librarian-bots/model_cards_with_metadata · Datasets at Hugging Face

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Hagidr/gemma-tatsu_lab-Instruct-Finetune-test_02	Hagidr	2024-02-29T13:26:21Z	116	0	transformers	[ "transformers", "safetensors", "gemma", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-02-29T13:22:02Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [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. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
Nandreas/q-FrozenLake-v1-4x4-noSlippery	Nandreas	2024-02-29T13:24:05Z	0	0	null	[ "Taxi-v3-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2024-02-29T13:24:02Z	--- tags: - Taxi-v3-4x4-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: Taxi-v3-4x4-no_slippery type: Taxi-v3-4x4-no_slippery metrics: - type: mean_reward value: 1.00 +/- 0.00 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="Nandreas/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"]) ```
mogmyij/yelp-model-5k-10layer-2Epoch	mogmyij	2024-02-29T13:14:23Z	106	0	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:google-bert/bert-base-uncased", "base_model:finetune:google-bert/bert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-02-29T12:53:20Z	--- license: apache-2.0 base_model: google-bert/bert-base-uncased tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: yelp-model-5k-10layer-2Epoch 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. --> # yelp-model-5k-10layer-2Epoch This model is a fine-tuned version of [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.9048 - Accuracy: 0.61 - F1: 0.6143 ## 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: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| 1.0394 \| 1.0 \| 1250 \| 0.8914 \| 0.594 \| 0.5961 \| \| 0.7221 \| 2.0 \| 2500 \| 0.9048 \| 0.61 \| 0.6143 \| ### Framework versions - Transformers 4.37.2 - Pytorch 2.1.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2
TransferGraph/connectivity_cola_6ep_ft-22-finetuned-lora-tweet_eval_sentiment	TransferGraph	2024-02-29T13:14:21Z	0	0	peft	[ "peft", "safetensors", "parquet", "text-classification", "dataset:tweet_eval", "base_model:connectivity/cola_6ep_ft-22", "base_model:adapter:connectivity/cola_6ep_ft-22", "model-index", "region:us" ]	text-classification	2024-02-29T13:14:19Z	--- library_name: peft tags: - parquet - text-classification datasets: - tweet_eval metrics: - accuracy base_model: connectivity/cola_6ep_ft-22 model-index: - name: connectivity_cola_6ep_ft-22-finetuned-lora-tweet_eval_sentiment results: - task: type: text-classification name: Text Classification dataset: name: tweet_eval type: tweet_eval config: sentiment split: validation args: sentiment metrics: - type: accuracy value: 0.7165 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. --> # connectivity_cola_6ep_ft-22-finetuned-lora-tweet_eval_sentiment This model is a fine-tuned version of [connectivity/cola_6ep_ft-22](https://huggingface.co/connectivity/cola_6ep_ft-22) on the tweet_eval dataset. It achieves the following results on the evaluation set: - accuracy: 0.7165 ## 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: 0.0004 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results \| accuracy \| train_loss \| epoch \| \|:--------:\|:----------:\|:-----:\| \| 0.339 \| None \| 0 \| \| 0.692 \| 0.7477 \| 0 \| \| 0.708 \| 0.6590 \| 1 \| \| 0.7155 \| 0.6373 \| 2 \| \| 0.7165 \| 0.6261 \| 3 \| ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.16.1 - Tokenizers 0.15.2
TransferGraph/Jeevesh8_init_bert_ft_qqp-33-finetuned-lora-tweet_eval_sentiment	TransferGraph	2024-02-29T13:13:49Z	0	0	peft	[ "peft", "safetensors", "parquet", "text-classification", "dataset:tweet_eval", "base_model:Jeevesh8/init_bert_ft_qqp-33", "base_model:adapter:Jeevesh8/init_bert_ft_qqp-33", "model-index", "region:us" ]	text-classification	2024-02-29T13:13:47Z	--- library_name: peft tags: - parquet - text-classification datasets: - tweet_eval metrics: - accuracy base_model: Jeevesh8/init_bert_ft_qqp-33 model-index: - name: Jeevesh8_init_bert_ft_qqp-33-finetuned-lora-tweet_eval_sentiment results: - task: type: text-classification name: Text Classification dataset: name: tweet_eval type: tweet_eval config: sentiment split: validation args: sentiment metrics: - type: accuracy value: 0.7085 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. --> # Jeevesh8_init_bert_ft_qqp-33-finetuned-lora-tweet_eval_sentiment This model is a fine-tuned version of [Jeevesh8/init_bert_ft_qqp-33](https://huggingface.co/Jeevesh8/init_bert_ft_qqp-33) on the tweet_eval dataset. It achieves the following results on the evaluation set: - accuracy: 0.7085 ## 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: 0.0004 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results \| accuracy \| train_loss \| epoch \| \|:--------:\|:----------:\|:-----:\| \| 0.4065 \| None \| 0 \| \| 0.6805 \| 0.8189 \| 0 \| \| 0.7015 \| 0.6812 \| 1 \| \| 0.705 \| 0.6572 \| 2 \| \| 0.7085 \| 0.6455 \| 3 \| ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.16.1 - Tokenizers 0.15.2
TransferGraph/yukta10_finetuning-sentiment-model-3000-samples-finetuned-lora-tweet_eval_sentiment	TransferGraph	2024-02-29T13:10:51Z	1	0	peft	[ "peft", "safetensors", "parquet", "text-classification", "dataset:tweet_eval", "base_model:yukta10/finetuning-sentiment-model-3000-samples", "base_model:adapter:yukta10/finetuning-sentiment-model-3000-samples", "license:apache-2.0", "model-index", "region:us" ]	text-classification	2024-02-29T13:10:49Z	--- license: apache-2.0 library_name: peft tags: - parquet - text-classification datasets: - tweet_eval metrics: - accuracy base_model: yukta10/finetuning-sentiment-model-3000-samples model-index: - name: yukta10_finetuning-sentiment-model-3000-samples-finetuned-lora-tweet_eval_sentiment results: - task: type: text-classification name: Text Classification dataset: name: tweet_eval type: tweet_eval config: sentiment split: validation args: sentiment metrics: - type: accuracy value: 0.7195 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. --> # yukta10_finetuning-sentiment-model-3000-samples-finetuned-lora-tweet_eval_sentiment This model is a fine-tuned version of [yukta10/finetuning-sentiment-model-3000-samples](https://huggingface.co/yukta10/finetuning-sentiment-model-3000-samples) on the tweet_eval dataset. It achieves the following results on the evaluation set: - accuracy: 0.7195 ## 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: 0.0004 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results \| accuracy \| train_loss \| epoch \| \|:--------:\|:----------:\|:-----:\| \| 0.415 \| None \| 0 \| \| 0.7065 \| 0.7064 \| 0 \| \| 0.7165 \| 0.6522 \| 1 \| \| 0.703 \| 0.6282 \| 2 \| \| 0.7195 \| 0.6119 \| 3 \| ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.16.1 - Tokenizers 0.15.2
TransferGraph/riyadhctg_distilbert-base-uncased-finetuned-cola-finetuned-lora-tweet_eval_sentiment	TransferGraph	2024-02-29T13:10:23Z	0	0	peft	[ "peft", "safetensors", "parquet", "text-classification", "dataset:tweet_eval", "base_model:riyadhctg/distilbert-base-uncased-finetuned-cola", "base_model:adapter:riyadhctg/distilbert-base-uncased-finetuned-cola", "license:apache-2.0", "model-index", "region:us" ]	text-classification	2024-02-29T13:10:17Z	--- license: apache-2.0 library_name: peft tags: - parquet - text-classification datasets: - tweet_eval metrics: - accuracy base_model: riyadhctg/distilbert-base-uncased-finetuned-cola model-index: - name: riyadhctg_distilbert-base-uncased-finetuned-cola-finetuned-lora-tweet_eval_sentiment results: - task: type: text-classification name: Text Classification dataset: name: tweet_eval type: tweet_eval config: sentiment split: validation args: sentiment metrics: - type: accuracy value: 0.709 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. --> # riyadhctg_distilbert-base-uncased-finetuned-cola-finetuned-lora-tweet_eval_sentiment This model is a fine-tuned version of [riyadhctg/distilbert-base-uncased-finetuned-cola](https://huggingface.co/riyadhctg/distilbert-base-uncased-finetuned-cola) on the tweet_eval dataset. It achieves the following results on the evaluation set: - accuracy: 0.709 ## 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: 0.0004 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results \| accuracy \| train_loss \| epoch \| \|:--------:\|:----------:\|:-----:\| \| 0.209 \| None \| 0 \| \| 0.6985 \| 0.7312 \| 0 \| \| 0.6995 \| 0.6561 \| 1 \| \| 0.702 \| 0.6327 \| 2 \| \| 0.709 \| 0.6149 \| 3 \| ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.16.1 - Tokenizers 0.15.2
buelfhood/SOCO_Adapter_Java_LoRA_0	buelfhood	2024-02-29T13:10:05Z	1	0	adapter-transformers	[ "adapter-transformers", "roberta", "dataset:SOCO", "region:us" ]	null	2024-02-29T13:06:15Z	--- tags: - roberta - adapter-transformers datasets: - SOCO --- # Adapter `buelfhood/SOCO_Adapter_Java_LoRA` for huggingface/CodeBERTa-small-v1 An [adapter](https://adapterhub.ml) for the `huggingface/CodeBERTa-small-v1` model that was trained on the [SOCO](https://huggingface.co/datasets/SOCO/) dataset and includes a prediction head for classification. This adapter was created for usage with the [Adapters](https://github.com/Adapter-Hub/adapters) library. ## Usage First, install `adapters`: ``` pip install -U adapters ``` Now, the adapter can be loaded and activated like this: ```python from adapters import AutoAdapterModel model = AutoAdapterModel.from_pretrained("huggingface/CodeBERTa-small-v1") adapter_name = model.load_adapter("buelfhood/SOCO_Adapter_Java_LoRA", source="hf", set_active=True) ``` ## Architecture & Training max_length=256 learning_rate=5e-4 epochs=10 batch_size=30 LoRAConfig(r=8, alpha=8) ## Architecture & Training <!-- Add some description here --> ## Evaluation results <!-- Add some description here --> ## Citation <!-- Add some description here -->
TransferGraph/heranm_finetuning-sentiment-model-3000-samples-finetuned-lora-tweet_eval_sentiment	TransferGraph	2024-02-29T13:09:55Z	0	0	peft	[ "peft", "safetensors", "parquet", "text-classification", "dataset:tweet_eval", "base_model:heranm/finetuning-sentiment-model-3000-samples", "base_model:adapter:heranm/finetuning-sentiment-model-3000-samples", "license:apache-2.0", "model-index", "region:us" ]	text-classification	2024-02-29T13:09:52Z	--- license: apache-2.0 library_name: peft tags: - parquet - text-classification datasets: - tweet_eval metrics: - accuracy base_model: heranm/finetuning-sentiment-model-3000-samples model-index: - name: heranm_finetuning-sentiment-model-3000-samples-finetuned-lora-tweet_eval_sentiment results: - task: type: text-classification name: Text Classification dataset: name: tweet_eval type: tweet_eval config: sentiment split: validation args: sentiment metrics: - type: accuracy value: 0.7185 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. --> # heranm_finetuning-sentiment-model-3000-samples-finetuned-lora-tweet_eval_sentiment This model is a fine-tuned version of [heranm/finetuning-sentiment-model-3000-samples](https://huggingface.co/heranm/finetuning-sentiment-model-3000-samples) on the tweet_eval dataset. It achieves the following results on the evaluation set: - accuracy: 0.7185 ## 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: 0.0004 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results \| accuracy \| train_loss \| epoch \| \|:--------:\|:----------:\|:-----:\| \| 0.372 \| None \| 0 \| \| 0.709 \| 0.7049 \| 0 \| \| 0.705 \| 0.6518 \| 1 \| \| 0.7065 \| 0.6283 \| 2 \| \| 0.7185 \| 0.6111 \| 3 \| ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.16.1 - Tokenizers 0.15.2
TransferGraph/jasonyim2_distilbert-base-uncased-finetuned-emotion-finetuned-lora-tweet_eval_sentiment	TransferGraph	2024-02-29T13:09:52Z	1	0	peft	[ "peft", "safetensors", "parquet", "text-classification", "dataset:tweet_eval", "base_model:jasonyim2/distilbert-base-uncased-finetuned-emotion", "base_model:adapter:jasonyim2/distilbert-base-uncased-finetuned-emotion", "license:apache-2.0", "model-index", "region:us" ]	text-classification	2024-02-29T13:09:49Z	--- license: apache-2.0 library_name: peft tags: - parquet - text-classification datasets: - tweet_eval metrics: - accuracy base_model: jasonyim2/distilbert-base-uncased-finetuned-emotion model-index: - name: jasonyim2_distilbert-base-uncased-finetuned-emotion-finetuned-lora-tweet_eval_sentiment results: - task: type: text-classification name: Text Classification dataset: name: tweet_eval type: tweet_eval config: sentiment split: validation args: sentiment metrics: - type: accuracy value: 0.7135 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. --> # jasonyim2_distilbert-base-uncased-finetuned-emotion-finetuned-lora-tweet_eval_sentiment This model is a fine-tuned version of [jasonyim2/distilbert-base-uncased-finetuned-emotion](https://huggingface.co/jasonyim2/distilbert-base-uncased-finetuned-emotion) on the tweet_eval dataset. It achieves the following results on the evaluation set: - accuracy: 0.7135 ## 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: 0.0004 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results \| accuracy \| train_loss \| epoch \| \|:--------:\|:----------:\|:-----:\| \| 0.3925 \| None \| 0 \| \| 0.6855 \| 0.7244 \| 0 \| \| 0.701 \| 0.6611 \| 1 \| \| 0.6975 \| 0.6374 \| 2 \| \| 0.7135 \| 0.6205 \| 3 \| ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.16.1 - Tokenizers 0.15.2
kbberendsen/deberta-v3-large-finetuned-cola-midterm	kbberendsen	2024-02-29T13:08:55Z	15	0	transformers	[ "transformers", "tensorboard", "safetensors", "deberta-v2", "text-classification", "generated_from_trainer", "base_model:microsoft/deberta-v3-large", "base_model:finetune:microsoft/deberta-v3-large", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-02-29T09:37:07Z	--- license: mit base_model: microsoft/deberta-v3-large tags: - generated_from_trainer metrics: - matthews_correlation model-index: - name: deberta-v3-large-finetuned-cola-midterm 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. --> # deberta-v3-large-finetuned-cola-midterm This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.6555 - Matthews Correlation: 0.7173 ## 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 \| Matthews Correlation \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------------------:\| \| 0.3739 \| 1.0 \| 535 \| 0.3250 \| 0.7041 \| \| 0.2223 \| 2.0 \| 1070 \| 0.4253 \| 0.6893 \| \| 0.1459 \| 3.0 \| 1605 \| 0.5346 \| 0.7065 \| \| 0.0878 \| 4.0 \| 2140 \| 0.6422 \| 0.7112 \| \| 0.0466 \| 5.0 \| 2675 \| 0.6555 \| 0.7173 \| ### Framework versions - Transformers 4.38.1 - Pytorch 2.1.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2
TransferGraph/dapang_distilroberta-base-mic-sym-finetuned-lora-tweet_eval_sentiment	TransferGraph	2024-02-29T13:08:54Z	0	0	peft	[ "peft", "safetensors", "parquet", "text-classification", "dataset:tweet_eval", "base_model:agi-css/distilroberta-base-mic-sym", "base_model:adapter:agi-css/distilroberta-base-mic-sym", "license:apache-2.0", "model-index", "region:us" ]	text-classification	2024-02-29T13:08:51Z	--- license: apache-2.0 library_name: peft tags: - parquet - text-classification datasets: - tweet_eval metrics: - accuracy base_model: dapang/distilroberta-base-mic-sym model-index: - name: dapang_distilroberta-base-mic-sym-finetuned-lora-tweet_eval_sentiment results: - task: type: text-classification name: Text Classification dataset: name: tweet_eval type: tweet_eval config: sentiment split: validation args: sentiment metrics: - type: accuracy value: 0.7155 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. --> # dapang_distilroberta-base-mic-sym-finetuned-lora-tweet_eval_sentiment This model is a fine-tuned version of [dapang/distilroberta-base-mic-sym](https://huggingface.co/dapang/distilroberta-base-mic-sym) on the tweet_eval dataset. It achieves the following results on the evaluation set: - accuracy: 0.7155 ## 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: 0.0004 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results \| accuracy \| train_loss \| epoch \| \|:--------:\|:----------:\|:-----:\| \| 0.1705 \| None \| 0 \| \| 0.7 \| 0.7062 \| 0 \| \| 0.713 \| 0.6484 \| 1 \| \| 0.715 \| 0.6303 \| 2 \| \| 0.7155 \| 0.6217 \| 3 \| ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.16.1 - Tokenizers 0.15.2
TransferGraph/PrasunMishra_finetuning-sentiment-model-3000-samples-finetuned-lora-tweet_eval_sentiment	TransferGraph	2024-02-29T13:08:54Z	1	0	peft	[ "peft", "safetensors", "parquet", "text-classification", "dataset:tweet_eval", "base_model:PrasunMishra/finetuning-sentiment-model-3000-samples", "base_model:adapter:PrasunMishra/finetuning-sentiment-model-3000-samples", "license:apache-2.0", "model-index", "region:us" ]	text-classification	2024-02-29T13:08:52Z	--- license: apache-2.0 library_name: peft tags: - parquet - text-classification datasets: - tweet_eval metrics: - accuracy base_model: PrasunMishra/finetuning-sentiment-model-3000-samples model-index: - name: PrasunMishra_finetuning-sentiment-model-3000-samples-finetuned-lora-tweet_eval_sentiment results: - task: type: text-classification name: Text Classification dataset: name: tweet_eval type: tweet_eval config: sentiment split: validation args: sentiment metrics: - type: accuracy value: 0.711 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. --> # PrasunMishra_finetuning-sentiment-model-3000-samples-finetuned-lora-tweet_eval_sentiment This model is a fine-tuned version of [PrasunMishra/finetuning-sentiment-model-3000-samples](https://huggingface.co/PrasunMishra/finetuning-sentiment-model-3000-samples) on the tweet_eval dataset. It achieves the following results on the evaluation set: - accuracy: 0.711 ## 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: 0.0004 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results \| accuracy \| train_loss \| epoch \| \|:--------:\|:----------:\|:-----:\| \| 0.221 \| None \| 0 \| \| 0.707 \| 0.7083 \| 0 \| \| 0.7025 \| 0.6520 \| 1 \| \| 0.707 \| 0.6308 \| 2 \| \| 0.711 \| 0.6143 \| 3 \| ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.16.1 - Tokenizers 0.15.2
PJMixers-Archive/MV01-7B-SFT-QLoRA-run_33-perscengen-only-maskinputs	PJMixers-Archive	2024-02-29T13:08:35Z	5	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2024-01-28T09:31:22Z	--- language: - en --- https://gist.github.com/xzuyn/fe00ae8895550f3bfaddaa773e55146e
TransferGraph/moshew_bert-mini-sst2-distilled-finetuned-lora-tweet_eval_sentiment	TransferGraph	2024-02-29T13:08:19Z	0	0	peft	[ "peft", "safetensors", "parquet", "text-classification", "dataset:tweet_eval", "base_model:moshew/bert-mini-sst2-distilled", "base_model:adapter:moshew/bert-mini-sst2-distilled", "model-index", "region:us" ]	text-classification	2024-02-29T13:08:17Z	--- library_name: peft tags: - parquet - text-classification datasets: - tweet_eval metrics: - accuracy base_model: moshew/bert-mini-sst2-distilled model-index: - name: moshew_bert-mini-sst2-distilled-finetuned-lora-tweet_eval_sentiment results: - task: type: text-classification name: Text Classification dataset: name: tweet_eval type: tweet_eval config: sentiment split: validation args: sentiment metrics: - type: accuracy value: 0.6765 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. --> # moshew_bert-mini-sst2-distilled-finetuned-lora-tweet_eval_sentiment This model is a fine-tuned version of [moshew/bert-mini-sst2-distilled](https://huggingface.co/moshew/bert-mini-sst2-distilled) on the tweet_eval dataset. It achieves the following results on the evaluation set: - accuracy: 0.6765 ## 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: 0.0004 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results \| accuracy \| train_loss \| epoch \| \|:--------:\|:----------:\|:-----:\| \| 0.2375 \| None \| 0 \| \| 0.6665 \| 0.7985 \| 0 \| \| 0.672 \| 0.7376 \| 1 \| \| 0.675 \| 0.7293 \| 2 \| \| 0.6765 \| 0.7231 \| 3 \| ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.16.1 - Tokenizers 0.15.2
kkimdev/gemma7b-test-1	kkimdev	2024-02-29T13:07:14Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "gemma", "trl", "en", "base_model:unsloth/gemma-7b-it-bnb-4bit", "base_model:finetune:unsloth/gemma-7b-it-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-02-29T13:07:06Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - gemma - trl base_model: unsloth/gemma-7b-it-bnb-4bit --- # Uploaded model - Developed by: kkimdev - License: apache-2.0 - Finetuned from model : unsloth/gemma-7b-it-bnb-4bit This gemma model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
TransferGraph/philschmid_tiny-distilbert-classification-finetuned-lora-tweet_eval_sentiment	TransferGraph	2024-02-29T13:06:52Z	0	0	peft	[ "peft", "safetensors", "parquet", "text-classification", "dataset:tweet_eval", "base_model:philschmid/tiny-distilbert-classification", "base_model:adapter:philschmid/tiny-distilbert-classification", "model-index", "region:us" ]	text-classification	2024-02-29T13:06:50Z	--- library_name: peft tags: - parquet - text-classification datasets: - tweet_eval metrics: - accuracy base_model: philschmid/tiny-distilbert-classification model-index: - name: philschmid_tiny-distilbert-classification-finetuned-lora-tweet_eval_sentiment results: - task: type: text-classification name: Text Classification dataset: name: tweet_eval type: tweet_eval config: sentiment split: validation args: sentiment metrics: - type: accuracy value: 0.4345 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. --> # philschmid_tiny-distilbert-classification-finetuned-lora-tweet_eval_sentiment This model is a fine-tuned version of [philschmid/tiny-distilbert-classification](https://huggingface.co/philschmid/tiny-distilbert-classification) on the tweet_eval dataset. It achieves the following results on the evaluation set: - accuracy: 0.4345 ## 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: 0.0004 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results \| accuracy \| train_loss \| epoch \| \|:--------:\|:----------:\|:-----:\| \| 0.4095 \| None \| 0 \| \| 0.4345 \| 1.0374 \| 0 \| \| 0.4345 \| 1.0207 \| 1 \| \| 0.4345 \| 1.0183 \| 2 \| \| 0.4345 \| 1.0179 \| 3 \| ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.16.1 - Tokenizers 0.15.2
TransferGraph/saattrupdan_job-listing-relevance-model-finetuned-lora-tweet_eval_emotion	TransferGraph	2024-02-29T13:06:22Z	0	0	peft	[ "peft", "safetensors", "parquet", "text-classification", "dataset:tweet_eval", "base_model:saattrupdan/job-listing-relevance-model", "base_model:adapter:saattrupdan/job-listing-relevance-model", "license:mit", "model-index", "region:us" ]	text-classification	2024-02-29T12:49:23Z	--- license: mit library_name: peft tags: - parquet - text-classification datasets: - tweet_eval metrics: - accuracy base_model: saattrupdan/job-listing-relevance-model model-index: - name: saattrupdan_job-listing-relevance-model-finetuned-lora-tweet_eval_emotion results: - task: type: text-classification name: Text Classification dataset: name: tweet_eval type: tweet_eval config: emotion split: validation args: emotion metrics: - type: accuracy value: 0.6229946524064172 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. --> # saattrupdan_job-listing-relevance-model-finetuned-lora-tweet_eval_emotion This model is a fine-tuned version of [saattrupdan/job-listing-relevance-model](https://huggingface.co/saattrupdan/job-listing-relevance-model) on the tweet_eval dataset. It achieves the following results on the evaluation set: - accuracy: 0.6230 ## 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: 0.0004 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results \| accuracy \| train_loss \| epoch \| \|:--------:\|:----------:\|:-----:\| \| 0.4358 \| None \| 0 \| \| 0.4733 \| 1.3004 \| 0 \| \| 0.5829 \| 1.1392 \| 1 \| \| 0.6203 \| 1.0111 \| 2 \| \| 0.6230 \| 0.9552 \| 3 \| ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.16.1 - Tokenizers 0.15.2
TransferGraph/Guscode_DKbert-hatespeech-detection-finetuned-lora-tweet_eval_emotion	TransferGraph	2024-02-29T13:06:14Z	1	0	peft	[ "peft", "safetensors", "parquet", "text-classification", "dataset:tweet_eval", "base_model:Guscode/DKbert-hatespeech-detection", "base_model:adapter:Guscode/DKbert-hatespeech-detection", "license:mit", "model-index", "region:us" ]	text-classification	2024-02-29T12:53:07Z	--- license: mit library_name: peft tags: - parquet - text-classification datasets: - tweet_eval metrics: - accuracy base_model: Guscode/DKbert-hatespeech-detection model-index: - name: Guscode_DKbert-hatespeech-detection-finetuned-lora-tweet_eval_emotion results: - task: type: text-classification name: Text Classification dataset: name: tweet_eval type: tweet_eval config: emotion split: validation args: emotion metrics: - type: accuracy value: 0.48663101604278075 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. --> # Guscode_DKbert-hatespeech-detection-finetuned-lora-tweet_eval_emotion This model is a fine-tuned version of [Guscode/DKbert-hatespeech-detection](https://huggingface.co/Guscode/DKbert-hatespeech-detection) on the tweet_eval dataset. It achieves the following results on the evaluation set: - accuracy: 0.4866 ## 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: 0.0004 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results \| accuracy \| train_loss \| epoch \| \|:--------:\|:----------:\|:-----:\| \| 0.2567 \| None \| 0 \| \| 0.4465 \| 1.2842 \| 0 \| \| 0.4920 \| 1.2342 \| 1 \| \| 0.5 \| 1.1954 \| 2 \| \| 0.4866 \| 1.1742 \| 3 \| ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.16.1 - Tokenizers 0.15.2
TransferGraph/cross-encoder_ms-marco-MiniLM-L-4-v2-finetuned-lora-tweet_eval_emotion	TransferGraph	2024-02-29T13:05:47Z	0	0	peft	[ "peft", "safetensors", "parquet", "text-classification", "dataset:tweet_eval", "base_model:cross-encoder/ms-marco-MiniLM-L-4-v2", "base_model:adapter:cross-encoder/ms-marco-MiniLM-L-4-v2", "license:apache-2.0", "model-index", "region:us" ]	text-classification	2024-02-29T13:05:44Z	--- license: apache-2.0 library_name: peft tags: - parquet - text-classification datasets: - tweet_eval metrics: - accuracy base_model: cross-encoder/ms-marco-MiniLM-L-4-v2 model-index: - name: cross-encoder_ms-marco-MiniLM-L-4-v2-finetuned-lora-tweet_eval_emotion results: - task: type: text-classification name: Text Classification dataset: name: tweet_eval type: tweet_eval config: emotion split: validation args: emotion metrics: - type: accuracy value: 0.4358288770053476 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. --> # cross-encoder_ms-marco-MiniLM-L-4-v2-finetuned-lora-tweet_eval_emotion This model is a fine-tuned version of [cross-encoder/ms-marco-MiniLM-L-4-v2](https://huggingface.co/cross-encoder/ms-marco-MiniLM-L-4-v2) on the tweet_eval dataset. It achieves the following results on the evaluation set: - accuracy: 0.4358 ## 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: 0.0004 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results \| accuracy \| train_loss \| epoch \| \|:--------:\|:----------:\|:-----:\| \| 0.2380 \| None \| 0 \| \| 0.4278 \| 1.2772 \| 0 \| \| 0.4278 \| 1.2622 \| 1 \| \| 0.4385 \| 1.2263 \| 2 \| \| 0.4358 \| 1.2005 \| 3 \| ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.16.1 - Tokenizers 0.15.2
TransferGraph/phailyoor_distilbert-base-uncased-finetuned-yahd-finetuned-lora-tweet_eval_emotion	TransferGraph	2024-02-29T13:04:48Z	1	0	peft	[ "peft", "safetensors", "parquet", "text-classification", "dataset:tweet_eval", "base_model:phailyoor/distilbert-base-uncased-finetuned-yahd", "base_model:adapter:phailyoor/distilbert-base-uncased-finetuned-yahd", "license:apache-2.0", "model-index", "region:us" ]	text-classification	2024-02-29T13:04:42Z	--- license: apache-2.0 library_name: peft tags: - parquet - text-classification datasets: - tweet_eval metrics: - accuracy base_model: phailyoor/distilbert-base-uncased-finetuned-yahd model-index: - name: phailyoor_distilbert-base-uncased-finetuned-yahd-finetuned-lora-tweet_eval_emotion results: - task: type: text-classification name: Text Classification dataset: name: tweet_eval type: tweet_eval config: emotion split: validation args: emotion metrics: - type: accuracy value: 0.6844919786096256 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. --> # phailyoor_distilbert-base-uncased-finetuned-yahd-finetuned-lora-tweet_eval_emotion This model is a fine-tuned version of [phailyoor/distilbert-base-uncased-finetuned-yahd](https://huggingface.co/phailyoor/distilbert-base-uncased-finetuned-yahd) on the tweet_eval dataset. It achieves the following results on the evaluation set: - accuracy: 0.6845 ## 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: 0.0004 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results \| accuracy \| train_loss \| epoch \| \|:--------:\|:----------:\|:-----:\| \| 0.3556 \| None \| 0 \| \| 0.5535 \| 1.2405 \| 0 \| \| 0.6283 \| 0.9864 \| 1 \| \| 0.6738 \| 0.8234 \| 2 \| \| 0.6845 \| 0.7686 \| 3 \| ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.16.1 - Tokenizers 0.15.2
TransferGraph/ncduy_roberta-imdb-sentiment-analysis-finetuned-lora-tweet_eval_emotion	TransferGraph	2024-02-29T13:03:39Z	4	0	peft	[ "peft", "safetensors", "parquet", "text-classification", "dataset:tweet_eval", "base_model:ncduy/roberta-imdb-sentiment-analysis", "base_model:adapter:ncduy/roberta-imdb-sentiment-analysis", "model-index", "region:us" ]	text-classification	2024-02-29T13:03:36Z	--- library_name: peft tags: - parquet - text-classification datasets: - tweet_eval metrics: - accuracy base_model: ncduy/roberta-imdb-sentiment-analysis model-index: - name: ncduy_roberta-imdb-sentiment-analysis-finetuned-lora-tweet_eval_emotion results: - task: type: text-classification name: Text Classification dataset: name: tweet_eval type: tweet_eval config: emotion split: validation args: emotion metrics: - type: accuracy value: 0.7379679144385026 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. --> # ncduy_roberta-imdb-sentiment-analysis-finetuned-lora-tweet_eval_emotion This model is a fine-tuned version of [ncduy/roberta-imdb-sentiment-analysis](https://huggingface.co/ncduy/roberta-imdb-sentiment-analysis) on the tweet_eval dataset. It achieves the following results on the evaluation set: - accuracy: 0.7380 ## 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: 0.0004 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results \| accuracy \| train_loss \| epoch \| \|:--------:\|:----------:\|:-----:\| \| 0.2594 \| None \| 0 \| \| 0.7059 \| 0.9399 \| 0 \| \| 0.7353 \| 0.6872 \| 1 \| \| 0.7380 \| 0.6120 \| 2 \| \| 0.7380 \| 0.5775 \| 3 \| ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.16.1 - Tokenizers 0.15.2
TransferGraph/moghis_distilbert-base-uncased-finetuned-emotion-finetuned-lora-tweet_eval_emotion	TransferGraph	2024-02-29T13:03:31Z	0	0	peft	[ "peft", "safetensors", "parquet", "text-classification", "dataset:tweet_eval", "base_model:moghis/distilbert-base-uncased-finetuned-emotion", "base_model:adapter:moghis/distilbert-base-uncased-finetuned-emotion", "license:apache-2.0", "model-index", "region:us" ]	text-classification	2024-02-29T13:03:26Z	--- license: apache-2.0 library_name: peft tags: - parquet - text-classification datasets: - tweet_eval metrics: - accuracy base_model: moghis/distilbert-base-uncased-finetuned-emotion model-index: - name: moghis_distilbert-base-uncased-finetuned-emotion-finetuned-lora-tweet_eval_emotion results: - task: type: text-classification name: Text Classification dataset: name: tweet_eval type: tweet_eval config: emotion split: validation args: emotion metrics: - type: accuracy value: 0.7245989304812834 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. --> # moghis_distilbert-base-uncased-finetuned-emotion-finetuned-lora-tweet_eval_emotion This model is a fine-tuned version of [moghis/distilbert-base-uncased-finetuned-emotion](https://huggingface.co/moghis/distilbert-base-uncased-finetuned-emotion) on the tweet_eval dataset. It achieves the following results on the evaluation set: - accuracy: 0.7246 ## 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: 0.0004 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results \| accuracy \| train_loss \| epoch \| \|:--------:\|:----------:\|:-----:\| \| 0.1684 \| None \| 0 \| \| 0.7005 \| 0.8896 \| 0 \| \| 0.7086 \| 0.7329 \| 1 \| \| 0.7139 \| 0.6568 \| 2 \| \| 0.7246 \| 0.6240 \| 3 \| ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.16.1 - Tokenizers 0.15.2
TransferGraph/jasonyim2_distilbert-base-uncased-finetuned-emotion-finetuned-lora-tweet_eval_emotion	TransferGraph	2024-02-29T13:03:30Z	0	0	peft	[ "peft", "safetensors", "parquet", "text-classification", "dataset:tweet_eval", "base_model:jasonyim2/distilbert-base-uncased-finetuned-emotion", "base_model:adapter:jasonyim2/distilbert-base-uncased-finetuned-emotion", "license:apache-2.0", "model-index", "region:us" ]	text-classification	2024-02-29T13:03:25Z	--- license: apache-2.0 library_name: peft tags: - parquet - text-classification datasets: - tweet_eval metrics: - accuracy base_model: jasonyim2/distilbert-base-uncased-finetuned-emotion model-index: - name: jasonyim2_distilbert-base-uncased-finetuned-emotion-finetuned-lora-tweet_eval_emotion results: - task: type: text-classification name: Text Classification dataset: name: tweet_eval type: tweet_eval config: emotion split: validation args: emotion metrics: - type: accuracy value: 0.7219251336898396 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. --> # jasonyim2_distilbert-base-uncased-finetuned-emotion-finetuned-lora-tweet_eval_emotion This model is a fine-tuned version of [jasonyim2/distilbert-base-uncased-finetuned-emotion](https://huggingface.co/jasonyim2/distilbert-base-uncased-finetuned-emotion) on the tweet_eval dataset. It achieves the following results on the evaluation set: - accuracy: 0.7219 ## 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: 0.0004 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results \| accuracy \| train_loss \| epoch \| \|:--------:\|:----------:\|:-----:\| \| 0.3529 \| None \| 0 \| \| 0.6684 \| 0.8859 \| 0 \| \| 0.6925 \| 0.7423 \| 1 \| \| 0.7059 \| 0.6689 \| 2 \| \| 0.7219 \| 0.6358 \| 3 \| ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.16.1 - Tokenizers 0.15.2
doroshroman/finetuned_sd_xl	doroshroman	2024-02-29T13:01:39Z	0	1	diffusers	[ "diffusers", "tensorboard", "text-to-image", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "lora", "template:sd-lora", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:openrail++", "region:us" ]	text-to-image	2024-02-12T14:23:09Z	--- license: openrail++ library_name: diffusers tags: - text-to-image - stable-diffusion-xl - stable-diffusion-xl-diffusers - text-to-image - diffusers - lora - template:sd-lora - text-to-image - stable-diffusion-xl - stable-diffusion-xl-diffusers - text-to-image - diffusers - lora - template:sd-lora - text-to-image - stable-diffusion-xl - stable-diffusion-xl-diffusers - text-to-image - diffusers - lora - template:sd-lora - text-to-image - stable-diffusion-xl - stable-diffusion-xl-diffusers - text-to-image - diffusers - lora - template:sd-lora - text-to-image - stable-diffusion-xl - stable-diffusion-xl-diffusers - text-to-image - diffusers - lora - template:sd-lora - text-to-image - stable-diffusion-xl - stable-diffusion-xl-diffusers - text-to-image - diffusers - lora - template:sd-lora - text-to-image - stable-diffusion-xl - stable-diffusion-xl-diffusers - text-to-image - diffusers - lora - template:sd-lora - text-to-image - stable-diffusion-xl - stable-diffusion-xl-diffusers - text-to-image - diffusers - lora - template:sd-lora - text-to-image - stable-diffusion-xl - stable-diffusion-xl-diffusers - text-to-image - diffusers - lora - template:sd-lora - text-to-image - stable-diffusion-xl - stable-diffusion-xl-diffusers - text-to-image - diffusers - lora - template:sd-lora - text-to-image - stable-diffusion-xl - stable-diffusion-xl-diffusers - text-to-image - diffusers - lora - template:sd-lora base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: a photo of guy raise money for army widget: [] --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # SDXL LoRA DreamBooth - doroshroman/finetuned_sd_xl <Gallery /> ## Model description These are doroshroman/finetuned_sd_xl LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained using [DreamBooth](https://dreambooth.github.io/). LoRA for the text encoder was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix. ## Trigger words You should use a photo of guy raise money for army to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](doroshroman/finetuned_sd_xl/tree/main) them in the Files & versions tab. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]
22x99/w2v2-ru-prre	22x99	2024-02-29T12:59:11Z	4	0	transformers	[ "transformers", "safetensors", "wav2vec2-bert", "automatic-speech-recognition", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-02-29T08:29:02Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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SKNahin/NER_Deberta55	SKNahin	2024-02-29T12:56:37Z	108	0	transformers	[ "transformers", "tensorboard", "safetensors", "deberta", "token-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2024-02-25T23:44:00Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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TransferGraph/fgaim_tiroberta-geezswitch-finetuned-lora-tweet_eval_emotion	TransferGraph	2024-02-29T12:53:41Z	0	0	peft	[ "peft", "safetensors", "parquet", "text-classification", "dataset:tweet_eval", "base_model:fgaim/tiroberta-geezswitch", "base_model:adapter:fgaim/tiroberta-geezswitch", "license:cc-by-4.0", "model-index", "region:us" ]	text-classification	2024-02-29T12:53:38Z	--- license: cc-by-4.0 library_name: peft tags: - parquet - text-classification datasets: - tweet_eval metrics: - accuracy base_model: fgaim/tiroberta-geezswitch model-index: - name: fgaim_tiroberta-geezswitch-finetuned-lora-tweet_eval_emotion results: - task: type: text-classification name: Text Classification dataset: name: tweet_eval type: tweet_eval config: emotion split: validation args: emotion metrics: - type: accuracy value: 0.45454545454545453 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. --> # fgaim_tiroberta-geezswitch-finetuned-lora-tweet_eval_emotion This model is a fine-tuned version of [fgaim/tiroberta-geezswitch](https://huggingface.co/fgaim/tiroberta-geezswitch) on the tweet_eval dataset. It achieves the following results on the evaluation set: - accuracy: 0.4545 ## 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: 0.0004 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results \| accuracy \| train_loss \| epoch \| \|:--------:\|:----------:\|:-----:\| \| 0.2487 \| None \| 0 \| \| 0.4037 \| 1.2938 \| 0 \| \| 0.4519 \| 1.2385 \| 1 \| \| 0.4545 \| 1.2156 \| 2 \| \| 0.4545 \| 1.1901 \| 3 \| ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.16.1 - Tokenizers 0.15.2
