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token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Tagged_Uni_250v2_NER_Model_3Epochs_AUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["tagged_uni250v2_wikigold_split"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "Tagged_Uni_250v2_NER_Model_3Epochs_AUGMENTED", "results": [{"task": {"type": "token-classification", "name": "Token Classification... | DOOGLAK/Tagged_Uni_250v2_NER_Model_3Epochs_AUGMENTED | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:tagged_uni250v2_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-11T18:01:07+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-tagged_uni250v2_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Tagged\_Uni\_250v2\_NER\_Model\_3Epochs\_AUGMENTED
==================================================
This model is a fine-tuned version of bert-base-cased on the tagged\_uni250v2\_wikigold\_split dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3254
* Precision: 0.6102
* Recall: 0.5595
*... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-tagged_uni250v2_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during trai... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Tagged_Uni_250v3_NER_Model_3Epochs_AUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["tagged_uni250v3_wikigold_split"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "Tagged_Uni_250v3_NER_Model_3Epochs_AUGMENTED", "results": [{"task": {"type": "token-classification", "name": "Token Classification... | DOOGLAK/Tagged_Uni_250v3_NER_Model_3Epochs_AUGMENTED | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:tagged_uni250v3_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-11T18:06:44+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-tagged_uni250v3_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Tagged\_Uni\_250v3\_NER\_Model\_3Epochs\_AUGMENTED
==================================================
This model is a fine-tuned version of bert-base-cased on the tagged\_uni250v3\_wikigold\_split dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3093
* Precision: 0.5831
* Recall: 0.4849
*... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-tagged_uni250v3_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during trai... |
fill-mask | transformers |
# Please use 'Roberta' related functions to load this model!
This repository contains the resources in our paper
**[Protest Stance Detection: Leveraging heterogeneous user interactions for extrapolation in out-of-sample country contexts]**
*Ramon Villa-Cox, Evan Williams, Kathleen M. Carley*
We pre-trained a BERT ... | {"language": ["es"], "license": "apache-2.0", "tags": ["roberta"]} | Ramavill/twBETO_v0 | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"es",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-11T18:10:16+00:00 | [] | [
"es"
] | TAGS
#transformers #pytorch #roberta #fill-mask #es #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# Please use 'Roberta' related functions to load this model!
This repository contains the resources in our paper
[Protest Stance Detection: Leveraging heterogeneous user interactions for extrapolation in out-of-sample country contexts]
*Ramon Villa-Cox, Evan Williams, Kathleen M. Carley*
We pre-trained a BERT lang... | [
"# Please use 'Roberta' related functions to load this model!\n\nThis repository contains the resources in our paper\n[Protest Stance Detection: Leveraging heterogeneous user interactions for extrapolation in out-of-sample country contexts] \n*Ramon Villa-Cox, Evan Williams, Kathleen M. Carley*\n\nWe pre-trained a... | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #es #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Please use 'Roberta' related functions to load this model!\n\nThis repository contains the resources in our paper\n[Protest Stance Detection: Leveraging heterogeneous user inter... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Tagged_Uni_250v4_NER_Model_3Epochs_AUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["tagged_uni250v4_wikigold_split"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "Tagged_Uni_250v4_NER_Model_3Epochs_AUGMENTED", "results": [{"task": {"type": "token-classification", "name": "Token Classification... | DOOGLAK/Tagged_Uni_250v4_NER_Model_3Epochs_AUGMENTED | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:tagged_uni250v4_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-11T18:12:24+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-tagged_uni250v4_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Tagged\_Uni\_250v4\_NER\_Model\_3Epochs\_AUGMENTED
==================================================
This model is a fine-tuned version of bert-base-cased on the tagged\_uni250v4\_wikigold\_split dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2997
* Precision: 0.5478
* Recall: 0.4742
*... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-tagged_uni250v4_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during trai... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Tagged_Uni_250v5_NER_Model_3Epochs_AUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["tagged_uni250v5_wikigold_split"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "Tagged_Uni_250v5_NER_Model_3Epochs_AUGMENTED", "results": [{"task": {"type": "token-classification", "name": "Token Classification... | DOOGLAK/Tagged_Uni_250v5_NER_Model_3Epochs_AUGMENTED | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:tagged_uni250v5_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-11T18:17:54+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-tagged_uni250v5_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Tagged\_Uni\_250v5\_NER\_Model\_3Epochs\_AUGMENTED
==================================================
This model is a fine-tuned version of bert-base-cased on the tagged\_uni250v5\_wikigold\_split dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3324
* Precision: 0.5808
* Recall: 0.5341
*... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-tagged_uni250v5_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during trai... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Tagged_Uni_250v6_NER_Model_3Epochs_AUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["tagged_uni250v6_wikigold_split"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "Tagged_Uni_250v6_NER_Model_3Epochs_AUGMENTED", "results": [{"task": {"type": "token-classification", "name": "Token Classification... | DOOGLAK/Tagged_Uni_250v6_NER_Model_3Epochs_AUGMENTED | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:tagged_uni250v6_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-11T18:23:31+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-tagged_uni250v6_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Tagged\_Uni\_250v6\_NER\_Model\_3Epochs\_AUGMENTED
==================================================
This model is a fine-tuned version of bert-base-cased on the tagged\_uni250v6\_wikigold\_split dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3080
* Precision: 0.5572
* Recall: 0.4573
*... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-tagged_uni250v6_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during trai... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Tagged_Uni_250v7_NER_Model_3Epochs_AUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["tagged_uni250v7_wikigold_split"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "Tagged_Uni_250v7_NER_Model_3Epochs_AUGMENTED", "results": [{"task": {"type": "token-classification", "name": "Token Classification... | DOOGLAK/Tagged_Uni_250v7_NER_Model_3Epochs_AUGMENTED | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:tagged_uni250v7_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-11T18:29:51+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-tagged_uni250v7_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Tagged\_Uni\_250v7\_NER\_Model\_3Epochs\_AUGMENTED
==================================================
This model is a fine-tuned version of bert-base-cased on the tagged\_uni250v7\_wikigold\_split dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3477
* Precision: 0.5765
* Recall: 0.4909
*... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-tagged_uni250v7_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during trai... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Tagged_Uni_250v8_NER_Model_3Epochs_AUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["tagged_uni250v8_wikigold_split"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "Tagged_Uni_250v8_NER_Model_3Epochs_AUGMENTED", "results": [{"task": {"type": "token-classification", "name": "Token Classification... | DOOGLAK/Tagged_Uni_250v8_NER_Model_3Epochs_AUGMENTED | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:tagged_uni250v8_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-11T18:35:03+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-tagged_uni250v8_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Tagged\_Uni\_250v8\_NER\_Model\_3Epochs\_AUGMENTED
==================================================
This model is a fine-tuned version of bert-base-cased on the tagged\_uni250v8\_wikigold\_split dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3186
* Precision: 0.5548
* Recall: 0.4939
*... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-tagged_uni250v8_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during trai... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Tagged_Uni_250v9_NER_Model_3Epochs_AUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["tagged_uni250v9_wikigold_split"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "Tagged_Uni_250v9_NER_Model_3Epochs_AUGMENTED", "results": [{"task": {"type": "token-classification", "name": "Token Classification... | DOOGLAK/Tagged_Uni_250v9_NER_Model_3Epochs_AUGMENTED | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:tagged_uni250v9_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-11T18:40:11+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-tagged_uni250v9_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Tagged\_Uni\_250v9\_NER\_Model\_3Epochs\_AUGMENTED
==================================================
This model is a fine-tuned version of bert-base-cased on the tagged\_uni250v9\_wikigold\_split dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2786
* Precision: 0.5877
* Recall: 0.5263
*... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-tagged_uni250v9_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during trai... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Tagged_Uni_500v0_NER_Model_3Epochs_AUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["tagged_uni500v0_wikigold_split"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "Tagged_Uni_500v0_NER_Model_3Epochs_AUGMENTED", "results": [{"task": {"type": "token-classification", "name": "Token Classification... | DOOGLAK/Tagged_Uni_500v0_NER_Model_3Epochs_AUGMENTED | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:tagged_uni500v0_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-11T18:45:24+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-tagged_uni500v0_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Tagged\_Uni\_500v0\_NER\_Model\_3Epochs\_AUGMENTED
==================================================
This model is a fine-tuned version of bert-base-cased on the tagged\_uni500v0\_wikigold\_split dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2431
* Precision: 0.6686
* Recall: 0.7194
*... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-tagged_uni500v0_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during trai... |
text-generation | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# PromptGenerator_32_topic_finetuned
This model is a fine-tuned version of [kmkarakaya/turkishReviews-ds](https://huggingface.co/kmkarak... | {"license": "mit", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "PromptGenerator_32_topic_finetuned", "results": []}]} | cansen88/PromptGenerator_32_topic_finetuned | null | [
"transformers",
"tf",
"gpt2",
"text-generation",
"generated_from_keras_callback",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-11T18:49:31+00:00 | [] | [] | TAGS
#transformers #tf #gpt2 #text-generation #generated_from_keras_callback #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| PromptGenerator\_32\_topic\_finetuned
=====================================
This model is a fine-tuned version of kmkarakaya/turkishReviews-ds on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.0569
* Train Sparse Categorical Accuracy: 1.0
* Validation Loss: 0.0787
* Val... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'learning\\_rate': 5e-05, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}\n* training\\_precision: float32",
"### Training results",
"### Framework... | [
"TAGS\n#transformers #tf #gpt2 #text-generation #generated_from_keras_callback #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'learnin... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Tagged_Uni_500v1_NER_Model_3Epochs_AUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["tagged_uni500v1_wikigold_split"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "Tagged_Uni_500v1_NER_Model_3Epochs_AUGMENTED", "results": [{"task": {"type": "token-classification", "name": "Token Classification... | DOOGLAK/Tagged_Uni_500v1_NER_Model_3Epochs_AUGMENTED | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:tagged_uni500v1_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-11T18:50:49+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-tagged_uni500v1_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Tagged\_Uni\_500v1\_NER\_Model\_3Epochs\_AUGMENTED
==================================================
This model is a fine-tuned version of bert-base-cased on the tagged\_uni500v1\_wikigold\_split dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2425
* Precision: 0.7049
* Recall: 0.7077
*... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-tagged_uni500v1_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during trai... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Tagged_Uni_500v2_NER_Model_3Epochs_AUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["tagged_uni500v2_wikigold_split"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "Tagged_Uni_500v2_NER_Model_3Epochs_AUGMENTED", "results": [{"task": {"type": "token-classification", "name": "Token Classification... | DOOGLAK/Tagged_Uni_500v2_NER_Model_3Epochs_AUGMENTED | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:tagged_uni500v2_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-11T18:56:44+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-tagged_uni500v2_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Tagged\_Uni\_500v2\_NER\_Model\_3Epochs\_AUGMENTED
