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train · 787k rows

modelId stringlengths 5 139	author stringlengths 2 42	last_modified timestamp[us, tz=UTC]date 2020-02-15 11:33:14 2025-05-30 06:27:13	downloads int64 0 223M	likes int64 0 11.7k	library_name stringclasses 459 values	tags sequencelengths 1 4.05k	pipeline_tag stringclasses 54 values	createdAt timestamp[us, tz=UTC]date 2022-03-02 23:29:04 2025-05-30 06:25:49	card stringlengths 11 1.01M
timjwhite/Pyramids	timjwhite	2023-06-19T03:46:20Z	1	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "Pyramids", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Pyramids", "region:us" ]	reinforcement-learning	2023-06-19T03:46:11Z	--- library_name: ml-agents tags: - Pyramids - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Pyramids --- # ppo Agent playing Pyramids This is a trained model of a ppo agent playing Pyramids using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A short tutorial where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A longer tutorial to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: timjwhite/Pyramids 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
twidfeel/xlm-roberta-base-finetuned-panx-de	twidfeel	2023-06-19T03:23:34Z	103	0	transformers	[ "transformers", "pytorch", "tensorboard", "xlm-roberta", "token-classification", "generated_from_trainer", "dataset:xtreme", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2023-06-19T03:13:47Z	--- license: mit tags: - generated_from_trainer datasets: - xtreme metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-de results: - task: name: Token Classification type: token-classification dataset: name: xtreme type: xtreme config: PAN-X.de split: validation args: PAN-X.de metrics: - name: F1 type: f1 value: 0.8608314849570609 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # xlm-roberta-base-finetuned-panx-de This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.1378 - F1: 0.8608 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 0.258 \| 1.0 \| 525 \| 0.1619 \| 0.8168 \| \| 0.1295 \| 2.0 \| 1050 \| 0.1357 \| 0.8468 \| \| 0.0827 \| 3.0 \| 1575 \| 0.1378 \| 0.8608 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.0 - Tokenizers 0.13.3
draziert/SpaceInvadersNoFrameskip-v4	draziert	2023-06-19T03:12:09Z	2	0	stable-baselines3	[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-06-19T03:11:30Z	--- 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: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 metrics: - type: mean_reward value: 576.00 +/- 223.45 name: mean_reward verified: false --- # 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 framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga draziert -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga draziert -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga draziert ``` ## Hyperparameters ```python OrderedDict([('batch_size', 32), ('buffer_size', 100000), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_final_eps', 0.01), ('exploration_fraction', 0.1), ('frame_stack', 4), ('gradient_steps', 1), ('learning_rate', 0.0001), ('learning_starts', 100000), ('n_timesteps', 1500000), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ``` # Environment Arguments ```python {'render_mode': 'rgb_array'} ```
AhsanZaidi/Reinforce-PixelCopter	AhsanZaidi	2023-06-19T03:07:40Z	0	0	null	[ "Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2023-06-19T03:07:30Z	--- tags: - Pixelcopter-PLE-v0 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-PixelCopter results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Pixelcopter-PLE-v0 type: Pixelcopter-PLE-v0 metrics: - type: mean_reward value: 60.10 +/- 45.96 name: mean_reward verified: false --- # 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
nolanaatama/nvrndngdrm	nolanaatama	2023-06-19T02:50:39Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-02-23T03:01:38Z	--- license: creativeml-openrail-m ---
timjwhite/ppo-SnowballTarget	timjwhite	2023-06-19T02:42:15Z	4	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "SnowballTarget", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SnowballTarget", "region:us" ]	reinforcement-learning	2023-06-18T06:19:56Z	--- library_name: ml-agents tags: - SnowballTarget - deep-reinforcement-learning - reinforcement-learning - ML-Agents-SnowballTarget --- # ppo Agent playing SnowballTarget This is a trained model of a ppo agent playing SnowballTarget using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A short tutorial where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A longer tutorial to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: timjwhite/ppo-SnowballTarget 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
openaccess-ai-collective/minotaur-15b	openaccess-ai-collective	2023-06-19T02:41:57Z	9	15	transformers	[ "transformers", "pytorch", "gpt_bigcode", "text-generation", "code", "dataset:bigcode/the-stack-dedup", "dataset:tiiuae/falcon-refinedweb", "dataset:ehartford/WizardLM_alpaca_evol_instruct_70k_unfiltered", "dataset:QingyiSi/Alpaca-CoT", "dataset:teknium/GPTeacher-General-Instruct", "dataset:metaeval/ScienceQA_text_only", "dataset:hellaswag", "dataset:openai/summarize_from_feedback", "dataset:riddle_sense", "dataset:gsm8k", "dataset:camel-ai/math", "dataset:camel-ai/biology", "dataset:camel-ai/physics", "dataset:camel-ai/chemistry", "dataset:winglian/evals", "arxiv:1911.02150", "arxiv:2205.14135", "arxiv:2207.14255", "arxiv:2305.06161", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-06-16T17:19:19Z	--- pipeline_tag: text-generation inference: true widget: - text: 'def print_hello_world():' example_title: Hello world group: Python - text: 'Gradient descent is' example_title: Machine Learning group: English - license: bigcode-openrail-m datasets: - bigcode/the-stack-dedup - tiiuae/falcon-refinedweb - ehartford/WizardLM_alpaca_evol_instruct_70k_unfiltered - QingyiSi/Alpaca-CoT - teknium/GPTeacher-General-Instruct - metaeval/ScienceQA_text_only - hellaswag - openai/summarize_from_feedback - riddle_sense - gsm8k - camel-ai/math - camel-ai/biology - camel-ai/physics - camel-ai/chemistry - winglian/evals metrics: - code_eval - mmlu - arc - hellaswag - truthfulqa library_name: transformers tags: - code extra_gated_prompt: >- ## Model License Agreement Please read the BigCode [OpenRAIL-M license](https://huggingface.co/spaces/bigcode/bigcode-model-license-agreement) agreement before accepting it. extra_gated_fields: I accept the above license agreement, and will use the Model complying with the set of use restrictions and sharing requirements: checkbox --- [<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl) [💵 Donate to OpenAccess AI Collective](https://github.com/sponsors/OpenAccess-AI-Collective) to help us keep building great tools and models! # Minotaur 15B 8K Minotaur 15B is an instruct fine-tuned model on top of Starcoder Plus. Minotaur 15B is fine-tuned on only completely open datasets making this model reproducible by anyone. Minotaur 15B has a context length of 8K tokens, allowing for strong recall at long contexts. Questions, comments, feedback, looking to donate, or want to help? Reach out on our [Discord](https://discord.gg/PugNNHAF5r) or email [[email protected]](mailto:[email protected]) # Prompts Chat only style prompts using `USER:`,`ASSISTANT:`. <img src="https://huggingface.co/openaccess-ai-collective/minotaur-13b/resolve/main/minotaur.png" alt="minotaur" width="600" height="600"/> # Training Datasets Minotaur 15B model is fine-tuned on the following openly available datasets: - [WizardLM](https://huggingface.co/datasets/ehartford/WizardLM_alpaca_evol_instruct_70k_unfiltered) - [subset of QingyiSi/Alpaca-CoT for roleplay and CoT](https://huggingface.co/QingyiSi/Alpaca-CoT) - [GPTeacher-General-Instruct](https://huggingface.co/datasets/teknium/GPTeacher-General-Instruct) - [metaeval/ScienceQA_text_only](https://huggingface.co/datasets/metaeval/ScienceQA_text_only) - instruct for concise responses - [openai/summarize_from_feedback](https://huggingface.co/datasets/openai/summarize_from_feedback) - instruct augmented tl;dr summarization - [camel-ai/math](https://huggingface.co/datasets/camel-ai/math) - [camel-ai/physics](https://huggingface.co/datasets/camel-ai/physics) - [camel-ai/chemistry](https://huggingface.co/datasets/camel-ai/chemistry) - [camel-ai/biology](https://huggingface.co/datasets/camel-ai/biology) - [winglian/evals](https://huggingface.co/datasets/winglian/evals) - instruct augmented datasets - custom sysnthetic datasets around misconceptions, in-context qa, jokes, N-tasks problems, and context-insensitivity - ARC-Easy & ARC-Challenge - instruct augmented for detailed responses, derived from the `train` split - [hellaswag](https://huggingface.co/datasets/hellaswag) - 30K+ rows of instruct augmented for detailed explanations w 30K+ rows, derived from the `train` split - [riddle_sense](https://huggingface.co/datasets/riddle_sense) - instruct augmented, derived from the `train` split - [gsm8k](https://huggingface.co/datasets/gsm8k) - instruct augmented, derived from the `train` split - prose generation # Shoutouts Special thanks to Nanobit for helping with Axolotl and TheBloke for quantizing these models are more accessible to all. # Demo HF Demo in Spaces available in the [Community ChatBot Arena](https://huggingface.co/spaces/openaccess-ai-collective/rlhf-arena) under the OAAIC Chatbots tab. ## Release Notes - https://wandb.ai/wing-lian/minotaur-16b-8k/runs/tshgbl2k ## Build Minotaur was built with [Axolotl](https://github.com/OpenAccess-AI-Collective/axolotl) on 4XA100 80GB - 1 epochs taking approximately 30 hours - Trained using QLoRA techniques ## Bias, Risks, and Limitations Minotaur has not been aligned to human preferences with techniques like RLHF or deployed with in-the-loop filtering of responses like ChatGPT, so the model can produce problematic outputs (especially when prompted to do so). Minotaur was fine-tuned from the base model StarCoder, please refer to its model card's Limitations Section for relevant information. (included below) ## Benchmarks TBD ## Examples TBD # StarCoderPlus Play with the instruction-tuned StarCoderPlus at [StarChat-Beta](https://huggingface.co/spaces/HuggingFaceH4/starchat-playground). ## Table of Contents 1. [Model Summary](##model-summary) 2. [Use](##use) 3. [Limitations](##limitations) 4. [Training](##training) 5. [License](##license) 6. [Citation](##citation) ## Model Summary StarCoderPlus is a fine-tuned version of [StarCoderBase](https://huggingface.co/bigcode/starcoderbase) on 600B tokens from the English web dataset [RedefinedWeb](https://huggingface.co/datasets/tiiuae/falcon-refinedweb) combined with [StarCoderData](https://huggingface.co/datasets/bigcode/starcoderdata) from [The Stack (v1.2)](https://huggingface.co/datasets/bigcode/the-stack) and a Wikipedia dataset. It's a 15.5B parameter Language Model trained on English and 80+ programming languages. The model uses [Multi Query Attention](https://arxiv.org/abs/1911.02150), [a context window of 8192 tokens](https://arxiv.org/abs/2205.14135), and was trained using the [Fill-in-the-Middle objective](https://arxiv.org/abs/2207.14255) on 1.6 trillion tokens. - Repository: [bigcode/Megatron-LM](https://github.com/bigcode-project/Megatron-LM) - Project Website: [bigcode-project.org](https://www.bigcode-project.org) - Point of Contact: [[email protected]](mailto:[email protected]) - Languages: English & 80+ Programming languages ## Use ### Intended use The model was trained on English and GitHub code. As such it is _not_ an instruction model and commands like "Write a function that computes the square root." do not work well. However, the instruction-tuned version in [StarChat](hhttps://huggingface.co/spaces/HuggingFaceH4/starchat-playground) makes a capable assistant. Feel free to share your generations in the Community tab! ### Generation ```python # pip install -q transformers from transformers import AutoModelForCausalLM, AutoTokenizer checkpoint = "bigcode/starcoderplus" device = "cuda" # for GPU usage or "cpu" for CPU usage tokenizer = AutoTokenizer.from_pretrained(checkpoint) model = AutoModelForCausalLM.from_pretrained(checkpoint).to(device) inputs = tokenizer.encode("def print_hello_world():", return_tensors="pt").to(device) outputs = model.generate(inputs) print(tokenizer.decode(outputs[0])) ``` ### Fill-in-the-middle Fill-in-the-middle uses special tokens to identify the prefix/middle/suffix part of the input and output: ```python input_text = "<fim_prefix>def print_hello_world():\n <fim_suffix>\n print('Hello world!')<fim_middle>" inputs = tokenizer.encode(input_text, return_tensors="pt").to(device) outputs = model.generate(inputs) print(tokenizer.decode(outputs[0])) ``` ### Attribution & Other Requirements The training code dataset of the model was filtered for permissive licenses only. Nevertheless, the model can generate source code verbatim from the dataset. The code's license might require attribution and/or other specific requirements that must be respected. We provide a [search index](https://huggingface.co/spaces/bigcode/starcoder-search) that let's you search through the pretraining data to identify where generated code came from and apply the proper attribution to your code. # Limitations The model has been trained on a mixture of English text from the web and GitHub code. Therefore it might encounter limitations when working with non-English text, and can carry the stereotypes and biases commonly encountered online. Additionally, the generated code should be used with caution as it may contain errors, inefficiencies, or potential vulnerabilities. For a more comprehensive understanding of the base model's code limitations, please refer to See [StarCoder paper](hhttps://arxiv.org/abs/2305.06161). # Training StarCoderPlus is a fine-tuned version on 600B English and code tokens of StarCoderBase, which was pre-trained on 1T code tokens. Below are the fine-tuning details: ## Model - Architecture: GPT-2 model with multi-query attention and Fill-in-the-Middle objective - Finetuning steps: 150k - Finetuning tokens: 600B - Precision: bfloat16 ## Hardware - GPUs: 512 Tesla A100 - Training time: 14 days ## Software - Orchestration: [Megatron-LM](https://github.com/bigcode-project/Megatron-LM) - Neural networks: [PyTorch](https://github.com/pytorch/pytorch) - BP16 if applicable: [apex](https://github.com/NVIDIA/apex) # License The model is licensed under the BigCode OpenRAIL-M v1 license agreement. You can find the full agreement [here](https://huggingface.co/spaces/bigcode/bigcode-model-license-agreement).
pooruss-lsh/tool-llama7b-multi-tool-lora	pooruss-lsh	2023-06-19T02:34:46Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2023-06-19T02:29:18Z	--- license: apache-2.0 --- # Model Card for Model ID This is a lora version tool-llama model introduced in [ToolBench](https://github.com/OpenBMB/ToolBench) under multi-tool scenario. ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - License: apache-2.0 - Finetuned from model [optional]: LLaMA-7b ## Uses Refer to [ToolBench](https://github.com/OpenBMB/ToolBench). ## Training Details Trained with the released multi-tool data in ToolBench, including 3 scenarios.
