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danielv835/PF_Coach
danielv835
2023-05-09T19:29:49Z
5
0
transformers
[ "transformers", "pytorch", "opt", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2023-05-08T15:21:40Z
--- # For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1 # Doc / guide: https://huggingface.co/docs/hub/model-cards {} --- # Model Card for PF_Coach Coach (final) models for the PF Coach project for the Tribe LLM Hackathon, May 2023 Current version is for integration test. ## Model Details ### Model Description This model is based on OPT-1.3B, after brief DeepSpeed instruction following finetuning and RLHF on a generic (non PF) dataset. - **Developed by:** Daniel Vainsencher - **Model type:** Causal language modeling, tuned for chat. - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** facebook/OPT-1.3B ### Model Sources [optional] - **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]
ratish/DBERT_CleanDesc_MAKE_v12
ratish
2023-05-09T19:21:07Z
4
0
transformers
[ "transformers", "tf", "distilbert", "text-classification", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2023-05-09T19:13:38Z
--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: ratish/DBERT_CleanDesc_MAKE_v12 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. --> # ratish/DBERT_CleanDesc_MAKE_v12 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: - Train Loss: nan - Validation Loss: nan - Train Accuracy: 0.0 - Epoch: 0 ## 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': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 4635, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results | Train Loss | Validation Loss | Train Accuracy | Epoch | |:----------:|:---------------:|:--------------:|:-----:| | nan | nan | 0.0 | 0 | ### Framework versions - Transformers 4.28.1 - TensorFlow 2.12.0 - Datasets 2.12.0 - Tokenizers 0.13.3
Multi-Domain-Expert-Learning/expert-min-pile-instruct
Multi-Domain-Expert-Learning
2023-05-09T19:08:57Z
16
1
transformers
[ "transformers", "pytorch", "gpt_neox", "text-generation", "generated_from_trainer", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2023-05-06T16:15:06Z
--- tags: - generated_from_trainer datasets: - pile-instruct/ metrics: - accuracy model-index: - name: layer_4,5,6,7,8 results: - task: type: text-generation name: Causal Language Modeling dataset: name: pile-instruct/ type: pile-instruct/ split: None metrics: - type: accuracy value: 0.3842425129408517 name: Accuracy --- <!-- 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. --> # layer_4,5,6,7,8 This model is a fine-tuned version of [P1ayer-1/pythia-deduped-1b-chat-base](https://huggingface.co/P1ayer-1/pythia-deduped-1b-chat-base) on the pile-instruct/ dataset. It achieves the following results on the evaluation set: - Loss: 4.9648 - Accuracy: 0.3842 ## 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: 0.0001 - train_batch_size: 12 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - total_train_batch_size: 96 - total_eval_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 6000 ### Training results | Training Loss | Epoch | Step | Accuracy | Validation Loss | |:-------------:|:-----:|:----:|:--------:|:---------------:| | 7.4574 | 0.1 | 200 | 0.1688 | 7.4961 | | 7.0445 | 0.2 | 400 | 0.1997 | 7.0547 | | 6.7483 | 0.3 | 600 | 0.2190 | 6.7930 | | 6.4568 | 0.4 | 800 | 0.2376 | 6.5703 | | 6.2865 | 0.5 | 1000 | 0.2552 | 6.375 | | 6.1028 | 0.6 | 1200 | 0.2793 | 6.1484 | | 5.8888 | 0.7 | 1400 | 0.2982 | 5.9570 | | 5.7362 | 0.8 | 1600 | 0.3121 | 5.8008 | | 5.6507 | 0.9 | 1800 | 0.3238 | 5.6797 | | 5.565 | 1.0 | 2000 | 0.3318 | 5.5781 | | 5.4688 | 1.1 | 2200 | 0.3392 | 5.4961 | | 5.4044 | 1.2 | 2400 | 0.3456 | 5.4219 | | 5.3323 | 1.3 | 2600 | 0.3516 | 5.3594 | | 5.2598 | 1.4 | 2800 | 0.3562 | 5.3047 | | 5.2159 | 1.5 | 3000 | 0.3596 | 5.2578 | | 5.1992 | 1.6 | 3200 | 0.3638 | 5.2148 | | 5.1429 | 1.69 | 3400 | 0.3672 | 5.1797 | | 5.095 | 1.79 | 3600 | 0.3696 | 5.1445 | | 5.0646 | 1.89 | 3800 | 0.3715 | 5.1172 | | 5.059 | 1.99 | 4000 | 0.3742 | 5.0859 | | 5.0152 | 2.09 | 4200 | 0.3756 | 5.0664 | | 5.0251 | 2.19 | 4400 | 0.3769 | 5.0469 | | 5.022 | 2.29 | 4600 | 0.3790 | 5.0273 | | 4.9939 | 2.39 | 4800 | 0.3798 | 5.0156 | | 4.924 | 2.49 | 5000 | 0.3811 | 5.0 | | 4.9335 | 2.59 | 5200 | 0.3821 | 4.9883 | | 4.9231 | 2.69 | 5400 | 0.3829 | 4.9805 | | 4.8886 | 2.79 | 5600 | 4.9727 | 0.3835 | | 4.9419 | 2.89 | 5800 | 4.9648 | 0.3843 | | 4.9227 | 2.99 | 6000 | 4.9648 | 0.3842 | ### Framework versions - Transformers 4.28.1 - Pytorch 2.0.0+cu117 - Datasets 2.11.0 - Tokenizers 0.13.3 ## Wandb Report https://wandb.ai/ontocord/pythia-1b-deduped-layer-test-min-pile-instruct/runs/kqlipkt3
Vailla-Rohit/bart-base-finetuned-samsum
Vailla-Rohit
2023-05-09T18:57:35Z
3
0
transformers
[ "transformers", "pytorch", "bart", "text2text-generation", "generated_from_trainer", "dataset:samsum", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text2text-generation
2023-05-09T18:56:22Z
--- license: apache-2.0 tags: - generated_from_trainer datasets: - samsum metrics: - rouge model-index: - name: test-dialogue-summarization results: - task: name: Sequence-to-sequence Language Modeling type: text2text-generation dataset: name: samsum type: samsum config: samsum split: validation args: samsum metrics: - name: Rouge1 type: rouge value: 48.0348 --- <!-- 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. --> # test-dialogue-summarization This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on the samsum dataset. It achieves the following results on the evaluation set: - Loss: 1.5507 - Rouge1: 48.0348 - Rouge2: 24.8215 - Rougel: 40.5048 - Rougelsum: 44.3467 - Gen Len: 18.1638 ## 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: 9e-06 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | No log | 1.0 | 460 | 1.5507 | 48.0348 | 24.8215 | 40.5048 | 44.3467 | 18.1638 | ### Framework versions - Transformers 4.28.1 - Pytorch 2.0.0+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3
lucakuehne/mobilenetv3-imagenet-rev1
lucakuehne
2023-05-09T18:53:06Z
4
0
tf-keras
[ "tf-keras", "mobilenet", "image-classification", "region:us" ]
image-classification
2023-05-09T18:43:51Z
--- pipeline_tag: image-classification ---
openaccess-ai-collective/llama-13b-alpaca-wizard-vicuna
openaccess-ai-collective
2023-05-09T18:06:55Z
11
5
transformers
[ "transformers", "pytorch", "llama", "text-generation", "en", "dataset:vicgalle/alpaca-gpt4", "dataset:ehartford/WizardLM_alpaca_evol_instruct_70k_unfiltered", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2023-05-01T19:54:33Z
--- license: apache-2.0 datasets: - vicgalle/alpaca-gpt4 - ehartford/WizardLM_alpaca_evol_instruct_70k_unfiltered language: - en library_name: transformers pipeline_tag: text-generation --- # openaccess-ai-collective/llama-13b-alpaca-wizard ## Trained - `vicgalle/alpaca-gpt4` 1 epoch, learning rate 3e-5 https://wandb.ai/wing-lian/wizard-vicuna-gpt4/overview - `deepspeed scripts/finetune.py configs/axolotl/wizard-vicuna-13b-step1.yml --deepspeed configs/ds_config.json --num_epochs 2 --warmup_steps 46 --logging_steps 1 --save_steps 23` - `wizardlm` https://wandb.ai/wing-lian/wizard-vicuna-gpt4/runs/4y38knw4 - `deepspeed scripts/finetune.py configs/axolotl/wizard-vicuna-13b-step2.yml --deepspeed configs/ds_config-step2.json --num_epochs 2 --logging_steps 1` - `vicuna` TBD <pre>Brought to you by the OpenAccess AI Collective</pre>
jhutchinson25/ppo-LunarLander-v2
jhutchinson25
2023-05-09T18:03:23Z
0
0
stable-baselines3
[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-05-09T18:03:00Z
--- 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: 272.05 +/- 17.30 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 ... ```
harvinder676/bert-news
harvinder676
2023-05-09T18:02:03Z
4
0
transformers
[ "transformers", "pytorch", "distilbert", "fill-mask", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
fill-mask
2023-05-09T17:44:52Z
--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: bert-news 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-news 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: 2.5512 ## 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: 32 - eval_batch_size: 32 - seed: 0 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 100 - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.7548 | 1.0 | 1531 | 2.6146 | | 2.6217 | 2.0 | 3062 | 2.5512 | ### Framework versions - Transformers 4.28.1 - Pytorch 2.0.0 - Datasets 2.1.0 - Tokenizers 0.13.3
muhammadravi251001/fine-tuned-DatasetQAS-IDK-MRC-with-xlm-roberta-large-with-ITTL-with-freeze-LR-1e-05
muhammadravi251001
2023-05-09T17:59:05Z
9
0
transformers
[ "transformers", "pytorch", "tensorboard", "xlm-roberta", "question-answering", "generated_from_trainer", "license:mit", "endpoints_compatible", "region:us" ]
question-answering
2023-05-07T13:05:44Z
--- license: mit tags: - generated_from_trainer metrics: - f1 model-index: - name: fine-tuned-DatasetQAS-IDK-MRC-with-xlm-roberta-large-with-ITTL-with-freeze-LR-1e-05 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. --> # fine-tuned-DatasetQAS-IDK-MRC-with-xlm-roberta-large-with-ITTL-with-freeze-LR-1e-05 This model is a fine-tuned version of [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.8698 - Exact Match: 74.6073 - F1: 81.6214 ## 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: 1e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Exact Match | F1 | |:-------------:|:-----:|:----:|:---------------:|:-----------:|:-------:| | 6.2825 | 0.49 | 36 | 2.2341 | 49.2147 | 49.3071 | | 3.465 | 0.98 | 72 | 1.8139 | 49.2147 | 49.4968 | | 1.9165 | 1.48 | 108 | 1.3110 | 50.6545 | 59.1184 | | 1.9165 | 1.97 | 144 | 0.9907 | 65.0524 | 72.4023 | | 1.2487 | 2.46 | 180 | 0.9051 | 68.1937 | 75.7323 | | 0.9426 | 2.95 | 216 | 0.8485 | 67.8010 | 75.3684 | | 0.8069 | 3.45 | 252 | 0.8499 | 70.0262 | 77.7548 | | 0.8069 | 3.94 | 288 | 0.9202 | 67.5393 | 74.8123 | | 0.7193 | 4.44 | 324 | 0.7897 | 73.0366 | 79.9552 | | 0.6234 | 4.92 | 360 | 0.7973 | 73.6911 | 80.5009 | | 0.6234 | 5.42 | 396 | 0.8353 | 72.9058 | 80.2879 | | 0.5583 | 5.91 | 432 | 0.8392 | 73.4293 | 80.6345 | | 0.5263 | 6.41 | 468 | 0.8477 | 73.5602 | 81.0016 | | 0.4642 | 6.9 | 504 | 0.8355 | 74.6073 | 81.7391 | | 0.4642 | 7.39 | 540 | 0.8383 | 73.5602 | 81.1187 | | 0.4381 | 7.88 | 576 | 0.8828 | 73.0366 | 79.8504 | | 0.4099 | 8.38 | 612 | 0.8698 | 74.6073 | 81.6214 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1+cu117 - Datasets 2.2.0 - Tokenizers 0.13.2
JonatanGk/roberta-base-bne-finetuned-catalonia-independence-detector
JonatanGk
2023-05-09T17:53:13Z
12
1
transformers
[ "transformers", "pytorch", "tensorboard", "roberta", "text-classification", "spanish", "es", "dataset:catalonia_independence", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2022-03-02T23:29:04Z
--- license: apache-2.0 language: es tags: - spanish datasets: - catalonia_independence metrics: - accuracy model-index: - name: roberta-base-bne-finetuned-mnli results: - task: name: Text Classification type: text-classification dataset: name: catalonia_independence type: catalonia_independence args: spanish metrics: - name: Accuracy type: accuracy value: 0.7880893300248138 - task: type: text-classification name: Text Classification dataset: name: catalonia_independence type: catalonia_independence config: catalan split: test metrics: - name: Accuracy type: accuracy value: 0.4592039800995025 verified: true - name: Precision Macro type: precision value: 0.6104489964825159 verified: true - name: Precision Micro type: precision value: 0.4592039800995025 verified: true - name: Precision Weighted type: precision value: 0.6167123723406555 verified: true - name: Recall Macro type: recall value: 0.4146479268294389 verified: true - name: Recall Micro type: recall value: 0.4592039800995025 verified: true - name: Recall Weighted type: recall value: 0.4592039800995025 verified: true - name: F1 Macro type: f1 value: 0.33416407167650636 verified: true - name: F1 Micro type: f1 value: 0.4592039800995025 verified: true - name: F1 Weighted type: f1 value: 0.34549318538357193 verified: true - name: loss type: loss value: 3.393402099609375 verified: true widget: - text: "Junqueras, sobre la decisi\xF3n judicial sobre Puigdemont: La justicia que\ \ falta en el Estado llega y llegar\xE1 de Europa" - text: "Desconvocada la manifestaci\xF3n del domingo en Barcelona en apoyo a Puigdemont" --- # roberta-base-bne-finetuned-catalonia-independence-detector This model is a fine-tuned version of [BSC-TeMU/roberta-base-bne](https://huggingface.co/BSC-TeMU/roberta-base-bne) on the catalonia_independence dataset. It achieves the following results on the evaluation set: - Loss: 0.9415 - Accuracy: 0.7881 <details> ## Model description The data was collected over 12 days during February and March of 2019 from tweets posted in Barcelona, and during September of 2018 from tweets posted in the town of Terrassa, Catalonia. Each corpus is annotated with three classes: AGAINST, FAVOR and NEUTRAL, which express the stance towards the target - independence of Catalonia. ## 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: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 378 | 0.5534 | 0.7558 | | 0.6089 | 2.0 | 756 | 0.5315 | 0.7643 | | 0.2678 | 3.0 | 1134 | 0.7336 | 0.7816 | | 0.0605 | 4.0 | 1512 | 0.8809 | 0.7866 | | 0.0605 | 5.0 | 1890 | 0.9415 | 0.7881 | </details> ### Model in action 🚀 Fast usage with **pipelines**: ```python from transformers import pipeline model_path = "JonatanGk/roberta-base-bne-finetuned-catalonia-independence-detector" independence_analysis = pipeline("text-classification", model=model_path, tokenizer=model_path) independence_analysis( "Junqueras, sobre la decisión judicial sobre Puigdemont: La justicia que falta en el Estado llega y llegará de Europa" ) # Output: [{'label': 'FAVOR', 'score': 0.9936726093292236}] independence_analysis( "El desafío independentista queda adormecido, y eso que el Gobierno ha sido muy claro en que su propuesta para Cataluña es una agenda de reencuentro, centrada en inversiones e infraestructuras") # Output: [{'label': 'AGAINST', 'score': 0.7508948445320129}] independence_analysis( "Desconvocada la manifestación del domingo en Barcelona en apoyo a Puigdemont" ) # Output: [{'label': 'NEUTRAL', 'score': 0.9966907501220703}] ``` [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JonatanGk/Shared-Colab/blob/master/Catalonia_independence_Detector_(SPANISH).ipynb#scrollTo=uNMOXJz38W6U) ### Framework versions - Transformers 4.11.3 - Pytorch 1.9.0+cu111 - Datasets 1.12.1 - Tokenizers 0.10.3 ## Citation Thx to HF.co & [@lewtun](https://github.com/lewtun) for Dataset ;) > Special thx to [Manuel Romero/@mrm8488](https://huggingface.co/mrm8488) as my mentor & R.C. > Created by [Jonatan Luna](https://JonatanGk.github.io) | [LinkedIn](https://www.linkedin.com/in/JonatanGk/)
JonatanGk/roberta-base-bne-finetuned-cyberbullying-spanish
JonatanGk
2023-05-09T17:51:31Z
45
5
transformers
[ "transformers", "pytorch", "tensorboard", "safetensors", "roberta", "text-classification", "spanish", "es", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2022-03-02T23:29:04Z
--- language: es tags: - "spanish" metrics: - accuracy widget: - text: "Eres mas pequeño que un pitufo!" - text: "Eres muy feo!" - text: "Odio tu forma de hablar!" - text: "Eres tan fea que cuando eras pequeña te echaban de comer por debajo de la puerta." --- # roberta-base-bne-finetuned-ciberbullying-spanish This model is a fine-tuned version of [BSC-TeMU/roberta-base-bne](https://huggingface.co/BSC-TeMU/roberta-base-bne) on the dataset generated scrapping all social networks (Twitter, Youtube ...) to detect ciberbullying on Spanish. It achieves the following results on the evaluation set: - Loss: 0.1657 - Accuracy: 0.9607 ## Training and evaluation data I use the concatenation from multiple datasets generated scrapping social networks (Twitter,Youtube,Discord...) to fine-tune this model. The total number of sentence pairs is above 360k sentences. ## Training procedure <details> ### 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: 4 ### Training results | Training Loss | Epoch | Step | Accuracy | Validation Loss | |:-------------:|:-----:|:-----:|:--------:|:---------------:| | 0.1512 | 1.0 | 22227 | 0.9501 | 0.1418 | | 0.1253 | 2.0 | 44454 | 0.9567 | 0.1499 | | 0.0973 | 3.0 | 66681 | 0.9594 | 0.1397 | | 0.0658 | 4.0 | 88908 | 0.9607 | 0.1657 | </details> ### Model in action 🚀 Fast usage with **pipelines**: ```python from transformers import pipeline model_path = "JonatanGk/roberta-base-bne-finetuned-ciberbullying-spanish" bullying_analysis = pipeline("text-classification", model=model_path, tokenizer=model_path) bullying_analysis( "Desde que te vi me enamoré de ti." ) # Output: [{'label': 'Not_bullying', 'score': 0.9995710253715515}] bullying_analysis( "Eres tan fea que cuando eras pequeña te echaban de comer por debajo de la puerta." ) # Output: [{'label': 'Bullying', 'score': 0.9918262958526611}] ``` [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JonatanGk/Shared-Colab/blob/master/Cyberbullying_detection_(SPANISH).ipynb) ### Framework versions - Transformers 4.10.3 - Pytorch 1.9.0+cu102 - Datasets 1.12.1 - Tokenizers 0.10.3 > Special thx to [Manuel Romero/@mrm8488](https://huggingface.co/mrm8488) as my mentor & R.C. > Created by [Jonatan Luna](https://JonatanGk.github.io) | [LinkedIn](https://www.linkedin.com/in/JonatanGk/)
dxli/berry_bowl
dxli
2023-05-09T17:31:12Z
33
0
diffusers
[ "diffusers", "tensorboard", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "textual_inversion", "base_model:runwayml/stable-diffusion-v1-5", "base_model:adapter:runwayml/stable-diffusion-v1-5", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]
text-to-image
2023-05-09T08:19:37Z
--- license: creativeml-openrail-m base_model: runwayml/stable-diffusion-v1-5 tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - textual_inversion inference: true --- # Textual inversion text2image fine-tuning - dxli/berry_bowl These are textual inversion adaption weights for runwayml/stable-diffusion-v1-5. You can find some example images in the following.
