modelId stringlengths 4 111 | lastModified stringlengths 24 24 | tags list | pipeline_tag stringlengths 5 30 ⌀ | author stringlengths 2 34 ⌀ | config null | securityStatus null | id stringlengths 4 111 | likes int64 0 9.53k | downloads int64 2 73.6M | library_name stringlengths 2 84 ⌀ | created timestamp[us] | card stringlengths 101 901k | card_len int64 101 901k | embeddings list |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
tum-nlp/IDMGSP-Galactica-TRAIN-CG | 2023-07-31T15:21:47.000Z | [
"transformers",
"pytorch",
"opt",
"text-classification",
"scientific paper",
"fake papers",
"science",
"scientific text",
"en",
"dataset:tum-nlp/IDMGSP",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-classification | tum-nlp | null | null | tum-nlp/IDMGSP-Galactica-TRAIN-CG | 0 | 2 | transformers | 2023-05-30T19:26:02 | ---
datasets:
- tum-nlp/IDMGSP
language:
- en
tags:
- scientific paper
- fake papers
- science
- scientific text
widget:
- text: |
Abstract:
The Hartree-Fock (HF) method is a widely used method for approximating the electronic structure of many-electron systems. In this work, we study the properties of HF solutions of the three-dimensional electron gas (3DEG), a model system consisting of a uniform, non-interacting electron gas in three dimensions. We find that the HF solutions accurately reproduce the known analytic results for the ground state energy and the static structure factor of the 3DEG. However, we also find that the HF solutions fail to accurately describe the excitation spectrum of the 3DEG, particularly at high energies.
Introduction:
The HF method is a self-consistent method for approximating the electronic structure of many-electron systems. It is based on the assumption that the electrons in a system can be described as non-interacting quasiparticles, each with its own effective potential. The HF method is commonly used to study the ground state properties of systems, such as the energy and the density distribution, but it can also be used to study excited states.
The 3DEG is a model system that has been widely studied as a test case for electronic structure methods. It consists of a uniform, non-interacting electron gas in three dimensions, with a finite density and a periodic boundary condition. The 3DEG has a number of known analytic results for its ground state properties, such as the ground state energy and the static structure factor, which can be used to test the accuracy of approximate methods.
Conclusion:
In this work, we have studied the properties of HF solutions of the 3DEG. We find that the HF solutions accurately reproduce the known analytic results for the ground state energy and the static structure factor of the 3DEG. However, we also find that the HF solutions fail to accurately describe the excitation spectrum of the 3DEG, particularly at high energies. This suggests that the HF method may not be suitable for accurately describing the excited states of the 3DEG. Further work is needed to understand the limitations of the HF method and to develop improved methods for studying the electronic structure of many-electron systems.
example_title: "Example ChatGPT fake"
- text: |
Abstract:
Recent calculations have pointed to a 2.8 $\sigma$ tension between data on $\epsilon^{\prime}_K / \epsilon_K$ and the standard-model (SM) prediction. Several new physics (NP) models can explain this discrepancy, and such NP models are likely to predict deviations of $\mathcal{B}(K\to \pi \nu \overline{\nu})$ from the SM predictions, which can be probed precisely in the near future by NA62 and KOTO experiments. We present correlations between $\epsilon^{\prime}_K / \epsilon_K$ and $\mathcal{B}(K\to \pi \nu \overline{\nu})$ in two types of NP scenarios: a box dominated scenario and a $Z$-penguin dominated one. It is shown that different correlations are predicted and the future precision measurements of $K \to \pi \nu \overline{\nu}$ can distinguish both scenarios.
Introduction:
CP violating flavor-changing neutral current decays of K mesons are extremely sensitive to new physics (NP) and can probe virtual effects of particles with masses far above the reach of the Large Hadron Collider. Prime examples of such observables are ϵ′ K measuring direct CP violation in K → ππ decays and B(KL → π0νν). Until recently, large theoretical uncertainties precluded reliable predictions for ϵ′ K. Although standard-model (SM) predictions of ϵ′ K using chiral perturbation theory are consistent with the experimental value, their theoretical uncertainties are large. In contrast, calculation by the dual QCD approach 1 finds the SM value much below the experimental one. A major breakthrough has been the recent lattice-QCD calculation of the hadronic matrix elements by RBC-UKQCD collaboration 2, which gives support to the latter result. The SM value at the next-to-leading order divided by the indirect CP violating measure ϵK is 3 which is consistent with (ϵ′ K/ϵK)SM = (1.9±4.5)×10−4 given by Buras et al 4.a Both results are based on the lattice numbers, and further use CP-conserving K → ππ data to constrain some of the hadronic matrix elements involved. Compared to the world average of the experimental results 6, Re (ϵ′ K/ϵK)exp = (16.6 ± 2.3) × 10−4, (2) the SM prediction lies below the experimental value by 2.8 σ. Several NP models including supersymmetry (SUSY) can explain this discrepancy. It is known that such NP models are likely to predict deviations of the kaon rare decay branching ratios from the SM predictions, especially B(K → πνν) which can be probed precisely in the near future by NA62 and KOTO experiments.b In this contribution, we present correlations between ϵ′ K/ϵK and B(K → πνν) in two types of NP scenarios: a box dominated scenario and a Z-penguin dominated one. Presented at the 52th Rencontres de Moriond electroweak interactions and unified theories, La Thuile, Italy, 18-25 March, 2017. aOther estimations of the SM value are listed in Kitahara et al 5. b The correlations between ϵ′ K/ϵK, B(K → πνν) and ϵK through the CKM components in the SM are discussed in Ref. 7.
Conclusion:
We have presented the correlations between ϵ′ K/ϵK, B(KL → π0νν), and B(K+ → π+νν) in the box dominated scenario and the Z-penguin dominated one. It is shown that the constraint from ϵK produces different correlations between two NP scenarios. In the future, measurements of B(K → πνν) will be significantly improved. The NA62 experiment at CERN measuring B(K+ → π+νν) is aiming to reach a precision of 10 % compared to the SM value already in 2018. In order to achieve 5% accuracy more time is needed. Concerning KL → π0νν, the KOTO experiment at J-PARC aims in a first step at measuring B(KL → π0νν) around the SM sensitivity. Furthermore, the KOTO-step2 experiment will aim at 100 events for the SM branching ratio, implying a precision of 10 % of this measurement. Therefore, we conclude that when the ϵ′ K/ϵK discrepancy is explained by the NP contribution, NA62 experiment could probe whether a modified Z-coupling scenario is realized or not, and KOTO-step2 experiment can distinguish the box dominated scenario and the simplified modified Z-coupling scenario.
example_title: "Example real"
---
# Model Card for IDMGSP-Galactica-TRAIN-CG
A fine-tuned Galactica model to detect machine-generated scientific papers based on their abstract, introduction, and conclusion.
This model is trained on the `train-cg` dataset found in https://huggingface.co/datasets/tum-nlp/IDMGSP.
# this model card is WIP, please check the repository, the dataset card and the paper for more details.
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** Technical University of Munich (TUM)
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** English
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** Galactica
### Model Sources
<!-- Provide the basic links for the model. -->
- **Repository:** https://github.com/qwenzo/-IDMGSP
- **Paper:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
```python
from transformers import AutoTokenizer, OPTForSequenceClassification, pipeline
model = OPTForSequenceClassification.from_pretrained("tum-nlp/IDMGSP-Galactica-TRAIN-CG")
tokenizer = AutoTokenizer.from_pretrained("tum-nlp/IDMGSP-Galactica-TRAIN-CG")
reader = pipeline("text-classification", model=model, tokenizer = tokenizer)
reader(
'''
Abstract:
....
Introduction:
....
Conclusion:
...'''
)
```
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
## Training Details
### Training Data
The training dataset comprises scientific papers generated by the Galactica, GPT-2, and SCIgen models, as well as papers extracted from the arXiv database.
The provided table displays the sample counts from each source utilized in constructing the training dataset.
The dataset could be found in https://huggingface.co/datasets/tum-nlp/IDMGSP.
| Dataset | arXiv (real) | ChatGPT (fake) | GPT-2 (fake) | SCIgen (fake) | Galactica (fake) | GPT-3 (fake) |
|------------------------------|--------------|----------------|--------------|----------------|------------------|--------------|
| TRAIN without ChatGPT (TRAIN-CG) | 8k | - | 2k | 2k | 2k | - |
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
[More Information Needed]
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Data Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
| 11,586 | [
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YakovElm/Hyperledger10Classic_Balance_DATA_ratio_2 | 2023-05-30T19:29:13.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/Hyperledger10Classic_Balance_DATA_ratio_2 | 0 | 2 | transformers | 2023-05-30T19:28:35 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Hyperledger10Classic_Balance_DATA_ratio_2
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Hyperledger10Classic_Balance_DATA_ratio_2
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.5273
- Train Accuracy: 0.7262
- Validation Loss: 0.6784
- Validation Accuracy: 0.6576
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.6215 | 0.6646 | 0.6233 | 0.6685 | 0 |
| 0.5866 | 0.6782 | 0.5921 | 0.6522 | 1 |
| 0.5273 | 0.7262 | 0.6784 | 0.6576 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,826 | [
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YakovElm/Hyperledger10Classic_Balance_DATA_ratio_3 | 2023-05-30T19:49:04.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/Hyperledger10Classic_Balance_DATA_ratio_3 | 0 | 2 | transformers | 2023-05-30T19:48:29 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Hyperledger10Classic_Balance_DATA_ratio_3
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Hyperledger10Classic_Balance_DATA_ratio_3
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.4784
- Train Accuracy: 0.7641
- Validation Loss: 0.4682
- Validation Accuracy: 0.7755
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.5434 | 0.7396 | 0.4923 | 0.7531 | 0 |
| 0.5131 | 0.7498 | 0.4739 | 0.7551 | 1 |
| 0.4784 | 0.7641 | 0.4682 | 0.7755 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,826 | [
[
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YakovElm/Hyperledger10Classic_Balance_DATA_ratio_4 | 2023-05-30T20:13:47.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/Hyperledger10Classic_Balance_DATA_ratio_4 | 0 | 2 | transformers | 2023-05-30T20:13:12 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Hyperledger10Classic_Balance_DATA_ratio_4
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Hyperledger10Classic_Balance_DATA_ratio_4
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.4452
- Train Accuracy: 0.8037
- Validation Loss: 0.4715
- Validation Accuracy: 0.7993
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.4893 | 0.7945 | 0.4696 | 0.7993 | 0 |
| 0.4671 | 0.8032 | 0.4655 | 0.7993 | 1 |
| 0.4452 | 0.8037 | 0.4715 | 0.7993 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,826 | [
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YakovElm/Hyperledger15Classic_Balance_DATA_ratio_Half | 2023-05-30T20:21:26.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/Hyperledger15Classic_Balance_DATA_ratio_Half | 0 | 2 | transformers | 2023-05-30T20:20:50 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Hyperledger15Classic_Balance_DATA_ratio_Half
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Hyperledger15Classic_Balance_DATA_ratio_Half
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.5381
- Train Accuracy: 0.7134
- Validation Loss: 0.5600
- Validation Accuracy: 0.7548
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.6293 | 0.6724 | 0.6315 | 0.6452 | 0 |
| 0.6051 | 0.6746 | 0.5948 | 0.7097 | 1 |
| 0.5381 | 0.7134 | 0.5600 | 0.7548 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,832 | [
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YakovElm/Hyperledger15Classic_Balance_DATA_ratio_1 | 2023-05-30T20:30:37.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/Hyperledger15Classic_Balance_DATA_ratio_1 | 0 | 2 | transformers | 2023-05-30T20:29:58 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Hyperledger15Classic_Balance_DATA_ratio_1
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Hyperledger15Classic_Balance_DATA_ratio_1