Alexisbal/distilbert-base-uncased-finetuned-emo	Alexisbal	2023-06-19T02:12:05Z	4	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-06-10T16:43:27Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: distilbert-base-uncased-finetuned-emo results: [] --- <!-- 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-emo This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3127 - Accuracy: 0.8775 - F1: 0.8775 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| 0.4049 \| 1.0 \| 500 \| 0.3183 \| 0.8655 \| 0.8653 \| \| 0.247 \| 2.0 \| 1000 \| 0.3127 \| 0.8775 \| 0.8775 \| ### Framework versions - Transformers 4.29.2 - Pytorch 2.0.0 - Datasets 2.13.0 - Tokenizers 0.13.3
echrisantus/taxi-v3	echrisantus	2023-06-19T02:04:09Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-06-19T01:53:31Z	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: taxi-v3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.56 +/- 2.71 name: mean_reward verified: false --- # Q-Learning Agent playing1 Taxi-v3 This is a trained model of a Q-Learning agent playing Taxi-v3 . ## Usage ```python model = load_from_hub(repo_id="echrisantus/taxi-v3", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
phi0112358/Zicklein-7B-german_Alpaca-ggml	phi0112358	2023-06-19T01:57:43Z	0	4	null	[ "llama", "alpaca", "ggml", "german", "deutsch", "zicklein", "de", "dataset:yahma/alpaca-cleaned", "license:apache-2.0", "region:us" ]	null	2023-06-18T23:54:37Z	--- license: apache-2.0 datasets: - yahma/alpaca-cleaned language: - de tags: - llama - alpaca - ggml - german - deutsch - zicklein --- # --- --- # Zicklein: A german finetuned instructions following LLaMA ## This is a ggml conversion of [Zicklein](https://github.com/avocardio/zicklein) 7B. ## Zicklein itself is a LLaMA finetuned model with a cleaned and german translated [Alpaca](https://github.com/tatsu-lab/stanford_alpaca) [dataset](https://github.com/LEL-A/GerAlpacaDataCleaned). Currently I have only converted it into new k-quant method Q5_K_M. I will gladly make more versions on request. Other possible quantizations include: q2_K, q3_K_S, q3_K_M, q3_K_L, q4_K_S, q4_K_M, q5_K_S, q5_K_M, q6_K A f-16 version could be found here: [nikuya3/alpaca-lora-7b-german-base-51k-ggml](https://huggingface.co/nikuya3/alpaca-lora-7b-german-base-51k-ggml) Compatible with llama.cpp, but also with: - text-generation-webui - KoboldCpp - ParisNeo/GPT4All-UI - llama-cpp-python - ctransformers --- ## Prompt format Since this model is based on alpaca dataset, the right prompt formatting should look like this: ``` Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request. ### Instruction: {instruction} ### Input: {input} ### Response: ``` Or without addiotional input: ``` Below is an instruction that describes a task. Write a response that appropriately completes the request. ### Instruction: {instruction} ### Response: ``` --- ### That's it! If you have any further questions, feel free to contact me or start a discussion
openaccess-ai-collective/dodona-pyg-v8p4-15b-preview	openaccess-ai-collective	2023-06-19T01:49:14Z	5	0	transformers	[ "transformers", "pytorch", "gpt_bigcode", "text-generation", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-06-18T21:50:37Z	[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl) [💵 Donate to OpenAccess AI Collective](https://github.com/sponsors/OpenAccess-AI-Collective) to help us keep building great tools and models! # Dodona Pyg 15B 8K Preview This finetune adds about 100MB of the v8p4 dataset to Dodona 15B 8K. Dodona 15B 8K Preview is an experiment for fan-fiction and character ai use cases. It is built on Starcoder Plus to give it 8K context length and pretrained on a corpus of fanfiction and visual novels. Lots of mistakes were made during the creation of this model, but we didn't want to throw $300 of model training time out the window, so we are releasing this as a preview. If you would like to see us continue to build more models like this, please consider donating by sponsoring us on GitHub on the link above or [Buy me a coffee](https://www.buymeacoffee.com/winglian). Questions, comments, feedback, looking to donate, or want to help? Reach out on our [Discord](https://discord.gg/PugNNHAF5r) or email [[email protected]](mailto:[email protected]) # Prompts While this model is minimally finetuned with USER: / ASSISTANT: prompts, it seems to respond better to Alpaca style prompts: ``` Below is an instruction that describes a task. Write a response that appropriately completes the request. ### Instruction: ... ### Response: ``` <img src="https://huggingface.co/openaccess-ai-collective/dodona-15b-preview/resolve/main/dodona.png" alt="oracle of dodona" width="600" height="500"/>
Calluna/Asuna	Calluna	2023-06-19T01:47:54Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-06-19T01:47:54Z	--- license: creativeml-openrail-m ---
nolanaatama/nylr	nolanaatama	2023-06-19T01:20:48Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-04-14T07:28:35Z	--- license: creativeml-openrail-m ---
nolanaatama/nmnpcprndpsttmskplr	nolanaatama	2023-06-19T01:06:21Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-06-19T01:00:44Z	--- license: creativeml-openrail-m ---
ponponnsan/LoRAInstruction	ponponnsan	2023-06-19T00:45:32Z	0	0	null	[ "region:us" ]	null	2023-06-19T00:37:34Z	LoRAを用いた指示文のファインチューニング。
FALLENSTAR/JSR_style_LoRa	FALLENSTAR	2023-06-19T00:37:48Z	0	0	null	[ "region:us" ]	null	2023-06-18T09:38:49Z	Inspired by the streets of Tokyo-to Dedicated to Jet Set Radio I decided to make a Jet Set Radio style LoRa Works well with the characters as well as everything else imo best settings: Steps: 25 Sampler: DPM++ SDE Karras, Euler a, CFG scale: 6.5-11 and with LoRa strength 1 ![00004-3836112645.png](https://s3.amazonaws.com/moonup/production/uploads/640761fd2e309e65452364fa/nV-H8Mpr73GmElwGW6ZGK.png) ![00010-349973646.png](https://s3.amazonaws.com/moonup/production/uploads/640761fd2e309e65452364fa/kxOLLlOlJhrMMzjoBHimO.png) ![00026-3628689958.png](https://s3.amazonaws.com/moonup/production/uploads/640761fd2e309e65452364fa/kEwhcx4Sn1eCTd7CCv1ab.png) ![00032-3937542360.png](https://s3.amazonaws.com/moonup/production/uploads/640761fd2e309e65452364fa/NkhFcJZg9AQKzYYbPu824.png)
ponponnsan/sakura-CALM	ponponnsan	2023-06-19T00:36:49Z	0	0	null	[ "region:us" ]	null	2023-06-18T13:49:01Z	LoRAを用いたファインチューニングにsakura-datasetを加えたモデル。あまりうまくいっていない気がする。。。
ardhies/mej	ardhies	2023-06-19T00:34:54Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-06-19T00:33:26Z	--- license: creativeml-openrail-m ---
lewdryuna/A-BmixB	lewdryuna	2023-06-19T00:01:50Z	0	3	null	[ "region:us" ]	null	2023-06-19T00:01:47Z	--- duplicated_from: malikxseto/Necromancing-BBMixes --- # BBMixes Backup Backup of different BBMixes ## BBMIX-ALICE - [BB-Mix-ALICE_V1.0](https://huggingface.co/malikxseto/Necromancing-BBMixes/resolve/main/BBMIX-ALICE/bbMIXALICE_v10.safetensors) ## BBMIX ANN - [BB-Mix-ANN_V1.0](https://huggingface.co/malikxseto/Necromancing-BBMixes/resolve/main/BBMIX-ANN/bbmixANN_v10.safetensors) ## BBMIX EIMI - [BB-Mix-EIMI_V1.0](https://huggingface.co/malikxseto/Necromancing-BBMixes/resolve/main/BBMIX-EIMI/bbmixEIMI_v10.safetensors) ## BBMIX EVE - [BB-Mix-EVE_V1.0](https://huggingface.co/malikxseto/Necromancing-BBMixes/resolve/main/BBMIX-EVE/bbmixEVE_v10.safetensors) ## BBMIX HANNA - [BB-Mix-HANNA_V1.0](https://huggingface.co/malikxseto/Necromancing-BBMixes/resolve/main/BBMIX-HANNA/bbmixHANNA_v10.safetensors) ## BBMIX JOY - [BB-Mix-JOY_V1.0](https://huggingface.co/malikxseto/Necromancing-BBMixes/resolve/main/BBMIX-JOY/bbmixJOY_v10.safetensors) - [BB-Mix-JOY_V2.0](https://huggingface.co/malikxseto/Necromancing-BBMixes/resolve/main/BBMIX-JOY/bbmixJOY_v20.safetensors) ## BBMIX JUDE - [BB-Mix-JUDE_V1.0](https://huggingface.co/malikxseto/Necromancing-BBMixes/resolve/main/BBMIX-JUDE/bbmixJUDE_v10.safetensors) ## BBMIX JULIA - [BB-Mix-JULIA_V1.0](https://huggingface.co/malikxseto/Necromancing-BBMixes/resolve/main/BBMIX-JULIA/bbmixJULIA_v10.safetensors) ## BBMIX KALI - [BB-Mix-KALI_V1.0](https://huggingface.co/malikxseto/Necromancing-BBMixes/resolve/main/BBMIX-KALI/bbmixKALI_v10.safetensors) ## BBMIX LIN - [BB-Mix-LIN_V1.0](https://huggingface.co/malikxseto/Necromancing-BBMixes/resolve/main/BBMIX-LIN/bbmixLIN_v10.safetensors) ## BBMIX LUCI - [BB-Mix-LUCI_V1.0](https://huggingface.co/malikxseto/Necromancing-BBMixes/resolve/main/BBMIX-LUCI/bbmixLUCI_v10.safetensors) - [BB-Mix-LUCI_V2.0](https://huggingface.co/malikxseto/Necromancing-BBMixes/resolve/main/BBMIX-LUCI/bbmixLUCI_v20.safetensors) ## BBMIX NUNU - [BB-Mix-NUNU_V1.0](https://huggingface.co/malikxseto/Necromancing-BBMixes/resolve/main/BBMIX-NUNU/bbmixNUNU_v10.safetensors) ## BBMIX RUIS - [BB-Mix-RUIS_V1.0](https://huggingface.co/malikxseto/Necromancing-BBMixes/resolve/main/BBMIX-RUIS/bbMIXRUIS_v10.safetensors) - [BB-Mix-RUIS_V1.1](https://huggingface.co/malikxseto/Necromancing-BBMixes/resolve/main/BBMIX-RUIS/bbMIXRUIS_v11.safetensors) - [BB-Mix-RUIS_V1.2](https://huggingface.co/malikxseto/Necromancing-BBMixes/resolve/main/BBMIX-RUIS/bbMIXRUIS_v12.safetensors) - [BB-Mix-RUIS_V1.5](https://huggingface.co/malikxseto/Necromancing-BBMixes/resolve/main/BBMIX-RUIS/bbMIXRUIS_v15.safetensors) - [BB-Mix-RUIS+](https://huggingface.co/malikxseto/Necromancing-BBMixes/resolve/main/BBMIX-RUIS/bbmixRUIS_bbmixRUIS.safetensors) ## BBMIX SANDY - [BB-Mix-SANDY_V1.0](https://huggingface.co/malikxseto/Necromancing-BBMixes/resolve/main/BBMIX-SANDY/bbMIXSANDY_v10.safetensors) ## BBMIX VERONICA - [BB-Mix-VERONICA_V1.0](https://huggingface.co/malikxseto/Necromancing-BBMixes/resolve/main/BBMIX-VERONICA/bbmixVERONICA_v10.safetensors)
NasimB/gpt2_left_out_open_subtitles	NasimB	2023-06-18T23:58:42Z	7	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "generated_from_trainer", "dataset:generator", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-06-18T21:32:05Z	--- license: mit tags: - generated_from_trainer datasets: - generator model-index: - name: gpt2_left_out_open_subtitles results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # gpt2_left_out_open_subtitles This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the generator dataset. It achieves the following results on the evaluation set: - Loss: 4.3147 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 1000 - num_epochs: 10 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:-----:\|:---------------:\| \| 6.353 \| 0.35 \| 500 \| 5.4389 \| \| 5.0517 \| 0.71 \| 1000 \| 4.9981 \| \| 4.6553 \| 1.06 \| 1500 \| 4.7472 \| \| 4.3892 \| 1.41 \| 2000 \| 4.5957 \| \| 4.258 \| 1.77 \| 2500 \| 4.4664 \| \| 4.0843 \| 2.12 \| 3000 \| 4.3902 \| \| 3.922 \| 2.48 \| 3500 \| 4.3282 \| \| 3.89 \| 2.83 \| 4000 \| 4.2537 \| \| 3.713 \| 3.18 \| 4500 \| 4.2408 \| \| 3.6155 \| 3.54 \| 5000 \| 4.2007 \| \| 3.6227 \| 3.89 \| 5500 \| 4.1522 \| \| 3.4072 \| 4.24 \| 6000 \| 4.1829 \| \| 3.3651 \| 4.6 \| 6500 \| 4.1499 \| \| 3.3798 \| 4.95 \| 7000 \| 4.1168 \| \| 3.1038 \| 5.3 \| 7500 \| 4.1744 \| \| 3.1121 \| 5.66 \| 8000 \| 4.1606 \| \| 3.1104 \| 6.01 \| 8500 \| 4.1563 \| \| 2.8101 \| 6.36 \| 9000 \| 4.2017 \| \| 2.8457 \| 6.72 \| 9500 \| 4.1979 \| \| 2.7947 \| 7.07 \| 10000 \| 4.2241 \| \| 2.5839 \| 7.43 \| 10500 \| 4.2544 \| \| 2.6024 \| 7.78 \| 11000 \| 4.2558 \| \| 2.5182 \| 8.13 \| 11500 \| 4.2838 \| \| 2.41 \| 8.49 \| 12000 \| 4.2963 \| \| 2.416 \| 8.84 \| 12500 \| 4.3009 \| \| 2.3609 \| 9.19 \| 13000 \| 4.3117 \| \| 2.3182 \| 9.55 \| 13500 \| 4.3138 \| \| 2.3187 \| 9.9 \| 14000 \| 4.3147 \| ### Framework versions - Transformers 4.26.1 - Pytorch 1.11.0+cu113 - Datasets 2.13.0 - Tokenizers 0.13.3
ngkuissi/Pixelcopter-PLE-v0	ngkuissi	2023-06-18T23:53:44Z	0	0	null	[ "Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2023-06-10T19:03:24Z	--- tags: - Pixelcopter-PLE-v0 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Pixelcopter-PLE-v0 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Pixelcopter-PLE-v0 type: Pixelcopter-PLE-v0 metrics: - type: mean_reward value: 45.70 +/- 42.58 name: mean_reward verified: false --- # 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
Hodurr/Sarah	Hodurr	2023-06-18T23:49:11Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-06-18T23:42:52Z	--- license: creativeml-openrail-m ---
RajkNakka/distilbert-base-uncased-finetuned-imdb	RajkNakka	2023-06-18T23:49:02Z	125	0	transformers	[ "transformers", "pytorch", "distilbert", "fill-mask", "generated_from_trainer", "dataset:imdb", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2023-06-18T23:34:50Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - imdb model-index: - name: distilbert-base-uncased-finetuned-imdb results: [] --- <!-- 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-imdb This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 2.4745 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 2.7111 \| 1.0 \| 157 \| 2.5003 \| \| 2.5841 \| 2.0 \| 314 \| 2.4255 \| \| 2.5275 \| 3.0 \| 471 \| 2.4364 \| ### Framework versions - Transformers 4.29.2 - Pytorch 2.0.1+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3
teostereciu/wikidata-property-recognizer	teostereciu	2023-06-18T23:11:04Z	5	0	sentence-transformers	[ "sentence-transformers", "pytorch", "mpnet", "setfit", "text-classification", "en", "arxiv:2209.11055", "license:apache-2.0", "region:us" ]	text-classification	2023-06-16T11:28:37Z	--- license: apache-2.0 tags: - setfit - sentence-transformers - text-classification pipeline_tag: text-classification language: - en metrics: - accuracy --- # /var/folders/x1/dl1z_tcs7zb6pppfbf65d5sh0000gn/T/tmp5w7ybndg/teostereciu/wikidata-property-recognizer This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for Wikidata property recognition in the domain of videogames. The model has been trained using an efficient few-shot learning technique that involves: 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning. 2. Training a classification head with features from the fine-tuned Sentence Transformer. ## Usage To use this model for inference, first install the SetFit library: ```bash python -m pip install setfit ``` You can then run inference as follows: ```python from setfit import SetFitModel # Download from Hub and run inference model = SetFitModel.from_pretrained("teostereciu/wikidata-property-recognizer") # Run inference preds = model(["Who developed Celeste?", "How many copies has The Last of Us Part II sold?"]) ``` ## BibTeX entry and citation info ```bibtex @article{https://doi.org/10.48550/arxiv.2209.11055, doi = {10.48550/ARXIV.2209.11055}, url = {https://arxiv.org/abs/2209.11055}, author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren}, keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Efficient Few-Shot Learning Without Prompts}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution 4.0 International} } ```
Gage888/GageAI	Gage888	2023-06-18T22:51:32Z	0	0	adapter-transformers	[ "adapter-transformers", "robotics", "zh", "en", "dataset:tiiuae/falcon-refinedweb", "dataset:OpenAssistant/oasst1", "dataset:databricks/databricks-dolly-15k", "dataset:fka/awesome-chatgpt-prompts", "arxiv:1910.09700", "license:pddl", "region:us" ]	robotics	2023-06-18T22:45:39Z	--- license: pddl datasets: - tiiuae/falcon-refinedweb - OpenAssistant/oasst1 - databricks/databricks-dolly-15k - fka/awesome-chatgpt-prompts language: - zh - en library_name: adapter-transformers pipeline_tag: robotics --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1). ## 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 Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Data Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