==================================================
This model is a fine-tuned version of bert-base-cased on the tagged\_uni500v2\_wikigold\_split dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2645
* Precision: 0.7018
* Recall: 0.6812
*... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-tagged_uni500v2_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during trai... |
text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# story_spanish_gpt2_by_category
This model is a fine-tuned version of [datificate/gpt2-small-spanish](https://huggingface.co/dati... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "story_spanish_gpt2_by_category", "results": []}]} | dquisi/story_spanish_gpt2_by_category | null | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-08-11T18:58:56+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
# story_spanish_gpt2_by_category
This model is a fine-tuned version of datificate/gpt2-small-spanish on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Trai... | [
"# story_spanish_gpt2_by_category\n\nThis model is a fine-tuned version of datificate/gpt2-small-spanish on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Tr... | [
"TAGS\n#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# story_spanish_gpt2_by_category\n\nThis model is a fine-tuned version of datificate/gpt2-small-spanish o... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Tagged_Uni_500v3_NER_Model_3Epochs_AUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["tagged_uni500v3_wikigold_split"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "Tagged_Uni_500v3_NER_Model_3Epochs_AUGMENTED", "results": [{"task": {"type": "token-classification", "name": "Token Classification... | DOOGLAK/Tagged_Uni_500v3_NER_Model_3Epochs_AUGMENTED | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:tagged_uni500v3_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-11T19:02:21+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-tagged_uni500v3_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Tagged\_Uni\_500v3\_NER\_Model\_3Epochs\_AUGMENTED
==================================================
This model is a fine-tuned version of bert-base-cased on the tagged\_uni500v3\_wikigold\_split dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2350
* Precision: 0.7144
* Recall: 0.7115
*... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-tagged_uni500v3_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during trai... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Tagged_Uni_500v4_NER_Model_3Epochs_AUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["tagged_uni500v4_wikigold_split"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "Tagged_Uni_500v4_NER_Model_3Epochs_AUGMENTED", "results": [{"task": {"type": "token-classification", "name": "Token Classification... | DOOGLAK/Tagged_Uni_500v4_NER_Model_3Epochs_AUGMENTED | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:tagged_uni500v4_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-11T19:07:53+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-tagged_uni500v4_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Tagged\_Uni\_500v4\_NER\_Model\_3Epochs\_AUGMENTED
==================================================
This model is a fine-tuned version of bert-base-cased on the tagged\_uni500v4\_wikigold\_split dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2629
* Precision: 0.6813
* Recall: 0.6431
*... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-tagged_uni500v4_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during trai... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Tagged_Uni_500v5_NER_Model_3Epochs_AUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["tagged_uni500v5_wikigold_split"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "Tagged_Uni_500v5_NER_Model_3Epochs_AUGMENTED", "results": [{"task": {"type": "token-classification", "name": "Token Classification... | DOOGLAK/Tagged_Uni_500v5_NER_Model_3Epochs_AUGMENTED | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:tagged_uni500v5_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-11T19:13:09+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-tagged_uni500v5_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Tagged\_Uni\_500v5\_NER\_Model\_3Epochs\_AUGMENTED
==================================================
This model is a fine-tuned version of bert-base-cased on the tagged\_uni500v5\_wikigold\_split dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2258
* Precision: 0.7005
* Recall: 0.7075
*... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-tagged_uni500v5_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during trai... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Tagged_Uni_500v6_NER_Model_3Epochs_AUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["tagged_uni500v6_wikigold_split"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "Tagged_Uni_500v6_NER_Model_3Epochs_AUGMENTED", "results": [{"task": {"type": "token-classification", "name": "Token Classification... | DOOGLAK/Tagged_Uni_500v6_NER_Model_3Epochs_AUGMENTED | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:tagged_uni500v6_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-11T19:19:30+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-tagged_uni500v6_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Tagged\_Uni\_500v6\_NER\_Model\_3Epochs\_AUGMENTED
==================================================
This model is a fine-tuned version of bert-base-cased on the tagged\_uni500v6\_wikigold\_split dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2386
* Precision: 0.6992
* Recall: 0.6987
*... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-tagged_uni500v6_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during trai... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Tagged_Uni_500v7_NER_Model_3Epochs_AUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["tagged_uni500v7_wikigold_split"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "Tagged_Uni_500v7_NER_Model_3Epochs_AUGMENTED", "results": [{"task": {"type": "token-classification", "name": "Token Classification... | DOOGLAK/Tagged_Uni_500v7_NER_Model_3Epochs_AUGMENTED | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:tagged_uni500v7_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-11T19:25:39+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-tagged_uni500v7_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Tagged\_Uni\_500v7\_NER\_Model\_3Epochs\_AUGMENTED
==================================================
This model is a fine-tuned version of bert-base-cased on the tagged\_uni500v7\_wikigold\_split dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2465
* Precision: 0.7087
* Recall: 0.7069
*... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-tagged_uni500v7_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during trai... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Tagged_Uni_500v8_NER_Model_3Epochs_AUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["tagged_uni500v8_wikigold_split"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "Tagged_Uni_500v8_NER_Model_3Epochs_AUGMENTED", "results": [{"task": {"type": "token-classification", "name": "Token Classification... | DOOGLAK/Tagged_Uni_500v8_NER_Model_3Epochs_AUGMENTED | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:tagged_uni500v8_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-11T19:31:43+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-tagged_uni500v8_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Tagged\_Uni\_500v8\_NER\_Model\_3Epochs\_AUGMENTED
==================================================
This model is a fine-tuned version of bert-base-cased on the tagged\_uni500v8\_wikigold\_split dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2501
* Precision: 0.7046
* Recall: 0.6968
*... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-tagged_uni500v8_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during trai... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Tagged_Uni_500v9_NER_Model_3Epochs_AUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["tagged_uni500v9_wikigold_split"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "Tagged_Uni_500v9_NER_Model_3Epochs_AUGMENTED", "results": [{"task": {"type": "token-classification", "name": "Token Classification... | DOOGLAK/Tagged_Uni_500v9_NER_Model_3Epochs_AUGMENTED | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:tagged_uni500v9_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-11T19:37:39+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-tagged_uni500v9_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Tagged\_Uni\_500v9\_NER\_Model\_3Epochs\_AUGMENTED
==================================================
This model is a fine-tuned version of bert-base-cased on the tagged\_uni500v9\_wikigold\_split dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2209
* Precision: 0.7117
* Recall: 0.7177
*... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-tagged_uni500v9_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during trai... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Article_50v0_NER_Model_3Epochs_UNAUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-b... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["article50v0_wikigold_split"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "Article_50v0_NER_Model_3Epochs_UNAUGMENTED", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "d... | DOOGLAK/Article_50v0_NER_Model_3Epochs_UNAUGMENTED | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:article50v0_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-11T19:48:14+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-article50v0_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Article\_50v0\_NER\_Model\_3Epochs\_UNAUGMENTED
===============================================
This model is a fine-tuned version of bert-base-cased on the article50v0\_wikigold\_split dataset.
It achieves the following results on the evaluation set:
* Loss: 0.7728
* Precision: 0.0
* Recall: 0.0
* F1: 0.0
* Accura... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-article50v0_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Article_50v1_NER_Model_3Epochs_UNAUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-b... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["article50v1_wikigold_split"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "Article_50v1_NER_Model_3Epochs_UNAUGMENTED", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "d... | DOOGLAK/Article_50v1_NER_Model_3Epochs_UNAUGMENTED | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:article50v1_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-11T19:53:51+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-article50v1_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Article\_50v1\_NER\_Model\_3Epochs\_UNAUGMENTED
===============================================
This model is a fine-tuned version of bert-base-cased on the article50v1\_wikigold\_split dataset.
It achieves the following results on the evaluation set:
* Loss: 0.7237
* Precision: 0.0
* Recall: 0.0
* F1: 0.0
* Accura... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-article50v1_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Article_50v2_NER_Model_3Epochs_UNAUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-b... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["article50v2_wikigold_split"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "Article_50v2_NER_Model_3Epochs_UNAUGMENTED", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "d... | DOOGLAK/Article_50v2_NER_Model_3Epochs_UNAUGMENTED | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:article50v2_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-11T19:59:12+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-article50v2_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Article\_50v2\_NER\_Model\_3Epochs\_UNAUGMENTED
===============================================
This model is a fine-tuned version of bert-base-cased on the article50v2\_wikigold\_split dataset.
It achieves the following results on the evaluation set:
* Loss: 0.7694
* Precision: 0.0
* Recall: 0.0
* F1: 0.0
* Accura... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-article50v2_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Article_50v3_NER_Model_3Epochs_UNAUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-b... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["article50v3_wikigold_split"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "Article_50v3_NER_Model_3Epochs_UNAUGMENTED", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "d... | DOOGLAK/Article_50v3_NER_Model_3Epochs_UNAUGMENTED | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:article50v3_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-11T20:04:53+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-article50v3_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Article\_50v3\_NER\_Model\_3Epochs\_UNAUGMENTED
===============================================
This model is a fine-tuned version of bert-base-cased on the article50v3\_wikigold\_split dataset.
It achieves the following results on the evaluation set:
* Loss: 0.7382
* Precision: 0.0
* Recall: 0.0
* F1: 0.0
* Accura... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-article50v3_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Article_50v4_NER_Model_3Epochs_UNAUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-b... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["article50v4_wikigold_split"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "Article_50v4_NER_Model_3Epochs_UNAUGMENTED", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "d... | DOOGLAK/Article_50v4_NER_Model_3Epochs_UNAUGMENTED | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:article50v4_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-11T20:10:23+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-article50v4_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Article\_50v4\_NER\_Model\_3Epochs\_UNAUGMENTED
===============================================
This model is a fine-tuned version of bert-base-cased on the article50v4\_wikigold\_split dataset.