TransferGraph/aditeyabaral_finetuned-sail2017-xlm-roberta-base-finetuned-lora-tweet_eval_emotion	TransferGraph	2024-02-29T12:53:15Z	0	0	peft	[ "peft", "safetensors", "parquet", "text-classification", "dataset:tweet_eval", "base_model:aditeyabaral/finetuned-sail2017-xlm-roberta-base", "base_model:adapter:aditeyabaral/finetuned-sail2017-xlm-roberta-base", "model-index", "region:us" ]	text-classification	2024-02-29T12:53:09Z	--- library_name: peft tags: - parquet - text-classification datasets: - tweet_eval metrics: - accuracy base_model: aditeyabaral/finetuned-sail2017-xlm-roberta-base model-index: - name: aditeyabaral_finetuned-sail2017-xlm-roberta-base-finetuned-lora-tweet_eval_emotion results: - task: type: text-classification name: Text Classification dataset: name: tweet_eval type: tweet_eval config: emotion split: validation args: emotion metrics: - type: accuracy value: 0.6925133689839572 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. --> # aditeyabaral_finetuned-sail2017-xlm-roberta-base-finetuned-lora-tweet_eval_emotion This model is a fine-tuned version of [aditeyabaral/finetuned-sail2017-xlm-roberta-base](https://huggingface.co/aditeyabaral/finetuned-sail2017-xlm-roberta-base) on the tweet_eval dataset. It achieves the following results on the evaluation set: - accuracy: 0.6925 ## 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: 0.0004 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results \| accuracy \| train_loss \| epoch \| \|:--------:\|:----------:\|:-----:\| \| 0.1364 \| None \| 0 \| \| 0.6043 \| 1.0439 \| 0 \| \| 0.6738 \| 0.9083 \| 1 \| \| 0.7032 \| 0.8345 \| 2 \| \| 0.6925 \| 0.7925 \| 3 \| ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.16.1 - Tokenizers 0.15.2
TransferGraph/ASCCCCCCCC_distilbert-base-chinese-amazon_zh_20000-finetuned-lora-tweet_eval_emotion	TransferGraph	2024-02-29T12:53:11Z	7	0	peft	[ "peft", "safetensors", "parquet", "text-classification", "dataset:tweet_eval", "base_model:ASCCCCCCCC/distilbert-base-chinese-amazon_zh_20000", "base_model:adapter:ASCCCCCCCC/distilbert-base-chinese-amazon_zh_20000", "model-index", "region:us" ]	text-classification	2024-02-29T12:53:09Z	--- library_name: peft tags: - parquet - text-classification datasets: - tweet_eval metrics: - accuracy base_model: ASCCCCCCCC/distilbert-base-chinese-amazon_zh_20000 model-index: - name: ASCCCCCCCC_distilbert-base-chinese-amazon_zh_20000-finetuned-lora-tweet_eval_emotion results: - task: type: text-classification name: Text Classification dataset: name: tweet_eval type: tweet_eval config: emotion split: validation args: emotion metrics: - type: accuracy value: 0.4919786096256685 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. --> # ASCCCCCCCC_distilbert-base-chinese-amazon_zh_20000-finetuned-lora-tweet_eval_emotion This model is a fine-tuned version of [ASCCCCCCCC/distilbert-base-chinese-amazon_zh_20000](https://huggingface.co/ASCCCCCCCC/distilbert-base-chinese-amazon_zh_20000) on the tweet_eval dataset. It achieves the following results on the evaluation set: - accuracy: 0.4920 ## 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: 0.0004 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results \| accuracy \| train_loss \| epoch \| \|:--------:\|:----------:\|:-----:\| \| 0.2380 \| None \| 0 \| \| 0.4840 \| 1.2523 \| 0 \| \| 0.4973 \| 1.1999 \| 1 \| \| 0.4893 \| 1.1651 \| 2 \| \| 0.4920 \| 1.1285 \| 3 \| ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.16.1 - Tokenizers 0.15.2
TransferGraph/ChrisUPM_BioBERT_Re_trained-finetuned-lora-tweet_eval_emotion	TransferGraph	2024-02-29T12:53:09Z	1	0	peft	[ "peft", "safetensors", "parquet", "text-classification", "dataset:tweet_eval", "base_model:ChrisUPM/BioBERT_Re_trained", "base_model:adapter:ChrisUPM/BioBERT_Re_trained", "model-index", "region:us" ]	text-classification	2024-02-29T12:53:07Z	--- library_name: peft tags: - parquet - text-classification datasets: - tweet_eval metrics: - accuracy base_model: ChrisUPM/BioBERT_Re_trained model-index: - name: ChrisUPM_BioBERT_Re_trained-finetuned-lora-tweet_eval_emotion results: - task: type: text-classification name: Text Classification dataset: name: tweet_eval type: tweet_eval config: emotion split: validation args: emotion metrics: - type: accuracy value: 0.5347593582887701 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. --> # ChrisUPM_BioBERT_Re_trained-finetuned-lora-tweet_eval_emotion This model is a fine-tuned version of [ChrisUPM/BioBERT_Re_trained](https://huggingface.co/ChrisUPM/BioBERT_Re_trained) on the tweet_eval dataset. It achieves the following results on the evaluation set: - accuracy: 0.5348 ## 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: 0.0004 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results \| accuracy \| train_loss \| epoch \| \|:--------:\|:----------:\|:-----:\| \| 0.2754 \| None \| 0 \| \| 0.4332 \| 1.2735 \| 0 \| \| 0.4947 \| 1.2404 \| 1 \| \| 0.5267 \| 1.1690 \| 2 \| \| 0.5348 \| 1.1118 \| 3 \| ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.16.1 - Tokenizers 0.15.2
TransferGraph/classla_bcms-bertic-parlasent-bcs-ter-finetuned-lora-tweet_eval_emotion	TransferGraph	2024-02-29T12:53:07Z	4	0	peft	[ "peft", "safetensors", "parquet", "text-classification", "dataset:tweet_eval", "base_model:classla/bcms-bertic-parlasent-bcs-ter", "base_model:adapter:classla/bcms-bertic-parlasent-bcs-ter", "model-index", "region:us" ]	text-classification	2024-02-29T12:53:05Z	--- library_name: peft tags: - parquet - text-classification datasets: - tweet_eval metrics: - accuracy base_model: classla/bcms-bertic-parlasent-bcs-ter model-index: - name: classla_bcms-bertic-parlasent-bcs-ter-finetuned-lora-tweet_eval_emotion results: - task: type: text-classification name: Text Classification dataset: name: tweet_eval type: tweet_eval config: emotion split: validation args: emotion metrics: - type: accuracy value: 0.4946524064171123 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. --> # classla_bcms-bertic-parlasent-bcs-ter-finetuned-lora-tweet_eval_emotion This model is a fine-tuned version of [classla/bcms-bertic-parlasent-bcs-ter](https://huggingface.co/classla/bcms-bertic-parlasent-bcs-ter) on the tweet_eval dataset. It achieves the following results on the evaluation set: - accuracy: 0.4947 ## 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: 0.0004 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results \| accuracy \| train_loss \| epoch \| \|:--------:\|:----------:\|:-----:\| \| 0.1818 \| None \| 0 \| \| 0.4679 \| 1.2475 \| 0 \| \| 0.4786 \| 1.1874 \| 1 \| \| 0.4920 \| 1.1567 \| 2 \| \| 0.4947 \| 1.1286 \| 3 \| ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.16.1 - Tokenizers 0.15.2
TransferGraph/Jeevesh8_feather_berts_92-finetuned-lora-tweet_eval_emotion	TransferGraph	2024-02-29T12:52:52Z	0	0	peft	[ "peft", "safetensors", "parquet", "text-classification", "dataset:tweet_eval", "base_model:Jeevesh8/feather_berts_92", "base_model:adapter:Jeevesh8/feather_berts_92", "model-index", "region:us" ]	text-classification	2024-02-29T12:52:51Z	--- library_name: peft tags: - parquet - text-classification datasets: - tweet_eval metrics: - accuracy base_model: Jeevesh8/feather_berts_92 model-index: - name: Jeevesh8_feather_berts_92-finetuned-lora-tweet_eval_emotion results: - task: type: text-classification name: Text Classification dataset: name: tweet_eval type: tweet_eval config: emotion split: validation args: emotion metrics: - type: accuracy value: 0.7406417112299465 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. --> # Jeevesh8_feather_berts_92-finetuned-lora-tweet_eval_emotion This model is a fine-tuned version of [Jeevesh8/feather_berts_92](https://huggingface.co/Jeevesh8/feather_berts_92) on the tweet_eval dataset. It achieves the following results on the evaluation set: - accuracy: 0.7406 ## 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: 0.0004 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results \| accuracy \| train_loss \| epoch \| \|:--------:\|:----------:\|:-----:\| \| 0.4278 \| None \| 0 \| \| 0.6257 \| 1.1915 \| 0 \| \| 0.6738 \| 0.9857 \| 1 \| \| 0.7299 \| 0.8524 \| 2 \| \| 0.7406 \| 0.7986 \| 3 \| ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.16.1 - Tokenizers 0.15.2
TransferGraph/YeRyeongLee_electra-base-discriminator-finetuned-filtered-0602-finetuned-lora-tweet_eval_emotion	TransferGraph	2024-02-29T12:52:50Z	0	0	peft	[ "peft", "safetensors", "parquet", "text-classification", "dataset:tweet_eval", "base_model:YeRyeongLee/electra-base-discriminator-finetuned-filtered-0602", "base_model:adapter:YeRyeongLee/electra-base-discriminator-finetuned-filtered-0602", "license:apache-2.0", "model-index", "region:us" ]	text-classification	2024-02-29T12:52:48Z	--- license: apache-2.0 library_name: peft tags: - parquet - text-classification datasets: - tweet_eval metrics: - accuracy base_model: YeRyeongLee/electra-base-discriminator-finetuned-filtered-0602 model-index: - name: YeRyeongLee_electra-base-discriminator-finetuned-filtered-0602-finetuned-lora-tweet_eval_emotion results: - task: type: text-classification name: Text Classification dataset: name: tweet_eval type: tweet_eval config: emotion split: validation args: emotion metrics: - type: accuracy value: 0.44919786096256686 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. --> # YeRyeongLee_electra-base-discriminator-finetuned-filtered-0602-finetuned-lora-tweet_eval_emotion This model is a fine-tuned version of [YeRyeongLee/electra-base-discriminator-finetuned-filtered-0602](https://huggingface.co/YeRyeongLee/electra-base-discriminator-finetuned-filtered-0602) on the tweet_eval dataset. It achieves the following results on the evaluation set: - accuracy: 0.4492 ## 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: 0.0004 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results \| accuracy \| train_loss \| epoch \| \|:--------:\|:----------:\|:-----:\| \| 0.0775 \| None \| 0 \| \| 0.4465 \| 1.2791 \| 0 \| \| 0.4439 \| 1.2467 \| 1 \| \| 0.4545 \| 1.2331 \| 2 \| \| 0.4492 \| 1.2253 \| 3 \| ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.16.1 - Tokenizers 0.15.2
TransferGraph/M47Labs_spanish_news_classification_headlines_untrained-finetuned-lora-tweet_eval_emotion	TransferGraph	2024-02-29T12:52:42Z	0	0	peft	[ "peft", "safetensors", "parquet", "text-classification", "dataset:tweet_eval", "base_model:M47Labs/spanish_news_classification_headlines_untrained", "base_model:adapter:M47Labs/spanish_news_classification_headlines_untrained", "model-index", "region:us" ]	text-classification	2024-02-29T12:52:39Z	--- library_name: peft tags: - parquet - text-classification datasets: - tweet_eval metrics: - accuracy base_model: M47Labs/spanish_news_classification_headlines_untrained model-index: - name: M47Labs_spanish_news_classification_headlines_untrained-finetuned-lora-tweet_eval_emotion results: - task: type: text-classification name: Text Classification dataset: name: tweet_eval type: tweet_eval config: emotion split: validation args: emotion metrics: - type: accuracy value: 0.5053475935828877 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. --> # M47Labs_spanish_news_classification_headlines_untrained-finetuned-lora-tweet_eval_emotion This model is a fine-tuned version of [M47Labs/spanish_news_classification_headlines_untrained](https://huggingface.co/M47Labs/spanish_news_classification_headlines_untrained) on the tweet_eval dataset. It achieves the following results on the evaluation set: - accuracy: 0.5053 ## 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: 0.0004 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results \| accuracy \| train_loss \| epoch \| \|:--------:\|:----------:\|:-----:\| \| 0.2032 \| None \| 0 \| \| 0.4733 \| 1.2328 \| 0 \| \| 0.4973 \| 1.1826 \| 1 \| \| 0.4920 \| 1.1451 \| 2 \| \| 0.5053 \| 1.1119 \| 3 \| ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.16.1 - Tokenizers 0.15.2
TransferGraph/cardiffnlp_bertweet-base-stance-climate-finetuned-lora-tweet_eval_emotion	TransferGraph	2024-02-29T12:52:38Z	3	0	peft	[ "peft", "safetensors", "parquet", "text-classification", "dataset:tweet_eval", "base_model:cardiffnlp/bertweet-base-stance-climate", "base_model:adapter:cardiffnlp/bertweet-base-stance-climate", "model-index", "region:us" ]	text-classification	2024-02-29T12:52:37Z	--- library_name: peft tags: - parquet - text-classification datasets: - tweet_eval metrics: - accuracy base_model: cardiffnlp/bertweet-base-stance-climate model-index: - name: cardiffnlp_bertweet-base-stance-climate-finetuned-lora-tweet_eval_emotion results: - task: type: text-classification name: Text Classification dataset: name: tweet_eval type: tweet_eval config: emotion split: validation args: emotion metrics: - type: accuracy value: 0.7058823529411765 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. --> # cardiffnlp_bertweet-base-stance-climate-finetuned-lora-tweet_eval_emotion This model is a fine-tuned version of [cardiffnlp/bertweet-base-stance-climate](https://huggingface.co/cardiffnlp/bertweet-base-stance-climate) on the tweet_eval dataset. It achieves the following results on the evaluation set: - accuracy: 0.7059 ## 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: 0.0004 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results \| accuracy \| train_loss \| epoch \| \|:--------:\|:----------:\|:-----:\| \| 0.2567 \| None \| 0 \| \| 0.5160 \| 1.2144 \| 0 \| \| 0.6631 \| 0.9743 \| 1 \| \| 0.6979 \| 0.8127 \| 2 \| \| 0.7059 \| 0.7347 \| 3 \| ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.16.1 - Tokenizers 0.15.2
TransferGraph/Hate-speech-CNERG_bert-base-uncased-hatexplain-rationale-two-finetuned-lora-tweet_eval_emotion	TransferGraph	2024-02-29T12:52:34Z	4	0	peft	[ "peft", "safetensors", "parquet", "text-classification", "dataset:tweet_eval", "base_model:Hate-speech-CNERG/bert-base-uncased-hatexplain-rationale-two", "base_model:adapter:Hate-speech-CNERG/bert-base-uncased-hatexplain-rationale-two", "license:apache-2.0", "model-index", "region:us" ]	text-classification	2024-02-29T12:52:32Z	--- license: apache-2.0 library_name: peft tags: - parquet - text-classification datasets: - tweet_eval metrics: - accuracy base_model: Hate-speech-CNERG/bert-base-uncased-hatexplain-rationale-two model-index: - name: Hate-speech-CNERG_bert-base-uncased-hatexplain-rationale-two-finetuned-lora-tweet_eval_emotion results: - task: type: text-classification name: Text Classification dataset: name: tweet_eval type: tweet_eval config: emotion split: validation args: emotion metrics: - type: accuracy value: 0.7352941176470589 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. --> # Hate-speech-CNERG_bert-base-uncased-hatexplain-rationale-two-finetuned-lora-tweet_eval_emotion This model is a fine-tuned version of [Hate-speech-CNERG/bert-base-uncased-hatexplain-rationale-two](https://huggingface.co/Hate-speech-CNERG/bert-base-uncased-hatexplain-rationale-two) on the tweet_eval dataset. It achieves the following results on the evaluation set: - accuracy: 0.7353 ## 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: 0.0004 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results \| accuracy \| train_loss \| epoch \| \|:--------:\|:----------:\|:-----:\| \| 0.4037 \| None \| 0 \| \| 0.5160 \| 1.2275 \| 0 \| \| 0.6979 \| 0.9809 \| 1 \| \| 0.7193 \| 0.8033 \| 2 \| \| 0.7353 \| 0.7538 \| 3 \| ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.16.1 - Tokenizers 0.15.2
TransferGraph/boychaboy_MNLI_roberta-base-finetuned-lora-tweet_eval_emotion	TransferGraph	2024-02-29T12:52:23Z	0	0	peft	[ "peft", "safetensors", "parquet", "text-classification", "dataset:tweet_eval", "model-index", "region:us" ]	text-classification	2024-02-29T12:52:20Z	--- library_name: peft tags: - parquet - text-classification datasets: - tweet_eval metrics: - accuracy base_model: boychaboy/MNLI_roberta-base model-index: - name: boychaboy_MNLI_roberta-base-finetuned-lora-tweet_eval_emotion results: - task: type: text-classification name: Text Classification dataset: name: tweet_eval type: tweet_eval config: emotion split: validation args: emotion metrics: - type: accuracy value: 0.7700534759358288 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. --> # boychaboy_MNLI_roberta-base-finetuned-lora-tweet_eval_emotion This model is a fine-tuned version of [boychaboy/MNLI_roberta-base](https://huggingface.co/boychaboy/MNLI_roberta-base) on the tweet_eval dataset. It achieves the following results on the evaluation set: - accuracy: 0.7701 ## 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: 0.0004 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results \| accuracy \| train_loss \| epoch \| \|:--------:\|:----------:\|:-----:\| \| 0.3316 \| None \| 0 \| \| 0.7299 \| 0.9658 \| 0 \| \| 0.7781 \| 0.6329 \| 1 \| \| 0.7674 \| 0.5839 \| 2 \| \| 0.7701 \| 0.5558 \| 3 \| ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.16.1 - Tokenizers 0.15.2
TransferGraph/mrm8488_codebert-base-finetuned-detect-insecure-code-finetuned-lora-tweet_eval_emotion	TransferGraph	2024-02-29T12:52:23Z	1	0	peft	[ "peft", "safetensors", "parquet", "text-classification", "dataset:tweet_eval", "base_model:mrm8488/codebert-base-finetuned-detect-insecure-code", "base_model:adapter:mrm8488/codebert-base-finetuned-detect-insecure-code", "model-index", "region:us" ]	text-classification	2024-02-29T12:52:20Z	--- library_name: peft tags: - parquet - text-classification datasets: - tweet_eval metrics: - accuracy base_model: mrm8488/codebert-base-finetuned-detect-insecure-code model-index: - name: mrm8488_codebert-base-finetuned-detect-insecure-code-finetuned-lora-tweet_eval_emotion results: - task: type: text-classification name: Text Classification dataset: name: tweet_eval type: tweet_eval config: emotion split: validation args: emotion metrics: - type: accuracy value: 0.6256684491978609 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. --> # mrm8488_codebert-base-finetuned-detect-insecure-code-finetuned-lora-tweet_eval_emotion This model is a fine-tuned version of [mrm8488/codebert-base-finetuned-detect-insecure-code](https://huggingface.co/mrm8488/codebert-base-finetuned-detect-insecure-code) on the tweet_eval dataset. It achieves the following results on the evaluation set: - accuracy: 0.6257 ## 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: 0.0004 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results \| accuracy \| train_loss \| epoch \| \|:--------:\|:----------:\|:-----:\| \| 0.1417 \| None \| 0 \| \| 0.4893 \| 1.2212 \| 0 \| \| 0.5882 \| 1.0598 \| 1 \| \| 0.6283 \| 0.9872 \| 2 \| \| 0.6257 \| 0.9374 \| 3 \| ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.16.1 - Tokenizers 0.15.2
TransferGraph/CAMeL-Lab_bert-base-arabic-camelbert-da-sentiment-finetuned-lora-tweet_eval_emotion	TransferGraph	2024-02-29T12:52:22Z	2	0	peft	[ "peft", "safetensors", "parquet", "text-classification", "dataset:tweet_eval", "base_model:CAMeL-Lab/bert-base-arabic-camelbert-da-sentiment", "base_model:adapter:CAMeL-Lab/bert-base-arabic-camelbert-da-sentiment", "license:apache-2.0", "model-index", "region:us" ]	text-classification	2024-02-29T12:52:18Z	--- license: apache-2.0 library_name: peft tags: - parquet - text-classification datasets: - tweet_eval metrics: - accuracy base_model: CAMeL-Lab/bert-base-arabic-camelbert-da-sentiment model-index: - name: CAMeL-Lab_bert-base-arabic-camelbert-da-sentiment-finetuned-lora-tweet_eval_emotion results: - task: type: text-classification name: Text Classification dataset: name: tweet_eval type: tweet_eval config: emotion split: validation args: emotion metrics: - type: accuracy value: 0.4304812834224599 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. --> # CAMeL-Lab_bert-base-arabic-camelbert-da-sentiment-finetuned-lora-tweet_eval_emotion This model is a fine-tuned version of [CAMeL-Lab/bert-base-arabic-camelbert-da-sentiment](https://huggingface.co/CAMeL-Lab/bert-base-arabic-camelbert-da-sentiment) on the tweet_eval dataset. It achieves the following results on the evaluation set: - accuracy: 0.4305 ## 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: 0.0004 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results \| accuracy \| train_loss \| epoch \| \|:--------:\|:----------:\|:-----:\| \| 0.0909 \| None \| 0 \| \| 0.4385 \| 1.2835 \| 0 \| \| 0.4278 \| 1.2635 \| 1 \| \| 0.4305 \| 1.2579 \| 2 \| \| 0.4305 \| 1.2545 \| 3 \| ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.16.1 - Tokenizers 0.15.2
TransferGraph/ishan_bert-base-uncased-mnli-finetuned-lora-tweet_eval_emotion	TransferGraph	2024-02-29T12:52:22Z	0	0	peft	[ "peft", "safetensors", "parquet", "text-classification", "dataset:tweet_eval", "base_model:ishan/bert-base-uncased-mnli", "base_model:adapter:ishan/bert-base-uncased-mnli", "model-index", "region:us" ]	text-classification	2024-02-29T12:52:17Z	--- library_name: peft tags: - parquet - text-classification datasets: - tweet_eval metrics: - accuracy base_model: ishan/bert-base-uncased-mnli model-index: - name: ishan_bert-base-uncased-mnli-finetuned-lora-tweet_eval_emotion results: - task: type: text-classification name: Text Classification dataset: name: tweet_eval type: tweet_eval config: emotion split: validation args: emotion metrics: - type: accuracy value: 0.7700534759358288 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. --> # ishan_bert-base-uncased-mnli-finetuned-lora-tweet_eval_emotion This model is a fine-tuned version of [ishan/bert-base-uncased-mnli](https://huggingface.co/ishan/bert-base-uncased-mnli) on the tweet_eval dataset. It achieves the following results on the evaluation set: - accuracy: 0.7701 ## 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: 0.0004 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results \| accuracy \| train_loss \| epoch \| \|:--------:\|:----------:\|:-----:\| \| 0.3369 \| None \| 0 \| \| 0.6230 \| 1.1865 \| 0 \| \| 0.7059 \| 0.9572 \| 1 \| \| 0.7701 \| 0.8155 \| 2 \| \| 0.7701 \| 0.7561 \| 3 \| ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.16.1 - Tokenizers 0.15.2
TransferGraph/anferico_bert-for-patents-finetuned-lora-tweet_eval_emotion	TransferGraph	2024-02-29T12:52:22Z	0	0	peft	[ "peft", "safetensors", "parquet", "text-classification", "dataset:tweet_eval", "base_model:anferico/bert-for-patents", "base_model:adapter:anferico/bert-for-patents", "license:apache-2.0", "model-index", "region:us" ]	text-classification	2024-02-29T12:52:20Z	--- license: apache-2.0 library_name: peft tags: - parquet - text-classification datasets: - tweet_eval metrics: - accuracy base_model: anferico/bert-for-patents model-index: - name: anferico_bert-for-patents-finetuned-lora-tweet_eval_emotion results: - task: type: text-classification name: Text Classification dataset: name: tweet_eval type: tweet_eval config: emotion split: validation args: emotion metrics: - type: accuracy value: 0.5614973262032086 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. --> # anferico_bert-for-patents-finetuned-lora-tweet_eval_emotion This model is a fine-tuned version of [anferico/bert-for-patents](https://huggingface.co/anferico/bert-for-patents) on the tweet_eval dataset. It achieves the following results on the evaluation set: - accuracy: 0.5615 ## 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: 0.0004 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results \| accuracy \| train_loss \| epoch \| \|:--------:\|:----------:\|:-----:\| \| 0.2701 \| None \| 0 \| \| 0.4251 \| 1.2818 \| 0 \| \| 0.5187 \| 1.1616 \| 1 \| \| 0.5401 \| 1.0477 \| 2 \| \| 0.5615 \| 0.9809 \| 3 \| ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.16.1 - Tokenizers 0.15.2
TransferGraph/aychang_bert-base-cased-trec-coarse-finetuned-lora-tweet_eval_emotion	TransferGraph	2024-02-29T12:52:13Z	1	0	peft	[ "peft", "safetensors", "parquet", "text-classification", "dataset:tweet_eval", "base_model:aychang/bert-base-cased-trec-coarse", "base_model:adapter:aychang/bert-base-cased-trec-coarse", "license:mit", "model-index", "region:us" ]	text-classification	2024-02-29T12:52:10Z	--- license: mit library_name: peft tags: - parquet - text-classification datasets: - tweet_eval metrics: - accuracy base_model: aychang/bert-base-cased-trec-coarse model-index: - name: aychang_bert-base-cased-trec-coarse-finetuned-lora-tweet_eval_emotion results: - task: type: text-classification name: Text Classification dataset: name: tweet_eval type: tweet_eval config: emotion split: validation args: emotion metrics: - type: accuracy value: 0.7406417112299465 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. --> # aychang_bert-base-cased-trec-coarse-finetuned-lora-tweet_eval_emotion This model is a fine-tuned version of [aychang/bert-base-cased-trec-coarse](https://huggingface.co/aychang/bert-base-cased-trec-coarse) on the tweet_eval dataset. It achieves the following results on the evaluation set: - accuracy: 0.7406 ## 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: 0.0004 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results \| accuracy \| train_loss \| epoch \| \|:--------:\|:----------:\|:-----:\| \| 0.2460 \| None \| 0 \| \| 0.4545 \| 1.2636 \| 0 \| \| 0.6043 \| 1.1509 \| 1 \| \| 0.7193 \| 0.9356 \| 2 \| \| 0.7406 \| 0.8091 \| 3 \| ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.16.1 - Tokenizers 0.15.2
TransferGraph/bert-large-uncased-finetuned-lora-tweet_eval_emotion	TransferGraph	2024-02-29T12:51:53Z	0	0	peft	[ "peft", "safetensors", "parquet", "text-classification", "dataset:tweet_eval", "base_model:google-bert/bert-large-uncased", "base_model:adapter:google-bert/bert-large-uncased", "license:apache-2.0", "model-index", "region:us" ]	text-classification	2024-02-29T12:51:51Z	--- license: apache-2.0 library_name: peft tags: - parquet - text-classification datasets: - tweet_eval metrics: - accuracy base_model: bert-large-uncased model-index: - name: bert-large-uncased-finetuned-lora-tweet_eval_emotion results: - task: type: text-classification name: Text Classification dataset: name: tweet_eval type: tweet_eval config: emotion split: validation args: emotion metrics: - type: accuracy value: 0.56951871657754 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. --> # bert-large-uncased-finetuned-lora-tweet_eval_emotion This model is a fine-tuned version of [bert-large-uncased](https://huggingface.co/bert-large-uncased) on the tweet_eval dataset. It achieves the following results on the evaluation set: - accuracy: 0.5695 ## 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: 0.0004 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results \| accuracy \| train_loss \| epoch \| \|:--------:\|:----------:\|:-----:\| \| 0.4171 \| None \| 0 \| \| 0.4278 \| 1.2852 \| 0 \| \| 0.4652 \| 1.2330 \| 1 \| \| 0.5588 \| 1.1183 \| 2 \| \| 0.5695 \| 1.0442 \| 3 \| ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.16.1 - Tokenizers 0.15.2
TransferGraph/matthewburke_korean_sentiment-finetuned-lora-tweet_eval_emotion	TransferGraph	2024-02-29T12:51:50Z	0	0	peft	[ "peft", "safetensors", "parquet", "text-classification", "dataset:tweet_eval", "base_model:matthewburke/korean_sentiment", "base_model:adapter:matthewburke/korean_sentiment", "model-index", "region:us" ]	text-classification	2024-02-29T12:51:48Z	--- library_name: peft tags: - parquet - text-classification datasets: - tweet_eval metrics: - accuracy base_model: matthewburke/korean_sentiment model-index: - name: matthewburke_korean_sentiment-finetuned-lora-tweet_eval_emotion results: - task: type: text-classification name: Text Classification dataset: name: tweet_eval type: tweet_eval config: emotion split: validation args: emotion metrics: - type: accuracy value: 0.4946524064171123 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. --> # matthewburke_korean_sentiment-finetuned-lora-tweet_eval_emotion This model is a fine-tuned version of [matthewburke/korean_sentiment](https://huggingface.co/matthewburke/korean_sentiment) on the tweet_eval dataset. It achieves the following results on the evaluation set: - accuracy: 0.4947 ## 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: 0.0004 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results \| accuracy \| train_loss \| epoch \| \|:--------:\|:----------:\|:-----:\| \| 0.2353 \| None \| 0 \| \| 0.4813 \| 1.2502 \| 0 \| \| 0.4305 \| 1.2180 \| 1 \| \| 0.4973 \| 1.1976 \| 2 \| \| 0.4947 \| 1.1844 \| 3 \| ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.16.1 - Tokenizers 0.15.2
TransferGraph/navteca_quora-roberta-base-finetuned-lora-tweet_eval_emotion	TransferGraph	2024-02-29T12:51:41Z	1	0	peft	[ "peft", "safetensors", "parquet", "text-classification", "dataset:tweet_eval", "base_model:navteca/quora-roberta-base", "base_model:adapter:navteca/quora-roberta-base", "license:mit", "model-index", "region:us" ]	text-classification	2024-02-29T12:51:38Z	--- license: mit library_name: peft tags: - parquet - text-classification datasets: - tweet_eval metrics: - accuracy base_model: navteca/quora-roberta-base model-index: - name: navteca_quora-roberta-base-finetuned-lora-tweet_eval_emotion results: - task: type: text-classification name: Text Classification dataset: name: tweet_eval type: tweet_eval config: emotion split: validation args: emotion metrics: - type: accuracy value: 0.5641711229946524 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. --> # navteca_quora-roberta-base-finetuned-lora-tweet_eval_emotion This model is a fine-tuned version of [navteca/quora-roberta-base](https://huggingface.co/navteca/quora-roberta-base) on the tweet_eval dataset. It achieves the following results on the evaluation set: - accuracy: 0.5642 ## 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: 0.0004 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results \| accuracy \| train_loss \| epoch \| \|:--------:\|:----------:\|:-----:\| \| 0.0722 \| None \| 0 \| \| 0.3717 \| 1.3164 \| 0 \| \| 0.4412 \| 1.2845 \| 1 \| \| 0.5428 \| 1.2363 \| 2 \| \| 0.5642 \| 1.1378 \| 3 \| ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.16.1 - Tokenizers 0.15.2
TransferGraph/morenolq_SumTO_FNS2020-finetuned-lora-tweet_eval_emotion	TransferGraph	2024-02-29T12:51:40Z	0	0	peft	[ "peft", "safetensors", "parquet", "text-classification", "dataset:tweet_eval", "base_model:morenolq/SumTO_FNS2020", "base_model:adapter:morenolq/SumTO_FNS2020", "model-index", "region:us" ]	text-classification	2024-02-29T12:51:37Z	--- library_name: peft tags: - parquet - text-classification datasets: - tweet_eval metrics: - accuracy base_model: morenolq/SumTO_FNS2020 model-index: - name: morenolq_SumTO_FNS2020-finetuned-lora-tweet_eval_emotion results: - task: type: text-classification name: Text Classification dataset: name: tweet_eval type: tweet_eval config: emotion split: validation args: emotion metrics: - type: accuracy value: 0.45187165775401067 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. --> # morenolq_SumTO_FNS2020-finetuned-lora-tweet_eval_emotion This model is a fine-tuned version of [morenolq/SumTO_FNS2020](https://huggingface.co/morenolq/SumTO_FNS2020) on the tweet_eval dataset. It achieves the following results on the evaluation set: - accuracy: 0.4519 ## 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: 0.0004 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results \| accuracy \| train_loss \| epoch \| \|:--------:\|:----------:\|:-----:\| \| 0.1818 \| None \| 0 \| \| 0.4278 \| 1.2862 \| 0 \| \| 0.4278 \| 1.2472 \| 1 \| \| 0.4572 \| 1.2178 \| 2 \| \| 0.4519 \| 1.1990 \| 3 \| ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.16.1 - Tokenizers 0.15.2
TransferGraph/michiyasunaga_LinkBERT-base-finetuned-lora-tweet_eval_emotion	TransferGraph	2024-02-29T12:51:36Z	0	0	peft	[ "peft", "safetensors", "parquet", "text-classification", "dataset:tweet_eval", "base_model:michiyasunaga/LinkBERT-base", "base_model:adapter:michiyasunaga/LinkBERT-base", "license:apache-2.0", "model-index", "region:us" ]	text-classification	2024-02-29T12:51:34Z	--- license: apache-2.0 library_name: peft tags: - parquet - text-classification datasets: - tweet_eval metrics: - accuracy base_model: michiyasunaga/LinkBERT-base model-index: - name: michiyasunaga_LinkBERT-base-finetuned-lora-tweet_eval_emotion results: - task: type: text-classification name: Text Classification dataset: name: tweet_eval type: tweet_eval config: emotion split: validation args: emotion metrics: - type: accuracy value: 0.6898395721925134 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. --> # michiyasunaga_LinkBERT-base-finetuned-lora-tweet_eval_emotion This model is a fine-tuned version of [michiyasunaga/LinkBERT-base](https://huggingface.co/michiyasunaga/LinkBERT-base) on the tweet_eval dataset. It achieves the following results on the evaluation set: - accuracy: 0.6898 ## 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: 0.0004 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results \| accuracy \| train_loss \| epoch \| \|:--------:\|:----------:\|:-----:\| \| 0.0749 \| None \| 0 \| \| 0.4278 \| 1.2771 \| 0 \| \| 0.6310 \| 1.2050 \| 1 \| \| 0.6818 \| 0.9793 \| 2 \| \| 0.6898 \| 0.8838 \| 3 \| ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.16.1 - Tokenizers 0.15.2
TransferGraph/connectivity_bert_ft_qqp-94-finetuned-lora-tweet_eval_emotion	TransferGraph	2024-02-29T12:51:35Z	0	0	peft	[ "peft", "safetensors", "parquet", "text-classification", "dataset:tweet_eval", "base_model:connectivity/bert_ft_qqp-94", "base_model:adapter:connectivity/bert_ft_qqp-94", "model-index", "region:us" ]	text-classification	2024-02-29T12:51:33Z	--- library_name: peft tags: - parquet - text-classification datasets: - tweet_eval metrics: - accuracy base_model: connectivity/bert_ft_qqp-94 model-index: - name: connectivity_bert_ft_qqp-94-finetuned-lora-tweet_eval_emotion results: - task: type: text-classification name: Text Classification dataset: name: tweet_eval type: tweet_eval config: emotion split: validation args: emotion metrics: - type: accuracy value: 0.6497326203208557 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. --> # connectivity_bert_ft_qqp-94-finetuned-lora-tweet_eval_emotion This model is a fine-tuned version of [connectivity/bert_ft_qqp-94](https://huggingface.co/connectivity/bert_ft_qqp-94) on the tweet_eval dataset. It achieves the following results on the evaluation set: - accuracy: 0.6497 ## 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: 0.0004 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results \| accuracy \| train_loss \| epoch \| \|:--------:\|:----------:\|:-----:\| \| 0.2353 \| None \| 0 \| \| 0.4465 \| 1.2773 \| 0 \| \| 0.5802 \| 1.1764 \| 1 \| \| 0.6444 \| 1.0266 \| 2 \| \| 0.6497 \| 0.9588 \| 3 \| ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.16.1 - Tokenizers 0.15.2
TransferGraph/Jeevesh8_lecun_feather_berts-7-finetuned-lora-tweet_eval_emotion	TransferGraph	2024-02-29T12:51:26Z	0	0	peft	[ "peft", "safetensors", "parquet", "text-classification", "dataset:tweet_eval", "base_model:Jeevesh8/lecun_feather_berts-7", "base_model:adapter:Jeevesh8/lecun_feather_berts-7", "model-index", "region:us" ]	text-classification	2024-02-29T12:51:23Z	--- library_name: peft tags: - parquet - text-classification datasets: - tweet_eval metrics: - accuracy base_model: Jeevesh8/lecun_feather_berts-7 model-index: - name: Jeevesh8_lecun_feather_berts-7-finetuned-lora-tweet_eval_emotion results: - task: type: text-classification name: Text Classification dataset: name: tweet_eval type: tweet_eval config: emotion split: validation args: emotion metrics: - type: accuracy value: 0.7272727272727273 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. --> # Jeevesh8_lecun_feather_berts-7-finetuned-lora-tweet_eval_emotion This model is a fine-tuned version of [Jeevesh8/lecun_feather_berts-7](https://huggingface.co/Jeevesh8/lecun_feather_berts-7) on the tweet_eval dataset. It achieves the following results on the evaluation set: - accuracy: 0.7273 ## 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: 0.0004 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results \| accuracy \| train_loss \| epoch \| \|:--------:\|:----------:\|:-----:\| \| 0.3369 \| None \| 0 \| \| 0.6070 \| 1.1667 \| 0 \| \| 0.6711 \| 0.9766 \| 1 \| \| 0.6979 \| 0.8727 \| 2 \| \| 0.7273 \| 0.8140 \| 3 \| ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.16.1 - Tokenizers 0.15.2
TransferGraph/Jeevesh8_6ep_bert_ft_cola-29-finetuned-lora-tweet_eval_emotion	TransferGraph	2024-02-29T12:51:23Z	0	0	peft	[ "peft", "safetensors", "parquet", "text-classification", "dataset:tweet_eval", "base_model:Jeevesh8/6ep_bert_ft_cola-29", "base_model:adapter:Jeevesh8/6ep_bert_ft_cola-29", "model-index", "region:us" ]	text-classification	2024-02-29T12:51:21Z	--- library_name: peft tags: - parquet - text-classification datasets: - tweet_eval metrics: - accuracy base_model: Jeevesh8/6ep_bert_ft_cola-29 model-index: - name: Jeevesh8_6ep_bert_ft_cola-29-finetuned-lora-tweet_eval_emotion results: - task: type: text-classification name: Text Classification dataset: name: tweet_eval type: tweet_eval config: emotion split: validation args: emotion metrics: - type: accuracy value: 0.7299465240641712 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. --> # Jeevesh8_6ep_bert_ft_cola-29-finetuned-lora-tweet_eval_emotion This model is a fine-tuned version of [Jeevesh8/6ep_bert_ft_cola-29](https://huggingface.co/Jeevesh8/6ep_bert_ft_cola-29) on the tweet_eval dataset. It achieves the following results on the evaluation set: - accuracy: 0.7299 ## 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: 0.0004 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results \| accuracy \| train_loss \| epoch \| \|:--------:\|:----------:\|:-----:\| \| 0.4412 \| None \| 0 \| \| 0.4358 \| 1.2650 \| 0 \| \| 0.6444 \| 1.1346 \| 1 \| \| 0.6952 \| 0.9012 \| 2 \| \| 0.7299 \| 0.8322 \| 3 \| ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.16.1 - Tokenizers 0.15.2