BenjaminOcampo/model-bert__trained-in-dynahate__seed-1	BenjaminOcampo	2023-06-18T22:22:16Z	106	0	transformers	[ "transformers", "pytorch", "bert", "text-classification", "en", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-06-18T22:20:27Z	--- language: en --- # Model Card for BenjaminOcampo/model-bert__trained-in-dynahate__seed-1 <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> Classification results dev set ``` precision recall f1-score support 0 0.8132 0.7993 0.8062 1933 1 0.8236 0.8362 0.8299 2167 accuracy 0.8188 4100 macro avg 0.8184 0.8177 0.8180 4100 weighted avg 0.8187 0.8188 0.8187 4100 ``` Classification results test set ``` precision recall f1-score support 0 0.7585 0.7597 0.7591 1852 1 0.8035 0.8025 0.8030 2268 accuracy 0.7833 4120 macro avg 0.7810 0.7811 0.7811 4120 weighted avg 0.7833 0.7833 0.7833 4120 ``` - Developed by: Benjamin Ocampo - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): en - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: https://github.com/huggingface/huggingface_hub - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ### How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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BenjaminOcampo/model-bert__trained-in-dynahate__seed-2	BenjaminOcampo	2023-06-18T22:19:12Z	105	0	transformers	[ "transformers", "pytorch", "bert", "text-classification", "en", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-06-18T22:18:12Z	--- language: en --- # Model Card for BenjaminOcampo/model-bert__trained-in-dynahate__seed-2 <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> Classification results dev set ``` precision recall f1-score support 0 0.8245 0.7900 0.8069 1933 1 0.8194 0.8500 0.8344 2167 accuracy 0.8217 4100 macro avg 0.8220 0.8200 0.8206 4100 weighted avg 0.8218 0.8217 0.8214 4100 ``` Classification results test set ``` precision recall f1-score support 0 0.7637 0.7522 0.7579 1852 1 0.8001 0.8100 0.8050 2268 accuracy 0.7840 4120 macro avg 0.7819 0.7811 0.7814 4120 weighted avg 0.7837 0.7840 0.7838 4120 ``` - Developed by: Benjamin Ocampo - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): en - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: https://github.com/huggingface/huggingface_hub - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ### How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Data Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
BenjaminOcampo/model-bert__trained-in-ihc__seed-42	BenjaminOcampo	2023-06-18T22:18:09Z	103	0	transformers	[ "transformers", "pytorch", "bert", "text-classification", "en", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-06-18T22:16:55Z	--- language: en --- # Model Card for BenjaminOcampo/model-bert__trained-in-ihc__seed-42 <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> Classification results dev set ``` precision recall f1-score support 0 0.8243 0.8243 0.8243 2658 1 0.7149 0.7149 0.7149 1638 accuracy 0.7826 4296 macro avg 0.7696 0.7696 0.7696 4296 weighted avg 0.7826 0.7826 0.7826 4296 ``` Classification results test set ``` precision recall f1-score support 0 0.8234 0.7983 0.8107 2658 1 0.6882 0.7222 0.7048 1638 accuracy 0.7693 4296 macro avg 0.7558 0.7603 0.7577 4296 weighted avg 0.7719 0.7693 0.7703 4296 ``` - Developed by: Benjamin Ocampo - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): en - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: https://github.com/huggingface/huggingface_hub - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ### How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
BenjaminOcampo/model-bert__trained-in-ihc__seed-2	BenjaminOcampo	2023-06-18T22:16:52Z	106	0	transformers	[ "transformers", "pytorch", "bert", "text-classification", "en", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-06-18T22:15:18Z	--- language: en --- # Model Card for BenjaminOcampo/model-bert__trained-in-ihc__seed-2 <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> Classification results dev set ``` precision recall f1-score support 0 0.8299 0.8149 0.8223 2658 1 0.7082 0.7289 0.7184 1638 accuracy 0.7821 4296 macro avg 0.7690 0.7719 0.7704 4296 weighted avg 0.7835 0.7821 0.7827 4296 ``` Classification results test set ``` precision recall f1-score support 0 0.8252 0.7886 0.8065 2658 1 0.6800 0.7289 0.7036 1638 accuracy 0.7658 4296 macro avg 0.7526 0.7588 0.7550 4296 weighted avg 0.7698 0.7658 0.7672 4296 ``` - Developed by: Benjamin Ocampo - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): en - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: https://github.com/huggingface/huggingface_hub - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ### How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Data Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
sakharamg/NMTKD	sakharamg	2023-06-18T22:15:44Z	0	0	null	[ "region:us" ]	null	2023-06-18T20:57:36Z	# Knowledge_Distillation # Knowledge_Distillation # Knowledge_Distillation
BenjaminOcampo/model-bert__trained-in-ihc__seed-1	BenjaminOcampo	2023-06-18T22:15:15Z	103	0	transformers	[ "transformers", "pytorch", "bert", "text-classification", "en", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-06-18T22:14:19Z	--- language: en --- # Model Card for BenjaminOcampo/model-bert__trained-in-ihc__seed-1 <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> Classification results dev set ``` precision recall f1-score support 0 0.8204 0.8401 0.8301 2658 1 0.7300 0.7015 0.7154 1638 accuracy 0.7872 4296 macro avg 0.7752 0.7708 0.7728 4296 weighted avg 0.7859 0.7872 0.7864 4296 ``` Classification results test set ``` precision recall f1-score support 0 0.8216 0.8284 0.8250 2658 1 0.7178 0.7082 0.7130 1638 accuracy 0.7826 4296 macro avg 0.7697 0.7683 0.7690 4296 weighted avg 0.7821 0.7826 0.7823 4296 ``` - Developed by: Benjamin Ocampo - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): en - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: https://github.com/huggingface/huggingface_hub - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ### How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Data Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
BenjaminOcampo/model-bert__trained-in-ihc__seed-3	BenjaminOcampo	2023-06-18T22:12:49Z	103	0	transformers	[ "transformers", "pytorch", "bert", "text-classification", "en", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-06-18T22:11:31Z	--- language: en --- # Model Card for BenjaminOcampo/model-bert__trained-in-ihc__seed-3 <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> Classification results dev set ``` precision recall f1-score support 0 0.8230 0.8465 0.8346 2658 1 0.7388 0.7045 0.7213 1638 accuracy 0.7924 4296 macro avg 0.7809 0.7755 0.7779 4296 weighted avg 0.7909 0.7924 0.7914 4296 ``` Classification results test set ``` precision recall f1-score support 0 0.8160 0.8243 0.8201 2658 1 0.7101 0.6984 0.7042 1638 accuracy 0.7763 4296 macro avg 0.7631 0.7614 0.7622 4296 weighted avg 0.7756 0.7763 0.7759 4296 ``` - Developed by: Benjamin Ocampo - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): en - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: https://github.com/huggingface/huggingface_hub - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ### How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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BenjaminOcampo/model-bert__trained-in-ishate__seed-3	BenjaminOcampo	2023-06-18T22:11:28Z	105	0	transformers	[ "transformers", "pytorch", "bert", "text-classification", "en", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-06-18T22:10:02Z	--- language: en --- # Model Card for BenjaminOcampo/model-bert__trained-in-ishate__seed-3 <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> Classification results dev set ``` precision recall f1-score support 0 0.8995 0.8888 0.8941 2680 1 0.8266 0.8423 0.8344 1687 accuracy 0.8708 4367 macro avg 0.8631 0.8656 0.8643 4367 weighted avg 0.8714 0.8708 0.8711 4367 ``` Classification results test set ``` precision recall f1-score support 0 0.8983 0.8900 0.8941 2681 1 0.8277 0.8400 0.8338 1687 accuracy 0.8707 4368 macro avg 0.8630 0.8650 0.8640 4368 weighted avg 0.8711 0.8707 0.8708 4368 ``` - Developed by: Benjamin Ocampo - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): en - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: https://github.com/huggingface/huggingface_hub - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ### How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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BenjaminOcampo/model-bert__trained-in-ishate__seed-42	BenjaminOcampo	2023-06-18T22:09:01Z	105	0	transformers	[ "transformers", "pytorch", "bert", "text-classification", "en", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-06-18T22:08:08Z	--- language: en --- # Model Card for BenjaminOcampo/model-bert__trained-in-ishate__seed-42 <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> Classification results dev set ``` precision recall f1-score support 0 0.9043 0.8851 0.8946 2680 1 0.8234 0.8512 0.8371 1687 accuracy 0.8720 4367 macro avg 0.8639 0.8681 0.8658 4367 weighted avg 0.8731 0.8720 0.8724 4367 ``` Classification results test set ``` precision recall f1-score support 0 0.9064 0.8851 0.8956 2681 1 0.8240 0.8548 0.8391 1687 accuracy 0.8734 4368 macro avg 0.8652 0.8699 0.8674 4368 weighted avg 0.8746 0.8734 0.8738 4368 ``` - Developed by: Benjamin Ocampo - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): en - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: https://github.com/huggingface/huggingface_hub - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ### How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
BenjaminOcampo/model-bert__trained-in-sbic__seed-0	BenjaminOcampo	2023-06-18T22:05:50Z	105	0	transformers	[ "transformers", "pytorch", "bert", "text-classification", "en", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-06-18T22:04:45Z	--- language: en --- # Model Card for BenjaminOcampo/model-bert__trained-in-sbic__seed-0 <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> Classification results dev set ``` precision recall f1-score support 0 0.8686 0.8503 0.8594 8756 1 0.8346 0.8545 0.8445 7741 accuracy 0.8523 16497 macro avg 0.8516 0.8524 0.8519 16497 weighted avg 0.8527 0.8523 0.8524 16497 ``` Classification results test set ``` precision recall f1-score support 0 0.8641 0.8559 0.8600 8471 1 0.8625 0.8704 0.8664 8798 accuracy 0.8633 17269 macro avg 0.8633 0.8631 0.8632 17269 weighted avg 0.8633 0.8633 0.8633 17269 ``` - Developed by: Benjamin Ocampo - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): en - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: https://github.com/huggingface/huggingface_hub - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ### How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
rttl-ai/bert-large-uncased-sentiment	rttl-ai	2023-06-18T22:05:43Z	12	0	transformers	[ "transformers", "pytorch", "bert", "text-classification", "en", "dataset:sst2", "dataset:sst", "arxiv:2004.10964", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-06-15T18:48:57Z	--- license: apache-2.0 language: - en model-index: - name: rttl-ai/SentyBert results: - task: type: task-classification name: Text Classification dataset: type: sst2 name: sst2 config: default split: validation metrics: - type: f1 value: 0.9992 name: F1 Macro - type: accuracy value: 0.9992 name: Accuracy datasets: - sst2 - sst --- # rttl-ai/SentyBert ## Model Details Model Description: This model is a fine-tune checkpoint of [bert-large-uncased](https://huggingface.co/bert-large-uncased), fine-tuned on SST-2. This model reaches an accuracy of 99.92 on the dev set. - Developed by: rttl-ai - Model Type: Text Classification - Language(s): English - License: Apache-2.0 - Resources for more information: - The model was pre-trained with task-adaptive pre-training [TAPT](https://arxiv.org/pdf/2004.10964.pdf) with an increased masking rate, no corruption strategy, and using WWM, following [this paper](https://aclanthology.org/2023.eacl-main.217.pdf) - fine-tuned on sst with subtrees - fine-tuned on sst2
BenjaminOcampo/model-bert__trained-in-toxigen__seed-3	BenjaminOcampo	2023-06-18T21:59:10Z	105	0	transformers	[ "transformers", "pytorch", "bert", "text-classification", "en", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-06-18T21:57:55Z	--- language: en --- # Model Card for BenjaminOcampo/model-bert__trained-in-toxigen__seed-3 <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> Classification results dev set ``` precision recall f1-score support 0 0.8333 0.8778 0.8550 900 1 0.8697 0.8229 0.8456 892 accuracy 0.8504 1792 macro avg 0.8515 0.8503 0.8503 1792 weighted avg 0.8514 0.8504 0.8503 1792 ``` Classification results test set ``` precision recall f1-score support 0 0.8775 0.7323 0.7984 538 1 0.6849 0.8505 0.7588 368 accuracy 0.7804 906 macro avg 0.7812 0.7914 0.7786 906 weighted avg 0.7993 0.7804 0.7823 906 ``` - Developed by: Benjamin Ocampo - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): en - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: https://github.com/huggingface/huggingface_hub - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ### How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Data Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