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.5761
- Train Accuracy: 0.6688
- Validation Loss: 0.6144
- Validation Accuracy: 0.6359
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.6766 | 0.5929 | 0.6081 | 0.7087 | 0 |
| 0.6340 | 0.6187 | 0.5659 | 0.7184 | 1 |
| 0.5761 | 0.6688 | 0.6144 | 0.6359 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,826 | [
[
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YakovElm/Hyperledger15Classic_Balance_DATA_ratio_2 | 2023-05-30T20:44:46.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/Hyperledger15Classic_Balance_DATA_ratio_2 | 0 | 2 | transformers | 2023-05-30T20:43:48 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Hyperledger15Classic_Balance_DATA_ratio_2
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Hyperledger15Classic_Balance_DATA_ratio_2
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.4921
- Train Accuracy: 0.7403
- Validation Loss: 0.6094
- Validation Accuracy: 0.6968
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.6037 | 0.6649 | 0.5731 | 0.6806 | 0 |
| 0.5526 | 0.6875 | 0.5595 | 0.7032 | 1 |
| 0.4921 | 0.7403 | 0.6094 | 0.6968 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,826 | [
[
-0.047149658203125,
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0.0098876953125,
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0.0146484375,
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mikemosc/distilbert-base-uncased-finetuned-mnli | 2023-05-30T23:33:58.000Z | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | text-classification | mikemosc | null | null | mikemosc/distilbert-base-uncased-finetuned-mnli | 0 | 2 | transformers | 2023-05-30T20:47:05 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased-finetuned-mnli
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
config: mnli
split: validation_matched
args: mnli
metrics:
- name: Accuracy
type: accuracy
value: 0.8188487009679063
---
<!-- 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-mnli
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5546
- Accuracy: 0.8188
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:------:|:---------------:|:--------:|
| 0.5153 | 1.0 | 24544 | 0.4999 | 0.8029 |
| 0.4194 | 2.0 | 49088 | 0.4788 | 0.8138 |
| 0.3065 | 3.0 | 73632 | 0.5546 | 0.8188 |
| 0.2172 | 4.0 | 98176 | 0.7237 | 0.8142 |
| 0.1784 | 5.0 | 122720 | 0.8463 | 0.8165 |
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,932 | [
[
-0.02838134765625,
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0.01312255859375,
0.0137481689453125,
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0.01251220703125,
0.01515960693359375,
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-0.044189453125,
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YakovElm/Hyperledger15Classic_Balance_DATA_ratio_3 | 2023-05-30T21:05:05.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/Hyperledger15Classic_Balance_DATA_ratio_3 | 0 | 2 | transformers | 2023-05-30T21:01:17 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Hyperledger15Classic_Balance_DATA_ratio_3
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Hyperledger15Classic_Balance_DATA_ratio_3
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.4230
- Train Accuracy: 0.7633
- Validation Loss: 0.5957
- Validation Accuracy: 0.7361
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.5250 | 0.7447 | 0.5194 | 0.7579 | 0 |
| 0.4694 | 0.7625 | 0.5376 | 0.7603 | 1 |
| 0.4230 | 0.7633 | 0.5957 | 0.7361 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,826 | [
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YakovElm/Hyperledger15Classic_Balance_DATA_ratio_4 | 2023-05-30T21:27:51.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/Hyperledger15Classic_Balance_DATA_ratio_4 | 0 | 2 | transformers | 2023-05-30T21:25:40 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Hyperledger15Classic_Balance_DATA_ratio_4
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Hyperledger15Classic_Balance_DATA_ratio_4
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.3612
- Train Accuracy: 0.8379
- Validation Loss: 0.5238
- Validation Accuracy: 0.7442
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.4905 | 0.7933 | 0.4737 | 0.7674 | 0 |
| 0.4290 | 0.8127 | 0.4847 | 0.75 | 1 |
| 0.3612 | 0.8379 | 0.5238 | 0.7442 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,826 | [
[
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0.0146636962890625,
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-0.0... |
YakovElm/Hyperledger20Classic_Balance_DATA_ratio_Half | 2023-05-30T21:36:46.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/Hyperledger20Classic_Balance_DATA_ratio_Half | 0 | 2 | transformers | 2023-05-30T21:34:24 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Hyperledger20Classic_Balance_DATA_ratio_Half
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Hyperledger20Classic_Balance_DATA_ratio_Half
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.4412
- Train Accuracy: 0.7976
- Validation Loss: 0.6211
- Validation Accuracy: 0.6569
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.5896 | 0.6976 | 0.6079 | 0.7080 | 0 |
| 0.5253 | 0.7659 | 0.6139 | 0.7153 | 1 |
| 0.4412 | 0.7976 | 0.6211 | 0.6569 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,832 | [
[
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YakovElm/Hyperledger20Classic_Balance_DATA_ratio_1 | 2023-05-30T21:47:29.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/Hyperledger20Classic_Balance_DATA_ratio_1 | 0 | 2 | transformers | 2023-05-30T21:44:29 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Hyperledger20Classic_Balance_DATA_ratio_1
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Hyperledger20Classic_Balance_DATA_ratio_1
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.5573
- Train Accuracy: 0.7130
- Validation Loss: 0.6381
- Validation Accuracy: 0.6154
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.6848 | 0.5539 | 0.6371 | 0.6648 | 0 |
| 0.6290 | 0.6362 | 0.6070 | 0.6648 | 1 |
| 0.5573 | 0.7130 | 0.6381 | 0.6154 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,826 | [
[
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YakovElm/Hyperledger20Classic_Balance_DATA_ratio_2 | 2023-05-30T21:59:17.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/Hyperledger20Classic_Balance_DATA_ratio_2 | 0 | 2 | transformers | 2023-05-30T21:58:38 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Hyperledger20Classic_Balance_DATA_ratio_2
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Hyperledger20Classic_Balance_DATA_ratio_2
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.5201
- Train Accuracy: 0.6976
- Validation Loss: 0.6229
- Validation Accuracy: 0.6788
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.5906 | 0.6427 | 0.5674 | 0.7263 | 0 |
| 0.5513 | 0.6549 | 0.5635 | 0.6825 | 1 |
| 0.5201 | 0.6976 | 0.6229 | 0.6788 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,826 | [
[
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razerblade072611/EleutherAI | 2023-05-31T14:02:03.000Z | [
"transformers",
"pytorch",
"jax",
"rust",
"gpt_neo",
"text-generation",
"doi:10.57967/hf/0703",
"endpoints_compatible",
"region:us"
] | text-generation | razerblade072611 | null | null | razerblade072611/EleutherAI | 0 | 2 | transformers | 2023-05-30T22:02:54 | import atexit
import pyttsx3
import speech_recognition as sr
import torch
import nltk
from nltk.corpus import stopwords
from nltk.stem import WordNetLemmatizer
from sklearn.feature_extraction.text import TfidfVectorizer
from transformers import GPT2Model, AutoTokenizer, AutoModelForCausalLM, GPTNeoForCausalLM, pipeline
from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer
import numpy as np
import json
import os
import spacy
import sys
import transformers
import spacy as nlp
import nltk
import spacy
import site
from pathlib import Path
model_path = spacy.util.get_package_path('en_core_web_sm')
print(model_path)
print("transformers version:", transformers.__version__)
print("spacy version:", spacy.__version__)
print("nltk version:", nltk.__version__)
sys.path.append(r"C:\Users\withe\PycharmProjects\no hope2\Gpt-Neo1")
# Download necessary NLTK resources
nltk.download('punkt')
nltk.download('stopwords')
nltk.download('wordnet')
nltk.download('omw-1.4')
# Load the API key from the environment file
dotenv_path = './API_KEY.env'
(dotenv_path)
# Check if GPU is available and set the device accordingly
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
# Initialize the speech engine
speech_engine = pyttsx3.init()
# Get the list of available voices
voices = speech_engine.getProperty('voices')
for voice in voices:
print(voice.id, voice.name)
# Set the desired voice
voice_id = "HKEY_LOCAL_MACHINE\SOFTWARE\Microsoft\Speech\Voices\Tokens\TTS_MS_EN-GB_HAZEL_11.0 Microsoft Hazel Desktop - English (Great Britain)"
speech_engine.setProperty('voice', voice_id)
voices = speech_engine.getProperty('voices')
for voice in voices:
print(voice.id, voice.name)
# Set the desired voice
desired_voice = "Microsoft Hazel Desktop - English (Great Britain)"
voice_id = None
# Find the voice ID based on the desired voice name
for voice in voices:
if desired_voice in voice.name:
voice_id = voice.id
break
if voice_id:
speech_engine.setProperty('voice', voice_id)
print("Desired voice set successfully.")
else:
print("Desired voice not found.")
class CommonModule:
def __init__(self, model, name, param1, param2):
# Initialize the instance variables using the provided arguments
self.model = model
self.name = name
self.param1 = param1
self.param2 = param2
self.tokenizer = AutoTokenizer.from_pretrained(model) # Load the tokenizer
self.tokenizer.add_special_tokens({'pad_token': '[PAD]'})
self.gpt3_model = GPTNeoForCausalLM.from_pretrained('EleutherAI/gpt-neo-1.3B')
self.gpt3_model.to(device) # Move model to the device (GPU or CPU)
self.memory_module = MemoryModule()
self.sentiment_module = SentimentAnalysisModule()
self.speech_engine = speech_engine # Assign the initialized speech engine
self.max_sequence_length = 10 # Decrease the value for faster response
self.num_beams = 4 # Reduce the value for faster response
self.no_repeat_ngram_size = 2
self.temperature = 0.3
self.response_cache = {} # Cache for storing frequently occurring responses
def reset_conversation(self):
self.memory_module.reset_memory()
def retrieve_cached_response(self, input_text):
named_entities = self.memory_module.get_named_entities()
for entity in named_entities:
if entity.lower() in input_text.lower():
return self.response_cache.get(entity)
return None
def generate_gpt3_response(self, input_text, conversation_history, temperature=0.3):
prompt = '\n'.join(conversation_history) + '\n' + input_text + '\n'
generator = pipeline('text-generation', model='EleutherAI/gpt-neo-1.3B')
output = generator(
prompt,
do_sample=True,
min_length=10,
max_length=300,
num_return_sequences=1,
temperature=0.3
)
if output:
generated_response = output[0]['generated_text'].strip()
return generated_response
return ""
def process_input(self, input_text, conversation_history):
named_entities = list(self.memory_module.get_named_entities())
for entity in named_entities:
if entity in input_text:
response = "Nice to meet you again, {}!".format(entity)
self.memory_module.add_to_memory(response)
return response
# Check if the input contains a question
if '?' in input_text:
return "You're making me angry, you wouldn't like me when I'm angry."
# Check if the input contains a keyword for memory search
if 'search' in input_text.lower():
keyword = input_text.lower().split('search ')[-1]
matches = self.memory_module.search_memory(keyword)
if matches:
return "I found some related information in the memory:\n" + '\n'.join(matches)
else:
return "Sorry, I couldn't find any relevant information in the memory."
# Retrieve the cached response
response = self.retrieve_cached_response(input_text)
if response is None:
response = self.generate_gpt3_response(input_text, conversation_history)
self.cache_response(input_text, response)
named_entities = self.memory_module.get_named_entities()
if named_entities and any(entity in input_text for entity in named_entities):
response = "Nice to meet you, {}! I'm still {}".format(named_entities[0], self.name)
self.memory_module.add_to_memory(response)
return response
self.memory_module.add_to_memory(response)
return response
def cache_response(self, input_text, response):
self.response_cache[input_text] = response
def speak(self, text, conversation_history=None):
if conversation_history is None:
conversation_history = []
conversation_history.append(text)
full_text = "\n".join(conversation_history)
print(text)
self.speech_engine.say(text)
self.speech_engine.runAndWait()
def listen(self):
recognizer = sr.Recognizer()
with sr.Microphone() as source:
print("Listening...")
audio = recognizer.listen(source)
try:
user_input = recognizer.recognize_google(audio)
print("You said:", user_input)
return user_input
except sr.UnknownValueError:
print("Sorry, I could not understand your speech.")
except sr.RequestError as e:
print("Sorry, an error occurred while processing your request. Please try again.")
