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null | null | transformers | # Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
Made by finetuning [google/flan-t5-small](https://huggingface.co/google/flan-t5-small). | {"license": "unknown", "metrics": ["bleu"], "pipeline_tag": "translation"} | translation | aboli-marathe/flan_t5_3185BLEU | [
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"region:us"
] | 2023-11-12T17:02:03+00:00 | [] | [] | TAGS
#transformers #safetensors #t5 #text2text-generation #translation #license-unknown #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # Model Card for Model ID
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null | 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. -->
# swin-tiny-patch4-window7-224-finetuned-eurosat
This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](ht... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "microsoft/swin-tiny-patch4-window7-224", "model-index": [{"name": "swin-tiny-patch4-window7-224-finetuned-eurosat", "results": []}]} | image-classification | Artemiy27/swin-tiny-patch4-window7-224-finetuned-eurosat | [
"transformers",
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"safetensors",
"swin",
"image-classification",
"generated_from_trainer",
"base_model:microsoft/swin-tiny-patch4-window7-224",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2023-11-12T17:02:41+00:00 | [] | [] | TAGS
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| swin-tiny-patch4-window7-224-finetuned-eurosat
==============================================
This model is a fine-tuned version of microsoft/swin-tiny-patch4-window7-224 on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0136
* Accuracy: 0.9938
Model description
-----------... | [
"### 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: 32\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilo... | [
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null | null | transformers | All Rights Reserved | {} | text-generation | f0rGoTTen000/AgroGPT_125M | [
"transformers",
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"gpt_neo",
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"autotrain_compatible",
"endpoints_compatible",
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] | 2023-11-12T17:11:20+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt_neo #text-generation #autotrain_compatible #endpoints_compatible #region-us
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null | null | diffusers | ### My-Pet-rabbit Dreambooth model trained by MANASA-1919 following the "Build your own Gen AI model" session by NxtWave.
Project Submission Code: PIETW-212
Sample pictures of this concept:
.jpg)
... | {"license": "creativeml-openrail-m", "tags": ["NxtWave-GenAI-Webinar", "text-to-image", "stable-diffusion"]} | text-to-image | MANASA-1919/my-pet-rabbit | [
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"stable-diffusion",
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] | 2023-11-12T17:23:49+00:00 | [] | [] | TAGS
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| ### My-Pet-rabbit Dreambooth model trained by MANASA-1919 following the "Build your own Gen AI model" session by NxtWave.
Project Submission Code: PIETW-212
Sample pictures of this concept:
!0.jpg)
| [
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null | null | transformers |
<div align="center">
<h1>
TransNormerLLM -- A Faster and Better LLM
</h1>
</div>
<p align="center">
💻 <a href="https://github.com/OpenNLPLab/TransnormerLLM" target="_blank">GitHub </a> • 💬 <a href="https://discord.gg/W4Vr7AKW" target="_blank">Discord</a> • 💬 <a href="./images/contact_me_qr.png" target="_blank">W... | {"language": ["en", "zh"], "license": "other", "tags": [" TransNormerLLM"], "pipeline_tag": "text-generation"} | text-generation | OpenNLPLab/TransNormerLLM-7B | [
"transformers",
"pytorch",
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" TransNormerLLM",
"custom_code",
"en",
"zh",
"arxiv:2307.14995",
"arxiv:2009.03300",
"license:other",
"autotrain_compatible",
"region:us"
] | 2023-11-12T17:25:50+00:00 | [
"2307.14995",
"2009.03300"
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"en",
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#transformers #pytorch #text-generation # TransNormerLLM #custom_code #en #zh #arxiv-2307.14995 #arxiv-2009.03300 #license-other #autotrain_compatible #region-us
|
TransNormerLLM -- A Faster and Better LLM
===========================================
[GitHub](URL target=) • [Discord](URL target=) • [Wechat](./images/contact_me_qr.png)
Table of Contents
=================
* Introduction
* Released Weights
* Benchmark Results
+ General Domain
- Model Results
* Inferen... | [
"### Model Results\n\n\nPerformance Comparison on Commonsense Reasoning and Aggregated Benchmarks. For a fair comparison, we report competing methods' results reproduced by us using their released models. PS: parameter size (billion). T: tokens (trillion). HS: HellaSwag. WG: WinoGrande.\n\n\n\nInference and Deploym... | [
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null | null | null |
# Lora of haruka_makino_onichichi
This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion). And the auto-training framework is maintained by [DeepGHS Team](https://huggingface.co/deepghs).