TransferGraph/Jeevesh8_bert_ft_cola-60-finetuned-lora-tweet_eval_emotion	TransferGraph	2024-02-29T12:51:23Z	0	0	peft	[ "peft", "safetensors", "parquet", "text-classification", "dataset:tweet_eval", "base_model:Jeevesh8/bert_ft_cola-60", "base_model:adapter:Jeevesh8/bert_ft_cola-60", "model-index", "region:us" ]	text-classification	2024-02-29T12:51:20Z	--- library_name: peft tags: - parquet - text-classification datasets: - tweet_eval metrics: - accuracy base_model: Jeevesh8/bert_ft_cola-60 model-index: - name: Jeevesh8_bert_ft_cola-60-finetuned-lora-tweet_eval_emotion results: - task: type: text-classification name: Text Classification dataset: name: tweet_eval type: tweet_eval config: emotion split: validation args: emotion metrics: - type: accuracy value: 0.6470588235294118 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. --> # Jeevesh8_bert_ft_cola-60-finetuned-lora-tweet_eval_emotion This model is a fine-tuned version of [Jeevesh8/bert_ft_cola-60](https://huggingface.co/Jeevesh8/bert_ft_cola-60) on the tweet_eval dataset. It achieves the following results on the evaluation set: - accuracy: 0.6471 ## 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: 0.0004 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results \| accuracy \| train_loss \| epoch \| \|:--------:\|:----------:\|:-----:\| \| 0.0963 \| None \| 0 \| \| 0.4412 \| 1.2649 \| 0 \| \| 0.5 \| 1.1765 \| 1 \| \| 0.6096 \| 1.0309 \| 2 \| \| 0.6471 \| 0.9186 \| 3 \| ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.16.1 - Tokenizers 0.15.2
TransferGraph/Jeevesh8_bert_ft_qqp-39-finetuned-lora-tweet_eval_emotion	TransferGraph	2024-02-29T12:51:19Z	0	0	peft	[ "peft", "safetensors", "parquet", "text-classification", "dataset:tweet_eval", "base_model:Jeevesh8/bert_ft_qqp-39", "base_model:adapter:Jeevesh8/bert_ft_qqp-39", "model-index", "region:us" ]	text-classification	2024-02-29T12:51:17Z	--- library_name: peft tags: - parquet - text-classification datasets: - tweet_eval metrics: - accuracy base_model: Jeevesh8/bert_ft_qqp-39 model-index: - name: Jeevesh8_bert_ft_qqp-39-finetuned-lora-tweet_eval_emotion results: - task: type: text-classification name: Text Classification dataset: name: tweet_eval type: tweet_eval config: emotion split: validation args: emotion metrics: - type: accuracy value: 0.6443850267379679 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. --> # Jeevesh8_bert_ft_qqp-39-finetuned-lora-tweet_eval_emotion This model is a fine-tuned version of [Jeevesh8/bert_ft_qqp-39](https://huggingface.co/Jeevesh8/bert_ft_qqp-39) on the tweet_eval dataset. It achieves the following results on the evaluation set: - accuracy: 0.6444 ## 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: 0.0004 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results \| accuracy \| train_loss \| epoch \| \|:--------:\|:----------:\|:-----:\| \| 0.3102 \| None \| 0 \| \| 0.4545 \| 1.2731 \| 0 \| \| 0.5829 \| 1.1474 \| 1 \| \| 0.6524 \| 1.0011 \| 2 \| \| 0.6444 \| 0.9448 \| 3 \| ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.16.1 - Tokenizers 0.15.2
TransferGraph/Jeevesh8_lecun_feather_berts-8-finetuned-lora-tweet_eval_emotion	TransferGraph	2024-02-29T12:51:19Z	1	0	peft	[ "peft", "safetensors", "parquet", "text-classification", "dataset:tweet_eval", "base_model:Jeevesh8/lecun_feather_berts-8", "base_model:adapter:Jeevesh8/lecun_feather_berts-8", "model-index", "region:us" ]	text-classification	2024-02-29T12:51:17Z	--- library_name: peft tags: - parquet - text-classification datasets: - tweet_eval metrics: - accuracy base_model: Jeevesh8/lecun_feather_berts-8 model-index: - name: Jeevesh8_lecun_feather_berts-8-finetuned-lora-tweet_eval_emotion results: - task: type: text-classification name: Text Classification dataset: name: tweet_eval type: tweet_eval config: emotion split: validation args: emotion metrics: - type: accuracy value: 0.6336898395721925 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. --> # Jeevesh8_lecun_feather_berts-8-finetuned-lora-tweet_eval_emotion This model is a fine-tuned version of [Jeevesh8/lecun_feather_berts-8](https://huggingface.co/Jeevesh8/lecun_feather_berts-8) on the tweet_eval dataset. It achieves the following results on the evaluation set: - accuracy: 0.6337 ## 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: 0.0004 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results \| accuracy \| train_loss \| epoch \| \|:--------:\|:----------:\|:-----:\| \| 0.2380 \| None \| 0 \| \| 0.5856 \| 1.1833 \| 0 \| \| 0.6390 \| 1.0057 \| 1 \| \| 0.6364 \| 0.9549 \| 2 \| \| 0.6337 \| 0.9384 \| 3 \| ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.16.1 - Tokenizers 0.15.2
TransferGraph/connectivity_bert_ft_qqp-1-finetuned-lora-tweet_eval_emotion	TransferGraph	2024-02-29T12:51:13Z	0	0	peft	[ "peft", "safetensors", "parquet", "text-classification", "dataset:tweet_eval", "base_model:connectivity/bert_ft_qqp-1", "base_model:adapter:connectivity/bert_ft_qqp-1", "model-index", "region:us" ]	text-classification	2024-02-29T12:51:09Z	--- library_name: peft tags: - parquet - text-classification datasets: - tweet_eval metrics: - accuracy base_model: connectivity/bert_ft_qqp-1 model-index: - name: connectivity_bert_ft_qqp-1-finetuned-lora-tweet_eval_emotion results: - task: type: text-classification name: Text Classification dataset: name: tweet_eval type: tweet_eval config: emotion split: validation args: emotion metrics: - type: accuracy value: 0.7058823529411765 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. --> # connectivity_bert_ft_qqp-1-finetuned-lora-tweet_eval_emotion This model is a fine-tuned version of [connectivity/bert_ft_qqp-1](https://huggingface.co/connectivity/bert_ft_qqp-1) on the tweet_eval dataset. It achieves the following results on the evaluation set: - accuracy: 0.7059 ## 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: 0.0004 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results \| accuracy \| train_loss \| epoch \| \|:--------:\|:----------:\|:-----:\| \| 0.4118 \| None \| 0 \| \| 0.4572 \| 1.2690 \| 0 \| \| 0.6364 \| 1.1587 \| 1 \| \| 0.6952 \| 0.9070 \| 2 \| \| 0.7059 \| 0.8192 \| 3 \| ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.16.1 - Tokenizers 0.15.2
TransferGraph/AnonymousSub_dummy_2-finetuned-lora-tweet_eval_emotion	TransferGraph	2024-02-29T12:50:50Z	0	0	peft	[ "peft", "safetensors", "parquet", "text-classification", "dataset:tweet_eval", "base_model:AnonymousSub/dummy_2", "base_model:adapter:AnonymousSub/dummy_2", "model-index", "region:us" ]	text-classification	2024-02-29T12:50:48Z	--- library_name: peft tags: - parquet - text-classification datasets: - tweet_eval metrics: - accuracy base_model: AnonymousSub/dummy_2 model-index: - name: AnonymousSub_dummy_2-finetuned-lora-tweet_eval_emotion results: - task: type: text-classification name: Text Classification dataset: name: tweet_eval type: tweet_eval config: emotion split: validation args: emotion metrics: - type: accuracy value: 0.553475935828877 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. --> # AnonymousSub_dummy_2-finetuned-lora-tweet_eval_emotion This model is a fine-tuned version of [AnonymousSub/dummy_2](https://huggingface.co/AnonymousSub/dummy_2) on the tweet_eval dataset. It achieves the following results on the evaluation set: - accuracy: 0.5535 ## 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: 0.0004 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results \| accuracy \| train_loss \| epoch \| \|:--------:\|:----------:\|:-----:\| \| 0.2594 \| None \| 0 \| \| 0.4599 \| 1.2515 \| 0 \| \| 0.5348 \| 1.1725 \| 1 \| \| 0.5481 \| 1.1362 \| 2 \| \| 0.5535 \| 1.1001 \| 3 \| ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.16.1 - Tokenizers 0.15.2
TransferGraph/bert-base-uncased-finetuned-lora-tweet_eval_emotion	TransferGraph	2024-02-29T12:50:49Z	0	0	peft	[ "peft", "safetensors", "parquet", "text-classification", "dataset:tweet_eval", "base_model:google-bert/bert-base-uncased", "base_model:adapter:google-bert/bert-base-uncased", "license:apache-2.0", "model-index", "region:us" ]	text-classification	2024-02-29T12:50:47Z	--- license: apache-2.0 library_name: peft tags: - parquet - text-classification datasets: - tweet_eval metrics: - accuracy base_model: bert-base-uncased model-index: - name: bert-base-uncased-finetuned-lora-tweet_eval_emotion results: - task: type: text-classification name: Text Classification dataset: name: tweet_eval type: tweet_eval config: emotion split: validation args: emotion metrics: - type: accuracy value: 0.7406417112299465 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. --> # bert-base-uncased-finetuned-lora-tweet_eval_emotion This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the tweet_eval dataset. It achieves the following results on the evaluation set: - accuracy: 0.7406 ## 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: 0.0004 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results \| accuracy \| train_loss \| epoch \| \|:--------:\|:----------:\|:-----:\| \| 0.3048 \| None \| 0 \| \| 0.4412 \| 1.2579 \| 0 \| \| 0.7193 \| 1.1064 \| 1 \| \| 0.7406 \| 0.8318 \| 2 \| \| 0.7406 \| 0.7559 \| 3 \| ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.16.1 - Tokenizers 0.15.2
TransferGraph/Nanatan_distilbert-base-uncased-finetuned-emotion-finetuned-lora-tweet_eval_emotion	TransferGraph	2024-02-29T12:50:40Z	0	0	peft	[ "peft", "safetensors", "parquet", "text-classification", "dataset:tweet_eval", "base_model:Nanatan/distilbert-base-uncased-finetuned-emotion", "base_model:adapter:Nanatan/distilbert-base-uncased-finetuned-emotion", "license:apache-2.0", "model-index", "region:us" ]	text-classification	2024-02-29T12:50:38Z	--- license: apache-2.0 library_name: peft tags: - parquet - text-classification datasets: - tweet_eval metrics: - accuracy base_model: Nanatan/distilbert-base-uncased-finetuned-emotion model-index: - name: Nanatan_distilbert-base-uncased-finetuned-emotion-finetuned-lora-tweet_eval_emotion results: - task: type: text-classification name: Text Classification dataset: name: tweet_eval type: tweet_eval config: emotion split: validation args: emotion metrics: - type: accuracy value: 0.7379679144385026 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. --> # Nanatan_distilbert-base-uncased-finetuned-emotion-finetuned-lora-tweet_eval_emotion This model is a fine-tuned version of [Nanatan/distilbert-base-uncased-finetuned-emotion](https://huggingface.co/Nanatan/distilbert-base-uncased-finetuned-emotion) on the tweet_eval dataset. It achieves the following results on the evaluation set: - accuracy: 0.7380 ## 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: 0.0004 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results \| accuracy \| train_loss \| epoch \| \|:--------:\|:----------:\|:-----:\| \| 0.3021 \| None \| 0 \| \| 0.6872 \| 0.8774 \| 0 \| \| 0.7246 \| 0.7358 \| 1 \| \| 0.7326 \| 0.6590 \| 2 \| \| 0.7380 \| 0.6254 \| 3 \| ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.16.1 - Tokenizers 0.15.2
TransferGraph/dhimskyy_wiki-bert-finetuned-lora-tweet_eval_emotion	TransferGraph	2024-02-29T12:50:33Z	0	0	peft	[ "peft", "safetensors", "parquet", "text-classification", "dataset:tweet_eval", "base_model:dhimskyy/wiki-bert", "base_model:adapter:dhimskyy/wiki-bert", "model-index", "region:us" ]	text-classification	2024-02-29T12:50:31Z	--- library_name: peft tags: - parquet - text-classification datasets: - tweet_eval metrics: - accuracy base_model: dhimskyy/wiki-bert model-index: - name: dhimskyy_wiki-bert-finetuned-lora-tweet_eval_emotion results: - task: type: text-classification name: Text Classification dataset: name: tweet_eval type: tweet_eval config: emotion split: validation args: emotion metrics: - type: accuracy value: 0.43315508021390375 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. --> # dhimskyy_wiki-bert-finetuned-lora-tweet_eval_emotion This model is a fine-tuned version of [dhimskyy/wiki-bert](https://huggingface.co/dhimskyy/wiki-bert) on the tweet_eval dataset. It achieves the following results on the evaluation set: - accuracy: 0.4332 ## 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: 0.0004 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results \| accuracy \| train_loss \| epoch \| \|:--------:\|:----------:\|:-----:\| \| 0.2353 \| None \| 0 \| \| 0.4251 \| 1.2739 \| 0 \| \| 0.4305 \| 1.2626 \| 1 \| \| 0.4278 \| 1.2564 \| 2 \| \| 0.4332 \| 1.2526 \| 3 \| ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.16.1 - Tokenizers 0.15.2
TransferGraph/roberta-base-finetuned-lora-tweet_eval_emotion	TransferGraph	2024-02-29T12:50:25Z	0	0	peft	[ "peft", "safetensors", "parquet", "text-classification", "dataset:tweet_eval", "base_model:FacebookAI/roberta-base", "base_model:adapter:FacebookAI/roberta-base", "license:mit", "model-index", "region:us" ]	text-classification	2024-02-29T12:50:22Z	--- license: mit library_name: peft tags: - parquet - text-classification datasets: - tweet_eval metrics: - accuracy base_model: roberta-base model-index: - name: roberta-base-finetuned-lora-tweet_eval_emotion results: - task: type: text-classification name: Text Classification dataset: name: tweet_eval type: tweet_eval config: emotion split: validation args: emotion metrics: - type: accuracy value: 0.7593582887700535 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. --> # roberta-base-finetuned-lora-tweet_eval_emotion This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the tweet_eval dataset. It achieves the following results on the evaluation set: - accuracy: 0.7594 ## 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: 0.0004 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results \| accuracy \| train_loss \| epoch \| \|:--------:\|:----------:\|:-----:\| \| 0.2380 \| None \| 0 \| \| 0.6818 \| 1.1695 \| 0 \| \| 0.7299 \| 0.7084 \| 1 \| \| 0.7513 \| 0.6157 \| 2 \| \| 0.7594 \| 0.5666 \| 3 \| ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.16.1 - Tokenizers 0.15.2
TransferGraph/jb2k_bert-base-multilingual-cased-language-detection-finetuned-lora-tweet_eval_emotion	TransferGraph	2024-02-29T12:50:06Z	2	0	peft	[ "peft", "safetensors", "parquet", "text-classification", "dataset:tweet_eval", "base_model:jb2k/bert-base-multilingual-cased-language-detection", "base_model:adapter:jb2k/bert-base-multilingual-cased-language-detection", "model-index", "region:us" ]	text-classification	2024-02-29T12:50:03Z	--- library_name: peft tags: - parquet - text-classification datasets: - tweet_eval metrics: - accuracy base_model: jb2k/bert-base-multilingual-cased-language-detection model-index: - name: jb2k_bert-base-multilingual-cased-language-detection-finetuned-lora-tweet_eval_emotion results: - task: type: text-classification name: Text Classification dataset: name: tweet_eval type: tweet_eval config: emotion split: validation args: emotion metrics: - type: accuracy value: 0.45187165775401067 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. --> # jb2k_bert-base-multilingual-cased-language-detection-finetuned-lora-tweet_eval_emotion This model is a fine-tuned version of [jb2k/bert-base-multilingual-cased-language-detection](https://huggingface.co/jb2k/bert-base-multilingual-cased-language-detection) on the tweet_eval dataset. It achieves the following results on the evaluation set: - accuracy: 0.4519 ## 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: 0.0004 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results \| accuracy \| train_loss \| epoch \| \|:--------:\|:----------:\|:-----:\| \| 0.2433 \| None \| 0 \| \| 0.4332 \| 1.2647 \| 0 \| \| 0.4439 \| 1.2429 \| 1 \| \| 0.4439 \| 1.2280 \| 2 \| \| 0.4519 \| 1.2111 \| 3 \| ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.16.1 - Tokenizers 0.15.2
TransferGraph/Jeevesh8_bert_ft_qqp-55-finetuned-lora-tweet_eval_emotion	TransferGraph	2024-02-29T12:50:06Z	0	0	peft	[ "peft", "safetensors", "parquet", "text-classification", "dataset:tweet_eval", "base_model:Jeevesh8/bert_ft_qqp-55", "base_model:adapter:Jeevesh8/bert_ft_qqp-55", "model-index", "region:us" ]	text-classification	2024-02-29T12:50:04Z	--- library_name: peft tags: - parquet - text-classification datasets: - tweet_eval metrics: - accuracy base_model: Jeevesh8/bert_ft_qqp-55 model-index: - name: Jeevesh8_bert_ft_qqp-55-finetuned-lora-tweet_eval_emotion results: - task: type: text-classification name: Text Classification dataset: name: tweet_eval type: tweet_eval config: emotion split: validation args: emotion metrics: - type: accuracy value: 0.5614973262032086 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. --> # Jeevesh8_bert_ft_qqp-55-finetuned-lora-tweet_eval_emotion This model is a fine-tuned version of [Jeevesh8/bert_ft_qqp-55](https://huggingface.co/Jeevesh8/bert_ft_qqp-55) on the tweet_eval dataset. It achieves the following results on the evaluation set: - accuracy: 0.5615 ## 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: 0.0004 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results \| accuracy \| train_loss \| epoch \| \|:--------:\|:----------:\|:-----:\| \| 0.2086 \| None \| 0 \| \| 0.4519 \| 1.2748 \| 0 \| \| 0.5080 \| 1.1791 \| 1 \| \| 0.5481 \| 1.0682 \| 2 \| \| 0.5615 \| 1.0189 \| 3 \| ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.16.1 - Tokenizers 0.15.2
TransferGraph/Jeevesh8_lecun_feather_berts-51-finetuned-lora-tweet_eval_emotion	TransferGraph	2024-02-29T12:50:05Z	0	0	peft	[ "peft", "safetensors", "parquet", "text-classification", "dataset:tweet_eval", "base_model:Jeevesh8/lecun_feather_berts-51", "base_model:adapter:Jeevesh8/lecun_feather_berts-51", "model-index", "region:us" ]	text-classification	2024-02-29T12:50:03Z	--- library_name: peft tags: - parquet - text-classification datasets: - tweet_eval metrics: - accuracy base_model: Jeevesh8/lecun_feather_berts-51 model-index: - name: Jeevesh8_lecun_feather_berts-51-finetuned-lora-tweet_eval_emotion results: - task: type: text-classification name: Text Classification dataset: name: tweet_eval type: tweet_eval config: emotion split: validation args: emotion metrics: - type: accuracy value: 0.713903743315508 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. --> # Jeevesh8_lecun_feather_berts-51-finetuned-lora-tweet_eval_emotion This model is a fine-tuned version of [Jeevesh8/lecun_feather_berts-51](https://huggingface.co/Jeevesh8/lecun_feather_berts-51) on the tweet_eval dataset. It achieves the following results on the evaluation set: - accuracy: 0.7139 ## 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: 0.0004 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results \| accuracy \| train_loss \| epoch \| \|:--------:\|:----------:\|:-----:\| \| 0.4278 \| None \| 0 \| \| 0.5802 \| 1.2090 \| 0 \| \| 0.6711 \| 1.0003 \| 1 \| \| 0.7086 \| 0.9109 \| 2 \| \| 0.7139 \| 0.8573 \| 3 \| ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.16.1 - Tokenizers 0.15.2
TransferGraph/distilbert-base-uncased-finetuned-lora-tweet_eval_emotion	TransferGraph	2024-02-29T12:50:05Z	1	0	peft	[ "peft", "safetensors", "parquet", "text-classification", "dataset:tweet_eval", "base_model:distilbert/distilbert-base-uncased", "base_model:adapter:distilbert/distilbert-base-uncased", "license:apache-2.0", "model-index", "region:us" ]	text-classification	2024-02-29T12:50:02Z	--- license: apache-2.0 library_name: peft tags: - parquet - text-classification datasets: - tweet_eval metrics: - accuracy base_model: distilbert-base-uncased model-index: - name: distilbert-base-uncased-finetuned-lora-tweet_eval_emotion results: - task: type: text-classification name: Text Classification dataset: name: tweet_eval type: tweet_eval config: emotion split: validation args: emotion metrics: - type: accuracy value: 0.732620320855615 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. --> # distilbert-base-uncased-finetuned-lora-tweet_eval_emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the tweet_eval dataset. It achieves the following results on the evaluation set: - accuracy: 0.7326 ## 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: 0.0004 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results \| accuracy \| train_loss \| epoch \| \|:--------:\|:----------:\|:-----:\| \| 0.2353 \| None \| 0 \| \| 0.6631 \| 1.0175 \| 0 \| \| 0.7139 \| 0.6889 \| 1 \| \| 0.7246 \| 0.6209 \| 2 \| \| 0.7326 \| 0.5840 \| 3 \| ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.16.1 - Tokenizers 0.15.2
TransferGraph/Jeevesh8_6ep_bert_ft_cola-12-finetuned-lora-tweet_eval_emotion	TransferGraph	2024-02-29T12:50:00Z	0	0	peft	[ "peft", "safetensors", "parquet", "text-classification", "dataset:tweet_eval", "base_model:Jeevesh8/6ep_bert_ft_cola-12", "base_model:adapter:Jeevesh8/6ep_bert_ft_cola-12", "model-index", "region:us" ]	text-classification	2024-02-29T12:49:55Z	--- library_name: peft tags: - parquet - text-classification datasets: - tweet_eval metrics: - accuracy base_model: Jeevesh8/6ep_bert_ft_cola-12 model-index: - name: Jeevesh8_6ep_bert_ft_cola-12-finetuned-lora-tweet_eval_emotion results: - task: type: text-classification name: Text Classification dataset: name: tweet_eval type: tweet_eval config: emotion split: validation args: emotion metrics: - type: accuracy value: 0.6443850267379679 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. --> # Jeevesh8_6ep_bert_ft_cola-12-finetuned-lora-tweet_eval_emotion This model is a fine-tuned version of [Jeevesh8/6ep_bert_ft_cola-12](https://huggingface.co/Jeevesh8/6ep_bert_ft_cola-12) on the tweet_eval dataset. It achieves the following results on the evaluation set: - accuracy: 0.6444 ## 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: 0.0004 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results \| accuracy \| train_loss \| epoch \| \|:--------:\|:----------:\|:-----:\| \| 0.3235 \| None \| 0 \| \| 0.4171 \| 1.2687 \| 0 \| \| 0.4626 \| 1.2149 \| 1 \| \| 0.6123 \| 1.0727 \| 2 \| \| 0.6444 \| 0.9374 \| 3 \| ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.16.1 - Tokenizers 0.15.2
TransferGraph/aXhyra_presentation_emotion_31415-finetuned-lora-tweet_eval_emotion	TransferGraph	2024-02-29T12:49:50Z	0	0	peft	[ "peft", "safetensors", "parquet", "text-classification", "dataset:tweet_eval", "base_model:aXhyra/presentation_emotion_31415", "base_model:adapter:aXhyra/presentation_emotion_31415", "license:apache-2.0", "model-index", "region:us" ]	text-classification	2024-02-29T12:49:46Z	--- license: apache-2.0 library_name: peft tags: - parquet - text-classification datasets: - tweet_eval metrics: - accuracy base_model: aXhyra/presentation_emotion_31415 model-index: - name: aXhyra_presentation_emotion_31415-finetuned-lora-tweet_eval_emotion results: - task: type: text-classification name: Text Classification dataset: name: tweet_eval type: tweet_eval config: emotion split: validation args: emotion metrics: - type: accuracy value: 0.7780748663101604 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. --> # aXhyra_presentation_emotion_31415-finetuned-lora-tweet_eval_emotion This model is a fine-tuned version of [aXhyra/presentation_emotion_31415](https://huggingface.co/aXhyra/presentation_emotion_31415) on the tweet_eval dataset. It achieves the following results on the evaluation set: - accuracy: 0.7781 ## 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: 0.0004 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results \| accuracy \| train_loss \| epoch \| \|:--------:\|:----------:\|:-----:\| \| 0.7781 \| None \| 0 \| \| 0.7807 \| 0.1641 \| 0 \| \| 0.7834 \| 0.1484 \| 1 \| \| 0.7834 \| 0.1305 \| 2 \| \| 0.7781 \| 0.1284 \| 3 \| ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.16.1 - Tokenizers 0.15.2
TransferGraph/JB173_distilbert-base-uncased-finetuned-emotion-finetuned-lora-tweet_eval_emotion	TransferGraph	2024-02-29T12:49:41Z	0	0	peft	[ "peft", "safetensors", "parquet", "text-classification", "dataset:tweet_eval", "base_model:JB173/distilbert-base-uncased-finetuned-emotion", "base_model:adapter:JB173/distilbert-base-uncased-finetuned-emotion", "model-index", "region:us" ]	text-classification	2024-02-29T12:49:38Z	--- library_name: peft tags: - parquet - text-classification datasets: - tweet_eval metrics: - accuracy base_model: JB173/distilbert-base-uncased-finetuned-emotion model-index: - name: JB173_distilbert-base-uncased-finetuned-emotion-finetuned-lora-tweet_eval_emotion results: - task: type: text-classification name: Text Classification dataset: name: tweet_eval type: tweet_eval config: emotion split: validation args: emotion metrics: - type: accuracy value: 0.7245989304812834 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. --> # JB173_distilbert-base-uncased-finetuned-emotion-finetuned-lora-tweet_eval_emotion This model is a fine-tuned version of [JB173/distilbert-base-uncased-finetuned-emotion](https://huggingface.co/JB173/distilbert-base-uncased-finetuned-emotion) on the tweet_eval dataset. It achieves the following results on the evaluation set: - accuracy: 0.7246 ## 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: 0.0004 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results \| accuracy \| train_loss \| epoch \| \|:--------:\|:----------:\|:-----:\| \| 0.4064 \| None \| 0 \| \| 0.6898 \| 0.8641 \| 0 \| \| 0.7246 \| 0.7247 \| 1 \| \| 0.7166 \| 0.6561 \| 2 \| \| 0.7246 \| 0.6172 \| 3 \| ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.16.1 - Tokenizers 0.15.2
TransferGraph/Jeevesh8_bert_ft_qqp-88-finetuned-lora-tweet_eval_emotion	TransferGraph	2024-02-29T12:49:36Z	0	0	peft	[ "peft", "safetensors", "parquet", "text-classification", "dataset:tweet_eval", "base_model:Jeevesh8/bert_ft_qqp-88", "base_model:adapter:Jeevesh8/bert_ft_qqp-88", "model-index", "region:us" ]	text-classification	2024-02-29T12:49:34Z	--- library_name: peft tags: - parquet - text-classification datasets: - tweet_eval metrics: - accuracy base_model: Jeevesh8/bert_ft_qqp-88 model-index: - name: Jeevesh8_bert_ft_qqp-88-finetuned-lora-tweet_eval_emotion results: - task: type: text-classification name: Text Classification dataset: name: tweet_eval type: tweet_eval config: emotion split: validation args: emotion metrics: - type: accuracy value: 0.5106951871657754 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. --> # Jeevesh8_bert_ft_qqp-88-finetuned-lora-tweet_eval_emotion This model is a fine-tuned version of [Jeevesh8/bert_ft_qqp-88](https://huggingface.co/Jeevesh8/bert_ft_qqp-88) on the tweet_eval dataset. It achieves the following results on the evaluation set: - accuracy: 0.5107 ## 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: 0.0004 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results \| accuracy \| train_loss \| epoch \| \|:--------:\|:----------:\|:-----:\| \| 0.4091 \| None \| 0 \| \| 0.4251 \| 1.2672 \| 0 \| \| 0.5080 \| 1.1863 \| 1 \| \| 0.5027 \| 1.1127 \| 2 \| \| 0.5107 \| 1.0784 \| 3 \| ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.16.1 - Tokenizers 0.15.2
TransferGraph/aXhyra_demo_sentiment_31415-finetuned-lora-tweet_eval_emotion	TransferGraph	2024-02-29T12:49:36Z	0	0	peft	[ "peft", "safetensors", "parquet", "text-classification", "dataset:tweet_eval", "base_model:aXhyra/demo_sentiment_31415", "base_model:adapter:aXhyra/demo_sentiment_31415", "license:apache-2.0", "model-index", "region:us" ]	text-classification	2024-02-29T12:49:33Z	--- license: apache-2.0 library_name: peft tags: - parquet - text-classification datasets: - tweet_eval metrics: - accuracy base_model: aXhyra/demo_sentiment_31415 model-index: - name: aXhyra_demo_sentiment_31415-finetuned-lora-tweet_eval_emotion results: - task: type: text-classification name: Text Classification dataset: name: tweet_eval type: tweet_eval config: emotion split: validation args: emotion metrics: - type: accuracy value: 0.7406417112299465 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. --> # aXhyra_demo_sentiment_31415-finetuned-lora-tweet_eval_emotion This model is a fine-tuned version of [aXhyra/demo_sentiment_31415](https://huggingface.co/aXhyra/demo_sentiment_31415) on the tweet_eval dataset. It achieves the following results on the evaluation set: - accuracy: 0.7406 ## 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: 0.0004 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results \| accuracy \| train_loss \| epoch \| \|:--------:\|:----------:\|:-----:\| \| 0.1738 \| None \| 0 \| \| 0.7086 \| 0.7749 \| 0 \| \| 0.7326 \| 0.6331 \| 1 \| \| 0.7433 \| 0.5832 \| 2 \| \| 0.7406 \| 0.5645 \| 3 \| ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.16.1 - Tokenizers 0.15.2
TransferGraph/oferweintraub_bert-base-finance-sentiment-noisy-search-finetuned-lora-tweet_eval_emotion	TransferGraph	2024-02-29T12:49:27Z	0	0	peft	[ "peft", "safetensors", "parquet", "text-classification", "dataset:tweet_eval", "base_model:oferweintraub/bert-base-finance-sentiment-noisy-search", "base_model:adapter:oferweintraub/bert-base-finance-sentiment-noisy-search", "license:apache-2.0", "model-index", "region:us" ]	text-classification	2024-02-29T12:49:25Z	--- license: apache-2.0 library_name: peft tags: - parquet - text-classification datasets: - tweet_eval metrics: - accuracy base_model: oferweintraub/bert-base-finance-sentiment-noisy-search model-index: - name: oferweintraub_bert-base-finance-sentiment-noisy-search-finetuned-lora-tweet_eval_emotion results: - task: type: text-classification name: Text Classification dataset: name: tweet_eval type: tweet_eval config: emotion split: validation args: emotion metrics: - type: accuracy value: 0.7032085561497327 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. --> # oferweintraub_bert-base-finance-sentiment-noisy-search-finetuned-lora-tweet_eval_emotion This model is a fine-tuned version of [oferweintraub/bert-base-finance-sentiment-noisy-search](https://huggingface.co/oferweintraub/bert-base-finance-sentiment-noisy-search) on the tweet_eval dataset. It achieves the following results on the evaluation set: - accuracy: 0.7032 ## 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: 0.0004 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results \| accuracy \| train_loss \| epoch \| \|:--------:\|:----------:\|:-----:\| \| 0.3396 \| None \| 0 \| \| 0.5428 \| 1.1723 \| 0 \| \| 0.6551 \| 1.0773 \| 1 \| \| 0.6925 \| 0.9860 \| 2 \| \| 0.7032 \| 0.9167 \| 3 \| ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.16.1 - Tokenizers 0.15.2
TransferGraph/aXhyra_emotion_trained_31415-finetuned-lora-tweet_eval_emotion	TransferGraph	2024-02-29T12:49:25Z	2	0	peft	[ "peft", "safetensors", "parquet", "text-classification", "dataset:tweet_eval", "base_model:aXhyra/emotion_trained_31415", "base_model:adapter:aXhyra/emotion_trained_31415", "license:apache-2.0", "model-index", "region:us" ]	text-classification	2024-02-29T12:49:23Z	--- license: apache-2.0 library_name: peft tags: - parquet - text-classification datasets: - tweet_eval metrics: - accuracy base_model: aXhyra/emotion_trained_31415 model-index: - name: aXhyra_emotion_trained_31415-finetuned-lora-tweet_eval_emotion results: - task: type: text-classification name: Text Classification dataset: name: tweet_eval type: tweet_eval config: emotion split: validation args: emotion metrics: - type: accuracy value: 0.7807486631016043 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. --> # aXhyra_emotion_trained_31415-finetuned-lora-tweet_eval_emotion This model is a fine-tuned version of [aXhyra/emotion_trained_31415](https://huggingface.co/aXhyra/emotion_trained_31415) on the tweet_eval dataset. It achieves the following results on the evaluation set: - accuracy: 0.7807 ## 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: 0.0004 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results \| accuracy \| train_loss \| epoch \| \|:--------:\|:----------:\|:-----:\| \| 0.7941 \| None \| 0 \| \| 0.7914 \| 0.0853 \| 0 \| \| 0.7888 \| 0.0652 \| 1 \| \| 0.7754 \| 0.0602 \| 2 \| \| 0.7807 \| 0.0562 \| 3 \| ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.16.1 - Tokenizers 0.15.2
TransferGraph/JNK789_distilbert-base-uncased-finetuned-tweets-emoji-dataset-finetuned-lora-tweet_eval_emotion	TransferGraph	2024-02-29T12:49:25Z	2	0	peft	[ "peft", "safetensors", "parquet", "text-classification", "dataset:tweet_eval", "base_model:JNK789/distilbert-base-uncased-finetuned-tweets-emoji-dataset", "base_model:adapter:JNK789/distilbert-base-uncased-finetuned-tweets-emoji-dataset", "model-index", "region:us" ]	text-classification	2024-02-29T12:49:22Z	--- library_name: peft tags: - parquet - text-classification datasets: - tweet_eval metrics: - accuracy base_model: JNK789/distilbert-base-uncased-finetuned-tweets-emoji-dataset model-index: - name: JNK789_distilbert-base-uncased-finetuned-tweets-emoji-dataset-finetuned-lora-tweet_eval_emotion results: - task: type: text-classification name: Text Classification dataset: name: tweet_eval type: tweet_eval config: emotion split: validation args: emotion metrics: - type: accuracy value: 0.42780748663101603 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. --> # JNK789_distilbert-base-uncased-finetuned-tweets-emoji-dataset-finetuned-lora-tweet_eval_emotion This model is a fine-tuned version of [JNK789/distilbert-base-uncased-finetuned-tweets-emoji-dataset](https://huggingface.co/JNK789/distilbert-base-uncased-finetuned-tweets-emoji-dataset) on the tweet_eval dataset. It achieves the following results on the evaluation set: - accuracy: 0.4278 ## 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: 0.0004 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results \| accuracy \| train_loss \| epoch \| \|:--------:\|:----------:\|:-----:\| \| 0.4278 \| None \| 0 \| \| 0.4278 \| 1.3804 \| 0 \| \| 0.4278 \| 1.3711 \| 1 \| \| 0.4278 \| 1.3652 \| 2 \| \| 0.4278 \| 1.3623 \| 3 \| ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.16.1 - Tokenizers 0.15.2
lvcalucioli/llamantino7b_2_question-answering_merged	lvcalucioli	2024-02-29T12:49:24Z	5	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2024-02-22T12:08:42Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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TransferGraph/Jeevesh8_bert_ft_qqp-9-finetuned-lora-tweet_eval_emotion	TransferGraph	2024-02-29T12:49:08Z	0	0	peft	[ "peft", "safetensors", "parquet", "text-classification", "dataset:tweet_eval", "base_model:Jeevesh8/bert_ft_qqp-9", "base_model:adapter:Jeevesh8/bert_ft_qqp-9", "model-index", "region:us" ]	text-classification	2024-02-29T12:49:06Z	--- library_name: peft tags: - parquet - text-classification datasets: - tweet_eval metrics: - accuracy base_model: Jeevesh8/bert_ft_qqp-9 model-index: - name: Jeevesh8_bert_ft_qqp-9-finetuned-lora-tweet_eval_emotion results: - task: type: text-classification name: Text Classification dataset: name: tweet_eval type: tweet_eval config: emotion split: validation args: emotion metrics: - type: accuracy value: 0.6336898395721925 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. --> # Jeevesh8_bert_ft_qqp-9-finetuned-lora-tweet_eval_emotion This model is a fine-tuned version of [Jeevesh8/bert_ft_qqp-9](https://huggingface.co/Jeevesh8/bert_ft_qqp-9) on the tweet_eval dataset. It achieves the following results on the evaluation set: - accuracy: 0.6337 ## 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: 0.0004 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results \| accuracy \| train_loss \| epoch \| \|:--------:\|:----------:\|:-----:\| \| 0.2647 \| None \| 0 \| \| 0.4679 \| 1.2645 \| 0 \| \| 0.5160 \| 1.1796 \| 1 \| \| 0.6283 \| 0.9874 \| 2 \| \| 0.6337 \| 0.8972 \| 3 \| ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.16.1 - Tokenizers 0.15.2
TransferGraph/strickvl_nlp-redaction-classifier-finetuned-lora-tweet_eval_emotion	TransferGraph	2024-02-29T12:49:05Z	1	0	peft	[ "peft", "safetensors", "parquet", "text-classification", "dataset:tweet_eval", "base_model:strickvl/nlp-redaction-classifier", "base_model:adapter:strickvl/nlp-redaction-classifier", "license:mit", "model-index", "region:us" ]	text-classification	2024-02-29T12:49:03Z	--- license: mit library_name: peft tags: - parquet - text-classification datasets: - tweet_eval metrics: - accuracy base_model: strickvl/nlp-redaction-classifier model-index: - name: strickvl_nlp-redaction-classifier-finetuned-lora-tweet_eval_emotion results: - task: type: text-classification name: Text Classification dataset: name: tweet_eval type: tweet_eval config: emotion split: validation args: emotion metrics: - type: accuracy value: 0.5401069518716578 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. --> # strickvl_nlp-redaction-classifier-finetuned-lora-tweet_eval_emotion This model is a fine-tuned version of [strickvl/nlp-redaction-classifier](https://huggingface.co/strickvl/nlp-redaction-classifier) on the tweet_eval dataset. It achieves the following results on the evaluation set: - accuracy: 0.5401 ## 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: 0.0004 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results \| accuracy \| train_loss \| epoch \| \|:--------:\|:----------:\|:-----:\| \| 0.4278 \| None \| 0 \| \| 0.4278 \| 1.2709 \| 0 \| \| 0.4840 \| 1.2408 \| 1 \| \| 0.5160 \| 1.1781 \| 2 \| \| 0.5401 \| 1.1320 \| 3 \| ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.16.1 - Tokenizers 0.15.2