return ""
def converse(self):
self.reset_conversation()
self.speak("Hey, what's up bro? I'm {}".format(self.name))
conversation_history = []
while True:
user_input = self.listen()
if user_input:
response = self.process_input(user_input, conversation_history)
self.speak(response, conversation_history)
# Check if the user input contains a named entity (name)
named_entities = self.memory_module.get_named_entities()
if named_entities and any(entity in user_input for entity in named_entities):
self.speak("Nice to meet you, {}! I'm still {}".format(named_entities[0], self.name),
conversation_history)
conversation_history.append(user_input)
# Check if the conversation is over (you can define your own condition here)
if user_input == "bye":
self.save_memory('C:\\Users\\withe\PycharmProjects\\no hope\\Chat_Bot_Main\\save_memory.json')
break
def save_memory(self, file_path):
data = {
'memory': self.memory_module.memory,
'named_entities': list(self.memory_module.named_entities) # Convert set to list
}
with open(file_path, 'w') as file:
json.dump(data, file)
def load_memory_data(self, memory_data):
self.memory_module.memory = memory_data['memory']
self.memory_module.named_entities = set(memory_data['named_entities'])
class MemoryModule:
def __init__(self):
self.memory = []
self.vectorizer = TfidfVectorizer(stop_words=stopwords.words('english'))
self.lemmatizer = WordNetLemmatizer()
self.tokenizer = nltk.tokenize.word_tokenize
self.named_entities = set() # Set to store named entities like names
def get_named_entities(self):
return self.named_entities
def preprocess_text(self, text):
tokens = self.tokenizer(text.lower())
tokens = [self.lemmatizer.lemmatize(token) for token in tokens if token.isalnum()]
preprocessed_text = ' '.join(tokens)
return preprocessed_text
def add_to_memory(self, text):
preprocessed_text = self.preprocess_text(text)
self.memory.append(preprocessed_text)
# Update named entities if any
named_entity = self.extract_named_entity(text)
if named_entity:
self.named_entities.add(named_entity)
def extract_named_entity(self, text):
doc = nlp(text)
for entity in doc.ents:
if entity.label_ in ['PERSON', 'ORG', 'GPE']:
return entity.text
return None
def search_memory(self, keyword):
preprocessed_keyword = self.preprocess_text(keyword)
vectorized_memory = self.vectorizer.transform(self.memory)
vectorized_keyword = self.vectorizer.transform([preprocessed_keyword])
similarity_scores = np.dot(vectorized_memory, vectorized_keyword.T).toarray().flatten()
sorted_indices = np.argsort(similarity_scores)[::-1]
matches = [self.memory[i] for i in sorted_indices if similarity_scores[i] > 0.5]
return matches
def reset_memory(self):
self.memory = []
self.named_entities = set()
class SentimentAnalysisModule:
def __init__(self):
self.analyzer = SentimentIntensityAnalyzer()
def analyze_sentiment(self, text):
sentiment_scores = self.analyzer.polarity_scores(text)
return sentiment_scores
def get_sentiment_label(self, sentiment_scores):
compound_score = sentiment_scores['compound']
if compound_score >= 0.05:
return 'positive'
elif compound_score <= -0.05:
return 'negative'
else:
return 'neutral'
# Define an exit handler function
def exit_handler(common_module):
memory_data = {
'memory': common_module.memory_module.memory,
'named_entities': list(common_module.memory_module.named_entities)
}
common_module.save_memory('C:\\Users\\withe\\PycharmProjects\\no hope2\\Chat_Bot1\\save_memory.json')
print("Memory data saved successfully.")
return memory_data
# Define a method to check if the load_memory.json file exists
def check_memory_file(file_path):
return os.path.isfile(file_path)
# Modify the main section of the code to load memory data if the file exists
if __name__ == "__main__":
model = 'gpt2'
name = "Chat bot1"
param1 = 'value1'
param2 = 'value2'
common_module = CommonModule(model, name, param1, param2)
memory_file_path = 'C:\\Users\\withe\\PycharmProjects\\no hope2\\Chat_Bot1\\load_memory1.json'
if check_memory_file(memory_file_path):
with open(memory_file_path, 'r') as file:
memory_data = json.load(file)
common_module.load_memory_data(memory_data)
# Register the exit handler
atexit.register(exit_handler, common_module)
common_module.converse()
common_module.save_memory(memory_file_path)
| 11,960 | [
[
-0.0200958251953125,
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0.0113372802734375,
0.021087646484375,
-0.04241943359375,
-0.0396728515625,
-0.025543212890625,
... |
YakovElm/Hyperledger20Classic_Balance_DATA_ratio_3 | 2023-05-30T22:14:31.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/Hyperledger20Classic_Balance_DATA_ratio_3 | 0 | 2 | transformers | 2023-05-30T22:13:44 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Hyperledger20Classic_Balance_DATA_ratio_3
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Hyperledger20Classic_Balance_DATA_ratio_3
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.4850
- Train Accuracy: 0.7678
- Validation Loss: 0.5013
- Validation Accuracy: 0.7068
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.5443 | 0.7477 | 0.5624 | 0.7205 | 0 |
| 0.4953 | 0.7660 | 0.4982 | 0.7096 | 1 |
| 0.4850 | 0.7678 | 0.5013 | 0.7068 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,826 | [
[
-0.047271728515625,
-0.042755126953125,
0.016754150390625,
0.00904083251953125,
-0.0279541015625,
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0.0167388916015625,
0.017303466796875,
-0.05438232421875,
-0.0406494140625,
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-0... |
YakovElm/Hyperledger20Classic_Balance_DATA_ratio_4 | 2023-05-30T22:33:42.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/Hyperledger20Classic_Balance_DATA_ratio_4 | 0 | 2 | transformers | 2023-05-30T22:32:52 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Hyperledger20Classic_Balance_DATA_ratio_4
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Hyperledger20Classic_Balance_DATA_ratio_4
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.3697
- Train Accuracy: 0.8194
- Validation Loss: 0.5944
- Validation Accuracy: 0.8026
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.4770 | 0.8085 | 0.4682 | 0.7807 | 0 |
| 0.4307 | 0.8114 | 0.4549 | 0.7763 | 1 |
| 0.3697 | 0.8194 | 0.5944 | 0.8026 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,826 | [
[
-0.046783447265625,
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0.016357421875,
0.00823211669921875,
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0.0174560546875,
0.0159759521484375,
-0.05511474609375,
-0.040557861328125,
-0.05047607421875,
-0.01... |
YakovElm/IntelDAOS5Classic_Balance_DATA_ratio_1 | 2023-05-30T22:42:19.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/IntelDAOS5Classic_Balance_DATA_ratio_1 | 0 | 2 | transformers | 2023-05-30T22:41:46 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: IntelDAOS5Classic_Balance_DATA_ratio_1
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# IntelDAOS5Classic_Balance_DATA_ratio_1
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.6248
- Train Accuracy: 0.6548
- Validation Loss: 0.6878
- Validation Accuracy: 0.5714
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.6815 | 0.5714 | 0.6853 | 0.5357 | 0 |
| 0.6431 | 0.6270 | 0.7439 | 0.4762 | 1 |
| 0.6248 | 0.6548 | 0.6878 | 0.5714 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,820 | [
[
-0.045257568359375,
-0.035858154296875,
0.0139007568359375,
0.00283050537109375,
-0.03057861328125,
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-0.011962890625,
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0.0183868408203125,
0.00807952880859375,
-0.055511474609375,
-0.0428466796875,
-0.049713134765625,
-0.025... |
YakovElm/IntelDAOS5Classic_Balance_DATA_ratio_2 | 2023-05-30T22:48:24.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/IntelDAOS5Classic_Balance_DATA_ratio_2 | 0 | 2 | transformers | 2023-05-30T22:47:50 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: IntelDAOS5Classic_Balance_DATA_ratio_2
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# IntelDAOS5Classic_Balance_DATA_ratio_2
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.6102
- Train Accuracy: 0.6772
- Validation Loss: 0.6331
- Validation Accuracy: 0.6349
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.6426 | 0.6693 | 0.6235 | 0.6825 | 0 |
| 0.6319 | 0.6614 | 0.6161 | 0.6825 | 1 |
| 0.6102 | 0.6772 | 0.6331 | 0.6349 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,820 | [
[
-0.043243408203125,
-0.035858154296875,
0.0147705078125,
0.0030193328857421875,
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YakovElm/IntelDAOS5Classic_Balance_DATA_ratio_3 | 2023-05-30T22:56:10.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/IntelDAOS5Classic_Balance_DATA_ratio_3 | 0 | 2 | transformers | 2023-05-30T22:55:34 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: IntelDAOS5Classic_Balance_DATA_ratio_3
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# IntelDAOS5Classic_Balance_DATA_ratio_3
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.5535
- Train Accuracy: 0.7460
- Validation Loss: 0.4840
- Validation Accuracy: 0.7857
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.5752 | 0.7401 | 0.5166 | 0.7857 | 0 |
| 0.5614 | 0.7460 | 0.5229 | 0.7857 | 1 |
| 0.5535 | 0.7460 | 0.4840 | 0.7857 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,820 | [
[
-0.043853759765625,
-0.036895751953125,
0.0162353515625,
0.003082275390625,
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YakovElm/IntelDAOS5Classic_Balance_DATA_ratio_4 | 2023-05-30T23:05:31.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/IntelDAOS5Classic_Balance_DATA_ratio_4 | 0 | 2 | transformers | 2023-05-30T23:04:53 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: IntelDAOS5Classic_Balance_DATA_ratio_4
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# IntelDAOS5Classic_Balance_DATA_ratio_4
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.4740
- Train Accuracy: 0.8111
- Validation Loss: 0.4501
- Validation Accuracy: 0.8238
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.5092 | 0.7889 | 0.4630 | 0.8238 | 0 |
| 0.4821 | 0.8111 | 0.4603 | 0.8238 | 1 |
| 0.4740 | 0.8111 | 0.4501 | 0.8238 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,820 | [
[
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-0.036163330078125,
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0.0031070709228515625,
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YakovElm/IntelDAOS10Classic_Balance_DATA_ratio_Half | 2023-05-30T23:08:49.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/IntelDAOS10Classic_Balance_DATA_ratio_Half | 0 | 2 | transformers | 2023-05-30T23:08:14 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: IntelDAOS10Classic_Balance_DATA_ratio_Half
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# IntelDAOS10Classic_Balance_DATA_ratio_Half
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.6711
- Train Accuracy: 0.5630
- Validation Loss: 0.6026
- Validation Accuracy: 0.6889
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.6811 | 0.6000 | 0.6098 | 0.6889 | 0 |
| 0.6291 | 0.6815 | 0.6062 | 0.6444 | 1 |
| 0.6711 | 0.5630 | 0.6026 | 0.6889 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,828 | [
[
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-0.025848388671875,
0.0225677490234375,
0.008026123046875,
-0.056427001953125,
-0.041107177734375,
-0.050079345703125,
-0... |
YakovElm/IntelDAOS10Classic_Balance_DATA_ratio_1 | 2023-05-30T23:12:21.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/IntelDAOS10Classic_Balance_DATA_ratio_1 | 0 | 2 | transformers | 2023-05-30T23:11:44 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: IntelDAOS10Classic_Balance_DATA_ratio_1
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# IntelDAOS10Classic_Balance_DATA_ratio_1
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.6684
- Train Accuracy: 0.5801
- Validation Loss: 0.6749
- Validation Accuracy: 0.6000
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.7113 | 0.4365 | 0.6936 | 0.5 | 0 |
| 0.6836 | 0.5635 | 0.6832 | 0.5167 | 1 |
| 0.6684 | 0.5801 | 0.6749 | 0.6000 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,822 | [
[
-0.04498291015625,
-0.038604736328125,
0.01442718505859375,
0.0037479400634765625,
-0.02978515625,
-0.0273895263671875,
-0.01239776611328125,
-0.02532958984375,
0.0207061767578125,
0.0093536376953125,
-0.05438232421875,
-0.039642333984375,
-0.050048828125,
-... |
YakovElm/IntelDAOS10Classic_Balance_DATA_ratio_2 | 2023-05-30T23:17:11.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/IntelDAOS10Classic_Balance_DATA_ratio_2 | 0 | 2 | transformers | 2023-05-30T23:16:36 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: IntelDAOS10Classic_Balance_DATA_ratio_2
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# IntelDAOS10Classic_Balance_DATA_ratio_2
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.6093
- Train Accuracy: 0.6605
- Validation Loss: 0.6365
- Validation Accuracy: 0.6923
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.6664 | 0.6162 | 0.6270 | 0.6484 | 0 |