BenjaminOcampo/model-bert__trained-in-toxigen__seed-1	BenjaminOcampo	2023-06-18T21:56:19Z	105	0	transformers	[ "transformers", "pytorch", "bert", "text-classification", "en", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-06-18T21:55:12Z	--- language: en --- # Model Card for BenjaminOcampo/model-bert__trained-in-toxigen__seed-1 <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> Classification results dev set ``` precision recall f1-score support 0 0.8533 0.8789 0.8659 900 1 0.8740 0.8475 0.8606 892 accuracy 0.8633 1792 macro avg 0.8636 0.8632 0.8632 1792 weighted avg 0.8636 0.8633 0.8632 1792 ``` Classification results test set ``` precision recall f1-score support 0 0.8508 0.7844 0.8162 538 1 0.7171 0.7989 0.7558 368 accuracy 0.7903 906 macro avg 0.7839 0.7916 0.7860 906 weighted avg 0.7965 0.7903 0.7917 906 ``` - Developed by: Benjamin Ocampo - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): en - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: https://github.com/huggingface/huggingface_hub - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ### How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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BenjaminOcampo/model-bert__trained-in-toxigen__seed-0	BenjaminOcampo	2023-06-18T21:55:02Z	105	0	transformers	[ "transformers", "pytorch", "bert", "text-classification", "en", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-06-18T21:53:54Z	--- language: en --- # Model Card for BenjaminOcampo/model-bert__trained-in-toxigen__seed-0 <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> Classification results dev set ``` precision recall f1-score support 0 0.8565 0.8622 0.8594 900 1 0.8600 0.8543 0.8571 892 accuracy 0.8583 1792 macro avg 0.8583 0.8582 0.8583 1792 weighted avg 0.8583 0.8583 0.8583 1792 ``` Classification results test set ``` precision recall f1-score support 0 0.8932 0.7305 0.8037 538 1 0.6888 0.8723 0.7698 368 accuracy 0.7881 906 macro avg 0.7910 0.8014 0.7867 906 weighted avg 0.8102 0.7881 0.7899 906 ``` - Developed by: Benjamin Ocampo - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): en - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: https://github.com/huggingface/huggingface_hub - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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BenjaminOcampo/model-hatebert__trained-in-dynahate__seed-42	BenjaminOcampo	2023-06-18T21:52:16Z	105	0	transformers	[ "transformers", "pytorch", "bert", "text-classification", "en", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-06-18T21:51:01Z	--- language: en --- # Model Card for BenjaminOcampo/model-hatebert__trained-in-dynahate__seed-42 <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> Classification results dev set ``` precision recall f1-score support 0 0.8204 0.8293 0.8248 1933 1 0.8462 0.8380 0.8421 2167 accuracy 0.8339 4100 macro avg 0.8333 0.8337 0.8335 4100 weighted avg 0.8340 0.8339 0.8339 4100 ``` Classification results test set ``` precision recall f1-score support 0 0.7747 0.7705 0.7726 1852 1 0.8134 0.8170 0.8152 2268 accuracy 0.7961 4120 macro avg 0.7941 0.7938 0.7939 4120 weighted avg 0.7960 0.7961 0.7961 4120 ``` - Developed by: Benjamin Ocampo - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): en - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: https://github.com/huggingface/huggingface_hub - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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BenjaminOcampo/model-hatebert__trained-in-dynahate__seed-0	BenjaminOcampo	2023-06-18T21:50:53Z	105	0	transformers	[ "transformers", "pytorch", "bert", "text-classification", "en", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-06-18T21:49:39Z	--- language: en --- # Model Card for BenjaminOcampo/model-hatebert__trained-in-dynahate__seed-0 <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> Classification results dev set ``` precision recall f1-score support 0 0.8212 0.8200 0.8206 1933 1 0.8396 0.8408 0.8402 2167 accuracy 0.8310 4100 macro avg 0.8304 0.8304 0.8304 4100 weighted avg 0.8310 0.8310 0.8310 4100 ``` Classification results test set ``` precision recall f1-score support 0 0.7605 0.7700 0.7652 1852 1 0.8102 0.8020 0.8061 2268 accuracy 0.7876 4120 macro avg 0.7854 0.7860 0.7857 4120 weighted avg 0.7879 0.7876 0.7877 4120 ``` - Developed by: Benjamin Ocampo - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): en - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: https://github.com/huggingface/huggingface_hub - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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TheBloke/vicuna-7B-v1.3-GGML	TheBloke	2023-06-18T21:49:48Z	0	20	null	[ "arxiv:2302.13971", "arxiv:2306.05685", "license:other", "region:us" ]	null	2023-06-18T19:52:31Z	--- inference: false license: other --- <!-- header start --> <div style="width: 100%;"> <img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </div> <div style="display: flex; justify-content: space-between; width: 100%;"> <div style="display: flex; flex-direction: column; align-items: flex-start;"> <p><a href="https://discord.gg/Jq4vkcDakD">Chat & support: my new Discord server</a></p> </div> <div style="display: flex; flex-direction: column; align-items: flex-end;"> <p><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p> </div> </div> <!-- header end --> # LmSys' Vicuna 7B v1.3 GGML These files are GGML format model files for [LmSys' Vicuna 7B v1.3](https://huggingface.co/lmsys/vicuna-7b-v1.3). GGML files are for CPU + GPU inference using [llama.cpp](https://github.com/ggerganov/llama.cpp) and libraries and UIs which support this format, such as: * [text-generation-webui](https://github.com/oobabooga/text-generation-webui) * [KoboldCpp](https://github.com/LostRuins/koboldcpp) * [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui) * [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) * [ctransformers](https://github.com/marella/ctransformers) ## Repositories available * [4-bit GPTQ models for GPU inference](https://huggingface.co/TheBloke/vicuna-7B-v1.3-GPTQ) * [2, 3, 4, 5, 6 and 8-bit GGML models for CPU+GPU inference](https://huggingface.co/TheBloke/vicuna-7B-v1.3-GGML) * [Unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/lmsys/vicuna-7b-v1.3) ## Prompt template ``` A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: prompt ASSISTANT: ``` <!-- compatibility_ggml start --> ## Compatibility ### Original llama.cpp quant methods: `q4_0, q4_1, q5_0, q5_1, q8_0` I have quantized these 'original' quantisation methods using an older version of llama.cpp so that they remain compatible with llama.cpp as of May 19th, commit `2d5db48`. These are guaranteed to be compatbile with any UIs, tools and libraries released since late May. ### New k-quant methods: `q2_K, q3_K_S, q3_K_M, q3_K_L, q4_K_S, q4_K_M, q5_K_S, q6_K` These new quantisation methods are compatible with llama.cpp as of June 6th, commit `2d43387`. They are now also compatible with recent releases of text-generation-webui, KoboldCpp, llama-cpp-python and ctransformers. Other tools and libraries may or may not be compatible - check their documentation if in doubt. ## Explanation of the new k-quant methods The new methods available are: * GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw) * GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw. * GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw. * GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw * GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw * GGML_TYPE_Q8_K - "type-0" 8-bit quantization. Only used for quantizing intermediate results. The difference to the existing Q8_0 is that the block size is 256. All 2-6 bit dot products are implemented for this quantization type. Refer to the Provided Files table below to see what files use which methods, and how. <!-- compatibility_ggml end --> ## Provided files \| Name \| Quant method \| Bits \| Size \| Max RAM required \| Use case \| \| ---- \| ---- \| ---- \| ---- \| ---- \| ----- \| \| vicuna-7b-v1.3.ggmlv3.q2_K.bin \| q2_K \| 2 \| 2.87 GB \| 5.37 GB \| New k-quant method. Uses GGML_TYPE_Q4_K for the attention.vw and feed_forward.w2 tensors, GGML_TYPE_Q2_K for the other tensors. \| \| vicuna-7b-v1.3.ggmlv3.q3_K_L.bin \| q3_K_L \| 3 \| 3.60 GB \| 6.10 GB \| New k-quant method. Uses GGML_TYPE_Q5_K for the attention.wv, attention.wo, and feed_forward.w2 tensors, else GGML_TYPE_Q3_K \| \| vicuna-7b-v1.3.ggmlv3.q3_K_M.bin \| q3_K_M \| 3 \| 3.28 GB \| 5.78 GB \| New k-quant method. Uses GGML_TYPE_Q4_K for the attention.wv, attention.wo, and feed_forward.w2 tensors, else GGML_TYPE_Q3_K \| \| vicuna-7b-v1.3.ggmlv3.q3_K_S.bin \| q3_K_S \| 3 \| 2.95 GB \| 5.45 GB \| New k-quant method. Uses GGML_TYPE_Q3_K for all tensors \| \| vicuna-7b-v1.3.ggmlv3.q4_0.bin \| q4_0 \| 4 \| 3.79 GB \| 6.29 GB \| Original llama.cpp quant method, 4-bit. \| \| vicuna-7b-v1.3.ggmlv3.q4_1.bin \| q4_1 \| 4 \| 4.21 GB \| 6.71 GB \| Original llama.cpp quant method, 4-bit. Higher accuracy than q4_0 but not as high as q5_0. However has quicker inference than q5 models. \| \| vicuna-7b-v1.3.ggmlv3.q4_K_M.bin \| q4_K_M \| 4 \| 4.08 GB \| 6.58 GB \| New k-quant method. Uses GGML_TYPE_Q6_K for half of the attention.wv and feed_forward.w2 tensors, else GGML_TYPE_Q4_K \| \| vicuna-7b-v1.3.ggmlv3.q4_K_S.bin \| q4_K_S \| 4 \| 3.83 GB \| 6.33 GB \| New k-quant method. Uses GGML_TYPE_Q4_K for all tensors \| \| vicuna-7b-v1.3.ggmlv3.q5_0.bin \| q5_0 \| 5 \| 4.63 GB \| 7.13 GB \| Original llama.cpp quant method, 5-bit. Higher accuracy, higher resource usage and slower inference. \| \| vicuna-7b-v1.3.ggmlv3.q5_1.bin \| q5_1 \| 5 \| 5.06 GB \| 7.56 GB \| Original llama.cpp quant method, 5-bit. Even higher accuracy, resource usage and slower inference. \| \| vicuna-7b-v1.3.ggmlv3.q5_K_M.bin \| q5_K_M \| 5 \| 4.78 GB \| 7.28 GB \| New k-quant method. Uses GGML_TYPE_Q6_K for half of the attention.wv and feed_forward.w2 tensors, else GGML_TYPE_Q5_K \| \| vicuna-7b-v1.3.ggmlv3.q5_K_S.bin \| q5_K_S \| 5 \| 4.65 GB \| 7.15 GB \| New k-quant method. Uses GGML_TYPE_Q5_K for all tensors \| \| vicuna-7b-v1.3.ggmlv3.q6_K.bin \| q6_K \| 6 \| 5.53 GB \| 8.03 GB \| New k-quant method. Uses GGML_TYPE_Q8_K - 6-bit quantization - for all tensors \| \| vicuna-7b-v1.3.ggmlv3.q8_0.bin \| q8_0 \| 8 \| 7.16 GB \| 9.66 GB \| Original llama.cpp quant method, 8-bit. Almost indistinguishable from float16. High resource use and slow. Not recommended for most users. \| Note: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead. ## How to run in `llama.cpp` I use the following command line; adjust for your tastes and needs: ``` ./main -t 10 -ngl 32 -m vicuna-7b-v1.3.ggmlv3.q5_0.bin --color -c 2048 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "USER: Write a story about llamas\nASSISTANT:" ``` Change `-t 10` to the number of physical CPU cores you have. For example if your system has 8 cores/16 threads, use `-t 8`. Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration. If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins` ## How to run in `text-generation-webui` Further instructions here: [text-generation-webui/docs/llama.cpp-models.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/llama.cpp-models.md). <!-- footer start --> ## Discord For further support, and discussions on these models and AI in general, join us at: [TheBloke AI's Discord server](https://discord.gg/Jq4vkcDakD) ## Thanks, and how to contribute. Thanks to the [chirper.ai](https://chirper.ai) team! I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training. If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects. Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits. * Patreon: https://patreon.com/TheBlokeAI * Ko-Fi: https://ko-fi.com/TheBlokeAI Special thanks to: Luke from CarbonQuill, Aemon Algiz, Dmitriy Samsonov. Patreon special mentions: Mano Prime, Fen Risland, Derek Yates, Preetika Verma, webtim, Sean Connelly, Alps Aficionado, Karl Bernard, Junyu Yang, Nathan LeClaire, Chris McCloskey, Lone Striker, Asp the Wyvern, Eugene Pentland, Imad Khwaja, trip7s trip, WelcomeToTheClub, John Detwiler, Artur Olbinski, Khalefa Al-Ahmad, Trenton Dambrowitz, Talal Aujan, Kevin Schuppel, Luke Pendergrass, Pyrater, Joseph William Delisle, terasurfer , vamX, Gabriel Puliatti, David Flickinger, Jonathan Leane, Iucharbius , Luke, Deep Realms, Cory Kujawski, ya boyyy, Illia Dulskyi, senxiiz, Johann-Peter Hartmann, John Villwock, K, Ghost , Spiking Neurons AB, Nikolai Manek, Rainer Wilmers, Pierre Kircher, biorpg, Space Cruiser, Ai Maven, subjectnull, Willem Michiel, Ajan Kanaga, Kalila, chris gileta, Oscar Rangel. Thank you to all my generous patrons and donaters! <!-- footer end --> # Original model card: LmSys' Vicuna 7B v1.3 # Vicuna Model Card ## Model Details Vicuna is a chat assistant trained by fine-tuning LLaMA on user-shared conversations collected from ShareGPT. - Developed by: [LMSYS](https://lmsys.org/) - Model type: An auto-regressive language model based on the transformer architecture. - License: Non-commercial license - Finetuned from model: [LLaMA](https://arxiv.org/abs/2302.13971). ### Model Sources - Repository: https://github.com/lm-sys/FastChat - Blog: https://lmsys.org/blog/2023-03-30-vicuna/ - Paper: https://arxiv.org/abs/2306.05685 - Demo: https://chat.lmsys.org/ ## Uses The primary use of Vicuna is research on large language models and chatbots. The primary intended users of the model are researchers and hobbyists in natural language processing, machine learning, and artificial intelligence. ## How to Get Started with the Model Command line interface: https://github.com/lm-sys/FastChat#vicuna-weights. APIs (OpenAI API, Huggingface API): https://github.com/lm-sys/FastChat/tree/main#api. ## Training Details Vicuna v1.3 is fine-tuned from LLaMA with supervised instruction fine-tuning. The training data is around 140K conversations collected from ShareGPT.com. See more details in the "Training Details of Vicuna Models" section in the appendix of this [paper](https://arxiv.org/pdf/2306.05685.pdf). ## Evaluation Vicuna is evaluated with standard benchmarks, human preference, and LLM-as-a-judge. See more details in this [paper](https://arxiv.org/pdf/2306.05685.pdf). ## Difference between different versions of Vicuna See [vicuna_weights_version.md](https://github.com/lm-sys/FastChat/blob/main/docs/vicuna_weights_version.md)
BenjaminOcampo/model-hatebert__trained-in-dynahate__seed-1	BenjaminOcampo	2023-06-18T21:49:30Z	103	0	transformers	[ "transformers", "pytorch", "bert", "text-classification", "en", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-06-18T21:48:04Z	--- language: en --- # Model Card for BenjaminOcampo/model-hatebert__trained-in-dynahate__seed-1 <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> Classification results dev set ``` precision recall f1-score support 0 0.8259 0.8174 0.8216 1933 1 0.8386 0.8463 0.8424 2167 accuracy 0.8327 4100 macro avg 0.8323 0.8319 0.8320 4100 weighted avg 0.8326 0.8327 0.8326 4100 ``` Classification results test set ``` precision recall f1-score support 0 0.7733 0.7624 0.7678 1852 1 0.8082 0.8175 0.8128 2268 accuracy 0.7927 4120 macro avg 0.7907 0.7899 0.7903 4120 weighted avg 0.7925 0.7927 0.7926 4120 ``` - Developed by: Benjamin Ocampo - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): en - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: https://github.com/huggingface/huggingface_hub - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ### How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Data Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
BenjaminOcampo/model-hatebert__trained-in-dynahate__seed-2	BenjaminOcampo	2023-06-18T21:46:19Z	105	0	transformers	[ "transformers", "pytorch", "bert", "text-classification", "en", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-06-18T21:44:40Z	--- language: en --- # Model Card for BenjaminOcampo/model-hatebert__trained-in-dynahate__seed-2 <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> Classification results dev set ``` precision recall f1-score support 0 0.8233 0.8314 0.8273 1933 1 0.8482 0.8408 0.8445 2167 accuracy 0.8363 4100 macro avg 0.8357 0.8361 0.8359 4100 weighted avg 0.8365 0.8363 0.8364 4100 ``` Classification results test set ``` precision recall f1-score support 0 0.7742 0.7700 0.7721 1852 1 0.8130 0.8166 0.8148 2268 accuracy 0.7956 4120 macro avg 0.7936 0.7933 0.7934 4120 weighted avg 0.7955 0.7956 0.7956 4120 ``` - Developed by: Benjamin Ocampo - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): en - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: https://github.com/huggingface/huggingface_hub - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ### How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Data Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