directtt/wine-reviews-roberta
directtt
2023-05-09T17:28:12Z
3
0
transformers
[ "transformers", "tf", "roberta", "text-classification", "generated_from_keras_callback", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2023-05-09T17:26:11Z
--- license: mit tags: - generated_from_keras_callback model-index: - name: wine-reviews-roberta 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. --> # wine-reviews-roberta This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.2715 - Train Acc: 0.8906 - Validation Loss: 0.6536 - Validation Acc: 0.7701 - Epoch: 4 ## 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': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 24455, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results | Train Loss | Train Acc | Validation Loss | Validation Acc | Epoch | |:----------:|:---------:|:---------------:|:--------------:|:-----:| | 0.6164 | 0.7297 | 0.5360 | 0.7665 | 0 | | 0.5040 | 0.7820 | 0.5145 | 0.7739 | 1 | | 0.4248 | 0.8206 | 0.5470 | 0.7744 | 2 | | 0.3413 | 0.8583 | 0.6132 | 0.7699 | 3 | | 0.2715 | 0.8906 | 0.6536 | 0.7701 | 4 | ### Framework versions - Transformers 4.28.1 - TensorFlow 2.11.0 - Datasets 2.1.0 - Tokenizers 0.13.3
Mael7307/sbiobert
Mael7307
2023-05-09T17:18:19Z
3
0
sentence-transformers
[ "sentence-transformers", "pytorch", "distilbert", "feature-extraction", "sentence-similarity", "transformers", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]
sentence-similarity
2023-05-09T16:39:01Z
--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers --- # {MODEL_NAME} This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. <!--- Describe your model here --> ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('{MODEL_NAME}') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) # Sentences we want sentence embeddings for sentences = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}') model = AutoModel.from_pretrained('{MODEL_NAME}') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(**encoded_input) # Perform pooling. In this case, mean pooling. sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Evaluation Results <!--- Describe how your model was evaluated --> For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME}) ## Training The model was trained with the parameters: **DataLoader**: `torch.utils.data.dataloader.DataLoader` of length 140000 with parameters: ``` {'batch_size': 32, 'sampler': 'torch.utils.data.sampler.SequentialSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` **Loss**: `gpl.toolkit.loss.MarginDistillationLoss` Parameters of the fit()-Method: ``` { "epochs": 1, "evaluation_steps": 0, "evaluator": "NoneType", "max_grad_norm": 1, "optimizer_class": "<class 'transformers.optimization.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": 140000, "warmup_steps": 1000, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 350, 'do_lower_case': False}) with Transformer model: DistilBertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) ) ``` ## Citing & Authors <!--- Describe where people can find more information -->
YashGajjar/Reinforce-CartPole-v1
YashGajjar
2023-05-09T17:15:38Z
0
0
null
[ "CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]
reinforcement-learning
2023-05-09T17:15:28Z
--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-CartPole-v1 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 metrics: - type: mean_reward value: 500.00 +/- 0.00 name: mean_reward verified: false --- # **Reinforce** Agent playing **CartPole-v1** This is a trained model of a **Reinforce** agent playing **CartPole-v1** . 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
reyhanemyr/bert-base-uncased-finetuned-recruitment-exp
reyhanemyr
2023-05-09T17:09:51Z
4
0
transformers
[ "transformers", "pytorch", "tensorboard", "bert", "token-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
token-classification
2023-05-09T16:59:57Z
--- license: apache-2.0 tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: bert-base-uncased-finetuned-recruitment-exp 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-finetuned-recruitment-exp This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1433 - Precision: 0.6113 - Recall: 0.7250 - F1: 0.6633 - Accuracy: 0.9612 ## 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: 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: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 17 | 0.5254 | 0.0 | 0.0 | 0.0 | 0.8991 | | No log | 2.0 | 34 | 0.3199 | 0.2854 | 0.2255 | 0.2519 | 0.9152 | | No log | 3.0 | 51 | 0.2303 | 0.3948 | 0.5232 | 0.4500 | 0.9370 | | No log | 4.0 | 68 | 0.1878 | 0.4876 | 0.6212 | 0.5463 | 0.9496 | | No log | 5.0 | 85 | 0.1630 | 0.5544 | 0.6548 | 0.6005 | 0.9553 | | No log | 6.0 | 102 | 0.1433 | 0.6113 | 0.7250 | 0.6633 | 0.9612 | | No log | 7.0 | 119 | 0.1469 | 0.6412 | 0.7458 | 0.6895 | 0.9621 | | No log | 8.0 | 136 | 0.1463 | 0.6516 | 0.7418 | 0.6938 | 0.9633 | | No log | 9.0 | 153 | 0.1446 | 0.6664 | 0.7527 | 0.7069 | 0.9643 | | No log | 10.0 | 170 | 0.1447 | 0.6579 | 0.7646 | 0.7072 | 0.9641 | ### Framework versions - Transformers 4.28.1 - Pytorch 2.0.0+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3
J001/mt5-ch-en-v1
J001
2023-05-09T17:01:40Z
12
0
transformers
[ "transformers", "pytorch", "tensorboard", "mt5", "text2text-generation", "translation", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]
translation
2023-05-09T13:53:50Z
--- tags: - translation - generated_from_trainer model-index: - name: mt5-ch-en-v1 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. --> # mt5-ch-en-v1 This model is a fine-tuned version of [IDEA-CCNL/Randeng-T5-77M](https://huggingface.co/IDEA-CCNL/Randeng-T5-77M) 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: 2e-05 - train_batch_size: 60 - eval_batch_size: 60 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 6 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.28.1 - Pytorch 2.0.0 - Datasets 2.1.0 - Tokenizers 0.13.3
reyhanemyr/distilbert-base-uncased-finetuned-recruitment-exp
reyhanemyr
2023-05-09T16:57:10Z
3
0
transformers
[ "transformers", "pytorch", "tensorboard", "distilbert", "token-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
token-classification
2023-05-09T16:51:34Z
--- license: apache-2.0 tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: distilbert-base-uncased-finetuned-recruitment-exp 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-recruitment-exp 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: 0.1742 - Precision: 0.6204 - Recall: 0.6855 - F1: 0.6513 - Accuracy: 0.9561 ## 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: 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: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 17 | 0.5203 | 0.0 | 0.0 | 0.0 | 0.8991 | | No log | 2.0 | 34 | 0.3797 | 0.2979 | 0.0277 | 0.0507 | 0.9030 | | No log | 3.0 | 51 | 0.2983 | 0.3171 | 0.4194 | 0.3612 | 0.9222 | | No log | 4.0 | 68 | 0.2321 | 0.4219 | 0.4916 | 0.4541 | 0.9375 | | No log | 5.0 | 85 | 0.2100 | 0.5076 | 0.5262 | 0.5168 | 0.9453 | | No log | 6.0 | 102 | 0.1899 | 0.5174 | 0.5885 | 0.5507 | 0.9506 | | No log | 7.0 | 119 | 0.1775 | 0.5395 | 0.6350 | 0.5834 | 0.9509 | | No log | 8.0 | 136 | 0.1817 | 0.6282 | 0.6617 | 0.6445 | 0.9550 | | No log | 9.0 | 153 | 0.1775 | 0.6262 | 0.6726 | 0.6485 | 0.9558 | | No log | 10.0 | 170 | 0.1742 | 0.6204 | 0.6855 | 0.6513 | 0.9561 | ### Framework versions - Transformers 4.28.1 - Pytorch 2.0.0+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3
dxli/bear_plushie
dxli
2023-05-09T16:52:25Z
18
0
diffusers
[ "diffusers", "tensorboard", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "textual_inversion", "base_model:runwayml/stable-diffusion-v1-5", "base_model:adapter:runwayml/stable-diffusion-v1-5", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]
text-to-image
2023-05-09T07:24:41Z
--- license: creativeml-openrail-m base_model: runwayml/stable-diffusion-v1-5 tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - textual_inversion inference: true --- # Textual inversion text2image fine-tuning - dxli/bear_plushie These are textual inversion adaption weights for runwayml/stable-diffusion-v1-5. You can find some example images in the following.
vldnechai/LunarLander_v2_PPO
vldnechai
2023-05-09T16:52:12Z
0
0
null
[ "tensorboard", "LunarLander-v2", "ppo", "deep-reinforcement-learning", "reinforcement-learning", "custom-implementation", "deep-rl-course", "model-index", "region:us" ]
reinforcement-learning
2023-05-09T15:30:17Z
--- tags: - LunarLander-v2 - ppo - deep-reinforcement-learning - reinforcement-learning - custom-implementation - deep-rl-course model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: -37.32 +/- 13.17 name: mean_reward verified: false --- # PPO Agent Playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2. # Hyperparameters ```python {'exp_name': 'ppo' 'seed': 1 'torch_deterministic': True 'cuda': True 'track': False 'wandb_project_name': 'cleanRL' 'wandb_entity': None 'capture_video': False 'env_id': 'LunarLander-v2' 'total_timesteps': 1000000 'learning_rate': 0.001 'num_envs': 32 'num_steps': 2048 'anneal_lr': True 'gae': True 'gamma': 0.99 'gae_lambda': 0.95 'num_minibatches': 128 'update_epochs': 4 'norm_adv': True 'clip_coef': 0.2 'clip_vloss': True 'ent_coef': 0.01 'vf_coef': 0.5 'max_grad_norm': 0.5 'target_kl': None 'repo_id': 'vldnechai/LunarLander_v2_PPO' 'batch_size': 65536 'minibatch_size': 512} ```
zeyefkey/q-Taxi-v3.2
zeyefkey
2023-05-09T16:51:42Z
0
0
null
[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]
reinforcement-learning
2023-05-09T16:48:51Z
--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-Taxi-v3.2 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.56 +/- 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="zeyefkey/q-Taxi-v3.2", 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"]) ```
tastytoast/ppo-LunarLander-v2
tastytoast
2023-05-09T16:48:28Z
0
0
stable-baselines3
[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-05-09T16:48:03Z
--- 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: 258.98 +/- 28.22 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 ... ```
reyhanemyr/roberta-base-finetuned-recruitment-exp
reyhanemyr
2023-05-09T16:46:55Z
3
0
transformers
[ "transformers", "pytorch", "tensorboard", "roberta", "token-classification", "generated_from_trainer", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]
token-classification
2023-05-09T16:35:51Z
--- license: mit tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: roberta-base-finetuned-recruitment-exp 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. --> # roberta-base-finetuned-recruitment-exp This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1044 - Precision: 0.7320 - Recall: 0.8560 - F1: 0.7892 - Accuracy: 0.9713 ## 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: 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: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 17 | 0.4051 | 0.25 | 0.0061 | 0.0119 | 0.8984 | | No log | 2.0 | 34 | 0.2450 | 0.4040 | 0.3732 | 0.3880 | 0.9280 | | No log | 3.0 | 51 | 0.1481 | 0.5385 | 0.6663 | 0.5956 | 0.9555 | | No log | 4.0 | 68 | 0.1269 | 0.6295 | 0.7961 | 0.7031 | 0.964 | | No log | 5.0 | 85 | 0.1101 | 0.6639 | 0.8235 | 0.7352 | 0.9679 | | No log | 6.0 | 102 | 0.1116 | 0.7287 | 0.7819 | 0.7544 | 0.9701 | | No log | 7.0 | 119 | 0.1160 | 0.7026 | 0.8266 | 0.7596 | 0.9684 | | No log | 8.0 | 136 | 0.1071 | 0.7442 | 0.8499 | 0.7936 | 0.9717 | | No log | 9.0 | 153 | 0.1044 | 0.7320 | 0.8560 | 0.7892 | 0.9713 | | No log | 10.0 | 170 | 0.1081 | 0.7532 | 0.8448 | 0.7964 | 0.9722 | ### Framework versions - Transformers 4.28.1 - Pytorch 2.0.0+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3
VictorGil75/autotrain-rm-soccer_class-56881131860
VictorGil75
2023-05-09T16:45:00Z
3
0
transformers
[ "transformers", "pytorch", "bert", "text-classification", "autotrain", "en", "dataset:VictorGil75/autotrain-data-rm-soccer_class", "co2_eq_emissions", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2023-05-09T16:43:58Z
--- tags: - autotrain - text-classification language: - en widget: - text: "I love AutoTrain 🤗" datasets: - VictorGil75/autotrain-data-rm-soccer_class co2_eq_emissions: emissions: 0.4133097011272339 --- # Model Trained Using AutoTrain - Problem type: Binary Classification - Model ID: 56881131860 - CO2 Emissions (in grams): 0.4133 ## Validation Metrics - Loss: 0.064 - Accuracy: 0.985 - Precision: 0.990 - Recall: 0.980 - AUC: 0.995 - F1: 0.985 ## Usage You can use cURL to access this model: ``` $ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/VictorGil75/autotrain-rm-soccer_class-56881131860 ``` Or Python API: ``` from transformers import AutoModelForSequenceClassification, AutoTokenizer model = AutoModelForSequenceClassification.from_pretrained("VictorGil75/autotrain-rm-soccer_class-56881131860", use_auth_token=True) tokenizer = AutoTokenizer.from_pretrained("VictorGil75/autotrain-rm-soccer_class-56881131860", use_auth_token=True) inputs = tokenizer("I love AutoTrain", return_tensors="pt") outputs = model(**inputs) ```
JustFrederik/sugoi-v4-ja-en-ct2-int8
JustFrederik
2023-05-09T16:36:28Z
1
0
transformers
[ "transformers", "translation", "ja", "en", "license:unknown", "endpoints_compatible", "region:us" ]
translation
2023-05-09T16:15:55Z
--- license: unknown language: - ja - en pipeline_tag: translation --- https://sugoitranslator.com <br /> https://blog.sugoitranslator.com <br /> https://www.patreon.com/mingshiba <br /> ``` ct2-fairseq-converter --model_path big.pretrain.pt --data_dir . --source_lang ja --target_lang en --quantization int8 --output_dir ../converted/sugoi-v4-ja-en-ct2-int8 ```
JustFrederik/jparacrawl-v3-small-ct2-float16
JustFrederik
2023-05-09T16:31:05Z
0
0
null
[ "translation", "ja", "en", "license:unknown", "region:us" ]
translation
2023-05-09T16:05:43Z
--- license: unknown language: - ja - en pipeline_tag: translation --- https://www.kecl.ntt.co.jp/icl/lirg/jparacrawl/ <br /> ``` ct2-fairseq-converter --model_path small.pretrain.pt --data_dir . --source_lang en --target_lang ja --quantization float16 --output_dir ../converted/jparacrawl-v3-small-ct2-float16/en-ja ``` ``` ct2-fairseq-converter --model_path ./small/small.pretrain.pt --data_dir ./small --source_lang ja --target_lang en --quantization float16 --output_dir ../converted/jparacrawl-v3-small-ct2-float16/ja-en ```
JustFrederik/jparacrawl-v3-big-ct2-int8
JustFrederik
2023-05-09T16:30:02Z
0
0
null
[ "translation", "ja", "en", "license:unknown", "region:us" ]
translation
2023-05-09T15:58:33Z
--- license: unknown language: - ja - en pipeline_tag: translation --- https://www.kecl.ntt.co.jp/icl/lirg/jparacrawl/ <br /> ``` ct2-fairseq-converter --model_path big.pretrain.pt --data_dir . --source_lang en --target_lang ja --quantization int8 --output_dir ../converted/jparacrawl-v3-big-ct2-int8/en-ja ``` ``` ct2-fairseq-converter --model_path ./big/big.pretrain.pt --data_dir ./big --source_lang ja --target_lang en --quantization int8 --output_dir ../converted/jparacrawl-v3-big-ct2-int8/ja-en ```
Luca77/a2c-AntBulletEnv-v0
Luca77
2023-05-09T16:24:19Z
4
0
stable-baselines3
[ "stable-baselines3", "AntBulletEnv-v0", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-04-01T14:22:14Z
--- library_name: stable-baselines3 tags: - AntBulletEnv-v0 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: A2C results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: AntBulletEnv-v0 type: AntBulletEnv-v0 metrics: - type: mean_reward value: 1506.49 +/- 61.06 name: mean_reward verified: false --- # **A2C** Agent playing **AntBulletEnv-v0** This is a trained model of a **A2C** agent playing **AntBulletEnv-v0** 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 ... ```
hr-elrond/autotrain-p2_finbert_training_100-56875131853
hr-elrond
2023-05-09T16:23:24Z
4
0
transformers
[ "transformers", "pytorch", "bert", "text-classification", "autotrain", "unk", "dataset:hr-elrond/autotrain-data-p2_finbert_training_100", "co2_eq_emissions", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2023-05-09T16:22:38Z
--- tags: - autotrain - text-classification language: - unk widget: - text: "I love AutoTrain 🤗" datasets: - hr-elrond/autotrain-data-p2_finbert_training_100 co2_eq_emissions: emissions: 0.2967273355715001 --- # Model Trained Using AutoTrain - Problem type: Binary Classification - Model ID: 56875131853 - CO2 Emissions (in grams): 0.2967 ## Validation Metrics - Loss: 0.068 - Accuracy: 0.984 - Precision: 0.993 - Recall: 0.983 - AUC: 0.996 - F1: 0.988 ## Usage You can use cURL to access this model: ``` $ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/hr-elrond/autotrain-p2_finbert_training_100-56875131853 ``` Or Python API: ``` from transformers import AutoModelForSequenceClassification, AutoTokenizer model = AutoModelForSequenceClassification.from_pretrained("hr-elrond/autotrain-p2_finbert_training_100-56875131853", use_auth_token=True) tokenizer = AutoTokenizer.from_pretrained("hr-elrond/autotrain-p2_finbert_training_100-56875131853", use_auth_token=True) inputs = tokenizer("I love AutoTrain", return_tensors="pt") outputs = model(**inputs) ```
xinyixiuxiu/albert-xxlarge-v2-SST2-incremental_pre_training-epoch1-5
xinyixiuxiu
2023-05-09T16:21:49Z
3
0
transformers
[ "transformers", "tf", "albert", "text-classification", "generated_from_keras_callback", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2023-05-09T15:45:59Z
--- tags: - generated_from_keras_callback model-index: - name: xinyixiuxiu/albert-xxlarge-v2-SST2-incremental_pre_training-epoch1-5 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. --> # xinyixiuxiu/albert-xxlarge-v2-SST2-incremental_pre_training-epoch1-5 This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.0332 - Train Accuracy: 0.9897 - Validation Loss: 0.1438 - Validation Accuracy: 0.9599 - Epoch: 0 ## 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': 'Adam', 'learning_rate': 3e-06, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False} - training_precision: float32 ### Training results | Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch | |:----------:|:--------------:|:---------------:|:-------------------:|:-----:| | 0.0332 | 0.9897 | 0.1438 | 0.9599 | 0 | ### Framework versions - Transformers 4.28.1 - TensorFlow 2.7.0 - Datasets 2.10.1 - Tokenizers 0.12.1
eason0203/swin-tiny-patch4-window7-224-finetuned-arty
eason0203
2023-05-09T16:07:40Z
5
0
transformers
[ "transformers", "pytorch", "swin", "image-classification", "generated_from_trainer", "dataset:imagefolder", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