The base model used during training is [NAI](https://huggingface.co/deepghs/animefull-latest), and th... | {"license": "mit", "tags": ["art"], "datasets": ["CyberHarem/haruka_makino_onichichi"], "pipeline_tag": "text-to-image"} | text-to-image | CyberHarem/haruka_makino_onichichi | [
"art",
"text-to-image",
"dataset:CyberHarem/haruka_makino_onichichi",
"license:mit",
"region:us"
] | 2023-11-12T17:26:47+00:00 | [] | [] | TAGS
#art #text-to-image #dataset-CyberHarem/haruka_makino_onichichi #license-mit #region-us
| Lora of haruka\_makino\_onichichi
=================================
This model is trained with HCP-Diffusion. And the auto-training framework is maintained by DeepGHS Team.
The base model used during training is NAI, and the base model used for generating preview images is Meina/MeinaMix\_V11.
After downloading t... | [] | [
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null | 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. -->
# pegasus-xsum_readme_summarization
This model is a fine-tuned version of [google/pegasus-xsum](https://huggingface.co/google/pega... | {"tags": ["generated_from_trainer"], "metrics": ["rouge"], "base_model": "google/pegasus-xsum", "model-index": [{"name": "pegasus-xsum_readme_summarization", "results": []}]} | text2text-generation | bunbohue/pegasus-xsum_readme_summarization | [
"transformers",
"safetensors",
"pegasus",
"text2text-generation",
"generated_from_trainer",
"base_model:google/pegasus-xsum",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2023-11-12T17:33:52+00:00 | [] | [] | TAGS
#transformers #safetensors #pegasus #text2text-generation #generated_from_trainer #base_model-google/pegasus-xsum #autotrain_compatible #endpoints_compatible #region-us
| pegasus-xsum\_readme\_summarization
===================================
This model is a fine-tuned version of google/pegasus-xsum on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 2.3151
* Rouge1: 0.4555
* Rouge2: 0.313
* Rougel: 0.43
* Rougelsum: 0.4306
* Gen Len: 20.4628
Mode... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 4",
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null | null | peft | # Model Card for CNC-7b
## Model Details
- Name: CNC-7b
- Version: 1.0
- Release Date: November 13, 2023
## Intended Use
CNC-7b is a lora adapter for Mistral-7b (Instruct) intended to be clear, concise, and helpful in short text conversations. It is designed for conversational agents and assistants.
## Training Da... | {"language": ["en", "tl"], "license": "cc-by-sa-4.0", "library_name": "peft", "tags": ["mistral", "lora", "instruct", "custom code"], "datasets": ["NewstaR/clearNconcise"], "pipeline_tag": "text-generation", "inference": false, "base_model": "mistralai/Mistral-7B-v0.1"} | text-generation | NewstaR/CNC-7b-lora | [
"peft",
"mistral",
"lora",
"instruct",
"custom code",
"text-generation",
"en",
"tl",
"dataset:NewstaR/clearNconcise",
"base_model:mistralai/Mistral-7B-v0.1",
"license:cc-by-sa-4.0",
"region:us"
] | 2023-11-12T17:35:38+00:00 | [] | [
"en",
"tl"
] | TAGS
#peft #mistral #lora #instruct #custom code #text-generation #en #tl #dataset-NewstaR/clearNconcise #base_model-mistralai/Mistral-7B-v0.1 #license-cc-by-sa-4.0 #region-us
| # Model Card for CNC-7b
## Model Details
- Name: CNC-7b
- Version: 1.0
- Release Date: November 13, 2023
## Intended Use
CNC-7b is a lora adapter for Mistral-7b (Instruct) intended to be clear, concise, and helpful in short text conversations. It is designed for conversational agents and assistants.