TransferGraph/milyiyo_selectra-small-finetuned-amazon-review-finetuned-lora-tweet_eval_emotion	TransferGraph	2024-02-29T12:48:57Z	1	0	peft	[ "peft", "safetensors", "parquet", "text-classification", "dataset:tweet_eval", "base_model:milyiyo/selectra-small-finetuned-amazon-review", "base_model:adapter:milyiyo/selectra-small-finetuned-amazon-review", "license:apache-2.0", "model-index", "region:us" ]	text-classification	2024-02-29T12:48:55Z	--- license: apache-2.0 library_name: peft tags: - parquet - text-classification datasets: - tweet_eval metrics: - accuracy base_model: milyiyo/selectra-small-finetuned-amazon-review model-index: - name: milyiyo_selectra-small-finetuned-amazon-review-finetuned-lora-tweet_eval_emotion results: - task: type: text-classification name: Text Classification dataset: name: tweet_eval type: tweet_eval config: emotion split: validation args: emotion metrics: - type: accuracy value: 0.47593582887700536 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. --> # milyiyo_selectra-small-finetuned-amazon-review-finetuned-lora-tweet_eval_emotion This model is a fine-tuned version of [milyiyo/selectra-small-finetuned-amazon-review](https://huggingface.co/milyiyo/selectra-small-finetuned-amazon-review) on the tweet_eval dataset. It achieves the following results on the evaluation set: - accuracy: 0.4759 ## 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: 0.0004 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results \| accuracy \| train_loss \| epoch \| \|:--------:\|:----------:\|:-----:\| \| 0.2380 \| None \| 0 \| \| 0.4840 \| 1.2635 \| 0 \| \| 0.5027 \| 1.2380 \| 1 \| \| 0.4626 \| 1.2238 \| 2 \| \| 0.4759 \| 1.2154 \| 3 \| ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.16.1 - Tokenizers 0.15.2
TransferGraph/chiragasarpota_scotus-bert-finetuned-lora-tweet_eval_emotion	TransferGraph	2024-02-29T12:48:56Z	0	0	peft	[ "peft", "safetensors", "parquet", "text-classification", "dataset:tweet_eval", "base_model:chiragasarpota/scotus-bert", "base_model:adapter:chiragasarpota/scotus-bert", "license:apache-2.0", "model-index", "region:us" ]	text-classification	2024-02-29T12:48:53Z	--- license: apache-2.0 library_name: peft tags: - parquet - text-classification datasets: - tweet_eval metrics: - accuracy base_model: chiragasarpota/scotus-bert model-index: - name: chiragasarpota_scotus-bert-finetuned-lora-tweet_eval_emotion results: - task: type: text-classification name: Text Classification dataset: name: tweet_eval type: tweet_eval config: emotion split: validation args: emotion metrics: - type: accuracy value: 0.42780748663101603 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. --> # chiragasarpota_scotus-bert-finetuned-lora-tweet_eval_emotion This model is a fine-tuned version of [chiragasarpota/scotus-bert](https://huggingface.co/chiragasarpota/scotus-bert) on the tweet_eval dataset. It achieves the following results on the evaluation set: - accuracy: 0.4278 ## 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: 0.0004 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results \| accuracy \| train_loss \| epoch \| \|:--------:\|:----------:\|:-----:\| \| 0.2594 \| None \| 0 \| \| 0.4278 \| 1.3091 \| 0 \| \| 0.4278 \| 1.2684 \| 1 \| \| 0.4278 \| 1.2646 \| 2 \| \| 0.4278 \| 1.2645 \| 3 \| ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.16.1 - Tokenizers 0.15.2
TransferGraph/jaesun_distilbert-base-uncased-finetuned-cola-finetuned-lora-tweet_eval_emotion	TransferGraph	2024-02-29T12:48:38Z	0	0	peft	[ "peft", "safetensors", "parquet", "text-classification", "dataset:tweet_eval", "base_model:jaesun/distilbert-base-uncased-finetuned-cola", "base_model:adapter:jaesun/distilbert-base-uncased-finetuned-cola", "license:apache-2.0", "model-index", "region:us" ]	text-classification	2024-02-29T12:48:36Z	--- license: apache-2.0 library_name: peft tags: - parquet - text-classification datasets: - tweet_eval metrics: - accuracy base_model: jaesun/distilbert-base-uncased-finetuned-cola model-index: - name: jaesun_distilbert-base-uncased-finetuned-cola-finetuned-lora-tweet_eval_emotion results: - task: type: text-classification name: Text Classification dataset: name: tweet_eval type: tweet_eval config: emotion split: validation args: emotion metrics: - type: accuracy value: 0.7032085561497327 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. --> # jaesun_distilbert-base-uncased-finetuned-cola-finetuned-lora-tweet_eval_emotion This model is a fine-tuned version of [jaesun/distilbert-base-uncased-finetuned-cola](https://huggingface.co/jaesun/distilbert-base-uncased-finetuned-cola) on the tweet_eval dataset. It achieves the following results on the evaluation set: - accuracy: 0.7032 ## 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: 0.0004 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results \| accuracy \| train_loss \| epoch \| \|:--------:\|:----------:\|:-----:\| \| 0.3316 \| None \| 0 \| \| 0.6016 \| 1.1651 \| 0 \| \| 0.6818 \| 0.8386 \| 1 \| \| 0.7166 \| 0.6910 \| 2 \| \| 0.7032 \| 0.6499 \| 3 \| ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.16.1 - Tokenizers 0.15.2
TransferGraph/neibla_distilbert-base-uncased-finetuned-emotion-finetuned-lora-tweet_eval_emotion	TransferGraph	2024-02-29T12:48:35Z	0	0	peft	[ "peft", "safetensors", "parquet", "text-classification", "dataset:tweet_eval", "base_model:neibla/distilbert-base-uncased-finetuned-emotion", "base_model:adapter:neibla/distilbert-base-uncased-finetuned-emotion", "license:apache-2.0", "model-index", "region:us" ]	text-classification	2024-02-29T12:48:33Z	--- license: apache-2.0 library_name: peft tags: - parquet - text-classification datasets: - tweet_eval metrics: - accuracy base_model: neibla/distilbert-base-uncased-finetuned-emotion model-index: - name: neibla_distilbert-base-uncased-finetuned-emotion-finetuned-lora-tweet_eval_emotion results: - task: type: text-classification name: Text Classification dataset: name: tweet_eval type: tweet_eval config: emotion split: validation args: emotion metrics: - type: accuracy value: 0.7299465240641712 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. --> # neibla_distilbert-base-uncased-finetuned-emotion-finetuned-lora-tweet_eval_emotion This model is a fine-tuned version of [neibla/distilbert-base-uncased-finetuned-emotion](https://huggingface.co/neibla/distilbert-base-uncased-finetuned-emotion) on the tweet_eval dataset. It achieves the following results on the evaluation set: - accuracy: 0.7299 ## 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: 0.0004 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results \| accuracy \| train_loss \| epoch \| \|:--------:\|:----------:\|:-----:\| \| 0.3422 \| None \| 0 \| \| 0.6818 \| 0.8986 \| 0 \| \| 0.7246 \| 0.7357 \| 1 \| \| 0.7273 \| 0.6598 \| 2 \| \| 0.7299 \| 0.6188 \| 3 \| ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.16.1 - Tokenizers 0.15.2
TransferGraph/moshew_bert-mini-sst2-distilled-finetuned-lora-tweet_eval_emotion	TransferGraph	2024-02-29T12:48:35Z	0	0	peft	[ "peft", "safetensors", "parquet", "text-classification", "dataset:tweet_eval", "base_model:moshew/bert-mini-sst2-distilled", "base_model:adapter:moshew/bert-mini-sst2-distilled", "model-index", "region:us" ]	text-classification	2024-02-29T12:48:33Z	--- library_name: peft tags: - parquet - text-classification datasets: - tweet_eval metrics: - accuracy base_model: moshew/bert-mini-sst2-distilled model-index: - name: moshew_bert-mini-sst2-distilled-finetuned-lora-tweet_eval_emotion results: - task: type: text-classification name: Text Classification dataset: name: tweet_eval type: tweet_eval config: emotion split: validation args: emotion metrics: - type: accuracy value: 0.5721925133689839 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. --> # moshew_bert-mini-sst2-distilled-finetuned-lora-tweet_eval_emotion This model is a fine-tuned version of [moshew/bert-mini-sst2-distilled](https://huggingface.co/moshew/bert-mini-sst2-distilled) on the tweet_eval dataset. It achieves the following results on the evaluation set: - accuracy: 0.5722 ## 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: 0.0004 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results \| accuracy \| train_loss \| epoch \| \|:--------:\|:----------:\|:-----:\| \| 0.5668 \| None \| 0 \| \| 0.5668 \| 1.0973 \| 0 \| \| 0.5749 \| 1.0593 \| 1 \| \| 0.5722 \| 1.0378 \| 2 \| \| 0.5722 \| 1.0263 \| 3 \| ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.16.1 - Tokenizers 0.15.2
TransferGraph/JonatanGk_roberta-base-bne-finetuned-cyberbullying-spanish-finetuned-lora-tweet_eval_emotion	TransferGraph	2024-02-29T12:48:35Z	8	0	peft	[ "peft", "safetensors", "parquet", "text-classification", "dataset:tweet_eval", "base_model:JonatanGk/roberta-base-bne-finetuned-cyberbullying-spanish", "base_model:adapter:JonatanGk/roberta-base-bne-finetuned-cyberbullying-spanish", "model-index", "region:us" ]	text-classification	2024-02-29T12:48:33Z	--- library_name: peft tags: - parquet - text-classification datasets: - tweet_eval metrics: - accuracy base_model: JonatanGk/roberta-base-bne-finetuned-cyberbullying-spanish model-index: - name: JonatanGk_roberta-base-bne-finetuned-cyberbullying-spanish-finetuned-lora-tweet_eval_emotion results: - task: type: text-classification name: Text Classification dataset: name: tweet_eval type: tweet_eval config: emotion split: validation args: emotion metrics: - type: accuracy value: 0.4385026737967914 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. --> # JonatanGk_roberta-base-bne-finetuned-cyberbullying-spanish-finetuned-lora-tweet_eval_emotion This model is a fine-tuned version of [JonatanGk/roberta-base-bne-finetuned-cyberbullying-spanish](https://huggingface.co/JonatanGk/roberta-base-bne-finetuned-cyberbullying-spanish) on the tweet_eval dataset. It achieves the following results on the evaluation set: - accuracy: 0.4385 ## 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: 0.0004 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results \| accuracy \| train_loss \| epoch \| \|:--------:\|:----------:\|:-----:\| \| 0.2620 \| None \| 0 \| \| 0.4144 \| 1.2872 \| 0 \| \| 0.3984 \| 1.2571 \| 1 \| \| 0.4412 \| 1.2472 \| 2 \| \| 0.4385 \| 1.2295 \| 3 \| ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.16.1 - Tokenizers 0.15.2
TransferGraph/pietrotrope_emotion_final-finetuned-lora-tweet_eval_emotion	TransferGraph	2024-02-29T12:48:31Z	0	0	peft	[ "peft", "safetensors", "parquet", "text-classification", "dataset:tweet_eval", "base_model:pietrotrope/emotion_final", "base_model:adapter:pietrotrope/emotion_final", "model-index", "region:us" ]	text-classification	2024-02-29T12:48:29Z	--- library_name: peft tags: - parquet - text-classification datasets: - tweet_eval metrics: - accuracy base_model: pietrotrope/emotion_final model-index: - name: pietrotrope_emotion_final-finetuned-lora-tweet_eval_emotion results: - task: type: text-classification name: Text Classification dataset: name: tweet_eval type: tweet_eval config: emotion split: validation args: emotion metrics: - type: accuracy value: 0.786096256684492 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. --> # pietrotrope_emotion_final-finetuned-lora-tweet_eval_emotion This model is a fine-tuned version of [pietrotrope/emotion_final](https://huggingface.co/pietrotrope/emotion_final) on the tweet_eval dataset. It achieves the following results on the evaluation set: - accuracy: 0.7861 ## 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: 0.0004 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results \| accuracy \| train_loss \| epoch \| \|:--------:\|:----------:\|:-----:\| \| 0.7941 \| None \| 0 \| \| 0.7914 \| 0.0810 \| 0 \| \| 0.7888 \| 0.0640 \| 1 \| \| 0.7888 \| 0.0589 \| 2 \| \| 0.7861 \| 0.0520 \| 3 \| ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.16.1 - Tokenizers 0.15.2
TransferGraph/Jeevesh8_bert_ft_qqp-40-finetuned-lora-tweet_eval_emotion	TransferGraph	2024-02-29T12:48:07Z	0	0	peft	[ "peft", "safetensors", "parquet", "text-classification", "dataset:tweet_eval", "base_model:Jeevesh8/bert_ft_qqp-40", "base_model:adapter:Jeevesh8/bert_ft_qqp-40", "model-index", "region:us" ]	text-classification	2024-02-29T12:48:05Z	--- library_name: peft tags: - parquet - text-classification datasets: - tweet_eval metrics: - accuracy base_model: Jeevesh8/bert_ft_qqp-40 model-index: - name: Jeevesh8_bert_ft_qqp-40-finetuned-lora-tweet_eval_emotion results: - task: type: text-classification name: Text Classification dataset: name: tweet_eval type: tweet_eval config: emotion split: validation args: emotion metrics: - type: accuracy value: 0.6818181818181818 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. --> # Jeevesh8_bert_ft_qqp-40-finetuned-lora-tweet_eval_emotion This model is a fine-tuned version of [Jeevesh8/bert_ft_qqp-40](https://huggingface.co/Jeevesh8/bert_ft_qqp-40) on the tweet_eval dataset. It achieves the following results on the evaluation set: - accuracy: 0.6818 ## 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: 0.0004 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results \| accuracy \| train_loss \| epoch \| \|:--------:\|:----------:\|:-----:\| \| 0.3770 \| None \| 0 \| \| 0.4706 \| 1.2464 \| 0 \| \| 0.5963 \| 1.1009 \| 1 \| \| 0.6791 \| 0.9371 \| 2 \| \| 0.6818 \| 0.8690 \| 3 \| ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.16.1 - Tokenizers 0.15.2
TransferGraph/heranm_finetuning-sentiment-model-3000-samples-finetuned-lora-tweet_eval_emotion	TransferGraph	2024-02-29T12:47:56Z	0	0	peft	[ "peft", "safetensors", "parquet", "text-classification", "dataset:tweet_eval", "base_model:heranm/finetuning-sentiment-model-3000-samples", "base_model:adapter:heranm/finetuning-sentiment-model-3000-samples", "license:apache-2.0", "model-index", "region:us" ]	text-classification	2024-02-29T12:47:54Z	--- license: apache-2.0 library_name: peft tags: - parquet - text-classification datasets: - tweet_eval metrics: - accuracy base_model: heranm/finetuning-sentiment-model-3000-samples model-index: - name: heranm_finetuning-sentiment-model-3000-samples-finetuned-lora-tweet_eval_emotion results: - task: type: text-classification name: Text Classification dataset: name: tweet_eval type: tweet_eval config: emotion split: validation args: emotion metrics: - type: accuracy value: 0.7379679144385026 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. --> # heranm_finetuning-sentiment-model-3000-samples-finetuned-lora-tweet_eval_emotion This model is a fine-tuned version of [heranm/finetuning-sentiment-model-3000-samples](https://huggingface.co/heranm/finetuning-sentiment-model-3000-samples) on the tweet_eval dataset. It achieves the following results on the evaluation set: - accuracy: 0.7380 ## 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: 0.0004 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results \| accuracy \| train_loss \| epoch \| \|:--------:\|:----------:\|:-----:\| \| 0.2032 \| None \| 0 \| \| 0.7059 \| 0.8609 \| 0 \| \| 0.7620 \| 0.6577 \| 1 \| \| 0.7487 \| 0.6057 \| 2 \| \| 0.7380 \| 0.5801 \| 3 \| ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.16.1 - Tokenizers 0.15.2
TransferGraph/philschmid_tiny-distilbert-classification-finetuned-lora-tweet_eval_emotion	TransferGraph	2024-02-29T12:47:56Z	0	0	peft	[ "peft", "safetensors", "parquet", "text-classification", "dataset:tweet_eval", "base_model:philschmid/tiny-distilbert-classification", "base_model:adapter:philschmid/tiny-distilbert-classification", "model-index", "region:us" ]	text-classification	2024-02-29T12:47:54Z	--- library_name: peft tags: - parquet - text-classification datasets: - tweet_eval metrics: - accuracy base_model: philschmid/tiny-distilbert-classification model-index: - name: philschmid_tiny-distilbert-classification-finetuned-lora-tweet_eval_emotion results: - task: type: text-classification name: Text Classification dataset: name: tweet_eval type: tweet_eval config: emotion split: validation args: emotion metrics: - type: accuracy value: 0.42780748663101603 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. --> # philschmid_tiny-distilbert-classification-finetuned-lora-tweet_eval_emotion This model is a fine-tuned version of [philschmid/tiny-distilbert-classification](https://huggingface.co/philschmid/tiny-distilbert-classification) on the tweet_eval dataset. It achieves the following results on the evaluation set: - accuracy: 0.4278 ## 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: 0.0004 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results \| accuracy \| train_loss \| epoch \| \|:--------:\|:----------:\|:-----:\| \| 0.2380 \| None \| 0 \| \| 0.4278 \| 1.3801 \| 0 \| \| 0.4278 \| 1.3679 \| 1 \| \| 0.4278 \| 1.3588 \| 2 \| \| 0.4278 \| 1.3538 \| 3 \| ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.16.1 - Tokenizers 0.15.2
TransferGraph/nreimers_mmarco-mMiniLMv2-L6-H384-v1-finetuned-lora-tweet_eval_emotion	TransferGraph	2024-02-29T12:47:56Z	2	0	peft	[ "peft", "safetensors", "parquet", "text-classification", "dataset:tweet_eval", "base_model:nreimers/mmarco-mMiniLMv2-L6-H384-v1", "base_model:adapter:nreimers/mmarco-mMiniLMv2-L6-H384-v1", "model-index", "region:us" ]	text-classification	2024-02-29T12:47:52Z	--- library_name: peft tags: - parquet - text-classification datasets: - tweet_eval metrics: - accuracy base_model: nreimers/mmarco-mMiniLMv2-L6-H384-v1 model-index: - name: nreimers_mmarco-mMiniLMv2-L6-H384-v1-finetuned-lora-tweet_eval_emotion results: - task: type: text-classification name: Text Classification dataset: name: tweet_eval type: tweet_eval config: emotion split: validation args: emotion metrics: - type: accuracy value: 0.42513368983957217 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. --> # nreimers_mmarco-mMiniLMv2-L6-H384-v1-finetuned-lora-tweet_eval_emotion This model is a fine-tuned version of [nreimers/mmarco-mMiniLMv2-L6-H384-v1](https://huggingface.co/nreimers/mmarco-mMiniLMv2-L6-H384-v1) on the tweet_eval dataset. It achieves the following results on the evaluation set: - accuracy: 0.4251 ## 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: 0.0004 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results \| accuracy \| train_loss \| epoch \| \|:--------:\|:----------:\|:-----:\| \| 0.2353 \| None \| 0 \| \| 0.4251 \| 1.2736 \| 0 \| \| 0.4251 \| 1.2574 \| 1 \| \| 0.4251 \| 1.2516 \| 2 \| \| 0.4251 \| 1.2477 \| 3 \| ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.16.1 - Tokenizers 0.15.2
TransferGraph/Jeevesh8_init_bert_ft_qqp-49-finetuned-lora-tweet_eval_emotion	TransferGraph	2024-02-29T12:47:54Z	0	0	peft	[ "peft", "safetensors", "parquet", "text-classification", "dataset:tweet_eval", "base_model:Jeevesh8/init_bert_ft_qqp-49", "base_model:adapter:Jeevesh8/init_bert_ft_qqp-49", "model-index", "region:us" ]	text-classification	2024-02-29T12:47:53Z	--- library_name: peft tags: - parquet - text-classification datasets: - tweet_eval metrics: - accuracy base_model: Jeevesh8/init_bert_ft_qqp-49 model-index: - name: Jeevesh8_init_bert_ft_qqp-49-finetuned-lora-tweet_eval_emotion results: - task: type: text-classification name: Text Classification dataset: name: tweet_eval type: tweet_eval config: emotion split: validation args: emotion metrics: - type: accuracy value: 0.553475935828877 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. --> # Jeevesh8_init_bert_ft_qqp-49-finetuned-lora-tweet_eval_emotion This model is a fine-tuned version of [Jeevesh8/init_bert_ft_qqp-49](https://huggingface.co/Jeevesh8/init_bert_ft_qqp-49) on the tweet_eval dataset. It achieves the following results on the evaluation set: - accuracy: 0.5535 ## 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: 0.0004 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results \| accuracy \| train_loss \| epoch \| \|:--------:\|:----------:\|:-----:\| \| 0.4198 \| None \| 0 \| \| 0.4305 \| 1.2917 \| 0 \| \| 0.4840 \| 1.2411 \| 1 \| \| 0.5241 \| 1.1301 \| 2 \| \| 0.5535 \| 1.0543 \| 3 \| ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.16.1 - Tokenizers 0.15.2
TransferGraph/Jeevesh8_feather_berts_46-finetuned-lora-tweet_eval_emotion	TransferGraph	2024-02-29T12:47:54Z	0	0	peft	[ "peft", "safetensors", "parquet", "text-classification", "dataset:tweet_eval", "base_model:Jeevesh8/feather_berts_46", "base_model:adapter:Jeevesh8/feather_berts_46", "model-index", "region:us" ]	text-classification	2024-02-29T12:47:52Z	--- library_name: peft tags: - parquet - text-classification datasets: - tweet_eval metrics: - accuracy base_model: Jeevesh8/feather_berts_46 model-index: - name: Jeevesh8_feather_berts_46-finetuned-lora-tweet_eval_emotion results: - task: type: text-classification name: Text Classification dataset: name: tweet_eval type: tweet_eval config: emotion split: validation args: emotion metrics: - type: accuracy value: 0.7112299465240641 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. --> # Jeevesh8_feather_berts_46-finetuned-lora-tweet_eval_emotion This model is a fine-tuned version of [Jeevesh8/feather_berts_46](https://huggingface.co/Jeevesh8/feather_berts_46) on the tweet_eval dataset. It achieves the following results on the evaluation set: - accuracy: 0.7112 ## 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: 0.0004 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results \| accuracy \| train_loss \| epoch \| \|:--------:\|:----------:\|:-----:\| \| 0.2754 \| None \| 0 \| \| 0.5775 \| 1.2198 \| 0 \| \| 0.6604 \| 0.9839 \| 1 \| \| 0.6979 \| 0.8798 \| 2 \| \| 0.7112 \| 0.8237 \| 3 \| ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.16.1 - Tokenizers 0.15.2
TransferGraph/abdelkader_distilbert-base-uncased-finetuned-emotion-finetuned-lora-tweet_eval_emotion	TransferGraph	2024-02-29T12:47:42Z	0	0	peft	[ "peft", "safetensors", "parquet", "text-classification", "dataset:tweet_eval", "base_model:abdelkader/distilbert-base-uncased-finetuned-emotion", "base_model:adapter:abdelkader/distilbert-base-uncased-finetuned-emotion", "license:apache-2.0", "model-index", "region:us" ]	text-classification	2024-02-29T12:47:36Z	--- license: apache-2.0 library_name: peft tags: - parquet - text-classification datasets: - tweet_eval metrics: - accuracy base_model: abdelkader/distilbert-base-uncased-finetuned-emotion model-index: - name: abdelkader_distilbert-base-uncased-finetuned-emotion-finetuned-lora-tweet_eval_emotion results: - task: type: text-classification name: Text Classification dataset: name: tweet_eval type: tweet_eval config: emotion split: validation args: emotion metrics: - type: accuracy value: 0.7352941176470589 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. --> # abdelkader_distilbert-base-uncased-finetuned-emotion-finetuned-lora-tweet_eval_emotion This model is a fine-tuned version of [abdelkader/distilbert-base-uncased-finetuned-emotion](https://huggingface.co/abdelkader/distilbert-base-uncased-finetuned-emotion) on the tweet_eval dataset. It achieves the following results on the evaluation set: - accuracy: 0.7353 ## 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: 0.0004 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results \| accuracy \| train_loss \| epoch \| \|:--------:\|:----------:\|:-----:\| \| 0.2647 \| None \| 0 \| \| 0.6898 \| 0.8616 \| 0 \| \| 0.7246 \| 0.7169 \| 1 \| \| 0.7326 \| 0.6474 \| 2 \| \| 0.7353 \| 0.6150 \| 3 \| ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.16.1 - Tokenizers 0.15.2
TransferGraph/dapang_distilroberta-base-mic-sym-finetuned-lora-tweet_eval_emotion	TransferGraph	2024-02-29T12:47:42Z	1	0	peft	[ "peft", "safetensors", "parquet", "text-classification", "dataset:tweet_eval", "base_model:agi-css/distilroberta-base-mic-sym", "base_model:adapter:agi-css/distilroberta-base-mic-sym", "license:apache-2.0", "model-index", "region:us" ]	text-classification	2024-02-29T12:47:36Z	--- license: apache-2.0 library_name: peft tags: - parquet - text-classification datasets: - tweet_eval metrics: - accuracy base_model: dapang/distilroberta-base-mic-sym model-index: - name: dapang_distilroberta-base-mic-sym-finetuned-lora-tweet_eval_emotion results: - task: type: text-classification name: Text Classification dataset: name: tweet_eval type: tweet_eval config: emotion split: validation args: emotion metrics: - type: accuracy value: 0.732620320855615 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. --> # dapang_distilroberta-base-mic-sym-finetuned-lora-tweet_eval_emotion This model is a fine-tuned version of [dapang/distilroberta-base-mic-sym](https://huggingface.co/dapang/distilroberta-base-mic-sym) on the tweet_eval dataset. It achieves the following results on the evaluation set: - accuracy: 0.7326 ## 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: 0.0004 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results \| accuracy \| train_loss \| epoch \| \|:--------:\|:----------:\|:-----:\| \| 0.0749 \| None \| 0 \| \| 0.6444 \| 1.0939 \| 0 \| \| 0.6898 \| 0.8092 \| 1 \| \| 0.7139 \| 0.7366 \| 2 \| \| 0.7326 \| 0.6813 \| 3 \| ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.16.1 - Tokenizers 0.15.2
TransferGraph/yukta10_finetuning-sentiment-model-3000-samples-finetuned-lora-tweet_eval_emotion	TransferGraph	2024-02-29T12:47:42Z	0	0	peft	[ "peft", "safetensors", "parquet", "text-classification", "dataset:tweet_eval", "base_model:yukta10/finetuning-sentiment-model-3000-samples", "base_model:adapter:yukta10/finetuning-sentiment-model-3000-samples", "license:apache-2.0", "model-index", "region:us" ]	text-classification	2024-02-29T12:47:36Z	--- license: apache-2.0 library_name: peft tags: - parquet - text-classification datasets: - tweet_eval metrics: - accuracy base_model: yukta10/finetuning-sentiment-model-3000-samples model-index: - name: yukta10_finetuning-sentiment-model-3000-samples-finetuned-lora-tweet_eval_emotion results: - task: type: text-classification name: Text Classification dataset: name: tweet_eval type: tweet_eval config: emotion split: validation args: emotion metrics: - type: accuracy value: 0.7379679144385026 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. --> # yukta10_finetuning-sentiment-model-3000-samples-finetuned-lora-tweet_eval_emotion This model is a fine-tuned version of [yukta10/finetuning-sentiment-model-3000-samples](https://huggingface.co/yukta10/finetuning-sentiment-model-3000-samples) on the tweet_eval dataset. It achieves the following results on the evaluation set: - accuracy: 0.7380 ## 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: 0.0004 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results \| accuracy \| train_loss \| epoch \| \|:--------:\|:----------:\|:-----:\| \| 0.2647 \| None \| 0 \| \| 0.7032 \| 0.8680 \| 0 \| \| 0.7540 \| 0.6776 \| 1 \| \| 0.7353 \| 0.6196 \| 2 \| \| 0.7380 \| 0.5839 \| 3 \| ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.16.1 - Tokenizers 0.15.2
TransferGraph/riyadhctg_distilbert-base-uncased-finetuned-cola-finetuned-lora-tweet_eval_emotion	TransferGraph	2024-02-29T12:47:42Z	0	0	peft	[ "peft", "safetensors", "parquet", "text-classification", "dataset:tweet_eval", "base_model:riyadhctg/distilbert-base-uncased-finetuned-cola", "base_model:adapter:riyadhctg/distilbert-base-uncased-finetuned-cola", "license:apache-2.0", "model-index", "region:us" ]	text-classification	2024-02-29T12:47:36Z	--- license: apache-2.0 library_name: peft tags: - parquet - text-classification datasets: - tweet_eval metrics: - accuracy base_model: riyadhctg/distilbert-base-uncased-finetuned-cola model-index: - name: riyadhctg_distilbert-base-uncased-finetuned-cola-finetuned-lora-tweet_eval_emotion results: - task: type: text-classification name: Text Classification dataset: name: tweet_eval type: tweet_eval config: emotion split: validation args: emotion metrics: - type: accuracy value: 0.7299465240641712 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. --> # riyadhctg_distilbert-base-uncased-finetuned-cola-finetuned-lora-tweet_eval_emotion This model is a fine-tuned version of [riyadhctg/distilbert-base-uncased-finetuned-cola](https://huggingface.co/riyadhctg/distilbert-base-uncased-finetuned-cola) on the tweet_eval dataset. It achieves the following results on the evaluation set: - accuracy: 0.7299 ## 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: 0.0004 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results \| accuracy \| train_loss \| epoch \| \|:--------:\|:----------:\|:-----:\| \| 0.2406 \| None \| 0 \| \| 0.6123 \| 1.1167 \| 0 \| \| 0.7139 \| 0.7458 \| 1 \| \| 0.7299 \| 0.6476 \| 2 \| \| 0.7299 \| 0.6153 \| 3 \| ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.16.1 - Tokenizers 0.15.2
CatBarks/GPT2ES_spamming-email-classification1_1_tokenizer	CatBarks	2024-02-29T12:47:25Z	0	0	transformers	[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-02-29T12:47:24Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [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. 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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 Dataset 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]
jiaqianwu/ppo-Huggy	jiaqianwu	2024-02-29T12:46:51Z	1	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]	reinforcement-learning	2024-02-29T12:45:09Z	--- library_name: ml-agents tags: - Huggy - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Huggy --- # ppo Agent playing Huggy This is a trained model of a ppo agent playing Huggy using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A short tutorial where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A longer tutorial to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: jiaqianwu/ppo-Huggy 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
lewtun/gemma-7b-sft-full-dolly-v3	lewtun	2024-02-29T12:36:27Z	12	0	transformers	[ "transformers", "safetensors", "gemma", "text-generation", "alignment-handbook", "trl", "sft", "generated_from_trainer", "conversational", "dataset:philschmid/dolly-15k-oai-style", "base_model:google/gemma-7b", "base_model:finetune:google/gemma-7b", "license:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-02-29T12:24:16Z	--- license: other base_model: google/gemma-7b tags: - alignment-handbook - trl - sft - generated_from_trainer - trl - sft - generated_from_trainer datasets: - philschmid/dolly-15k-oai-style model-index: - name: gemma-7b-sft-full-dolly-v3 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. --> # gemma-7b-sft-full-dolly-v3 This model is a fine-tuned version of [google/gemma-7b](https://huggingface.co/google/gemma-7b) on the philschmid/dolly-15k-oai-style 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: 4 - eval_batch_size: 16 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - total_train_batch_size: 32 - total_eval_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3 ### Training results ### Framework versions - Transformers 4.39.0.dev0 - Pytorch 2.1.2+cu121 - Datasets 2.14.6 - Tokenizers 0.15.1
JaniShubh/gemma2b_FT	JaniShubh	2024-02-29T12:35:44Z	22	0	transformers	[ "transformers", "gguf", "gemma", "text-generation-inference", "unsloth", "en", "base_model:google/gemma-2b", "base_model:quantized:google/gemma-2b", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-02-29T12:33:49Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - gemma - gguf base_model: google/gemma-2b --- # Uploaded model - Developed by: JaniShubh - License: apache-2.0 - Finetuned from model : google/gemma-2b This gemma model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
VamsiPranav/hindi-mlm	VamsiPranav	2024-02-29T12:35:02Z	106	0	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "feature-extraction", "generated_from_trainer", "base_model:google-bert/bert-base-multilingual-cased", "base_model:finetune:google-bert/bert-base-multilingual-cased", "license:apache-2.0", "endpoints_compatible", "region:us" ]	feature-extraction	2024-02-29T12:34:05Z	--- license: apache-2.0 base_model: bert-base-multilingual-cased tags: - generated_from_trainer model-index: - name: hindi-mlm 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. --> # hindi-mlm This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 512 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 1000 - num_epochs: 100 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.38.1 - Pytorch 2.1.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2
Astral7/bert_base_cased_qa	Astral7	2024-02-29T12:25:05Z	61	0	transformers	[ "transformers", "tf", "bert", "question-answering", "generated_from_keras_callback", "base_model:google-bert/bert-base-cased", "base_model:finetune:google-bert/bert-base-cased", "license:apache-2.0", "endpoints_compatible", "region:us" ]	question-answering	2024-02-26T10:27:27Z	--- license: apache-2.0 tags: - generated_from_keras_callback base_model: bert-base-cased model-index: - name: bert_base_cased_qa 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. --> # bert_base_cased_qa This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 1.2898 - 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: {'inner_optimizer': {'module': 'transformers.optimization_tf', 'class_name': 'AdamWeightDecay', 'config': {'name': 'AdamWeightDecay', 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 5545, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'decay': 0.0, 'beta_1': 0.8999999761581421, 'beta_2': 0.9990000128746033, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}, 'registered_name': 'AdamWeightDecay'}, 'dynamic': True, 'initial_scale': 32768.0, 'dynamic_growth_steps': 2000} - training_precision: mixed_float16 ### Training results \| Train Loss \| Epoch \| \|:----------:\|:-----:\| \| 1.2898 \| 0 \| ### Framework versions - Transformers 4.37.2 - TensorFlow 2.15.0 - Datasets 2.17.1 - Tokenizers 0.15.2
philschmid/gemma-7b-dolly-chatml	philschmid	2024-02-29T12:21:31Z	12	13	peft	[ "peft", "tensorboard", "safetensors", "trl", "sft", "generated_from_trainer", "dataset:generator", "base_model:google/gemma-7b", "base_model:adapter:google/gemma-7b", "license:other", "region:us" ]	null	2024-02-27T14:20:13Z	--- license: other library_name: peft tags: - trl - sft - generated_from_trainer datasets: - generator base_model: google/gemma-7b model-index: - name: gemma-7b-dolly-chatml 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. --> # gemma-7b-dolly-chatml This model is a fine-tuned version of [google/gemma-7b](https://huggingface.co/google/gemma-7b) with [philschmid/gemma-tokenizer-chatml](https://huggingface.co/philschmid/gemma-tokenizer-chatml) tokenizer on the [philschmid/dolly-15k-oai-style](https://huggingface.co/datasets/philschmid/dolly-15k-oai-style) using the chatML format. The model was fine-tuned with the following [script using Lora (no, qlora)](https://huggingface.co/philschmid/gemma-7b-dolly-chatml/blob/main/trl-lora.py). I also included a [inference script](https://huggingface.co/philschmid/gemma-7b-dolly-chatml/blob/main/inference.py) to make sure it works since there were some issues with Gemma. Results of the inference test are ```bash prompt: What is the capital of Germany? Explain why thats the case and if it was different in the past? response: Berlin is the capital of Germany. It was the capital of Prussia until 1918, when the monarchy was abolished. It was also the capital of the Weimar Republic. It was the capital of the Third Reich until 1945, when it was liberated by the allies. It has been the capital of the Federal Republic of Germany since 1949. It is the largest city in the country with a population of 3.6 million people. It is also the seat of the government and parliament. prompt: In a town, 60% of the population are adults. Among the adults, 30% have a pet dog and 40% have a pet cat. What percentage of the total population has a pet dog? response: 60% of the total population have a pet dog. The calculation is 30% of adults multiplied by 60% of the total population. 30% of adults is 18% of the total population and 18% multiplied by 60% is 10.8% or 60% of the total population. ``` ### Run inference ```python import torch from peft import AutoPeftModelForCausalLM from transformers import AutoTokenizer, pipeline peft_model_id = "philschmid/gemma-7b-dolly-chatml" # Load Model with PEFT adapter tokenizer = AutoTokenizer.from_pretrained(peft_model_id) model = AutoPeftModelForCausalLM.from_pretrained(peft_model_id, device_map="auto", torch_dtype=torch.float16) pipe = pipeline("text-generation", model=model, tokenizer=tokenizer) eos_token = tokenizer("<\|im_end\|>",add_special_tokens=False)["input_ids"][0] print(f"eos_token: {eos_token}") # run inference messages = [ { "role": "user", "content": "What is the capital of Germany? Explain why thats the case and if it was different in the past?" } ] prompt = pipe.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) outputs = pipe(prompt, max_new_tokens=1024, do_sample=True, temperature=0.7, top_k=50, top_p=0.95, eos_token_id=eos_token) print(outputs[0]['generated_text'][len(prompt):]) # Berlin is the capital of Germany. It was the capital of Prussia until 1918, when the monarchy was abolished. It was also the capital of the Weimar Republic. It was the capital of the Third Reich until 1945, when it was liberated by the allies. It has been the capital of the Federal Republic of Germany since 1949. It is the largest city in the country with a population of 3.6 million people. It is also the seat of the government and parliament. ``` ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - 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: constant - lr_scheduler_warmup_ratio: 0.03 - num_epochs: 3 ### Framework versions - PEFT 0.8.2 - Transformers 4.38.1 - Pytorch 2.1.2+cu121 - Datasets 2.16.1 - Tokenizers 0.15.0
Aharneish/mistral-test_1	Aharneish	2024-02-29T12:18:32Z	0	0	peft	[ "peft", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:mistralai/Mistral-7B-Instruct-v0.1", "base_model:adapter:mistralai/Mistral-7B-Instruct-v0.1", "license:apache-2.0", "region:us" ]	null	2024-02-29T07:33:28Z	--- license: apache-2.0 library_name: peft tags: - trl - sft - generated_from_trainer base_model: mistralai/Mistral-7B-Instruct-v0.1 model-index: - name: mistral-test_1 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. --> # mistral-test_1 This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.2947 ## 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: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 1.8128 \| 1.36 \| 2500 \| 2.1855 \| \| 1.7961 \| 1.63 \| 3000 \| 2.1808 \| \| 1.7701 \| 1.9 \| 3500 \| 2.2271 \| \| 1.7186 \| 2.17 \| 4000 \| 2.2265 \| \| 1.6784 \| 2.44 \| 4500 \| 2.2547 \| \| 1.6692 \| 2.71 \| 5000 \| 2.2547 \| \| 1.6752 \| 2.98 \| 5500 \| 2.2470 \| \| 1.6206 \| 3.26 \| 6000 \| 2.2842 \| \| 1.599 \| 3.53 \| 6500 \| 2.2663 \| \| 1.6054 \| 3.8 \| 7000 \| 2.2560 \| \| 1.593 \| 4.07 \| 7500 \| 2.3039 \| \| 1.5771 \| 4.34 \| 8000 \| 2.2797 \| \| 1.5636 \| 4.61 \| 8500 \| 2.2915 \| \| 1.5551 \| 4.88 \| 9000 \| 2.2947 \| ### Framework versions - PEFT 0.9.0 - Transformers 4.38.1 - Pytorch 2.1.2 - Datasets 2.16.0 - Tokenizers 0.15.2