| 0.6266 | 0.6458 | 0.6179 | 0.6703 | 1 |
| 0.6093 | 0.6605 | 0.6365 | 0.6923 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,822 | [
[
-0.043365478515625,
-0.038482666015625,
0.014190673828125,
0.00334930419921875,
-0.030548095703125,
-0.0268402099609375,
-0.01302337646484375,
-0.026153564453125,
0.020111083984375,
0.00896453857421875,
-0.054656982421875,
-0.038848876953125,
-0.050689697265625,... |
YakovElm/IntelDAOS10Classic_Balance_DATA_ratio_3 | 2023-05-30T23:23:03.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/IntelDAOS10Classic_Balance_DATA_ratio_3 | 0 | 2 | transformers | 2023-05-30T23:22:27 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: IntelDAOS10Classic_Balance_DATA_ratio_3
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# IntelDAOS10Classic_Balance_DATA_ratio_3
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.5602
- Train Accuracy: 0.7541
- Validation Loss: 0.5507
- Validation Accuracy: 0.7603
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.6032 | 0.7293 | 0.5528 | 0.7603 | 0 |
| 0.5601 | 0.7486 | 0.5334 | 0.7603 | 1 |
| 0.5602 | 0.7541 | 0.5507 | 0.7603 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,822 | [
[
-0.045013427734375,
-0.038726806640625,
0.0163726806640625,
0.004055023193359375,
-0.0303802490234375,
-0.028350830078125,
-0.01285552978515625,
-0.0261383056640625,
0.0184783935546875,
0.01047515869140625,
-0.052459716796875,
-0.040924072265625,
-0.049285888671... |
YakovElm/IntelDAOS10Classic_Balance_DATA_ratio_4 | 2023-05-30T23:30:10.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/IntelDAOS10Classic_Balance_DATA_ratio_4 | 0 | 2 | transformers | 2023-05-30T23:29:34 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: IntelDAOS10Classic_Balance_DATA_ratio_4
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# IntelDAOS10Classic_Balance_DATA_ratio_4
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.5157
- Train Accuracy: 0.7837
- Validation Loss: 0.4183
- Validation Accuracy: 0.8543
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.5389 | 0.7837 | 0.4137 | 0.8543 | 0 |
| 0.5116 | 0.7837 | 0.4319 | 0.8543 | 1 |
| 0.5157 | 0.7837 | 0.4183 | 0.8543 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,822 | [
[
-0.0443115234375,
-0.03790283203125,
0.0163421630859375,
0.003589630126953125,
-0.030487060546875,
-0.0263519287109375,
-0.0135955810546875,
-0.0267486572265625,
0.01971435546875,
0.0100860595703125,
-0.054046630859375,
-0.04132080078125,
-0.04937744140625,
... |
YakovElm/IntelDAOS15Classic_Balance_DATA_ratio_Half | 2023-05-30T23:33:01.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/IntelDAOS15Classic_Balance_DATA_ratio_Half | 0 | 2 | transformers | 2023-05-30T23:32:20 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: IntelDAOS15Classic_Balance_DATA_ratio_Half
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# IntelDAOS15Classic_Balance_DATA_ratio_Half
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.5689
- Train Accuracy: 0.7404
- Validation Loss: 0.6286
- Validation Accuracy: 0.6571
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.6473 | 0.6442 | 0.5794 | 0.7429 | 0 |
| 0.6371 | 0.6442 | 0.5616 | 0.7714 | 1 |
| 0.5689 | 0.7404 | 0.6286 | 0.6571 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,828 | [
[
-0.04425048828125,
-0.0401611328125,
0.01294708251953125,
0.0036525726318359375,
-0.0305633544921875,
-0.0255584716796875,
-0.009979248046875,
-0.02496337890625,
0.0206451416015625,
0.00846099853515625,
-0.056793212890625,
-0.041900634765625,
-0.050323486328125,... |
YakovElm/IntelDAOS15Classic_Balance_DATA_ratio_1 | 2023-05-30T23:36:05.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/IntelDAOS15Classic_Balance_DATA_ratio_1 | 0 | 2 | transformers | 2023-05-30T23:35:31 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: IntelDAOS15Classic_Balance_DATA_ratio_1
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# IntelDAOS15Classic_Balance_DATA_ratio_1
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.6359
- Train Accuracy: 0.7050
- Validation Loss: 0.6330
- Validation Accuracy: 0.6522
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.7256 | 0.5036 | 0.6663 | 0.6522 | 0 |
| 0.6591 | 0.6619 | 0.6598 | 0.6087 | 1 |
| 0.6359 | 0.7050 | 0.6330 | 0.6522 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,822 | [
[
-0.04522705078125,
-0.03936767578125,
0.01415252685546875,
0.003627777099609375,
-0.0291748046875,
-0.027374267578125,
-0.0120391845703125,
-0.0254669189453125,
0.019561767578125,
0.00925445556640625,
-0.05517578125,
-0.042144775390625,
-0.048980712890625,
-... |
YakovElm/IntelDAOS15Classic_Balance_DATA_ratio_2 | 2023-05-30T23:40:07.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/IntelDAOS15Classic_Balance_DATA_ratio_2 | 0 | 2 | transformers | 2023-05-30T23:39:25 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: IntelDAOS15Classic_Balance_DATA_ratio_2
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# IntelDAOS15Classic_Balance_DATA_ratio_2
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.5300
- Train Accuracy: 0.7548
- Validation Loss: 0.5816
- Validation Accuracy: 0.6143
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.6203 | 0.6779 | 0.5805 | 0.7000 | 0 |
| 0.5601 | 0.7308 | 0.6075 | 0.6429 | 1 |
| 0.5300 | 0.7548 | 0.5816 | 0.6143 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,822 | [
[
-0.0435791015625,
-0.0396728515625,
0.01377105712890625,
0.004962921142578125,
-0.029998779296875,
-0.0283355712890625,
-0.0129852294921875,
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0.0181121826171875,
0.00908660888671875,
-0.05419921875,
-0.039642333984375,
-0.049591064453125,
... |
YakovElm/IntelDAOS15Classic_Balance_DATA_ratio_3 | 2023-05-30T23:44:57.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/IntelDAOS15Classic_Balance_DATA_ratio_3 | 0 | 2 | transformers | 2023-05-30T23:44:23 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: IntelDAOS15Classic_Balance_DATA_ratio_3
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# IntelDAOS15Classic_Balance_DATA_ratio_3
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.5163
- Train Accuracy: 0.7734
- Validation Loss: 0.4785
- Validation Accuracy: 0.7957
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.5689 | 0.7338 | 0.5002 | 0.7957 | 0 |
| 0.5411 | 0.7590 | 0.4894 | 0.7957 | 1 |
| 0.5163 | 0.7734 | 0.4785 | 0.7957 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,822 | [
[
-0.044281005859375,
-0.040130615234375,
0.0159454345703125,
0.00439453125,
-0.030975341796875,
-0.0279083251953125,
-0.01248931884765625,
-0.02618408203125,
0.018218994140625,
0.01081085205078125,
-0.053497314453125,
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-0.... |
YakovElm/IntelDAOS15Classic_Balance_DATA_ratio_4 | 2023-05-30T23:51:00.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/IntelDAOS15Classic_Balance_DATA_ratio_4 | 0 | 2 | transformers | 2023-05-30T23:50:25 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: IntelDAOS15Classic_Balance_DATA_ratio_4
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# IntelDAOS15Classic_Balance_DATA_ratio_4
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.4989
- Train Accuracy: 0.8017
- Validation Loss: 0.4107
- Validation Accuracy: 0.8621
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.5433 | 0.7759 | 0.4230 | 0.8621 | 0 |
| 0.5032 | 0.8017 | 0.4035 | 0.8621 | 1 |
| 0.4989 | 0.8017 | 0.4107 | 0.8621 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,822 | [
[
-0.044708251953125,
-0.038787841796875,
0.0158233642578125,
0.005126953125,
-0.031341552734375,
-0.02716064453125,
-0.01241302490234375,
-0.02545166015625,
0.0183258056640625,
0.010009765625,
-0.054901123046875,
-0.041717529296875,
-0.048309326171875,
-0.024... |
YakovElm/IntelDAOS20Classic_Balance_DATA_ratio_Half | 2023-05-30T23:53:29.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/IntelDAOS20Classic_Balance_DATA_ratio_Half | 0 | 2 | transformers | 2023-05-30T23:52:55 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: IntelDAOS20Classic_Balance_DATA_ratio_Half
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# IntelDAOS20Classic_Balance_DATA_ratio_Half
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.6178
- Train Accuracy: 0.6543
- Validation Loss: 0.5301
- Validation Accuracy: 0.7778
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.6696 | 0.5926 | 0.5580 | 0.7778 | 0 |
| 0.6485 | 0.6543 | 0.5306 | 0.7778 | 1 |
| 0.6178 | 0.6543 | 0.5301 | 0.7778 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,828 | [
[
-0.04412841796875,
-0.038848876953125,
0.01367950439453125,
0.0039520263671875,
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YakovElm/IntelDAOS20Classic_Balance_DATA_ratio_1 | 2023-05-30T23:56:07.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/IntelDAOS20Classic_Balance_DATA_ratio_1 | 0 | 2 | transformers | 2023-05-30T23:55:31 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: IntelDAOS20Classic_Balance_DATA_ratio_1
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# IntelDAOS20Classic_Balance_DATA_ratio_1
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.5707
- Train Accuracy: 0.6972
- Validation Loss: 0.5805
- Validation Accuracy: 0.75
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.6702 | 0.5229 | 0.6593 | 0.5556 | 0 |
| 0.6356 | 0.6147 | 0.6616 | 0.5833 | 1 |
| 0.5707 | 0.6972 | 0.5805 | 0.75 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,820 | [
[
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YakovElm/IntelDAOS20Classic_Balance_DATA_ratio_2 | 2023-05-30T23:59:35.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/IntelDAOS20Classic_Balance_DATA_ratio_2 | 0 | 2 | transformers | 2023-05-30T23:58:59 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: IntelDAOS20Classic_Balance_DATA_ratio_2
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# IntelDAOS20Classic_Balance_DATA_ratio_2
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.5250
- Train Accuracy: 0.7730
- Validation Loss: 0.5228
- Validation Accuracy: 0.7636
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.6488 | 0.6258 | 0.5223 | 0.7455 | 0 |
| 0.5682 | 0.6871 | 0.5957 | 0.6909 | 1 |
| 0.5250 | 0.7730 | 0.5228 | 0.7636 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,822 | [
[
-0.043792724609375,
-0.037933349609375,
0.01424407958984375,
0.004360198974609375,
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0.0189056396484375,
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YakovElm/IntelDAOS20Classic_Balance_DATA_ratio_3 | 2023-05-31T00:03:40.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/IntelDAOS20Classic_Balance_DATA_ratio_3 | 0 | 2 | transformers | 2023-05-31T00:03:04 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: IntelDAOS20Classic_Balance_DATA_ratio_3
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# IntelDAOS20Classic_Balance_DATA_ratio_3
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.4854
- Train Accuracy: 0.7661
- Validation Loss: 0.5602
- Validation Accuracy: 0.7123
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.5905 | 0.7615 | 0.5971 | 0.6986 | 0 |
| 0.5308 | 0.7615 | 0.6242 | 0.6986 | 1 |
| 0.4854 | 0.7661 | 0.5602 | 0.7123 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,822 | [
[
-0.043701171875,
-0.038909912109375,
0.0168304443359375,
0.00457000732421875,
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YakovElm/IntelDAOS20Classic_Balance_DATA_ratio_4 | 2023-05-31T00:08:31.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/IntelDAOS20Classic_Balance_DATA_ratio_4 | 0 | 2 | transformers | 2023-05-31T00:07:55 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: IntelDAOS20Classic_Balance_DATA_ratio_4
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# IntelDAOS20Classic_Balance_DATA_ratio_4
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.4344
- Train Accuracy: 0.8095
- Validation Loss: 0.5350
- Validation Accuracy: 0.7692
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.5505 | 0.7399 | 0.5835 | 0.7473 | 0 |
| 0.4818 | 0.8059 | 0.5470 | 0.7473 | 1 |
| 0.4344 | 0.8095 | 0.5350 | 0.7692 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,822 | [
[
-0.044677734375,
-0.037872314453125,
0.0166015625,
0.004062652587890625,
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0.01910400390625,
0.0103607177734375,
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YakovElm/Jira5Classic_Balance_DATA_ratio_1 | 2023-05-31T00:26:03.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/Jira5Classic_Balance_DATA_ratio_1 | 0 | 2 | transformers | 2023-05-31T00:25:27 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Jira5Classic_Balance_DATA_ratio_1
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Jira5Classic_Balance_DATA_ratio_1
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.5531
- Train Accuracy: 0.7171
- Validation Loss: 0.5869
- Validation Accuracy: 0.6697
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.6711 | 0.5780 | 0.6148 | 0.6560 | 0 |