## Training Da... | [
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null | 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. -->
# FakeNews-bert-large-cased-stable
This model is a fine-tuned version of [bert-large-cased](https://huggingface.co/bert-large-case... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "bert-large-cased", "model-index": [{"name": "FakeNews-bert-large-cased-stable", "results": []}]} | text-classification | Denyol/FakeNews-bert-large-cased-stable | [
"transformers",
"tensorboard",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:bert-large-cased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2023-11-12T17:39:25+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #bert #text-classification #generated_from_trainer #base_model-bert-large-cased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| FakeNews-bert-large-cased-stable
================================
This model is a fine-tuned version of bert-large-cased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1020
* Accuracy: 0.9827
Model description
-----------------
More information needed
Intended uses &... | [
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null | 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. -->
# gpt-neo-125M-couples_therapist_full_renamed
This model is a fine-tuned version of [EleutherAI/gpt-neo-125M](https://huggingface.... | {"license": "mit", "tags": ["generated_from_trainer"], "base_model": "EleutherAI/gpt-neo-125M", "model-index": [{"name": "gpt-neo-125M-couples_therapist_full_renamed", "results": []}]} | text-generation | ColleenMacklin/gpt-neo-125M-couples_therapist_full_renamed | [
"transformers",
"tensorboard",
"safetensors",
"gpt_neo",
"text-generation",
"generated_from_trainer",
"base_model:EleutherAI/gpt-neo-125M",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2023-11-12T17:41:44+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #gpt_neo #text-generation #generated_from_trainer #base_model-EleutherAI/gpt-neo-125M #license-mit #autotrain_compatible #endpoints_compatible #region-us
| gpt-neo-125M-couples\_therapist\_full\_renamed
==============================================
This model is a fine-tuned version of EleutherAI/gpt-neo-125M on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 3.0235
Model description
-----------------
More information needed
I... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Traini... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\... | [
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0.1594475656747818,
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0.060557398945093155,
0.13735359907150269,
0.11269506812095642,
0.018921131268143654,
0.13898272... |
null | 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. -->
# ICU_Returns_ClinicalBERT
This model is a fine-tuned version of [medicalai/ClinicalBERT](https://huggingface.co/medicalai/Clinica... | {"tags": ["generated_from_trainer"], "base_model": "medicalai/ClinicalBERT", "model-index": [{"name": "ICU_Returns_ClinicalBERT", "results": []}]} | text-classification | moro01525/ICU_Returns_ClinicalBERT | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:medicalai/ClinicalBERT",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2023-11-12T17:43:41+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #base_model-medicalai/ClinicalBERT #autotrain_compatible #endpoints_compatible #region-us
| ICU\_Returns\_ClinicalBERT
==========================
This model is a fine-tuned version of medicalai/ClinicalBERT on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 1.3201
* F1:: 0.7134
* Roc Auc: 0.7225
* Precision with 0:: 0.8462
* Precision with 1:: 0.6640
* Recall with 0:: ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 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: 13",
"### Trai... | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #base_model-medicalai/ClinicalBERT #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\... | [
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0.13512365520000458,
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0.11714092... |
null | null | 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... | reinforcement-learning | vones/ppo-LunarLander-v2 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | 2023-11-12T17:44:15+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... | [
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"passage: 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 your c... | [
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0.08338645845651627,
0.06030960753560066,
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0.2571701407432... |
null | null | peft | ## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_doub... | {"library_name": "peft"} | null | runse/OPS-koalpaca-polyglot-12.8b | [
"peft",
"tensorboard",
"region:us"
] | 2023-11-12T17:49:35+00:00 | [] | [] | TAGS
