05:48+00:00 | [] | [] | TAGS
#stable-baselines3 #Pendulum-v1 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing Pendulum-v1
This is a trained model of a PPO agent playing Pendulum-v1
using the stable-baselines3 library
and the RL Zoo.
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 R... | [
"# PPO Agent playing Pendulum-v1\nThis is a trained model of a PPO agent playing Pendulum-v1\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for Stable Baselines3\nreinforcement learning agents,\nwith hyperparameter optimization and pre-trained agents included.",
"## Us... | [
"TAGS\n#stable-baselines3 #Pendulum-v1 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing Pendulum-v1\nThis is a trained model of a PPO agent playing Pendulum-v1\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for Stab... |
text-classification | transformers |
# DistilBERT optimized for Apple Neural Engine
This is the [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) model, optimized for the Apple Neural Engine (ANE) as described in the article [Deploying Transformers on the Apple Neural Engine](https:... | {"language": "en", "license": "apache-2.0", "datasets": ["sst2"]} | apple/ane-distilbert-base-uncased-finetuned-sst-2-english | null | [
"transformers",
"pytorch",
"coreml",
"distilbert",
"text-classification",
"custom_code",
"en",
"dataset:sst2",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-07T13:08:22+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #coreml #distilbert #text-classification #custom_code #en #dataset-sst2 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# DistilBERT optimized for Apple Neural Engine
This is the distilbert-base-uncased-finetuned-sst-2-english model, optimized for the Apple Neural Engine (ANE) as described in the article Deploying Transformers on the Apple Neural Engine.
The source code is taken from Apple's ml-ane-transformers GitHub repo, modified ... | [
"# DistilBERT optimized for Apple Neural Engine\n\nThis is the distilbert-base-uncased-finetuned-sst-2-english model, optimized for the Apple Neural Engine (ANE) as described in the article Deploying Transformers on the Apple Neural Engine.\n\nThe source code is taken from Apple's ml-ane-transformers GitHub repo, m... | [
"TAGS\n#transformers #pytorch #coreml #distilbert #text-classification #custom_code #en #dataset-sst2 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# DistilBERT optimized for Apple Neural Engine\n\nThis is the distilbert-base-uncased-finetuned-sst-2-english model, optimized for ... |
text-classification | transformers |
# Quantized-distilbert-banking77
This model is a statically quantized version of [optimum/distilbert-base-uncased-finetuned-banking77](https://huggingface.co/optimum/distilbert-base-uncased-finetuned-banking77) on the `banking77` dataset.
The model was created using the [optimum-static-quantization](https://github.... | {"tags": ["optimum"], "datasets": ["banking77"], "metrics": ["accuracy"], "model-index": [{"name": "quantized-distilbert-banking77", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "banking77", "type": "banking77"}, "metrics": [{"type": "accuracy", "value": 0.922... | philschmid/quantized-distilbert-banking77 | null | [
"transformers",
"onnx",
"text-classification",
"optimum",
"dataset:banking77",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-07T13:18:48+00:00 | [] | [] | TAGS
#transformers #onnx #text-classification #optimum #dataset-banking77 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Quantized-distilbert-banking77
==============================
This model is a statically quantized version of optimum/distilbert-base-uncased-finetuned-banking77 on the 'banking77' dataset.
The model was created using the optimum-static-quantization notebook.
It achieves the following results on the evaluation se... | [] | [
"TAGS\n#transformers #onnx #text-classification #optimum #dataset-banking77 #model-index #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# MiniLM-evidence-types
This model is a fine-tuned version of [microsoft/MiniLM-L12-H384-uncased](https://huggingface.co/microsoft... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "MiniLM-evidence-types", "results": []}]} | marieke93/MiniLM-evidence-types | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-07T13:19:25+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| MiniLM-evidence-types
=====================
This model is a fine-tuned version of microsoft/MiniLM-L12-H384-uncased on the evidence types dataset.