It achieves the following results on the evaluation set:
* Loss: 0.7543
* Precision: 0.0
* Recall: 0.0
* F1: 0.0
* Accura... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-article50v4_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training... |
text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# story_spanish_gpt2_v2
This model is a fine-tuned version of [datificate/gpt2-small-spanish](https://huggingface.co/datificate/gp... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "story_spanish_gpt2_v2", "results": []}]} | dquisi/story_spanish_gpt2_v2 | null | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-08-11T20:11:10+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| story\_spanish\_gpt2\_v2
========================
This model is a fine-tuned version of datificate/gpt2-small-spanish on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 3.7640
Model description
-----------------
More information needed
Intended uses & limitations
-----------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 2\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learnin... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Article_50v5_NER_Model_3Epochs_UNAUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-b... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["article50v5_wikigold_split"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "Article_50v5_NER_Model_3Epochs_UNAUGMENTED", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "d... | DOOGLAK/Article_50v5_NER_Model_3Epochs_UNAUGMENTED | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:article50v5_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-11T20:15:39+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-article50v5_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Article\_50v5\_NER\_Model\_3Epochs\_UNAUGMENTED
===============================================
This model is a fine-tuned version of bert-base-cased on the article50v5\_wikigold\_split dataset.
It achieves the following results on the evaluation set:
* Loss: 0.7582
* Precision: 0.0
* Recall: 0.0
* F1: 0.0
* Accura... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-article50v5_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Article_50v6_NER_Model_3Epochs_UNAUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-b... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["article50v6_wikigold_split"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "Article_50v6_NER_Model_3Epochs_UNAUGMENTED", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "d... | DOOGLAK/Article_50v6_NER_Model_3Epochs_UNAUGMENTED | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:article50v6_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-11T20:21:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-article50v6_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Article\_50v6\_NER\_Model\_3Epochs\_UNAUGMENTED
===============================================
This model is a fine-tuned version of bert-base-cased on the article50v6\_wikigold\_split dataset.
It achieves the following results on the evaluation set:
* Loss: 0.7622
* Precision: 0.0
* Recall: 0.0
* F1: 0.0
* Accura... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-article50v6_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Article_50v7_NER_Model_3Epochs_UNAUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-b... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["article50v7_wikigold_split"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "Article_50v7_NER_Model_3Epochs_UNAUGMENTED", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "d... | DOOGLAK/Article_50v7_NER_Model_3Epochs_UNAUGMENTED | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:article50v7_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-11T20:26:35+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-article50v7_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Article\_50v7\_NER\_Model\_3Epochs\_UNAUGMENTED
===============================================
This model is a fine-tuned version of bert-base-cased on the article50v7\_wikigold\_split dataset.
It achieves the following results on the evaluation set:
* Loss: 0.7894
* Precision: 0.3333
* Recall: 0.0002
* F1: 0.0005... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-article50v7_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Article_50v8_NER_Model_3Epochs_UNAUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-b... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["article50v8_wikigold_split"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "Article_50v8_NER_Model_3Epochs_UNAUGMENTED", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "d... | DOOGLAK/Article_50v8_NER_Model_3Epochs_UNAUGMENTED | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:article50v8_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-11T20:32:16+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-article50v8_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Article\_50v8\_NER\_Model\_3Epochs\_UNAUGMENTED
===============================================
This model is a fine-tuned version of bert-base-cased on the article50v8\_wikigold\_split dataset.
It achieves the following results on the evaluation set:
* Loss: 0.7555
* Precision: 0.0
* Recall: 0.0
* F1: 0.0
* Accura... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-article50v8_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Article_50v9_NER_Model_3Epochs_UNAUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-b... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["article50v9_wikigold_split"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "Article_50v9_NER_Model_3Epochs_UNAUGMENTED", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "d... | DOOGLAK/Article_50v9_NER_Model_3Epochs_UNAUGMENTED | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:article50v9_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-11T20:37:41+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-article50v9_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Article\_50v9\_NER\_Model\_3Epochs\_UNAUGMENTED
===============================================
This model is a fine-tuned version of bert-base-cased on the article50v9\_wikigold\_split dataset.
It achieves the following results on the evaluation set:
* Loss: 0.7640
* Precision: 0.0
* Recall: 0.0
* F1: 0.0
* Accura... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-article50v9_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training... |
text-generation | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# PromptGenerator_5_topic_finetuned
This model is a fine-tuned version of [kmkarakaya/turkishReviews-ds](https://huggingface.co/kmkaraka... | {"license": "mit", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "PromptGenerator_5_topic_finetuned", "results": []}]} | cansen88/PromptGenerator_5_topic_finetuned | null | [
"transformers",
"tf",
"gpt2",
"text-generation",
"generated_from_keras_callback",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-11T20:39:35+00:00 | [] | [] | TAGS
#transformers #tf #gpt2 #text-generation #generated_from_keras_callback #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| PromptGenerator\_5\_topic\_finetuned
====================================
This model is a fine-tuned version of kmkarakaya/turkishReviews-ds on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 1.6861
* Train Sparse Categorical Accuracy: 0.8150
* Validation Loss: 1.9777
* Va... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'learning\\_rate': 5e-05, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}\n* training\\_precision: float32",
"### Training results",
"### Framework... | [
"TAGS\n#transformers #tf #gpt2 #text-generation #generated_from_keras_callback #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'learnin... |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-squad
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/d... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "distilbert-base-uncased-finetuned-squad", "results": []}]} | drewski/distilbert-base-uncased-finetuned-squad | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-08-11T20:42:15+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-squad
=======================================
This model is a fine-tuned version of distilbert-base-uncased on the squad dataset.
It achieves the following results on the evaluation set:
* Loss: 1.1564
Model description
-----------------
More information needed
Intended uses ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_s... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Article_100v0_NER_Model_3Epochs_UNAUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["article100v0_wikigold_split"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "Article_100v0_NER_Model_3Epochs_UNAUGMENTED", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, ... | DOOGLAK/Article_100v0_NER_Model_3Epochs_UNAUGMENTED | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:article100v0_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-11T20:43:03+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-article100v0_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Article\_100v0\_NER\_Model\_3Epochs\_UNAUGMENTED
================================================
This model is a fine-tuned version of bert-base-cased on the article100v0\_wikigold\_split dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6037
* Precision: 0.25
* Recall: 0.0003
* F1: 0.000... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-article100v0_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during trainin... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Article_100v1_NER_Model_3Epochs_UNAUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["article100v1_wikigold_split"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "Article_100v1_NER_Model_3Epochs_UNAUGMENTED", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, ... | DOOGLAK/Article_100v1_NER_Model_3Epochs_UNAUGMENTED | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:article100v1_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-11T20:48:36+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-article100v1_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Article\_100v1\_NER\_Model\_3Epochs\_UNAUGMENTED
================================================
This model is a fine-tuned version of bert-base-cased on the article100v1\_wikigold\_split dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5783
* Precision: 0.06
* Recall: 0.0016
* F1: 0.003... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-article100v1_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during trainin... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Article_100v2_NER_Model_3Epochs_UNAUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["article100v2_wikigold_split"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "Article_100v2_NER_Model_3Epochs_UNAUGMENTED", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, ... | DOOGLAK/Article_100v2_NER_Model_3Epochs_UNAUGMENTED | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:article100v2_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-11T20:53:59+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-article100v2_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Article\_100v2\_NER\_Model\_3Epochs\_UNAUGMENTED
================================================
This model is a fine-tuned version of bert-base-cased on the article100v2\_wikigold\_split dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6054
* Precision: 0.0339
* Recall: 0.0005
* F1: 0.0... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-article100v2_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during trainin... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Article_100v3_NER_Model_3Epochs_UNAUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["article100v3_wikigold_split"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "Article_100v3_NER_Model_3Epochs_UNAUGMENTED", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, ... | DOOGLAK/Article_100v3_NER_Model_3Epochs_UNAUGMENTED | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:article100v3_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-11T20:59:14+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-article100v3_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Article\_100v3\_NER\_Model\_3Epochs\_UNAUGMENTED
================================================
This model is a fine-tuned version of bert-base-cased on the article100v3\_wikigold\_split dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6272
* Precision: 0.0
* Recall: 0.0
* F1: 0.0
* Acc... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-article100v3_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during trainin... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Article_100v4_NER_Model_3Epochs_UNAUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["article100v4_wikigold_split"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "Article_100v4_NER_Model_3Epochs_UNAUGMENTED", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, ... | DOOGLAK/Article_100v4_NER_Model_3Epochs_UNAUGMENTED | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:article100v4_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-11T21:04:46+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-article100v4_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Article\_100v4\_NER\_Model\_3Epochs\_UNAUGMENTED
================================================
This model is a fine-tuned version of bert-base-cased on the article100v4\_wikigold\_split dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5862
* Precision: 0.1622
* Recall: 0.0031
* F1: 0.0... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-article100v4_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during trainin... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Article_100v5_NER_Model_3Epochs_UNAUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["article100v5_wikigold_split"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "Article_100v5_NER_Model_3Epochs_UNAUGMENTED", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, ... | DOOGLAK/Article_100v5_NER_Model_3Epochs_UNAUGMENTED | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:article100v5_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-11T21:10:57+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-article100v5_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Article\_100v5\_NER\_Model\_3Epochs\_UNAUGMENTED
================================================
This model is a fine-tuned version of bert-base-cased on the article100v5\_wikigold\_split dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5958
* Precision: 0.0241
* Recall: 0.0005
* F1: 0.0... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-article100v5_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during trainin... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Article_100v6_NER_Model_3Epochs_UNAUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["article100v6_wikigold_split"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "Article_100v6_NER_Model_3Epochs_UNAUGMENTED", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, ... | DOOGLAK/Article_100v6_NER_Model_3Epochs_UNAUGMENTED | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:article100v6_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-11T21:16:25+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-article100v6_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Article\_100v6\_NER\_Model\_3Epochs\_UNAUGMENTED
================================================
This model is a fine-tuned version of bert-base-cased on the article100v6\_wikigold\_split dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5955
* Precision: 0.0
* Recall: 0.0
* F1: 0.0
* Acc... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-article100v6_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during trainin... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Article_100v7_NER_Model_3Epochs_UNAUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["article100v7_wikigold_split"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "Article_100v7_NER_Model_3Epochs_UNAUGMENTED", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, ... | DOOGLAK/Article_100v7_NER_Model_3Epochs_UNAUGMENTED | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:article100v7_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-11T21:22:01+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-article100v7_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Article\_100v7\_NER\_Model\_3Epochs\_UNAUGMENTED
================================================
This model is a fine-tuned version of bert-base-cased on the article100v7\_wikigold\_split dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6011
* Precision: 0.1661
* Recall: 0.0138
* F1: 0.0... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-article100v7_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during trainin... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Article_100v8_NER_Model_3Epochs_UNAUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["article100v8_wikigold_split"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "Article_100v8_NER_Model_3Epochs_UNAUGMENTED", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, ... | DOOGLAK/Article_100v8_NER_Model_3Epochs_UNAUGMENTED | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:article100v8_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-11T21:27:35+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-article100v8_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Article\_100v8\_NER\_Model\_3Epochs\_UNAUGMENTED
================================================
This model is a fine-tuned version of bert-base-cased on the article100v8\_wikigold\_split dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6455
* Precision: 0.0
* Recall: 0.0
* F1: 0.0
* Acc... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-article100v8_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during trainin... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Article_100v9_NER_Model_3Epochs_UNAUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["article100v9_wikigold_split"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "Article_100v9_NER_Model_3Epochs_UNAUGMENTED", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, ... | DOOGLAK/Article_100v9_NER_Model_3Epochs_UNAUGMENTED | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:article100v9_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-11T21:33:07+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-article100v9_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Article\_100v9\_NER\_Model\_3Epochs\_UNAUGMENTED