image-classification
2023-05-09T15:12:51Z
--- license: apache-2.0 tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: swin-tiny-patch4-window7-224-finetuned-arty results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: default split: train args: default metrics: - name: Accuracy type: accuracy value: 1.0 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # swin-tiny-patch4-window7-224-finetuned-arty This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.0002 - Accuracy: 1.0 ## 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: 64 - eval_batch_size: 64 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 256 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.2386 | 0.43 | 50 | 0.0643 | 0.9967 | | 0.0359 | 0.87 | 100 | 0.0035 | 0.9996 | | 0.058 | 1.3 | 150 | 0.0015 | 0.9996 | | 0.0297 | 1.74 | 200 | 0.0003 | 1.0 | | 0.0175 | 2.17 | 250 | 0.0002 | 1.0 | | 0.0166 | 2.6 | 300 | 0.0002 | 1.0 | | 0.0318 | 3.04 | 350 | 0.0001 | 1.0 | | 0.0062 | 3.47 | 400 | 0.0002 | 1.0 | | 0.0101 | 3.9 | 450 | 0.0002 | 1.0 | | 0.0066 | 4.34 | 500 | 0.0002 | 1.0 | | 0.005 | 4.77 | 550 | 0.0002 | 1.0 | ### Framework versions - Transformers 4.28.1 - Pytorch 2.0.0+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3
CSHaitao/SAILER_zh
CSHaitao
2023-05-09T16:06:33Z
17
0
transformers
[ "transformers", "pytorch", "bert", "arxiv:2304.11370", "license:mit", "endpoints_compatible", "region:us" ]
null
2023-05-09T15:16:36Z
--- license: mit --- SAILER is a structure-aware pre-trained language model. It is highlighted in the following three aspects: - SAILER fully utilizes the structural information contained in legal case documents and pays more attention to key legal elements, similar to how legal experts browse legal case documents. - SAILER employs an asymmetric encoder-decoder architecture to integrate several different pre-training objectives. In this way, rich semantic information across tasks is encoded into dense vectors. - SAILER has powerful discriminative ability, even without any legal annotation data. It can distinguish legal cases with different charges accurately. SAILER_zh pre-training on Chinese criminal law legal case documents If you find our work useful, please do not save your star and cite our work: ``` @misc{SAILER, title={SAILER: Structure-aware Pre-trained Language Model for Legal Case Retrieval}, author={Haitao Li and Qingyao Ai and Jia Chen and Qian Dong and Yueyue Wu and Yiqun Liu and Chong Chen and Qi Tian}, year={2023}, eprint={2304.11370}, archivePrefix={arXiv}, primaryClass={cs.IR} } ```
judithrosell/sa_english_new
judithrosell
2023-05-09T16:03:08Z
3
0
transformers
[ "transformers", "pytorch", "tensorboard", "xlm-roberta", "text-classification", "generated_from_trainer", "dataset:imdb", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2023-05-09T14:19:18Z
--- license: mit tags: - generated_from_trainer datasets: - imdb metrics: - accuracy model-index: - name: sa_english_new results: - task: name: Text Classification type: text-classification dataset: name: imdb type: imdb config: plain_text split: test args: plain_text metrics: - name: Accuracy type: accuracy value: 0.9394 --- <!-- 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. --> # sa_english_new This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 0.3371 - Accuracy: 0.9394 ## 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: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.244 | 1.0 | 1563 | 0.2231 | 0.9151 | | 0.1826 | 2.0 | 3126 | 0.2054 | 0.9396 | | 0.1196 | 3.0 | 4689 | 0.2671 | 0.9350 | | 0.0769 | 4.0 | 6252 | 0.2950 | 0.9399 | | 0.0455 | 5.0 | 7815 | 0.3371 | 0.9394 | ### Framework versions - Transformers 4.28.1 - Pytorch 2.0.0+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3
dxli/backpack_dog
dxli
2023-05-09T16:00:22Z
25
0
diffusers
[ "diffusers", "tensorboard", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "textual_inversion", "base_model:runwayml/stable-diffusion-v1-5", "base_model:adapter:runwayml/stable-diffusion-v1-5", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]
text-to-image
2023-05-09T06:40:33Z
--- license: creativeml-openrail-m base_model: runwayml/stable-diffusion-v1-5 tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - textual_inversion inference: true --- # Textual inversion text2image fine-tuning - dxli/backpack_dog These are textual inversion adaption weights for runwayml/stable-diffusion-v1-5. You can find some example images in the following.
dxli/candle
dxli
2023-05-09T15:57:07Z
15
0
diffusers
[ "diffusers", "tensorboard", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "textual_inversion", "base_model:runwayml/stable-diffusion-v1-5", "base_model:adapter:runwayml/stable-diffusion-v1-5", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]
text-to-image
2023-05-09T15:03:41Z
--- license: creativeml-openrail-m base_model: runwayml/stable-diffusion-v1-5 tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - textual_inversion inference: true --- # Textual inversion text2image fine-tuning - dxli/candle These are textual inversion adaption weights for runwayml/stable-diffusion-v1-5. You can find some example images in the following.
Purus15987/Summarization_model
Purus15987
2023-05-09T15:56:11Z
5
0
transformers
[ "transformers", "pytorch", "tensorboard", "t5", "text2text-generation", "generated_from_trainer", "dataset:billsum", "license:apache-2.0", "model-index", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text2text-generation
2023-04-28T05:40:17Z
--- license: apache-2.0 tags: - generated_from_trainer datasets: - billsum metrics: - rouge model-index: - name: Summarization_model results: - task: name: Sequence-to-sequence Language Modeling type: text2text-generation dataset: name: billsum type: billsum config: default split: ca_test args: default metrics: - name: Rouge1 type: rouge value: 0.1392 --- <!-- 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. --> # Summarization_model This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the billsum dataset. It achieves the following results on the evaluation set: - Loss: 2.5515 - Rouge1: 0.1392 - Rouge2: 0.0503 - Rougel: 0.1161 - Rougelsum: 0.1159 - Gen Len: 19.0 ## 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: 4 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:| | No log | 1.0 | 62 | 2.8419 | 0.1272 | 0.0393 | 0.108 | 0.1079 | 19.0 | | No log | 2.0 | 124 | 2.6329 | 0.1333 | 0.0458 | 0.1133 | 0.1131 | 19.0 | | No log | 3.0 | 186 | 2.5693 | 0.1379 | 0.0494 | 0.1164 | 0.1162 | 19.0 | | No log | 4.0 | 248 | 2.5515 | 0.1392 | 0.0503 | 0.1161 | 0.1159 | 19.0 | ### Framework versions - Transformers 4.28.1 - Pytorch 2.0.0+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3
reyhanemyr/roberta-base-finetuned-scientific-exp
reyhanemyr
2023-05-09T15:48:48Z
3
0
transformers
[ "transformers", "pytorch", "tensorboard", "roberta", "token-classification", "generated_from_trainer", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]
token-classification
2023-05-09T15:38:15Z
--- license: mit tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: roberta-base-finetuned-scientific-exp 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. --> # roberta-base-finetuned-scientific-exp This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1255 - Precision: 0.7662 - Recall: 0.7484 - F1: 0.7572 - Accuracy: 0.9674 ## 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: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 81 | 0.2172 | 0.6049 | 0.5180 | 0.5581 | 0.9433 | | No log | 2.0 | 162 | 0.1470 | 0.7556 | 0.6469 | 0.6970 | 0.9582 | | No log | 3.0 | 243 | 0.1255 | 0.7662 | 0.7484 | 0.7572 | 0.9674 | | No log | 4.0 | 324 | 0.1261 | 0.7546 | 0.7738 | 0.7641 | 0.9666 | | No log | 5.0 | 405 | 0.1339 | 0.7184 | 0.8414 | 0.7751 | 0.9635 | | No log | 6.0 | 486 | 0.1350 | 0.7112 | 0.8330 | 0.7673 | 0.9627 | | 0.1498 | 7.0 | 567 | 0.1362 | 0.7471 | 0.8309 | 0.7868 | 0.9693 | | 0.1498 | 8.0 | 648 | 0.1530 | 0.7174 | 0.8266 | 0.7682 | 0.9644 | | 0.1498 | 9.0 | 729 | 0.1587 | 0.7392 | 0.8330 | 0.7833 | 0.9655 | | 0.1498 | 10.0 | 810 | 0.1610 | 0.7416 | 0.8372 | 0.7865 | 0.9651 | ### Framework versions - Transformers 4.28.1 - Pytorch 2.0.0+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3
rethem-expeditecommerce/MiniLM-L6-GPL
rethem-expeditecommerce
2023-05-09T15:46:31Z
4
0
sentence-transformers
[ "sentence-transformers", "pytorch", "bert", "feature-extraction", "sentence-similarity", "en", "dataset:s2orc", "dataset:flax-sentence-embeddings/stackexchange_xml", "dataset:ms_marco", "dataset:gooaq", "dataset:yahoo_answers_topics", "dataset:code_search_net", "dataset:search_qa", "dataset:eli5", "dataset:snli", "dataset:multi_nli", "dataset:wikihow", "dataset:natural_questions", "dataset:trivia_qa", "dataset:embedding-data/sentence-compression", "dataset:embedding-data/flickr30k-captions", "dataset:embedding-data/altlex", "dataset:embedding-data/simple-wiki", "dataset:embedding-data/QQP", "dataset:embedding-data/SPECTER", "dataset:embedding-data/PAQ_pairs", "dataset:embedding-data/WikiAnswers", "arxiv:1904.06472", "arxiv:2102.07033", "arxiv:2104.08727", "arxiv:1704.05179", "arxiv:1810.09305", "license:apache-2.0", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]
sentence-similarity
2023-05-05T15:17:37Z
--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity language: en license: apache-2.0 datasets: - s2orc - flax-sentence-embeddings/stackexchange_xml - ms_marco - gooaq - yahoo_answers_topics - code_search_net - search_qa - eli5 - snli - multi_nli - wikihow - natural_questions - trivia_qa - embedding-data/sentence-compression - embedding-data/flickr30k-captions - embedding-data/altlex - embedding-data/simple-wiki - embedding-data/QQP - embedding-data/SPECTER - embedding-data/PAQ_pairs - embedding-data/WikiAnswers --- # all-MiniLM-L6-v2 This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search. ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch import torch.nn.functional as F #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) # Sentences we want sentence embeddings for sentences = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('sentence-transformers/all-MiniLM-L6-v2') model = AutoModel.from_pretrained('sentence-transformers/all-MiniLM-L6-v2') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(**encoded_input) # Perform pooling sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) # Normalize embeddings sentence_embeddings = F.normalize(sentence_embeddings, p=2, dim=1) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Evaluation Results For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=sentence-transformers/all-MiniLM-L6-v2) ------ ## Background The project aims to train sentence embedding models on very large sentence level datasets using a self-supervised contrastive learning objective. We used the pretrained [`nreimers/MiniLM-L6-H384-uncased`](https://huggingface.co/nreimers/MiniLM-L6-H384-uncased) model and fine-tuned in on a 1B sentence pairs dataset. We use a contrastive learning objective: given a sentence from the pair, the model should predict which out of a set of randomly sampled other sentences, was actually paired with it in our dataset. We developped this model during the [Community week using JAX/Flax for NLP & CV](https://discuss.huggingface.co/t/open-to-the-community-community-week-using-jax-flax-for-nlp-cv/7104), organized by Hugging Face. We developped this model as part of the project: [Train the Best Sentence Embedding Model Ever with 1B Training Pairs](https://discuss.huggingface.co/t/train-the-best-sentence-embedding-model-ever-with-1b-training-pairs/7354). We benefited from efficient hardware infrastructure to run the project: 7 TPUs v3-8, as well as intervention from Googles Flax, JAX, and Cloud team member about efficient deep learning frameworks. ## Intended uses Our model is intented to be used as a sentence and short paragraph encoder. Given an input text, it ouptuts a vector which captures the semantic information. The sentence vector may be used for information retrieval, clustering or sentence similarity tasks. By default, input text longer than 256 word pieces is truncated. ## Training procedure ### Pre-training We use the pretrained [`nreimers/MiniLM-L6-H384-uncased`](https://huggingface.co/nreimers/MiniLM-L6-H384-uncased) model. Please refer to the model card for more detailed information about the pre-training procedure. ### Fine-tuning We fine-tune the model using a contrastive objective. Formally, we compute the cosine similarity from each possible sentence pairs from the batch. We then apply the cross entropy loss by comparing with true pairs. #### Hyper parameters We trained ou model on a TPU v3-8. We train the model during 100k steps using a batch size of 1024 (128 per TPU core). We use a learning rate warm up of 500. The sequence length was limited to 128 tokens. We used the AdamW optimizer with a 2e-5 learning rate. The full training script is accessible in this current repository: `train_script.py`. #### Training data We use the concatenation from multiple datasets to fine-tune our model. The total number of sentence pairs is above 1 billion sentences. We sampled each dataset given a weighted probability which configuration is detailed in the `data_config.json` file. | Dataset | Paper | Number of training tuples | |--------------------------------------------------------|:----------------------------------------:|:--------------------------:| | [Reddit comments (2015-2018)](https://github.com/PolyAI-LDN/conversational-datasets/tree/master/reddit) | [paper](https://arxiv.org/abs/1904.06472) | 726,484,430 | | [S2ORC](https://github.com/allenai/s2orc) Citation pairs (Abstracts) | [paper](https://aclanthology.org/2020.acl-main.447/) | 116,288,806 | | [WikiAnswers](https://github.com/afader/oqa#wikianswers-corpus) Duplicate question pairs | [paper](https://doi.org/10.1145/2623330.2623677) | 77,427,422 | | [PAQ](https://github.com/facebookresearch/PAQ) (Question, Answer) pairs | [paper](https://arxiv.org/abs/2102.07033) | 64,371,441 | | [S2ORC](https://github.com/allenai/s2orc) Citation pairs (Titles) | [paper](https://aclanthology.org/2020.acl-main.447/) | 52,603,982 | | [S2ORC](https://github.com/allenai/s2orc) (Title, Abstract) | [paper](https://aclanthology.org/2020.acl-main.447/) | 41,769,185 | | [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) (Title, Body) pairs | - | 25,316,456 | | [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) (Title+Body, Answer) pairs | - | 21,396,559 | | [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) (Title, Answer) pairs | - | 21,396,559 | | [MS MARCO](https://microsoft.github.io/msmarco/) triplets | [paper](https://doi.org/10.1145/3404835.3462804) | 9,144,553 | | [GOOAQ: Open Question Answering with Diverse Answer Types](https://github.com/allenai/gooaq) | [paper](https://arxiv.org/pdf/2104.08727.pdf) | 3,012,496 | | [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) (Title, Answer) | [paper](https://proceedings.neurips.cc/paper/2015/hash/250cf8b51c773f3f8dc8b4be867a9a02-Abstract.html) | 1,198,260 | | [Code Search](https://huggingface.co/datasets/code_search_net) | - | 1,151,414 | | [COCO](https://cocodataset.org/#home) Image captions | [paper](https://link.springer.com/chapter/10.1007%2F978-3-319-10602-1_48) | 828,395| | [SPECTER](https://github.com/allenai/specter) citation triplets | [paper](https://doi.org/10.18653/v1/2020.acl-main.207) | 684,100 | | [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) (Question, Answer) | [paper](https://proceedings.neurips.cc/paper/2015/hash/250cf8b51c773f3f8dc8b4be867a9a02-Abstract.html) | 681,164 | | [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) (Title, Question) | [paper](https://proceedings.neurips.cc/paper/2015/hash/250cf8b51c773f3f8dc8b4be867a9a02-Abstract.html) | 659,896 | | [SearchQA](https://huggingface.co/datasets/search_qa) | [paper](https://arxiv.org/abs/1704.05179) | 582,261 | | [Eli5](https://huggingface.co/datasets/eli5) | [paper](https://doi.org/10.18653/v1/p19-1346) | 325,475 | | [Flickr 30k](https://shannon.cs.illinois.edu/DenotationGraph/) | [paper](https://transacl.org/ojs/index.php/tacl/article/view/229/33) | 317,695 | | [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) Duplicate questions (titles) | | 304,525 | | AllNLI ([SNLI](https://nlp.stanford.edu/projects/snli/) and [MultiNLI](https://cims.nyu.edu/~sbowman/multinli/) | [paper SNLI](https://doi.org/10.18653/v1/d15-1075), [paper MultiNLI](https://doi.org/10.18653/v1/n18-1101) | 277,230 | | [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) Duplicate questions (bodies) | | 250,519 | | [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) Duplicate questions (titles+bodies) | | 250,460 | | [Sentence Compression](https://github.com/google-research-datasets/sentence-compression) | [paper](https://www.aclweb.org/anthology/D13-1155/) | 180,000 | | [Wikihow](https://github.com/pvl/wikihow_pairs_dataset) | [paper](https://arxiv.org/abs/1810.09305) | 128,542 | | [Altlex](https://github.com/chridey/altlex/) | [paper](https://aclanthology.org/P16-1135.pdf) | 112,696 | | [Quora Question Triplets](https://quoradata.quora.com/First-Quora-Dataset-Release-Question-Pairs) | - | 103,663 | | [Simple Wikipedia](https://cs.pomona.edu/~dkauchak/simplification/) | [paper](https://www.aclweb.org/anthology/P11-2117/) | 102,225 | | [Natural Questions (NQ)](https://ai.google.com/research/NaturalQuestions) | [paper](https://transacl.org/ojs/index.php/tacl/article/view/1455) | 100,231 | | [SQuAD2.0](https://rajpurkar.github.io/SQuAD-explorer/) | [paper](https://aclanthology.org/P18-2124.pdf) | 87,599 | | [TriviaQA](https://huggingface.co/datasets/trivia_qa) | - | 73,346 | | **Total** | | **1,170,060,424** |
dxli/dog8
dxli
2023-05-09T15:32:48Z
20
0
diffusers
[ "diffusers", "tensorboard", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "textual_inversion", "base_model:runwayml/stable-diffusion-v1-5", "base_model:adapter:runwayml/stable-diffusion-v1-5", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]
text-to-image
2023-05-09T05:28:32Z
--- license: creativeml-openrail-m base_model: runwayml/stable-diffusion-v1-5 tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - textual_inversion inference: true --- # Textual inversion text2image fine-tuning - dxli/dog8 These are textual inversion adaption weights for runwayml/stable-diffusion-v1-5. You can find some example images in the following.