#peft #tensorboard #region-us
| ## Training procedure
The following 'bitsandbytes' quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_doub... | [
"## Training procedure\n\n\nThe following 'bitsandbytes' quantization config was used during training:\n- load_in_8bit: False\n- load_in_4bit: True\n- llm_int8_threshold: 6.0\n- llm_int8_skip_modules: None\n- llm_int8_enable_fp32_cpu_offload: False\n- llm_int8_has_fp16_weight: False\n- bnb_4bit_quant_type: nf4\n- b... | [
"TAGS\n#peft #tensorboard #region-us \n",
"## Training procedure\n\n\nThe following 'bitsandbytes' quantization config was used during training:\n- load_in_8bit: False\n- load_in_4bit: True\n- llm_int8_threshold: 6.0\n- llm_int8_skip_modules: None\n- llm_int8_enable_fp32_cpu_offload: False\n- llm_int8_has_fp16_we... | [
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0.08869192004203796,
0.04804614931344986,
0.07064280658960342,
0.11643702536... |
null | 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. -->
# fine-tuned-text-summarization
This model is a fine-tuned version of [google/flan-t5-base](https://huggingface.co/google/flan-t5-... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "google/flan-t5-base", "model-index": [{"name": "fine-tuned-text-summarization", "results": []}]} | null | AlyGreo/fine-tuned-text-summarization | [
"tensorboard",
"safetensors",
"generated_from_trainer",
"base_model:google/flan-t5-base",
"license:apache-2.0",
"region:us"
] | 2023-11-12T17:50:40+00:00 | [] | [] | TAGS
#tensorboard #safetensors #generated_from_trainer #base_model-google/flan-t5-base #license-apache-2.0 #region-us
|
# fine-tuned-text-summarization
This model is a fine-tuned version of google/flan-t5-base on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hype... | [
"# fine-tuned-text-summarization\n\nThis model is a fine-tuned version of google/flan-t5-base on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training pr... | [
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"# fine-tuned-text-summarization\n\nThis model is a fine-tuned version of google/flan-t5-base on an unknown dataset.",
"## Model description\n\nMore information needed",
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0.09333959966897964,
-0.001905346056446433,
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0.212567627... |
null | 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. -->
# ICU_Returns_BioClinicalBERT
This model is a fine-tuned version of [emilyalsentzer/Bio_ClinicalBERT](https://huggingface.co/emily... | {"license": "mit", "tags": ["generated_from_trainer"], "base_model": "emilyalsentzer/Bio_ClinicalBERT", "model-index": [{"name": "ICU_Returns_BioClinicalBERT", "results": []}]} | text-classification | moro01525/ICU_Returns_BioClinicalBERT | [
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:emilyalsentzer/Bio_ClinicalBERT",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2023-11-12T17:53:50+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #text-classification #generated_from_trainer #base_model-emilyalsentzer/Bio_ClinicalBERT #license-mit #autotrain_compatible #endpoints_compatible #region-us
| ICU\_Returns\_BioClinicalBERT
=============================
This model is a fine-tuned version of emilyalsentzer/Bio\_ClinicalBERT on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 1.7775
* F1:: 0.7063
* Roc Auc: 0.7198
* Precision with 0:: 0.8846
* Precision with 1:: 0.6538
* ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 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: 13",
"### Trai... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: ... | [
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0.08475185930728912,
-0.0015843362780287862,
0.10697293281555176,
0.16269953548908234,
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0.1327241212129593,
0.1209334135055542,
-0.0702114850282669,
0.029579630121588707,
0.12896092236042023,
0.13817715644836426,
-0.0012983549386262894,
0.125189244... |
null | null | transformers |
ExllamaV2 version of the model created by BlueNipples!
Original Model https://huggingface.co/BlueNipples/TimeCrystal-l2-13B
Requires ExllamaV2, which is being developed by turboderp https://github.com/turboderp/exllamav2 under an MIT license.
Main branch is 8bpw 8h
----
This 13B model, TimeCrystal-l2-13B is bui... | {"license": "apache-2.0", "tags": ["llama-2", "roleplaying"]} | text-generation | AzureBlack/TimeCrystal-l2-13B-exl2 | [
"transformers",
"safetensors",
"llama",
"text-generation",
"llama-2",
"roleplaying",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-12T17:57:04+00:00 | [] | [] | TAGS
#transformers #safetensors #llama #text-generation #llama-2 #roleplaying #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
ExllamaV2 version of the model created by BlueNipples!
Original Model URL
Requires ExllamaV2, which is being developed by turboderp URL under an MIT license.