It achieved the following results on the evaluation set:
* Loss: 1.8672
* Macro f1: 0.3726
* Weighted f1: 0.7030
* Accuracy: 0.7161
* Balanced accuracy: 0.3616
Trainin... | [
"### 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: 20\n* mixed\\_pre... | [
"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: 2e-05\n* train\\_batch\\_size: ... |
image-classification | keras |
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'learning_rate': 0.001, 'decay': 0.0, 'b... | {"library_name": "keras", "tags": ["data-augmentation", "image-classification"]} | harsha163/CutMix_data_augmentation_for_image_classification | null | [
"keras",
"tensorboard",
"data-augmentation",
"image-classification",
"region:us"
] | null | 2022-06-07T14:06:28+00:00 | [] | [] | TAGS
#keras #tensorboard #data-augmentation #image-classification #region-us
|
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'learning_rate': 0.001, 'decay': 0.0, 'b... | [
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- optimizer: {'name': 'Adam', 'learning_rate'... | [
"TAGS\n#keras #tensorboard #data-augmentation #image-classification #region-us \n",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"### Training hyperparameters\n\nThe following hyp... |
fill-mask | 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. -->
# nouman10/robertabase-claims-2
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown ... | {"license": "mit", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "nouman10/robertabase-claims-2", "results": []}]} | nouman10/robertabase-claims-2 | null | [
"transformers",
"tf",
"roberta",
"fill-mask",
"generated_from_keras_callback",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-07T14:22:17+00:00 | [] | [] | TAGS
#transformers #tf #roberta #fill-mask #generated_from_keras_callback #license-mit #autotrain_compatible #endpoints_compatible #region-us
| nouman10/robertabase-claims-2
=============================
This model is a fine-tuned version of roberta-base on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.5836
* Validation Loss: 0.3727
* Epoch: 1
Model description
-----------------
More information needed
I... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'WarmUp', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_schedule\\_fn': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_le... | [
"TAGS\n#transformers #tf #roberta #fill-mask #generated_from_keras_callback #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\... |
sentence-similarity | sentence-transformers |
# inokufu/bertheo-en
A [sentence-transformers](https://www.SBERT.net) model fine-tuned on course sentences. It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Details
This model is based on the English bert-base-uncased pre-trained... | {"language": "en", "tags": ["sentence-similarity", "transformers", "Education", "en", "bert", "sentence-transformers", "feature-extraction", "xnli", "stsb_multi_mt"], "datasets": ["xnli", "stsb_multi_mt"], "pipeline_tag": "sentence-similarity"} | inokufu/bert-base-uncased-xnli-sts-finetuned-education | null | [
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"Education",
"en",
"xnli",
"stsb_multi_mt",
"dataset:xnli",
"dataset:stsb_multi_mt",
"arxiv:1810.04805",
"arxiv:1809.05053",
"endpoints_compatible",
"region:us"
] | null | 2022-06-07T14:36:19+00:00 | [
"1810.04805",
"1809.05053"
] | [
"en"
] | TAGS
#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #Education #en #xnli #stsb_multi_mt #dataset-xnli #dataset-stsb_multi_mt #arxiv-1810.04805 #arxiv-1809.05053 #endpoints_compatible #region-us
|
# inokufu/bertheo-en
A sentence-transformers model fine-tuned on course sentences. It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Details
This model is based on the English bert-base-uncased pre-trained model [1, 2].