dxli/backpack
dxli
2023-05-09T15:17:25Z
32
0
diffusers
[ "diffusers", "tensorboard", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "textual_inversion", "base_model:runwayml/stable-diffusion-v1-5", "base_model:adapter:runwayml/stable-diffusion-v1-5", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]
text-to-image
2023-05-09T06:00:30Z
--- license: creativeml-openrail-m base_model: runwayml/stable-diffusion-v1-5 tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - textual_inversion inference: true --- # Textual inversion text2image fine-tuning - dxli/backpack These are textual inversion adaption weights for runwayml/stable-diffusion-v1-5. You can find some example images in the following.
labicquette/Reinforce-Cartpole-v1
labicquette
2023-05-09T15:08:06Z
0
0
null
[ "CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]
reinforcement-learning
2023-05-09T15:07:58Z
--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-Cartpole-v1 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 metrics: - type: mean_reward value: 500.00 +/- 0.00 name: mean_reward verified: false --- # **Reinforce** Agent playing **CartPole-v1** This is a trained model of a **Reinforce** agent playing **CartPole-v1** . 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
Khayoon/ppo-Huggy
Khayoon
2023-05-09T15:07:52Z
14
0
ml-agents
[ "ml-agents", "tensorboard", "onnx", "Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]
reinforcement-learning
2023-05-09T15:07:45Z
--- 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://github.com/huggingface/ml-agents#get-started We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: ### Resume the training ``` 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. Go to https://huggingface.co/spaces/unity/ML-Agents-Huggy 2. Step 1: Find your model_id: Khayoon/ppo-Huggy 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀
agomberto/trocr-base-printed-fr
agomberto
2023-05-09T15:02:25Z
66
2
transformers
[ "transformers", "pytorch", "vision-encoder-decoder", "image-text-to-text", "trocr", "image-to-text", "fr", "arxiv:2109.10282", "license:mit", "endpoints_compatible", "region:us" ]
image-to-text
2023-05-04T08:39:53Z
--- license: mit language: - fr pipeline_tag: image-to-text tags: - trocr - vision-encoder-decoder metrics: - cer - wer widget: - src: >- https://raw.githubusercontent.com/agombert/trocr-base-printed-fr/main/sample_imgs/3.jpg example_title: Example 1 - src: >- https://raw.githubusercontent.com/agombert/trocr-base-printed-fr/main/sample_imgs/0.jpg example_title: Example 2 - src: >- https://raw.githubusercontent.com/agombert/trocr-base-printed-fr/main/sample_imgs/1.jpg example_title: Example 3 --- # TrOCR for French ## Overview TrOCR has not yet released for French, so we trained a French model for PoC purpose. Based on this model, it is recommended to collect more data to additionally train the 1st stage or perform fine-tuning as the 2nd stage. It's a special case of the [English trOCR model](https://huggingface.co/microsoft/trocr-base-printed) introduced in the paper [TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models](https://arxiv.org/abs/2109.10282) by Li et al. and first released in [this repository](https://github.com/microsoft/unilm/tree/master/trocr) This was possible thanks to [daekun-ml](https://huggingface.co/daekeun-ml/ko-trocr-base-nsmc-news-chatbot) and [Niels Rogge](https://github.com/NielsRogge/) than enabled us to publish this model with their tutorials and code. ## Collecting data ### Text data We created training data of ~723k examples by taking random samples of the following datasets: - [MultiLegalPile](https://huggingface.co/datasets/joelito/Multi_Legal_Pile) - 90k - [French book Reviews](https://huggingface.co/datasets/Abirate/french_book_reviews) - 20k - [WikiNeural](https://huggingface.co/datasets/Babelscape/wikineural) - 83k - [Multilingual cc news](https://huggingface.co/datasets/intfloat/multilingual_cc_news) - 119k - [Reviews Amazon Multi](https://huggingface.co/datasets/amazon_reviews_multi) - 153k - [Opus Book](https://huggingface.co/datasets/opus_books) - 70k - [BerlinText](https://huggingface.co/datasets/biglam/berlin_state_library_ocr) - 38k We collected parts of each of the datasets and then cut randomly the sentences to collect the final training set. ### Image Data Image data was generated with TextRecognitionDataGenerator (https://github.com/Belval/TextRecognitionDataGenerator) introduced in the TrOCR paper. Below is a code snippet for generating images. ```shell python3 ./trdg/run.py -i ocr_dataset_poc.txt -w 5 -t {num_cores} -f 64 -l ko -c {num_samples} -na 2 --output_dir {dataset_dir} ``` ## Training ### Base model The encoder model used `facebook/deit-base-distilled-patch16-384` and the decoder model used `camembert-base`. It is easier than training by starting weights from `microsoft/trocr-base-stage1`. ### Parameters We used heuristic parameters without separate hyperparameter tuning. - learning_rate = 4e-5 - epochs = 25 - fp16 = True - max_length = 32 ### Results on dev set For the dev set we got those results - size of the test set: 72k examples - CER: 0.13 - WER: 0.26 - Val Loss: 0.424 ## Usage ```python from transformers import TrOCRProcessor, VisionEncoderDecoderModel, AutoTokenizer import requests from io import BytesIO from PIL import Image processor = TrOCRProcessor.from_pretrained("microsoft/trocr-base-handwritten") model = VisionEncoderDecoderModel.from_pretrained("agomberto/trocr-base-printed-fr") tokenizer = AutoTokenizer.from_pretrained("agomberto/trocr-base-printed-fr") url = "https://github.com/agombert/trocr-base-printed-fr/blob/main/sample_imgs/0.jpg" response = requests.get(url) img = Image.open(BytesIO(response.content)) pixel_values = processor(img, return_tensors="pt").pixel_values generated_ids = model.generate(pixel_values, max_length=32) generated_text = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] print(generated_text) ``` All the code required for data collection and model training has been published on the author's Github. - https://github.com/agombert/trocr-base-printed-fr/
togethercomputer/RedPajama-INCITE-Base-3B-v1
togethercomputer
2023-05-09T14:59:20Z
3,772
90
transformers
[ "transformers", "pytorch", "gpt_neox", "text-generation", "en", "dataset:togethercomputer/RedPajama-Data-1T", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2023-05-04T05:51:02Z
--- license: apache-2.0 language: - en datasets: - togethercomputer/RedPajama-Data-1T --- # RedPajama-INCITE-Base-3B-v1 RedPajama-INCITE-Base-3B-v1 was developed by Together and leaders from the open-source AI community including Ontocord.ai, ETH DS3Lab, AAI CERC, Université de Montréal, MILA - Québec AI Institute, Stanford Center for Research on Foundation Models (CRFM), Stanford Hazy Research research group and LAION. The training was done on 3,072 V100 GPUs provided as part of the INCITE 2023 project on Scalable Foundation Models for Transferrable Generalist AI, awarded to MILA, LAION, and EleutherAI in fall 2022, with support from the Oak Ridge Leadership Computing Facility (OLCF) and INCITE program. - Base Model: [RedPajama-INCITE-Base-3B-v1](https://huggingface.co/togethercomputer/RedPajama-INCITE-Base-3B-v1) - Instruction-tuned Version: [RedPajama-INCITE-Instruct-3B-v1](https://huggingface.co/togethercomputer/RedPajama-INCITE-Instruct-3B-v1) - Chat Version: [RedPajama-INCITE-Chat-3B-v1](https://huggingface.co/togethercomputer/RedPajama-INCITE-Chat-3B-v1) ## Model Details - **Developed by**: Together Computer. - **Model type**: Language Model - **Language(s)**: English - **License**: Apache 2.0 - **Model Description**: A 2.8B parameter pretrained language model. # Quick Start Please note that the model requires `transformers` version >= 4.25.1. ## GPU Inference This requires a GPU with 8GB memory. ```python import torch import transformers from transformers import AutoTokenizer, AutoModelForCausalLM MIN_TRANSFORMERS_VERSION = '4.25.1' # check transformers version assert transformers.__version__ >= MIN_TRANSFORMERS_VERSION, f'Please upgrade transformers to version {MIN_TRANSFORMERS_VERSION} or higher.' # init tokenizer = AutoTokenizer.from_pretrained("togethercomputer/RedPajama-INCITE-Base-3B-v1") model = AutoModelForCausalLM.from_pretrained("togethercomputer/RedPajama-INCITE-Base-3B-v1", torch_dtype=torch.float16) model = model.to('cuda:0') # infer prompt = "Alan Turing is" inputs = tokenizer(prompt, return_tensors='pt').to(model.device) input_length = inputs.input_ids.shape[1] outputs = model.generate( **inputs, max_new_tokens=128, do_sample=True, temperature=0.7, top_p=0.7, top_k=50, return_dict_in_generate=True, ) token = outputs.sequences[0, input_length:] output_str = tokenizer.decode(token) print(output_str) """ a name that has been synonymous with the computer age since the 1950s. The British mathematician, logician, and cryptanalyst is widely regarded as the father of modern computing. His contributions to the development of the modern computer and the theory of computation have had a profound impact on the world we live in today. Turing’s contributions to the development of the modern computer were made in the 1940s and 1950s. He is most famous for his work on the Turing machine, a theoretical model of a computing machine that was able to perform all the mathematical operations of a computer. Turing’s work on the... """ ``` ## GPU Inference in Int8 To run inference with int8, please ensure you have installed accelerate and bitandbytes. You can install them with the following command: ```bash pip install accelerate pip install bitsandbytes ``` Then you can run inference with int8 as follows: ```python import torch import transformers from transformers import AutoTokenizer, AutoModelForCausalLM MIN_TRANSFORMERS_VERSION = '4.25.1' # check transformers version assert transformers.__version__ >= MIN_TRANSFORMERS_VERSION, f'Please upgrade transformers to version {MIN_TRANSFORMERS_VERSION} or higher.' # init tokenizer = AutoTokenizer.from_pretrained("togethercomputer/RedPajama-INCITE-Base-3B-v1") model = AutoModelForCausalLM.from_pretrained("togethercomputer/RedPajama-INCITE-Base-3B-v1", device_map='auto', torch_dtype=torch.float16, load_in_8bit=True) # infer prompt = "Alan Turing is" inputs = tokenizer(prompt, return_tensors='pt').to(model.device) input_length = inputs.input_ids.shape[1] outputs = model.generate( **inputs, max_new_tokens=128, do_sample=True, temperature=0.7, top_p=0.7, top_k=50, return_dict_in_generate=True ) token = outputs.sequences[0, input_length:] output_str = tokenizer.decode(token) print(output_str) """ the man who cracked the Enigma code during World War II, and who was later convicted of homosexual acts. He was a brilliant mathematician, and a visionary who foresaw the computer age.... """ ``` ## CPU Inference You can run inference on CPU as follows: ```python import torch import transformers from transformers import AutoTokenizer, AutoModelForCausalLM MIN_TRANSFORMERS_VERSION = '4.25.1' # check transformers version assert transformers.__version__ >= MIN_TRANSFORMERS_VERSION, f'Please upgrade transformers to version {MIN_TRANSFORMERS_VERSION} or higher.' # init tokenizer = AutoTokenizer.from_pretrained("togethercomputer/RedPajama-INCITE-Base-3B-v1") model = AutoModelForCausalLM.from_pretrained("togethercomputer/RedPajama-INCITE-Base-3B-v1", torch_dtype=torch.bfloat16) # infer prompt = "Alan Turing is" inputs = tokenizer(prompt, return_tensors='pt').to(model.device) input_length = inputs.input_ids.shape[1] outputs = model.generate( **inputs, max_new_tokens=128, do_sample=True, temperature=0.7, top_p=0.7, top_k=50, return_dict_in_generate=True ) token = outputs.sequences[0, input_length:] output_str = tokenizer.decode(token) print(output_str) """ a name that is synonymous with the history of computer science. As the man who invented the Turing machine, the mathematical model that defines the limits of what can be computed, Turing is credited with the invention of the modern computer. Turing was also a mathematician and logician, and his work in these fields led to the development of the field of artificial intelligence... """ ``` Please note that since `LayerNormKernelImpl` is not implemented in fp16 for CPU, we use `bfloat16` for CPU inference. # Uses Excluded uses are described below. ### Misuse, Malicious Use, and Out-of-Scope Use It is the responsibility of the end user to ensure that the model is used in a responsible and ethical manner. #### Out-of-Scope Use `RedPajama-INCITE-Base-3B-v1` is a language model and may not perform well for other use cases outside of its intended scope. For example, it may not be suitable for use in safety-critical applications or for making decisions that have a significant impact on individuals or society. It is important to consider the limitations of the model and to only use it for its intended purpose. #### Misuse and Malicious Use `RedPajama-INCITE-Base-3B-v1` is designed for language modeling. Misuse of the model, such as using it to engage in illegal or unethical activities, is strictly prohibited and goes against the principles of the project. Using the model to generate content that is cruel to individuals is a misuse of this model. This includes, but is not limited to: - Generating fake news, misinformation, or propaganda - Promoting hate speech, discrimination, or violence against individuals or groups - Impersonating individuals or organizations without their consent - Engaging in cyberbullying or harassment - Defamatory content - Spamming or scamming - Sharing confidential or sensitive information without proper authorization - Violating the terms of use of the model or the data used to train it - Creating automated bots for malicious purposes such as spreading malware, phishing scams, or spamming ## Limitations `RedPajama-INCITE-Base-3B-v1`, like other language models, has limitations that should be taken into consideration. For example, the model may not always provide accurate or relevant answers, particularly for questions that are complex, ambiguous, or outside of its training data. We therefore welcome contributions from individuals and organizations, and encourage collaboration towards creating a more robust and inclusive chatbot. ## Training **Training Data** Please refer to [togethercomputer/RedPajama-Data-1T](https://huggingface.co/datasets/togethercomputer/RedPajama-Data-1T) **Training Procedure** - **Hardware:** 256 nodes of 6xV100 (IBM Power9), on the OLCF Summit cluster - **Optimizer:** Apex FusedAdam - **Parallelism:** Pipeline parallel 6, tensor parallel 2 - **Gradient Accumulations**: 8 (global batch size 4M tokens) - **Num of Tokens:** 800B Tokens - **Learning rate:** 0.00016 ## Benchmark Please refer to our [blog post](https://together.xyz) for benchmark results. ## Community Join us on [Together Discord](https://discord.gg/6ZVDU8tTD4)
dmitry-vorobiev/rubert_ria_headlines
dmitry-vorobiev
2023-05-09T14:56:55Z
329
2
transformers
[ "transformers", "pytorch", "safetensors", "encoder-decoder", "text2text-generation", "summarization", "bert", "rubert", "ru", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]
summarization
2022-03-02T23:29:05Z
--- language: - ru tags: - summarization - bert - rubert license: mit --- # rubert_ria_headlines ## Description *bert2bert* model, initialized with the `DeepPavlov/rubert-base-cased` pretrained weights and fine-tuned on the first 99% of ["Rossiya Segodnya" news dataset](https://github.com/RossiyaSegodnya/ria_news_dataset) for 2 epochs. ## Usage example ```python from transformers import AutoTokenizer, AutoModelForSeq2SeqLM MODEL_NAME = "dmitry-vorobiev/rubert_ria_headlines" tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME) model = AutoModelForSeq2SeqLM.from_pretrained(MODEL_NAME) text = "Скопируйте текст статьи / новости" encoded_batch = tokenizer.prepare_seq2seq_batch( [text], return_tensors="pt", padding="max_length", truncation=True, max_length=512) output_ids = model.generate( input_ids=encoded_batch["input_ids"], max_length=36, no_repeat_ngram_size=3, num_beams=5, top_k=0 ) headline = tokenizer.decode(output_ids[0], skip_special_tokens=True, clean_up_tokenization_spaces=False) print(headline) ``` ## Datasets - [ria_news](https://github.com/RossiyaSegodnya/ria_news_dataset) ## How it was trained? I used free TPUv3 on kaggle. The model was trained for 3 epochs with effective batch size 192 and soft restarts (warmup steps 1500 / 500 / 500 with new optimizer state on each epoch start). - [1 epoch notebook](https://www.kaggle.com/dvorobiev/try-train-seq2seq-ria-tpu?scriptVersionId=53254694) - [2 epoch notebook](https://www.kaggle.com/dvorobiev/try-train-seq2seq-ria-tpu?scriptVersionId=53269040) - [3 epoch notebook](https://www.kaggle.com/dvorobiev/try-train-seq2seq-ria-tpu?scriptVersionId=53280797) Common train params: ```shell export XLA_USE_BF16=1 export XLA_TENSOR_ALLOCATOR_MAXSIZE=100000000 python nlp_headline_rus/src/train_seq2seq.py \ --do_train \ --tie_encoder_decoder \ --max_source_length 512 \ --max_target_length 32 \ --val_max_target_length 48 \ --tpu_num_cores 8 \ --per_device_train_batch_size 24 \ --gradient_accumulation_steps 1 \ --learning_rate 5e-4 \ --adam_epsilon 1e-6 \ --weight_decay 1e-5 \ ``` ## Validation results - Using [last 1% of ria](https://drive.google.com/drive/folders/1ztAeyb1BiLMgXwOgOJS7WMR4PGiI1q92) dataset - Using [gazeta_ru test](https://drive.google.com/drive/folders/1CyowuRpecsLTcDbqEfmAvkCWOod58g_e) split - Using [gazeta_ru val](https://drive.google.com/drive/folders/1XZFOXHSXLKdhzm61ceVLw3aautrdskIu) split