================================================
This model is a fine-tuned version of bert-base-cased on the article100v9\_wikigold\_split dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5642
* Precision: 0.1490
* Recall: 0.0392
* F1: 0.0... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-article100v9_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during trainin... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Article_250v0_NER_Model_3Epochs_UNAUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["article250v0_wikigold_split"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "Article_250v0_NER_Model_3Epochs_UNAUGMENTED", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, ... | DOOGLAK/Article_250v0_NER_Model_3Epochs_UNAUGMENTED | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:article250v0_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-11T21:38:41+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-article250v0_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Article\_250v0\_NER\_Model\_3Epochs\_UNAUGMENTED
================================================
This model is a fine-tuned version of bert-base-cased on the article250v0\_wikigold\_split dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3397
* Precision: 0.316
* Recall: 0.2984
* F1: 0.30... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-article250v0_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during trainin... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Article_250v1_NER_Model_3Epochs_UNAUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["article250v1_wikigold_split"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "Article_250v1_NER_Model_3Epochs_UNAUGMENTED", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, ... | DOOGLAK/Article_250v1_NER_Model_3Epochs_UNAUGMENTED | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:article250v1_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-11T21:44:18+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-article250v1_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Article\_250v1\_NER\_Model\_3Epochs\_UNAUGMENTED
================================================
This model is a fine-tuned version of bert-base-cased on the article250v1\_wikigold\_split dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2871
* Precision: 0.4687
* Recall: 0.4757
* F1: 0.4... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-article250v1_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during trainin... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Article_250v2_NER_Model_3Epochs_UNAUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["article250v2_wikigold_split"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "Article_250v2_NER_Model_3Epochs_UNAUGMENTED", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, ... | DOOGLAK/Article_250v2_NER_Model_3Epochs_UNAUGMENTED | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:article250v2_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-11T21:49:50+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-article250v2_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Article\_250v2\_NER\_Model\_3Epochs\_UNAUGMENTED
================================================
This model is a fine-tuned version of bert-base-cased on the article250v2\_wikigold\_split dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2900
* Precision: 0.4665
* Recall: 0.5280
* F1: 0.4... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-article250v2_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during trainin... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Article_250v3_NER_Model_3Epochs_UNAUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["article250v3_wikigold_split"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "Article_250v3_NER_Model_3Epochs_UNAUGMENTED", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, ... | DOOGLAK/Article_250v3_NER_Model_3Epochs_UNAUGMENTED | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:article250v3_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-11T21:55:37+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-article250v3_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Article\_250v3\_NER\_Model\_3Epochs\_UNAUGMENTED
================================================
This model is a fine-tuned version of bert-base-cased on the article250v3\_wikigold\_split dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2795
* Precision: 0.4662
* Recall: 0.4709
* F1: 0.4... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-article250v3_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during trainin... |
text-generation | transformers | ```
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("BigSalmon/InformalToFormalLincoln64Paraphrase")
model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincoln64Paraphrase")
```
```
Demo:
https://huggingface.co/spaces/BigSalmon/FormalInforma... | {} | BigSalmon/InformalToFormalLincoln64Paraphrase | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-08-11T21:55:51+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
Most likely outputs:
Keywords to sentences or sentence.
Infill / Infilling / Masking / Phrase Masking
| [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n"
] |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Article_250v4_NER_Model_3Epochs_UNAUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["article250v4_wikigold_split"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "Article_250v4_NER_Model_3Epochs_UNAUGMENTED", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, ... | DOOGLAK/Article_250v4_NER_Model_3Epochs_UNAUGMENTED | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:article250v4_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-11T22:01:03+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-article250v4_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Article\_250v4\_NER\_Model\_3Epochs\_UNAUGMENTED
================================================
This model is a fine-tuned version of bert-base-cased on the article250v4\_wikigold\_split dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3243
* Precision: 0.4027
* Recall: 0.4337
* F1: 0.4... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-article250v4_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during trainin... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Article_250v5_NER_Model_3Epochs_UNAUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["article250v5_wikigold_split"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "Article_250v5_NER_Model_3Epochs_UNAUGMENTED", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, ... | DOOGLAK/Article_250v5_NER_Model_3Epochs_UNAUGMENTED | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:article250v5_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-11T22:06:42+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-article250v5_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Article\_250v5\_NER\_Model\_3Epochs\_UNAUGMENTED
================================================
This model is a fine-tuned version of bert-base-cased on the article250v5\_wikigold\_split dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3250
* Precision: 0.3979
* Recall: 0.4221
* F1: 0.4... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-article250v5_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during trainin... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Article_250v6_NER_Model_3Epochs_UNAUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["article250v6_wikigold_split"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "Article_250v6_NER_Model_3Epochs_UNAUGMENTED", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, ... | DOOGLAK/Article_250v6_NER_Model_3Epochs_UNAUGMENTED | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:article250v6_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-11T22:12:08+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-article250v6_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Article\_250v6\_NER\_Model\_3Epochs\_UNAUGMENTED
================================================
This model is a fine-tuned version of bert-base-cased on the article250v6\_wikigold\_split dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3052
* Precision: 0.3970
* Recall: 0.3699
* F1: 0.3... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-article250v6_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during trainin... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Article_250v7_NER_Model_3Epochs_UNAUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["article250v7_wikigold_split"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "Article_250v7_NER_Model_3Epochs_UNAUGMENTED", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, ... | DOOGLAK/Article_250v7_NER_Model_3Epochs_UNAUGMENTED | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:article250v7_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-11T22:17:58+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-article250v7_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Article\_250v7\_NER\_Model\_3Epochs\_UNAUGMENTED
================================================
This model is a fine-tuned version of bert-base-cased on the article250v7\_wikigold\_split dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3252
* Precision: 0.4384
* Recall: 0.4016
* F1: 0.4... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-article250v7_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during trainin... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Article_500v0_NER_Model_3Epochs_UNAUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["article500v0_wikigold_split"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "Article_500v0_NER_Model_3Epochs_UNAUGMENTED", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, ... | DOOGLAK/Article_500v0_NER_Model_3Epochs_UNAUGMENTED | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:article500v0_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-11T22:25:11+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-article500v0_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Article\_500v0\_NER\_Model\_3Epochs\_UNAUGMENTED
================================================
This model is a fine-tuned version of bert-base-cased on the article500v0\_wikigold\_split dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1853
* Precision: 0.6388
* Recall: 0.7250
* F1: 0.6... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-article500v0_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during trainin... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Article_500v1_NER_Model_3Epochs_UNAUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["article500v1_wikigold_split"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "Article_500v1_NER_Model_3Epochs_UNAUGMENTED", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, ... | DOOGLAK/Article_500v1_NER_Model_3Epochs_UNAUGMENTED | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:article500v1_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-11T22:31:14+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-article500v1_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Article\_500v1\_NER\_Model\_3Epochs\_UNAUGMENTED
================================================
This model is a fine-tuned version of bert-base-cased on the article500v1\_wikigold\_split dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2058
* Precision: 0.6615
* Recall: 0.6746
* F1: 0.6... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-article500v1_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during trainin... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Article_500v2_NER_Model_3Epochs_UNAUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["article500v2_wikigold_split"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "Article_500v2_NER_Model_3Epochs_UNAUGMENTED", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, ... | DOOGLAK/Article_500v2_NER_Model_3Epochs_UNAUGMENTED | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:article500v2_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-11T22:36:55+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-article500v2_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Article\_500v2\_NER\_Model\_3Epochs\_UNAUGMENTED
================================================
This model is a fine-tuned version of bert-base-cased on the article500v2\_wikigold\_split dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1886
* Precision: 0.6510
* Recall: 0.7377
* F1: 0.6... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-article500v2_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during trainin... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Article_500v3_NER_Model_3Epochs_UNAUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["article500v3_wikigold_split"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "Article_500v3_NER_Model_3Epochs_UNAUGMENTED", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, ... | DOOGLAK/Article_500v3_NER_Model_3Epochs_UNAUGMENTED | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:article500v3_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-11T22:42:30+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-article500v3_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Article\_500v3\_NER\_Model\_3Epochs\_UNAUGMENTED
================================================
This model is a fine-tuned version of bert-base-cased on the article500v3\_wikigold\_split dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2058
* Precision: 0.6713
* Recall: 0.6822
* F1: 0.6... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-article500v3_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during trainin... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Article_500v4_NER_Model_3Epochs_UNAUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["article500v4_wikigold_split"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "Article_500v4_NER_Model_3Epochs_UNAUGMENTED", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, ... | DOOGLAK/Article_500v4_NER_Model_3Epochs_UNAUGMENTED | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:article500v4_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-11T22:48:17+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-article500v4_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Article\_500v4\_NER\_Model\_3Epochs\_UNAUGMENTED
================================================
This model is a fine-tuned version of bert-base-cased on the article500v4\_wikigold\_split dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2062
* Precision: 0.6464
* Recall: 0.6730
* F1: 0.6... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-article500v4_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during trainin... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Article_500v5_NER_Model_3Epochs_UNAUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["article500v5_wikigold_split"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "Article_500v5_NER_Model_3Epochs_UNAUGMENTED", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, ... | DOOGLAK/Article_500v5_NER_Model_3Epochs_UNAUGMENTED | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:article500v5_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-11T22:54:14+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-article500v5_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Article\_500v5\_NER\_Model\_3Epochs\_UNAUGMENTED
================================================
This model is a fine-tuned version of bert-base-cased on the article500v5\_wikigold\_split dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1914
* Precision: 0.6408
* Recall: 0.7218
* F1: 0.6... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-article500v5_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during trainin... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Article_500v6_NER_Model_3Epochs_UNAUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["article500v6_wikigold_split"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "Article_500v6_NER_Model_3Epochs_UNAUGMENTED", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, ... | DOOGLAK/Article_500v6_NER_Model_3Epochs_UNAUGMENTED | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:article500v6_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-11T23:00:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-article500v6_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Article\_500v6\_NER\_Model\_3Epochs\_UNAUGMENTED
================================================
This model is a fine-tuned version of bert-base-cased on the article500v6\_wikigold\_split dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2025
* Precision: 0.6462
* Recall: 0.6930
* F1: 0.6... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-article500v6_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during trainin... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# trainer_log
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the None dataset.... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "trainer_log", "results": []}]} | ozioh/trainer_log | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-11T23:00:49+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| trainer\_log
============
This model is a fine-tuned version of bert-base-cased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4907
* Accuracy: 0.8742
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Article_500v7_NER_Model_3Epochs_UNAUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["article500v7_wikigold_split"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "Article_500v7_NER_Model_3Epochs_UNAUGMENTED", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, ... | DOOGLAK/Article_500v7_NER_Model_3Epochs_UNAUGMENTED | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:article500v7_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-11T23:05:39+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-article500v7_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Article\_500v7\_NER\_Model\_3Epochs\_UNAUGMENTED
================================================
This model is a fine-tuned version of bert-base-cased on the article500v7\_wikigold\_split dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1885
* Precision: 0.6722
* Recall: 0.7278
* F1: 0.6... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-article500v7_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during trainin... |
token-classification | transformers | # tner/roberta-large-bionlp2004
This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the
[tner/bionlp2004](https://huggingface.co/datasets/tner/bionlp2004) dataset.