BenjaminOcampo/model-hatebert__trained-in-ihc__seed-3	BenjaminOcampo	2023-06-18T21:40:58Z	105	0	transformers	[ "transformers", "pytorch", "bert", "text-classification", "en", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-06-18T21:35:28Z	--- language: en --- # Model Card for BenjaminOcampo/model-hatebert__trained-in-ihc__seed-3 <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> Classification results dev set ``` precision recall f1-score support 0 0.8175 0.8424 0.8297 2658 1 0.7309 0.6947 0.7124 1638 accuracy 0.7861 4296 macro avg 0.7742 0.7686 0.7710 4296 weighted avg 0.7844 0.7861 0.7850 4296 ``` Classification results test set ``` precision recall f1-score support 0 0.8141 0.8337 0.8238 2658 1 0.7192 0.6911 0.7049 1638 accuracy 0.7793 4296 macro avg 0.7666 0.7624 0.7643 4296 weighted avg 0.7779 0.7793 0.7784 4296 ``` - Developed by: Benjamin Ocampo - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): en - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: https://github.com/huggingface/huggingface_hub - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ### How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Data Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
BenjaminOcampo/model-hatebert__trained-in-ihc__seed-0	BenjaminOcampo	2023-06-18T21:39:48Z	103	0	transformers	[ "transformers", "pytorch", "bert", "text-classification", "en", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-06-18T21:38:30Z	--- language: en --- # Model Card for BenjaminOcampo/model-hatebert__trained-in-ihc__seed-0 <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> Classification results dev set ``` precision recall f1-score support 0 0.8173 0.8262 0.8217 2658 1 0.7129 0.7002 0.7065 1638 accuracy 0.7782 4296 macro avg 0.7651 0.7632 0.7641 4296 weighted avg 0.7775 0.7782 0.7778 4296 ``` Classification results test set ``` precision recall f1-score support 0 0.8164 0.8183 0.8174 2658 1 0.7040 0.7015 0.7028 1638 accuracy 0.7737 4296 macro avg 0.7602 0.7599 0.7601 4296 weighted avg 0.7736 0.7737 0.7737 4296 ``` - Developed by: Benjamin Ocampo - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): en - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: https://github.com/huggingface/huggingface_hub - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ### How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Data Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
BenjaminOcampo/model-hatebert__trained-in-ihc__seed-1	BenjaminOcampo	2023-06-18T21:35:20Z	105	0	transformers	[ "transformers", "pytorch", "bert", "text-classification", "en", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-06-18T21:34:16Z	--- language: en --- # Model Card for BenjaminOcampo/model-hatebert__trained-in-ihc__seed-1 <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> Classification results dev set ``` precision recall f1-score support 0 0.8098 0.8330 0.8212 2658 1 0.7157 0.6825 0.6988 1638 accuracy 0.7756 4296 macro avg 0.7628 0.7577 0.7600 4296 weighted avg 0.7739 0.7756 0.7745 4296 ``` Classification results test set ``` precision recall f1-score support 0 0.8147 0.8318 0.8232 2658 1 0.7174 0.6929 0.7050 1638 accuracy 0.7789 4296 macro avg 0.7661 0.7624 0.7641 4296 weighted avg 0.7776 0.7789 0.7781 4296 ``` - Developed by: Benjamin Ocampo - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): en - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: https://github.com/huggingface/huggingface_hub - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ### How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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BenjaminOcampo/model-hatebert__trained-in-ishate__seed-2	BenjaminOcampo	2023-06-18T21:34:08Z	106	0	transformers	[ "transformers", "pytorch", "bert", "text-classification", "en", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-06-18T21:31:10Z	--- language: en --- # Model Card for BenjaminOcampo/model-hatebert__trained-in-ishate__seed-2 <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> Classification results dev set ``` precision recall f1-score support 0 0.9009 0.8851 0.8929 2680 1 0.8224 0.8453 0.8337 1687 accuracy 0.8697 4367 macro avg 0.8616 0.8652 0.8633 4367 weighted avg 0.8705 0.8697 0.8700 4367 ``` Classification results test set ``` precision recall f1-score support 0 0.9069 0.8833 0.8949 2681 1 0.8219 0.8560 0.8386 1687 accuracy 0.8727 4368 macro avg 0.8644 0.8696 0.8667 4368 weighted avg 0.8741 0.8727 0.8732 4368 ``` - Developed by: Benjamin Ocampo - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): en - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: https://github.com/huggingface/huggingface_hub - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ### How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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BenjaminOcampo/model-hatebert__trained-in-ishate__seed-1	BenjaminOcampo	2023-06-18T21:31:00Z	105	0	transformers	[ "transformers", "pytorch", "bert", "text-classification", "en", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-06-18T21:29:35Z	--- language: en --- # Model Card for BenjaminOcampo/model-hatebert__trained-in-ishate__seed-1 <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> Classification results dev set ``` precision recall f1-score support 0 0.8964 0.8843 0.8903 2680 1 0.8201 0.8376 0.8287 1687 accuracy 0.8663 4367 macro avg 0.8582 0.8610 0.8595 4367 weighted avg 0.8669 0.8663 0.8665 4367 ``` Classification results test set ``` precision recall f1-score support 0 0.9008 0.8836 0.8921 2681 1 0.8205 0.8453 0.8327 1687 accuracy 0.8688 4368 macro avg 0.8606 0.8645 0.8624 4368 weighted avg 0.8698 0.8688 0.8692 4368 ``` - Developed by: Benjamin Ocampo - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): en - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: https://github.com/huggingface/huggingface_hub - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ### How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Data Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
NasimB/gpt2_left_out_bnc_spoken	NasimB	2023-06-18T21:29:41Z	6	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "generated_from_trainer", "dataset:generator", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-06-18T17:42:23Z	--- license: mit tags: - generated_from_trainer datasets: - generator model-index: - name: gpt2_left_out_bnc_spoken results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # gpt2_left_out_bnc_spoken This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the generator dataset. It achieves the following results on the evaluation set: - Loss: 3.9573 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 1000 - num_epochs: 10 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:-----:\|:---------------:\| \| 5.9941 \| 0.26 \| 500 \| 5.0785 \| \| 4.744 \| 0.52 \| 1000 \| 4.6923 \| \| 4.449 \| 0.78 \| 1500 \| 4.4585 \| \| 4.2281 \| 1.04 \| 2000 \| 4.3108 \| \| 4.0362 \| 1.3 \| 2500 \| 4.2194 \| \| 3.9581 \| 1.56 \| 3000 \| 4.1345 \| \| 3.8942 \| 1.82 \| 3500 \| 4.0589 \| \| 3.7653 \| 2.08 \| 4000 \| 4.0146 \| \| 3.6477 \| 2.34 \| 4500 \| 3.9820 \| \| 3.6314 \| 2.6 \| 5000 \| 3.9363 \| \| 3.5895 \| 2.86 \| 5500 \| 3.8927 \| \| 3.4677 \| 3.12 \| 6000 \| 3.8892 \| \| 3.3837 \| 3.39 \| 6500 \| 3.8736 \| \| 3.3922 \| 3.65 \| 7000 \| 3.8444 \| \| 3.387 \| 3.91 \| 7500 \| 3.8169 \| \| 3.2108 \| 4.17 \| 8000 \| 3.8439 \| \| 3.1722 \| 4.43 \| 8500 \| 3.8370 \| \| 3.1802 \| 4.69 \| 9000 \| 3.8128 \| \| 3.1877 \| 4.95 \| 9500 \| 3.7892 \| \| 2.9711 \| 5.21 \| 10000 \| 3.8382 \| \| 2.9515 \| 5.47 \| 10500 \| 3.8363 \| \| 2.9643 \| 5.73 \| 11000 \| 3.8184 \| \| 2.9776 \| 5.99 \| 11500 \| 3.8051 \| \| 2.7104 \| 6.25 \| 12000 \| 3.8626 \| \| 2.7359 \| 6.51 \| 12500 \| 3.8661 \| \| 2.7452 \| 6.77 \| 13000 \| 3.8605 \| \| 2.7255 \| 7.03 \| 13500 \| 3.8748 \| \| 2.5175 \| 7.29 \| 14000 \| 3.9038 \| \| 2.5252 \| 7.55 \| 14500 \| 3.9064 \| \| 2.5391 \| 7.81 \| 15000 \| 3.9065 \| \| 2.4972 \| 8.07 \| 15500 \| 3.9270 \| \| 2.3676 \| 8.33 \| 16000 \| 3.9408 \| \| 2.3852 \| 8.59 \| 16500 \| 3.9432 \| \| 2.3809 \| 8.85 \| 17000 \| 3.9458 \| \| 2.3448 \| 9.11 \| 17500 \| 3.9530 \| \| 2.2974 \| 9.38 \| 18000 \| 3.9563 \| \| 2.2979 \| 9.64 \| 18500 \| 3.9568 \| \| 2.3035 \| 9.9 \| 19000 \| 3.9573 \| ### Framework versions - Transformers 4.26.1 - Pytorch 1.11.0+cu113 - Datasets 2.13.0 - Tokenizers 0.13.3
BenjaminOcampo/model-hatebert__trained-in-ishate__seed-0	BenjaminOcampo	2023-06-18T21:29:21Z	108	0	transformers	[ "transformers", "pytorch", "bert", "text-classification", "en", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-06-18T21:28:10Z	--- language: en --- # Model Card for BenjaminOcampo/model-hatebert__trained-in-ishate__seed-0 <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> Classification results dev set ``` precision recall f1-score support 0 0.8901 0.8884 0.8893 2680 1 0.8233 0.8257 0.8245 1687 accuracy 0.8642 4367 macro avg 0.8567 0.8571 0.8569 4367 weighted avg 0.8643 0.8642 0.8642 4367 ``` Classification results test set ``` precision recall f1-score support 0 0.9014 0.8967 0.8990 2681 1 0.8372 0.8441 0.8406 1687 accuracy 0.8764 4368 macro avg 0.8693 0.8704 0.8698 4368 weighted avg 0.8766 0.8764 0.8765 4368 ``` - Developed by: Benjamin Ocampo - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): en - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: https://github.com/huggingface/huggingface_hub - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ### How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Data Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
BenjaminOcampo/model-hatebert__trained-in-ishate__seed-3	BenjaminOcampo	2023-06-18T21:28:00Z	105	0	transformers	[ "transformers", "pytorch", "bert", "text-classification", "en", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-06-18T21:26:31Z	--- language: en --- # Model Card for BenjaminOcampo/model-hatebert__trained-in-ishate__seed-3 <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> Classification results dev set ``` precision recall f1-score support 0 0.8994 0.8772 0.8882 2680 1 0.8123 0.8441 0.8279 1687 accuracy 0.8644 4367 macro avg 0.8559 0.8607 0.8580 4367 weighted avg 0.8658 0.8644 0.8649 4367 ``` Classification results test set ``` precision recall f1-score support 0 0.9108 0.8870 0.8987 2681 1 0.8275 0.8619 0.8444 1687 accuracy 0.8773 4368 macro avg 0.8692 0.8744 0.8715 4368 weighted avg 0.8786 0.8773 0.8777 4368 ``` - Developed by: Benjamin Ocampo - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): en - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: https://github.com/huggingface/huggingface_hub - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ### How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Data Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
edu-linguistic/deberta-v3-large-edu-rm	edu-linguistic	2023-06-18T21:24:40Z	0	0	null	[ "en", "dataset:Dahoas/rm-static", "dataset:openai/webgpt_comparisons", "region:us" ]	null	2023-06-17T09:15:50Z	--- datasets: - Dahoas/rm-static - openai/webgpt_comparisons language: - en --- ## Inference Example: ```python from peft import PeftModel, PeftConfig from transformers import AutoModelForCausalLM, AutoTokenizer peft_model_id = "edu-linguistic/deberta-v3-large-edu-rm" model_name = 'microsoft/deberta-v3-large' config = PeftConfig.from_pretrained(peft_model_id) model_config = AutoConfig.from_pretrained(model_name, cache_dir=self.model_cache_dir) model_config.num_labels = 1 model = AutoModelForSequenceClassification.from_pretrained(model_name) model = PeftModelForSequenceClassification.from_pretrained(model, peft_model_id) tokenizer = AutoTokenizer.from_pretrained(model_name) texts = "<\|prompter\|> When using linear regression, how do you help prevent numerical instabilities? (One or multiple answers) \n <\|assistant\|> 4. add more features" inputs = tokenizer(texts, return_tensors='pt', padding=True, truncation=True) score = self.reward_model(**inputs).logits.cpu().detach() print(score) ```
BenjaminOcampo/model-hatebert__trained-in-sbic__seed-2	BenjaminOcampo	2023-06-18T21:22:14Z	103	0	transformers	[ "transformers", "pytorch", "bert", "text-classification", "en", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-06-18T21:21:03Z	--- language: en --- # Model Card for BenjaminOcampo/model-hatebert__trained-in-sbic__seed-2 <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> Classification results dev set ``` precision recall f1-score support 0 0.8793 0.8521 0.8655 8756 1 0.8384 0.8677 0.8528 7741 accuracy 0.8594 16497 macro avg 0.8588 0.8599 0.8591 16497 weighted avg 0.8601 0.8594 0.8595 16497 ``` Classification results test set ``` precision recall f1-score support 0 0.8729 0.8568 0.8648 8471 1 0.8645 0.8799 0.8721 8798 accuracy 0.8686 17269 macro avg 0.8687 0.8683 0.8684 17269 weighted avg 0.8686 0.8686 0.8685 17269 ``` - Developed by: Benjamin Ocampo - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): en - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: https://github.com/huggingface/huggingface_hub - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ### How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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BenjaminOcampo/model-hatebert__trained-in-sbic__seed-3	BenjaminOcampo	2023-06-18T21:20:54Z	105	0	transformers	[ "transformers", "pytorch", "bert", "text-classification", "en", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-06-18T21:19:43Z	--- language: en --- # Model Card for BenjaminOcampo/model-hatebert__trained-in-sbic__seed-3 <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> Classification results dev set ``` precision recall f1-score support 0 0.8835 0.8544 0.8687 8756 1 0.8412 0.8726 0.8566 7741 accuracy 0.8629 16497 macro avg 0.8624 0.8635 0.8627 16497 weighted avg 0.8637 0.8629 0.8631 16497 ``` Classification results test set ``` precision recall f1-score support 0 0.8733 0.8576 0.8654 8471 1 0.8653 0.8802 0.8727 8798 accuracy 0.8691 17269 macro avg 0.8693 0.8689 0.8690 17269 weighted avg 0.8692 0.8691 0.8691 17269 ``` - Developed by: Benjamin Ocampo - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): en - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: https://github.com/huggingface/huggingface_hub - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ### How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Data Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
BenjaminOcampo/model-hatebert__trained-in-sbic__seed-0	BenjaminOcampo	2023-06-18T21:19:35Z	105	0	transformers	[ "transformers", "pytorch", "bert", "text-classification", "en", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-06-18T21:18:12Z	--- language: en --- # Model Card for BenjaminOcampo/model-hatebert__trained-in-sbic__seed-0 <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> Classification results dev set ``` precision recall f1-score support 0 0.8759 0.8496 0.8625 8756 1 0.8355 0.8638 0.8494 7741 accuracy 0.8563 16497 macro avg 0.8557 0.8567 0.8560 16497 weighted avg 0.8569 0.8563 0.8564 16497 ``` Classification results test set ``` precision recall f1-score support 0 0.8806 0.8585 0.8694 8471 1 0.8669 0.8879 0.8773 8798 accuracy 0.8735 17269 macro avg 0.8738 0.8732 0.8733 17269 weighted avg 0.8736 0.8735 0.8734 17269 ``` - Developed by: Benjamin Ocampo - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): en - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: https://github.com/huggingface/huggingface_hub - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ### How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Data Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