| 0.5881 | 0.6713 | 0.5785 | 0.6789 | 1 |
| 0.5531 | 0.7171 | 0.5869 | 0.6697 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,810 | [
[
-0.03729248046875,
-0.03759765625,
0.01248931884765625,
0.00412750244140625,
-0.03131103515625,
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0.021453857421875,
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YakovElm/Jira10Classic_Balance_DATA_ratio_Half | 2023-05-31T00:33:54.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/Jira10Classic_Balance_DATA_ratio_Half | 0 | 2 | transformers | 2023-05-31T00:33:17 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Jira10Classic_Balance_DATA_ratio_Half
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Jira10Classic_Balance_DATA_ratio_Half
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.4887
- Train Accuracy: 0.7915
- Validation Loss: 0.4790
- Validation Accuracy: 0.8153
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.6094 | 0.7085 | 0.5579 | 0.7962 | 0 |
| 0.5428 | 0.7532 | 0.5758 | 0.6879 | 1 |
| 0.4887 | 0.7915 | 0.4790 | 0.8153 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,818 | [
[
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0.01104736328125,
0.0036411285400390625,
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YakovElm/Jira10Classic_Balance_DATA_ratio_1 | 2023-05-31T00:43:35.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/Jira10Classic_Balance_DATA_ratio_1 | 0 | 2 | transformers | 2023-05-31T00:42:58 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Jira10Classic_Balance_DATA_ratio_1
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Jira10Classic_Balance_DATA_ratio_1
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.4678
- Train Accuracy: 0.7863
- Validation Loss: 0.6591
- Validation Accuracy: 0.7081
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.6739 | 0.5726 | 0.6130 | 0.6555 | 0 |
| 0.5723 | 0.7033 | 0.5917 | 0.6746 | 1 |
| 0.4678 | 0.7863 | 0.6591 | 0.7081 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,812 | [
[
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0.01229095458984375,
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0.0248870849609375,
0.01097869873046875,
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YakovElm/Jira10Classic_Balance_DATA_ratio_2 | 2023-05-31T00:57:19.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/Jira10Classic_Balance_DATA_ratio_2 | 0 | 2 | transformers | 2023-05-31T00:56:44 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Jira10Classic_Balance_DATA_ratio_2
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Jira10Classic_Balance_DATA_ratio_2
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.4670
- Train Accuracy: 0.7906
- Validation Loss: 0.5642
- Validation Accuracy: 0.7484
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.6305 | 0.6429 | 0.5566 | 0.7197 | 0 |
| 0.5610 | 0.6950 | 0.4968 | 0.7293 | 1 |
| 0.4670 | 0.7906 | 0.5642 | 0.7484 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,812 | [
[
-0.035797119140625,
-0.042205810546875,
0.01209259033203125,
0.0052337646484375,
-0.03106689453125,
-0.0243072509765625,
-0.0112152099609375,
-0.022430419921875,
0.02362060546875,
0.011016845703125,
-0.0506591796875,
-0.03692626953125,
-0.04962158203125,
-0.... |
YakovElm/Jira15Classic_Balance_DATA_ratio_Half | 2023-05-31T01:04:51.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/Jira15Classic_Balance_DATA_ratio_Half | 0 | 2 | transformers | 2023-05-31T01:04:14 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Jira15Classic_Balance_DATA_ratio_Half
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Jira15Classic_Balance_DATA_ratio_Half
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.4481
- Train Accuracy: 0.7911
- Validation Loss: 0.5020
- Validation Accuracy: 0.8133
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.6042 | 0.6689 | 0.5017 | 0.7733 | 0 |
| 0.5242 | 0.7733 | 0.4695 | 0.8133 | 1 |
| 0.4481 | 0.7911 | 0.5020 | 0.8133 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,818 | [
[
-0.037628173828125,
-0.043304443359375,
0.01012420654296875,
0.005512237548828125,
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0.0245819091796875,
0.01071929931640625,
-0.054656982421875,
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YakovElm/Jira15Classic_Balance_DATA_ratio_1 | 2023-05-31T01:14:01.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/Jira15Classic_Balance_DATA_ratio_1 | 0 | 2 | transformers | 2023-05-31T01:13:26 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Jira15Classic_Balance_DATA_ratio_1
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Jira15Classic_Balance_DATA_ratio_1
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.4506
- Train Accuracy: 0.7967
- Validation Loss: 0.6458
- Validation Accuracy: 0.7250
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.6588 | 0.6050 | 0.5779 | 0.7050 | 0 |
| 0.5464 | 0.7267 | 0.5502 | 0.75 | 1 |
| 0.4506 | 0.7967 | 0.6458 | 0.7250 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,812 | [
[
-0.0382080078125,
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0.01190185546875,
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YakovElm/Jira15Classic_Balance_DATA_ratio_2 | 2023-05-31T01:27:02.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/Jira15Classic_Balance_DATA_ratio_2 | 0 | 2 | transformers | 2023-05-31T01:26:27 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Jira15Classic_Balance_DATA_ratio_2
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Jira15Classic_Balance_DATA_ratio_2
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.3906
- Train Accuracy: 0.8289
- Validation Loss: 0.4315
- Validation Accuracy: 0.8033
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.5956 | 0.6822 | 0.5581 | 0.6800 | 0 |
| 0.5190 | 0.7433 | 0.4423 | 0.7900 | 1 |
| 0.3906 | 0.8289 | 0.4315 | 0.8033 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,812 | [
[
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0.011016845703125,
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-0.037384033203125,
-0.04888916015625,
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YakovElm/Jira20Classic_Balance_DATA_ratio_Half | 2023-05-31T01:30:57.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/Jira20Classic_Balance_DATA_ratio_Half | 0 | 2 | transformers | 2023-05-31T01:30:21 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Jira20Classic_Balance_DATA_ratio_Half
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Jira20Classic_Balance_DATA_ratio_Half
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.5100
- Train Accuracy: 0.7531
- Validation Loss: 0.6473
- Validation Accuracy: 0.6481
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.6115 | 0.6914 | 0.7346 | 0.5556 | 0 |
| 0.5589 | 0.7160 | 0.6487 | 0.5741 | 1 |
| 0.5100 | 0.7531 | 0.6473 | 0.6481 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,818 | [
[
-0.035980224609375,
-0.04266357421875,
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0.02569580078125,
0.01043701171875,
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YakovElm/Jira20Classic_Balance_DATA_ratio_1 | 2023-05-31T01:35:10.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/Jira20Classic_Balance_DATA_ratio_1 | 0 | 2 | transformers | 2023-05-31T01:34:32 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Jira20Classic_Balance_DATA_ratio_1
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Jira20Classic_Balance_DATA_ratio_1
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.6347
- Train Accuracy: 0.6713
- Validation Loss: 0.6894
- Validation Accuracy: 0.5139
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.7128 | 0.4907 | 0.6885 | 0.5139 | 0 |
| 0.6632 | 0.5972 | 0.6791 | 0.5694 | 1 |
| 0.6347 | 0.6713 | 0.6894 | 0.5139 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,812 | [
[
-0.03668212890625,
-0.041778564453125,
0.012054443359375,
0.004680633544921875,
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0.0249481201171875,
0.01120758056640625,
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-0.039794921875,
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-0.0... |
YakovElm/Jira20Classic_Balance_DATA_ratio_2 | 2023-05-31T01:40:46.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/Jira20Classic_Balance_DATA_ratio_2 | 0 | 2 | transformers | 2023-05-31T01:40:07 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Jira20Classic_Balance_DATA_ratio_2
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Jira20Classic_Balance_DATA_ratio_2
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.6133
- Train Accuracy: 0.6852
- Validation Loss: 0.6297
- Validation Accuracy: 0.6296
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.6461 | 0.6481 | 0.6620 | 0.6296 | 0 |
| 0.6100 | 0.6883 | 0.6349 | 0.6296 | 1 |
| 0.6133 | 0.6852 | 0.6297 | 0.6296 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,812 | [
[
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0.01226043701171875,
0.006328582763671875,
-0.030975341796875,
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0.0228729248046875,
0.0114898681640625,
-0.05364990234375,
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YakovElm/Jira20Classic_Balance_DATA_ratio_3 | 2023-05-31T01:48:31.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/Jira20Classic_Balance_DATA_ratio_3 | 0 | 2 | transformers | 2023-05-31T01:47:57 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Jira20Classic_Balance_DATA_ratio_3
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Jira20Classic_Balance_DATA_ratio_3
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.3788
- Train Accuracy: 0.8472
- Validation Loss: 0.4907
- Validation Accuracy: 0.7708
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.5380 | 0.7569 | 0.5530 | 0.7153 | 0 |
| 0.4505 | 0.8218 | 0.5012 | 0.7708 | 1 |
| 0.3788 | 0.8472 | 0.4907 | 0.7708 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,812 | [
[
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0.01430511474609375,
0.005504608154296875,
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YakovElm/Jira20Classic_Balance_DATA_ratio_4 | 2023-05-31T01:56:58.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/Jira20Classic_Balance_DATA_ratio_4 | 0 | 2 | transformers | 2023-05-31T01:56:19 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Jira20Classic_Balance_DATA_ratio_4
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Jira20Classic_Balance_DATA_ratio_4
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.4387
- Train Accuracy: 0.7981
- Validation Loss: 0.3830
- Validation Accuracy: 0.8278
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.5397 | 0.7537 | 0.4603 | 0.8278 | 0 |
| 0.4846 | 0.7981 | 0.4194 | 0.8278 | 1 |
| 0.4387 | 0.7981 | 0.3830 | 0.8278 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,812 | [
[
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0.005390167236328125,
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YakovElm/MariaDB5Classic_Balance_DATA_ratio_1 | 2023-05-31T02:06:39.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/MariaDB5Classic_Balance_DATA_ratio_1 | 0 | 2 | transformers | 2023-05-31T02:05:49 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: MariaDB5Classic_Balance_DATA_ratio_1
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# MariaDB5Classic_Balance_DATA_ratio_1
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.6207
- Train Accuracy: 0.6535
- Validation Loss: 0.5631
- Validation Accuracy: 0.7412
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.6867 | 0.5157 | 0.6622 | 0.6471 | 0 |
| 0.6667 | 0.5787 | 0.6071 | 0.7176 | 1 |
| 0.6207 | 0.6535 | 0.5631 | 0.7412 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,816 | [
[
-0.04266357421875,
-0.040191650390625,
0.01427459716796875,
0.004344940185546875,
-0.03045654296875,
-0.0298919677734375,
-0.0062408447265625,
-0.0228271484375,
0.0209197998046875,
0.01444244384765625,
-0.059417724609375,
-0.046234130859375,
-0.0450439453125,
... |
YakovElm/MariaDB5Classic_Balance_DATA_ratio_2 | 2023-05-31T02:13:01.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/MariaDB5Classic_Balance_DATA_ratio_2 | 0 | 2 | transformers | 2023-05-31T02:12:26 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: MariaDB5Classic_Balance_DATA_ratio_2
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# MariaDB5Classic_Balance_DATA_ratio_2
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.5732
- Train Accuracy: 0.6903
- Validation Loss: 0.6107
- Validation Accuracy: 0.6693
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.6708 | 0.6037 | 0.6436 | 0.6378 | 0 |
| 0.6054 | 0.6824 | 0.6241 | 0.6142 | 1 |
| 0.5732 | 0.6903 | 0.6107 | 0.6693 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,816 | [
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YakovElm/MariaDB5Classic_Balance_DATA_ratio_3 | 2023-05-31T02:21:04.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/MariaDB5Classic_Balance_DATA_ratio_3 | 0 | 2 | transformers | 2023-05-31T02:20:30 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: MariaDB5Classic_Balance_DATA_ratio_3