Model fine-tuning is done via [T-NER](https://github.com/asahi417/tner)'s hyper-parameter search (see the repos... | {"datasets": ["tner/bionlp2004"], "metrics": ["f1", "precision", "recall"], "pipeline_tag": "token-classification", "widget": [{"text": "Jacob Collier is a Grammy awarded artist from England.", "example_title": "NER Example 1"}], "model-index": [{"name": "tner/roberta-large-bionlp2004", "results": [{"task": {"type": "t... | tner/roberta-large-bionlp2004 | null | [
"transformers",
"pytorch",
"roberta",
"token-classification",
"dataset:tner/bionlp2004",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-11T23:10:08+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #token-classification #dataset-tner/bionlp2004 #model-index #autotrain_compatible #endpoints_compatible #region-us
| # tner/roberta-large-bionlp2004
This model is a fine-tuned version of roberta-large on the
tner/bionlp2004 dataset.
Model fine-tuning is done via T-NER's hyper-parameter search (see the repository
for more detail). It achieves the following results on the test set:
- F1 (micro): 0.7513434294088912
- Precision (micro)... | [
"# tner/roberta-large-bionlp2004\n\nThis model is a fine-tuned version of roberta-large on the \ntner/bionlp2004 dataset.\nModel fine-tuning is done via T-NER's hyper-parameter search (see the repository\nfor more detail). It achieves the following results on the test set:\n- F1 (micro): 0.7513434294088912\n- Preci... | [
"TAGS\n#transformers #pytorch #roberta #token-classification #dataset-tner/bionlp2004 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# tner/roberta-large-bionlp2004\n\nThis model is a fine-tuned version of roberta-large on the \ntner/bionlp2004 dataset.\nModel fine-tuning is done via T-... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilled-mt5-small-0.02-0.5
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) o... | {"language": ["en", "ro"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["wmt16"], "metrics": ["bleu"], "model-index": [{"name": "distilled-mt5-small-0.02-0.5", "results": [{"task": {"type": "translation", "name": "Translation"}, "dataset": {"name": "wmt16 ro-en", "type": "wmt16", "args": "r... | Lvxue/distilled-mt5-small-0.02-0.5 | null | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"generated_from_trainer",
"en",
"ro",
"dataset:wmt16",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-11T23:10:42+00:00 | [] | [
"en",
"ro"
] | TAGS
#transformers #pytorch #mt5 #text2text-generation #generated_from_trainer #en #ro #dataset-wmt16 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# distilled-mt5-small-0.02-0.5
This model is a fine-tuned version of google/mt5-small on the wmt16 ro-en dataset.
It achieves the following results on the evaluation set:
- Loss: 2.8160
- Bleu: 7.448
- Gen Len: 44.2241
## Model description
More information needed
## Intended uses & limitations
More information ... | [
"# distilled-mt5-small-0.02-0.5\n\nThis model is a fine-tuned version of google/mt5-small on the wmt16 ro-en dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 2.8160\n- Bleu: 7.448\n- Gen Len: 44.2241",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\... | [
"TAGS\n#transformers #pytorch #mt5 #text2text-generation #generated_from_trainer #en #ro #dataset-wmt16 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# distilled-mt5-small-0.02-0.5\n\nThis model is a fine-tuned version of google/mt5-small ... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilled-mt5-small-0.02-1
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on ... | {"language": ["en", "ro"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["wmt16"], "metrics": ["bleu"], "model-index": [{"name": "distilled-mt5-small-0.02-1", "results": [{"task": {"type": "translation", "name": "Translation"}, "dataset": {"name": "wmt16 ro-en", "type": "wmt16", "args": "ro-... | Lvxue/distilled-mt5-small-0.02-1 | null | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"generated_from_trainer",
"en",
"ro",
"dataset:wmt16",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-11T23:11:11+00:00 | [] | [
"en",
"ro"
] | TAGS
#transformers #pytorch #mt5 #text2text-generation #generated_from_trainer #en #ro #dataset-wmt16 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# distilled-mt5-small-0.02-1
This model is a fine-tuned version of google/mt5-small on the wmt16 ro-en dataset.
It achieves the following results on the evaluation set:
- Loss: 2.8008
- Bleu: 7.2811
- Gen Len: 45.6168
## Model description
More information needed
## Intended uses & limitations
More information n... | [
"# distilled-mt5-small-0.02-1\n\nThis model is a fine-tuned version of google/mt5-small on the wmt16 ro-en dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 2.8008\n- Bleu: 7.2811\n- Gen Len: 45.6168",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n... | [
"TAGS\n#transformers #pytorch #mt5 #text2text-generation #generated_from_trainer #en #ro #dataset-wmt16 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# distilled-mt5-small-0.02-1\n\nThis model is a fine-tuned version of google/mt5-small on... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Article_500v8_NER_Model_3Epochs_UNAUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["article500v8_wikigold_split"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "Article_500v8_NER_Model_3Epochs_UNAUGMENTED", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, ... | DOOGLAK/Article_500v8_NER_Model_3Epochs_UNAUGMENTED | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:article500v8_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-11T23:11:25+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-article500v8_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Article\_500v8\_NER\_Model\_3Epochs\_UNAUGMENTED
================================================
This model is a fine-tuned version of bert-base-cased on the article500v8\_wikigold\_split dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1980
* Precision: 0.6780
* Recall: 0.7117
* F1: 0.6... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-article500v8_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during trainin... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilled-mt5-small-0.02-0.25
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) ... | {"language": ["en", "ro"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["wmt16"], "metrics": ["bleu"], "model-index": [{"name": "distilled-mt5-small-0.02-0.25", "results": [{"task": {"type": "translation", "name": "Translation"}, "dataset": {"name": "wmt16 ro-en", "type": "wmt16", "args": "... | Lvxue/distilled-mt5-small-0.02-0.25 | null | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"generated_from_trainer",
"en",
"ro",
"dataset:wmt16",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-11T23:12:00+00:00 | [] | [
"en",
"ro"
] | TAGS
#transformers #pytorch #mt5 #text2text-generation #generated_from_trainer #en #ro #dataset-wmt16 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# distilled-mt5-small-0.02-0.25
This model is a fine-tuned version of google/mt5-small on the wmt16 ro-en dataset.
It achieves the following results on the evaluation set:
- Loss: 2.8275
- Bleu: 7.5228
- Gen Len: 44.6403
## Model description
More information needed
## Intended uses & limitations
More informatio... | [
"# distilled-mt5-small-0.02-0.25\n\nThis model is a fine-tuned version of google/mt5-small on the wmt16 ro-en dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 2.8275\n- Bleu: 7.5228\n- Gen Len: 44.6403",
"## Model description\n\nMore information needed",
"## Intended uses & limitation... | [
"TAGS\n#transformers #pytorch #mt5 #text2text-generation #generated_from_trainer #en #ro #dataset-wmt16 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# distilled-mt5-small-0.02-0.25\n\nThis model is a fine-tuned version of google/mt5-small... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilled-mt5-small-0.005-0.25
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small)... | {"language": ["en", "ro"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["wmt16"], "metrics": ["bleu"], "model-index": [{"name": "distilled-mt5-small-0.005-0.25", "results": [{"task": {"type": "translation", "name": "Translation"}, "dataset": {"name": "wmt16 ro-en", "type": "wmt16", "args": ... | Lvxue/distilled-mt5-small-0.005-0.25 | null | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"generated_from_trainer",
"en",
"ro",
"dataset:wmt16",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-11T23:14:33+00:00 | [] | [
"en",
"ro"
] | TAGS
#transformers #pytorch #mt5 #text2text-generation #generated_from_trainer #en #ro #dataset-wmt16 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# distilled-mt5-small-0.005-0.25
This model is a fine-tuned version of google/mt5-small on the wmt16 ro-en dataset.