BenjaminOcampo/model-hatebert__trained-in-toxigen__seed-0	BenjaminOcampo	2023-06-18T21:16:49Z	105	0	transformers	[ "transformers", "pytorch", "bert", "text-classification", "en", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-06-18T21:15:31Z	--- language: en --- # Model Card for BenjaminOcampo/model-hatebert__trained-in-toxigen__seed-0 <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> Classification results dev set ``` precision recall f1-score support 0 0.8601 0.8678 0.8639 900 1 0.8654 0.8576 0.8615 892 accuracy 0.8627 1792 macro avg 0.8628 0.8627 0.8627 1792 weighted avg 0.8627 0.8627 0.8627 1792 ``` Classification results test set ``` precision recall f1-score support 0 0.9160 0.6691 0.7734 538 1 0.6530 0.9103 0.7605 368 accuracy 0.7671 906 macro avg 0.7845 0.7897 0.7669 906 weighted avg 0.8092 0.7671 0.7681 906 ``` - Developed by: Benjamin Ocampo - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): en - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: https://github.com/huggingface/huggingface_hub - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ### How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
BenjaminOcampo/model-hatebert__trained-in-toxigen__seed-42	BenjaminOcampo	2023-06-18T21:13:51Z	106	0	transformers	[ "transformers", "pytorch", "bert", "text-classification", "en", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-06-18T21:12:49Z	--- language: en --- # Model Card for BenjaminOcampo/model-hatebert__trained-in-toxigen__seed-42 <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> Classification results dev set ``` precision recall f1-score support 0 0.8575 0.8756 0.8664 900 1 0.8717 0.8531 0.8623 892 accuracy 0.8644 1792 macro avg 0.8646 0.8643 0.8644 1792 weighted avg 0.8645 0.8644 0.8644 1792 ``` Classification results test set ``` precision recall f1-score support 0 0.9297 0.6636 0.7744 538 1 0.6533 0.9266 0.7663 368 accuracy 0.7704 906 macro avg 0.7915 0.7951 0.7703 906 weighted avg 0.8174 0.7704 0.7711 906 ``` - Developed by: Benjamin Ocampo - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): en - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: https://github.com/huggingface/huggingface_hub - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ### How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
BenjaminOcampo/model-hatebert__trained-in-toxigen__seed-1	BenjaminOcampo	2023-06-18T21:12:35Z	105	0	transformers	[ "transformers", "pytorch", "bert", "text-classification", "en", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-06-18T21:07:58Z	--- language: en --- # Model Card for BenjaminOcampo/model-hatebert__trained-in-toxigen__seed-1 <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> Classification results dev set ``` precision recall f1-score support 0 0.8613 0.8489 0.8551 900 1 0.8497 0.8621 0.8559 892 accuracy 0.8555 1792 macro avg 0.8555 0.8555 0.8555 1792 weighted avg 0.8556 0.8555 0.8555 1792 ``` Classification results test set ``` precision recall f1-score support 0 0.9129 0.6822 0.7809 538 1 0.6607 0.9049 0.7638 368 accuracy 0.7726 906 macro avg 0.7868 0.7935 0.7723 906 weighted avg 0.8105 0.7726 0.7739 906 ``` - Developed by: Benjamin Ocampo - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): en - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: https://github.com/huggingface/huggingface_hub - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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marmolpen3/sla-obligations-rights	marmolpen3	2023-06-18T20:57:42Z	4	0	sentence-transformers	[ "sentence-transformers", "pytorch", "mpnet", "setfit", "text-classification", "en", "arxiv:2209.11055", "arxiv:1908.10084", "license:apache-2.0", "region:us" ]	text-classification	2023-05-19T08:28:34Z	--- license: apache-2.0 tags: - setfit - sentence-transformers - text-classification pipeline_tag: text-classification language: - en metrics: - accuracy --- # marmolpen3/sla-obligations-rights This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for SLA sentence obligation and right classification. The model has been trained using an efficient few-shot learning technique that involves: 1. Fitting a [Sentence Transformer](https://www.sbert.net) with contrastive learning. 2. Training a classification head with the features of the fitted sentence transformer. This model has been trained with SLA sentences from real providers that offer software as a service. The outputs will be of numerical value, 0, 1 or 2, these being obligation, right, none respectively. ## Usage To use this model for inference, first install the SetFit library: ```bash python -m pip install setfit ``` You can then run inference as follows: ```python from setfit import SetFitModel # Download from Hub and run inference model = SetFitModel.from_pretrained("marmolpen3/sla-obligations-rights") # Run inference preds = model(["NTTA's goal is to deliver SCD Content 100% of the time.", "You can request for a service credit by contacting Support."]) # Solution [0, 1] = ["Obligation", "Right"] ``` ## BibTeX entry and citation info We are developing a system, it is in process, but you can visit it and contribute [here](https://github.com/isa-group/iContracts) SetFit is the framework we use to train the model: ```bibtex @article{https://doi.org/10.48550/arxiv.2209.11055, doi = {10.48550/ARXIV.2209.11055}, url = {https://arxiv.org/abs/2209.11055}, author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren}, keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Efficient Few-Shot Learning Without Prompts}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution 4.0 International} } ``` The model used is [paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2). If you need it you can cite their publication Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks: ```bibtex @inproceedings{reimers-2019-sentence-bert, title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2019", publisher = "Association for Computational Linguistics", url = "http://arxiv.org/abs/1908.10084", } ```
VladmirPutgang/ppo-Huggy	VladmirPutgang	2023-06-18T20:56:20Z	0	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]	reinforcement-learning	2023-06-18T20:56:02Z	--- library_name: ml-agents tags: - Huggy - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Huggy --- # ppo Agent playing Huggy This is a trained model of a ppo agent playing Huggy using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A short tutorial where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A longer tutorial to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: VladmirPutgang/ppo-Huggy 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
magnustragardh/ppo-Huggy	magnustragardh	2023-06-18T20:55:30Z	3	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]	reinforcement-learning	2023-06-18T20:55:05Z	--- library_name: ml-agents tags: - Huggy - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Huggy --- # ppo Agent playing Huggy This is a trained model of a ppo agent playing Huggy using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A short tutorial where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A longer tutorial to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: magnustragardh/ppo-Huggy 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
justphil/delightful-sparrow	justphil	2023-06-18T20:39:21Z	16	0	transformers	[ "transformers", "pytorch", "gpt_neox", "text-generation", "gpt", "llm", "large language model", "h2o-llmstudio", "en", "autotrain_compatible", "text-generation-inference", "region:us" ]	text-generation	2023-06-18T17:41:41Z	--- language: - en library_name: transformers tags: - gpt - llm - large language model - h2o-llmstudio inference: false thumbnail: https://h2o.ai/etc.clientlibs/h2o/clientlibs/clientlib-site/resources/images/favicon.ico --- # Model Card ## Summary This model was trained using [H2O LLM Studio](https://github.com/h2oai/h2o-llmstudio). - Base model: [EleutherAI/pythia-2.8b-deduped](https://huggingface.co/EleutherAI/pythia-2.8b-deduped) ## Usage To use the model with the `transformers` library on a machine with GPUs, first make sure you have the `transformers`, `accelerate` and `torch` libraries installed. ```bash pip install transformers==4.30.1 pip install accelerate==0.19.0 pip install torch==2.0.0 ``` ```python import torch from transformers import pipeline generate_text = pipeline( model="justphil/delightful-sparrow", torch_dtype="auto", trust_remote_code=True, use_fast=True, device_map={"": "cuda:0"}, ) res = generate_text( "Why is drinking water so healthy?", min_new_tokens=2, max_new_tokens=256, do_sample=False, num_beams=1, temperature=float(0.3), repetition_penalty=float(1.2), renormalize_logits=True ) print(res[0]["generated_text"]) ``` You can print a sample prompt after the preprocessing step to see how it is feed to the tokenizer: ```python print(generate_text.preprocess("Why is drinking water so healthy?")["prompt_text"]) ``` ```bash <\|prompt\|>Why is drinking water so healthy?<\|endoftext\|><\|answer\|> ``` Alternatively, you can download [h2oai_pipeline.py](h2oai_pipeline.py), store it alongside your notebook, and construct the pipeline yourself from the loaded model and tokenizer. If the model and the tokenizer are fully supported in the `transformers` package, this will allow you to set `trust_remote_code=False`. ```python import torch from h2oai_pipeline import H2OTextGenerationPipeline from transformers import AutoModelForCausalLM, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained( "justphil/delightful-sparrow", use_fast=True, padding_side="left", trust_remote_code=True, ) model = AutoModelForCausalLM.from_pretrained( "justphil/delightful-sparrow", torch_dtype="auto", device_map={"": "cuda:0"}, trust_remote_code=True, ) generate_text = H2OTextGenerationPipeline(model=model, tokenizer=tokenizer) res = generate_text( "Why is drinking water so healthy?", min_new_tokens=2, max_new_tokens=256, do_sample=False, num_beams=1, temperature=float(0.3), repetition_penalty=float(1.2), renormalize_logits=True ) print(res[0]["generated_text"]) ``` You may also construct the pipeline from the loaded model and tokenizer yourself and consider the preprocessing steps: ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_name = "justphil/delightful-sparrow" # either local folder or huggingface model name # Important: The prompt needs to be in the same format the model was trained with. # You can find an example prompt in the experiment logs. prompt = "<\|prompt\|>How are you?<\|endoftext\|><\|answer\|>" tokenizer = AutoTokenizer.from_pretrained( model_name, use_fast=True, trust_remote_code=True, ) model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype="auto", device_map={"": "cuda:0"}, trust_remote_code=True, ) model.cuda().eval() inputs = tokenizer(prompt, return_tensors="pt", add_special_tokens=False).to("cuda") # generate configuration can be modified to your needs tokens = model.generate( **inputs, min_new_tokens=2, max_new_tokens=256, do_sample=False, num_beams=1, temperature=float(0.3), repetition_penalty=float(1.2), renormalize_logits=True )[0] tokens = tokens[inputs["input_ids"].shape[1]:] answer = tokenizer.decode(tokens, skip_special_tokens=True) print(answer) ``` ## Model Architecture ``` GPTNeoXForCausalLM( (gpt_neox): GPTNeoXModel( (embed_in): Embedding(50304, 2560) (layers): ModuleList( (0-31): 32 x GPTNeoXLayer( (input_layernorm): LayerNorm((2560,), eps=1e-05, elementwise_affine=True) (post_attention_layernorm): LayerNorm((2560,), eps=1e-05, elementwise_affine=True) (attention): GPTNeoXAttention( (rotary_emb): RotaryEmbedding() (query_key_value): Linear(in_features=2560, out_features=7680, bias=True) (dense): Linear(in_features=2560, out_features=2560, bias=True) ) (mlp): GPTNeoXMLP( (dense_h_to_4h): Linear(in_features=2560, out_features=10240, bias=True) (dense_4h_to_h): Linear(in_features=10240, out_features=2560, bias=True) (act): GELUActivation() ) ) ) (final_layer_norm): LayerNorm((2560,), eps=1e-05, elementwise_affine=True) ) (embed_out): Linear(in_features=2560, out_features=50304, bias=False) ) ``` ## Model Configuration This model was trained using H2O LLM Studio and with the configuration in [cfg.yaml](cfg.yaml). Visit [H2O LLM Studio](https://github.com/h2oai/h2o-llmstudio) to learn how to train your own large language models. ## Model Validation Model validation results using [EleutherAI lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness). ```bash CUDA_VISIBLE_DEVICES=0 python main.py --model hf-causal-experimental --model_args pretrained=justphil/delightful-sparrow --tasks openbookqa,arc_easy,winogrande,hellaswag,arc_challenge,piqa,boolq --device cuda &> eval.log ``` ## Disclaimer Please read this disclaimer carefully before using the large language model provided in this repository. Your use of the model signifies your agreement to the following terms and conditions. - Biases and Offensiveness: The large language model is trained on a diverse range of internet text data, which may contain biased, racist, offensive, or otherwise inappropriate content. By using this model, you acknowledge and accept that the generated content may sometimes exhibit biases or produce content that is offensive or inappropriate. The developers of this repository do not endorse, support, or promote any such content or viewpoints. - Limitations: The large language model is an AI-based tool and not a human. It may produce incorrect, nonsensical, or irrelevant responses. It is the user's responsibility to critically evaluate the generated content and use it at their discretion. - Use at Your Own Risk: Users of this large language model must assume full responsibility for any consequences that may arise from their use of the tool. The developers and contributors of this repository shall not be held liable for any damages, losses, or harm resulting from the use or misuse of the provided model. - Ethical Considerations: Users are encouraged to use the large language model responsibly and ethically. By using this model, you agree not to use it for purposes that promote hate speech, discrimination, harassment, or any form of illegal or harmful activities. - Reporting Issues: If you encounter any biased, offensive, or otherwise inappropriate content generated by the large language model, please report it to the repository maintainers through the provided channels. Your feedback will help improve the model and mitigate potential issues. - Changes to this Disclaimer: The developers of this repository reserve the right to modify or update this disclaimer at any time without prior notice. It is the user's responsibility to periodically review the disclaimer to stay informed about any changes. By using the large language model provided in this repository, you agree to accept and comply with the terms and conditions outlined in this disclaimer. If you do not agree with any part of this disclaimer, you should refrain from using the model and any content generated by it.