Nakul24/SM_Bot
Nakul24
2023-05-09T14:33:50Z
4
0
transformers
[ "transformers", "pytorch", "gpt2", "text-generation", "conversational", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2022-09-23T05:14:27Z
--- tags: - conversational ---
can34/Modill
can34
2023-05-09T14:33:36Z
3
0
diffusers
[ "diffusers", "art", "text-to-image", "region:us" ]
text-to-image
2023-05-09T13:35:18Z
--- library_name: diffusers pipeline_tag: text-to-image tags: - art --- 🔥Declaration: I suggest illustrators and arts cannot be replaced by AI, although these models can accelerate design/drawing, the details, sprite-inside, visual-logics cannot be Datafication in Neural Networks. Modill (Modern-Illustration) is a trained checkpoint to make attractive and creative illustrations/painting. I’am UI/UX designer, so I want to a model to generate some flat illustrations for both business and creative-design. 🔥Advantages: Modill is trained by 289 brilliant illustrations from different designers/illustrators. It can draw exaggerated-body characters and raster texture. No strictly restrictions on style. The train data includes different styles, no-overfitting can generate more special outputs. 🔥Recommendations of parameters: Sampler: DPM2 Karras, 20~40 steps. CFG Scale: 7-9. Resolutions: 512*512 Negatives: poorly lit, duplicated leg, no text, one shoe, multiple head, strange face , error head, missing hand, blur, stereopsis, sex, waterpoint *** Add some Style Lora models might generate great arts. *** -------------------------------------------------------------------------------
Althhecow/BlushyandSpicy_v4
Althhecow
2023-05-09T14:29:39Z
0
0
null
[ "region:us" ]
null
2023-05-09T14:11:23Z
Model made using ~100 images from Blushy&Spicy on Twitter Available as both LORA and full models MeinaMix recommended but not required.
ighina/roberta_wikidisease_topseg_topsam
ighina
2023-05-09T14:02:21Z
2
0
sentence-transformers
[ "sentence-transformers", "pytorch", "roberta", "feature-extraction", "sentence-similarity", "transformers", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]
sentence-similarity
2023-05-09T14:01:32Z
--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers --- # {MODEL_NAME} This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. <!--- Describe your model here --> ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('{MODEL_NAME}') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) # Sentences we want sentence embeddings for sentences = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}') model = AutoModel.from_pretrained('{MODEL_NAME}') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(**encoded_input) # Perform pooling. In this case, mean pooling. sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Evaluation Results <!--- Describe how your model was evaluated --> For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME}) ## Training The model was trained with the parameters: **DataLoader**: `torch.utils.data.dataloader.DataLoader` of length 6033 with parameters: ``` {'batch_size': 64, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` **Loss**: `sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss` Parameters of the fit()-Method: ``` { "epochs": 10, "evaluation_steps": 0, "evaluator": "sentence_transformers.evaluation.BinaryClassificationEvaluator.BinaryClassificationEvaluator", "max_grad_norm": 1, "optimizer_class": "<class 'transformers.optimization.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 10000, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: RobertaModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) ) ``` ## Citing & Authors <!--- Describe where people can find more information -->
danazoid/ppo-LunarLander-v2
danazoid
2023-05-09T13:59:02Z
0
0
stable-baselines3
[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-05-09T13:29:12Z
--- 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: 251.70 +/- 41.90 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 ... ```
ChrisOfLondon/ppo-LunarLander-v2
ChrisOfLondon
2023-05-09T13:42:29Z
0
0
stable-baselines3
[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-05-09T13:42:11Z
--- 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: 260.03 +/- 11.99 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 ... ```
kinshuk-h/t5-kelm-tekgen-kg-small-finetuned
kinshuk-h
2023-05-09T13:38:46Z
5
0
transformers
[ "transformers", "pytorch", "t5", "text2text-generation", "legal", "en", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text2text-generation
2023-05-09T13:37:46Z
--- license: mit language: - en pipeline_tag: text2text-generation tags: - legal --- # t5-kelm-tekgen-kg-small-finetuned Google's T5 model ([t5-small](https://huggingface.co/t5-small)) finetuned over KELM-TEKGEN KG triples for link prediction.
kinshuk-h/flan-t5-kelm-tekgen-kg-small-finetuned
kinshuk-h
2023-05-09T13:36:13Z
3
0
transformers
[ "transformers", "pytorch", "t5", "text2text-generation", "legal", "en", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text2text-generation
2023-05-09T13:35:42Z
--- license: mit language: - en pipeline_tag: text2text-generation tags: - legal --- # flan-t5-kelm-tekgen-kg-small-finetuned Google's Flan T5 model ([flan-t5-small](https://huggingface.co/google/flan-t5-small)) finetuned over KELM-TEKGEN KG triples for link prediction.
Jilas/Nar
Jilas
2023-05-09T13:08:08Z
0
0
null
[ "license:cc-by-nc-sa-2.0", "region:us" ]
null
2023-05-09T13:08:08Z
--- license: cc-by-nc-sa-2.0 ---
wiesmpas/testmodelforkiex
wiesmpas
2023-05-09T12:50:18Z
5
0
tf-keras
[ "tf-keras", "mobilenet", "image-classification", "region:us" ]
image-classification
2023-05-09T12:48:34Z
--- pipeline_tag: image-classification ---
AlienKevin/ipa_ocr
AlienKevin
2023-05-09T12:45:04Z
0
0
null
[ "tensorboard", "image-to-text", "zh", "license:mit", "region:us" ]
image-to-text
2023-05-08T13:43:03Z
--- license: mit language: - zh pipeline_tag: image-to-text --- # Target: Convert Scanned Images of IPA symbols to Pinyin Scanned images of IPA phonetic symbols for Chengdunese (成都话) in The Great Dictionary of Modern Chinese Dialects (現代漢語方言大詞典). # Training and Test Set * 2,553 images of IPA phonetic symbols generated from Pinyin pronunciations found in Sichuanese Dialect Dictionary (四川方言词典 教你一口地道的四川话) and the word list of the Shupin (蜀拼) input method. * 80/20 split on train/test # Results * Trained for 180 steps with a batch size of 32 * Final Character Error Rate of 0.795% on test set * TODO: label part of the scanned images to see if model generalizes on target task
alexandrualexandru/my-final-v1-text-to-sparql-combined-dataset-t5-base-2023-05-09_09-13
alexandrualexandru
2023-05-09T12:33:17Z
3
0
transformers
[ "transformers", "pytorch", "tensorboard", "t5", "text2text-generation", "generated_from_trainer", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text2text-generation
2023-05-09T09:17:02Z
--- tags: - generated_from_trainer model-index: - name: my-final-v1-text-to-sparql-combined-dataset-t5-base-2023-05-09_09-13 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. --> # my-final-v1-text-to-sparql-combined-dataset-t5-base-2023-05-09_09-13 This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3456 - Gen Len: 19.0 - Bertscorer-p: 0.5013 - Bertscorer-r: 0.1137 - Bertscorer-f1: 0.3000 - Sacrebleu-score: 6.1003 - Sacrebleu-precisions: [77.97754754552538, 64.74142628270293, 53.3199157675034, 47.63691495511611] - Bleu-bp: 0.1019 ## 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: 0.0003 - train_batch_size: 8 - eval_batch_size: 8 - 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 | Gen Len | Bertscorer-p | Bertscorer-r | Bertscorer-f1 | Sacrebleu-score | Sacrebleu-precisions | Bleu-bp | |:-------------:|:-----:|:-----:|:---------------:|:-------:|:------------:|:------------:|:-------------:|:---------------:|:----------------------------------------------------------------------------:|:-------:| | 0.4215 | 1.0 | 7822 | 0.3919 | 19.0 | 0.4997 | 0.1122 | 0.2984 | 5.8699 | [77.35323282257656, 63.16682990532158, 51.41608735111668, 45.63668646835748] | 0.1009 | | 0.3639 | 2.0 | 15644 | 0.3456 | 19.0 | 0.5013 | 0.1137 | 0.3000 | 6.1003 | [77.97754754552538, 64.74142628270293, 53.3199157675034, 47.63691495511611] | 0.1019 | ### Framework versions - Transformers 4.28.1 - Pytorch 2.0.0+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3
directtt/wine-reviews-distilbert
directtt
2023-05-09T10:46:50Z
4
0
transformers
[ "transformers", "tf", "distilbert", "text-classification", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2023-05-09T09:11:41Z
--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: wine-reviews-distilbert 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. --> # wine-reviews-distilbert 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: - Train Loss: 0.3834 - Train Acc: 0.8375 - Validation Loss: 0.5538 - Validation Acc: 0.7741 - Epoch: 2 ## 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': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 24455, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results | Train Loss | Train Acc | Validation Loss | Validation Acc | Epoch | |:----------:|:---------:|:---------------:|:--------------:|:-----:| | 0.6005 | 0.7381 | 0.5342 | 0.7661 | 0 | | 0.4822 | 0.7915 | 0.5570 | 0.7612 | 1 | | 0.3834 | 0.8375 | 0.5538 | 0.7741 | 2 | ### Framework versions - Transformers 4.28.1 - TensorFlow 2.11.0 - Datasets 2.1.0 - Tokenizers 0.13.3
SaberMolaei/speecht5_tts_ckb
SaberMolaei
2023-05-09T10:21:25Z
13
0
transformers
[ "transformers", "pytorch", "tensorboard", "speecht5", "text-to-audio", "hf-tts-leaderboard", "generated_from_trainer", "ckb", "dataset:mozilla-foundation/common_voice_11_0", "endpoints_compatible", "region:us" ]
text-to-audio
2023-05-09T08:14:35Z
--- language: - ckb tags: - hf-tts-leaderboard - generated_from_trainer datasets: - mozilla-foundation/common_voice_11_0 model-index: - name: SpeechT5 TTS ckb 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. --> # SpeechT5 TTS ckb This model is a fine-tuned version of [microsoft/speecht5_tts - Saber Molaei](https://huggingface.co/microsoft/speecht5_tts - Saber Molaei) on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set: - Loss: 0.5267 ## 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: 1e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 2000 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 0.5811 | 7.41 | 1000 | 0.5423 | | 0.5511 | 14.81 | 2000 | 0.5267 | ### Framework versions - Transformers 4.29.0.dev0 - Pytorch 2.0.0+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3
DIAG-PSSeng/cicero-gpt2
DIAG-PSSeng
2023-05-09T09:45:04Z
12
4
transformers
[ "transformers", "pytorch", "safetensors", "gpt2", "text-generation", "it", "license:openrail", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2023-05-08T20:03:43Z
--- license: openrail language: - it metrics: - perplexity --- # cicero-gpt2 <!-- Provide a quick summary of what the model is/does. --> GroNLP/gpt2-small-italian version fine-tuned with italian civil judgments. ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - **Developed by:** Marco Calamo, Francesca De Luzi, Mattia Macrì, Tommaso Mencattini, Massimo Mecella - **Model type:** gpt2-small-italian - **Language(s) (NLP):** italian - **License:** openrail - **Finetuned from model:** [GroNLP/gpt-2-small](https://huggingface.co/GroNLP/gpt2-small-italian) - ### Model Sources <!-- Provide the basic links for the model. --> - **Repository:** [Github](https://github.com/MattiaMacri/Cicero) - **Paper [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. --> Used to generate part of sentences based upon user input. All sensible data are hidden by design. ## 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 --> - **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]
babs001seye/distilbert-base-uncased-finetuned-squad
babs001seye
2023-05-09T09:18:02Z
3
0
transformers
[ "transformers", "pytorch", "tensorboard", "distilbert", "question-answering", "generated_from_trainer", "dataset:squad", "license:apache-2.0", "endpoints_compatible", "region:us" ]
question-answering
2023-05-05T13:42:29Z
--- license: apache-2.0 tags: - generated_from_trainer datasets: - squad model-index: - name: distilbert-base-uncased-finetuned-squad 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-squad This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training 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: 3 ### Framework versions - Transformers 4.28.1 - Pytorch 2.0.0+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3
aitslab/biobert_huner_chemical_v1
aitslab
2023-05-09T09:11:07Z
9
3
transformers
[ "transformers", "pytorch", "bert", "token-classification", "arxiv:2304.07805", "doi:10.57967/hf/2033", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
token-classification
2023-04-12T16:11:33Z
--- license: apache-2.0 --- More information can be found in our github repo and paper. Please cite the paper, if you use the model. https://github.com/Aitslab/EasyNER @article{ahmed2023easyner, title={EasyNER: A Customizable Easy-to-Use Pipeline for Deep Learning- and Dictionary-based Named Entity Recognition from Medical Text}, author={Rafsan Ahmed and Petter Berntsson and Alexander Skafte and Salma Kazemi Rashed and Marcus Klang and Adam Barvesten and Ola Olde and William Lindholm and Antton Lamarca Arrizabalaga and Pierre Nugues and Sonja Aits}, year={2023}, eprint={2304.07805}, archivePrefix={arXiv}, primaryClass={q-bio.QM} }
aitslab/biobert_huner_gene_v1
aitslab
2023-05-09T09:10:42Z
127
2
transformers
[ "transformers", "pytorch", "bert", "token-classification", "arxiv:2304.07805", "doi:10.57967/hf/2031", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
token-classification
2023-04-12T16:14:52Z
--- license: apache-2.0 --- More information can be found in our github repo and paper. Please cite the paper, if you use the model. https://github.com/Aitslab/EasyNER @article{ahmed2023easyner, title={EasyNER: A Customizable Easy-to-Use Pipeline for Deep Learning- and Dictionary-based Named Entity Recognition from Medical Text}, author={Rafsan Ahmed and Petter Berntsson and Alexander Skafte and Salma Kazemi Rashed and Marcus Klang and Adam Barvesten and Ola Olde and William Lindholm and Antton Lamarca Arrizabalaga and Pierre Nugues and Sonja Aits}, year={2023}, eprint={2304.07805}, archivePrefix={arXiv}, primaryClass={q-bio.QM} }
aitslab/biobert_huner_disease_v1
aitslab
2023-05-09T09:10:16Z
14
1
transformers
[ "transformers", "pytorch", "bert", "token-classification", "arxiv:2304.07805", "doi:10.57967/hf/2034", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
token-classification
2023-04-12T16:13:23Z
--- license: apache-2.0 --- More information can be found in our github repo and paper. Please cite the paper, if you use the model. https://github.com/Aitslab/EasyNER @article{ahmed2023easyner, title={EasyNER: A Customizable Easy-to-Use Pipeline for Deep Learning- and Dictionary-based Named Entity Recognition from Medical Text}, author={Rafsan Ahmed and Petter Berntsson and Alexander Skafte and Salma Kazemi Rashed and Marcus Klang and Adam Barvesten and Ola Olde and William Lindholm and Antton Lamarca Arrizabalaga and Pierre Nugues and Sonja Aits}, year={2023}, eprint={2304.07805}, archivePrefix={arXiv}, primaryClass={q-bio.QM} }
aitslab/biobert_huner_species_v1
aitslab
2023-05-09T09:09:35Z
5
0
transformers
[ "transformers", "pytorch", "bert", "token-classification", "arxiv:2304.07805", "doi:10.57967/hf/2032", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
token-classification
2023-04-12T16:16:19Z
--- license: apache-2.0 --- More information can be found in our github repo and paper. Please cite the paper, if you use the model. https://github.com/Aitslab/EasyNER @article{ahmed2023easyner, title={EasyNER: A Customizable Easy-to-Use Pipeline for Deep Learning- and Dictionary-based Named Entity Recognition from Medical Text}, author={Rafsan Ahmed and Petter Berntsson and Alexander Skafte and Salma Kazemi Rashed and Marcus Klang and Adam Barvesten and Ola Olde and William Lindholm and Antton Lamarca Arrizabalaga and Pierre Nugues and Sonja Aits}, year={2023}, eprint={2304.07805}, archivePrefix={arXiv}, primaryClass={q-bio.QM} }
dxli/dog2
dxli
2023-05-09T09:08:16Z
12
0
diffusers
[ "diffusers", "tensorboard", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "textual_inversion", "base_model:runwayml/stable-diffusion-v1-5", "base_model:adapter:runwayml/stable-diffusion-v1-5", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]
text-to-image
2023-05-09T08:26:09Z
--- license: creativeml-openrail-m base_model: runwayml/stable-diffusion-v1-5 tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - textual_inversion inference: true --- # Textual inversion text2image fine-tuning - dxli/dog2 These are textual inversion adaption weights for runwayml/stable-diffusion-v1-5. You can find some example images in the following.