Main branch is 8bpw 8h
----
This 13B model, TimeCrystal-l2-13B is built to maximize logic and instruct following, whilst also increasing the vividness of ... | [] | [
"TAGS\n#transformers #safetensors #llama #text-generation #llama-2 #roleplaying #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] | [
64
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0.14478813111782074,
0.1644625961780548,
-0.011986946687102318,
0.063405... |
null | 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. -->
# gpt-neo-125M-couples_therapist_full_renamed
This model is a fine-tuned version of [EleutherAI/gpt-neo-125M](https://huggingface.... | {"license": "mit", "tags": ["generated_from_trainer"], "base_model": "EleutherAI/gpt-neo-125M", "model-index": [{"name": "gpt-neo-125M-couples_therapist_full_renamed", "results": []}]} | text-generation | ailments/gpt-neo-125M-couples_therapist_full_renamed | [
"transformers",
"tensorboard",
"safetensors",
"gpt_neo",
"text-generation",
"generated_from_trainer",
"base_model:EleutherAI/gpt-neo-125M",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2023-11-12T18:00:45+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #gpt_neo #text-generation #generated_from_trainer #base_model-EleutherAI/gpt-neo-125M #license-mit #autotrain_compatible #endpoints_compatible #region-us
| gpt-neo-125M-couples\_therapist\_full\_renamed
==============================================
This model is a fine-tuned version of EleutherAI/gpt-neo-125M on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 3.0778
Model description
-----------------
More information needed
I... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Traini... | [
"TAGS\n#transformers #tensorboard #safetensors #gpt_neo #text-generation #generated_from_trainer #base_model-EleutherAI/gpt-neo-125M #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\... | [
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0.13735359907150269,
0.11269506812095642,
0.018921131268143654,
0.13898272... |
null | 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. -->
# ICU_Returns_COReClinicalBioBERT
This model is a fine-tuned version of [bvanaken/CORe-clinical-outcome-biobert-v1](https://huggin... | {"tags": ["generated_from_trainer"], "base_model": "bvanaken/CORe-clinical-outcome-biobert-v1", "model-index": [{"name": "ICU_Returns_COReClinicalBioBERT", "results": []}]} | text-classification | moro01525/ICU_Returns_COReClinicalBioBERT | [
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:bvanaken/CORe-clinical-outcome-biobert-v1",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2023-11-12T18:04:18+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #text-classification #generated_from_trainer #base_model-bvanaken/CORe-clinical-outcome-biobert-v1 #autotrain_compatible #endpoints_compatible #region-us
| ICU\_Returns\_COReClinicalBioBERT
=================================
This model is a fine-tuned version of bvanaken/CORe-clinical-outcome-biobert-v1 on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 1.8391
* F1:: 0.7210
* Roc Auc: 0.7335
* Precision with 0:: 0.9048
* Precision w... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 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: 13",
"### Trai... | [
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null | null | peft |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** ... | {"library_name": "peft", "base_model": "meta-llama/Llama-2-7b-hf"} | null | joshswartz/model_d2_llama_wikihow_cc | [
"peft",
"arxiv:1910.09700",
"base_model:meta-llama/Llama-2-7b-hf",
"region:us"
] | 2023-11-12T18:05:53+00:00 | [
"1910.09700"
] | [] | TAGS
#peft #arxiv-1910.09700 #base_model-meta-llama/Llama-2-7b-hf #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
- Developed by:
- Funded by [optional]:
- Shared by [optional]:
- Model type:
- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
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null | 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. -->
# segformer-finetuned-coasts-final
This model is a fine-tuned version of [peldrak/segformer-finetuned-coastalDataset](https://hugg... | {"license": "other", "tags": ["vision", "image-segmentation", "generated_from_trainer"], "base_model": "peldrak/segformer-finetuned-coastalDataset", "model-index": [{"name": "segformer-finetuned-coasts-final", "results": []}]} | image-segmentation | peldrak/segformer-finetuned-coasts-final | [
"transformers",
"pytorch",
"segformer",
"vision",
"image-segmentation",
"generated_from_trainer",
"base_model:peldrak/segformer-finetuned-coastalDataset",
"license:other",
"endpoints_compatible",
"region:us"
] | 2023-11-12T18:23:08+00:00 | [] | [] | TAGS
#transformers #pytorch #segformer #vision #image-segmentation #generated_from_trainer #base_model-peldrak/segformer-finetuned-coastalDataset #license-other #endpoints_compatible #region-us
| segformer-finetuned-coasts-final
================================
This model is a fine-tuned version of peldrak/segformer-finetuned-coastalDataset on the peldrak/coastal2 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2563
* Mean Iou: 0.5765
* Mean Accuracy: 0.7934
* Overall Accuracy: 0... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 6e-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: 2",
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null | null | null |
# Lora of akira_makino_onichichi
This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion). And the auto-training framework is maintained by [DeepGHS Team](https://huggingface.co/deepghs).