Lzhou286/Taxi-Q-learning	Lzhou286	2023-06-18T20:25:29Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-06-18T20:25:15Z	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: Taxi-Q-learning results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.56 +/- 2.71 name: mean_reward verified: false --- # Q-Learning Agent playing1 Taxi-v3 This is a trained model of a Q-Learning agent playing Taxi-v3 . ## Usage ```python model = load_from_hub(repo_id="Lzhou286/Taxi-Q-learning", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
anavarrete/bias-mitigation-t1-ceo	anavarrete	2023-06-18T20:20:36Z	34	0	diffusers	[ "diffusers", "text-to-image", "stable-diffusion", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2023-06-18T20:08:21Z	--- license: creativeml-openrail-m tags: - text-to-image - stable-diffusion --- ### bias_mitigation_t1_CEO Dreambooth model trained by anavarrete with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb) Sample pictures of this concept:
IvanKun/Reinforce-PixelCopter-1	IvanKun	2023-06-18T19:51:55Z	0	0	null	[ "Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2023-06-18T19:51:42Z	--- tags: - Pixelcopter-PLE-v0 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-PixelCopter-1 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Pixelcopter-PLE-v0 type: Pixelcopter-PLE-v0 metrics: - type: mean_reward value: 9.40 +/- 6.73 name: mean_reward verified: false --- # 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
Todmy/q-FrozenLake-v1-4x4-noSlippery	Todmy	2023-06-18T19:51:41Z	0	0	null	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-06-18T19:51:31Z	--- 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: FrozenLake-v1-4x4-no_slippery metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward verified: false --- # Q-Learning Agent playing1 FrozenLake-v1 This is a trained model of a Q-Learning agent playing FrozenLake-v1 . ## Usage ```python model = load_from_hub(repo_id="Todmy/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
grenlayk/gpt2-medium-socialiqa	grenlayk	2023-06-18T19:36:08Z	144	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-05-09T18:12:11Z	GPT2-Medium model QAC fine-tuned on SocialIQA dataset. Based on Leveraging QA Datasets to Improve Generative Data Augmentation paper (by Mekala, Dheeraj and Vu, Tu and Schick, Timo and Shang, Jingb) and https://github.com/dheeraj7596/CONDA
LarryAIDraw/leafa_14_2	LarryAIDraw	2023-06-18T19:22:20Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-06-18T18:58:03Z	--- license: creativeml-openrail-m --- https://civitai.com/models/92777/leafa-alicization-or-alicization-or-sword-art-online
nmitchko/medfalcon-40b-lora	nmitchko	2023-06-18T19:22:01Z	9	3	peft	[ "peft", "medical", "text-generation", "en", "arxiv:2106.09685", "license:cc-by-nc-3.0", "region:us" ]	text-generation	2023-06-14T16:19:36Z	--- language: - en library_name: peft pipeline_tag: text-generation tags: - medical license: cc-by-nc-3.0 --- # MedFalcon 40b LoRA ## Model Description ### Architecture `nmitchko/medfalcon-40b-lora` is a large language model LoRa specifically fine-tuned for medical domain tasks. It is based on [`Falcon-40b-instruct`](https://huggingface.co/tiiuae/falcon-40b-instruct/) at 40 billion parameters. The primary goal of this model is to improve question-answering and medical dialogue tasks. It was trained using [LoRA](https://arxiv.org/abs/2106.09685), specifically [QLora](https://github.com/artidoro/qlora), to reduce memory footprint. > This Lora supports 4-bit and 8-bit modes. ### Requirements ``` bitsandbytes>=0.39.0 peft transformers ``` Steps to load this model: 1. Load base model using QLORA 2. Apply LoRA using peft ```python # from transformers import AutoTokenizer, AutoModelForCausalLM import transformers import torch model = "tiiuae/falcon-40b-instruct" LoRA = "nmitchko/medfalcon-40b-lora" tokenizer = AutoTokenizer.from_pretrained(model) model = AutoModelForCausalLM.from_pretrained(model, load_in_8bit=load_8bit, torch_dtype=torch.float16, trust_remote_code=True, ) model = PeftModel.from_pretrained(model, LoRA) pipeline = transformers.pipeline( "text-generation", model=model, tokenizer=tokenizer, torch_dtype=torch.bfloat16, trust_remote_code=True, device_map="auto", ) sequences = pipeline( "What does the drug ceftrioxone do?\nDoctor:", max_length=200, do_sample=True, top_k=40, num_return_sequences=1, eos_token_id=tokenizer.eos_token_id, ) for seq in sequences: print(f"Result: {seq['generated_text']}") ```
LarryAIDraw/AngelBeats_Yuriv2-04	LarryAIDraw	2023-06-18T19:19:45Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-06-18T18:55:04Z	--- license: creativeml-openrail-m --- https://civitai.com/models/91800/angel-beats-yuri-nakamura-yurippe
LarryAIDraw/carenhortensiaamor	LarryAIDraw	2023-06-18T19:19:19Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-06-18T18:54:26Z	--- license: creativeml-openrail-m --- https://civitai.com/models/91301/caren-hortensia-amor-or-fate-grand-order
LarryAIDraw/MiyakoSaitouV1	LarryAIDraw	2023-06-18T19:18:56Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-06-18T18:53:55Z	--- license: creativeml-openrail-m --- https://civitai.com/models/41207/miyako-saitou-or-oshi-no-ko
Tyrranen/ppo-LunarLander-v2.1	Tyrranen	2023-06-18T18:36:24Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-06-18T18:35:51Z	--- 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: LunarLander-v2 metrics: - type: mean_reward value: 278.05 +/- 21.49 name: mean_reward verified: false --- # 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 import load_from_hub ... ```
latentcat/latentcat-controlnet	latentcat	2023-06-18T18:22:44Z	0	252	null	[ "controlnet", "base_model:runwayml/stable-diffusion-v1-5", "base_model:adapter:runwayml/stable-diffusion-v1-5", "region:us" ]	null	2023-04-19T06:41:57Z	--- tags: - controlnet base_model: runwayml/stable-diffusion-v1-5 --- Download our ControlNet Models for [AUTOMATIC1111 Stable Diffusion Web UI](https://github.com/AUTOMATIC1111/stable-diffusion-webui)! * [Brightness Control](https://huggingface.co/ioclab/ioc-controlnet/resolve/main/models/control_v1p_sd15_brightness.safetensors) * [Model Introduction](https://huggingface.co/ioclab/control_v1p_sd15_brightness) * [Illumination Control](https://huggingface.co/ioclab/ioc-controlnet/resolve/main/models/control_v1p_sd15_illumination.safetensors) * [Model Introduction](https://huggingface.co/ioclab/control_v1u_sd15_illumination_webui) * [civitai Introduction]https://civitai.com/models/80536/lighting-based-picture-control-controlnet * Best practice: * Recommendation Weight: 0.4-0.9 * Recommendation Exit Timing: 0.4-0.9
bjlutuo/ppo-Huggy	bjlutuo	2023-06-18T18:12:36Z	2	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]	reinforcement-learning	2023-06-18T18:12:18Z	--- library_name: ml-agents tags: - Huggy - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Huggy --- # ppo Agent playing Huggy This is a trained model of a ppo agent playing Huggy using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A short tutorial where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A longer tutorial to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: bjlutuo/ppo-Huggy 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
drumwell/GenerAd-AI	drumwell	2023-06-18T17:43:24Z	31	0	peft	[ "peft", "text-generation", "dataset:drumwell/generadai-sample", "license:bigscience-openrail-m", "region:us" ]	text-generation	2023-06-18T17:24:59Z	--- library_name: peft license: bigscience-openrail-m datasets: - drumwell/generadai-sample pipeline_tag: text-generation ---
bryan467/Joko_widodo1	bryan467	2023-06-18T17:27:34Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-06-18T17:20:11Z	--- license: creativeml-openrail-m ---
ArvinArora/ppo-LunarLander-v2	ArvinArora	2023-06-18T17:20:27Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-06-18T17:19:56Z	--- 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: LunarLander-v2 metrics: - type: mean_reward value: 223.86 +/- 36.05 name: mean_reward verified: false --- # 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 import load_from_hub ... ```
Bodolaz/Unit-5.1	Bodolaz	2023-06-18T17:14:09Z	0	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "SnowballTarget", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SnowballTarget", "region:us" ]	reinforcement-learning	2023-06-18T17:14:00Z	--- library_name: ml-agents tags: - SnowballTarget - deep-reinforcement-learning - reinforcement-learning - ML-Agents-SnowballTarget --- # ppo Agent playing SnowballTarget This is a trained model of a ppo agent playing SnowballTarget using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A short tutorial where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A longer tutorial to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: Bodolaz/Unit-5.1 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
jclynn/finetuning-sentiment-model-5000-samples	jclynn	2023-06-18T17:04:10Z	103	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-06-17T22:12:50Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: finetuning-sentiment-model-5000-samples results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # finetuning-sentiment-model-5000-samples This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1462 - Accuracy: 0.956 - F1: 0.9719 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.0 - Tokenizers 0.13.3
DicksonMassawe/finetuning-emotion-model	DicksonMassawe	2023-06-18T16:44:54Z	103	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "dataset:emotion", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-06-13T16:16:38Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - emotion metrics: - accuracy - f1 model-index: - name: finetuning-emotion-model results: - task: name: Text Classification type: text-classification dataset: name: emotion type: emotion config: split split: validation args: split metrics: - name: Accuracy type: accuracy value: 0.936 - name: F1 type: f1 value: 0.936245808894686 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # finetuning-emotion-model This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.1508 - Accuracy: 0.936 - F1: 0.9362 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| No log \| 1.0 \| 250 \| 0.2633 \| 0.9135 \| 0.9126 \| \| 0.5024 \| 2.0 \| 500 \| 0.1717 \| 0.9285 \| 0.9289 \| \| 0.5024 \| 3.0 \| 750 \| 0.1560 \| 0.9325 \| 0.9330 \| \| 0.1253 \| 4.0 \| 1000 \| 0.1508 \| 0.936 \| 0.9362 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3
BryanSwk/q-FrozenLake-v1-4x4-noSlippery	BryanSwk	2023-06-18T16:30:56Z	0	0	null	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-06-18T16:30:47Z	--- 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: FrozenLake-v1-4x4-no_slippery metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward verified: false --- # Q-Learning Agent playing1 FrozenLake-v1 This is a trained model of a Q-Learning agent playing FrozenLake-v1 . ## Usage ```python model = load_from_hub(repo_id="BryanSwk/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
azetaaa/a2c-PandaReachDense-v2	azetaaa	2023-06-18T16:29:57Z	1	0	stable-baselines3	[ "stable-baselines3", "PandaReachDense-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-06-18T14:22:51Z	--- library_name: stable-baselines3 tags: - PandaReachDense-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: A2C results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: PandaReachDense-v2 type: PandaReachDense-v2 metrics: - type: mean_reward value: -2.63 +/- 0.51 name: mean_reward verified: false --- # A2C Agent playing PandaReachDense-v2 This is a trained model of a A2C agent playing PandaReachDense-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 import load_from_hub ... ```
prognosis/falcon7b-chunks-10k-v3_e2000	prognosis	2023-06-18T16:19:18Z	0	0	null	[ "tensorboard", "generated_from_trainer", "license:apache-2.0", "region:us" ]	null	2023-06-18T02:01:02Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: falcon7b-chunks-10k-v3_e2000 results: [] --- <!-- 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. --> # falcon7b-chunks-10k-v3_e2000 This model is a fine-tuned version of [tiiuae/falcon-7b](https://huggingface.co/tiiuae/falcon-7b) 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 hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - lr_scheduler_warmup_ratio: 0.03 - training_steps: 2000 ### Training results ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.0 - Tokenizers 0.13.3
Alexa2012/RL_Course_models	Alexa2012	2023-06-18T16:06:40Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-06-18T07:03:41Z	--- 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: LunarLander-v2 metrics: - type: mean_reward value: 278.76 +/- 23.10 name: mean_reward verified: false --- # 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 import load_from_hub ... ```
huantd/all-mpnet-base-v2	huantd	2023-06-18T16:04:01Z	4	0	transformers.js	[ "transformers.js", "onnx", "mpnet", "fill-mask", "region:us" ]	fill-mask	2023-06-18T15:42:54Z	--- library_name: "transformers.js" --- https://huggingface.co/sentence-transformers/all-mpnet-base-v2 with ONNX weights to be compatible with Transformers.js.