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# MariaDB5Classic_Balance_DATA_ratio_3
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.4818
- Train Accuracy: 0.7657
- Validation Loss: 0.6199
- Validation Accuracy: 0.7118
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.5720 | 0.7382 | 0.5931 | 0.7118 | 0 |
| 0.5061 | 0.7657 | 0.5737 | 0.7118 | 1 |
| 0.4818 | 0.7657 | 0.6199 | 0.7118 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
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YakovElm/MariaDB5Classic_Balance_DATA_ratio_4 | 2023-05-31T02:31:17.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/MariaDB5Classic_Balance_DATA_ratio_4 | 0 | 2 | transformers | 2023-05-31T02:30:05 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: MariaDB5Classic_Balance_DATA_ratio_4
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# MariaDB5Classic_Balance_DATA_ratio_4
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.4175
- Train Accuracy: 0.8302
- Validation Loss: 0.4497
- Validation Accuracy: 0.7877
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.4696 | 0.8239 | 0.5012 | 0.7877 | 0 |
| 0.4243 | 0.8302 | 0.5060 | 0.7877 | 1 |
| 0.4175 | 0.8302 | 0.4497 | 0.7877 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
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YakovElm/MariaDB10Classic_Balance_DATA_ratio_Half | 2023-05-31T02:34:55.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/MariaDB10Classic_Balance_DATA_ratio_Half | 0 | 2 | transformers | 2023-05-31T02:34:21 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: MariaDB10Classic_Balance_DATA_ratio_Half
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# MariaDB10Classic_Balance_DATA_ratio_Half
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.5948
- Train Accuracy: 0.6828
- Validation Loss: 0.5626
- Validation Accuracy: 0.6735
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.7471 | 0.5310 | 0.6682 | 0.5918 | 0 |
| 0.6110 | 0.6690 | 0.6302 | 0.6122 | 1 |
| 0.5948 | 0.6828 | 0.5626 | 0.6735 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,824 | [
[
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Ibrahim-Alam/finetuning-distilbert-base-uncased-on-sst2 | 2023-05-31T02:41:31.000Z | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:sst2",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | text-classification | Ibrahim-Alam | null | null | Ibrahim-Alam/finetuning-distilbert-base-uncased-on-sst2 | 0 | 2 | transformers | 2023-05-31T02:35:41 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- sst2
metrics:
- accuracy
- f1
model-index:
- name: finetuning-distilbert-base-uncased-on-sst2
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: sst2
type: sst2
config: default
split: validation
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9013761467889908
- name: F1
type: f1
value: 0.9040178571428571
---
<!-- 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-distilbert-base-uncased-on-sst2
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the sst2 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2883
- Accuracy: 0.9014
- F1: 0.9040
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,570 | [
[
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YakovElm/MariaDB10Classic_Balance_DATA_ratio_1 | 2023-05-31T02:38:55.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/MariaDB10Classic_Balance_DATA_ratio_1 | 0 | 2 | transformers | 2023-05-31T02:38:20 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: MariaDB10Classic_Balance_DATA_ratio_1
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# MariaDB10Classic_Balance_DATA_ratio_1
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.5981
- Train Accuracy: 0.7010
- Validation Loss: 0.6804
- Validation Accuracy: 0.5692
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.6864 | 0.5464 | 0.6699 | 0.5846 | 0 |
| 0.6274 | 0.7010 | 0.6354 | 0.6923 | 1 |
| 0.5981 | 0.7010 | 0.6804 | 0.5692 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,818 | [
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TokyoNights/dqn-SpaceInvadersNoFrameskip-v4 | 2023-05-31T02:42:32.000Z | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | TokyoNights | null | null | TokyoNights/dqn-SpaceInvadersNoFrameskip-v4 | 0 | 2 | stable-baselines3 | 2023-05-31T02:41:58 | ---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 582.00 +/- 131.63
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga TokyoNights -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga TokyoNights -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga TokyoNights
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 1000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
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YakovElm/MariaDB10Classic_Balance_DATA_ratio_2 | 2023-05-31T02:43:58.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/MariaDB10Classic_Balance_DATA_ratio_2 | 0 | 2 | transformers | 2023-05-31T02:43:21 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: MariaDB10Classic_Balance_DATA_ratio_2
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# MariaDB10Classic_Balance_DATA_ratio_2
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.5036
- Train Accuracy: 0.7938
- Validation Loss: 0.6325
- Validation Accuracy: 0.6701
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.6291 | 0.6735 | 0.6125 | 0.6804 | 0 |
| 0.5694 | 0.7182 | 0.6580 | 0.6598 | 1 |
| 0.5036 | 0.7938 | 0.6325 | 0.6701 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,818 | [
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YakovElm/MariaDB10Classic_Balance_DATA_ratio_3 | 2023-05-31T02:50:25.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/MariaDB10Classic_Balance_DATA_ratio_3 | 0 | 2 | transformers | 2023-05-31T02:49:45 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: MariaDB10Classic_Balance_DATA_ratio_3
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# MariaDB10Classic_Balance_DATA_ratio_3
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.4529
- Train Accuracy: 0.7861
- Validation Loss: 0.4950
- Validation Accuracy: 0.7615
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.5801 | 0.7268 | 0.5206 | 0.7615 | 0 |
| 0.5010 | 0.7809 | 0.5068 | 0.7615 | 1 |
| 0.4529 | 0.7861 | 0.4950 | 0.7615 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,818 | [
[
-0.0418701171875,
-0.042083740234375,
0.0154266357421875,
0.005695343017578125,
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0.021270751953125,
0.01531219482421875,
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YakovElm/MariaDB10Classic_Balance_DATA_ratio_4 | 2023-05-31T02:58:07.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/MariaDB10Classic_Balance_DATA_ratio_4 | 0 | 2 | transformers | 2023-05-31T02:57:33 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: MariaDB10Classic_Balance_DATA_ratio_4
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# MariaDB10Classic_Balance_DATA_ratio_4
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.4451
- Train Accuracy: 0.8004
- Validation Loss: 0.5120
- Validation Accuracy: 0.7531
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.5466 | 0.7490 | 0.5117 | 0.7963 | 0 |
| 0.4802 | 0.7901 | 0.4927 | 0.8210 | 1 |
| 0.4451 | 0.8004 | 0.5120 | 0.7531 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,818 | [
[
-0.042724609375,
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YakovElm/MariaDB15Classic_Balance_DATA_ratio_Half | 2023-05-31T03:01:14.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/MariaDB15Classic_Balance_DATA_ratio_Half | 0 | 2 | transformers | 2023-05-31T03:00:39 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: MariaDB15Classic_Balance_DATA_ratio_Half
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# MariaDB15Classic_Balance_DATA_ratio_Half
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.5834
- Train Accuracy: 0.7143
- Validation Loss: 0.5577
- Validation Accuracy: 0.8000
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.6561 | 0.5966 | 0.6086 | 0.7000 | 0 |
| 0.5959 | 0.7059 | 0.5717 | 0.75 | 1 |
| 0.5834 | 0.7143 | 0.5577 | 0.8000 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,824 | [
[
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YakovElm/MariaDB15Classic_Balance_DATA_ratio_1 | 2023-05-31T03:04:38.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/MariaDB15Classic_Balance_DATA_ratio_1 | 0 | 2 | transformers | 2023-05-31T03:04:03 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: MariaDB15Classic_Balance_DATA_ratio_1
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# MariaDB15Classic_Balance_DATA_ratio_1
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.6360
- Train Accuracy: 0.6352
- Validation Loss: 0.6069
- Validation Accuracy: 0.6792
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.6965 | 0.4843 | 0.6664 | 0.6981 | 0 |
| 0.6670 | 0.6101 | 0.6446 | 0.6415 | 1 |
| 0.6360 | 0.6352 | 0.6069 | 0.6792 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,818 | [
[
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0.0140228271484375,
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YakovElm/MariaDB15Classic_Balance_DATA_ratio_2 | 2023-05-31T03:08:59.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/MariaDB15Classic_Balance_DATA_ratio_2 | 0 | 2 | transformers | 2023-05-31T03:08:23 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: MariaDB15Classic_Balance_DATA_ratio_2
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# MariaDB15Classic_Balance_DATA_ratio_2
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.5283
- Train Accuracy: 0.7448
- Validation Loss: 0.4299
- Validation Accuracy: 0.8125
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.6389 | 0.6569 | 0.5386 | 0.7375 | 0 |
| 0.5884 | 0.6569 | 0.4752 | 0.7875 | 1 |
| 0.5283 | 0.7448 | 0.4299 | 0.8125 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,818 | [
[
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0.01348876953125,
0.00597381591796875,
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markroot/my-test-model | 2023-05-31T03:12:23.000Z | [
"transformers",
"pytorch",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"endpoints_compatible",
"region:us"
] | text-classification | markroot | null | null | markroot/my-test-model | 0 | 2 | transformers | 2023-05-31T03:11:22 | ---
tags:
- generated_from_keras_callback
model-index:
- name: my-test-model
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# my-test-model
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: None
- training_precision: float32
### Training results
### Framework versions
- Transformers 4.28.0
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 862 | [
[
-0.04058837890625,
-0.0416259765625,
0.0302886962890625,
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0.0004494190216064453,
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0.0333251953125,
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-0.036102294921875,
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... |
YakovElm/MariaDB15Classic_Balance_DATA_ratio_3 | 2023-05-31T03:14:27.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/MariaDB15Classic_Balance_DATA_ratio_3 | 0 | 2 | transformers | 2023-05-31T03:13:48 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: MariaDB15Classic_Balance_DATA_ratio_3
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# MariaDB15Classic_Balance_DATA_ratio_3
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.4251
- Train Accuracy: 0.7868
- Validation Loss: 0.6687
- Validation Accuracy: 0.6792
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.5416 | 0.7618 | 0.6025 | 0.6792 | 0 |
| 0.4727 | 0.7806 | 0.6428 | 0.6792 | 1 |
| 0.4251 | 0.7868 | 0.6687 | 0.6792 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,818 | [
[
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YakovElm/MariaDB15Classic_Balance_DATA_ratio_4 | 2023-05-31T03:20:58.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/MariaDB15Classic_Balance_DATA_ratio_4 | 0 | 2 | transformers | 2023-05-31T03:20:24 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: MariaDB15Classic_Balance_DATA_ratio_4
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# MariaDB15Classic_Balance_DATA_ratio_4
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.4238
- Train Accuracy: 0.7995
- Validation Loss: 0.3912
- Validation Accuracy: 0.8346
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.5240 | 0.7845 | 0.4230 | 0.8346 | 0 |
| 0.4713 | 0.7945 | 0.4084 | 0.8346 | 1 |
| 0.4238 | 0.7995 | 0.3912 | 0.8346 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,818 | [
[
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0.01507568359375,
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YakovElm/MariaDB20Classic_Balance_DATA_ratio_Half | 2023-05-31T03:23:53.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/MariaDB20Classic_Balance_DATA_ratio_Half | 0 | 2 | transformers | 2023-05-31T03:23:16 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: MariaDB20Classic_Balance_DATA_ratio_Half