It achieves the following results on the evaluation set:
- Loss: 2.8536
- Bleu: 7.6069
- Gen Len: 45.1846
## Model description
More information needed
## Intended uses & limitations
More informati... | [
"# distilled-mt5-small-0.005-0.25\n\nThis model is a fine-tuned version of google/mt5-small on the wmt16 ro-en dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 2.8536\n- Bleu: 7.6069\n- Gen Len: 45.1846",
"## Model description\n\nMore information needed",
"## Intended uses & limitatio... | [
"TAGS\n#transformers #pytorch #mt5 #text2text-generation #generated_from_trainer #en #ro #dataset-wmt16 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# distilled-mt5-small-0.005-0.25\n\nThis model is a fine-tuned version of google/mt5-smal... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuning-sentiment-model-3000-samples
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/d... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imdb"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "finetuning-sentiment-model-3000-samples", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "imdb", "type": "imdb", "config"... | brookelove/finetuning-sentiment-model-3000-samples | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-11T23:16:58+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-imdb #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# finetuning-sentiment-model-3000-samples
This model is a fine-tuned version of distilbert-base-uncased on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3246
- Accuracy: 0.8633
- F1: 0.8673
## Model description
More information needed
## Intended uses & limitations
More in... | [
"# finetuning-sentiment-model-3000-samples\n\nThis model is a fine-tuned version of distilbert-base-uncased on the imdb dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.3246\n- Accuracy: 0.8633\n- F1: 0.8673",
"## Model description\n\nMore information needed",
"## Intended uses & li... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-imdb #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# finetuning-sentiment-model-3000-samples\n\nThis model is a fine-tuned version of distilbert-base-uncased... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Article_500v9_NER_Model_3Epochs_UNAUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["article500v9_wikigold_split"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "Article_500v9_NER_Model_3Epochs_UNAUGMENTED", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, ... | DOOGLAK/Article_500v9_NER_Model_3Epochs_UNAUGMENTED | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:article500v9_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-11T23:17:10+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-article500v9_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Article\_500v9\_NER\_Model\_3Epochs\_UNAUGMENTED
================================================
This model is a fine-tuned version of bert-base-cased on the article500v9\_wikigold\_split dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1975
* Precision: 0.6869
* Recall: 0.7022
* F1: 0.6... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-article500v9_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during trainin... |
token-classification | transformers | # tner/roberta-large-mit-restaurant
This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the
[tner/mit_restaurant](https://huggingface.co/datasets/tner/mit_restaurant) dataset.
Model fine-tuning is done via [T-NER](https://github.com/asahi417/tner)'s hyper-parameter search (s... | {"datasets": ["tner/mit_restaurant"], "metrics": ["f1", "precision", "recall"], "pipeline_tag": "token-classification", "widget": [{"text": "Jacob Collier is a Grammy awarded artist from England.", "example_title": "NER Example 1"}], "model-index": [{"name": "tner/roberta-large-mit-restaurant", "results": [{"task": {"t... | tner/roberta-large-mit-restaurant | null | [
"transformers",
"pytorch",
"roberta",
"token-classification",
"dataset:tner/mit_restaurant",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-08-11T23:20:40+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #token-classification #dataset-tner/mit_restaurant #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
| # tner/roberta-large-mit-restaurant
This model is a fine-tuned version of roberta-large on the
tner/mit_restaurant dataset.
Model fine-tuning is done via T-NER's hyper-parameter search (see the repository
for more detail). It achieves the following results on the test set:
- F1 (micro): 0.8164676304211189
- Precision... | [
"# tner/roberta-large-mit-restaurant\n\nThis model is a fine-tuned version of roberta-large on the \ntner/mit_restaurant dataset.\nModel fine-tuning is done via T-NER's hyper-parameter search (see the repository\nfor more detail). It achieves the following results on the test set:\n- F1 (micro): 0.8164676304211189\... | [
"TAGS\n#transformers #pytorch #roberta #token-classification #dataset-tner/mit_restaurant #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# tner/roberta-large-mit-restaurant\n\nThis model is a fine-tuned version of roberta-large on the \ntner/mit_restaurant dataset.\nModel fin... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base-50k
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-base-50k", "results": []}]} | Gausstein26/wav2vec2-base-50k | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-08-11T23:57:47+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-base-50k
=================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common\_voice dataset.
It achieves the following results on the evaluation set:
* Loss: 3.5640
* Wer: 1.0
Model description
-----------------
More information needed
Intended uses & limitations
------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 7.5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsil... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 7.5e-05\n* ... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilled-mt5-small-1-0.5
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on t... | {"language": ["en", "ro"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["wmt16"], "metrics": ["bleu"], "model-index": [{"name": "distilled-mt5-small-1-0.5", "results": [{"task": {"type": "translation", "name": "Translation"}, "dataset": {"name": "wmt16 ro-en", "type": "wmt16", "args": "ro-e... | Lvxue/distilled-mt5-small-1-0.5 | null | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"generated_from_trainer",
"en",
"ro",
"dataset:wmt16",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-12T01:06:37+00:00 | [] | [
"en",
"ro"
] | TAGS
#transformers #pytorch #mt5 #text2text-generation #generated_from_trainer #en #ro #dataset-wmt16 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# distilled-mt5-small-1-0.5
This model is a fine-tuned version of google/mt5-small on the wmt16 ro-en dataset.
It achieves the following results on the evaluation set:
- Loss: 3.8410
- Bleu: 5.3917
- Gen Len: 40.6103
## Model description
More information needed
## Intended uses & limitations
More information ne... | [
"# distilled-mt5-small-1-0.5\n\nThis model is a fine-tuned version of google/mt5-small on the wmt16 ro-en dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 3.8410\n- Bleu: 5.3917\n- Gen Len: 40.6103",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\... | [
"TAGS\n#transformers #pytorch #mt5 #text2text-generation #generated_from_trainer #en #ro #dataset-wmt16 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# distilled-mt5-small-1-0.5\n\nThis model is a fine-tuned version of google/mt5-small on ... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilled-mt5-small-1-0.25
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on ... | {"language": ["en", "ro"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["wmt16"], "metrics": ["bleu"], "model-index": [{"name": "distilled-mt5-small-1-0.25", "results": [{"task": {"type": "translation", "name": "Translation"}, "dataset": {"name": "wmt16 ro-en", "type": "wmt16", "args": "ro-... | Lvxue/distilled-mt5-small-1-0.25 | null | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"generated_from_trainer",
"en",
"ro",
"dataset:wmt16",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-12T01:06:48+00:00 | [] | [
"en",
"ro"
] | TAGS
#transformers #pytorch #mt5 #text2text-generation #generated_from_trainer #en #ro #dataset-wmt16 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# distilled-mt5-small-1-0.25
This model is a fine-tuned version of google/mt5-small on the wmt16 ro-en dataset.
It achieves the following results on the evaluation set:
- Loss: 6.8599
- Bleu: 4.0871
- Gen Len: 35.3267
## Model description
More information needed
## Intended uses & limitations
More information n... | [
"# distilled-mt5-small-1-0.25\n\nThis model is a fine-tuned version of google/mt5-small on the wmt16 ro-en dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 6.8599\n- Bleu: 4.0871\n- Gen Len: 35.3267",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n... | [
"TAGS\n#transformers #pytorch #mt5 #text2text-generation #generated_from_trainer #en #ro #dataset-wmt16 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# distilled-mt5-small-1-0.25\n\nThis model is a fine-tuned version of google/mt5-small on... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilled-mt5-small-0.005-1
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on... | {"language": ["en", "ro"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["wmt16"], "metrics": ["bleu"], "model-index": [{"name": "distilled-mt5-small-0.005-1", "results": [{"task": {"type": "translation", "name": "Translation"}, "dataset": {"name": "wmt16 ro-en", "type": "wmt16", "args": "ro... | Lvxue/distilled-mt5-small-0.005-1 | null | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"generated_from_trainer",
"en",
"ro",
"dataset:wmt16",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-12T01:08:07+00:00 | [] | [
"en",
"ro"
] | TAGS
#transformers #pytorch #mt5 #text2text-generation #generated_from_trainer #en #ro #dataset-wmt16 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# distilled-mt5-small-0.005-1
This model is a fine-tuned version of google/mt5-small on the wmt16 ro-en dataset.
It achieves the following results on the evaluation set:
- Loss: 2.8123
- Bleu: 7.6523
- Gen Len: 44.3867
## Model description
More information needed
## Intended uses & limitations
More information ... | [
"# distilled-mt5-small-0.005-1\n\nThis model is a fine-tuned version of google/mt5-small on the wmt16 ro-en dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 2.8123\n- Bleu: 7.6523\n- Gen Len: 44.3867",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\... | [
"TAGS\n#transformers #pytorch #mt5 #text2text-generation #generated_from_trainer #en #ro #dataset-wmt16 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# distilled-mt5-small-0.005-1\n\nThis model is a fine-tuned version of google/mt5-small o... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilled-mt5-small-1-1
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the... | {"language": ["en", "ro"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["wmt16"], "metrics": ["bleu"], "model-index": [{"name": "distilled-mt5-small-1-1", "results": [{"task": {"type": "translation", "name": "Translation"}, "dataset": {"name": "wmt16 ro-en", "type": "wmt16", "args": "ro-en"... | Lvxue/distilled-mt5-small-1-1 | null | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"generated_from_trainer",
"en",
"ro",
"dataset:wmt16",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-12T01:08:29+00:00 | [] | [
"en",
"ro"
] | TAGS
#transformers #pytorch #mt5 #text2text-generation #generated_from_trainer #en #ro #dataset-wmt16 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# distilled-mt5-small-1-1
This model is a fine-tuned version of google/mt5-small on the wmt16 ro-en dataset.