Hagidr/gemma-tatsu_lab-Instruct-Finetune-test_02

Hagidr

2024-02-29T13:26:21Z

116

0

transformers

[ "transformers", "safetensors", "gemma", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-02-29T13:22:02Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

Nandreas/q-FrozenLake-v1-4x4-noSlippery

Nandreas

2024-02-29T13:24:05Z

0

null

[ "Taxi-v3-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]

reinforcement-learning

2024-02-29T13:24:02Z

--- tags: - Taxi-v3-4x4-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: Taxi-v3-4x4-no_slippery type: Taxi-v3-4x4-no_slippery metrics: - type: mean_reward value: 1.00 +/- 0.00 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="Nandreas/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"]) ```

mogmyij/yelp-model-5k-10layer-2Epoch

mogmyij

2024-02-29T13:14:23Z

106

0

transformers

[ "transformers", "tensorboard", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:google-bert/bert-base-uncased", "base_model:finetune:google-bert/bert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2024-02-29T12:53:20Z

--- license: apache-2.0 base_model: google-bert/bert-base-uncased tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: yelp-model-5k-10layer-2Epoch results: [] ---  # yelp-model-5k-10layer-2Epoch This model is a fine-tuned version of [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.9048 - Accuracy: 0.61 - F1: 0.6143 ## 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: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 1.0394 | 1.0 | 1250 | 0.8914 | 0.594 | 0.5961 | | 0.7221 | 2.0 | 2500 | 0.9048 | 0.61 | 0.6143 | ### Framework versions - Transformers 4.37.2 - Pytorch 2.1.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2

TransferGraph/connectivity_cola_6ep_ft-22-finetuned-lora-tweet_eval_sentiment

TransferGraph

2024-02-29T13:14:21Z

0

peft

[ "peft", "safetensors", "parquet", "text-classification", "dataset:tweet_eval", "base_model:connectivity/cola_6ep_ft-22", "base_model:adapter:connectivity/cola_6ep_ft-22", "model-index", "region:us" ]

text-classification

2024-02-29T13:14:19Z

--- library_name: peft tags: - parquet - text-classification datasets: - tweet_eval metrics: - accuracy base_model: connectivity/cola_6ep_ft-22 model-index: - name: connectivity_cola_6ep_ft-22-finetuned-lora-tweet_eval_sentiment results: - task: type: text-classification name: Text Classification dataset: name: tweet_eval type: tweet_eval config: sentiment split: validation args: sentiment metrics: - type: accuracy value: 0.7165 name: accuracy ---  # connectivity_cola_6ep_ft-22-finetuned-lora-tweet_eval_sentiment This model is a fine-tuned version of [connectivity/cola_6ep_ft-22](https://huggingface.co/connectivity/cola_6ep_ft-22) on the tweet_eval dataset. It achieves the following results on the evaluation set: - accuracy: 0.7165 ## 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: 0.0004 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results | accuracy | train_loss | epoch | |:--------:|:----------:|:-----:| | 0.339 | None | 0 | | 0.692 | 0.7477 | 0 | | 0.708 | 0.6590 | 1 | | 0.7155 | 0.6373 | 2 | | 0.7165 | 0.6261 | 3 | ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.16.1 - Tokenizers 0.15.2

TransferGraph/Jeevesh8_init_bert_ft_qqp-33-finetuned-lora-tweet_eval_sentiment

TransferGraph

2024-02-29T13:13:49Z

0

peft

[ "peft", "safetensors", "parquet", "text-classification", "dataset:tweet_eval", "base_model:Jeevesh8/init_bert_ft_qqp-33", "base_model:adapter:Jeevesh8/init_bert_ft_qqp-33", "model-index", "region:us" ]

text-classification

2024-02-29T13:13:47Z

--- library_name: peft tags: - parquet - text-classification datasets: - tweet_eval metrics: - accuracy base_model: Jeevesh8/init_bert_ft_qqp-33 model-index: - name: Jeevesh8_init_bert_ft_qqp-33-finetuned-lora-tweet_eval_sentiment results: - task: type: text-classification name: Text Classification dataset: name: tweet_eval type: tweet_eval config: sentiment split: validation args: sentiment metrics: - type: accuracy value: 0.7085 name: accuracy ---  # Jeevesh8_init_bert_ft_qqp-33-finetuned-lora-tweet_eval_sentiment This model is a fine-tuned version of [Jeevesh8/init_bert_ft_qqp-33](https://huggingface.co/Jeevesh8/init_bert_ft_qqp-33) on the tweet_eval dataset. It achieves the following results on the evaluation set: - accuracy: 0.7085 ## 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: 0.0004 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results | accuracy | train_loss | epoch | |:--------:|:----------:|:-----:| | 0.4065 | None | 0 | | 0.6805 | 0.8189 | 0 | | 0.7015 | 0.6812 | 1 | | 0.705 | 0.6572 | 2 | | 0.7085 | 0.6455 | 3 | ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.16.1 - Tokenizers 0.15.2

TransferGraph/yukta10_finetuning-sentiment-model-3000-samples-finetuned-lora-tweet_eval_sentiment

TransferGraph

2024-02-29T13:10:51Z

1

0

peft

[ "peft", "safetensors", "parquet", "text-classification", "dataset:tweet_eval", "base_model:yukta10/finetuning-sentiment-model-3000-samples", "base_model:adapter:yukta10/finetuning-sentiment-model-3000-samples", "license:apache-2.0", "model-index", "region:us" ]

text-classification

2024-02-29T13:10:49Z

--- license: apache-2.0 library_name: peft tags: - parquet - text-classification datasets: - tweet_eval metrics: - accuracy base_model: yukta10/finetuning-sentiment-model-3000-samples model-index: - name: yukta10_finetuning-sentiment-model-3000-samples-finetuned-lora-tweet_eval_sentiment results: - task: type: text-classification name: Text Classification dataset: name: tweet_eval type: tweet_eval config: sentiment split: validation args: sentiment metrics: - type: accuracy value: 0.7195 name: accuracy ---  # yukta10_finetuning-sentiment-model-3000-samples-finetuned-lora-tweet_eval_sentiment This model is a fine-tuned version of [yukta10/finetuning-sentiment-model-3000-samples](https://huggingface.co/yukta10/finetuning-sentiment-model-3000-samples) on the tweet_eval dataset. It achieves the following results on the evaluation set: - accuracy: 0.7195 ## 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: 0.0004 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results | accuracy | train_loss | epoch | |:--------:|:----------:|:-----:| | 0.415 | None | 0 | | 0.7065 | 0.7064 | 0 | | 0.7165 | 0.6522 | 1 | | 0.703 | 0.6282 | 2 | | 0.7195 | 0.6119 | 3 | ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.16.1 - Tokenizers 0.15.2

TransferGraph/riyadhctg_distilbert-base-uncased-finetuned-cola-finetuned-lora-tweet_eval_sentiment

TransferGraph

2024-02-29T13:10:23Z

0

peft

[ "peft", "safetensors", "parquet", "text-classification", "dataset:tweet_eval", "base_model:riyadhctg/distilbert-base-uncased-finetuned-cola", "base_model:adapter:riyadhctg/distilbert-base-uncased-finetuned-cola", "license:apache-2.0", "model-index", "region:us" ]

text-classification

2024-02-29T13:10:17Z

--- license: apache-2.0 library_name: peft tags: - parquet - text-classification datasets: - tweet_eval metrics: - accuracy base_model: riyadhctg/distilbert-base-uncased-finetuned-cola model-index: - name: riyadhctg_distilbert-base-uncased-finetuned-cola-finetuned-lora-tweet_eval_sentiment results: - task: type: text-classification name: Text Classification dataset: name: tweet_eval type: tweet_eval config: sentiment split: validation args: sentiment metrics: - type: accuracy value: 0.709 name: accuracy ---  # riyadhctg_distilbert-base-uncased-finetuned-cola-finetuned-lora-tweet_eval_sentiment This model is a fine-tuned version of [riyadhctg/distilbert-base-uncased-finetuned-cola](https://huggingface.co/riyadhctg/distilbert-base-uncased-finetuned-cola) on the tweet_eval dataset. It achieves the following results on the evaluation set: - accuracy: 0.709 ## 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: 0.0004 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results | accuracy | train_loss | epoch | |:--------:|:----------:|:-----:| | 0.209 | None | 0 | | 0.6985 | 0.7312 | 0 | | 0.6995 | 0.6561 | 1 | | 0.702 | 0.6327 | 2 | | 0.709 | 0.6149 | 3 | ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.16.1 - Tokenizers 0.15.2

buelfhood/SOCO_Adapter_Java_LoRA_0

buelfhood

2024-02-29T13:10:05Z

1

0

adapter-transformers

[ "adapter-transformers", "roberta", "dataset:SOCO", "region:us" ]

null

2024-02-29T13:06:15Z

--- tags: - roberta - adapter-transformers datasets: - SOCO --- # Adapter `buelfhood/SOCO_Adapter_Java_LoRA` for huggingface/CodeBERTa-small-v1 An [adapter](https://adapterhub.ml) for the `huggingface/CodeBERTa-small-v1` model that was trained on the [SOCO](https://huggingface.co/datasets/SOCO/) dataset and includes a prediction head for classification. This adapter was created for usage with the **[Adapters](https://github.com/Adapter-Hub/adapters)** library. ## Usage First, install `adapters`: ``` pip install -U adapters ``` Now, the adapter can be loaded and activated like this: ```python from adapters import AutoAdapterModel model = AutoAdapterModel.from_pretrained("huggingface/CodeBERTa-small-v1") adapter_name = model.load_adapter("buelfhood/SOCO_Adapter_Java_LoRA", source="hf", set_active=True) ``` ## Architecture & Training max_length=256 learning_rate=5e-4 epochs=10 batch_size=30 LoRAConfig(r=8, alpha=8) ## Architecture & Training  ## Evaluation results  ## Citation

TransferGraph/heranm_finetuning-sentiment-model-3000-samples-finetuned-lora-tweet_eval_sentiment

TransferGraph

2024-02-29T13:09:55Z

0

peft

[ "peft", "safetensors", "parquet", "text-classification", "dataset:tweet_eval", "base_model:heranm/finetuning-sentiment-model-3000-samples", "base_model:adapter:heranm/finetuning-sentiment-model-3000-samples", "license:apache-2.0", "model-index", "region:us" ]

text-classification

2024-02-29T13:09:52Z

--- license: apache-2.0 library_name: peft tags: - parquet - text-classification datasets: - tweet_eval metrics: - accuracy base_model: heranm/finetuning-sentiment-model-3000-samples model-index: - name: heranm_finetuning-sentiment-model-3000-samples-finetuned-lora-tweet_eval_sentiment results: - task: type: text-classification name: Text Classification dataset: name: tweet_eval type: tweet_eval config: sentiment split: validation args: sentiment metrics: - type: accuracy value: 0.7185 name: accuracy ---  # heranm_finetuning-sentiment-model-3000-samples-finetuned-lora-tweet_eval_sentiment This model is a fine-tuned version of [heranm/finetuning-sentiment-model-3000-samples](https://huggingface.co/heranm/finetuning-sentiment-model-3000-samples) on the tweet_eval dataset. It achieves the following results on the evaluation set: - accuracy: 0.7185 ## 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: 0.0004 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results | accuracy | train_loss | epoch | |:--------:|:----------:|:-----:| | 0.372 | None | 0 | | 0.709 | 0.7049 | 0 | | 0.705 | 0.6518 | 1 | | 0.7065 | 0.6283 | 2 | | 0.7185 | 0.6111 | 3 | ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.16.1 - Tokenizers 0.15.2

TransferGraph/jasonyim2_distilbert-base-uncased-finetuned-emotion-finetuned-lora-tweet_eval_sentiment

TransferGraph

2024-02-29T13:09:52Z

1

0

peft

[ "peft", "safetensors", "parquet", "text-classification", "dataset:tweet_eval", "base_model:jasonyim2/distilbert-base-uncased-finetuned-emotion", "base_model:adapter:jasonyim2/distilbert-base-uncased-finetuned-emotion", "license:apache-2.0", "model-index", "region:us" ]

text-classification

2024-02-29T13:09:49Z

--- license: apache-2.0 library_name: peft tags: - parquet - text-classification datasets: - tweet_eval metrics: - accuracy base_model: jasonyim2/distilbert-base-uncased-finetuned-emotion model-index: - name: jasonyim2_distilbert-base-uncased-finetuned-emotion-finetuned-lora-tweet_eval_sentiment results: - task: type: text-classification name: Text Classification dataset: name: tweet_eval type: tweet_eval config: sentiment split: validation args: sentiment metrics: - type: accuracy value: 0.7135 name: accuracy ---  # jasonyim2_distilbert-base-uncased-finetuned-emotion-finetuned-lora-tweet_eval_sentiment This model is a fine-tuned version of [jasonyim2/distilbert-base-uncased-finetuned-emotion](https://huggingface.co/jasonyim2/distilbert-base-uncased-finetuned-emotion) on the tweet_eval dataset. It achieves the following results on the evaluation set: - accuracy: 0.7135 ## 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: 0.0004 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results | accuracy | train_loss | epoch | |:--------:|:----------:|:-----:| | 0.3925 | None | 0 | | 0.6855 | 0.7244 | 0 | | 0.701 | 0.6611 | 1 | | 0.6975 | 0.6374 | 2 | | 0.7135 | 0.6205 | 3 | ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.16.1 - Tokenizers 0.15.2

kbberendsen/deberta-v3-large-finetuned-cola-midterm

kbberendsen

2024-02-29T13:08:55Z

15

0

transformers

[ "transformers", "tensorboard", "safetensors", "deberta-v2", "text-classification", "generated_from_trainer", "base_model:microsoft/deberta-v3-large", "base_model:finetune:microsoft/deberta-v3-large", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2024-02-29T09:37:07Z

--- license: mit base_model: microsoft/deberta-v3-large tags: - generated_from_trainer metrics: - matthews_correlation model-index: - name: deberta-v3-large-finetuned-cola-midterm results: [] ---  # deberta-v3-large-finetuned-cola-midterm This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.6555 - Matthews Correlation: 0.7173 ## 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 | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | 0.3739 | 1.0 | 535 | 0.3250 | 0.7041 | | 0.2223 | 2.0 | 1070 | 0.4253 | 0.6893 | | 0.1459 | 3.0 | 1605 | 0.5346 | 0.7065 | | 0.0878 | 4.0 | 2140 | 0.6422 | 0.7112 | | 0.0466 | 5.0 | 2675 | 0.6555 | 0.7173 | ### Framework versions - Transformers 4.38.1 - Pytorch 2.1.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2

TransferGraph/dapang_distilroberta-base-mic-sym-finetuned-lora-tweet_eval_sentiment

TransferGraph

2024-02-29T13:08:54Z

0

peft

[ "peft", "safetensors", "parquet", "text-classification", "dataset:tweet_eval", "base_model:agi-css/distilroberta-base-mic-sym", "base_model:adapter:agi-css/distilroberta-base-mic-sym", "license:apache-2.0", "model-index", "region:us" ]

text-classification

2024-02-29T13:08:51Z

--- license: apache-2.0 library_name: peft tags: - parquet - text-classification datasets: - tweet_eval metrics: - accuracy base_model: dapang/distilroberta-base-mic-sym model-index: - name: dapang_distilroberta-base-mic-sym-finetuned-lora-tweet_eval_sentiment results: - task: type: text-classification name: Text Classification dataset: name: tweet_eval type: tweet_eval config: sentiment split: validation args: sentiment metrics: - type: accuracy value: 0.7155 name: accuracy ---  # dapang_distilroberta-base-mic-sym-finetuned-lora-tweet_eval_sentiment This model is a fine-tuned version of [dapang/distilroberta-base-mic-sym](https://huggingface.co/dapang/distilroberta-base-mic-sym) on the tweet_eval dataset. It achieves the following results on the evaluation set: - accuracy: 0.7155 ## 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: 0.0004 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results | accuracy | train_loss | epoch | |:--------:|:----------:|:-----:| | 0.1705 | None | 0 | | 0.7 | 0.7062 | 0 | | 0.713 | 0.6484 | 1 | | 0.715 | 0.6303 | 2 | | 0.7155 | 0.6217 | 3 | ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.16.1 - Tokenizers 0.15.2

TransferGraph/PrasunMishra_finetuning-sentiment-model-3000-samples-finetuned-lora-tweet_eval_sentiment

TransferGraph

2024-02-29T13:08:54Z

1

0

peft

[ "peft", "safetensors", "parquet", "text-classification", "dataset:tweet_eval", "base_model:PrasunMishra/finetuning-sentiment-model-3000-samples", "base_model:adapter:PrasunMishra/finetuning-sentiment-model-3000-samples", "license:apache-2.0", "model-index", "region:us" ]

text-classification

2024-02-29T13:08:52Z

--- license: apache-2.0 library_name: peft tags: - parquet - text-classification datasets: - tweet_eval metrics: - accuracy base_model: PrasunMishra/finetuning-sentiment-model-3000-samples model-index: - name: PrasunMishra_finetuning-sentiment-model-3000-samples-finetuned-lora-tweet_eval_sentiment results: - task: type: text-classification name: Text Classification dataset: name: tweet_eval type: tweet_eval config: sentiment split: validation args: sentiment metrics: - type: accuracy value: 0.711 name: accuracy ---  # PrasunMishra_finetuning-sentiment-model-3000-samples-finetuned-lora-tweet_eval_sentiment This model is a fine-tuned version of [PrasunMishra/finetuning-sentiment-model-3000-samples](https://huggingface.co/PrasunMishra/finetuning-sentiment-model-3000-samples) on the tweet_eval dataset. It achieves the following results on the evaluation set: - accuracy: 0.711 ## 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: 0.0004 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results | accuracy | train_loss | epoch | |:--------:|:----------:|:-----:| | 0.221 | None | 0 | | 0.707 | 0.7083 | 0 | | 0.7025 | 0.6520 | 1 | | 0.707 | 0.6308 | 2 | | 0.711 | 0.6143 | 3 | ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.16.1 - Tokenizers 0.15.2

PJMixers-Archive/MV01-7B-SFT-QLoRA-run_33-perscengen-only-maskinputs

PJMixers-Archive

2024-02-29T13:08:35Z

5

0

transformers

[ "transformers", "safetensors", "mistral", "text-generation", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]

text-generation

2024-01-28T09:31:22Z

--- language: - en --- https://gist.github.com/xzuyn/fe00ae8895550f3bfaddaa773e55146e

TransferGraph/moshew_bert-mini-sst2-distilled-finetuned-lora-tweet_eval_sentiment

TransferGraph

2024-02-29T13:08:19Z

0

peft

[ "peft", "safetensors", "parquet", "text-classification", "dataset:tweet_eval", "base_model:moshew/bert-mini-sst2-distilled", "base_model:adapter:moshew/bert-mini-sst2-distilled", "model-index", "region:us" ]

text-classification

2024-02-29T13:08:17Z

--- library_name: peft tags: - parquet - text-classification datasets: - tweet_eval metrics: - accuracy base_model: moshew/bert-mini-sst2-distilled model-index: - name: moshew_bert-mini-sst2-distilled-finetuned-lora-tweet_eval_sentiment results: - task: type: text-classification name: Text Classification dataset: name: tweet_eval type: tweet_eval config: sentiment split: validation args: sentiment metrics: - type: accuracy value: 0.6765 name: accuracy ---  # moshew_bert-mini-sst2-distilled-finetuned-lora-tweet_eval_sentiment This model is a fine-tuned version of [moshew/bert-mini-sst2-distilled](https://huggingface.co/moshew/bert-mini-sst2-distilled) on the tweet_eval dataset. It achieves the following results on the evaluation set: - accuracy: 0.6765 ## 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: 0.0004 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results | accuracy | train_loss | epoch | |:--------:|:----------:|:-----:| | 0.2375 | None | 0 | | 0.6665 | 0.7985 | 0 | | 0.672 | 0.7376 | 1 | | 0.675 | 0.7293 | 2 | | 0.6765 | 0.7231 | 3 | ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.16.1 - Tokenizers 0.15.2

kkimdev/gemma7b-test-1

kkimdev

2024-02-29T13:07:14Z

0

transformers

[ "transformers", "safetensors", "text-generation-inference", "unsloth", "gemma", "trl", "en", "base_model:unsloth/gemma-7b-it-bnb-4bit", "base_model:finetune:unsloth/gemma-7b-it-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]

null

2024-02-29T13:07:06Z

--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - gemma - trl base_model: unsloth/gemma-7b-it-bnb-4bit --- # Uploaded model - **Developed by:** kkimdev - **License:** apache-2.0 - **Finetuned from model :** unsloth/gemma-7b-it-bnb-4bit This gemma model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

TransferGraph/philschmid_tiny-distilbert-classification-finetuned-lora-tweet_eval_sentiment

TransferGraph

2024-02-29T13:06:52Z

0

peft

[ "peft", "safetensors", "parquet", "text-classification", "dataset:tweet_eval", "base_model:philschmid/tiny-distilbert-classification", "base_model:adapter:philschmid/tiny-distilbert-classification", "model-index", "region:us" ]

text-classification

2024-02-29T13:06:50Z

--- library_name: peft tags: - parquet - text-classification datasets: - tweet_eval metrics: - accuracy base_model: philschmid/tiny-distilbert-classification model-index: - name: philschmid_tiny-distilbert-classification-finetuned-lora-tweet_eval_sentiment results: - task: type: text-classification name: Text Classification dataset: name: tweet_eval type: tweet_eval config: sentiment split: validation args: sentiment metrics: - type: accuracy value: 0.4345 name: accuracy ---  # philschmid_tiny-distilbert-classification-finetuned-lora-tweet_eval_sentiment This model is a fine-tuned version of [philschmid/tiny-distilbert-classification](https://huggingface.co/philschmid/tiny-distilbert-classification) on the tweet_eval dataset. It achieves the following results on the evaluation set: - accuracy: 0.4345 ## 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: 0.0004 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results | accuracy | train_loss | epoch | |:--------:|:----------:|:-----:| | 0.4095 | None | 0 | | 0.4345 | 1.0374 | 0 | | 0.4345 | 1.0207 | 1 | | 0.4345 | 1.0183 | 2 | | 0.4345 | 1.0179 | 3 | ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.16.1 - Tokenizers 0.15.2

TransferGraph/saattrupdan_job-listing-relevance-model-finetuned-lora-tweet_eval_emotion

TransferGraph

2024-02-29T13:06:22Z

0

peft

[ "peft", "safetensors", "parquet", "text-classification", "dataset:tweet_eval", "base_model:saattrupdan/job-listing-relevance-model", "base_model:adapter:saattrupdan/job-listing-relevance-model", "license:mit", "model-index", "region:us" ]

text-classification

2024-02-29T12:49:23Z

--- license: mit library_name: peft tags: - parquet - text-classification datasets: - tweet_eval metrics: - accuracy base_model: saattrupdan/job-listing-relevance-model model-index: - name: saattrupdan_job-listing-relevance-model-finetuned-lora-tweet_eval_emotion results: - task: type: text-classification name: Text Classification dataset: name: tweet_eval type: tweet_eval config: emotion split: validation args: emotion metrics: - type: accuracy value: 0.6229946524064172 name: accuracy ---  # saattrupdan_job-listing-relevance-model-finetuned-lora-tweet_eval_emotion This model is a fine-tuned version of [saattrupdan/job-listing-relevance-model](https://huggingface.co/saattrupdan/job-listing-relevance-model) on the tweet_eval dataset. It achieves the following results on the evaluation set: - accuracy: 0.6230 ## 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: 0.0004 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results | accuracy | train_loss | epoch | |:--------:|:----------:|:-----:| | 0.4358 | None | 0 | | 0.4733 | 1.3004 | 0 | | 0.5829 | 1.1392 | 1 | | 0.6203 | 1.0111 | 2 | | 0.6230 | 0.9552 | 3 | ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.16.1 - Tokenizers 0.15.2

TransferGraph/Guscode_DKbert-hatespeech-detection-finetuned-lora-tweet_eval_emotion

TransferGraph

2024-02-29T13:06:14Z

1

0

peft

[ "peft", "safetensors", "parquet", "text-classification", "dataset:tweet_eval", "base_model:Guscode/DKbert-hatespeech-detection", "base_model:adapter:Guscode/DKbert-hatespeech-detection", "license:mit", "model-index", "region:us" ]

text-classification

2024-02-29T12:53:07Z

--- license: mit library_name: peft tags: - parquet - text-classification datasets: - tweet_eval metrics: - accuracy base_model: Guscode/DKbert-hatespeech-detection model-index: - name: Guscode_DKbert-hatespeech-detection-finetuned-lora-tweet_eval_emotion results: - task: type: text-classification name: Text Classification dataset: name: tweet_eval type: tweet_eval config: emotion split: validation args: emotion metrics: - type: accuracy value: 0.48663101604278075 name: accuracy ---  # Guscode_DKbert-hatespeech-detection-finetuned-lora-tweet_eval_emotion This model is a fine-tuned version of [Guscode/DKbert-hatespeech-detection](https://huggingface.co/Guscode/DKbert-hatespeech-detection) on the tweet_eval dataset. It achieves the following results on the evaluation set: - accuracy: 0.4866 ## 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: 0.0004 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results | accuracy | train_loss | epoch | |:--------:|:----------:|:-----:| | 0.2567 | None | 0 | | 0.4465 | 1.2842 | 0 | | 0.4920 | 1.2342 | 1 | | 0.5 | 1.1954 | 2 | | 0.4866 | 1.1742 | 3 | ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.16.1 - Tokenizers 0.15.2

TransferGraph/cross-encoder_ms-marco-MiniLM-L-4-v2-finetuned-lora-tweet_eval_emotion

TransferGraph

2024-02-29T13:05:47Z

0

peft

[ "peft", "safetensors", "parquet", "text-classification", "dataset:tweet_eval", "base_model:cross-encoder/ms-marco-MiniLM-L-4-v2", "base_model:adapter:cross-encoder/ms-marco-MiniLM-L-4-v2", "license:apache-2.0", "model-index", "region:us" ]

text-classification

2024-02-29T13:05:44Z

--- license: apache-2.0 library_name: peft tags: - parquet - text-classification datasets: - tweet_eval metrics: - accuracy base_model: cross-encoder/ms-marco-MiniLM-L-4-v2 model-index: - name: cross-encoder_ms-marco-MiniLM-L-4-v2-finetuned-lora-tweet_eval_emotion results: - task: type: text-classification name: Text Classification dataset: name: tweet_eval type: tweet_eval config: emotion split: validation args: emotion metrics: - type: accuracy value: 0.4358288770053476 name: accuracy ---  # cross-encoder_ms-marco-MiniLM-L-4-v2-finetuned-lora-tweet_eval_emotion This model is a fine-tuned version of [cross-encoder/ms-marco-MiniLM-L-4-v2](https://huggingface.co/cross-encoder/ms-marco-MiniLM-L-4-v2) on the tweet_eval dataset. It achieves the following results on the evaluation set: - accuracy: 0.4358 ## 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: 0.0004 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results | accuracy | train_loss | epoch | |:--------:|:----------:|:-----:| | 0.2380 | None | 0 | | 0.4278 | 1.2772 | 0 | | 0.4278 | 1.2622 | 1 | | 0.4385 | 1.2263 | 2 | | 0.4358 | 1.2005 | 3 | ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.16.1 - Tokenizers 0.15.2

TransferGraph/phailyoor_distilbert-base-uncased-finetuned-yahd-finetuned-lora-tweet_eval_emotion

TransferGraph

2024-02-29T13:04:48Z

1

0

peft

[ "peft", "safetensors", "parquet", "text-classification", "dataset:tweet_eval", "base_model:phailyoor/distilbert-base-uncased-finetuned-yahd", "base_model:adapter:phailyoor/distilbert-base-uncased-finetuned-yahd", "license:apache-2.0", "model-index", "region:us" ]

text-classification

2024-02-29T13:04:42Z

--- license: apache-2.0 library_name: peft tags: - parquet - text-classification datasets: - tweet_eval metrics: - accuracy base_model: phailyoor/distilbert-base-uncased-finetuned-yahd model-index: - name: phailyoor_distilbert-base-uncased-finetuned-yahd-finetuned-lora-tweet_eval_emotion results: - task: type: text-classification name: Text Classification dataset: name: tweet_eval type: tweet_eval config: emotion split: validation args: emotion metrics: - type: accuracy value: 0.6844919786096256 name: accuracy ---  # phailyoor_distilbert-base-uncased-finetuned-yahd-finetuned-lora-tweet_eval_emotion This model is a fine-tuned version of [phailyoor/distilbert-base-uncased-finetuned-yahd](https://huggingface.co/phailyoor/distilbert-base-uncased-finetuned-yahd) on the tweet_eval dataset. It achieves the following results on the evaluation set: - accuracy: 0.6845 ## 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: 0.0004 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results | accuracy | train_loss | epoch | |:--------:|:----------:|:-----:| | 0.3556 | None | 0 | | 0.5535 | 1.2405 | 0 | | 0.6283 | 0.9864 | 1 | | 0.6738 | 0.8234 | 2 | | 0.6845 | 0.7686 | 3 | ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.16.1 - Tokenizers 0.15.2

TransferGraph/ncduy_roberta-imdb-sentiment-analysis-finetuned-lora-tweet_eval_emotion

TransferGraph

2024-02-29T13:03:39Z

4

0

peft

[ "peft", "safetensors", "parquet", "text-classification", "dataset:tweet_eval", "base_model:ncduy/roberta-imdb-sentiment-analysis", "base_model:adapter:ncduy/roberta-imdb-sentiment-analysis", "model-index", "region:us" ]

text-classification

2024-02-29T13:03:36Z

--- library_name: peft tags: - parquet - text-classification datasets: - tweet_eval metrics: - accuracy base_model: ncduy/roberta-imdb-sentiment-analysis model-index: - name: ncduy_roberta-imdb-sentiment-analysis-finetuned-lora-tweet_eval_emotion results: - task: type: text-classification name: Text Classification dataset: name: tweet_eval type: tweet_eval config: emotion split: validation args: emotion metrics: - type: accuracy value: 0.7379679144385026 name: accuracy ---  # ncduy_roberta-imdb-sentiment-analysis-finetuned-lora-tweet_eval_emotion This model is a fine-tuned version of [ncduy/roberta-imdb-sentiment-analysis](https://huggingface.co/ncduy/roberta-imdb-sentiment-analysis) on the tweet_eval dataset. It achieves the following results on the evaluation set: - accuracy: 0.7380 ## 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: 0.0004 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results | accuracy | train_loss | epoch | |:--------:|:----------:|:-----:| | 0.2594 | None | 0 | | 0.7059 | 0.9399 | 0 | | 0.7353 | 0.6872 | 1 | | 0.7380 | 0.6120 | 2 | | 0.7380 | 0.5775 | 3 | ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.16.1 - Tokenizers 0.15.2

TransferGraph/moghis_distilbert-base-uncased-finetuned-emotion-finetuned-lora-tweet_eval_emotion

TransferGraph

2024-02-29T13:03:31Z

0

peft

[ "peft", "safetensors", "parquet", "text-classification", "dataset:tweet_eval", "base_model:moghis/distilbert-base-uncased-finetuned-emotion", "base_model:adapter:moghis/distilbert-base-uncased-finetuned-emotion", "license:apache-2.0", "model-index", "region:us" ]

text-classification

2024-02-29T13:03:26Z

--- license: apache-2.0 library_name: peft tags: - parquet - text-classification datasets: - tweet_eval metrics: - accuracy base_model: moghis/distilbert-base-uncased-finetuned-emotion model-index: - name: moghis_distilbert-base-uncased-finetuned-emotion-finetuned-lora-tweet_eval_emotion results: - task: type: text-classification name: Text Classification dataset: name: tweet_eval type: tweet_eval config: emotion split: validation args: emotion metrics: - type: accuracy value: 0.7245989304812834 name: accuracy ---  # moghis_distilbert-base-uncased-finetuned-emotion-finetuned-lora-tweet_eval_emotion This model is a fine-tuned version of [moghis/distilbert-base-uncased-finetuned-emotion](https://huggingface.co/moghis/distilbert-base-uncased-finetuned-emotion) on the tweet_eval dataset. It achieves the following results on the evaluation set: - accuracy: 0.7246 ## 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: 0.0004 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results | accuracy | train_loss | epoch | |:--------:|:----------:|:-----:| | 0.1684 | None | 0 | | 0.7005 | 0.8896 | 0 | | 0.7086 | 0.7329 | 1 | | 0.7139 | 0.6568 | 2 | | 0.7246 | 0.6240 | 3 | ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.16.1 - Tokenizers 0.15.2

TransferGraph/jasonyim2_distilbert-base-uncased-finetuned-emotion-finetuned-lora-tweet_eval_emotion

TransferGraph

2024-02-29T13:03:30Z

0

peft

[ "peft", "safetensors", "parquet", "text-classification", "dataset:tweet_eval", "base_model:jasonyim2/distilbert-base-uncased-finetuned-emotion", "base_model:adapter:jasonyim2/distilbert-base-uncased-finetuned-emotion", "license:apache-2.0", "model-index", "region:us" ]

text-classification

2024-02-29T13:03:25Z

--- license: apache-2.0 library_name: peft tags: - parquet - text-classification datasets: - tweet_eval metrics: - accuracy base_model: jasonyim2/distilbert-base-uncased-finetuned-emotion model-index: - name: jasonyim2_distilbert-base-uncased-finetuned-emotion-finetuned-lora-tweet_eval_emotion results: - task: type: text-classification name: Text Classification dataset: name: tweet_eval type: tweet_eval config: emotion split: validation args: emotion metrics: - type: accuracy value: 0.7219251336898396 name: accuracy ---  # jasonyim2_distilbert-base-uncased-finetuned-emotion-finetuned-lora-tweet_eval_emotion This model is a fine-tuned version of [jasonyim2/distilbert-base-uncased-finetuned-emotion](https://huggingface.co/jasonyim2/distilbert-base-uncased-finetuned-emotion) on the tweet_eval dataset. It achieves the following results on the evaluation set: - accuracy: 0.7219 ## 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: 0.0004 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results | accuracy | train_loss | epoch | |:--------:|:----------:|:-----:| | 0.3529 | None | 0 | | 0.6684 | 0.8859 | 0 | | 0.6925 | 0.7423 | 1 | | 0.7059 | 0.6689 | 2 | | 0.7219 | 0.6358 | 3 | ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.16.1 - Tokenizers 0.15.2

doroshroman/finetuned_sd_xl

doroshroman

2024-02-29T13:01:39Z

0

1

diffusers

[ "diffusers", "tensorboard", "text-to-image", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "lora", "template:sd-lora", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:openrail++", "region:us" ]

text-to-image

2024-02-12T14:23:09Z

--- license: openrail++ library_name: diffusers tags: - text-to-image - stable-diffusion-xl - stable-diffusion-xl-diffusers - text-to-image - diffusers - lora - template:sd-lora - text-to-image - stable-diffusion-xl - stable-diffusion-xl-diffusers - text-to-image - diffusers - lora - template:sd-lora - text-to-image - stable-diffusion-xl - stable-diffusion-xl-diffusers - text-to-image - diffusers - lora - template:sd-lora - text-to-image - stable-diffusion-xl - stable-diffusion-xl-diffusers - text-to-image - diffusers - lora - template:sd-lora - text-to-image - stable-diffusion-xl - stable-diffusion-xl-diffusers - text-to-image - diffusers - lora - template:sd-lora - text-to-image - stable-diffusion-xl - stable-diffusion-xl-diffusers - text-to-image - diffusers - lora - template:sd-lora - text-to-image - stable-diffusion-xl - stable-diffusion-xl-diffusers - text-to-image - diffusers - lora - template:sd-lora - text-to-image - stable-diffusion-xl - stable-diffusion-xl-diffusers - text-to-image - diffusers - lora - template:sd-lora - text-to-image - stable-diffusion-xl - stable-diffusion-xl-diffusers - text-to-image - diffusers - lora - template:sd-lora - text-to-image - stable-diffusion-xl - stable-diffusion-xl-diffusers - text-to-image - diffusers - lora - template:sd-lora - text-to-image - stable-diffusion-xl - stable-diffusion-xl-diffusers - text-to-image - diffusers - lora - template:sd-lora base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: a photo of guy raise money for army widget: [] ---  # SDXL LoRA DreamBooth - doroshroman/finetuned_sd_xl <Gallery /> ## Model description These are doroshroman/finetuned_sd_xl LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained using [DreamBooth](https://dreambooth.github.io/). LoRA for the text encoder was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix. ## Trigger words You should use a photo of guy raise money for army to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](doroshroman/finetuned_sd_xl/tree/main) them in the Files & versions tab. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]