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# MariaDB20Classic_Balance_DATA_ratio_Half
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.6302
- Train Accuracy: 0.6762
- Validation Loss: 0.5937
- Validation Accuracy: 0.7429
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.6763 | 0.5524 | 0.6207 | 0.7429 | 0 |
| 0.6582 | 0.6190 | 0.6358 | 0.7429 | 1 |
| 0.6302 | 0.6762 | 0.5937 | 0.7429 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,824 | [
[
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0.00437164306640625,
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YakovElm/MariaDB20Classic_Balance_DATA_ratio_1 | 2023-05-31T03:26:55.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/MariaDB20Classic_Balance_DATA_ratio_1 | 0 | 2 | transformers | 2023-05-31T03:26:19 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: MariaDB20Classic_Balance_DATA_ratio_1
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# MariaDB20Classic_Balance_DATA_ratio_1
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.5688
- Train Accuracy: 0.7071
- Validation Loss: 0.5979
- Validation Accuracy: 0.6809
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.6930 | 0.5214 | 0.6401 | 0.6596 | 0 |
| 0.6195 | 0.7000 | 0.6279 | 0.5957 | 1 |
| 0.5688 | 0.7071 | 0.5979 | 0.6809 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,818 | [
[
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0.01493072509765625,
0.0040130615234375,
-0.03082275390625,
-0.03094482421875,
-0.0066070556640625,
-0.022308349609375,
0.0237579345703125,
0.015869140625,
-0.05999755859375,
-0.045654296875,
-0.046295166015625,
-0.026870727... |
YakovElm/MariaDB20Classic_Balance_DATA_ratio_2 | 2023-05-31T03:30:49.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/MariaDB20Classic_Balance_DATA_ratio_2 | 0 | 2 | transformers | 2023-05-31T03:30:15 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: MariaDB20Classic_Balance_DATA_ratio_2
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# MariaDB20Classic_Balance_DATA_ratio_2
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.5870
- Train Accuracy: 0.6714
- Validation Loss: 0.5487
- Validation Accuracy: 0.7143
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.6712 | 0.6143 | 0.5982 | 0.7143 | 0 |
| 0.6334 | 0.6476 | 0.5734 | 0.7143 | 1 |
| 0.5870 | 0.6714 | 0.5487 | 0.7143 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,818 | [
[
-0.040863037109375,
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YakovElm/MariaDB20Classic_Balance_DATA_ratio_3 | 2023-05-31T03:35:37.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/MariaDB20Classic_Balance_DATA_ratio_3 | 0 | 2 | transformers | 2023-05-31T03:35:00 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: MariaDB20Classic_Balance_DATA_ratio_3
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# MariaDB20Classic_Balance_DATA_ratio_3
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.5028
- Train Accuracy: 0.75
- Validation Loss: 0.5287
- Validation Accuracy: 0.7447
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.5976 | 0.7071 | 0.5633 | 0.7447 | 0 |
| 0.5556 | 0.75 | 0.5370 | 0.7447 | 1 |
| 0.5028 | 0.75 | 0.5287 | 0.7447 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,816 | [
[
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YakovElm/MariaDB20Classic_Balance_DATA_ratio_4 | 2023-05-31T03:41:24.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/MariaDB20Classic_Balance_DATA_ratio_4 | 0 | 2 | transformers | 2023-05-31T03:40:50 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: MariaDB20Classic_Balance_DATA_ratio_4
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# MariaDB20Classic_Balance_DATA_ratio_4
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.3891
- Train Accuracy: 0.8205
- Validation Loss: 0.4660
- Validation Accuracy: 0.7778
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.5057 | 0.7949 | 0.4990 | 0.7778 | 0 |
| 0.4474 | 0.8148 | 0.4729 | 0.7778 | 1 |
| 0.3891 | 0.8205 | 0.4660 | 0.7778 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,818 | [
[
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YakovElm/Qt5Classic_Balance_DATA_ratio_1 | 2023-05-31T03:59:58.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/Qt5Classic_Balance_DATA_ratio_1 | 0 | 2 | transformers | 2023-05-31T03:59:23 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Qt5Classic_Balance_DATA_ratio_1
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Qt5Classic_Balance_DATA_ratio_1
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.5569
- Train Accuracy: 0.7207
- Validation Loss: 0.6633
- Validation Accuracy: 0.6314
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.6783 | 0.5628 | 0.6909 | 0.6017 | 0 |
| 0.6449 | 0.6361 | 0.6829 | 0.6102 | 1 |
| 0.5569 | 0.7207 | 0.6633 | 0.6314 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,806 | [
[
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Ibrahim-Alam/finetuning-bert-base-uncased-on-sst2 | 2023-05-31T04:22:35.000Z | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:sst2",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | text-classification | Ibrahim-Alam | null | null | Ibrahim-Alam/finetuning-bert-base-uncased-on-sst2 | 0 | 2 | transformers | 2023-05-31T04:12:55 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- sst2
metrics:
- accuracy
- f1
model-index:
- name: finetuning-bert-base-uncased-on-sst2
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: sst2
type: sst2
config: default
split: validation
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.926605504587156
- name: F1
type: f1
value: 0.9285714285714286
---
<!-- 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-bert-base-uncased-on-sst2
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the sst2 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2424
- Accuracy: 0.9266
- F1: 0.9286
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,545 | [
[
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YakovElm/Qt5Classic_Balance_DATA_ratio_2 | 2023-05-31T04:14:15.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/Qt5Classic_Balance_DATA_ratio_2 | 0 | 2 | transformers | 2023-05-31T04:13:39 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Qt5Classic_Balance_DATA_ratio_2
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Qt5Classic_Balance_DATA_ratio_2
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.5704
- Train Accuracy: 0.6990
- Validation Loss: 0.6007
- Validation Accuracy: 0.6366
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.6162 | 0.6736 | 0.6984 | 0.6254 | 0 |
| 0.5881 | 0.6980 | 0.6512 | 0.6366 | 1 |
| 0.5704 | 0.6990 | 0.6007 | 0.6366 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,806 | [
[
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0.005718231201171875,
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YakovElm/Qt5Classic_Balance_DATA_ratio_3 | 2023-05-31T04:32:47.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/Qt5Classic_Balance_DATA_ratio_3 | 0 | 2 | transformers | 2023-05-31T04:32:12 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Qt5Classic_Balance_DATA_ratio_3
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Qt5Classic_Balance_DATA_ratio_3
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.4434
- Train Accuracy: 0.7877
- Validation Loss: 0.5431
- Validation Accuracy: 0.7125
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.5376 | 0.7602 | 0.5287 | 0.7548 | 0 |
| 0.5125 | 0.7616 | 0.5183 | 0.7548 | 1 |
| 0.4434 | 0.7877 | 0.5431 | 0.7125 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,806 | [
[
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YakovElm/Qt5Classic_Balance_DATA_ratio_4 | 2023-05-31T04:55:44.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/Qt5Classic_Balance_DATA_ratio_4 | 0 | 2 | transformers | 2023-05-31T04:55:08 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Qt5Classic_Balance_DATA_ratio_4
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Qt5Classic_Balance_DATA_ratio_4
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.4133
- Train Accuracy: 0.8297
- Validation Loss: 0.5084
- Validation Accuracy: 0.8105
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.4883 | 0.8009 | 0.4751 | 0.8020 | 0 |
| 0.4483 | 0.8116 | 0.4644 | 0.8020 | 1 |
| 0.4133 | 0.8297 | 0.5084 | 0.8105 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,806 | [
[
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sadananda/distilbert-base-uncased-finetuned-clinc | 2023-05-31T07:12:03.000Z | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:clinc_oos",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | text-classification | sadananda | null | null | sadananda/distilbert-base-uncased-finetuned-clinc | 0 | 2 | transformers | 2023-05-31T04:56:50 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- clinc_oos
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased-finetuned-clinc
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: clinc_oos
type: clinc_oos
config: plus
split: validation
args: plus
metrics:
- name: Accuracy
type: accuracy
value: 0.9180645161290323
---
<!-- 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-clinc
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the clinc_oos dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7720
- Accuracy: 0.9181
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 48
- eval_batch_size: 48
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 318 | 3.2887 | 0.7419 |
| 3.7868 | 2.0 | 636 | 1.8753 | 0.8371 |
| 3.7868 | 3.0 | 954 | 1.1570 | 0.8961 |
| 1.6927 | 4.0 | 1272 | 0.8573 | 0.9129 |
| 0.9056 | 5.0 | 1590 | 0.7720 | 0.9181 |
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,932 | [
[
-0.03399658203125,
-0.041656494140625,
0.01201629638671875,
0.007144927978515625,
-0.0271148681640625,
-0.02545166015625,
-0.012969970703125,
-0.00949859619140625,
0.0020160675048828125,
0.021759033203125,
-0.04620361328125,
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smarquie/dqn-SpaceInvadersNoFrameskip-v4 | 2023-05-31T05:01:02.000Z | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | smarquie | null | null | smarquie/dqn-SpaceInvadersNoFrameskip-v4 | 0 | 2 | stable-baselines3 | 2023-05-31T05:00:26 | ---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 582.00 +/- 249.58
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga smarquie -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga smarquie -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga smarquie
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 1000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
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YakovElm/Qt10Classic_Balance_DATA_ratio_Half | 2023-05-31T05:02:14.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/Qt10Classic_Balance_DATA_ratio_Half | 0 | 2 | transformers | 2023-05-31T05:01:39 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Qt10Classic_Balance_DATA_ratio_Half
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Qt10Classic_Balance_DATA_ratio_Half
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.5781
- Train Accuracy: 0.7063
- Validation Loss: 0.5501
- Validation Accuracy: 0.7222
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.6592 | 0.5741 | 0.6015 | 0.6746 | 0 |
| 0.6047 | 0.6825 | 0.5738 | 0.7540 | 1 |
| 0.5781 | 0.7063 | 0.5501 | 0.7222 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,814 | [
[
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0.01513671875,
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YakovElm/Qt10Classic_Balance_DATA_ratio_1 | 2023-05-31T05:09:45.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/Qt10Classic_Balance_DATA_ratio_1 | 0 | 2 | transformers | 2023-05-31T05:09:10 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Qt10Classic_Balance_DATA_ratio_1
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Qt10Classic_Balance_DATA_ratio_1
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.5377
- Train Accuracy: 0.7599
- Validation Loss: 0.7260
- Validation Accuracy: 0.5833
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.6707 | 0.5853 | 0.6418 | 0.6369 | 0 |
| 0.6033 | 0.6687 | 0.5982 | 0.6905 | 1 |
| 0.5377 | 0.7599 | 0.7260 | 0.5833 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,808 | [
[
-0.038665771484375,
-0.033782958984375,
0.0161590576171875,
0.005931854248046875,
-0.032501220703125,
-0.0233154296875,
-0.0045623779296875,
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0.01314544677734375,
0.01041412353515625,
-0.05364990234375,
-0.039703369140625,
-0.046844482421875,... |
p208p2002/gpt2-babi | 2023-05-31T08:06:12.000Z | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"dataset:facebook/babi_qa",
"arxiv:1502.05698",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | p208p2002 | null | null | p208p2002/gpt2-babi | 0 | 2 | transformers | 2023-05-31T05:15:41 | ---
datasets:
- facebook/babi_qa
---
Fine tune and evaluate transformer model on facebook's bAbi tasks.