It was f... | [
"# inokufu/bertheo-en\n\nA sentence-transformers model fine-tuned on course sentences. It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Details\n\nThis model is based on the English bert-base-uncased pre-trained model [1, 2]... | [
"TAGS\n#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #Education #en #xnli #stsb_multi_mt #dataset-xnli #dataset-stsb_multi_mt #arxiv-1810.04805 #arxiv-1809.05053 #endpoints_compatible #region-us \n",
"# inokufu/bertheo-en\n\nA sentence-transformers model fine-tuned o... |
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. -->
# rubert-tiny2_best_finetuned_emotion_experiment_augmented_anger_fear
This model is a fine-tuned version of [cointegrated/rubert-t... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1", "precision", "recall"], "model-index": [{"name": "rubert-tiny2_best_finetuned_emotion_experiment_augmented_anger_fear", "results": []}]} | mmillet/rubert-tiny2_best_finetuned_emotion_experiment_augmented_anger_fear | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-07T14:44:34+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| rubert-tiny2\_best\_finetuned\_emotion\_experiment\_augmented\_anger\_fear
==========================================================================
This model is a fine-tuned version of cointegrated/rubert-tiny2 on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3902
* Accur... | [
"### 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=0.0001\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 40",
"### 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: 2e-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. -->
# t5-small-finetuned-samsum-en
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the samsum dat... | {"license": "apache-2.0", "tags": ["summarization", "generated_from_trainer"], "datasets": ["samsum"], "metrics": ["rouge"], "base_model": "t5-small", "model-index": [{"name": "t5-small-finetuned-samsum-en", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "datase... | santiviquez/t5-small-finetuned-samsum-en | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"summarization",
"generated_from_trainer",
"dataset:samsum",
"base_model:t5-small",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-07T14:52:00+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #summarization #generated_from_trainer #dataset-samsum #base_model-t5-small #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| t5-small-finetuned-samsum-en
============================
This model is a fine-tuned version of t5-small on the samsum dataset.
It achieves the following results on the evaluation set:
* Loss: 1.9335
* Rouge1: 44.3313
* Rouge2: 20.71
* Rougel: 37.221
* Rougelsum: 40.9603
Model description
-----------------
More... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5.6e-05\n* train\\_batch\\_size: 10\n* eval\\_batch\\_size: 10\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10",
"### Tra... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #summarization #generated_from_trainer #dataset-samsum #base_model-t5-small #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following ... |
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/1488896240083517453/Bu0l... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true", "widget": [{"text": "My dream is"}]} | huggingtweets/mizefian | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-07T15:10:37+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Mizefian 🇺🇦
@mizefian
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"
] |
null | 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. -->
# rule_learning_margin_test
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_trainer"], "datasets": ["enoriega/odinsynth_dataset"], "model-index": [{"name": "rule_learning_margin_test", "results": []}]} | enoriega/rule_learning_margin_test | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"generated_from_trainer",
"dataset:enoriega/odinsynth_dataset",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-06-07T15:17:08+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #generated_from_trainer #dataset-enoriega/odinsynth_dataset #license-apache-2.0 #endpoints_compatible #region-us
| rule\_learning\_margin\_test
============================
This model is a fine-tuned version of bert-base-uncased on the enoriega/odinsynth\_dataset dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4104
Model description
-----------------
More information needed
Intended uses & limi... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* gradient\\_accumulation\\_steps: 2000\n* total\\_train\\_batch\\_size: 8000\n* optimizer: Adam with betas=(0.9,0.999) and epsi... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #generated_from_trainer #dataset-enoriega/odinsynth_dataset #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: 5e-05\n* train\\_batch\\_size: ... |
text-generation | transformers |
# Harry Potter DialoGPT Model | {"tags": ["conversational"]} | kozlovtsev/DialoGPT-medium-harrypotter | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-07T15:22:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Harry Potter DialoGPT Model | [
"# Harry Potter DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Harry Potter DialoGPT Model"
] |
fill-mask | transformers |
# Swedish BERT Models
The National Library of Sweden / KBLab releases three pretrained language models based on BERT and ALBERT. The models are trained on aproximately 15-20GB of text (200M sentences, 3000M tokens) from various sources (books, news, government publications, swedish wikipedia and internet forums) aimi... | {"language": "sv"} | KB/bert-base-swedish-cased | null | [