22x99/w2v2-ru-prre

22x99

2024-02-29T12:59:11Z

4

0

transformers

[ "transformers", "safetensors", "wav2vec2-bert", "automatic-speech-recognition", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2024-02-29T08:29:02Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

SKNahin/NER_Deberta55

SKNahin

2024-02-29T12:56:37Z

108

0

transformers

[ "transformers", "tensorboard", "safetensors", "deberta", "token-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2024-02-25T23:44:00Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

TransferGraph/fgaim_tiroberta-geezswitch-finetuned-lora-tweet_eval_emotion

TransferGraph

2024-02-29T12:53:41Z

0

peft

[ "peft", "safetensors", "parquet", "text-classification", "dataset:tweet_eval", "base_model:fgaim/tiroberta-geezswitch", "base_model:adapter:fgaim/tiroberta-geezswitch", "license:cc-by-4.0", "model-index", "region:us" ]

text-classification

2024-02-29T12:53:38Z

--- license: cc-by-4.0 library_name: peft tags: - parquet - text-classification datasets: - tweet_eval metrics: - accuracy base_model: fgaim/tiroberta-geezswitch model-index: - name: fgaim_tiroberta-geezswitch-finetuned-lora-tweet_eval_emotion results: - task: type: text-classification name: Text Classification dataset: name: tweet_eval type: tweet_eval config: emotion split: validation args: emotion metrics: - type: accuracy value: 0.45454545454545453 name: accuracy ---  # fgaim_tiroberta-geezswitch-finetuned-lora-tweet_eval_emotion This model is a fine-tuned version of [fgaim/tiroberta-geezswitch](https://huggingface.co/fgaim/tiroberta-geezswitch) on the tweet_eval dataset. It achieves the following results on the evaluation set: - accuracy: 0.4545 ## 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: 0.0004 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results | accuracy | train_loss | epoch | |:--------:|:----------:|:-----:| | 0.2487 | None | 0 | | 0.4037 | 1.2938 | 0 | | 0.4519 | 1.2385 | 1 | | 0.4545 | 1.2156 | 2 | | 0.4545 | 1.1901 | 3 | ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.16.1 - Tokenizers 0.15.2

TransferGraph/aditeyabaral_finetuned-sail2017-xlm-roberta-base-finetuned-lora-tweet_eval_emotion

TransferGraph

2024-02-29T12:53:15Z

0

peft

[ "peft", "safetensors", "parquet", "text-classification", "dataset:tweet_eval", "base_model:aditeyabaral/finetuned-sail2017-xlm-roberta-base", "base_model:adapter:aditeyabaral/finetuned-sail2017-xlm-roberta-base", "model-index", "region:us" ]

text-classification

2024-02-29T12:53:09Z

--- library_name: peft tags: - parquet - text-classification datasets: - tweet_eval metrics: - accuracy base_model: aditeyabaral/finetuned-sail2017-xlm-roberta-base model-index: - name: aditeyabaral_finetuned-sail2017-xlm-roberta-base-finetuned-lora-tweet_eval_emotion results: - task: type: text-classification name: Text Classification dataset: name: tweet_eval type: tweet_eval config: emotion split: validation args: emotion metrics: - type: accuracy value: 0.6925133689839572 name: accuracy ---  # aditeyabaral_finetuned-sail2017-xlm-roberta-base-finetuned-lora-tweet_eval_emotion This model is a fine-tuned version of [aditeyabaral/finetuned-sail2017-xlm-roberta-base](https://huggingface.co/aditeyabaral/finetuned-sail2017-xlm-roberta-base) on the tweet_eval dataset. It achieves the following results on the evaluation set: - accuracy: 0.6925 ## 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: 0.0004 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results | accuracy | train_loss | epoch | |:--------:|:----------:|:-----:| | 0.1364 | None | 0 | | 0.6043 | 1.0439 | 0 | | 0.6738 | 0.9083 | 1 | | 0.7032 | 0.8345 | 2 | | 0.6925 | 0.7925 | 3 | ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.16.1 - Tokenizers 0.15.2

TransferGraph/ASCCCCCCCC_distilbert-base-chinese-amazon_zh_20000-finetuned-lora-tweet_eval_emotion

TransferGraph

2024-02-29T12:53:11Z

7

0

peft

[ "peft", "safetensors", "parquet", "text-classification", "dataset:tweet_eval", "base_model:ASCCCCCCCC/distilbert-base-chinese-amazon_zh_20000", "base_model:adapter:ASCCCCCCCC/distilbert-base-chinese-amazon_zh_20000", "model-index", "region:us" ]

text-classification

2024-02-29T12:53:09Z

--- library_name: peft tags: - parquet - text-classification datasets: - tweet_eval metrics: - accuracy base_model: ASCCCCCCCC/distilbert-base-chinese-amazon_zh_20000 model-index: - name: ASCCCCCCCC_distilbert-base-chinese-amazon_zh_20000-finetuned-lora-tweet_eval_emotion results: - task: type: text-classification name: Text Classification dataset: name: tweet_eval type: tweet_eval config: emotion split: validation args: emotion metrics: - type: accuracy value: 0.4919786096256685 name: accuracy ---  # ASCCCCCCCC_distilbert-base-chinese-amazon_zh_20000-finetuned-lora-tweet_eval_emotion This model is a fine-tuned version of [ASCCCCCCCC/distilbert-base-chinese-amazon_zh_20000](https://huggingface.co/ASCCCCCCCC/distilbert-base-chinese-amazon_zh_20000) on the tweet_eval dataset. It achieves the following results on the evaluation set: - accuracy: 0.4920 ## 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: 0.0004 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results | accuracy | train_loss | epoch | |:--------:|:----------:|:-----:| | 0.2380 | None | 0 | | 0.4840 | 1.2523 | 0 | | 0.4973 | 1.1999 | 1 | | 0.4893 | 1.1651 | 2 | | 0.4920 | 1.1285 | 3 | ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.16.1 - Tokenizers 0.15.2

TransferGraph/ChrisUPM_BioBERT_Re_trained-finetuned-lora-tweet_eval_emotion

TransferGraph

2024-02-29T12:53:09Z

1

0

peft

[ "peft", "safetensors", "parquet", "text-classification", "dataset:tweet_eval", "base_model:ChrisUPM/BioBERT_Re_trained", "base_model:adapter:ChrisUPM/BioBERT_Re_trained", "model-index", "region:us" ]

text-classification

2024-02-29T12:53:07Z

--- library_name: peft tags: - parquet - text-classification datasets: - tweet_eval metrics: - accuracy base_model: ChrisUPM/BioBERT_Re_trained model-index: - name: ChrisUPM_BioBERT_Re_trained-finetuned-lora-tweet_eval_emotion results: - task: type: text-classification name: Text Classification dataset: name: tweet_eval type: tweet_eval config: emotion split: validation args: emotion metrics: - type: accuracy value: 0.5347593582887701 name: accuracy ---  # ChrisUPM_BioBERT_Re_trained-finetuned-lora-tweet_eval_emotion This model is a fine-tuned version of [ChrisUPM/BioBERT_Re_trained](https://huggingface.co/ChrisUPM/BioBERT_Re_trained) on the tweet_eval dataset. It achieves the following results on the evaluation set: - accuracy: 0.5348 ## 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: 0.0004 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results | accuracy | train_loss | epoch | |:--------:|:----------:|:-----:| | 0.2754 | None | 0 | | 0.4332 | 1.2735 | 0 | | 0.4947 | 1.2404 | 1 | | 0.5267 | 1.1690 | 2 | | 0.5348 | 1.1118 | 3 | ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.16.1 - Tokenizers 0.15.2

TransferGraph/classla_bcms-bertic-parlasent-bcs-ter-finetuned-lora-tweet_eval_emotion

TransferGraph

2024-02-29T12:53:07Z

4

0

peft

[ "peft", "safetensors", "parquet", "text-classification", "dataset:tweet_eval", "base_model:classla/bcms-bertic-parlasent-bcs-ter", "base_model:adapter:classla/bcms-bertic-parlasent-bcs-ter", "model-index", "region:us" ]

text-classification

2024-02-29T12:53:05Z

--- library_name: peft tags: - parquet - text-classification datasets: - tweet_eval metrics: - accuracy base_model: classla/bcms-bertic-parlasent-bcs-ter model-index: - name: classla_bcms-bertic-parlasent-bcs-ter-finetuned-lora-tweet_eval_emotion results: - task: type: text-classification name: Text Classification dataset: name: tweet_eval type: tweet_eval config: emotion split: validation args: emotion metrics: - type: accuracy value: 0.4946524064171123 name: accuracy ---  # classla_bcms-bertic-parlasent-bcs-ter-finetuned-lora-tweet_eval_emotion This model is a fine-tuned version of [classla/bcms-bertic-parlasent-bcs-ter](https://huggingface.co/classla/bcms-bertic-parlasent-bcs-ter) on the tweet_eval dataset. It achieves the following results on the evaluation set: - accuracy: 0.4947 ## 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: 0.0004 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results | accuracy | train_loss | epoch | |:--------:|:----------:|:-----:| | 0.1818 | None | 0 | | 0.4679 | 1.2475 | 0 | | 0.4786 | 1.1874 | 1 | | 0.4920 | 1.1567 | 2 | | 0.4947 | 1.1286 | 3 | ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.16.1 - Tokenizers 0.15.2

TransferGraph/Jeevesh8_feather_berts_92-finetuned-lora-tweet_eval_emotion

TransferGraph

2024-02-29T12:52:52Z

0

peft

[ "peft", "safetensors", "parquet", "text-classification", "dataset:tweet_eval", "base_model:Jeevesh8/feather_berts_92", "base_model:adapter:Jeevesh8/feather_berts_92", "model-index", "region:us" ]

text-classification

2024-02-29T12:52:51Z

--- library_name: peft tags: - parquet - text-classification datasets: - tweet_eval metrics: - accuracy base_model: Jeevesh8/feather_berts_92 model-index: - name: Jeevesh8_feather_berts_92-finetuned-lora-tweet_eval_emotion results: - task: type: text-classification name: Text Classification dataset: name: tweet_eval type: tweet_eval config: emotion split: validation args: emotion metrics: - type: accuracy value: 0.7406417112299465 name: accuracy ---  # Jeevesh8_feather_berts_92-finetuned-lora-tweet_eval_emotion This model is a fine-tuned version of [Jeevesh8/feather_berts_92](https://huggingface.co/Jeevesh8/feather_berts_92) on the tweet_eval dataset. It achieves the following results on the evaluation set: - accuracy: 0.7406 ## 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: 0.0004 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results | accuracy | train_loss | epoch | |:--------:|:----------:|:-----:| | 0.4278 | None | 0 | | 0.6257 | 1.1915 | 0 | | 0.6738 | 0.9857 | 1 | | 0.7299 | 0.8524 | 2 | | 0.7406 | 0.7986 | 3 | ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.16.1 - Tokenizers 0.15.2

TransferGraph/YeRyeongLee_electra-base-discriminator-finetuned-filtered-0602-finetuned-lora-tweet_eval_emotion

TransferGraph

2024-02-29T12:52:50Z

0

peft

[ "peft", "safetensors", "parquet", "text-classification", "dataset:tweet_eval", "base_model:YeRyeongLee/electra-base-discriminator-finetuned-filtered-0602", "base_model:adapter:YeRyeongLee/electra-base-discriminator-finetuned-filtered-0602", "license:apache-2.0", "model-index", "region:us" ]

text-classification

2024-02-29T12:52:48Z

--- license: apache-2.0 library_name: peft tags: - parquet - text-classification datasets: - tweet_eval metrics: - accuracy base_model: YeRyeongLee/electra-base-discriminator-finetuned-filtered-0602 model-index: - name: YeRyeongLee_electra-base-discriminator-finetuned-filtered-0602-finetuned-lora-tweet_eval_emotion results: - task: type: text-classification name: Text Classification dataset: name: tweet_eval type: tweet_eval config: emotion split: validation args: emotion metrics: - type: accuracy value: 0.44919786096256686 name: accuracy ---  # YeRyeongLee_electra-base-discriminator-finetuned-filtered-0602-finetuned-lora-tweet_eval_emotion This model is a fine-tuned version of [YeRyeongLee/electra-base-discriminator-finetuned-filtered-0602](https://huggingface.co/YeRyeongLee/electra-base-discriminator-finetuned-filtered-0602) on the tweet_eval dataset. It achieves the following results on the evaluation set: - accuracy: 0.4492 ## 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: 0.0004 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results | accuracy | train_loss | epoch | |:--------:|:----------:|:-----:| | 0.0775 | None | 0 | | 0.4465 | 1.2791 | 0 | | 0.4439 | 1.2467 | 1 | | 0.4545 | 1.2331 | 2 | | 0.4492 | 1.2253 | 3 | ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.16.1 - Tokenizers 0.15.2

TransferGraph/M47Labs_spanish_news_classification_headlines_untrained-finetuned-lora-tweet_eval_emotion

TransferGraph

2024-02-29T12:52:42Z

0

peft

[ "peft", "safetensors", "parquet", "text-classification", "dataset:tweet_eval", "base_model:M47Labs/spanish_news_classification_headlines_untrained", "base_model:adapter:M47Labs/spanish_news_classification_headlines_untrained", "model-index", "region:us" ]

text-classification

2024-02-29T12:52:39Z

--- library_name: peft tags: - parquet - text-classification datasets: - tweet_eval metrics: - accuracy base_model: M47Labs/spanish_news_classification_headlines_untrained model-index: - name: M47Labs_spanish_news_classification_headlines_untrained-finetuned-lora-tweet_eval_emotion results: - task: type: text-classification name: Text Classification dataset: name: tweet_eval type: tweet_eval config: emotion split: validation args: emotion metrics: - type: accuracy value: 0.5053475935828877 name: accuracy ---  # M47Labs_spanish_news_classification_headlines_untrained-finetuned-lora-tweet_eval_emotion This model is a fine-tuned version of [M47Labs/spanish_news_classification_headlines_untrained](https://huggingface.co/M47Labs/spanish_news_classification_headlines_untrained) on the tweet_eval dataset. It achieves the following results on the evaluation set: - accuracy: 0.5053 ## 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: 0.0004 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results | accuracy | train_loss | epoch | |:--------:|:----------:|:-----:| | 0.2032 | None | 0 | | 0.4733 | 1.2328 | 0 | | 0.4973 | 1.1826 | 1 | | 0.4920 | 1.1451 | 2 | | 0.5053 | 1.1119 | 3 | ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.16.1 - Tokenizers 0.15.2

TransferGraph/cardiffnlp_bertweet-base-stance-climate-finetuned-lora-tweet_eval_emotion

TransferGraph

2024-02-29T12:52:38Z

3

0

peft

[ "peft", "safetensors", "parquet", "text-classification", "dataset:tweet_eval", "base_model:cardiffnlp/bertweet-base-stance-climate", "base_model:adapter:cardiffnlp/bertweet-base-stance-climate", "model-index", "region:us" ]

text-classification

2024-02-29T12:52:37Z

--- library_name: peft tags: - parquet - text-classification datasets: - tweet_eval metrics: - accuracy base_model: cardiffnlp/bertweet-base-stance-climate model-index: - name: cardiffnlp_bertweet-base-stance-climate-finetuned-lora-tweet_eval_emotion results: - task: type: text-classification name: Text Classification dataset: name: tweet_eval type: tweet_eval config: emotion split: validation args: emotion metrics: - type: accuracy value: 0.7058823529411765 name: accuracy ---  # cardiffnlp_bertweet-base-stance-climate-finetuned-lora-tweet_eval_emotion This model is a fine-tuned version of [cardiffnlp/bertweet-base-stance-climate](https://huggingface.co/cardiffnlp/bertweet-base-stance-climate) on the tweet_eval dataset. It achieves the following results on the evaluation set: - accuracy: 0.7059 ## 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: 0.0004 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results | accuracy | train_loss | epoch | |:--------:|:----------:|:-----:| | 0.2567 | None | 0 | | 0.5160 | 1.2144 | 0 | | 0.6631 | 0.9743 | 1 | | 0.6979 | 0.8127 | 2 | | 0.7059 | 0.7347 | 3 | ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.16.1 - Tokenizers 0.15.2

TransferGraph/Hate-speech-CNERG_bert-base-uncased-hatexplain-rationale-two-finetuned-lora-tweet_eval_emotion

TransferGraph

2024-02-29T12:52:34Z

4

0

peft

[ "peft", "safetensors", "parquet", "text-classification", "dataset:tweet_eval", "base_model:Hate-speech-CNERG/bert-base-uncased-hatexplain-rationale-two", "base_model:adapter:Hate-speech-CNERG/bert-base-uncased-hatexplain-rationale-two", "license:apache-2.0", "model-index", "region:us" ]

text-classification

2024-02-29T12:52:32Z

--- license: apache-2.0 library_name: peft tags: - parquet - text-classification datasets: - tweet_eval metrics: - accuracy base_model: Hate-speech-CNERG/bert-base-uncased-hatexplain-rationale-two model-index: - name: Hate-speech-CNERG_bert-base-uncased-hatexplain-rationale-two-finetuned-lora-tweet_eval_emotion results: - task: type: text-classification name: Text Classification dataset: name: tweet_eval type: tweet_eval config: emotion split: validation args: emotion metrics: - type: accuracy value: 0.7352941176470589 name: accuracy ---  # Hate-speech-CNERG_bert-base-uncased-hatexplain-rationale-two-finetuned-lora-tweet_eval_emotion This model is a fine-tuned version of [Hate-speech-CNERG/bert-base-uncased-hatexplain-rationale-two](https://huggingface.co/Hate-speech-CNERG/bert-base-uncased-hatexplain-rationale-two) on the tweet_eval dataset. It achieves the following results on the evaluation set: - accuracy: 0.7353 ## 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: 0.0004 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results | accuracy | train_loss | epoch | |:--------:|:----------:|:-----:| | 0.4037 | None | 0 | | 0.5160 | 1.2275 | 0 | | 0.6979 | 0.9809 | 1 | | 0.7193 | 0.8033 | 2 | | 0.7353 | 0.7538 | 3 | ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.16.1 - Tokenizers 0.15.2

TransferGraph/boychaboy_MNLI_roberta-base-finetuned-lora-tweet_eval_emotion

TransferGraph

2024-02-29T12:52:23Z

0

peft

[ "peft", "safetensors", "parquet", "text-classification", "dataset:tweet_eval", "model-index", "region:us" ]

text-classification

2024-02-29T12:52:20Z

--- library_name: peft tags: - parquet - text-classification datasets: - tweet_eval metrics: - accuracy base_model: boychaboy/MNLI_roberta-base model-index: - name: boychaboy_MNLI_roberta-base-finetuned-lora-tweet_eval_emotion results: - task: type: text-classification name: Text Classification dataset: name: tweet_eval type: tweet_eval config: emotion split: validation args: emotion metrics: - type: accuracy value: 0.7700534759358288 name: accuracy ---  # boychaboy_MNLI_roberta-base-finetuned-lora-tweet_eval_emotion This model is a fine-tuned version of [boychaboy/MNLI_roberta-base](https://huggingface.co/boychaboy/MNLI_roberta-base) on the tweet_eval dataset. It achieves the following results on the evaluation set: - accuracy: 0.7701 ## 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: 0.0004 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results | accuracy | train_loss | epoch | |:--------:|:----------:|:-----:| | 0.3316 | None | 0 | | 0.7299 | 0.9658 | 0 | | 0.7781 | 0.6329 | 1 | | 0.7674 | 0.5839 | 2 | | 0.7701 | 0.5558 | 3 | ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.16.1 - Tokenizers 0.15.2

TransferGraph/mrm8488_codebert-base-finetuned-detect-insecure-code-finetuned-lora-tweet_eval_emotion

TransferGraph

2024-02-29T12:52:23Z

1

0

peft

[ "peft", "safetensors", "parquet", "text-classification", "dataset:tweet_eval", "base_model:mrm8488/codebert-base-finetuned-detect-insecure-code", "base_model:adapter:mrm8488/codebert-base-finetuned-detect-insecure-code", "model-index", "region:us" ]

text-classification

2024-02-29T12:52:20Z

--- library_name: peft tags: - parquet - text-classification datasets: - tweet_eval metrics: - accuracy base_model: mrm8488/codebert-base-finetuned-detect-insecure-code model-index: - name: mrm8488_codebert-base-finetuned-detect-insecure-code-finetuned-lora-tweet_eval_emotion results: - task: type: text-classification name: Text Classification dataset: name: tweet_eval type: tweet_eval config: emotion split: validation args: emotion metrics: - type: accuracy value: 0.6256684491978609 name: accuracy ---  # mrm8488_codebert-base-finetuned-detect-insecure-code-finetuned-lora-tweet_eval_emotion This model is a fine-tuned version of [mrm8488/codebert-base-finetuned-detect-insecure-code](https://huggingface.co/mrm8488/codebert-base-finetuned-detect-insecure-code) on the tweet_eval dataset. It achieves the following results on the evaluation set: - accuracy: 0.6257 ## 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: 0.0004 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results | accuracy | train_loss | epoch | |:--------:|:----------:|:-----:| | 0.1417 | None | 0 | | 0.4893 | 1.2212 | 0 | | 0.5882 | 1.0598 | 1 | | 0.6283 | 0.9872 | 2 | | 0.6257 | 0.9374 | 3 | ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.16.1 - Tokenizers 0.15.2

TransferGraph/CAMeL-Lab_bert-base-arabic-camelbert-da-sentiment-finetuned-lora-tweet_eval_emotion

TransferGraph

2024-02-29T12:52:22Z

2

0

peft

[ "peft", "safetensors", "parquet", "text-classification", "dataset:tweet_eval", "base_model:CAMeL-Lab/bert-base-arabic-camelbert-da-sentiment", "base_model:adapter:CAMeL-Lab/bert-base-arabic-camelbert-da-sentiment", "license:apache-2.0", "model-index", "region:us" ]

text-classification

2024-02-29T12:52:18Z

--- license: apache-2.0 library_name: peft tags: - parquet - text-classification datasets: - tweet_eval metrics: - accuracy base_model: CAMeL-Lab/bert-base-arabic-camelbert-da-sentiment model-index: - name: CAMeL-Lab_bert-base-arabic-camelbert-da-sentiment-finetuned-lora-tweet_eval_emotion results: - task: type: text-classification name: Text Classification dataset: name: tweet_eval type: tweet_eval config: emotion split: validation args: emotion metrics: - type: accuracy value: 0.4304812834224599 name: accuracy ---  # CAMeL-Lab_bert-base-arabic-camelbert-da-sentiment-finetuned-lora-tweet_eval_emotion This model is a fine-tuned version of [CAMeL-Lab/bert-base-arabic-camelbert-da-sentiment](https://huggingface.co/CAMeL-Lab/bert-base-arabic-camelbert-da-sentiment) on the tweet_eval dataset. It achieves the following results on the evaluation set: - accuracy: 0.4305 ## 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: 0.0004 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results | accuracy | train_loss | epoch | |:--------:|:----------:|:-----:| | 0.0909 | None | 0 | | 0.4385 | 1.2835 | 0 | | 0.4278 | 1.2635 | 1 | | 0.4305 | 1.2579 | 2 | | 0.4305 | 1.2545 | 3 | ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.16.1 - Tokenizers 0.15.2

TransferGraph/ishan_bert-base-uncased-mnli-finetuned-lora-tweet_eval_emotion

TransferGraph

2024-02-29T12:52:22Z

0

peft

[ "peft", "safetensors", "parquet", "text-classification", "dataset:tweet_eval", "base_model:ishan/bert-base-uncased-mnli", "base_model:adapter:ishan/bert-base-uncased-mnli", "model-index", "region:us" ]

text-classification

2024-02-29T12:52:17Z

--- library_name: peft tags: - parquet - text-classification datasets: - tweet_eval metrics: - accuracy base_model: ishan/bert-base-uncased-mnli model-index: - name: ishan_bert-base-uncased-mnli-finetuned-lora-tweet_eval_emotion results: - task: type: text-classification name: Text Classification dataset: name: tweet_eval type: tweet_eval config: emotion split: validation args: emotion metrics: - type: accuracy value: 0.7700534759358288 name: accuracy ---  # ishan_bert-base-uncased-mnli-finetuned-lora-tweet_eval_emotion This model is a fine-tuned version of [ishan/bert-base-uncased-mnli](https://huggingface.co/ishan/bert-base-uncased-mnli) on the tweet_eval dataset. It achieves the following results on the evaluation set: - accuracy: 0.7701 ## 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: 0.0004 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results | accuracy | train_loss | epoch | |:--------:|:----------:|:-----:| | 0.3369 | None | 0 | | 0.6230 | 1.1865 | 0 | | 0.7059 | 0.9572 | 1 | | 0.7701 | 0.8155 | 2 | | 0.7701 | 0.7561 | 3 | ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.16.1 - Tokenizers 0.15.2

TransferGraph/anferico_bert-for-patents-finetuned-lora-tweet_eval_emotion

TransferGraph

2024-02-29T12:52:22Z

0

peft

[ "peft", "safetensors", "parquet", "text-classification", "dataset:tweet_eval", "base_model:anferico/bert-for-patents", "base_model:adapter:anferico/bert-for-patents", "license:apache-2.0", "model-index", "region:us" ]

text-classification

2024-02-29T12:52:20Z

--- license: apache-2.0 library_name: peft tags: - parquet - text-classification datasets: - tweet_eval metrics: - accuracy base_model: anferico/bert-for-patents model-index: - name: anferico_bert-for-patents-finetuned-lora-tweet_eval_emotion results: - task: type: text-classification name: Text Classification dataset: name: tweet_eval type: tweet_eval config: emotion split: validation args: emotion metrics: - type: accuracy value: 0.5614973262032086 name: accuracy ---  # anferico_bert-for-patents-finetuned-lora-tweet_eval_emotion This model is a fine-tuned version of [anferico/bert-for-patents](https://huggingface.co/anferico/bert-for-patents) on the tweet_eval dataset. It achieves the following results on the evaluation set: - accuracy: 0.5615 ## 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: 0.0004 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results | accuracy | train_loss | epoch | |:--------:|:----------:|:-----:| | 0.2701 | None | 0 | | 0.4251 | 1.2818 | 0 | | 0.5187 | 1.1616 | 1 | | 0.5401 | 1.0477 | 2 | | 0.5615 | 0.9809 | 3 | ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.16.1 - Tokenizers 0.15.2

TransferGraph/aychang_bert-base-cased-trec-coarse-finetuned-lora-tweet_eval_emotion

TransferGraph

2024-02-29T12:52:13Z

1

0

peft

[ "peft", "safetensors", "parquet", "text-classification", "dataset:tweet_eval", "base_model:aychang/bert-base-cased-trec-coarse", "base_model:adapter:aychang/bert-base-cased-trec-coarse", "license:mit", "model-index", "region:us" ]

text-classification

2024-02-29T12:52:10Z

--- license: mit library_name: peft tags: - parquet - text-classification datasets: - tweet_eval metrics: - accuracy base_model: aychang/bert-base-cased-trec-coarse model-index: - name: aychang_bert-base-cased-trec-coarse-finetuned-lora-tweet_eval_emotion results: - task: type: text-classification name: Text Classification dataset: name: tweet_eval type: tweet_eval config: emotion split: validation args: emotion metrics: - type: accuracy value: 0.7406417112299465 name: accuracy ---  # aychang_bert-base-cased-trec-coarse-finetuned-lora-tweet_eval_emotion This model is a fine-tuned version of [aychang/bert-base-cased-trec-coarse](https://huggingface.co/aychang/bert-base-cased-trec-coarse) on the tweet_eval dataset. It achieves the following results on the evaluation set: - accuracy: 0.7406 ## 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: 0.0004 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results | accuracy | train_loss | epoch | |:--------:|:----------:|:-----:| | 0.2460 | None | 0 | | 0.4545 | 1.2636 | 0 | | 0.6043 | 1.1509 | 1 | | 0.7193 | 0.9356 | 2 | | 0.7406 | 0.8091 | 3 | ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.16.1 - Tokenizers 0.15.2

TransferGraph/bert-large-uncased-finetuned-lora-tweet_eval_emotion

TransferGraph

2024-02-29T12:51:53Z

0

peft

[ "peft", "safetensors", "parquet", "text-classification", "dataset:tweet_eval", "base_model:google-bert/bert-large-uncased", "base_model:adapter:google-bert/bert-large-uncased", "license:apache-2.0", "model-index", "region:us" ]

text-classification

2024-02-29T12:51:51Z

--- license: apache-2.0 library_name: peft tags: - parquet - text-classification datasets: - tweet_eval metrics: - accuracy base_model: bert-large-uncased model-index: - name: bert-large-uncased-finetuned-lora-tweet_eval_emotion results: - task: type: text-classification name: Text Classification dataset: name: tweet_eval type: tweet_eval config: emotion split: validation args: emotion metrics: - type: accuracy value: 0.56951871657754 name: accuracy ---  # bert-large-uncased-finetuned-lora-tweet_eval_emotion This model is a fine-tuned version of [bert-large-uncased](https://huggingface.co/bert-large-uncased) on the tweet_eval dataset. It achieves the following results on the evaluation set: - accuracy: 0.5695 ## 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: 0.0004 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results | accuracy | train_loss | epoch | |:--------:|:----------:|:-----:| | 0.4171 | None | 0 | | 0.4278 | 1.2852 | 0 | | 0.4652 | 1.2330 | 1 | | 0.5588 | 1.1183 | 2 | | 0.5695 | 1.0442 | 3 | ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.16.1 - Tokenizers 0.15.2

TransferGraph/matthewburke_korean_sentiment-finetuned-lora-tweet_eval_emotion

TransferGraph

2024-02-29T12:51:50Z

0

peft

[ "peft", "safetensors", "parquet", "text-classification", "dataset:tweet_eval", "base_model:matthewburke/korean_sentiment", "base_model:adapter:matthewburke/korean_sentiment", "model-index", "region:us" ]

text-classification

2024-02-29T12:51:48Z

--- library_name: peft tags: - parquet - text-classification datasets: - tweet_eval metrics: - accuracy base_model: matthewburke/korean_sentiment model-index: - name: matthewburke_korean_sentiment-finetuned-lora-tweet_eval_emotion results: - task: type: text-classification name: Text Classification dataset: name: tweet_eval type: tweet_eval config: emotion split: validation args: emotion metrics: - type: accuracy value: 0.4946524064171123 name: accuracy ---  # matthewburke_korean_sentiment-finetuned-lora-tweet_eval_emotion This model is a fine-tuned version of [matthewburke/korean_sentiment](https://huggingface.co/matthewburke/korean_sentiment) on the tweet_eval dataset. It achieves the following results on the evaluation set: - accuracy: 0.4947 ## 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: 0.0004 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results | accuracy | train_loss | epoch | |:--------:|:----------:|:-----:| | 0.2353 | None | 0 | | 0.4813 | 1.2502 | 0 | | 0.4305 | 1.2180 | 1 | | 0.4973 | 1.1976 | 2 | | 0.4947 | 1.1844 | 3 | ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.16.1 - Tokenizers 0.15.2

TransferGraph/navteca_quora-roberta-base-finetuned-lora-tweet_eval_emotion

TransferGraph

2024-02-29T12:51:41Z

1

0

peft

[ "peft", "safetensors", "parquet", "text-classification", "dataset:tweet_eval", "base_model:navteca/quora-roberta-base", "base_model:adapter:navteca/quora-roberta-base", "license:mit", "model-index", "region:us" ]

text-classification

2024-02-29T12:51:38Z

--- license: mit library_name: peft tags: - parquet - text-classification datasets: - tweet_eval metrics: - accuracy base_model: navteca/quora-roberta-base model-index: - name: navteca_quora-roberta-base-finetuned-lora-tweet_eval_emotion results: - task: type: text-classification name: Text Classification dataset: name: tweet_eval type: tweet_eval config: emotion split: validation args: emotion metrics: - type: accuracy value: 0.5641711229946524 name: accuracy ---  # navteca_quora-roberta-base-finetuned-lora-tweet_eval_emotion This model is a fine-tuned version of [navteca/quora-roberta-base](https://huggingface.co/navteca/quora-roberta-base) on the tweet_eval dataset. It achieves the following results on the evaluation set: - accuracy: 0.5642 ## 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: 0.0004 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results | accuracy | train_loss | epoch | |:--------:|:----------:|:-----:| | 0.0722 | None | 0 | | 0.3717 | 1.3164 | 0 | | 0.4412 | 1.2845 | 1 | | 0.5428 | 1.2363 | 2 | | 0.5642 | 1.1378 | 3 | ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.16.1 - Tokenizers 0.15.2

TransferGraph/morenolq_SumTO_FNS2020-finetuned-lora-tweet_eval_emotion

TransferGraph

2024-02-29T12:51:40Z

0

peft

[ "peft", "safetensors", "parquet", "text-classification", "dataset:tweet_eval", "base_model:morenolq/SumTO_FNS2020", "base_model:adapter:morenolq/SumTO_FNS2020", "model-index", "region:us" ]

text-classification

2024-02-29T12:51:37Z

--- library_name: peft tags: - parquet - text-classification datasets: - tweet_eval metrics: - accuracy base_model: morenolq/SumTO_FNS2020 model-index: - name: morenolq_SumTO_FNS2020-finetuned-lora-tweet_eval_emotion results: - task: type: text-classification name: Text Classification dataset: name: tweet_eval type: tweet_eval config: emotion split: validation args: emotion metrics: - type: accuracy value: 0.45187165775401067 name: accuracy ---  # morenolq_SumTO_FNS2020-finetuned-lora-tweet_eval_emotion This model is a fine-tuned version of [morenolq/SumTO_FNS2020](https://huggingface.co/morenolq/SumTO_FNS2020) on the tweet_eval dataset. It achieves the following results on the evaluation set: - accuracy: 0.4519 ## 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: 0.0004 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results | accuracy | train_loss | epoch | |:--------:|:----------:|:-----:| | 0.1818 | None | 0 | | 0.4278 | 1.2862 | 0 | | 0.4278 | 1.2472 | 1 | | 0.4572 | 1.2178 | 2 | | 0.4519 | 1.1990 | 3 | ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.16.1 - Tokenizers 0.15.2

TransferGraph/michiyasunaga_LinkBERT-base-finetuned-lora-tweet_eval_emotion

TransferGraph

2024-02-29T12:51:36Z

0

peft

[ "peft", "safetensors", "parquet", "text-classification", "dataset:tweet_eval", "base_model:michiyasunaga/LinkBERT-base", "base_model:adapter:michiyasunaga/LinkBERT-base", "license:apache-2.0", "model-index", "region:us" ]

text-classification

2024-02-29T12:51:34Z

--- license: apache-2.0 library_name: peft tags: - parquet - text-classification datasets: - tweet_eval metrics: - accuracy base_model: michiyasunaga/LinkBERT-base model-index: - name: michiyasunaga_LinkBERT-base-finetuned-lora-tweet_eval_emotion results: - task: type: text-classification name: Text Classification dataset: name: tweet_eval type: tweet_eval config: emotion split: validation args: emotion metrics: - type: accuracy value: 0.6898395721925134 name: accuracy ---  # michiyasunaga_LinkBERT-base-finetuned-lora-tweet_eval_emotion This model is a fine-tuned version of [michiyasunaga/LinkBERT-base](https://huggingface.co/michiyasunaga/LinkBERT-base) on the tweet_eval dataset. It achieves the following results on the evaluation set: - accuracy: 0.6898 ## 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: 0.0004 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results | accuracy | train_loss | epoch | |:--------:|:----------:|:-----:| | 0.0749 | None | 0 | | 0.4278 | 1.2771 | 0 | | 0.6310 | 1.2050 | 1 | | 0.6818 | 0.9793 | 2 | | 0.6898 | 0.8838 | 3 | ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.16.1 - Tokenizers 0.15.2

TransferGraph/connectivity_bert_ft_qqp-94-finetuned-lora-tweet_eval_emotion

TransferGraph

2024-02-29T12:51:35Z

0

peft

[ "peft", "safetensors", "parquet", "text-classification", "dataset:tweet_eval", "base_model:connectivity/bert_ft_qqp-94", "base_model:adapter:connectivity/bert_ft_qqp-94", "model-index", "region:us" ]

text-classification

2024-02-29T12:51:33Z

--- library_name: peft tags: - parquet - text-classification datasets: - tweet_eval metrics: - accuracy base_model: connectivity/bert_ft_qqp-94 model-index: - name: connectivity_bert_ft_qqp-94-finetuned-lora-tweet_eval_emotion results: - task: type: text-classification name: Text Classification dataset: name: tweet_eval type: tweet_eval config: emotion split: validation args: emotion metrics: - type: accuracy value: 0.6497326203208557 name: accuracy ---  # connectivity_bert_ft_qqp-94-finetuned-lora-tweet_eval_emotion This model is a fine-tuned version of [connectivity/bert_ft_qqp-94](https://huggingface.co/connectivity/bert_ft_qqp-94) on the tweet_eval dataset. It achieves the following results on the evaluation set: - accuracy: 0.6497 ## 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: 0.0004 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results | accuracy | train_loss | epoch | |:--------:|:----------:|:-----:| | 0.2353 | None | 0 | | 0.4465 | 1.2773 | 0 | | 0.5802 | 1.1764 | 1 | | 0.6444 | 1.0266 | 2 | | 0.6497 | 0.9588 | 3 | ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.16.1 - Tokenizers 0.15.2

TransferGraph/Jeevesh8_lecun_feather_berts-7-finetuned-lora-tweet_eval_emotion

TransferGraph

2024-02-29T12:51:26Z

0

peft

[ "peft", "safetensors", "parquet", "text-classification", "dataset:tweet_eval", "base_model:Jeevesh8/lecun_feather_berts-7", "base_model:adapter:Jeevesh8/lecun_feather_berts-7", "model-index", "region:us" ]

text-classification

2024-02-29T12:51:23Z

--- library_name: peft tags: - parquet - text-classification datasets: - tweet_eval metrics: - accuracy base_model: Jeevesh8/lecun_feather_berts-7 model-index: - name: Jeevesh8_lecun_feather_berts-7-finetuned-lora-tweet_eval_emotion results: - task: type: text-classification name: Text Classification dataset: name: tweet_eval type: tweet_eval config: emotion split: validation args: emotion metrics: - type: accuracy value: 0.7272727272727273 name: accuracy ---  # Jeevesh8_lecun_feather_berts-7-finetuned-lora-tweet_eval_emotion This model is a fine-tuned version of [Jeevesh8/lecun_feather_berts-7](https://huggingface.co/Jeevesh8/lecun_feather_berts-7) on the tweet_eval dataset. It achieves the following results on the evaluation set: - accuracy: 0.7273 ## 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: 0.0004 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results | accuracy | train_loss | epoch | |:--------:|:----------:|:-----:| | 0.3369 | None | 0 | | 0.6070 | 1.1667 | 0 | | 0.6711 | 0.9766 | 1 | | 0.6979 | 0.8727 | 2 | | 0.7273 | 0.8140 | 3 | ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.16.1 - Tokenizers 0.15.2