The base model used during training is [NAI](https://huggingface.co/deepghs/animefull-latest), and the... | {"license": "mit", "tags": ["art"], "datasets": ["CyberHarem/akira_makino_onichichi"], "pipeline_tag": "text-to-image"} | text-to-image | CyberHarem/akira_makino_onichichi | [
"art",
"text-to-image",
"dataset:CyberHarem/akira_makino_onichichi",
"license:mit",
"region:us"
] | 2023-11-12T18:27:36+00:00 | [] | [] | TAGS
#art #text-to-image #dataset-CyberHarem/akira_makino_onichichi #license-mit #region-us
| Lora of akira\_makino\_onichichi
================================
This model is trained with HCP-Diffusion. And the auto-training framework is maintained by DeepGHS Team.
The base model used during training is NAI, and the base model used for generating preview images is Meina/MeinaMix\_V11.
After downloading the... | [] | [
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null | null | 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... | reinforcement-learning | VenomAI/DQN-SpaceInvadersNFS-v4 | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | 2023-11-12T18:30:13+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... | [
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null | 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. -->
# arabert-fully-supervised-arabic-propaganda
This model is a fine-tuned version of [aubmindlab/bert-base-arabertv02-twitter](https... | {"tags": ["generated_from_trainer"], "metrics": ["accuracy", "precision", "recall", "f1"], "base_model": "aubmindlab/bert-base-arabertv02-twitter", "model-index": [{"name": "arabert-fully-supervised-arabic-propaganda", "results": []}]} | text-classification | Bmalmotairy/arabert-fully-supervised-arabic-propaganda | [
"transformers",
"tensorboard",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:aubmindlab/bert-base-arabertv02-twitter",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2023-11-12T18:30:16+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #bert #text-classification #generated_from_trainer #base_model-aubmindlab/bert-base-arabertv02-twitter #autotrain_compatible #endpoints_compatible #region-us
| arabert-fully-supervised-arabic-propaganda
==========================================
This model is a fine-tuned version of aubmindlab/bert-base-arabertv02-twitter on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4417
* Accuracy: 0.9167
* Precision: 0.5577
* Recall: 0.7073
* F... | [
"### 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* lr\\_scheduler\\_warmup\\_ratio... | [
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null | 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. -->
# deberta-v3-large-survey-new_fact_main_passage-rater
This model is a fine-tuned version of [microsoft/deberta-v3-large](https://h... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "deberta-v3-large-survey-new_fact_main_passage-rater", "results": []}]} | text-classification | domenicrosati/deberta-v3-large-survey-new_fact_main_passage-rater | [
"transformers",
"pytorch",
"tensorboard",
"deberta-v2",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2023-11-12T18:37:29+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #deberta-v2 #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| deberta-v3-large-survey-new\_fact\_main\_passage-rater
======================================================
This model is a fine-tuned version of microsoft/deberta-v3-large on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2742
* Krippendorff: 0.9302
* Spearman: 0.9541
* Abso... | [
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null | null | peft |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Info... | {"library_name": "peft", "base_model": "Deci/DeciCoder-1b"} | null | CShorten/decicoder-50m-updated-schemaSplit-10k-steps | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Deci/DeciCoder-1b",
"region:us"
] | 2023-11-12T18:37:32+00:00 | [
"1910.09700"
] | [] | TAGS
#peft #safetensors #arxiv-1910.09700 #base_model-Deci/DeciCoder-1b #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
- Developed by:
- Shared by [optional]:
- Model type:
- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
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### Direct Use
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null | null | transformers | # Model Card
## Overview
This document provides details about the training process and performance metrics for a machine learning model. The model is designed for a specific task, and the following table summarizes its performance at different training steps.