It achieves the following results on the evaluation set:
- Loss: 2.8289
- Bleu: 6.6959
- Gen Len: 45.7539
## Model description
More information needed
## Intended uses & limitations
More information need... | [
"# distilled-mt5-small-1-1\n\nThis model is a fine-tuned version of google/mt5-small on the wmt16 ro-en dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 2.8289\n- Bleu: 6.6959\n- Gen Len: 45.7539",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nM... | [
"TAGS\n#transformers #pytorch #mt5 #text2text-generation #generated_from_trainer #en #ro #dataset-wmt16 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# distilled-mt5-small-1-1\n\nThis model is a fine-tuned version of google/mt5-small on th... |
audio-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base-ks-finetuning
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2v... | {"license": "apache-2.0", "tags": ["audio-classification", "generated_from_trainer"], "datasets": ["superb"], "metrics": ["accuracy"], "model-index": [{"name": "wav2vec2-base-ks-finetuning", "results": []}]} | Jungwoo4021/wav2vec2-base-ks-finetuning | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"text-classification",
"audio-classification",
"generated_from_trainer",
"dataset:superb",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-12T01:24:38+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #text-classification #audio-classification #generated_from_trainer #dataset-superb #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| wav2vec2-base-ks-finetuning
===========================
This model is a fine-tuned version of facebook/wav2vec2-base on the superb dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2261
* Accuracy: 0.9813
Model description
-----------------
More information needed
Intended uses & lim... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 256\n* eval\\_batch\\_size: 256\n* seed: 0\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 1024\n* optimizer: Adam with betas=(0.9,0.999) and epsi... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #text-classification #audio-classification #generated_from_trainer #dataset-superb #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n*... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta_large-chunking_0811_v7
This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on an... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "roberta_large-chunking_0811_v7", "results": []}]} | mariolinml/roberta_large-chunking_0811_v7 | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"token-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-12T02:57:43+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| roberta\_large-chunking\_0811\_v7
=================================
This model is a fine-tuned version of roberta-large on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3687
* Precision: 0.8237
* Recall: 0.8406
* F1: 0.8320
* Accuracy: 0.9134
Model description
------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 7",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_si... |
image-classification | transformers |
# animal-classifier
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/nateraw/hu... | {"tags": ["image-classification", "pytorch", "huggingpics"], "metrics": ["accuracy"]} | Falcom/animal-classifier | null | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-12T03:02:02+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# animal-classifier
Autogenerated by HuggingPics️
Create your own image classifier for anything by running the demo on Google Colab.
Report any issues with the demo at the github repo.
## Example Images
#### butterfly
!butterfly
#### cat
!cat
#### chicken
!chicken
#### cow
!cow
#### dog
!dog
#### ele... | [
"# animal-classifier\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nReport any issues with the demo at the github repo.",
"## Example Images",
"#### butterfly\n\n!butterfly",
"#### cat\n\n!cat",
"#### chicken\n\n!chicken",
"#### ... | [
"TAGS\n#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# animal-classifier\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nReport any is... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["emotion"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "distilbert-base-uncased-finetuned-emotion", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "emotion", "type": "emotion... | User-leanring-HI/distilbert-base-uncased-finetuned-emotion | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-12T04:27:10+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-emotion
=========================================
This model is a fine-tuned version of distilbert-base-uncased on the emotion dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2264
* Accuracy: 0.928
* F1: 0.9280
Model description
-----------------
Mor... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learn... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["emotion"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "distilbert-base-uncased-finetuned-emotion", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "emotion", "type": "emotion... | susank/distilbert-base-uncased-finetuned-emotion | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-12T04:33:23+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-emotion
=========================================
This model is a fine-tuned version of distilbert-base-uncased on the emotion dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2281
* Accuracy: 0.924
* F1: 0.9240
Model description
-----------------
Mor... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learn... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# urdumodel
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["wer", "cer"], "model-index": [{"name": "urdumodel", "results": []}]} | Talha/urdumodel | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-08-12T04:37:10+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
|
# urdumodel
This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4939
- Wer: 0.3698
- Cer: 0.1465
## Model description
More information needed
## Intended uses & limitations
More information needed
## Train... | [
"# urdumodel\n\nThis model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the None dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.4939\n- Wer: 0.3698\n- Cer: 0.1465",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore informat... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"# urdumodel\n\nThis model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the None dataset.\nIt achieves the following results on the evaluation se... |
text-generation | transformers |
DialoGPT-small finetuned in the Switchboard Dialogue Act (SwDa) Corpus. The [repository](https://github.com/KonstSkouras/Switchboard-Corpus/tree/develop/) with additionally pre-processed SwDa dialogues of concatenated utterances per speaker, 80/10/10 train/val/test split and metadata, is a fork of the [Nathan Duran's ... | {"tags": ["conversational"]} | skouras/DialoGPT-small-swda | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-12T05:01:01+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
DialoGPT-small finetuned in the Switchboard Dialogue Act (SwDa) Corpus. The repository with additionally pre-processed SwDa dialogues of concatenated utterances per speaker, 80/10/10 train/val/test split and metadata, is a fork of the Nathan Duran's repository. For finetuning the 'train_dialogpt.ipynb' notebook from N... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilled-mt5-small-0.005-0.5
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) ... | {"language": ["en", "ro"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["wmt16"], "metrics": ["bleu"], "model-index": [{"name": "distilled-mt5-small-0.005-0.5", "results": [{"task": {"type": "translation", "name": "Translation"}, "dataset": {"name": "wmt16 ro-en", "type": "wmt16", "args": "... | Lvxue/distilled-mt5-small-0.005-0.5 | null | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"generated_from_trainer",
"en",
"ro",
"dataset:wmt16",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-12T05:09:48+00:00 | [] | [
"en",
"ro"
] | TAGS
#transformers #pytorch #mt5 #text2text-generation #generated_from_trainer #en #ro #dataset-wmt16 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# distilled-mt5-small-0.005-0.5
This model is a fine-tuned version of google/mt5-small on the wmt16 ro-en dataset.
It achieves the following results on the evaluation set:
- Loss: 2.8309
- Bleu: 7.642
- Gen Len: 44.9085
## Model description
More information needed
## Intended uses & limitations
More information... | [
"# distilled-mt5-small-0.005-0.5\n\nThis model is a fine-tuned version of google/mt5-small on the wmt16 ro-en dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 2.8309\n- Bleu: 7.642\n- Gen Len: 44.9085",
"## Model description\n\nMore information needed",
"## Intended uses & limitations... | [
"TAGS\n#transformers #pytorch #mt5 #text2text-generation #generated_from_trainer #en #ro #dataset-wmt16 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# distilled-mt5-small-0.005-0.5\n\nThis model is a fine-tuned version of google/mt5-small... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilled-mt5-small-1-2
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the... | {"language": ["en", "ro"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["wmt16"], "metrics": ["bleu"], "model-index": [{"name": "distilled-mt5-small-1-2", "results": [{"task": {"type": "translation", "name": "Translation"}, "dataset": {"name": "wmt16 ro-en", "type": "wmt16", "args": "ro-en"... | Lvxue/distilled-mt5-small-1-2 | null | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"generated_from_trainer",
"en",
"ro",
"dataset:wmt16",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-12T05:11:08+00:00 | [] | [
"en",
"ro"
] | TAGS
#transformers #pytorch #mt5 #text2text-generation #generated_from_trainer #en #ro #dataset-wmt16 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# distilled-mt5-small-1-2
This model is a fine-tuned version of google/mt5-small on the wmt16 ro-en dataset.
It achieves the following results on the evaluation set:
- Loss: 3.7760
- Bleu: 1.1101
- Gen Len: 99.5898
## Model description
More information needed
## Intended uses & limitations
More information need... | [
"# distilled-mt5-small-1-2\n\nThis model is a fine-tuned version of google/mt5-small on the wmt16 ro-en dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 3.7760\n- Bleu: 1.1101\n- Gen Len: 99.5898",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nM... | [
"TAGS\n#transformers #pytorch #mt5 #text2text-generation #generated_from_trainer #en #ro #dataset-wmt16 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# distilled-mt5-small-1-2\n\nThis model is a fine-tuned version of google/mt5-small on th... |
text-generation | transformers |
DialoGPT-small finetuned in the Maptask Corpus. The [repository](https://github.com/KonstSkouras/Maptask-Corpus/tree/develop) with additionally pre-processed Maptask dialogues of concatenated utterances per speaker, 80/10/10 train/val/test split and metadata, is a fork of the [Nathan Duran's repository](https://github... | {"tags": ["conversational"]} | skouras/DialoGPT-small-maptask | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-12T05:36:56+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
DialoGPT-small finetuned in the Maptask Corpus. The repository with additionally pre-processed Maptask dialogues of concatenated utterances per speaker, 80/10/10 train/val/test split and metadata, is a fork of the Nathan Duran's repository. For finetuning the 'train_dialogpt.ipynb' notebook from Nathan Cooper's Tutori... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilled-mt5-small-0.01-0.5-full
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-sma... | {"language": ["en", "ro"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["wmt16"], "metrics": ["bleu"], "model-index": [{"name": "distilled-mt5-small-0.01-0.5-full", "results": [{"task": {"type": "translation", "name": "Translation"}, "dataset": {"name": "wmt16 ro-en", "type": "wmt16", "args... | Lvxue/distilled-mt5-small-0.01-0.5-full | null | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"generated_from_trainer",
"en",
"ro",
"dataset:wmt16",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-12T05:37:42+00:00 | [] | [
"en",
"ro"
] | TAGS
#transformers #pytorch #mt5 #text2text-generation #generated_from_trainer #en #ro #dataset-wmt16 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# distilled-mt5-small-0.01-0.5-full
This model is a fine-tuned version of google/mt5-small on the wmt16 ro-en dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6451
- Bleu: 24.224
- Gen Len: 43.8584
## Model description
More information needed
## Intended uses & limitations
More inform... | [
"# distilled-mt5-small-0.01-0.5-full\n\nThis model is a fine-tuned version of google/mt5-small on the wmt16 ro-en dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 1.6451\n- Bleu: 24.224\n- Gen Len: 43.8584",
"## Model description\n\nMore information needed",
"## Intended uses & limita... | [
"TAGS\n#transformers #pytorch #mt5 #text2text-generation #generated_from_trainer #en #ro #dataset-wmt16 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# distilled-mt5-small-0.01-0.5-full\n\nThis model is a fine-tuned version of google/mt5-s... |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-squad
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/d... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "distilbert-base-uncased-finetuned-squad", "results": []}]} | Walterchamy/distilbert-base-uncased-finetuned-squad | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-08-12T05:44:33+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-squad
=======================================
This model is a fine-tuned version of distilbert-base-uncased on the squad dataset.