ardhies/breastinclassBetter	ardhies	2023-06-18T16:01:18Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-06-18T16:01:18Z	--- license: creativeml-openrail-m ---
Mustru/KAMU_KOTAN	Mustru	2023-06-18T16:00:40Z	0	0	null	[ "license:bigscience-openrail-m", "region:us" ]	null	2023-06-18T15:57:36Z	--- license: bigscience-openrail-m ---
TheFools/Celline	TheFools	2023-06-18T15:52:46Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-06-18T15:52:46Z	--- license: creativeml-openrail-m ---
rafay/ppo-Huggy	rafay	2023-06-18T15:49:38Z	4	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]	reinforcement-learning	2023-06-18T15:49:29Z	--- library_name: ml-agents tags: - Huggy - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Huggy --- # ppo Agent playing Huggy This is a trained model of a ppo agent playing Huggy using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A short tutorial where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A longer tutorial to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: rafay/ppo-Huggy 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
synpjh/bert-base-uncased-issues-128	synpjh	2023-06-18T15:43:51Z	116	0	transformers	[ "transformers", "pytorch", "bert", "fill-mask", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2023-06-18T14:16:56Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: bert-base-uncased-issues-128 results: [] --- <!-- 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-issues-128 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1675 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 16 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 0.239 \| 1.0 \| 291 \| 0.2306 \| \| 0.1865 \| 2.0 \| 582 \| 0.1971 \| \| 0.169 \| 3.0 \| 873 \| 0.1918 \| \| 0.1603 \| 4.0 \| 1164 \| 0.1875 \| \| 0.1536 \| 5.0 \| 1455 \| 0.1567 \| \| 0.1461 \| 6.0 \| 1746 \| 0.1755 \| \| 0.1411 \| 7.0 \| 2037 \| 0.1719 \| \| 0.1374 \| 8.0 \| 2328 \| 0.1658 \| \| 0.1341 \| 9.0 \| 2619 \| 0.1594 \| \| 0.1302 \| 10.0 \| 2910 \| 0.1666 \| \| 0.1284 \| 11.0 \| 3201 \| 0.1634 \| \| 0.1264 \| 12.0 \| 3492 \| 0.1588 \| \| 0.1238 \| 13.0 \| 3783 \| 0.1690 \| \| 0.1237 \| 14.0 \| 4074 \| 0.1558 \| \| 0.1218 \| 15.0 \| 4365 \| 0.1523 \| \| 0.1213 \| 16.0 \| 4656 \| 0.1675 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1 - Datasets 2.13.0 - Tokenizers 0.13.3
bjlutuo/ppo-LunarLander-v2	bjlutuo	2023-06-18T15:32:56Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-06-18T15:32:30Z	--- 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: LunarLander-v2 metrics: - type: mean_reward value: 230.67 +/- 18.68 name: mean_reward verified: false --- # 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 import load_from_hub ... ```
nic70/q-FrozenLake-v1-4x4-noSlippery	nic70	2023-06-18T15:02:26Z	0	0	null	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-06-18T08:17:50Z	--- 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: FrozenLake-v1-4x4-no_slippery metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward verified: false --- # Q-Learning Agent playing1 FrozenLake-v1 This is a trained model of a Q-Learning agent playing FrozenLake-v1 . ## Usage ```python model = load_from_hub(repo_id="nic70/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
Kenapaini/Normanvtsr	Kenapaini	2023-06-18T14:45:57Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-06-18T14:45:54Z	--- license: creativeml-openrail-m ---
ikaith/Reinforce-v3	ikaith	2023-06-18T14:33:08Z	0	0	null	[ "Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2023-06-18T14:32:55Z	--- tags: - Pixelcopter-PLE-v0 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-v3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Pixelcopter-PLE-v0 type: Pixelcopter-PLE-v0 metrics: - type: mean_reward value: 45.30 +/- 38.68 name: mean_reward verified: false --- # 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
TheBloke/gpt4-x-vicuna-13B-GGML	TheBloke	2023-06-18T14:31:23Z	0	97	null	[ "license:other", "region:us" ]	null	2023-05-05T19:46:40Z	--- inference: false license: other --- <!-- header start --> <div style="width: 100%;"> <img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </div> <div style="display: flex; justify-content: space-between; width: 100%;"> <div style="display: flex; flex-direction: column; align-items: flex-start;"> <p><a href="https://discord.gg/Jq4vkcDakD">Chat & support: my new Discord server</a></p> </div> <div style="display: flex; flex-direction: column; align-items: flex-end;"> <p><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p> </div> </div> <!-- header end --> # NousResearch's GPT4-x-Vicuna-13B GGML These files are GGML format model files for [NousResearch's GPT4-x-Vicuna-13B](https://huggingface.co/NousResearch/gpt4-x-vicuna-13b). GGML files are for CPU + GPU inference using [llama.cpp](https://github.com/ggerganov/llama.cpp) and libraries and UIs which support this format, such as: * [text-generation-webui](https://github.com/oobabooga/text-generation-webui) * [KoboldCpp](https://github.com/LostRuins/koboldcpp) * [ParisNeo/GPT4All-UI](https://github.com/ParisNeo/gpt4all-ui) * [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) * [ctransformers](https://github.com/marella/ctransformers) ## Repositories available * [4-bit GPTQ models for GPU inference](https://huggingface.co/TheBloke/gpt4-x-vicuna-13B-GPTQ) * [2, 3, 4, 5, 6 and 8-bit GGML models for CPU+GPU inference](https://huggingface.co/TheBloke/gpt4-x-vicuna-13B-GGML) * [Unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/TheBloke/gpt4-x-vicuna-13B-HF) <!-- compatibility_ggml start --> ## Compatibility ### Original llama.cpp quant methods: `q4_0, q4_1, q5_0, q5_1, q8_0` I have quantized these 'original' quantisation methods using an older version of llama.cpp so that they remain compatible with llama.cpp as of May 19th, commit `2d5db48`. These are guaranteed to be compatbile with any UIs, tools and libraries released since late May. ### New k-quant methods: `q2_K, q3_K_S, q3_K_M, q3_K_L, q4_K_S, q4_K_M, q5_K_S, q6_K` These new quantisation methods are compatible with llama.cpp as of June 6th, commit `2d43387`. They are now also compatible with recent releases of text-generation-webui, KoboldCpp, llama-cpp-python and ctransformers. Other tools and libraries may or may not be compatible - check their documentation if in doubt. ## Explanation of the new k-quant methods The new methods available are: * GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw) * GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw. * GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw. * GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw * GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw * GGML_TYPE_Q8_K - "type-0" 8-bit quantization. Only used for quantizing intermediate results. The difference to the existing Q8_0 is that the block size is 256. All 2-6 bit dot products are implemented for this quantization type. Refer to the Provided Files table below to see what files use which methods, and how. <!-- compatibility_ggml end --> ## Provided files \| Name \| Quant method \| Bits \| Size \| Max RAM required \| Use case \| \| ---- \| ---- \| ---- \| ---- \| ---- \| ----- \| \| gpt4-x-vicuna-13B.ggmlv3.q2_K.bin \| q2_K \| 2 \| 5.51 GB \| 8.01 GB \| New k-quant method. Uses GGML_TYPE_Q4_K for the attention.vw and feed_forward.w2 tensors, GGML_TYPE_Q2_K for the other tensors. \| \| gpt4-x-vicuna-13B.ggmlv3.q3_K_L.bin \| q3_K_L \| 3 \| 6.93 GB \| 9.43 GB \| New k-quant method. Uses GGML_TYPE_Q5_K for the attention.wv, attention.wo, and feed_forward.w2 tensors, else GGML_TYPE_Q3_K \| \| gpt4-x-vicuna-13B.ggmlv3.q3_K_M.bin \| q3_K_M \| 3 \| 6.31 GB \| 8.81 GB \| New k-quant method. Uses GGML_TYPE_Q4_K for the attention.wv, attention.wo, and feed_forward.w2 tensors, else GGML_TYPE_Q3_K \| \| gpt4-x-vicuna-13B.ggmlv3.q3_K_S.bin \| q3_K_S \| 3 \| 5.66 GB \| 8.16 GB \| New k-quant method. Uses GGML_TYPE_Q3_K for all tensors \| \| gpt4-x-vicuna-13B.ggmlv3.q4_0.bin \| q4_0 \| 4 \| 7.32 GB \| 9.82 GB \| Original llama.cpp quant method, 4-bit. \| \| gpt4-x-vicuna-13B.ggmlv3.q4_1.bin \| q4_1 \| 4 \| 8.14 GB \| 10.64 GB \| Original llama.cpp quant method, 4-bit. Higher accuracy than q4_0 but not as high as q5_0. However has quicker inference than q5 models. \| \| gpt4-x-vicuna-13B.ggmlv3.q4_K_M.bin \| q4_K_M \| 4 \| 7.87 GB \| 10.37 GB \| New k-quant method. Uses GGML_TYPE_Q6_K for half of the attention.wv and feed_forward.w2 tensors, else GGML_TYPE_Q4_K \| \| gpt4-x-vicuna-13B.ggmlv3.q4_K_S.bin \| q4_K_S \| 4 \| 7.37 GB \| 9.87 GB \| New k-quant method. Uses GGML_TYPE_Q4_K for all tensors \| \| gpt4-x-vicuna-13B.ggmlv3.q5_0.bin \| q5_0 \| 5 \| 8.95 GB \| 11.45 GB \| Original llama.cpp quant method, 5-bit. Higher accuracy, higher resource usage and slower inference. \| \| gpt4-x-vicuna-13B.ggmlv3.q5_1.bin \| q5_1 \| 5 \| 9.76 GB \| 12.26 GB \| Original llama.cpp quant method, 5-bit. Even higher accuracy, resource usage and slower inference. \| \| gpt4-x-vicuna-13B.ggmlv3.q5_K_M.bin \| q5_K_M \| 5 \| 9.23 GB \| 11.73 GB \| New k-quant method. Uses GGML_TYPE_Q6_K for half of the attention.wv and feed_forward.w2 tensors, else GGML_TYPE_Q5_K \| \| gpt4-x-vicuna-13B.ggmlv3.q5_K_S.bin \| q5_K_S \| 5 \| 8.97 GB \| 11.47 GB \| New k-quant method. Uses GGML_TYPE_Q5_K for all tensors \| \| gpt4-x-vicuna-13B.ggmlv3.q6_K.bin \| q6_K \| 6 \| 10.68 GB \| 13.18 GB \| New k-quant method. Uses GGML_TYPE_Q8_K - 6-bit quantization - for all tensors \| \| gpt4-x-vicuna-13B.ggmlv3.q8_0.bin \| q8_0 \| 8 \| 13.83 GB \| 16.33 GB \| Original llama.cpp quant method, 8-bit. Almost indistinguishable from float16. High resource use and slow. Not recommended for most users. \| Note: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead. ## How to run in `llama.cpp` I use the following command line; adjust for your tastes and needs: ``` ./main -t 10 -ngl 32 -m gpt4-x-vicuna-13B.ggmlv3.q5_0.bin --color -c 2048 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "### Instruction: Write a story about llamas\n### Response:" ``` Change `-t 10` to the number of physical CPU cores you have. For example if your system has 8 cores/16 threads, use `-t 8`. Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration. If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins` ## How to run in `text-generation-webui` Further instructions here: [text-generation-webui/docs/llama.cpp-models.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/llama.cpp-models.md). <!-- footer start --> ## Discord For further support, and discussions on these models and AI in general, join us at: [TheBloke AI's Discord server](https://discord.gg/Jq4vkcDakD) ## Thanks, and how to contribute. Thanks to the [chirper.ai](https://chirper.ai) team! I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training. If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects. Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits. * Patreon: https://patreon.com/TheBlokeAI * Ko-Fi: https://ko-fi.com/TheBlokeAI Special thanks to: Luke from CarbonQuill, Aemon Algiz, Dmitriy Samsonov. Patreon special mentions: vamX, K, Jonathan Leane, Lone Striker, Sean Connelly, Chris McCloskey, WelcomeToTheClub, Nikolai Manek, John Detwiler, Kalila, David Flickinger, Fen Risland, subjectnull, Johann-Peter Hartmann, Talal Aujan, John Villwock, senxiiz, Khalefa Al-Ahmad, Kevin Schuppel, Alps Aficionado, Derek Yates, Mano Prime, Nathan LeClaire, biorpg, trip7s trip, Asp the Wyvern, chris gileta, Iucharbius , Artur Olbinski, Ai Maven, Joseph William Delisle, Luke Pendergrass, Illia Dulskyi, Eugene Pentland, Ajan Kanaga, Willem Michiel, Space Cruiser, Pyrater, Preetika Verma, Junyu Yang, Oscar Rangel, Spiking Neurons AB, Pierre Kircher, webtim, Cory Kujawski, terasurfer , Trenton Dambrowitz, Gabriel Puliatti, Imad Khwaja, Luke. Thank you to all my generous patrons and donaters! <!-- footer end --> # Original model card: NousResearch's GPT4-x-Vicuna-13B As a base model used https://huggingface.co/eachadea/vicuna-13b-1.1 Finetuned on Teknium's GPTeacher dataset, unreleased Roleplay v2 dataset, GPT-4-LLM dataset Uncensored, WizardLM Uncensored and Nous Research Instruct Dataset Approx 180k instructions, all from GPT-4, all cleaned of any OpenAI censorship/"As an AI Language Model" etc. Base model still has OpenAI censorship. Soon, a new version will be released with cleaned vicuna from https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltere Trained on 8 A100-80GB GPUs for 5 epochs following Alpaca deepspeed training code. Nous Research Instruct Dataset will be released soon. Prompt format is Alpaca: ``` ### Instruction: ### Response: ``` or ``` ### Instruction: ### Input: ### Response: ``` GPTeacher, Roleplay v2 by https://huggingface.co/teknium Wizard LM by https://github.com/nlpxucan Nous Research Instruct Dataset by https://huggingface.co/karan4d and https://huggingface.co/huemin Benchmark results: ``` "arc_challenge": { "acc": 0.4189419795221843, "acc_stderr": 0.01441810695363901, "acc_norm": 0.439419795221843, "acc_norm_stderr": 0.014503747823580123 }, "arc_easy": { "acc": 0.7159090909090909, "acc_stderr": 0.009253921261885768, "acc_norm": 0.5867003367003367, "acc_norm_stderr": 0.010104361780747527 }, "boolq": { "acc": 0.8137614678899082, "acc_stderr": 0.006808882985424063 }, "hellaswag": { "acc": 0.5790679147580163, "acc_stderr": 0.004926996830194234, "acc_norm": 0.7518422624975104, "acc_norm_stderr": 0.004310610616845708 }, "openbookqa": { "acc": 0.288, "acc_stderr": 0.02027150383507522, "acc_norm": 0.436, "acc_norm_stderr": 0.0221989546414768 }, "piqa": { "acc": 0.7529923830250272, "acc_stderr": 0.010062268140772622, "acc_norm": 0.749727965179543, "acc_norm_stderr": 0.01010656188008979 }, "winogrande": { "acc": 0.6495659037095501, "acc_stderr": 0.01340904767667019 } ``` Compute provided by our project sponsor https://redmond.ai/
LLukas22/falcon-7B-ggml	LLukas22	2023-06-18T14:24:36Z	0	2	null	[ "license:apache-2.0", "region:us" ]	null	2023-06-18T13:55:29Z	--- license: apache-2.0 --- Debug-Models for [rustformers/llm](https://github.com/rustformers/llm)'s Falcon implementation.
Middelz2/roberta-large-aphasia-narration-weightdecay0-lr2e4_eps_10	Middelz2	2023-06-18T14:24:22Z	3	0	transformers	[ "transformers", "tf", "roberta", "fill-mask", "generated_from_keras_callback", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2023-06-18T13:32:21Z	--- license: mit tags: - generated_from_keras_callback model-index: - name: Middelz2/roberta-large-aphasia-narration-weightdecay0-lr2e4_eps_10 results: [] --- <!-- 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. --> # Middelz2/roberta-large-aphasia-narration-weightdecay0-lr2e4_eps_10 This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 5.6128 - Validation Loss: 5.5802 - Epoch: 9 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 0.0002, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0} - training_precision: float32 ### Training results \| Train Loss \| Validation Loss \| Epoch \| \|:----------:\|:---------------:\|:-----:\| \| 2.1420 \| 1.6450 \| 0 \| \| 1.6971 \| 1.4556 \| 1 \| \| 1.6025 \| 1.3882 \| 2 \| \| 1.4763 \| 1.2997 \| 3 \| \| 1.4301 \| 1.3055 \| 4 \| \| 1.4358 \| 1.3317 \| 5 \| \| 2.2816 \| 2.4774 \| 6 \| \| 2.7754 \| 2.0994 \| 7 \| \| 4.5272 \| 5.5713 \| 8 \| \| 5.6128 \| 5.5802 \| 9 \| ### Framework versions - Transformers 4.30.2 - TensorFlow 2.12.0 - Datasets 2.13.0 - Tokenizers 0.13.3

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