hitachi-nlp/roberta-base_last-4-chars_acl2023
hitachi-nlp
2023-05-09T09:03:10Z
3
0
transformers
[ "transformers", "pytorch", "roberta", "fill-mask", "en", "dataset:wikipedia", "dataset:bookcorpus", "license:cc-by-nc-sa-4.0", "endpoints_compatible", "region:us" ]
fill-mask
2023-05-09T07:13:40Z
--- license: cc-by-nc-sa-4.0 datasets: - wikipedia - bookcorpus language: - en library_name: transformers pipeline_tag: fill-mask --- This is the pretrained model of Last 4 Chars. Please refer to our [GitHub](https://github.com/hitachi-nlp/mlm-probe-acl2023) page for more details.
hitachi-nlp/roberta-base_last-5-chars_acl2023
hitachi-nlp
2023-05-09T09:02:33Z
4
0
transformers
[ "transformers", "pytorch", "roberta", "fill-mask", "en", "dataset:wikipedia", "dataset:bookcorpus", "license:cc-by-nc-sa-4.0", "endpoints_compatible", "region:us" ]
fill-mask
2023-05-09T07:14:03Z
--- license: cc-by-nc-sa-4.0 datasets: - wikipedia - bookcorpus language: - en library_name: transformers pipeline_tag: fill-mask --- This is the pretrained model of Last 5 Chars. Please refer to our [GitHub](https://github.com/hitachi-nlp/mlm-probe-acl2023) page for more details.
hitachi-nlp/roberta-base_last-9-chars_acl2023
hitachi-nlp
2023-05-09T09:00:39Z
4
0
transformers
[ "transformers", "pytorch", "roberta", "fill-mask", "en", "dataset:wikipedia", "dataset:bookcorpus", "license:cc-by-nc-sa-4.0", "endpoints_compatible", "region:us" ]
fill-mask
2023-05-09T07:14:27Z
--- license: cc-by-nc-sa-4.0 datasets: - wikipedia - bookcorpus language: - en library_name: transformers pipeline_tag: fill-mask --- This is the pretrained model of Last 9 Chars. Please refer to our [GitHub](https://github.com/hitachi-nlp/mlm-probe-acl2023) page for more details.
hitachi-nlp/roberta-base_last-2-chars_acl2023
hitachi-nlp
2023-05-09T08:59:54Z
4
0
transformers
[ "transformers", "pytorch", "roberta", "fill-mask", "en", "dataset:wikipedia", "dataset:bookcorpus", "license:cc-by-nc-sa-4.0", "endpoints_compatible", "region:us" ]
fill-mask
2023-05-09T07:12:56Z
--- license: cc-by-nc-sa-4.0 datasets: - wikipedia - bookcorpus language: - en library_name: transformers pipeline_tag: fill-mask --- This is the pretrained model of Last 2 Chars. Please refer to our [GitHub](https://github.com/hitachi-nlp/mlm-probe-acl2023) page for more details.
dxli/grey_sloth_plushie
dxli
2023-05-09T08:55:25Z
4
0
diffusers
[ "diffusers", "tensorboard", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "textual_inversion", "base_model:runwayml/stable-diffusion-v1-5", "base_model:adapter:runwayml/stable-diffusion-v1-5", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]
text-to-image
2023-05-09T08:00:48Z
--- license: creativeml-openrail-m base_model: runwayml/stable-diffusion-v1-5 tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - textual_inversion inference: true --- # Textual inversion text2image fine-tuning - dxli/grey_sloth_plushie These are textual inversion adaption weights for runwayml/stable-diffusion-v1-5. You can find some example images in the following.
FatihC/swin-tiny-patch4-window7-224-finetuned-eurosat-people
FatihC
2023-05-09T08:51:40Z
9
0
transformers
[ "transformers", "pytorch", "tensorboard", "swin", "image-classification", "generated_from_trainer", "dataset:imagefolder", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
image-classification
2023-05-09T08:18:45Z
--- license: apache-2.0 tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: swin-tiny-patch4-window7-224-finetuned-eurosat-people results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: images split: train args: images metrics: - name: Accuracy type: accuracy value: 0.952 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # swin-tiny-patch4-window7-224-finetuned-eurosat-people This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.1711 - Accuracy: 0.952 ## 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: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 4 | 0.3073 | 0.912 | | No log | 2.0 | 8 | 0.2076 | 0.92 | | 0.4055 | 3.0 | 12 | 0.1789 | 0.928 | | 0.4055 | 4.0 | 16 | 0.1911 | 0.928 | | 0.3045 | 5.0 | 20 | 0.1695 | 0.928 | | 0.3045 | 6.0 | 24 | 0.1756 | 0.944 | | 0.3045 | 7.0 | 28 | 0.1751 | 0.944 | | 0.2419 | 8.0 | 32 | 0.1727 | 0.944 | | 0.2419 | 9.0 | 36 | 0.1711 | 0.952 | | 0.2375 | 10.0 | 40 | 0.1711 | 0.952 | ### Framework versions - Transformers 4.28.1 - Pytorch 2.0.0+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3
funasr/fsmn-vad-onnx
funasr
2023-05-09T08:50:58Z
0
17
null
[ "onnx", "FunASR", "FSMN-VAD", "voice-activity-detection", "arxiv:1803.05030", "license:apache-2.0", "region:us" ]
voice-activity-detection
2023-04-22T13:55:01Z
--- license: apache-2.0 pipeline_tag: voice-activity-detection tags: - FunASR - FSMN-VAD --- ## Introduce Voice activity detection (VAD) plays a important role in speech recognition systems by detecting the beginning and end of effective speech. FunASR provides an efficient VAD model based on the [FSMN structure](https://arxiv.org/abs/1803.05030). To improve model discrimination, we use monophones as modeling units, given the relatively rich speech information. During inference, the VAD system requires post-processing for improved robustness, including operations such as threshold settings and sliding windows. This repository demonstrates how to leverage FSMN-VAD in conjunction with the funasr_onnx runtime. The underlying model is derived from [FunASR](https://github.com/alibaba-damo-academy/FunASR), which was trained on a massive 5,000-hour dataset. We have relesed numerous industrial-grade models, including speech recognition, voice activity detection, punctuation restoration, speaker verification, speaker diarization, and timestamp prediction (force alignment). To learn more about these models, kindly refer to the [documentation](https://alibaba-damo-academy.github.io/FunASR/en/index.html) available on FunASR. If you are interested in leveraging advanced AI technology for your speech-related projects, we invite you to explore the possibilities offered by [FunASR](https://github.com/alibaba-damo-academy/FunASR). ## Install funasr_onnx ```shell pip install -U funasr_onnx # For the users in China, you could install with the command: # pip install -U funasr_onnx -i https://mirror.sjtu.edu.cn/pypi/web/simple ``` ## Download the model ```shell git lfs install git clone https://huggingface.co/funasr/FSMN-VAD ``` ## Inference with runtime ### Voice Activity Detection #### FSMN-VAD ```python from funasr_onnx import Fsmn_vad model_dir = "./FSMN-VAD" model = Fsmn_vad(model_dir, quantize=True) wav_path = "./FSMN-VAD/asr_example.wav" result = model(wav_path) print(result) ``` - `model_dir`: the model path, which contains `model.onnx`, `config.yaml`, `am.mvn` - `batch_size`: `1` (Default), the batch size duration inference - `device_id`: `-1` (Default), infer on CPU. If you want to infer with GPU, set it to gpu_id (Please make sure that you have install the onnxruntime-gpu) - `quantize`: `False` (Default), load the model of `model.onnx` in `model_dir`. If set `True`, load the model of `model_quant.onnx` in `model_dir` - `intra_op_num_threads`: `4` (Default), sets the number of threads used for intraop parallelism on CPU Input: wav formt file, support formats: `str, np.ndarray, List[str]` Output: `List[str]`: recognition result ## Citations ``` bibtex @inproceedings{gao2022paraformer, title={Paraformer: Fast and Accurate Parallel Transformer for Non-autoregressive End-to-End Speech Recognition}, author={Gao, Zhifu and Zhang, Shiliang and McLoughlin, Ian and Yan, Zhijie}, booktitle={INTERSPEECH}, year={2022} } ```
rimOPS/embeddings
rimOPS
2023-05-09T08:47:03Z
0
0
null
[ "license:creativeml-openrail-m", "region:us" ]
null
2023-03-16T03:58:57Z
--- license: creativeml-openrail-m ---
fengbj/test-llm
fengbj
2023-05-09T08:35:26Z
0
0
transformers
[ "transformers", "text-to-speech", "arxiv:1910.09700", "license:mit", "endpoints_compatible", "region:us" ]
text-to-speech
2023-05-09T07:06:03Z
--- license: mit pipeline_tag: text-to-speech library_name: transformers --- # 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]
dxli/dog7
dxli
2023-05-09T08:32:36Z
21
0
diffusers
[ "diffusers", "tensorboard", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "textual_inversion", "base_model:runwayml/stable-diffusion-v1-5", "base_model:adapter:runwayml/stable-diffusion-v1-5", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]
text-to-image
2023-05-09T07:47:26Z
--- license: creativeml-openrail-m base_model: runwayml/stable-diffusion-v1-5 tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - textual_inversion inference: true --- # Textual inversion text2image fine-tuning - dxli/dog7 These are textual inversion adaption weights for runwayml/stable-diffusion-v1-5. You can find some example images in the following.
DmitriyVasiliev/autotrain-xls-mt5-rua-par-rua-sent-dia-56800131755
DmitriyVasiliev
2023-05-09T08:29:51Z
8
0
transformers
[ "transformers", "pytorch", "mt5", "text2text-generation", "autotrain", "summarization", "unk", "dataset:DmitriyVasiliev/autotrain-data-xls-mt5-rua-par-rua-sent-dia", "co2_eq_emissions", "autotrain_compatible", "endpoints_compatible", "region:us" ]
summarization
2023-05-09T08:14:15Z
--- tags: - autotrain - summarization language: - unk widget: - text: "I love AutoTrain 🤗" datasets: - DmitriyVasiliev/autotrain-data-xls-mt5-rua-par-rua-sent-dia co2_eq_emissions: emissions: 5.948993226966507 --- # Model Trained Using AutoTrain - Problem type: Summarization - Model ID: 56800131755 - CO2 Emissions (in grams): 5.9490 ## Validation Metrics - Loss: 1.627 - Rouge1: 4.517 - Rouge2: 1.694 - RougeL: 4.556 - RougeLsum: 4.550 - Gen Len: 29.800 ## Usage You can use cURL to access this model: ``` $ curl -X POST -H "Authorization: Bearer YOUR_HUGGINGFACE_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/DmitriyVasiliev/autotrain-xls-mt5-rua-par-rua-sent-dia-56800131755 ```
kasunw/PPO-LunarLander-v2
kasunw
2023-05-09T08:25:30Z
0
0
stable-baselines3
[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-05-09T08:25:11Z
--- 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: 253.94 +/- 24.90 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 ... ```
photel/ppo-LunarLander-v2
photel
2023-05-09T08:15:40Z
1
0
stable-baselines3
[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-05-09T08:15:19Z
--- 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: 255.54 +/- 24.46 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 ... ```
dxli/fancy_boot
dxli
2023-05-09T08:00:35Z
5
0
diffusers
[ "diffusers", "tensorboard", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "textual_inversion", "base_model:runwayml/stable-diffusion-v1-5", "base_model:adapter:runwayml/stable-diffusion-v1-5", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]
text-to-image
2023-05-09T07:06:16Z
--- license: creativeml-openrail-m base_model: runwayml/stable-diffusion-v1-5 tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - textual_inversion inference: true --- # Textual inversion text2image fine-tuning - dxli/fancy_boot These are textual inversion adaption weights for runwayml/stable-diffusion-v1-5. You can find some example images in the following.
rohan1221/inpaint-furniture
rohan1221
2023-05-09T07:58:29Z
2
0
diffusers
[ "diffusers", "safetensors", "text-to-image", "stable-diffusion", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]
text-to-image
2023-05-08T17:38:49Z
--- license: creativeml-openrail-m tags: - text-to-image - stable-diffusion --- ### inpaint_furniture Dreambooth model trained by rohan1221 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:
dxli/teapot
dxli
2023-05-09T07:49:41Z
28
0
diffusers
[ "diffusers", "tensorboard", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "textual_inversion", "base_model:runwayml/stable-diffusion-v1-5", "base_model:adapter:runwayml/stable-diffusion-v1-5", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]
text-to-image
2023-05-09T07:10:43Z
--- license: creativeml-openrail-m base_model: runwayml/stable-diffusion-v1-5 tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - textual_inversion inference: true --- # Textual inversion text2image fine-tuning - dxli/teapot These are textual inversion adaption weights for runwayml/stable-diffusion-v1-5. You can find some example images in the following.