It achieves the following results on the evaluation set:
* Loss: 1.1473
Model description
-----------------
More information needed
Intended uses ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_s... |
reinforcement-learning | null |
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes ... | {"tags": ["FrozenLake-v1-4x4", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-FrozenLake-v1-4x4-noSlippery", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "FrozenLake-v1-4x4", "type": "FrozenLake-v1-4x4"}, "m... | marii/q-FrozenLake-v1-4x4-noSlippery | null | [
"FrozenLake-v1-4x4",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-08-12T05:45:30+00:00 | [] | [] | TAGS
#FrozenLake-v1-4x4 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing FrozenLake-v1
This is a trained model of a Q-Learning agent playing FrozenLake-v1 .
## Usage
| [
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] | [
"TAGS\n#FrozenLake-v1-4x4 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-squad
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/d... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "distilbert-base-uncased-finetuned-squad", "results": []}]} | NitishKumar/distilbert-base-uncased-finetuned-squad | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-08-12T05:45:51+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-squad
=======================================
This model is a fine-tuned version of distilbert-base-uncased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 2.9423
Model description
-----------------
More information needed
Intended uses &... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["emotion"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "distilbert-base-uncased-finetuned-emotion", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "emotion", "type": "emotion... | hhffxx/distilbert-base-uncased-finetuned-emotion | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-12T05:49:57+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-emotion
=========================================
This model is a fine-tuned version of distilbert-base-uncased on the emotion dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2716
* Accuracy: 0.9385
* F1: 0.9382
Model description
-----------------
Mo... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learn... |
reinforcement-learning | null |
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="rhiga/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attrib... | {"tags": ["FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-FrozenLake-v1-4x4-noSlippery", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "FrozenLake-v1-4x4-no_slippery", "type": ... | rhiga/q-FrozenLake-v1-4x4-noSlippery | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-08-12T06:43:45+00:00 | [] | [] | TAGS
#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing FrozenLake-v1
This is a trained model of a Q-Learning agent playing FrozenLake-v1 .
## Usage
| [
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] | [
"TAGS\n#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] |
reinforcement-learning | null |
# **Q-Learning** Agent playing **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="rhiga/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
en... | {"tags": ["Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-Taxi-v3", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Taxi-v3", "type": "Taxi-v3"}, "metrics": [{"type": "mean_reward", "value": "7.50 +/... | rhiga/q-Taxi-v3 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-08-12T06:50:03+00:00 | [] | [] | TAGS
#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing Taxi-v3
This is a trained model of a Q-Learning agent playing Taxi-v3 .
## Usage
| [
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] | [
"TAGS\n#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] |
translation | transformers | # opus-mt-tc-big-en-ko
## Table of Contents
- [Model Details](#model-details)
- [Uses](#uses)
- [Risks, Limitations and Biases](#risks-limitations-and-biases)
- [How to Get Started With the Model](#how-to-get-started-with-the-model)
- [Training](#training)
- [Evaluation](#evaluation)
- [Citation Information](#citation... | {"language": ["en", "ko"], "license": "cc-by-4.0", "tags": ["translation", "opus-mt-tc"], "model-index": [{"name": "opus-mt-tc-big-en-ko", "results": [{"task": {"type": "translation", "name": "Translation eng-kor"}, "dataset": {"name": "flores101-devtest", "type": "flores_101", "args": "eng kor devtest"}, "metrics": [{... | Helsinki-NLP/opus-mt-tc-big-en-ko | null | [
"transformers",
"pytorch",
"tf",
"safetensors",
"marian",
"text2text-generation",
"translation",
"opus-mt-tc",
"en",
"ko",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-08-12T07:02:12+00:00 | [] | [
"en",
"ko"
] | TAGS
#transformers #pytorch #tf #safetensors #marian #text2text-generation #translation #opus-mt-tc #en #ko #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
| opus-mt-tc-big-en-ko
====================
Table of Contents
-----------------
* Model Details
* Uses
* Risks, Limitations and Biases
* How to Get Started With the Model
* Training
* Evaluation
* Citation Information
* Acknowledgements
Model Details
-------------
Neural machine translation model for translating ... | [] | [
"TAGS\n#transformers #pytorch #tf #safetensors #marian #text2text-generation #translation #opus-mt-tc #en #ko #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
text-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1433108361839472642/3d54... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/shaanvp/1668571298343/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/shaanvp | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-12T07:15:28+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Shaan Puri
@shaanvp
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-------------... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
translation | transformers | # opus-mt-tc-big-ko-en
## Table of Contents
- [Model Details](#model-details)
- [Uses](#uses)
- [Risks, Limitations and Biases](#risks-limitations-and-biases)
- [How to Get Started With the Model](#how-to-get-started-with-the-model)
- [Training](#training)
- [Evaluation](#evaluation)
- [Citation Information](#citation... | {"language": ["en", "ko"], "license": "cc-by-4.0", "tags": ["translation", "opus-mt-tc"], "model-index": [{"name": "opus-mt-tc-big-ko-en", "results": [{"task": {"type": "translation", "name": "Translation kor-eng"}, "dataset": {"name": "flores101-devtest", "type": "flores_101", "args": "kor eng devtest"}, "metrics": [{... | Helsinki-NLP/opus-mt-tc-big-ko-en | null | [
"transformers",
"pytorch",
"tf",
"safetensors",
"marian",
"text2text-generation",
"translation",
"opus-mt-tc",
"en",
"ko",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-08-12T07:19:11+00:00 | [] | [
"en",
"ko"
] | TAGS
#transformers #pytorch #tf #safetensors #marian #text2text-generation #translation #opus-mt-tc #en #ko #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
| opus-mt-tc-big-ko-en
====================
Table of Contents
-----------------
* Model Details
* Uses
* Risks, Limitations and Biases
* How to Get Started With the Model
* Training
* Evaluation
* Citation Information
* Acknowledgements
Model Details
-------------
Neural machine translation model for translating ... | [] | [
"TAGS\n#transformers #pytorch #tf #safetensors #marian #text2text-generation #translation #opus-mt-tc #en #ko #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
image-classification | transformers |
This model is a fork of [facebook/levit-256](https://huggingface.co/facebook/levit-256), where:
* `nn.BatchNorm2d` and `nn.Conv2d` are fused
* `nn.BatchNorm1d` and `nn.Linear` are fused
and the optimized model is converted to the onnx format.
The fusion of layers leverages torch.fx, using the transformations `FuseB... | {"license": "apache-2.0", "tags": ["vision", "image-classification"], "datasets": ["imagenet-1k"]} | fxmarty/levit-256-onnx | null | [
"transformers",
"onnx",
"levit",
"image-classification",
"vision",
"dataset:imagenet-1k",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-12T07:52:23+00:00 | [] | [] | TAGS
#transformers #onnx #levit #image-classification #vision #dataset-imagenet-1k #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
This model is a fork of facebook/levit-256, where:
* 'nn.BatchNorm2d' and 'nn.Conv2d' are fused
* 'nn.BatchNorm1d' and 'nn.Linear' are fused
and the optimized model is converted to the onnx format.
The fusion of layers leverages URL, using the transformations 'FuseBatchNorm2dInConv2d' and 'FuseBatchNorm1dInLinear' ... | [
"## How to use\n\n\n\nTo be safe, check as well that the onnx model returns the same logits as the PyTorch model:",
"## Benchmarking\n\nMore than x2 throughput with batch normalization folding and onnxruntime \n\nBelow you can find latency percentiles and mean (in ms), and the models throughput (in iterations/s).... | [
"TAGS\n#transformers #onnx #levit #image-classification #vision #dataset-imagenet-1k #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"## How to use\n\n\n\nTo be safe, check as well that the onnx model returns the same logits as the PyTorch model:",
"## Benchmarking\n\nMore than x... |
text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilgpt2-wikitext2
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the wikitext wikit... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["wikitext"], "metrics": ["accuracy"], "model-index": [{"name": "distilgpt2-wikitext2", "results": [{"task": {"type": "text-generation", "name": "Causal Language Modeling"}, "dataset": {"name": "wikitext wikitext-2-raw-v1", "type": "wikitext", "a... | Intel/distilgpt2-wikitext2 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"generated_from_trainer",
"dataset:wikitext",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-12T07:59:15+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #generated_from_trainer #dataset-wikitext #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# distilgpt2-wikitext2
This model is a fine-tuned version of distilgpt2 on the wikitext wikitext-2-raw-v1 dataset.
It achieves the following results on the evaluation set:
- Loss: 3.3259
- Accuracy: 0.3932
- perplexity: 27.8235
## Model description
More information needed
## Intended uses & limitations
More inf... | [
"# distilgpt2-wikitext2\n\nThis model is a fine-tuned version of distilgpt2 on the wikitext wikitext-2-raw-v1 dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 3.3259\n- Accuracy: 0.3932\n- perplexity: 27.8235",
"## Model description\n\nMore information needed",
"## Intended uses & lim... | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #generated_from_trainer #dataset-wikitext #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# distilgpt2-wikitext2\n\nThis model is a fine-tuned version of distilgpt2 on the wikitext wikitex... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec-large-xls-r-300-ha-colab_2
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/f... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice_10_0"], "model-index": [{"name": "wav2vec-large-xls-r-300-ha-colab_2", "results": []}]} | moro23/wav2vec-large-xls-r-300-ha-colab_2 | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice_10_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-08-12T08:14:16+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice_10_0 #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec-large-xls-r-300-ha-colab\_2
===================================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common\_voice\_10\_0 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4473
* Wer: 0.4392
Model description
-----------------
More informati... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice_10_0 #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Article_250v8_NER_Model_3Epochs_UNAUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["article250v8_wikigold_split"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "Article_250v8_NER_Model_3Epochs_UNAUGMENTED", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, ... | DOOGLAK/Article_250v8_NER_Model_3Epochs_UNAUGMENTED | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:article250v8_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-12T08:16:55+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-article250v8_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Article\_250v8\_NER\_Model\_3Epochs\_UNAUGMENTED
================================================
This model is a fine-tuned version of bert-base-cased on the article250v8\_wikigold\_split dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3329
* Precision: 0.4216
* Recall: 0.3991
* F1: 0.4... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-article250v8_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during trainin... |
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