TransferGraph/Jeevesh8_6ep_bert_ft_cola-29-finetuned-lora-tweet_eval_emotion

TransferGraph

2024-02-29T12:51:23Z

0

peft

[ "peft", "safetensors", "parquet", "text-classification", "dataset:tweet_eval", "base_model:Jeevesh8/6ep_bert_ft_cola-29", "base_model:adapter:Jeevesh8/6ep_bert_ft_cola-29", "model-index", "region:us" ]

text-classification

2024-02-29T12:51:21Z

--- library_name: peft tags: - parquet - text-classification datasets: - tweet_eval metrics: - accuracy base_model: Jeevesh8/6ep_bert_ft_cola-29 model-index: - name: Jeevesh8_6ep_bert_ft_cola-29-finetuned-lora-tweet_eval_emotion results: - task: type: text-classification name: Text Classification dataset: name: tweet_eval type: tweet_eval config: emotion split: validation args: emotion metrics: - type: accuracy value: 0.7299465240641712 name: accuracy ---  # Jeevesh8_6ep_bert_ft_cola-29-finetuned-lora-tweet_eval_emotion This model is a fine-tuned version of [Jeevesh8/6ep_bert_ft_cola-29](https://huggingface.co/Jeevesh8/6ep_bert_ft_cola-29) on the tweet_eval dataset. It achieves the following results on the evaluation set: - accuracy: 0.7299 ## 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: 0.0004 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results | accuracy | train_loss | epoch | |:--------:|:----------:|:-----:| | 0.4412 | None | 0 | | 0.4358 | 1.2650 | 0 | | 0.6444 | 1.1346 | 1 | | 0.6952 | 0.9012 | 2 | | 0.7299 | 0.8322 | 3 | ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.16.1 - Tokenizers 0.15.2

TransferGraph/Jeevesh8_bert_ft_cola-60-finetuned-lora-tweet_eval_emotion

TransferGraph

2024-02-29T12:51:23Z

0

peft

[ "peft", "safetensors", "parquet", "text-classification", "dataset:tweet_eval", "base_model:Jeevesh8/bert_ft_cola-60", "base_model:adapter:Jeevesh8/bert_ft_cola-60", "model-index", "region:us" ]

text-classification

2024-02-29T12:51:20Z

--- library_name: peft tags: - parquet - text-classification datasets: - tweet_eval metrics: - accuracy base_model: Jeevesh8/bert_ft_cola-60 model-index: - name: Jeevesh8_bert_ft_cola-60-finetuned-lora-tweet_eval_emotion results: - task: type: text-classification name: Text Classification dataset: name: tweet_eval type: tweet_eval config: emotion split: validation args: emotion metrics: - type: accuracy value: 0.6470588235294118 name: accuracy ---  # Jeevesh8_bert_ft_cola-60-finetuned-lora-tweet_eval_emotion This model is a fine-tuned version of [Jeevesh8/bert_ft_cola-60](https://huggingface.co/Jeevesh8/bert_ft_cola-60) on the tweet_eval dataset. It achieves the following results on the evaluation set: - accuracy: 0.6471 ## 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: 0.0004 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results | accuracy | train_loss | epoch | |:--------:|:----------:|:-----:| | 0.0963 | None | 0 | | 0.4412 | 1.2649 | 0 | | 0.5 | 1.1765 | 1 | | 0.6096 | 1.0309 | 2 | | 0.6471 | 0.9186 | 3 | ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.16.1 - Tokenizers 0.15.2

TransferGraph/Jeevesh8_bert_ft_qqp-39-finetuned-lora-tweet_eval_emotion

TransferGraph

2024-02-29T12:51:19Z

0

peft

[ "peft", "safetensors", "parquet", "text-classification", "dataset:tweet_eval", "base_model:Jeevesh8/bert_ft_qqp-39", "base_model:adapter:Jeevesh8/bert_ft_qqp-39", "model-index", "region:us" ]

text-classification

2024-02-29T12:51:17Z

--- library_name: peft tags: - parquet - text-classification datasets: - tweet_eval metrics: - accuracy base_model: Jeevesh8/bert_ft_qqp-39 model-index: - name: Jeevesh8_bert_ft_qqp-39-finetuned-lora-tweet_eval_emotion results: - task: type: text-classification name: Text Classification dataset: name: tweet_eval type: tweet_eval config: emotion split: validation args: emotion metrics: - type: accuracy value: 0.6443850267379679 name: accuracy ---  # Jeevesh8_bert_ft_qqp-39-finetuned-lora-tweet_eval_emotion This model is a fine-tuned version of [Jeevesh8/bert_ft_qqp-39](https://huggingface.co/Jeevesh8/bert_ft_qqp-39) on the tweet_eval dataset. It achieves the following results on the evaluation set: - accuracy: 0.6444 ## 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: 0.0004 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results | accuracy | train_loss | epoch | |:--------:|:----------:|:-----:| | 0.3102 | None | 0 | | 0.4545 | 1.2731 | 0 | | 0.5829 | 1.1474 | 1 | | 0.6524 | 1.0011 | 2 | | 0.6444 | 0.9448 | 3 | ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.16.1 - Tokenizers 0.15.2

TransferGraph/Jeevesh8_lecun_feather_berts-8-finetuned-lora-tweet_eval_emotion

TransferGraph

2024-02-29T12:51:19Z

1

0

peft

[ "peft", "safetensors", "parquet", "text-classification", "dataset:tweet_eval", "base_model:Jeevesh8/lecun_feather_berts-8", "base_model:adapter:Jeevesh8/lecun_feather_berts-8", "model-index", "region:us" ]

text-classification

2024-02-29T12:51:17Z

--- library_name: peft tags: - parquet - text-classification datasets: - tweet_eval metrics: - accuracy base_model: Jeevesh8/lecun_feather_berts-8 model-index: - name: Jeevesh8_lecun_feather_berts-8-finetuned-lora-tweet_eval_emotion results: - task: type: text-classification name: Text Classification dataset: name: tweet_eval type: tweet_eval config: emotion split: validation args: emotion metrics: - type: accuracy value: 0.6336898395721925 name: accuracy ---  # Jeevesh8_lecun_feather_berts-8-finetuned-lora-tweet_eval_emotion This model is a fine-tuned version of [Jeevesh8/lecun_feather_berts-8](https://huggingface.co/Jeevesh8/lecun_feather_berts-8) on the tweet_eval dataset. It achieves the following results on the evaluation set: - accuracy: 0.6337 ## 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: 0.0004 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results | accuracy | train_loss | epoch | |:--------:|:----------:|:-----:| | 0.2380 | None | 0 | | 0.5856 | 1.1833 | 0 | | 0.6390 | 1.0057 | 1 | | 0.6364 | 0.9549 | 2 | | 0.6337 | 0.9384 | 3 | ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.16.1 - Tokenizers 0.15.2

TransferGraph/connectivity_bert_ft_qqp-1-finetuned-lora-tweet_eval_emotion

TransferGraph

2024-02-29T12:51:13Z

0

peft

[ "peft", "safetensors", "parquet", "text-classification", "dataset:tweet_eval", "base_model:connectivity/bert_ft_qqp-1", "base_model:adapter:connectivity/bert_ft_qqp-1", "model-index", "region:us" ]

text-classification

2024-02-29T12:51:09Z

--- library_name: peft tags: - parquet - text-classification datasets: - tweet_eval metrics: - accuracy base_model: connectivity/bert_ft_qqp-1 model-index: - name: connectivity_bert_ft_qqp-1-finetuned-lora-tweet_eval_emotion results: - task: type: text-classification name: Text Classification dataset: name: tweet_eval type: tweet_eval config: emotion split: validation args: emotion metrics: - type: accuracy value: 0.7058823529411765 name: accuracy ---  # connectivity_bert_ft_qqp-1-finetuned-lora-tweet_eval_emotion This model is a fine-tuned version of [connectivity/bert_ft_qqp-1](https://huggingface.co/connectivity/bert_ft_qqp-1) on the tweet_eval dataset. It achieves the following results on the evaluation set: - accuracy: 0.7059 ## 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: 0.0004 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results | accuracy | train_loss | epoch | |:--------:|:----------:|:-----:| | 0.4118 | None | 0 | | 0.4572 | 1.2690 | 0 | | 0.6364 | 1.1587 | 1 | | 0.6952 | 0.9070 | 2 | | 0.7059 | 0.8192 | 3 | ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.16.1 - Tokenizers 0.15.2

TransferGraph/AnonymousSub_dummy_2-finetuned-lora-tweet_eval_emotion

TransferGraph

2024-02-29T12:50:50Z

0

peft

[ "peft", "safetensors", "parquet", "text-classification", "dataset:tweet_eval", "base_model:AnonymousSub/dummy_2", "base_model:adapter:AnonymousSub/dummy_2", "model-index", "region:us" ]

text-classification

2024-02-29T12:50:48Z

--- library_name: peft tags: - parquet - text-classification datasets: - tweet_eval metrics: - accuracy base_model: AnonymousSub/dummy_2 model-index: - name: AnonymousSub_dummy_2-finetuned-lora-tweet_eval_emotion results: - task: type: text-classification name: Text Classification dataset: name: tweet_eval type: tweet_eval config: emotion split: validation args: emotion metrics: - type: accuracy value: 0.553475935828877 name: accuracy ---  # AnonymousSub_dummy_2-finetuned-lora-tweet_eval_emotion This model is a fine-tuned version of [AnonymousSub/dummy_2](https://huggingface.co/AnonymousSub/dummy_2) on the tweet_eval dataset. It achieves the following results on the evaluation set: - accuracy: 0.5535 ## 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: 0.0004 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results | accuracy | train_loss | epoch | |:--------:|:----------:|:-----:| | 0.2594 | None | 0 | | 0.4599 | 1.2515 | 0 | | 0.5348 | 1.1725 | 1 | | 0.5481 | 1.1362 | 2 | | 0.5535 | 1.1001 | 3 | ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.16.1 - Tokenizers 0.15.2

TransferGraph/bert-base-uncased-finetuned-lora-tweet_eval_emotion

TransferGraph

2024-02-29T12:50:49Z

0

peft

[ "peft", "safetensors", "parquet", "text-classification", "dataset:tweet_eval", "base_model:google-bert/bert-base-uncased", "base_model:adapter:google-bert/bert-base-uncased", "license:apache-2.0", "model-index", "region:us" ]

text-classification

2024-02-29T12:50:47Z

--- license: apache-2.0 library_name: peft tags: - parquet - text-classification datasets: - tweet_eval metrics: - accuracy base_model: bert-base-uncased model-index: - name: bert-base-uncased-finetuned-lora-tweet_eval_emotion results: - task: type: text-classification name: Text Classification dataset: name: tweet_eval type: tweet_eval config: emotion split: validation args: emotion metrics: - type: accuracy value: 0.7406417112299465 name: accuracy ---  # bert-base-uncased-finetuned-lora-tweet_eval_emotion This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the tweet_eval dataset. It achieves the following results on the evaluation set: - accuracy: 0.7406 ## 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: 0.0004 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results | accuracy | train_loss | epoch | |:--------:|:----------:|:-----:| | 0.3048 | None | 0 | | 0.4412 | 1.2579 | 0 | | 0.7193 | 1.1064 | 1 | | 0.7406 | 0.8318 | 2 | | 0.7406 | 0.7559 | 3 | ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.16.1 - Tokenizers 0.15.2

TransferGraph/Nanatan_distilbert-base-uncased-finetuned-emotion-finetuned-lora-tweet_eval_emotion

TransferGraph

2024-02-29T12:50:40Z

0

peft

[ "peft", "safetensors", "parquet", "text-classification", "dataset:tweet_eval", "base_model:Nanatan/distilbert-base-uncased-finetuned-emotion", "base_model:adapter:Nanatan/distilbert-base-uncased-finetuned-emotion", "license:apache-2.0", "model-index", "region:us" ]

text-classification

2024-02-29T12:50:38Z

--- license: apache-2.0 library_name: peft tags: - parquet - text-classification datasets: - tweet_eval metrics: - accuracy base_model: Nanatan/distilbert-base-uncased-finetuned-emotion model-index: - name: Nanatan_distilbert-base-uncased-finetuned-emotion-finetuned-lora-tweet_eval_emotion results: - task: type: text-classification name: Text Classification dataset: name: tweet_eval type: tweet_eval config: emotion split: validation args: emotion metrics: - type: accuracy value: 0.7379679144385026 name: accuracy ---  # Nanatan_distilbert-base-uncased-finetuned-emotion-finetuned-lora-tweet_eval_emotion This model is a fine-tuned version of [Nanatan/distilbert-base-uncased-finetuned-emotion](https://huggingface.co/Nanatan/distilbert-base-uncased-finetuned-emotion) on the tweet_eval dataset. It achieves the following results on the evaluation set: - accuracy: 0.7380 ## 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: 0.0004 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results | accuracy | train_loss | epoch | |:--------:|:----------:|:-----:| | 0.3021 | None | 0 | | 0.6872 | 0.8774 | 0 | | 0.7246 | 0.7358 | 1 | | 0.7326 | 0.6590 | 2 | | 0.7380 | 0.6254 | 3 | ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.16.1 - Tokenizers 0.15.2

TransferGraph/dhimskyy_wiki-bert-finetuned-lora-tweet_eval_emotion

TransferGraph

2024-02-29T12:50:33Z

0

peft

[ "peft", "safetensors", "parquet", "text-classification", "dataset:tweet_eval", "base_model:dhimskyy/wiki-bert", "base_model:adapter:dhimskyy/wiki-bert", "model-index", "region:us" ]

text-classification

2024-02-29T12:50:31Z

--- library_name: peft tags: - parquet - text-classification datasets: - tweet_eval metrics: - accuracy base_model: dhimskyy/wiki-bert model-index: - name: dhimskyy_wiki-bert-finetuned-lora-tweet_eval_emotion results: - task: type: text-classification name: Text Classification dataset: name: tweet_eval type: tweet_eval config: emotion split: validation args: emotion metrics: - type: accuracy value: 0.43315508021390375 name: accuracy ---  # dhimskyy_wiki-bert-finetuned-lora-tweet_eval_emotion This model is a fine-tuned version of [dhimskyy/wiki-bert](https://huggingface.co/dhimskyy/wiki-bert) on the tweet_eval dataset. It achieves the following results on the evaluation set: - accuracy: 0.4332 ## 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: 0.0004 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results | accuracy | train_loss | epoch | |:--------:|:----------:|:-----:| | 0.2353 | None | 0 | | 0.4251 | 1.2739 | 0 | | 0.4305 | 1.2626 | 1 | | 0.4278 | 1.2564 | 2 | | 0.4332 | 1.2526 | 3 | ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.16.1 - Tokenizers 0.15.2

TransferGraph/roberta-base-finetuned-lora-tweet_eval_emotion

TransferGraph

2024-02-29T12:50:25Z

0

peft

[ "peft", "safetensors", "parquet", "text-classification", "dataset:tweet_eval", "base_model:FacebookAI/roberta-base", "base_model:adapter:FacebookAI/roberta-base", "license:mit", "model-index", "region:us" ]

text-classification

2024-02-29T12:50:22Z

--- license: mit library_name: peft tags: - parquet - text-classification datasets: - tweet_eval metrics: - accuracy base_model: roberta-base model-index: - name: roberta-base-finetuned-lora-tweet_eval_emotion results: - task: type: text-classification name: Text Classification dataset: name: tweet_eval type: tweet_eval config: emotion split: validation args: emotion metrics: - type: accuracy value: 0.7593582887700535 name: accuracy ---  # roberta-base-finetuned-lora-tweet_eval_emotion This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the tweet_eval dataset. It achieves the following results on the evaluation set: - accuracy: 0.7594 ## 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: 0.0004 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results | accuracy | train_loss | epoch | |:--------:|:----------:|:-----:| | 0.2380 | None | 0 | | 0.6818 | 1.1695 | 0 | | 0.7299 | 0.7084 | 1 | | 0.7513 | 0.6157 | 2 | | 0.7594 | 0.5666 | 3 | ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.16.1 - Tokenizers 0.15.2

TransferGraph/jb2k_bert-base-multilingual-cased-language-detection-finetuned-lora-tweet_eval_emotion

TransferGraph

2024-02-29T12:50:06Z

2

0

peft

[ "peft", "safetensors", "parquet", "text-classification", "dataset:tweet_eval", "base_model:jb2k/bert-base-multilingual-cased-language-detection", "base_model:adapter:jb2k/bert-base-multilingual-cased-language-detection", "model-index", "region:us" ]

text-classification

2024-02-29T12:50:03Z

--- library_name: peft tags: - parquet - text-classification datasets: - tweet_eval metrics: - accuracy base_model: jb2k/bert-base-multilingual-cased-language-detection model-index: - name: jb2k_bert-base-multilingual-cased-language-detection-finetuned-lora-tweet_eval_emotion results: - task: type: text-classification name: Text Classification dataset: name: tweet_eval type: tweet_eval config: emotion split: validation args: emotion metrics: - type: accuracy value: 0.45187165775401067 name: accuracy ---  # jb2k_bert-base-multilingual-cased-language-detection-finetuned-lora-tweet_eval_emotion This model is a fine-tuned version of [jb2k/bert-base-multilingual-cased-language-detection](https://huggingface.co/jb2k/bert-base-multilingual-cased-language-detection) on the tweet_eval dataset. It achieves the following results on the evaluation set: - accuracy: 0.4519 ## 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: 0.0004 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results | accuracy | train_loss | epoch | |:--------:|:----------:|:-----:| | 0.2433 | None | 0 | | 0.4332 | 1.2647 | 0 | | 0.4439 | 1.2429 | 1 | | 0.4439 | 1.2280 | 2 | | 0.4519 | 1.2111 | 3 | ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.16.1 - Tokenizers 0.15.2

TransferGraph/Jeevesh8_bert_ft_qqp-55-finetuned-lora-tweet_eval_emotion

TransferGraph

2024-02-29T12:50:06Z

0

peft

[ "peft", "safetensors", "parquet", "text-classification", "dataset:tweet_eval", "base_model:Jeevesh8/bert_ft_qqp-55", "base_model:adapter:Jeevesh8/bert_ft_qqp-55", "model-index", "region:us" ]

text-classification

2024-02-29T12:50:04Z

--- library_name: peft tags: - parquet - text-classification datasets: - tweet_eval metrics: - accuracy base_model: Jeevesh8/bert_ft_qqp-55 model-index: - name: Jeevesh8_bert_ft_qqp-55-finetuned-lora-tweet_eval_emotion results: - task: type: text-classification name: Text Classification dataset: name: tweet_eval type: tweet_eval config: emotion split: validation args: emotion metrics: - type: accuracy value: 0.5614973262032086 name: accuracy ---  # Jeevesh8_bert_ft_qqp-55-finetuned-lora-tweet_eval_emotion This model is a fine-tuned version of [Jeevesh8/bert_ft_qqp-55](https://huggingface.co/Jeevesh8/bert_ft_qqp-55) on the tweet_eval dataset. It achieves the following results on the evaluation set: - accuracy: 0.5615 ## 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: 0.0004 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results | accuracy | train_loss | epoch | |:--------:|:----------:|:-----:| | 0.2086 | None | 0 | | 0.4519 | 1.2748 | 0 | | 0.5080 | 1.1791 | 1 | | 0.5481 | 1.0682 | 2 | | 0.5615 | 1.0189 | 3 | ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.16.1 - Tokenizers 0.15.2

TransferGraph/Jeevesh8_lecun_feather_berts-51-finetuned-lora-tweet_eval_emotion

TransferGraph

2024-02-29T12:50:05Z

0

peft

[ "peft", "safetensors", "parquet", "text-classification", "dataset:tweet_eval", "base_model:Jeevesh8/lecun_feather_berts-51", "base_model:adapter:Jeevesh8/lecun_feather_berts-51", "model-index", "region:us" ]

text-classification

2024-02-29T12:50:03Z

--- library_name: peft tags: - parquet - text-classification datasets: - tweet_eval metrics: - accuracy base_model: Jeevesh8/lecun_feather_berts-51 model-index: - name: Jeevesh8_lecun_feather_berts-51-finetuned-lora-tweet_eval_emotion results: - task: type: text-classification name: Text Classification dataset: name: tweet_eval type: tweet_eval config: emotion split: validation args: emotion metrics: - type: accuracy value: 0.713903743315508 name: accuracy ---  # Jeevesh8_lecun_feather_berts-51-finetuned-lora-tweet_eval_emotion This model is a fine-tuned version of [Jeevesh8/lecun_feather_berts-51](https://huggingface.co/Jeevesh8/lecun_feather_berts-51) on the tweet_eval dataset. It achieves the following results on the evaluation set: - accuracy: 0.7139 ## 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: 0.0004 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results | accuracy | train_loss | epoch | |:--------:|:----------:|:-----:| | 0.4278 | None | 0 | | 0.5802 | 1.2090 | 0 | | 0.6711 | 1.0003 | 1 | | 0.7086 | 0.9109 | 2 | | 0.7139 | 0.8573 | 3 | ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.16.1 - Tokenizers 0.15.2

TransferGraph/distilbert-base-uncased-finetuned-lora-tweet_eval_emotion

TransferGraph

2024-02-29T12:50:05Z

1

0

peft

[ "peft", "safetensors", "parquet", "text-classification", "dataset:tweet_eval", "base_model:distilbert/distilbert-base-uncased", "base_model:adapter:distilbert/distilbert-base-uncased", "license:apache-2.0", "model-index", "region:us" ]

text-classification

2024-02-29T12:50:02Z

--- license: apache-2.0 library_name: peft tags: - parquet - text-classification datasets: - tweet_eval metrics: - accuracy base_model: distilbert-base-uncased model-index: - name: distilbert-base-uncased-finetuned-lora-tweet_eval_emotion results: - task: type: text-classification name: Text Classification dataset: name: tweet_eval type: tweet_eval config: emotion split: validation args: emotion metrics: - type: accuracy value: 0.732620320855615 name: accuracy ---  # distilbert-base-uncased-finetuned-lora-tweet_eval_emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the tweet_eval dataset. It achieves the following results on the evaluation set: - accuracy: 0.7326 ## 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: 0.0004 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results | accuracy | train_loss | epoch | |:--------:|:----------:|:-----:| | 0.2353 | None | 0 | | 0.6631 | 1.0175 | 0 | | 0.7139 | 0.6889 | 1 | | 0.7246 | 0.6209 | 2 | | 0.7326 | 0.5840 | 3 | ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.16.1 - Tokenizers 0.15.2

TransferGraph/Jeevesh8_6ep_bert_ft_cola-12-finetuned-lora-tweet_eval_emotion

TransferGraph

2024-02-29T12:50:00Z

0

peft

[ "peft", "safetensors", "parquet", "text-classification", "dataset:tweet_eval", "base_model:Jeevesh8/6ep_bert_ft_cola-12", "base_model:adapter:Jeevesh8/6ep_bert_ft_cola-12", "model-index", "region:us" ]

text-classification

2024-02-29T12:49:55Z

--- library_name: peft tags: - parquet - text-classification datasets: - tweet_eval metrics: - accuracy base_model: Jeevesh8/6ep_bert_ft_cola-12 model-index: - name: Jeevesh8_6ep_bert_ft_cola-12-finetuned-lora-tweet_eval_emotion results: - task: type: text-classification name: Text Classification dataset: name: tweet_eval type: tweet_eval config: emotion split: validation args: emotion metrics: - type: accuracy value: 0.6443850267379679 name: accuracy ---  # Jeevesh8_6ep_bert_ft_cola-12-finetuned-lora-tweet_eval_emotion This model is a fine-tuned version of [Jeevesh8/6ep_bert_ft_cola-12](https://huggingface.co/Jeevesh8/6ep_bert_ft_cola-12) on the tweet_eval dataset. It achieves the following results on the evaluation set: - accuracy: 0.6444 ## 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: 0.0004 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results | accuracy | train_loss | epoch | |:--------:|:----------:|:-----:| | 0.3235 | None | 0 | | 0.4171 | 1.2687 | 0 | | 0.4626 | 1.2149 | 1 | | 0.6123 | 1.0727 | 2 | | 0.6444 | 0.9374 | 3 | ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.16.1 - Tokenizers 0.15.2

TransferGraph/aXhyra_presentation_emotion_31415-finetuned-lora-tweet_eval_emotion

TransferGraph

2024-02-29T12:49:50Z

0

peft

[ "peft", "safetensors", "parquet", "text-classification", "dataset:tweet_eval", "base_model:aXhyra/presentation_emotion_31415", "base_model:adapter:aXhyra/presentation_emotion_31415", "license:apache-2.0", "model-index", "region:us" ]

text-classification

2024-02-29T12:49:46Z

--- license: apache-2.0 library_name: peft tags: - parquet - text-classification datasets: - tweet_eval metrics: - accuracy base_model: aXhyra/presentation_emotion_31415 model-index: - name: aXhyra_presentation_emotion_31415-finetuned-lora-tweet_eval_emotion results: - task: type: text-classification name: Text Classification dataset: name: tweet_eval type: tweet_eval config: emotion split: validation args: emotion metrics: - type: accuracy value: 0.7780748663101604 name: accuracy ---  # aXhyra_presentation_emotion_31415-finetuned-lora-tweet_eval_emotion This model is a fine-tuned version of [aXhyra/presentation_emotion_31415](https://huggingface.co/aXhyra/presentation_emotion_31415) on the tweet_eval dataset. It achieves the following results on the evaluation set: - accuracy: 0.7781 ## 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: 0.0004 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results | accuracy | train_loss | epoch | |:--------:|:----------:|:-----:| | 0.7781 | None | 0 | | 0.7807 | 0.1641 | 0 | | 0.7834 | 0.1484 | 1 | | 0.7834 | 0.1305 | 2 | | 0.7781 | 0.1284 | 3 | ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.16.1 - Tokenizers 0.15.2

TransferGraph/JB173_distilbert-base-uncased-finetuned-emotion-finetuned-lora-tweet_eval_emotion

TransferGraph

2024-02-29T12:49:41Z

0

peft

[ "peft", "safetensors", "parquet", "text-classification", "dataset:tweet_eval", "base_model:JB173/distilbert-base-uncased-finetuned-emotion", "base_model:adapter:JB173/distilbert-base-uncased-finetuned-emotion", "model-index", "region:us" ]

text-classification

2024-02-29T12:49:38Z

--- library_name: peft tags: - parquet - text-classification datasets: - tweet_eval metrics: - accuracy base_model: JB173/distilbert-base-uncased-finetuned-emotion model-index: - name: JB173_distilbert-base-uncased-finetuned-emotion-finetuned-lora-tweet_eval_emotion results: - task: type: text-classification name: Text Classification dataset: name: tweet_eval type: tweet_eval config: emotion split: validation args: emotion metrics: - type: accuracy value: 0.7245989304812834 name: accuracy ---  # JB173_distilbert-base-uncased-finetuned-emotion-finetuned-lora-tweet_eval_emotion This model is a fine-tuned version of [JB173/distilbert-base-uncased-finetuned-emotion](https://huggingface.co/JB173/distilbert-base-uncased-finetuned-emotion) on the tweet_eval dataset. It achieves the following results on the evaluation set: - accuracy: 0.7246 ## 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: 0.0004 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results | accuracy | train_loss | epoch | |:--------:|:----------:|:-----:| | 0.4064 | None | 0 | | 0.6898 | 0.8641 | 0 | | 0.7246 | 0.7247 | 1 | | 0.7166 | 0.6561 | 2 | | 0.7246 | 0.6172 | 3 | ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.16.1 - Tokenizers 0.15.2

TransferGraph/Jeevesh8_bert_ft_qqp-88-finetuned-lora-tweet_eval_emotion

TransferGraph

2024-02-29T12:49:36Z

0

peft

[ "peft", "safetensors", "parquet", "text-classification", "dataset:tweet_eval", "base_model:Jeevesh8/bert_ft_qqp-88", "base_model:adapter:Jeevesh8/bert_ft_qqp-88", "model-index", "region:us" ]

text-classification

2024-02-29T12:49:34Z

--- library_name: peft tags: - parquet - text-classification datasets: - tweet_eval metrics: - accuracy base_model: Jeevesh8/bert_ft_qqp-88 model-index: - name: Jeevesh8_bert_ft_qqp-88-finetuned-lora-tweet_eval_emotion results: - task: type: text-classification name: Text Classification dataset: name: tweet_eval type: tweet_eval config: emotion split: validation args: emotion metrics: - type: accuracy value: 0.5106951871657754 name: accuracy ---  # Jeevesh8_bert_ft_qqp-88-finetuned-lora-tweet_eval_emotion This model is a fine-tuned version of [Jeevesh8/bert_ft_qqp-88](https://huggingface.co/Jeevesh8/bert_ft_qqp-88) on the tweet_eval dataset. It achieves the following results on the evaluation set: - accuracy: 0.5107 ## 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: 0.0004 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results | accuracy | train_loss | epoch | |:--------:|:----------:|:-----:| | 0.4091 | None | 0 | | 0.4251 | 1.2672 | 0 | | 0.5080 | 1.1863 | 1 | | 0.5027 | 1.1127 | 2 | | 0.5107 | 1.0784 | 3 | ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.16.1 - Tokenizers 0.15.2

TransferGraph/aXhyra_demo_sentiment_31415-finetuned-lora-tweet_eval_emotion

TransferGraph

2024-02-29T12:49:36Z

0

peft

[ "peft", "safetensors", "parquet", "text-classification", "dataset:tweet_eval", "base_model:aXhyra/demo_sentiment_31415", "base_model:adapter:aXhyra/demo_sentiment_31415", "license:apache-2.0", "model-index", "region:us" ]

text-classification

2024-02-29T12:49:33Z

--- license: apache-2.0 library_name: peft tags: - parquet - text-classification datasets: - tweet_eval metrics: - accuracy base_model: aXhyra/demo_sentiment_31415 model-index: - name: aXhyra_demo_sentiment_31415-finetuned-lora-tweet_eval_emotion results: - task: type: text-classification name: Text Classification dataset: name: tweet_eval type: tweet_eval config: emotion split: validation args: emotion metrics: - type: accuracy value: 0.7406417112299465 name: accuracy ---  # aXhyra_demo_sentiment_31415-finetuned-lora-tweet_eval_emotion This model is a fine-tuned version of [aXhyra/demo_sentiment_31415](https://huggingface.co/aXhyra/demo_sentiment_31415) on the tweet_eval dataset. It achieves the following results on the evaluation set: - accuracy: 0.7406 ## 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: 0.0004 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results | accuracy | train_loss | epoch | |:--------:|:----------:|:-----:| | 0.1738 | None | 0 | | 0.7086 | 0.7749 | 0 | | 0.7326 | 0.6331 | 1 | | 0.7433 | 0.5832 | 2 | | 0.7406 | 0.5645 | 3 | ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.16.1 - Tokenizers 0.15.2

TransferGraph/oferweintraub_bert-base-finance-sentiment-noisy-search-finetuned-lora-tweet_eval_emotion

TransferGraph

2024-02-29T12:49:27Z

0

peft

[ "peft", "safetensors", "parquet", "text-classification", "dataset:tweet_eval", "base_model:oferweintraub/bert-base-finance-sentiment-noisy-search", "base_model:adapter:oferweintraub/bert-base-finance-sentiment-noisy-search", "license:apache-2.0", "model-index", "region:us" ]

text-classification

2024-02-29T12:49:25Z

--- license: apache-2.0 library_name: peft tags: - parquet - text-classification datasets: - tweet_eval metrics: - accuracy base_model: oferweintraub/bert-base-finance-sentiment-noisy-search model-index: - name: oferweintraub_bert-base-finance-sentiment-noisy-search-finetuned-lora-tweet_eval_emotion results: - task: type: text-classification name: Text Classification dataset: name: tweet_eval type: tweet_eval config: emotion split: validation args: emotion metrics: - type: accuracy value: 0.7032085561497327 name: accuracy ---  # oferweintraub_bert-base-finance-sentiment-noisy-search-finetuned-lora-tweet_eval_emotion This model is a fine-tuned version of [oferweintraub/bert-base-finance-sentiment-noisy-search](https://huggingface.co/oferweintraub/bert-base-finance-sentiment-noisy-search) on the tweet_eval dataset. It achieves the following results on the evaluation set: - accuracy: 0.7032 ## 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: 0.0004 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results | accuracy | train_loss | epoch | |:--------:|:----------:|:-----:| | 0.3396 | None | 0 | | 0.5428 | 1.1723 | 0 | | 0.6551 | 1.0773 | 1 | | 0.6925 | 0.9860 | 2 | | 0.7032 | 0.9167 | 3 | ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.16.1 - Tokenizers 0.15.2

TransferGraph/aXhyra_emotion_trained_31415-finetuned-lora-tweet_eval_emotion

TransferGraph

2024-02-29T12:49:25Z

2

0

peft

[ "peft", "safetensors", "parquet", "text-classification", "dataset:tweet_eval", "base_model:aXhyra/emotion_trained_31415", "base_model:adapter:aXhyra/emotion_trained_31415", "license:apache-2.0", "model-index", "region:us" ]

text-classification

2024-02-29T12:49:23Z

--- license: apache-2.0 library_name: peft tags: - parquet - text-classification datasets: - tweet_eval metrics: - accuracy base_model: aXhyra/emotion_trained_31415 model-index: - name: aXhyra_emotion_trained_31415-finetuned-lora-tweet_eval_emotion results: - task: type: text-classification name: Text Classification dataset: name: tweet_eval type: tweet_eval config: emotion split: validation args: emotion metrics: - type: accuracy value: 0.7807486631016043 name: accuracy ---  # aXhyra_emotion_trained_31415-finetuned-lora-tweet_eval_emotion This model is a fine-tuned version of [aXhyra/emotion_trained_31415](https://huggingface.co/aXhyra/emotion_trained_31415) on the tweet_eval dataset. It achieves the following results on the evaluation set: - accuracy: 0.7807 ## 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: 0.0004 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results | accuracy | train_loss | epoch | |:--------:|:----------:|:-----:| | 0.7941 | None | 0 | | 0.7914 | 0.0853 | 0 | | 0.7888 | 0.0652 | 1 | | 0.7754 | 0.0602 | 2 | | 0.7807 | 0.0562 | 3 | ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.16.1 - Tokenizers 0.15.2

TransferGraph/JNK789_distilbert-base-uncased-finetuned-tweets-emoji-dataset-finetuned-lora-tweet_eval_emotion

TransferGraph

2024-02-29T12:49:25Z

2

0

peft

[ "peft", "safetensors", "parquet", "text-classification", "dataset:tweet_eval", "base_model:JNK789/distilbert-base-uncased-finetuned-tweets-emoji-dataset", "base_model:adapter:JNK789/distilbert-base-uncased-finetuned-tweets-emoji-dataset", "model-index", "region:us" ]

text-classification

2024-02-29T12:49:22Z

--- library_name: peft tags: - parquet - text-classification datasets: - tweet_eval metrics: - accuracy base_model: JNK789/distilbert-base-uncased-finetuned-tweets-emoji-dataset model-index: - name: JNK789_distilbert-base-uncased-finetuned-tweets-emoji-dataset-finetuned-lora-tweet_eval_emotion results: - task: type: text-classification name: Text Classification dataset: name: tweet_eval type: tweet_eval config: emotion split: validation args: emotion metrics: - type: accuracy value: 0.42780748663101603 name: accuracy ---  # JNK789_distilbert-base-uncased-finetuned-tweets-emoji-dataset-finetuned-lora-tweet_eval_emotion This model is a fine-tuned version of [JNK789/distilbert-base-uncased-finetuned-tweets-emoji-dataset](https://huggingface.co/JNK789/distilbert-base-uncased-finetuned-tweets-emoji-dataset) on the tweet_eval dataset. It achieves the following results on the evaluation set: - accuracy: 0.4278 ## 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: 0.0004 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results | accuracy | train_loss | epoch | |:--------:|:----------:|:-----:| | 0.4278 | None | 0 | | 0.4278 | 1.3804 | 0 | | 0.4278 | 1.3711 | 1 | | 0.4278 | 1.3652 | 2 | | 0.4278 | 1.3623 | 3 | ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.16.1 - Tokenizers 0.15.2

lvcalucioli/llamantino7b_2_question-answering_merged

lvcalucioli

2024-02-29T12:49:24Z

5

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]

text-generation

2024-02-22T12:08:42Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

TransferGraph/Jeevesh8_bert_ft_qqp-9-finetuned-lora-tweet_eval_emotion

TransferGraph

2024-02-29T12:49:08Z

0

peft

[ "peft", "safetensors", "parquet", "text-classification", "dataset:tweet_eval", "base_model:Jeevesh8/bert_ft_qqp-9", "base_model:adapter:Jeevesh8/bert_ft_qqp-9", "model-index", "region:us" ]

text-classification

2024-02-29T12:49:06Z

--- library_name: peft tags: - parquet - text-classification datasets: - tweet_eval metrics: - accuracy base_model: Jeevesh8/bert_ft_qqp-9 model-index: - name: Jeevesh8_bert_ft_qqp-9-finetuned-lora-tweet_eval_emotion results: - task: type: text-classification name: Text Classification dataset: name: tweet_eval type: tweet_eval config: emotion split: validation args: emotion metrics: - type: accuracy value: 0.6336898395721925 name: accuracy ---  # Jeevesh8_bert_ft_qqp-9-finetuned-lora-tweet_eval_emotion This model is a fine-tuned version of [Jeevesh8/bert_ft_qqp-9](https://huggingface.co/Jeevesh8/bert_ft_qqp-9) on the tweet_eval dataset. It achieves the following results on the evaluation set: - accuracy: 0.6337 ## 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: 0.0004 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results | accuracy | train_loss | epoch | |:--------:|:----------:|:-----:| | 0.2647 | None | 0 | | 0.4679 | 1.2645 | 0 | | 0.5160 | 1.1796 | 1 | | 0.6283 | 0.9874 | 2 | | 0.6337 | 0.8972 | 3 | ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.16.1 - Tokenizers 0.15.2

TransferGraph/strickvl_nlp-redaction-classifier-finetuned-lora-tweet_eval_emotion

TransferGraph

2024-02-29T12:49:05Z

1

0

peft

[ "peft", "safetensors", "parquet", "text-classification", "dataset:tweet_eval", "base_model:strickvl/nlp-redaction-classifier", "base_model:adapter:strickvl/nlp-redaction-classifier", "license:mit", "model-index", "region:us" ]

text-classification

2024-02-29T12:49:03Z

--- license: mit library_name: peft tags: - parquet - text-classification datasets: - tweet_eval metrics: - accuracy base_model: strickvl/nlp-redaction-classifier model-index: - name: strickvl_nlp-redaction-classifier-finetuned-lora-tweet_eval_emotion results: - task: type: text-classification name: Text Classification dataset: name: tweet_eval type: tweet_eval config: emotion split: validation args: emotion metrics: - type: accuracy value: 0.5401069518716578 name: accuracy ---  # strickvl_nlp-redaction-classifier-finetuned-lora-tweet_eval_emotion This model is a fine-tuned version of [strickvl/nlp-redaction-classifier](https://huggingface.co/strickvl/nlp-redaction-classifier) on the tweet_eval dataset. It achieves the following results on the evaluation set: - accuracy: 0.5401 ## 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: 0.0004 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results | accuracy | train_loss | epoch | |:--------:|:----------:|:-----:| | 0.4278 | None | 0 | | 0.4278 | 1.2709 | 0 | | 0.4840 | 1.2408 | 1 | | 0.5160 | 1.1781 | 2 | | 0.5401 | 1.1320 | 3 | ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.16.1 - Tokenizers 0.15.2