christianutama/ppo-LunarLander-v2
christianutama
2023-05-09T07:49:02Z
0
0
stable-baselines3
[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-05-09T07:48:42Z
--- 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: 273.02 +/- 19.73 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 ... ```
Oscar-chen/roberta-base
Oscar-chen
2023-05-09T07:48:54Z
6
0
transformers
[ "transformers", "pytorch", "tensorboard", "roberta", "text-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2023-05-04T07:19:48Z
--- tags: - generated_from_trainer metrics: - accuracy model-index: - name: roberta-base 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. --> # roberta-base This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1131 - Accuracy: 0.9637 ## 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: 4 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 100 | 0.3406 | 0.8619 | | No log | 2.0 | 200 | 0.2220 | 0.9119 | | No log | 3.0 | 300 | 0.1429 | 0.9487 | | No log | 4.0 | 400 | 0.1131 | 0.9637 | ### Framework versions - Transformers 4.28.1 - Pytorch 2.0.0+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3
djkcyl/DDSP-SVC
djkcyl
2023-05-09T07:46:43Z
0
2
null
[ "svc", "audio-to-audio", "zh", "license:agpl-3.0", "region:us" ]
audio-to-audio
2023-05-08T17:56:31Z
--- license: agpl-3.0 language: - zh pipeline_tag: audio-to-audio tags: - svc --- # DDSP-SVC 3.0 一键包 source: https://github.com/yxlllc/DDSP-SVC password: DDSP@60
Team-PIXEL/pixel-tiny-continuous
Team-PIXEL
2023-05-09T07:39:56Z
9
0
transformers
[ "transformers", "pytorch", "pixel", "masked-auto-encoding", "generated_from_trainer", "endpoints_compatible", "region:us" ]
null
2023-05-02T21:13:43Z
--- tags: - masked-auto-encoding - generated_from_trainer model-index: - name: pixel-tiny-cont 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. --> # pixel-tiny-cont This model was trained from scratch on the wikipedia + bookcorpus dataset. It achieves the following results on the evaluation set: - Loss: 0.8024 ## 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: 0.0006 - train_batch_size: 128 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - total_train_batch_size: 1024 - total_eval_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.05 - training_steps: 250000 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:------:|:---------------:| | 0.7411 | 0.06 | 1000 | 0.9070 | | 0.7395 | 0.12 | 2000 | 0.9064 | | 0.7387 | 0.18 | 3000 | 0.9047 | | 0.7382 | 0.25 | 4000 | 0.9015 | | 0.7381 | 0.31 | 5000 | 0.9044 | | 0.7379 | 0.37 | 6000 | 0.9042 | | 0.7379 | 0.43 | 7000 | 0.9054 | | 0.7378 | 0.49 | 8000 | 0.9035 | | 0.7378 | 0.55 | 9000 | 0.9026 | | 0.7371 | 0.61 | 10000 | 0.9038 | | 0.7369 | 0.67 | 11000 | 0.9027 | | 0.7368 | 0.74 | 12000 | 0.9022 | | 0.7368 | 0.8 | 13000 | 0.8987 | | 0.7374 | 0.86 | 14000 | 0.9014 | | 0.7369 | 0.92 | 15000 | 0.9002 | | 0.7369 | 0.98 | 16000 | 0.9002 | | 0.7372 | 1.04 | 17000 | 0.9019 | | 0.737 | 1.1 | 18000 | 0.9001 | | 0.737 | 1.16 | 19000 | 0.9006 | | 0.7369 | 1.23 | 20000 | 0.9007 | | 0.7365 | 1.29 | 21000 | 0.8698 | | 0.7363 | 1.35 | 22000 | 0.8700 | | 0.7366 | 1.41 | 23000 | 0.9021 | | 0.7362 | 1.47 | 24000 | 0.8763 | | 0.7082 | 1.53 | 25000 | 0.8719 | | 0.6774 | 1.59 | 26000 | 0.8876 | | 0.6525 | 1.65 | 27000 | 0.8905 | | 0.6022 | 1.72 | 28000 | 0.8856 | | 0.5874 | 1.78 | 29000 | 0.8794 | | 0.5765 | 1.84 | 30000 | 0.8806 | | 0.5685 | 1.9 | 31000 | 0.8747 | | 0.564 | 1.96 | 32000 | 0.8779 | | 0.5606 | 2.02 | 33000 | 0.8762 | | 0.5574 | 2.08 | 34000 | 0.8703 | | 0.5528 | 2.14 | 35000 | 0.8664 | | 0.5494 | 2.21 | 36000 | 0.8717 | | 0.5448 | 2.27 | 37000 | 0.8673 | | 0.5419 | 2.33 | 38000 | 0.8637 | | 0.5385 | 2.39 | 39000 | 0.8634 | | 0.536 | 2.45 | 40000 | 0.8661 | | 0.5336 | 2.51 | 41000 | 0.8631 | | 0.5316 | 2.57 | 42000 | 0.8606 | | 0.5297 | 2.63 | 43000 | 0.8589 | | 0.5305 | 2.7 | 44000 | 0.8570 | | 0.5262 | 2.76 | 45000 | 0.8559 | | 0.5247 | 2.82 | 46000 | 0.8634 | | 0.5235 | 2.88 | 47000 | 0.8606 | | 0.5227 | 2.94 | 48000 | 0.8610 | | 0.5206 | 3.0 | 49000 | 0.8610 | | 0.5194 | 3.06 | 50000 | 0.8611 | | 0.5183 | 3.12 | 51000 | 0.8579 | | 0.5175 | 3.19 | 52000 | 0.8598 | | 0.5163 | 3.25 | 53000 | 0.8521 | | 0.5156 | 3.31 | 54000 | 0.8550 | | 0.5148 | 3.37 | 55000 | 0.8504 | | 0.5139 | 3.43 | 56000 | 0.8530 | | 0.5133 | 3.49 | 57000 | 0.8589 | | 0.5126 | 3.55 | 58000 | 0.8561 | | 0.5119 | 3.62 | 59000 | 0.8574 | | 0.5127 | 3.68 | 60000 | 0.8624 | | 0.5105 | 3.74 | 61000 | 0.8522 | | 0.5099 | 3.8 | 62000 | 0.8550 | | 0.5094 | 3.86 | 63000 | 0.8537 | | 0.509 | 3.92 | 64000 | 0.8535 | | 0.5091 | 3.98 | 65000 | 0.8592 | | 0.5079 | 4.04 | 66000 | 0.8554 | | 0.5074 | 4.11 | 67000 | 0.8516 | | 0.5069 | 4.17 | 68000 | 0.8491 | | 0.5066 | 4.23 | 69000 | 0.8571 | | 0.5068 | 4.29 | 70000 | 0.8536 | | 0.5066 | 4.35 | 71000 | 0.9288 | | 0.5051 | 4.41 | 72000 | 0.8597 | | 0.5045 | 4.47 | 73000 | 0.8555 | | 0.5043 | 4.53 | 74000 | 0.8547 | | 0.5039 | 4.6 | 75000 | 0.8561 | | 0.504 | 4.66 | 76000 | 0.8541 | | 0.5026 | 4.72 | 77000 | 0.8490 | | 0.5024 | 4.78 | 78000 | 0.8499 | | 0.5019 | 4.84 | 79000 | 0.8522 | | 0.5014 | 4.9 | 80000 | 0.8508 | | 0.5008 | 4.96 | 81000 | 0.8512 | | 0.5002 | 5.02 | 82000 | 0.8470 | | 0.4995 | 5.09 | 83000 | 0.8462 | | 0.4991 | 5.15 | 84000 | 0.8455 | | 0.4982 | 5.21 | 85000 | 0.8465 | | 0.4978 | 5.27 | 86000 | 0.8434 | | 0.4969 | 5.33 | 87000 | 0.8432 | | 0.4964 | 5.39 | 88000 | 0.8417 | | 0.4957 | 5.45 | 89000 | 0.8363 | | 0.495 | 5.51 | 90000 | 0.8392 | | 0.4946 | 5.58 | 91000 | 0.8401 | | 0.4935 | 5.64 | 92000 | 0.8373 | | 0.4929 | 5.7 | 93000 | 0.8401 | | 0.492 | 5.76 | 94000 | 0.8356 | | 0.4912 | 5.82 | 95000 | 0.8334 | | 0.4904 | 5.88 | 96000 | 0.8281 | | 0.4898 | 5.94 | 97000 | 0.8338 | | 0.4891 | 6.0 | 98000 | 0.8300 | | 0.4882 | 6.07 | 99000 | 0.8262 | | 0.4876 | 6.13 | 100000 | 0.8172 | | 0.4868 | 6.19 | 101000 | 0.8240 | | 0.4861 | 6.25 | 102000 | 0.8212 | | 0.4854 | 6.31 | 103000 | 0.8243 | | 0.4847 | 6.37 | 104000 | 0.8228 | | 0.4841 | 6.43 | 105000 | 0.8185 | | 0.4837 | 6.5 | 106000 | 0.8177 | | 0.4827 | 6.56 | 107000 | 0.8140 | | 0.4819 | 6.62 | 108000 | 0.8147 | | 0.4813 | 6.68 | 109000 | 0.8172 | | 0.4807 | 6.74 | 110000 | 0.8149 | | 0.4801 | 6.8 | 111000 | 0.8152 | | 0.4792 | 6.86 | 112000 | 0.8089 | | 0.4785 | 6.92 | 113000 | 0.8084 | | 0.4777 | 6.99 | 114000 | 0.8103 | | 0.477 | 7.05 | 115000 | 0.8104 | | 0.4772 | 7.11 | 116000 | 0.8142 | | 0.4754 | 7.17 | 117000 | 0.8159 | | 0.4748 | 7.23 | 118000 | 0.8092 | | 0.4738 | 7.29 | 119000 | 0.8036 | | 0.473 | 7.35 | 120000 | 0.8085 | | 0.4724 | 7.41 | 121000 | 0.8084 | | 0.4714 | 7.48 | 122000 | 0.8066 | | 0.4705 | 7.54 | 123000 | 0.8094 | | 0.4699 | 7.6 | 124000 | 0.8095 | | 0.4693 | 7.66 | 125000 | 0.8101 | | 0.4685 | 7.72 | 126000 | 0.8092 | | 0.4679 | 7.78 | 127000 | 0.8025 | | 0.4672 | 7.84 | 128000 | 0.8000 | | 0.4665 | 7.9 | 129000 | 0.8020 | | 0.4659 | 7.97 | 130000 | 0.8022 | | 0.4653 | 8.03 | 131000 | 0.8071 | | 0.4647 | 8.09 | 132000 | 0.7994 | | 0.4639 | 8.15 | 133000 | 0.8034 | | 0.4634 | 8.21 | 134000 | 0.8022 | | 0.4656 | 8.27 | 135000 | 0.8052 | | 0.4623 | 8.33 | 136000 | 0.7989 | | 0.4617 | 8.39 | 137000 | 0.7993 | | 0.4612 | 8.46 | 138000 | 0.8003 | | 0.4608 | 8.52 | 139000 | 0.7990 | | 0.4603 | 8.58 | 140000 | 0.8074 | | 0.4597 | 8.64 | 141000 | 0.8089 | | 0.4591 | 8.7 | 142000 | 0.8040 | | 0.4586 | 8.76 | 143000 | 0.7993 | | 0.4584 | 8.82 | 144000 | 0.8004 | | 0.4594 | 8.88 | 145000 | 0.7991 | | 0.4574 | 8.95 | 146000 | 0.7956 | | 0.4571 | 9.01 | 147000 | 0.7948 | | 0.4565 | 9.07 | 148000 | 0.7982 | | 0.4563 | 9.13 | 149000 | 0.7960 | | 0.4555 | 9.19 | 150000 | 0.8043 | | 0.4551 | 9.25 | 151000 | 0.8021 | | 0.4549 | 9.31 | 152000 | 0.7972 | | 0.4545 | 9.38 | 153000 | 0.8003 | | 0.4542 | 9.44 | 154000 | 0.8000 | | 0.4539 | 9.5 | 155000 | 0.7960 | | 0.4533 | 9.56 | 156000 | 0.8035 | | 0.453 | 9.62 | 157000 | 0.7953 | | 0.4527 | 9.68 | 158000 | 0.7937 | | 0.4524 | 9.74 | 159000 | 0.8021 | | 0.4519 | 9.8 | 160000 | 0.8028 | | 0.4517 | 9.87 | 161000 | 0.8006 | | 0.4514 | 9.93 | 162000 | 0.8067 | | 0.4512 | 9.99 | 163000 | 0.7990 | | 0.4508 | 10.05 | 164000 | 0.8041 | | 0.4504 | 10.11 | 165000 | 0.7995 | | 0.4501 | 10.17 | 166000 | 0.7979 | | 0.4499 | 10.23 | 167000 | 0.7969 | | 0.4497 | 10.29 | 168000 | 0.8041 | | 0.4495 | 10.36 | 169000 | 0.8050 | | 0.4492 | 10.42 | 170000 | 0.7999 | | 0.4494 | 10.48 | 171000 | 0.7992 | | 0.4486 | 10.54 | 172000 | 0.8019 | | 0.4485 | 10.6 | 173000 | 0.8026 | | 0.4483 | 10.66 | 174000 | 0.8009 | | 0.448 | 10.72 | 175000 | 0.8022 | | 0.4479 | 10.78 | 176000 | 0.8016 | | 0.4476 | 10.85 | 177000 | 0.7988 | | 0.4474 | 10.91 | 178000 | 0.8025 | | 0.4471 | 10.97 | 179000 | 0.8035 | | 0.4471 | 11.03 | 180000 | 0.7983 | | 0.4467 | 11.09 | 181000 | 0.8010 | | 0.4463 | 11.15 | 182000 | 0.8035 | | 0.4463 | 11.21 | 183000 | 0.8049 | | 0.4462 | 11.27 | 184000 | 0.7998 | | 0.4459 | 11.34 | 185000 | 0.7988 | | 0.4457 | 11.4 | 186000 | 0.8064 | | 0.4456 | 11.46 | 187000 | 0.8042 | | 0.4454 | 11.52 | 188000 | 0.7998 | | 0.4453 | 11.58 | 189000 | 0.8026 | | 0.4449 | 11.64 | 190000 | 0.7993 | | 0.4448 | 11.7 | 191000 | 0.8037 | | 0.4448 | 11.76 | 192000 | 0.8038 | | 0.4445 | 11.83 | 193000 | 0.8010 | | 0.4442 | 11.89 | 194000 | 0.7977 | | 0.4443 | 11.95 | 195000 | 0.8008 | | 0.4441 | 12.01 | 196000 | 0.8048 | | 0.4439 | 12.07 | 197000 | 0.8034 | | 0.4438 | 12.13 | 198000 | 0.8052 | | 0.4437 | 12.19 | 199000 | 0.8041 | | 0.4434 | 12.25 | 200000 | 0.8001 | | 0.4434 | 12.32 | 201000 | 0.8013 | | 0.4432 | 12.38 | 202000 | 0.7987 | | 0.443 | 12.44 | 203000 | 0.7962 | | 0.443 | 12.5 | 204000 | 0.8017 | | 0.4429 | 12.56 | 205000 | 0.7996 | | 0.4428 | 12.62 | 206000 | 0.7997 | | 0.4425 | 12.68 | 207000 | 0.8017 | | 0.4424 | 12.75 | 208000 | 0.8008 | | 0.4424 | 12.81 | 209000 | 0.8052 | | 0.4422 | 12.87 | 210000 | 0.8004 | | 0.4421 | 12.93 | 211000 | 0.8023 | | 0.4421 | 12.99 | 212000 | 0.8014 | | 0.442 | 13.05 | 213000 | 0.7999 | | 0.4418 | 13.11 | 214000 | 0.8019 | | 0.4417 | 13.17 | 215000 | 0.7996 | | 0.4416 | 13.24 | 216000 | 0.8007 | | 0.4414 | 13.3 | 217000 | 0.8029 | | 0.4415 | 13.36 | 218000 | 0.7990 | | 0.4413 | 13.42 | 219000 | 0.7997 | | 0.4413 | 13.48 | 220000 | 0.7997 | | 0.4412 | 13.54 | 221000 | 0.7996 | | 0.4411 | 13.6 | 222000 | 0.8003 | | 0.4411 | 13.66 | 223000 | 0.7993 | | 0.4411 | 13.73 | 224000 | 0.8005 | | 0.4409 | 13.79 | 225000 | 0.8013 | | 0.4409 | 13.85 | 226000 | 0.8016 | | 0.4409 | 13.91 | 227000 | 0.7994 | | 0.4408 | 13.97 | 228000 | 0.8023 | | 0.4407 | 14.03 | 229000 | 0.8013 | | 0.4406 | 14.09 | 230000 | 0.8038 | | 0.4408 | 14.15 | 231000 | 0.7994 | | 0.4406 | 14.22 | 232000 | 0.8007 | | 0.4404 | 14.28 | 233000 | 0.8006 | | 0.4403 | 14.34 | 234000 | 0.7987 | | 0.4405 | 14.4 | 235000 | 0.8010 | | 0.4404 | 14.46 | 236000 | 0.7982 | | 0.4404 | 14.52 | 237000 | 0.7985 | | 0.4403 | 14.58 | 238000 | 0.8016 | | 0.4402 | 14.64 | 239000 | 0.8025 | | 0.4402 | 14.71 | 240000 | 0.8020 | | 0.4401 | 14.77 | 241000 | 0.8009 | | 0.4401 | 14.83 | 242000 | 0.8015 | | 0.4401 | 14.89 | 243000 | 0.8010 | | 0.44 | 14.95 | 244000 | 0.7996 | | 0.4402 | 15.01 | 245000 | 0.8014 | | 0.44 | 15.07 | 246000 | 0.8007 | | 0.44 | 15.13 | 247000 | 0.7984 | | 0.44 | 15.2 | 248000 | 0.8009 | | 0.4399 | 15.26 | 249000 | 0.8006 | | 0.4399 | 15.32 | 250000 | 0.8016 | ### Framework versions - Transformers 4.17.0 - Pytorch 1.11.0 - Datasets 2.1.1.dev0 - Tokenizers 0.12.1
thuyentruong/a2c-AntBulletEnv-v0
thuyentruong
2023-05-09T07:30:49Z
3
0
stable-baselines3
[ "stable-baselines3", "AntBulletEnv-v0", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-05-09T07:29:46Z
--- library_name: stable-baselines3 tags: - AntBulletEnv-v0 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: A2C results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: AntBulletEnv-v0 type: AntBulletEnv-v0 metrics: - type: mean_reward value: 1741.60 +/- 107.57 name: mean_reward verified: false --- # **A2C** Agent playing **AntBulletEnv-v0** This is a trained model of a **A2C** agent playing **AntBulletEnv-v0** 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 ... ```
xinyixiuxiu/albert-xxlarge-v2-SST2-incremental_pre_training-epoch1-3
xinyixiuxiu
2023-05-09T07:25:13Z
4
0
transformers
[ "transformers", "tf", "albert", "text-classification", "generated_from_keras_callback", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2023-05-09T06:49:25Z
--- tags: - generated_from_keras_callback model-index: - name: xinyixiuxiu/albert-xxlarge-v2-SST2-incremental_pre_training-epoch1-3 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. --> # xinyixiuxiu/albert-xxlarge-v2-SST2-incremental_pre_training-epoch1-3 This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.0713 - Train Accuracy: 0.9771 - Validation Loss: 0.1705 - Validation Accuracy: 0.9541 - Epoch: 0 ## 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': 'Adam', 'learning_rate': 3e-06, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False} - training_precision: float32 ### Training results | Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch | |:----------:|:--------------:|:---------------:|:-------------------:|:-----:| | 0.0713 | 0.9771 | 0.1705 | 0.9541 | 0 | ### Framework versions - Transformers 4.28.1 - TensorFlow 2.7.0 - Datasets 2.10.1 - Tokenizers 0.12.1
dxli/robot_toy
dxli
2023-05-09T07:10:12Z
13
0
diffusers
[ "diffusers", "tensorboard", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "textual_inversion", "base_model:runwayml/stable-diffusion-v1-5", "base_model:adapter:runwayml/stable-diffusion-v1-5", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]
text-to-image
2023-05-09T06:26:06Z
--- license: creativeml-openrail-m base_model: runwayml/stable-diffusion-v1-5 tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - textual_inversion inference: true --- # Textual inversion text2image fine-tuning - dxli/robot_toy These are textual inversion adaption weights for runwayml/stable-diffusion-v1-5. You can find some example images in the following.