## Performance Metrics
| Step | Training Loss | Validati... | {} | token-classification | DataIntelligenceTeam/Tansport1.4 | [
"transformers",
"pytorch",
"layoutlmv3",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2023-11-12T18:42:03+00:00 | [] | [] | TAGS
#transformers #pytorch #layoutlmv3 #token-classification #autotrain_compatible #endpoints_compatible #region-us
| Model Card
==========
Overview
--------
This document provides details about the training process and performance metrics for a machine learning model. The model is designed for a specific task, and the following table summarizes its performance at different training steps.
Performance Metrics
-------------------... | [] | [
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null | null | null |
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
| {"tags": ["Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class"], "model-index": [{"name": "Reinforce-Pixelcopter-PLE-v0", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Pixelcopter-PLE-v0", "type": "Pixelco... | reinforcement-learning | AF6ECHO/Reinforce-Pixelcopter-PLE-v0 | [
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | 2023-11-12T18:55:56+00:00 | [] | [] | TAGS
#Pixelcopter-PLE-v0 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us
|
# Reinforce Agent playing Pixelcopter-PLE-v0
This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: URL
| [
"# Reinforce Agent playing Pixelcopter-PLE-v0\n This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 .\n To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: URL"
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0.1513713151... |
null | 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. -->
# uplimit-project-3-phi-1.5
This model is a fine-tuned version of [microsoft/phi-1_5](https://huggingface.co/microsoft/phi-1_5) on... | {"license": "other", "tags": ["generated_from_trainer"], "datasets": ["scitldr"], "base_model": "microsoft/phi-1_5", "model-index": [{"name": "uplimit-project-3-phi-1.5", "results": []}]} | null | sergoumaya/uplimit-project-3-phi-1.5 | [
"tensorboard",
"safetensors",
"generated_from_trainer",
"dataset:scitldr",
"base_model:microsoft/phi-1_5",
"license:other",
"region:us"
] | 2023-11-12T18:57:20+00:00 | [] | [] | TAGS
#tensorboard #safetensors #generated_from_trainer #dataset-scitldr #base_model-microsoft/phi-1_5 #license-other #region-us
| uplimit-project-3-phi-1.5
=========================
This model is a fine-tuned version of microsoft/phi-1\_5 on the scitldr dataset.
It achieves the following results on the evaluation set:
* Loss: 2.5338
Model description
-----------------
More information needed
Intended uses & limitations
-----------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.001\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1",
"### Training... | [
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-0.10731098800897598,
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0.12170831... |
null | 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. -->
# fine-tuned-led-base-book-summary
This model is a fine-tuned version of [pszemraj/led-base-book-summary](https://huggingface.co/p... | {"license": "bsd-3-clause", "tags": ["generated_from_trainer"], "base_model": "pszemraj/led-base-book-summary", "model-index": [{"name": "fine-tuned-led-base-book-summary", "results": []}]} | text2text-generation | Narya-ai/fine-tuned-led-base-book-summary | [
"transformers",
"safetensors",
"led",
"text2text-generation",
"generated_from_trainer",
"base_model:pszemraj/led-base-book-summary",
"license:bsd-3-clause",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2023-11-12T19:01:34+00:00 | [] | [] | TAGS
#transformers #safetensors #led #text2text-generation #generated_from_trainer #base_model-pszemraj/led-base-book-summary #license-bsd-3-clause #autotrain_compatible #endpoints_compatible #region-us
| fine-tuned-led-base-book-summary
================================
This model is a fine-tuned version of pszemraj/led-base-book-summary on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 2.5918
* Rouge2 Precision: 0.0778
* Rouge2 Recall: 0.1291
* Rouge2 Fmeasure: 0.0958
Model d... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-06\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 4\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e... | [
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0.1578665... |
null | null | peft |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Info... | {"library_name": "peft", "base_model": "meta-llama/Llama-2-7b-hf"} | null | Mavitu56/LLamaEmergency | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:meta-llama/Llama-2-7b-hf",
"region:us"
] | 2023-11-12T19:04:51+00:00 | [
"1910.09700"
] | [] | TAGS
#peft #safetensors #arxiv-1910.09700 #base_model-meta-llama/Llama-2-7b-hf #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
- Developed by:
- Shared by [optional]:
- Model type:
- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
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