TransferGraph/milyiyo_selectra-small-finetuned-amazon-review-finetuned-lora-tweet_eval_emotion

TransferGraph

2024-02-29T12:48:57Z

1

0

peft

[ "peft", "safetensors", "parquet", "text-classification", "dataset:tweet_eval", "base_model:milyiyo/selectra-small-finetuned-amazon-review", "base_model:adapter:milyiyo/selectra-small-finetuned-amazon-review", "license:apache-2.0", "model-index", "region:us" ]

text-classification

2024-02-29T12:48:55Z

--- license: apache-2.0 library_name: peft tags: - parquet - text-classification datasets: - tweet_eval metrics: - accuracy base_model: milyiyo/selectra-small-finetuned-amazon-review model-index: - name: milyiyo_selectra-small-finetuned-amazon-review-finetuned-lora-tweet_eval_emotion results: - task: type: text-classification name: Text Classification dataset: name: tweet_eval type: tweet_eval config: emotion split: validation args: emotion metrics: - type: accuracy value: 0.47593582887700536 name: accuracy ---  # milyiyo_selectra-small-finetuned-amazon-review-finetuned-lora-tweet_eval_emotion This model is a fine-tuned version of [milyiyo/selectra-small-finetuned-amazon-review](https://huggingface.co/milyiyo/selectra-small-finetuned-amazon-review) on the tweet_eval dataset. It achieves the following results on the evaluation set: - accuracy: 0.4759 ## 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: 0.0004 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results | accuracy | train_loss | epoch | |:--------:|:----------:|:-----:| | 0.2380 | None | 0 | | 0.4840 | 1.2635 | 0 | | 0.5027 | 1.2380 | 1 | | 0.4626 | 1.2238 | 2 | | 0.4759 | 1.2154 | 3 | ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.16.1 - Tokenizers 0.15.2

TransferGraph/chiragasarpota_scotus-bert-finetuned-lora-tweet_eval_emotion

TransferGraph

2024-02-29T12:48:56Z

0

peft

[ "peft", "safetensors", "parquet", "text-classification", "dataset:tweet_eval", "base_model:chiragasarpota/scotus-bert", "base_model:adapter:chiragasarpota/scotus-bert", "license:apache-2.0", "model-index", "region:us" ]

text-classification

2024-02-29T12:48:53Z

--- license: apache-2.0 library_name: peft tags: - parquet - text-classification datasets: - tweet_eval metrics: - accuracy base_model: chiragasarpota/scotus-bert model-index: - name: chiragasarpota_scotus-bert-finetuned-lora-tweet_eval_emotion results: - task: type: text-classification name: Text Classification dataset: name: tweet_eval type: tweet_eval config: emotion split: validation args: emotion metrics: - type: accuracy value: 0.42780748663101603 name: accuracy ---  # chiragasarpota_scotus-bert-finetuned-lora-tweet_eval_emotion This model is a fine-tuned version of [chiragasarpota/scotus-bert](https://huggingface.co/chiragasarpota/scotus-bert) on the tweet_eval dataset. It achieves the following results on the evaluation set: - accuracy: 0.4278 ## 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: 0.0004 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results | accuracy | train_loss | epoch | |:--------:|:----------:|:-----:| | 0.2594 | None | 0 | | 0.4278 | 1.3091 | 0 | | 0.4278 | 1.2684 | 1 | | 0.4278 | 1.2646 | 2 | | 0.4278 | 1.2645 | 3 | ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.16.1 - Tokenizers 0.15.2

TransferGraph/jaesun_distilbert-base-uncased-finetuned-cola-finetuned-lora-tweet_eval_emotion

TransferGraph

2024-02-29T12:48:38Z

0

peft

[ "peft", "safetensors", "parquet", "text-classification", "dataset:tweet_eval", "base_model:jaesun/distilbert-base-uncased-finetuned-cola", "base_model:adapter:jaesun/distilbert-base-uncased-finetuned-cola", "license:apache-2.0", "model-index", "region:us" ]

text-classification

2024-02-29T12:48:36Z

--- license: apache-2.0 library_name: peft tags: - parquet - text-classification datasets: - tweet_eval metrics: - accuracy base_model: jaesun/distilbert-base-uncased-finetuned-cola model-index: - name: jaesun_distilbert-base-uncased-finetuned-cola-finetuned-lora-tweet_eval_emotion results: - task: type: text-classification name: Text Classification dataset: name: tweet_eval type: tweet_eval config: emotion split: validation args: emotion metrics: - type: accuracy value: 0.7032085561497327 name: accuracy ---  # jaesun_distilbert-base-uncased-finetuned-cola-finetuned-lora-tweet_eval_emotion This model is a fine-tuned version of [jaesun/distilbert-base-uncased-finetuned-cola](https://huggingface.co/jaesun/distilbert-base-uncased-finetuned-cola) on the tweet_eval dataset. It achieves the following results on the evaluation set: - accuracy: 0.7032 ## 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: 0.0004 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results | accuracy | train_loss | epoch | |:--------:|:----------:|:-----:| | 0.3316 | None | 0 | | 0.6016 | 1.1651 | 0 | | 0.6818 | 0.8386 | 1 | | 0.7166 | 0.6910 | 2 | | 0.7032 | 0.6499 | 3 | ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.16.1 - Tokenizers 0.15.2

TransferGraph/neibla_distilbert-base-uncased-finetuned-emotion-finetuned-lora-tweet_eval_emotion

TransferGraph

2024-02-29T12:48:35Z

0

peft

[ "peft", "safetensors", "parquet", "text-classification", "dataset:tweet_eval", "base_model:neibla/distilbert-base-uncased-finetuned-emotion", "base_model:adapter:neibla/distilbert-base-uncased-finetuned-emotion", "license:apache-2.0", "model-index", "region:us" ]

text-classification

2024-02-29T12:48:33Z

--- license: apache-2.0 library_name: peft tags: - parquet - text-classification datasets: - tweet_eval metrics: - accuracy base_model: neibla/distilbert-base-uncased-finetuned-emotion model-index: - name: neibla_distilbert-base-uncased-finetuned-emotion-finetuned-lora-tweet_eval_emotion results: - task: type: text-classification name: Text Classification dataset: name: tweet_eval type: tweet_eval config: emotion split: validation args: emotion metrics: - type: accuracy value: 0.7299465240641712 name: accuracy ---  # neibla_distilbert-base-uncased-finetuned-emotion-finetuned-lora-tweet_eval_emotion This model is a fine-tuned version of [neibla/distilbert-base-uncased-finetuned-emotion](https://huggingface.co/neibla/distilbert-base-uncased-finetuned-emotion) on the tweet_eval dataset. It achieves the following results on the evaluation set: - accuracy: 0.7299 ## 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: 0.0004 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results | accuracy | train_loss | epoch | |:--------:|:----------:|:-----:| | 0.3422 | None | 0 | | 0.6818 | 0.8986 | 0 | | 0.7246 | 0.7357 | 1 | | 0.7273 | 0.6598 | 2 | | 0.7299 | 0.6188 | 3 | ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.16.1 - Tokenizers 0.15.2

TransferGraph/moshew_bert-mini-sst2-distilled-finetuned-lora-tweet_eval_emotion

TransferGraph

2024-02-29T12:48:35Z

0

peft

[ "peft", "safetensors", "parquet", "text-classification", "dataset:tweet_eval", "base_model:moshew/bert-mini-sst2-distilled", "base_model:adapter:moshew/bert-mini-sst2-distilled", "model-index", "region:us" ]

text-classification

2024-02-29T12:48:33Z

--- library_name: peft tags: - parquet - text-classification datasets: - tweet_eval metrics: - accuracy base_model: moshew/bert-mini-sst2-distilled model-index: - name: moshew_bert-mini-sst2-distilled-finetuned-lora-tweet_eval_emotion results: - task: type: text-classification name: Text Classification dataset: name: tweet_eval type: tweet_eval config: emotion split: validation args: emotion metrics: - type: accuracy value: 0.5721925133689839 name: accuracy ---  # moshew_bert-mini-sst2-distilled-finetuned-lora-tweet_eval_emotion This model is a fine-tuned version of [moshew/bert-mini-sst2-distilled](https://huggingface.co/moshew/bert-mini-sst2-distilled) on the tweet_eval dataset. It achieves the following results on the evaluation set: - accuracy: 0.5722 ## 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: 0.0004 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results | accuracy | train_loss | epoch | |:--------:|:----------:|:-----:| | 0.5668 | None | 0 | | 0.5668 | 1.0973 | 0 | | 0.5749 | 1.0593 | 1 | | 0.5722 | 1.0378 | 2 | | 0.5722 | 1.0263 | 3 | ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.16.1 - Tokenizers 0.15.2

TransferGraph/JonatanGk_roberta-base-bne-finetuned-cyberbullying-spanish-finetuned-lora-tweet_eval_emotion

TransferGraph

2024-02-29T12:48:35Z

8

0

peft

[ "peft", "safetensors", "parquet", "text-classification", "dataset:tweet_eval", "base_model:JonatanGk/roberta-base-bne-finetuned-cyberbullying-spanish", "base_model:adapter:JonatanGk/roberta-base-bne-finetuned-cyberbullying-spanish", "model-index", "region:us" ]

text-classification

2024-02-29T12:48:33Z

--- library_name: peft tags: - parquet - text-classification datasets: - tweet_eval metrics: - accuracy base_model: JonatanGk/roberta-base-bne-finetuned-cyberbullying-spanish model-index: - name: JonatanGk_roberta-base-bne-finetuned-cyberbullying-spanish-finetuned-lora-tweet_eval_emotion results: - task: type: text-classification name: Text Classification dataset: name: tweet_eval type: tweet_eval config: emotion split: validation args: emotion metrics: - type: accuracy value: 0.4385026737967914 name: accuracy ---  # JonatanGk_roberta-base-bne-finetuned-cyberbullying-spanish-finetuned-lora-tweet_eval_emotion This model is a fine-tuned version of [JonatanGk/roberta-base-bne-finetuned-cyberbullying-spanish](https://huggingface.co/JonatanGk/roberta-base-bne-finetuned-cyberbullying-spanish) on the tweet_eval dataset. It achieves the following results on the evaluation set: - accuracy: 0.4385 ## 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: 0.0004 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results | accuracy | train_loss | epoch | |:--------:|:----------:|:-----:| | 0.2620 | None | 0 | | 0.4144 | 1.2872 | 0 | | 0.3984 | 1.2571 | 1 | | 0.4412 | 1.2472 | 2 | | 0.4385 | 1.2295 | 3 | ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.16.1 - Tokenizers 0.15.2

TransferGraph/pietrotrope_emotion_final-finetuned-lora-tweet_eval_emotion

TransferGraph

2024-02-29T12:48:31Z

0

peft

[ "peft", "safetensors", "parquet", "text-classification", "dataset:tweet_eval", "base_model:pietrotrope/emotion_final", "base_model:adapter:pietrotrope/emotion_final", "model-index", "region:us" ]

text-classification

2024-02-29T12:48:29Z

--- library_name: peft tags: - parquet - text-classification datasets: - tweet_eval metrics: - accuracy base_model: pietrotrope/emotion_final model-index: - name: pietrotrope_emotion_final-finetuned-lora-tweet_eval_emotion results: - task: type: text-classification name: Text Classification dataset: name: tweet_eval type: tweet_eval config: emotion split: validation args: emotion metrics: - type: accuracy value: 0.786096256684492 name: accuracy ---  # pietrotrope_emotion_final-finetuned-lora-tweet_eval_emotion This model is a fine-tuned version of [pietrotrope/emotion_final](https://huggingface.co/pietrotrope/emotion_final) on the tweet_eval dataset. It achieves the following results on the evaluation set: - accuracy: 0.7861 ## 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: 0.0004 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results | accuracy | train_loss | epoch | |:--------:|:----------:|:-----:| | 0.7941 | None | 0 | | 0.7914 | 0.0810 | 0 | | 0.7888 | 0.0640 | 1 | | 0.7888 | 0.0589 | 2 | | 0.7861 | 0.0520 | 3 | ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.16.1 - Tokenizers 0.15.2

TransferGraph/Jeevesh8_bert_ft_qqp-40-finetuned-lora-tweet_eval_emotion

TransferGraph

2024-02-29T12:48:07Z

0

peft

[ "peft", "safetensors", "parquet", "text-classification", "dataset:tweet_eval", "base_model:Jeevesh8/bert_ft_qqp-40", "base_model:adapter:Jeevesh8/bert_ft_qqp-40", "model-index", "region:us" ]

text-classification

2024-02-29T12:48:05Z

--- library_name: peft tags: - parquet - text-classification datasets: - tweet_eval metrics: - accuracy base_model: Jeevesh8/bert_ft_qqp-40 model-index: - name: Jeevesh8_bert_ft_qqp-40-finetuned-lora-tweet_eval_emotion results: - task: type: text-classification name: Text Classification dataset: name: tweet_eval type: tweet_eval config: emotion split: validation args: emotion metrics: - type: accuracy value: 0.6818181818181818 name: accuracy ---  # Jeevesh8_bert_ft_qqp-40-finetuned-lora-tweet_eval_emotion This model is a fine-tuned version of [Jeevesh8/bert_ft_qqp-40](https://huggingface.co/Jeevesh8/bert_ft_qqp-40) on the tweet_eval dataset. It achieves the following results on the evaluation set: - accuracy: 0.6818 ## 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: 0.0004 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results | accuracy | train_loss | epoch | |:--------:|:----------:|:-----:| | 0.3770 | None | 0 | | 0.4706 | 1.2464 | 0 | | 0.5963 | 1.1009 | 1 | | 0.6791 | 0.9371 | 2 | | 0.6818 | 0.8690 | 3 | ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.16.1 - Tokenizers 0.15.2

TransferGraph/heranm_finetuning-sentiment-model-3000-samples-finetuned-lora-tweet_eval_emotion

TransferGraph

2024-02-29T12:47:56Z

0

peft

[ "peft", "safetensors", "parquet", "text-classification", "dataset:tweet_eval", "base_model:heranm/finetuning-sentiment-model-3000-samples", "base_model:adapter:heranm/finetuning-sentiment-model-3000-samples", "license:apache-2.0", "model-index", "region:us" ]

text-classification

2024-02-29T12:47:54Z

--- license: apache-2.0 library_name: peft tags: - parquet - text-classification datasets: - tweet_eval metrics: - accuracy base_model: heranm/finetuning-sentiment-model-3000-samples model-index: - name: heranm_finetuning-sentiment-model-3000-samples-finetuned-lora-tweet_eval_emotion results: - task: type: text-classification name: Text Classification dataset: name: tweet_eval type: tweet_eval config: emotion split: validation args: emotion metrics: - type: accuracy value: 0.7379679144385026 name: accuracy ---  # heranm_finetuning-sentiment-model-3000-samples-finetuned-lora-tweet_eval_emotion This model is a fine-tuned version of [heranm/finetuning-sentiment-model-3000-samples](https://huggingface.co/heranm/finetuning-sentiment-model-3000-samples) on the tweet_eval dataset. It achieves the following results on the evaluation set: - accuracy: 0.7380 ## 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: 0.0004 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results | accuracy | train_loss | epoch | |:--------:|:----------:|:-----:| | 0.2032 | None | 0 | | 0.7059 | 0.8609 | 0 | | 0.7620 | 0.6577 | 1 | | 0.7487 | 0.6057 | 2 | | 0.7380 | 0.5801 | 3 | ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.16.1 - Tokenizers 0.15.2

TransferGraph/philschmid_tiny-distilbert-classification-finetuned-lora-tweet_eval_emotion

TransferGraph

2024-02-29T12:47:56Z

0

peft

[ "peft", "safetensors", "parquet", "text-classification", "dataset:tweet_eval", "base_model:philschmid/tiny-distilbert-classification", "base_model:adapter:philschmid/tiny-distilbert-classification", "model-index", "region:us" ]

text-classification

2024-02-29T12:47:54Z

--- library_name: peft tags: - parquet - text-classification datasets: - tweet_eval metrics: - accuracy base_model: philschmid/tiny-distilbert-classification model-index: - name: philschmid_tiny-distilbert-classification-finetuned-lora-tweet_eval_emotion results: - task: type: text-classification name: Text Classification dataset: name: tweet_eval type: tweet_eval config: emotion split: validation args: emotion metrics: - type: accuracy value: 0.42780748663101603 name: accuracy ---  # philschmid_tiny-distilbert-classification-finetuned-lora-tweet_eval_emotion This model is a fine-tuned version of [philschmid/tiny-distilbert-classification](https://huggingface.co/philschmid/tiny-distilbert-classification) on the tweet_eval dataset. It achieves the following results on the evaluation set: - accuracy: 0.4278 ## 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: 0.0004 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results | accuracy | train_loss | epoch | |:--------:|:----------:|:-----:| | 0.2380 | None | 0 | | 0.4278 | 1.3801 | 0 | | 0.4278 | 1.3679 | 1 | | 0.4278 | 1.3588 | 2 | | 0.4278 | 1.3538 | 3 | ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.16.1 - Tokenizers 0.15.2

TransferGraph/nreimers_mmarco-mMiniLMv2-L6-H384-v1-finetuned-lora-tweet_eval_emotion

TransferGraph

2024-02-29T12:47:56Z

2

0

peft

[ "peft", "safetensors", "parquet", "text-classification", "dataset:tweet_eval", "base_model:nreimers/mmarco-mMiniLMv2-L6-H384-v1", "base_model:adapter:nreimers/mmarco-mMiniLMv2-L6-H384-v1", "model-index", "region:us" ]

text-classification

2024-02-29T12:47:52Z

--- library_name: peft tags: - parquet - text-classification datasets: - tweet_eval metrics: - accuracy base_model: nreimers/mmarco-mMiniLMv2-L6-H384-v1 model-index: - name: nreimers_mmarco-mMiniLMv2-L6-H384-v1-finetuned-lora-tweet_eval_emotion results: - task: type: text-classification name: Text Classification dataset: name: tweet_eval type: tweet_eval config: emotion split: validation args: emotion metrics: - type: accuracy value: 0.42513368983957217 name: accuracy ---  # nreimers_mmarco-mMiniLMv2-L6-H384-v1-finetuned-lora-tweet_eval_emotion This model is a fine-tuned version of [nreimers/mmarco-mMiniLMv2-L6-H384-v1](https://huggingface.co/nreimers/mmarco-mMiniLMv2-L6-H384-v1) on the tweet_eval dataset. It achieves the following results on the evaluation set: - accuracy: 0.4251 ## 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: 0.0004 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results | accuracy | train_loss | epoch | |:--------:|:----------:|:-----:| | 0.2353 | None | 0 | | 0.4251 | 1.2736 | 0 | | 0.4251 | 1.2574 | 1 | | 0.4251 | 1.2516 | 2 | | 0.4251 | 1.2477 | 3 | ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.16.1 - Tokenizers 0.15.2

TransferGraph/Jeevesh8_init_bert_ft_qqp-49-finetuned-lora-tweet_eval_emotion

TransferGraph

2024-02-29T12:47:54Z

0

peft

[ "peft", "safetensors", "parquet", "text-classification", "dataset:tweet_eval", "base_model:Jeevesh8/init_bert_ft_qqp-49", "base_model:adapter:Jeevesh8/init_bert_ft_qqp-49", "model-index", "region:us" ]

text-classification

2024-02-29T12:47:53Z

--- library_name: peft tags: - parquet - text-classification datasets: - tweet_eval metrics: - accuracy base_model: Jeevesh8/init_bert_ft_qqp-49 model-index: - name: Jeevesh8_init_bert_ft_qqp-49-finetuned-lora-tweet_eval_emotion results: - task: type: text-classification name: Text Classification dataset: name: tweet_eval type: tweet_eval config: emotion split: validation args: emotion metrics: - type: accuracy value: 0.553475935828877 name: accuracy ---  # Jeevesh8_init_bert_ft_qqp-49-finetuned-lora-tweet_eval_emotion This model is a fine-tuned version of [Jeevesh8/init_bert_ft_qqp-49](https://huggingface.co/Jeevesh8/init_bert_ft_qqp-49) on the tweet_eval dataset. It achieves the following results on the evaluation set: - accuracy: 0.5535 ## 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: 0.0004 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results | accuracy | train_loss | epoch | |:--------:|:----------:|:-----:| | 0.4198 | None | 0 | | 0.4305 | 1.2917 | 0 | | 0.4840 | 1.2411 | 1 | | 0.5241 | 1.1301 | 2 | | 0.5535 | 1.0543 | 3 | ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.16.1 - Tokenizers 0.15.2

TransferGraph/Jeevesh8_feather_berts_46-finetuned-lora-tweet_eval_emotion

TransferGraph

2024-02-29T12:47:54Z

0

peft

[ "peft", "safetensors", "parquet", "text-classification", "dataset:tweet_eval", "base_model:Jeevesh8/feather_berts_46", "base_model:adapter:Jeevesh8/feather_berts_46", "model-index", "region:us" ]

text-classification

2024-02-29T12:47:52Z

--- library_name: peft tags: - parquet - text-classification datasets: - tweet_eval metrics: - accuracy base_model: Jeevesh8/feather_berts_46 model-index: - name: Jeevesh8_feather_berts_46-finetuned-lora-tweet_eval_emotion results: - task: type: text-classification name: Text Classification dataset: name: tweet_eval type: tweet_eval config: emotion split: validation args: emotion metrics: - type: accuracy value: 0.7112299465240641 name: accuracy ---  # Jeevesh8_feather_berts_46-finetuned-lora-tweet_eval_emotion This model is a fine-tuned version of [Jeevesh8/feather_berts_46](https://huggingface.co/Jeevesh8/feather_berts_46) on the tweet_eval dataset. It achieves the following results on the evaluation set: - accuracy: 0.7112 ## 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: 0.0004 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results | accuracy | train_loss | epoch | |:--------:|:----------:|:-----:| | 0.2754 | None | 0 | | 0.5775 | 1.2198 | 0 | | 0.6604 | 0.9839 | 1 | | 0.6979 | 0.8798 | 2 | | 0.7112 | 0.8237 | 3 | ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.16.1 - Tokenizers 0.15.2

TransferGraph/abdelkader_distilbert-base-uncased-finetuned-emotion-finetuned-lora-tweet_eval_emotion

TransferGraph

2024-02-29T12:47:42Z

0

peft

[ "peft", "safetensors", "parquet", "text-classification", "dataset:tweet_eval", "base_model:abdelkader/distilbert-base-uncased-finetuned-emotion", "base_model:adapter:abdelkader/distilbert-base-uncased-finetuned-emotion", "license:apache-2.0", "model-index", "region:us" ]

text-classification

2024-02-29T12:47:36Z

--- license: apache-2.0 library_name: peft tags: - parquet - text-classification datasets: - tweet_eval metrics: - accuracy base_model: abdelkader/distilbert-base-uncased-finetuned-emotion model-index: - name: abdelkader_distilbert-base-uncased-finetuned-emotion-finetuned-lora-tweet_eval_emotion results: - task: type: text-classification name: Text Classification dataset: name: tweet_eval type: tweet_eval config: emotion split: validation args: emotion metrics: - type: accuracy value: 0.7352941176470589 name: accuracy ---  # abdelkader_distilbert-base-uncased-finetuned-emotion-finetuned-lora-tweet_eval_emotion This model is a fine-tuned version of [abdelkader/distilbert-base-uncased-finetuned-emotion](https://huggingface.co/abdelkader/distilbert-base-uncased-finetuned-emotion) on the tweet_eval dataset. It achieves the following results on the evaluation set: - accuracy: 0.7353 ## 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: 0.0004 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results | accuracy | train_loss | epoch | |:--------:|:----------:|:-----:| | 0.2647 | None | 0 | | 0.6898 | 0.8616 | 0 | | 0.7246 | 0.7169 | 1 | | 0.7326 | 0.6474 | 2 | | 0.7353 | 0.6150 | 3 | ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.16.1 - Tokenizers 0.15.2

TransferGraph/dapang_distilroberta-base-mic-sym-finetuned-lora-tweet_eval_emotion

TransferGraph

2024-02-29T12:47:42Z

1

0

peft

[ "peft", "safetensors", "parquet", "text-classification", "dataset:tweet_eval", "base_model:agi-css/distilroberta-base-mic-sym", "base_model:adapter:agi-css/distilroberta-base-mic-sym", "license:apache-2.0", "model-index", "region:us" ]

text-classification

2024-02-29T12:47:36Z

--- license: apache-2.0 library_name: peft tags: - parquet - text-classification datasets: - tweet_eval metrics: - accuracy base_model: dapang/distilroberta-base-mic-sym model-index: - name: dapang_distilroberta-base-mic-sym-finetuned-lora-tweet_eval_emotion results: - task: type: text-classification name: Text Classification dataset: name: tweet_eval type: tweet_eval config: emotion split: validation args: emotion metrics: - type: accuracy value: 0.732620320855615 name: accuracy ---  # dapang_distilroberta-base-mic-sym-finetuned-lora-tweet_eval_emotion This model is a fine-tuned version of [dapang/distilroberta-base-mic-sym](https://huggingface.co/dapang/distilroberta-base-mic-sym) on the tweet_eval dataset. It achieves the following results on the evaluation set: - accuracy: 0.7326 ## 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: 0.0004 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results | accuracy | train_loss | epoch | |:--------:|:----------:|:-----:| | 0.0749 | None | 0 | | 0.6444 | 1.0939 | 0 | | 0.6898 | 0.8092 | 1 | | 0.7139 | 0.7366 | 2 | | 0.7326 | 0.6813 | 3 | ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.16.1 - Tokenizers 0.15.2

TransferGraph/yukta10_finetuning-sentiment-model-3000-samples-finetuned-lora-tweet_eval_emotion

TransferGraph

2024-02-29T12:47:42Z

0

peft

[ "peft", "safetensors", "parquet", "text-classification", "dataset:tweet_eval", "base_model:yukta10/finetuning-sentiment-model-3000-samples", "base_model:adapter:yukta10/finetuning-sentiment-model-3000-samples", "license:apache-2.0", "model-index", "region:us" ]

text-classification

2024-02-29T12:47:36Z

--- license: apache-2.0 library_name: peft tags: - parquet - text-classification datasets: - tweet_eval metrics: - accuracy base_model: yukta10/finetuning-sentiment-model-3000-samples model-index: - name: yukta10_finetuning-sentiment-model-3000-samples-finetuned-lora-tweet_eval_emotion results: - task: type: text-classification name: Text Classification dataset: name: tweet_eval type: tweet_eval config: emotion split: validation args: emotion metrics: - type: accuracy value: 0.7379679144385026 name: accuracy ---  # yukta10_finetuning-sentiment-model-3000-samples-finetuned-lora-tweet_eval_emotion This model is a fine-tuned version of [yukta10/finetuning-sentiment-model-3000-samples](https://huggingface.co/yukta10/finetuning-sentiment-model-3000-samples) on the tweet_eval dataset. It achieves the following results on the evaluation set: - accuracy: 0.7380 ## 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: 0.0004 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results | accuracy | train_loss | epoch | |:--------:|:----------:|:-----:| | 0.2647 | None | 0 | | 0.7032 | 0.8680 | 0 | | 0.7540 | 0.6776 | 1 | | 0.7353 | 0.6196 | 2 | | 0.7380 | 0.5839 | 3 | ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.16.1 - Tokenizers 0.15.2

TransferGraph/riyadhctg_distilbert-base-uncased-finetuned-cola-finetuned-lora-tweet_eval_emotion

TransferGraph

2024-02-29T12:47:42Z

0

peft

[ "peft", "safetensors", "parquet", "text-classification", "dataset:tweet_eval", "base_model:riyadhctg/distilbert-base-uncased-finetuned-cola", "base_model:adapter:riyadhctg/distilbert-base-uncased-finetuned-cola", "license:apache-2.0", "model-index", "region:us" ]

text-classification

2024-02-29T12:47:36Z

--- license: apache-2.0 library_name: peft tags: - parquet - text-classification datasets: - tweet_eval metrics: - accuracy base_model: riyadhctg/distilbert-base-uncased-finetuned-cola model-index: - name: riyadhctg_distilbert-base-uncased-finetuned-cola-finetuned-lora-tweet_eval_emotion results: - task: type: text-classification name: Text Classification dataset: name: tweet_eval type: tweet_eval config: emotion split: validation args: emotion metrics: - type: accuracy value: 0.7299465240641712 name: accuracy ---  # riyadhctg_distilbert-base-uncased-finetuned-cola-finetuned-lora-tweet_eval_emotion This model is a fine-tuned version of [riyadhctg/distilbert-base-uncased-finetuned-cola](https://huggingface.co/riyadhctg/distilbert-base-uncased-finetuned-cola) on the tweet_eval dataset. It achieves the following results on the evaluation set: - accuracy: 0.7299 ## 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: 0.0004 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results | accuracy | train_loss | epoch | |:--------:|:----------:|:-----:| | 0.2406 | None | 0 | | 0.6123 | 1.1167 | 0 | | 0.7139 | 0.7458 | 1 | | 0.7299 | 0.6476 | 2 | | 0.7299 | 0.6153 | 3 | ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.16.1 - Tokenizers 0.15.2

CatBarks/GPT2ES_spamming-email-classification1_1_tokenizer

CatBarks

2024-02-29T12:47:25Z

0

transformers

[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

2024-02-29T12:47:24Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

jiaqianwu/ppo-Huggy

jiaqianwu

2024-02-29T12:46:51Z

1

0

ml-agents

[ "ml-agents", "tensorboard", "onnx", "Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]

reinforcement-learning

2024-02-29T12:45:09Z

--- library_name: ml-agents tags: - Huggy - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Huggy --- # **ppo** Agent playing **Huggy** This is a trained model of a **ppo** agent playing **Huggy** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A *longer tutorial* to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent **playing directly in your browser** 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: jiaqianwu/ppo-Huggy 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

lewtun/gemma-7b-sft-full-dolly-v3

lewtun

2024-02-29T12:36:27Z

12

0

transformers

[ "transformers", "safetensors", "gemma", "text-generation", "alignment-handbook", "trl", "sft", "generated_from_trainer", "conversational", "dataset:philschmid/dolly-15k-oai-style", "base_model:google/gemma-7b", "base_model:finetune:google/gemma-7b", "license:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-02-29T12:24:16Z

--- license: other base_model: google/gemma-7b tags: - alignment-handbook - trl - sft - generated_from_trainer - trl - sft - generated_from_trainer datasets: - philschmid/dolly-15k-oai-style model-index: - name: gemma-7b-sft-full-dolly-v3 results: [] ---  # gemma-7b-sft-full-dolly-v3 This model is a fine-tuned version of [google/gemma-7b](https://huggingface.co/google/gemma-7b) on the philschmid/dolly-15k-oai-style 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: 4 - eval_batch_size: 16 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - total_train_batch_size: 32 - total_eval_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3 ### Training results ### Framework versions - Transformers 4.39.0.dev0 - Pytorch 2.1.2+cu121 - Datasets 2.14.6 - Tokenizers 0.15.1

JaniShubh/gemma2b_FT

JaniShubh

2024-02-29T12:35:44Z

22

0

transformers

[ "transformers", "gguf", "gemma", "text-generation-inference", "unsloth", "en", "base_model:google/gemma-2b", "base_model:quantized:google/gemma-2b", "license:apache-2.0", "endpoints_compatible", "region:us" ]

null

2024-02-29T12:33:49Z

--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - gemma - gguf base_model: google/gemma-2b --- # Uploaded model - **Developed by:** JaniShubh - **License:** apache-2.0 - **Finetuned from model :** google/gemma-2b This gemma model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

VamsiPranav/hindi-mlm

VamsiPranav

2024-02-29T12:35:02Z

106

0

transformers

[ "transformers", "tensorboard", "safetensors", "bert", "feature-extraction", "generated_from_trainer", "base_model:google-bert/bert-base-multilingual-cased", "base_model:finetune:google-bert/bert-base-multilingual-cased", "license:apache-2.0", "endpoints_compatible", "region:us" ]

feature-extraction

2024-02-29T12:34:05Z

--- license: apache-2.0 base_model: bert-base-multilingual-cased tags: - generated_from_trainer model-index: - name: hindi-mlm results: [] ---  # hindi-mlm This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 512 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 1000 - num_epochs: 100 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.38.1 - Pytorch 2.1.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2

Astral7/bert_base_cased_qa

Astral7

2024-02-29T12:25:05Z

61

0

transformers

[ "transformers", "tf", "bert", "question-answering", "generated_from_keras_callback", "base_model:google-bert/bert-base-cased", "base_model:finetune:google-bert/bert-base-cased", "license:apache-2.0", "endpoints_compatible", "region:us" ]

question-answering

2024-02-26T10:27:27Z

--- license: apache-2.0 tags: - generated_from_keras_callback base_model: bert-base-cased model-index: - name: bert_base_cased_qa results: [] ---  # bert_base_cased_qa This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 1.2898 - 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: {'inner_optimizer': {'module': 'transformers.optimization_tf', 'class_name': 'AdamWeightDecay', 'config': {'name': 'AdamWeightDecay', 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 5545, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'decay': 0.0, 'beta_1': 0.8999999761581421, 'beta_2': 0.9990000128746033, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}, 'registered_name': 'AdamWeightDecay'}, 'dynamic': True, 'initial_scale': 32768.0, 'dynamic_growth_steps': 2000} - training_precision: mixed_float16 ### Training results | Train Loss | Epoch | |:----------:|:-----:| | 1.2898 | 0 | ### Framework versions - Transformers 4.37.2 - TensorFlow 2.15.0 - Datasets 2.17.1 - Tokenizers 0.15.2

philschmid/gemma-7b-dolly-chatml

philschmid

2024-02-29T12:21:31Z

12

13

peft

[ "peft", "tensorboard", "safetensors", "trl", "sft", "generated_from_trainer", "dataset:generator", "base_model:google/gemma-7b", "base_model:adapter:google/gemma-7b", "license:other", "region:us" ]

null

2024-02-27T14:20:13Z

--- license: other library_name: peft tags: - trl - sft - generated_from_trainer datasets: - generator base_model: google/gemma-7b model-index: - name: gemma-7b-dolly-chatml results: [] ---  # gemma-7b-dolly-chatml This model is a fine-tuned version of [google/gemma-7b](https://huggingface.co/google/gemma-7b) with [philschmid/gemma-tokenizer-chatml](https://huggingface.co/philschmid/gemma-tokenizer-chatml) tokenizer on the [philschmid/dolly-15k-oai-style](https://huggingface.co/datasets/philschmid/dolly-15k-oai-style) using the chatML format. The model was fine-tuned with the following [script using Lora (no, qlora)](https://huggingface.co/philschmid/gemma-7b-dolly-chatml/blob/main/trl-lora.py). I also included a [inference script](https://huggingface.co/philschmid/gemma-7b-dolly-chatml/blob/main/inference.py) to make sure it works since there were some issues with Gemma. Results of the inference test are ```bash prompt: What is the capital of Germany? Explain why thats the case and if it was different in the past? response: Berlin is the capital of Germany. It was the capital of Prussia until 1918, when the monarchy was abolished. It was also the capital of the Weimar Republic. It was the capital of the Third Reich until 1945, when it was liberated by the allies. It has been the capital of the Federal Republic of Germany since 1949. It is the largest city in the country with a population of 3.6 million people. It is also the seat of the government and parliament. prompt: In a town, 60% of the population are adults. Among the adults, 30% have a pet dog and 40% have a pet cat. What percentage of the total population has a pet dog? response: 60% of the total population have a pet dog. The calculation is 30% of adults multiplied by 60% of the total population. 30% of adults is 18% of the total population and 18% multiplied by 60% is 10.8% or 60% of the total population. ``` ### Run inference ```python import torch from peft import AutoPeftModelForCausalLM from transformers import AutoTokenizer, pipeline peft_model_id = "philschmid/gemma-7b-dolly-chatml" # Load Model with PEFT adapter tokenizer = AutoTokenizer.from_pretrained(peft_model_id) model = AutoPeftModelForCausalLM.from_pretrained(peft_model_id, device_map="auto", torch_dtype=torch.float16) pipe = pipeline("text-generation", model=model, tokenizer=tokenizer) eos_token = tokenizer("<|im_end|>",add_special_tokens=False)["input_ids"][0] print(f"eos_token: {eos_token}") # run inference messages = [ { "role": "user", "content": "What is the capital of Germany? Explain why thats the case and if it was different in the past?" } ] prompt = pipe.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) outputs = pipe(prompt, max_new_tokens=1024, do_sample=True, temperature=0.7, top_k=50, top_p=0.95, eos_token_id=eos_token) print(outputs[0]['generated_text'][len(prompt):]) # Berlin is the capital of Germany. It was the capital of Prussia until 1918, when the monarchy was abolished. It was also the capital of the Weimar Republic. It was the capital of the Third Reich until 1945, when it was liberated by the allies. It has been the capital of the Federal Republic of Germany since 1949. It is the largest city in the country with a population of 3.6 million people. It is also the seat of the government and parliament. ``` ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - 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: constant - lr_scheduler_warmup_ratio: 0.03 - num_epochs: 3 ### Framework versions - PEFT 0.8.2 - Transformers 4.38.1 - Pytorch 2.1.2+cu121 - Datasets 2.16.1 - Tokenizers 0.15.0

Aharneish/mistral-test_1

Aharneish

2024-02-29T12:18:32Z

0

peft

[ "peft", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:mistralai/Mistral-7B-Instruct-v0.1", "base_model:adapter:mistralai/Mistral-7B-Instruct-v0.1", "license:apache-2.0", "region:us" ]

null

2024-02-29T07:33:28Z

--- license: apache-2.0 library_name: peft tags: - trl - sft - generated_from_trainer base_model: mistralai/Mistral-7B-Instruct-v0.1 model-index: - name: mistral-test_1 results: [] ---  # mistral-test_1 This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.2947 ## 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: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 1.8128 | 1.36 | 2500 | 2.1855 | | 1.7961 | 1.63 | 3000 | 2.1808 | | 1.7701 | 1.9 | 3500 | 2.2271 | | 1.7186 | 2.17 | 4000 | 2.2265 | | 1.6784 | 2.44 | 4500 | 2.2547 | | 1.6692 | 2.71 | 5000 | 2.2547 | | 1.6752 | 2.98 | 5500 | 2.2470 | | 1.6206 | 3.26 | 6000 | 2.2842 | | 1.599 | 3.53 | 6500 | 2.2663 | | 1.6054 | 3.8 | 7000 | 2.2560 | | 1.593 | 4.07 | 7500 | 2.3039 | | 1.5771 | 4.34 | 8000 | 2.2797 | | 1.5636 | 4.61 | 8500 | 2.2915 | | 1.5551 | 4.88 | 9000 | 2.2947 | ### Framework versions - PEFT 0.9.0 - Transformers 4.38.1 - Pytorch 2.1.2 - Datasets 2.16.0 - Tokenizers 0.15.2