dxli/dog5
dxli
2023-05-09T07:05:13Z
21
0
diffusers
[ "diffusers", "tensorboard", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "textual_inversion", "base_model:runwayml/stable-diffusion-v1-5", "base_model:adapter:runwayml/stable-diffusion-v1-5", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]
text-to-image
2023-05-09T06:11:23Z
--- license: creativeml-openrail-m base_model: runwayml/stable-diffusion-v1-5 tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - textual_inversion inference: true --- # Textual inversion text2image fine-tuning - dxli/dog5 These are textual inversion adaption weights for runwayml/stable-diffusion-v1-5. You can find some example images in the following.
dxli/pink_sunglasses
dxli
2023-05-09T07:03:57Z
5
0
diffusers
[ "diffusers", "tensorboard", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "textual_inversion", "base_model:runwayml/stable-diffusion-v1-5", "base_model:adapter:runwayml/stable-diffusion-v1-5", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]
text-to-image
2023-05-09T06:21:03Z
--- license: creativeml-openrail-m base_model: runwayml/stable-diffusion-v1-5 tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - textual_inversion inference: true --- # Textual inversion text2image fine-tuning - dxli/pink_sunglasses These are textual inversion adaption weights for runwayml/stable-diffusion-v1-5. You can find some example images in the following.
gan11/q-FrozenLake-v1-4x4-noSlippery
gan11
2023-05-09T07:01:21Z
0
0
null
[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]
reinforcement-learning
2023-05-09T07:01:18Z
--- 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="gan11/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"]) ```
zxy1231/tm_simcse_zh_model
zxy1231
2023-05-09T06:59:09Z
1
0
sentence-transformers
[ "sentence-transformers", "pytorch", "bert", "feature-extraction", "sentence-similarity", "transformers", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]
sentence-similarity
2023-05-09T06:50:51Z
--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers --- # {MODEL_NAME} This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. <!--- Describe your model here --> ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('{MODEL_NAME}') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) # Sentences we want sentence embeddings for sentences = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}') model = AutoModel.from_pretrained('{MODEL_NAME}') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(**encoded_input) # Perform pooling. In this case, mean pooling. sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Evaluation Results <!--- Describe how your model was evaluated --> For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME}) ## Training The model was trained with the parameters: **DataLoader**: `torch.utils.data.dataloader.DataLoader` of length 313 with parameters: ``` {'batch_size': 32, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` **Loss**: `sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters: ``` {'scale': 20.0, 'similarity_fct': 'cos_sim'} ``` Parameters of the fit()-Method: ``` { "epochs": 3, "evaluation_steps": 0, "evaluator": "NoneType", "max_grad_norm": 1, "optimizer_class": "<class 'transformers.optimization.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 500, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 64, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) ) ``` ## Citing & Authors <!--- Describe where people can find more information -->
Cynthiaiii4/Text_classification_model_bbc
Cynthiaiii4
2023-05-09T06:57:06Z
5
0
transformers
[ "transformers", "pytorch", "tensorboard", "bert", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2023-05-09T06:52:56Z
--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: Text_classification_model_bbc 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. --> # Text_classification_model_bbc 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: 0.6851 - Accuracy: 0.78 ## 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 | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 100 | 0.6159 | 0.795 | | No log | 2.0 | 200 | 0.6851 | 0.78 | ### Framework versions - Transformers 4.28.1 - Pytorch 2.0.0+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3
dxli/clock
dxli
2023-05-09T06:55:26Z
5
0
diffusers
[ "diffusers", "tensorboard", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "textual_inversion", "base_model:runwayml/stable-diffusion-v1-5", "base_model:adapter:runwayml/stable-diffusion-v1-5", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]
text-to-image
2023-05-09T06:08:29Z
--- license: creativeml-openrail-m base_model: runwayml/stable-diffusion-v1-5 tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - textual_inversion inference: true --- # Textual inversion text2image fine-tuning - dxli/clock These are textual inversion adaption weights for runwayml/stable-diffusion-v1-5. You can find some example images in the following.
Renfeld/gabon
Renfeld
2023-05-09T06:53:59Z
0
0
null
[ "region:us" ]
null
2023-05-09T06:53:25Z
# ⚠️ Type of model/library unknown. # Feel free to open a Pull request # for integration of the huggingface model hub # into the corresponding library =)
xinyixiuxiu/albert-xxlarge-v2-SST2-incremental_pre_training-epoch1-2
xinyixiuxiu
2023-05-09T06:37:59Z
3
0
transformers
[ "transformers", "tf", "albert", "text-classification", "generated_from_keras_callback", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2023-05-09T06:02:09Z
--- tags: - generated_from_keras_callback model-index: - name: xinyixiuxiu/albert-xxlarge-v2-SST2-incremental_pre_training-epoch1-2 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. --> # xinyixiuxiu/albert-xxlarge-v2-SST2-incremental_pre_training-epoch1-2 This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.1049 - Train Accuracy: 0.9641 - Validation Loss: 0.1328 - Validation Accuracy: 0.9564 - Epoch: 0 ## 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': 'Adam', 'learning_rate': 3e-06, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False} - training_precision: float32 ### Training results | Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch | |:----------:|:--------------:|:---------------:|:-------------------:|:-----:| | 0.1049 | 0.9641 | 0.1328 | 0.9564 | 0 | ### Framework versions - Transformers 4.28.1 - TensorFlow 2.7.0 - Datasets 2.10.1 - Tokenizers 0.12.1
keldenl/RedPajama-INCITE-Chat-7B-v0.1-GGML
keldenl
2023-05-09T06:36:35Z
5
4
transformers
[ "transformers", "pytorch", "gpt_neox", "text-generation", "en", "dataset:togethercomputer/RedPajama-Data-1T", "dataset:OpenAssistant/oasst1", "dataset:databricks/databricks-dolly-15k", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2023-05-09T05:23:18Z
--- license: apache-2.0 language: - en datasets: - togethercomputer/RedPajama-Data-1T - OpenAssistant/oasst1 - databricks/databricks-dolly-15k widget: - text: "<human>: Write an email to my friends inviting them to come to my home on Friday for a dinner party, bring their own food to share.\n<bot>:" example_title: "Email Writing" - text: "<human>: Create a list of things to do in San Francisco\n<bot>:" example_title: "Brainstorming" inference: parameters: temperature: 0.7 top_p: 0.7 top_k: 50 max_new_tokens: 128 --- # RedPajama-INCITE-Chat-7B-v0.1 RedPajama-INCITE-Chat-7B-v0.1 was developed by Together and leaders from the open-source AI community including Ontocord.ai, ETH DS3Lab, AAI CERC, Université de Montréal, MILA - Québec AI Institute, Stanford Center for Research on Foundation Models (CRFM), Stanford Hazy Research research group and LAION. It is fine-tuned on OASST1 and Dolly2 to enhance chatting ability. ## Model Details - **Developed by**: Together Computer. - **Model type**: Language Model - **Language(s)**: English - **License**: Apache 2.0 - **Model Description**: A 6.9B parameter pretrained language model. # Quick Start Please note that the model requires `transformers` version >= 4.25.1. To prompt the chat model, use the following format: ``` <human>: [Instruction] <bot>: ``` ## GPU Inference This requires a GPU with 16GB memory. ```python import torch import transformers from transformers import AutoTokenizer, AutoModelForCausalLM MIN_TRANSFORMERS_VERSION = '4.25.1' # check transformers version assert transformers.__version__ >= MIN_TRANSFORMERS_VERSION, f'Please upgrade transformers to version {MIN_TRANSFORMERS_VERSION} or higher.' # init tokenizer = AutoTokenizer.from_pretrained("togethercomputer/RedPajama-INCITE-Chat-7B-v0.1") model = AutoModelForCausalLM.from_pretrained("togethercomputer/RedPajama-INCITE-Chat-7B-v0.1", torch_dtype=torch.float16) model = model.to('cuda:0') # infer prompt = "<human>: Who is Alan Turing?\n<bot>:" inputs = tokenizer(prompt, return_tensors='pt').to(model.device) input_length = inputs.input_ids.shape[1] outputs = model.generate( **inputs, max_new_tokens=128, do_sample=True, temperature=0.7, top_p=0.7, top_k=50, return_dict_in_generate=True ) token = outputs.sequences[0, input_length:] output_str = tokenizer.decode(token) print(output_str) """ Alan Mathison Turing (23 June 1912 7 June 1954) was an English computer scientist, mathematician, logician, cryptanalyst, philosopher, mathematician, and theoretical biologist. """ ``` ## GPU Inference in Int8 This requires a GPU with 12GB memory. To run inference with int8, please ensure you have installed accelerate and bitandbytes. You can install them with the following command: ```bash pip install accelerate pip install bitsandbytes ``` Then you can run inference with int8 as follows: ```python import torch import transformers from transformers import AutoTokenizer, AutoModelForCausalLM MIN_TRANSFORMERS_VERSION = '4.25.1' # check transformers version assert transformers.__version__ >= MIN_TRANSFORMERS_VERSION, f'Please upgrade transformers to version {MIN_TRANSFORMERS_VERSION} or higher.' # init tokenizer = AutoTokenizer.from_pretrained("togethercomputer/RedPajama-INCITE-Chat-7B-v0.1") model = AutoModelForCausalLM.from_pretrained("togethercomputer/RedPajama-INCITE-Chat-7B-v0.1", device_map='auto', torch_dtype=torch.float16, load_in_8bit=True) # infer prompt = "<human>: Who is Alan Turing?\n<bot>:" inputs = tokenizer(prompt, return_tensors='pt').to(model.device) input_length = inputs.input_ids.shape[1] outputs = model.generate( **inputs, max_new_tokens=128, do_sample=True, temperature=0.7, top_p=0.7, top_k=50, return_dict_in_generate=True ) token = outputs.sequences[0, input_length:] output_str = tokenizer.decode(token) print(output_str) """ Alan Mathison Turing (23 June 1912 – 7 June 1954) was an English computer scientist, mathematician, logician, cryptanalyst, philosopher, and theoretical biologist. """ ``` ## CPU Inference ```python import torch import transformers from transformers import AutoTokenizer, AutoModelForCausalLM MIN_TRANSFORMERS_VERSION = '4.25.1' # check transformers version assert transformers.__version__ >= MIN_TRANSFORMERS_VERSION, f'Please upgrade transformers to version {MIN_TRANSFORMERS_VERSION} or higher.' # init tokenizer = AutoTokenizer.from_pretrained("togethercomputer/RedPajama-INCITE-Chat-7B-v0.1") model = AutoModelForCausalLM.from_pretrained("togethercomputer/RedPajama-INCITE-Chat-7B-v0.1", torch_dtype=torch.bfloat16) # infer prompt = "<human>: Who is Alan Turing?\n<bot>:" inputs = tokenizer(prompt, return_tensors='pt').to(model.device) input_length = inputs.input_ids.shape[1] outputs = model.generate( **inputs, max_new_tokens=128, do_sample=True, temperature=0.7, top_p=0.7, top_k=50, return_dict_in_generate=True ) token = outputs.sequences[0, input_length:] output_str = tokenizer.decode(token) print(output_str) """ Alan Mathison Turing, OBE, FRS, (23 June 1912 – 7 June 1954) was an English computer scientist, mathematician, logician, cryptanalyst, philosopher, and theoretical biologist. """ ``` Please note that since `LayerNormKernelImpl` is not implemented in fp16 for CPU, we use `bfloat16` for CPU inference. # Uses ## Direct Use Excluded uses are described below. ### Misuse, Malicious Use, and Out-of-Scope Use It is the responsibility of the end user to ensure that the model is used in a responsible and ethical manner. #### Out-of-Scope Use `RedPajama-INCITE-Chat-7B-v0.1` is a language model and may not perform well for other use cases outside of its intended scope. For example, it may not be suitable for use in safety-critical applications or for making decisions that have a significant impact on individuals or society. It is important to consider the limitations of the model and to only use it for its intended purpose. #### Misuse and Malicious Use `RedPajama-INCITE-Chat-7B-v0.1` is designed for language modeling. Misuse of the model, such as using it to engage in illegal or unethical activities, is strictly prohibited and goes against the principles of the project. Using the model to generate content that is cruel to individuals is a misuse of this model. This includes, but is not limited to: - Generating fake news, misinformation, or propaganda - Promoting hate speech, discrimination, or violence against individuals or groups - Impersonating individuals or organizations without their consent - Engaging in cyberbullying or harassment - Defamatory content - Spamming or scamming - Sharing confidential or sensitive information without proper authorization - Violating the terms of use of the model or the data used to train it - Creating automated bots for malicious purposes such as spreading malware, phishing scams, or spamming ## Limitations `RedPajama-INCITE-Chat-7B-v0.1`, like other language models, has limitations that should be taken into consideration. For example, the model may not always provide accurate or relevant answers, particularly for questions that are complex, ambiguous, or outside of its training data. We therefore welcome contributions from individuals and organizations, and encourage collaboration towards creating a more robust and inclusive chatbot. ## Training **Training Data** Please refer to [togethercomputer/RedPajama-Data-1T](https://huggingface.co/datasets/togethercomputer/RedPajama-Data-1T) **Training Procedure** - **Hardware:** 8 A100 - **Optimizer:** Adam - **Gradient Accumulations**: 1 - **Num of Tokens:** 131M tokens - **Learning rate:** 1e-5 ## Community Join us on [Together Discord](https://discord.gg/6ZVDU8tTD4)
metarank/esci-MiniLM-L6-v2
metarank
2023-05-09T06:23:38Z
28
1
sentence-transformers
[ "sentence-transformers", "pytorch", "onnx", "bert", "feature-extraction", "sentence-similarity", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]
sentence-similarity
2023-04-03T22:13:38Z
--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity --- # metarank/esci-MiniLM-L6-v2 This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search. A [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) model fine-tuned on [Amazon ESCI dataset](https://github.com/amazon-science/esci-data). ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('metarank/esci-MiniLM-L6-v2') embeddings = model.encode(sentences) print(embeddings) ``` ## Training The model was trained with the parameters: **DataLoader**: `torch.utils.data.dataloader.DataLoader` of length 769 with parameters: ``` {'batch_size': 128, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` **Loss**: `sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters: ``` {'scale': 20.0, 'similarity_fct': 'cos_sim'} ``` Parameters of the fit()-Method: ``` { "epochs": 1, "evaluation_steps": 0, "evaluator": "NoneType", "max_grad_norm": 1, "optimizer_class": "<class 'torch.optim.adamw.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 1000, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) (2): Normalize() ) ``` ## Citing & Authors * Roman Grebennikov
Cynthiaiii4/Text_classification_bert-base-uncased
Cynthiaiii4
2023-05-09T06:18:51Z
4
0
transformers
[ "transformers", "pytorch", "tensorboard", "bert", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2023-05-09T06:14:50Z
--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: Text_classification_bert-base-uncased 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. --> # Text_classification_bert-base-uncased 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: 0.4491 - Accuracy: 0.79 ## 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 | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 100 | 0.5127 | 0.78 | | No log | 2.0 | 200 | 0.4491 | 0.79 | ### Framework versions - Transformers 4.28.1 - Pytorch 2.0.0+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3