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"sv",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-07T15:28:03+00:00 | [] | [
"sv"
] | TAGS
#transformers #pytorch #tf #jax #bert #fill-mask #sv #autotrain_compatible #endpoints_compatible #region-us
| Swedish BERT Models
===================
The National Library of Sweden / KBLab releases three pretrained language models based on BERT and ALBERT. The models are trained on aproximately 15-20GB of text (200M sentences, 3000M tokens) from various sources (books, news, government publications, swedish wikipedia and int... | [
"### BERT Base Swedish\n\n\nA standard BERT base for Swedish trained on a variety of sources. Vocabulary size is ~50k. Using Huggingface Transformers the model can be loaded in Python as follows:",
"### BERT base fine-tuned for Swedish NER\n\n\nThis model is fine-tuned on the SUC 3.0 dataset. Using the Huggingfac... | [
"TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #sv #autotrain_compatible #endpoints_compatible #region-us \n",
"### BERT Base Swedish\n\n\nA standard BERT base for Swedish trained on a variety of sources. Vocabulary size is ~50k. Using Huggingface Transformers the model can be loaded in Python as follows... |
token-classification | transformers |
# Swedish BERT Models
The National Library of Sweden / KBLab releases three pretrained language models based on BERT and ALBERT. The models are trained on approximately 15-20GB of text (200M sentences, 3000M tokens) from various sources (books, news, government publications, swedish wikipedia and internet forums) aim... | {"language": "sv"} | KB/bert-base-swedish-cased-ner | null | [
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"token-classification",
"sv",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-07T15:31:50+00:00 | [] | [
"sv"
] | TAGS
#transformers #pytorch #tf #jax #bert #token-classification #sv #autotrain_compatible #endpoints_compatible #region-us
| Swedish BERT Models
===================
The National Library of Sweden / KBLab releases three pretrained language models based on BERT and ALBERT. The models are trained on approximately 15-20GB of text (200M sentences, 3000M tokens) from various sources (books, news, government publications, swedish wikipedia and in... | [
"### BERT Base Swedish\n\n\nA standard BERT base for Swedish trained on a variety of sources. Vocabulary size is ~50k. Using Huggingface Transformers the model can be loaded in Python as follows:",
"### BERT base fine-tuned for Swedish NER\n\n\nThis model is fine-tuned on the SUC 3.0 dataset. Using the Huggingfac... | [
"TAGS\n#transformers #pytorch #tf #jax #bert #token-classification #sv #autotrain_compatible #endpoints_compatible #region-us \n",
"### BERT Base Swedish\n\n\nA standard BERT base for Swedish trained on a variety of sources. Vocabulary size is ~50k. Using Huggingface Transformers the model can be loaded in Python... |
null | null |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased-prefix-tuning
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncas... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "bert-base-uncased-prefix-tuning", "results": []}]} | anas-awadalla/bert-base-uncased-prefix-tuning-squad | null | [
"tensorboard",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"region:us"
] | null | 2022-06-07T15:54:13+00:00 | [] | [] | TAGS
#tensorboard #generated_from_trainer #dataset-squad #license-apache-2.0 #region-us
|
# bert-base-uncased-prefix-tuning
This model is a fine-tuned version of bert-base-uncased on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyper... | [
"# bert-base-uncased-prefix-tuning\n\nThis model is a fine-tuned version of bert-base-uncased on the squad dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training pro... | [
"TAGS\n#tensorboard #generated_from_trainer #dataset-squad #license-apache-2.0 #region-us \n",
"# bert-base-uncased-prefix-tuning\n\nThis model is a fine-tuned version of bert-base-uncased on the squad dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore inform... |
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. -->
# mt5-base-finetuned-xsum-data_prep_2021_12_26___t22027_162754.csv___topic_text_google_mt5_base
This model is a fine-tuned version... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "mt5-base-finetuned-xsum-data_prep_2021_12_26___t22027_162754.csv___topic_text_google_mt5_base", "results": []}]} | nestoralvaro/mt5-base-finetuned-xsum-data_prep_2021_12_26___t22027_162754.csv___topic_text_google_mt5_base | null | [
"transformers",
"pytorch",
"tensorboard",
"mt5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-07T16:06:07+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #mt5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| mt5-base-finetuned-xsum-data\_prep\_2021\_12\_26\_\_\_t22027\_162754.csv\_\_\_topic\_text\_google\_mt5\_base
============================================================================================================
This model is a fine-tuned version of google/mt5-base on an unknown dataset.
It achieves the followi... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1\n* mixed\\_precis... | [
"TAGS\n#transformers #pytorch #tensorboard #mt5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rat... |
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