> [Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks](https://arxiv.org/abs/1502.05698)
Training Code: [p208p2002/bAbi-tasks-with-transformer-model](https://github.com/p208p2002/bAbi-tasks-with-transformer-model)
|task_no|task_name |score|
|-------|----------------------|-----|
|qa1 |single-supporting-fact|100 |
|qa2 |two-supporting-facts |99.4 |
|qa3 |three-supporting-facts|62.0 |
|qa4 |two-arg-relations |100 |
|qa5 |three-arg-relations |96.0 |
|qa6 |yes-no-questions |100 |
|qa7 |counting |100 |
|qa8 |lists-sets |95.6 |
|qa9 |simple-negation |100 |
|qa10 | indefinite-knowledge |100 |
|qa11 | basic-coreference |100 |
|qa12 | conjunction |100 |
|qa13 | compound-coreference |100 |
|qa14 | time-reasoning |100 |
|qa15 | basic-deduction |100 |
|qa16 | basic-induction |100 |
|qa17 | positional-reasoning |100 |
|qa18 | size-reasoning |100 |
|qa19 | path-finding |100 |
|qa20 | agents-motivations |100 |
```python
# Please use with the follow template
INPUT_TEMPLATE = """
Context:
{context}
Question:
{question}
Answer:
{answer}
"""
input_text = INPUT_TEMPLATE.format_map({
"context":context,
"question":question,
"answer":answer
}).strip()
``` | 1,457 | [
[
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YakovElm/Qt10Classic_Balance_DATA_ratio_2 | 2023-05-31T05:20:14.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/Qt10Classic_Balance_DATA_ratio_2 | 0 | 2 | transformers | 2023-05-31T05:19:39 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Qt10Classic_Balance_DATA_ratio_2
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Qt10Classic_Balance_DATA_ratio_2
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.5061
- Train Accuracy: 0.7460
- Validation Loss: 0.5543
- Validation Accuracy: 0.6905
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.6105 | 0.6865 | 0.6598 | 0.6429 | 0 |
| 0.5750 | 0.6997 | 0.6021 | 0.6508 | 1 |
| 0.5061 | 0.7460 | 0.5543 | 0.6905 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,808 | [
[
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0.0158538818359375,
0.005512237548828125,
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YakovElm/Qt10Classic_Balance_DATA_ratio_3 | 2023-05-31T05:34:01.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/Qt10Classic_Balance_DATA_ratio_3 | 0 | 2 | transformers | 2023-05-31T05:33:25 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Qt10Classic_Balance_DATA_ratio_3
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Qt10Classic_Balance_DATA_ratio_3
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.4685
- Train Accuracy: 0.7708
- Validation Loss: 0.5219
- Validation Accuracy: 0.7440
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.5549 | 0.7520 | 0.5697 | 0.7381 | 0 |
| 0.5130 | 0.7510 | 0.5651 | 0.7440 | 1 |
| 0.4685 | 0.7708 | 0.5219 | 0.7440 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,808 | [
[
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0.017486572265625,
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0.01099395751953125,
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Akira10/distilbert-base-uncased-finetuned-clinc | 2023-05-31T07:12:13.000Z | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:clinc_oos",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | text-classification | Akira10 | null | null | Akira10/distilbert-base-uncased-finetuned-clinc | 0 | 2 | transformers | 2023-05-31T05:41:10 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- clinc_oos
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased-finetuned-clinc
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: clinc_oos
type: clinc_oos
config: plus
split: validation
args: plus
metrics:
- name: Accuracy
type: accuracy
value: 0.9145161290322581
---
<!-- 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-clinc
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the clinc_oos dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7724
- Accuracy: 0.9145
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 48
- eval_batch_size: 48
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 4.283 | 1.0 | 318 | 3.2777 | 0.7452 |
| 2.6225 | 2.0 | 636 | 1.8655 | 0.8371 |
| 1.5398 | 3.0 | 954 | 1.1527 | 0.8932 |
| 1.012 | 4.0 | 1272 | 0.8558 | 0.9090 |
| 0.7934 | 5.0 | 1590 | 0.7724 | 0.9145 |
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,932 | [
[
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0.003376007080078125,
0.022613525390625,
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YakovElm/Qt10Classic_Balance_DATA_ratio_4 | 2023-05-31T05:50:40.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/Qt10Classic_Balance_DATA_ratio_4 | 0 | 2 | transformers | 2023-05-31T05:50:06 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Qt10Classic_Balance_DATA_ratio_4
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Qt10Classic_Balance_DATA_ratio_4
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.4020
- Train Accuracy: 0.8230
- Validation Loss: 0.5593
- Validation Accuracy: 0.7619
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.4751 | 0.8103 | 0.5714 | 0.7571 | 0 |
| 0.4383 | 0.8119 | 0.5485 | 0.7571 | 1 |
| 0.4020 | 0.8230 | 0.5593 | 0.7619 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,808 | [
[
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0.01824951171875,
0.005828857421875,
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0.01195526123046875,
0.0115203857421875,
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YakovElm/Qt15Classic_Balance_DATA_ratio_Half | 2023-05-31T05:56:03.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/Qt15Classic_Balance_DATA_ratio_Half | 0 | 2 | transformers | 2023-05-31T05:55:28 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Qt15Classic_Balance_DATA_ratio_Half
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Qt15Classic_Balance_DATA_ratio_Half
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.5288
- Train Accuracy: 0.7700
- Validation Loss: 0.6552
- Validation Accuracy: 0.6600
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.5977 | 0.6933 | 0.6622 | 0.6100 | 0 |
| 0.5840 | 0.7300 | 0.6653 | 0.6400 | 1 |
| 0.5288 | 0.7700 | 0.6552 | 0.6600 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,814 | [
[
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0.01364898681640625,
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0.01023101806640625,
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-0.04156494140625,
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YakovElm/Qt15Classic_Balance_DATA_ratio_1 | 2023-05-31T06:02:32.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/Qt15Classic_Balance_DATA_ratio_1 | 0 | 2 | transformers | 2023-05-31T06:01:37 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Qt15Classic_Balance_DATA_ratio_1
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Qt15Classic_Balance_DATA_ratio_1
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.6387
- Train Accuracy: 0.6225
- Validation Loss: 0.6190
- Validation Accuracy: 0.6642
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.6983 | 0.5475 | 0.6646 | 0.4552 | 0 |
| 0.6680 | 0.5525 | 0.6700 | 0.6045 | 1 |
| 0.6387 | 0.6225 | 0.6190 | 0.6642 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,808 | [
[
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YakovElm/Qt15Classic_Balance_DATA_ratio_2 | 2023-05-31T06:11:14.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/Qt15Classic_Balance_DATA_ratio_2 | 0 | 2 | transformers | 2023-05-31T06:10:38 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Qt15Classic_Balance_DATA_ratio_2
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Qt15Classic_Balance_DATA_ratio_2
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.5878
- Train Accuracy: 0.7238
- Validation Loss: 0.6686
- Validation Accuracy: 0.5750
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.6288 | 0.6739 | 0.6947 | 0.6100 | 0 |
| 0.5963 | 0.7138 | 0.6473 | 0.6050 | 1 |
| 0.5878 | 0.7238 | 0.6686 | 0.5750 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,808 | [
[
-0.038787841796875,
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0.0154876708984375,
0.00745391845703125,
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anhdt-dsai-02/tuna_t0_v1.6 | 2023-05-31T12:30:02.000Z | [
"transformers",
"pytorch",
"tensorboard",
"mt5",
"text2text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text2text-generation | anhdt-dsai-02 | null | null | anhdt-dsai-02/tuna_t0_v1.6 | 0 | 2 | transformers | 2023-05-31T06:16:32 | ---
tags:
- generated_from_trainer
metrics:
- rouge
- bleu
model-index:
- name: tuna_t0_v1.6
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# tuna_t0_v1.6
This model is a fine-tuned version of [anhdt-dsai-02/tuna_t0_v1.4](https://huggingface.co/anhdt-dsai-02/tuna_t0_v1.4) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2728
- Rouge1: 48.7673
- Rouge2: 21.6362
- Rougel: 33.1174
- Rougelsum: 36.77
- Bleu: 5.5664
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Bleu |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:------:|
| 0.3131 | 1.0 | 7455 | 0.2811 | 47.5613 | 21.2753 | 32.3442 | 35.7895 | 5.3805 |
| 0.3524 | 2.0 | 14910 | 0.2757 | 48.8703 | 21.5877 | 33.0113 | 36.6099 | 6.0464 |
| 0.2785 | 3.0 | 22365 | 0.2734 | 48.9094 | 21.7508 | 33.3675 | 36.9757 | 5.9984 |
| 0.3074 | 4.0 | 29820 | 0.2728 | 48.7673 | 21.6362 | 33.1174 | 36.77 | 5.5664 |
### Framework versions
- Transformers 4.26.1
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.13.3
| 1,812 | [
[
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YakovElm/Qt15Classic_Balance_DATA_ratio_3 | 2023-05-31T06:22:35.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/Qt15Classic_Balance_DATA_ratio_3 | 0 | 2 | transformers | 2023-05-31T06:21:56 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Qt15Classic_Balance_DATA_ratio_3
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Qt15Classic_Balance_DATA_ratio_3
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.4696
- Train Accuracy: 0.7753
- Validation Loss: 0.6356
- Validation Accuracy: 0.5206
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.5718 | 0.7253 | 0.5475 | 0.7566 | 0 |
| 0.5318 | 0.7503 | 0.5253 | 0.7566 | 1 |
| 0.4696 | 0.7753 | 0.6356 | 0.5206 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,808 | [
[
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YakovElm/Qt15Classic_Balance_DATA_ratio_4 | 2023-05-31T06:36:11.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/Qt15Classic_Balance_DATA_ratio_4 | 0 | 2 | transformers | 2023-05-31T06:35:36 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Qt15Classic_Balance_DATA_ratio_4
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Qt15Classic_Balance_DATA_ratio_4
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.4523
- Train Accuracy: 0.8144
- Validation Loss: 0.4851
- Validation Accuracy: 0.7605
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.4776 | 0.8114 | 0.5124 | 0.7605 | 0 |
| 0.4510 | 0.8144 | 0.6845 | 0.7605 | 1 |
| 0.4523 | 0.8144 | 0.4851 | 0.7605 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,808 | [
[
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0.0073699951171875,
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0.01197052001953125,
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YakovElm/Qt20Classic_Balance_DATA_ratio_Half | 2023-05-31T06:40:56.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/Qt20Classic_Balance_DATA_ratio_Half | 0 | 2 | transformers | 2023-05-31T06:40:21 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Qt20Classic_Balance_DATA_ratio_Half
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Qt20Classic_Balance_DATA_ratio_Half
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.5791
- Train Accuracy: 0.6944
- Validation Loss: 0.6140
- Validation Accuracy: 0.7024
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.6662 | 0.5794 | 0.6231 | 0.6786 | 0 |
| 0.6139 | 0.6627 | 0.6083 | 0.6786 | 1 |
| 0.5791 | 0.6944 | 0.6140 | 0.7024 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,814 | [
[
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0.007022857666015625,
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Akira10/distilbert-base-uncased-distilled-clinc | 2023-05-31T08:00:03.000Z | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:clinc_oos",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | text-classification | Akira10 | null | null | Akira10/distilbert-base-uncased-distilled-clinc | 0 | 2 | transformers | 2023-05-31T06:43:22 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- clinc_oos
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased-distilled-clinc
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: clinc_oos
type: clinc_oos
config: plus
split: validation
args: plus
metrics:
- name: Accuracy
type: accuracy
value: 0.9438709677419355
---
<!-- 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-distilled-clinc
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the clinc_oos dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2192
- Accuracy: 0.9439
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 48
- eval_batch_size: 48
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.8173 | 1.0 | 318 | 1.2393 | 0.7426 |
| 0.9618 | 2.0 | 636 | 0.6148 | 0.8590 |
| 0.5073 | 3.0 | 954 | 0.3621 | 0.9158 |
| 0.3189 | 4.0 | 1272 | 0.2748 | 0.9319 |
| 0.2442 | 5.0 | 1590 | 0.2454 | 0.9394 |
| 0.2143 | 6.0 | 1908 | 0.2330 | 0.9419 |
| 0.1987 | 7.0 | 2226 | 0.2258 | 0.9432 |
| 0.1905 | 8.0 | 2544 | 0.2218 | 0.9442 |
| 0.1861 | 9.0 | 2862 | 0.2201 | 0.9439 |
| 0.1836 | 10.0 | 3180 | 0.2192 | 0.9439 |
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
| 2,243 | [
[
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0.01580810546875,
0.00600433349609375,
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YakovElm/Qt20Classic_Balance_DATA_ratio_1 | 2023-05-31T06:46:45.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/Qt20Classic_Balance_DATA_ratio_1 | 0 | 2 | transformers | 2023-05-31T06:46:11 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Qt20Classic_Balance_DATA_ratio_1
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Qt20Classic_Balance_DATA_ratio_1
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.5747
- Train Accuracy: 0.7054
- Validation Loss: 0.6443
- Validation Accuracy: 0.6607
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.6829 | 0.5387 | 0.6446 | 0.6607 | 0 |
| 0.6460 | 0.6220 | 0.6451 | 0.6071 | 1 |
| 0.5747 | 0.7054 | 0.6443 | 0.6607 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,808 | [
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-0.04620361328125,
-0.... |
YakovElm/Qt20Classic_Balance_DATA_ratio_2 | 2023-05-31T06:54:50.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/Qt20Classic_Balance_DATA_ratio_2 | 0 | 2 | transformers | 2023-05-31T06:54:16 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Qt20Classic_Balance_DATA_ratio_2
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Qt20Classic_Balance_DATA_ratio_2
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.6028
- Train Accuracy: 0.6706
- Validation Loss: 0.5639
- Validation Accuracy: 0.6905
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.6254 | 0.6667 | 0.5830 | 0.7143 | 0 |
| 0.6309 | 0.6429 | 0.5765 | 0.6905 | 1 |
| 0.6028 | 0.6706 | 0.5639 | 0.6905 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,808 | [
[
-0.037628173828125,
-0.033599853515625,
0.0161590576171875,
0.006771087646484375,
-0.03350830078125,
-0.0228271484375,
-0.00333404541015625,
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0.00975799560546875,
0.0124359130859375,
-0.054351806640625,
-0.03961181640625,
-0.046142578125,
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YakovElm/Qt20Classic_Balance_DATA_ratio_3 | 2023-05-31T07:04:49.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/Qt20Classic_Balance_DATA_ratio_3 | 0 | 2 | transformers | 2023-05-31T07:04:14 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Qt20Classic_Balance_DATA_ratio_3
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Qt20Classic_Balance_DATA_ratio_3
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.4749
- Train Accuracy: 0.7560
- Validation Loss: 0.5236
- Validation Accuracy: 0.7277
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.5616 | 0.7336 | 0.5621 | 0.7188 | 0 |
| 0.5182 | 0.7604 | 0.5585 | 0.7188 | 1 |
| 0.4749 | 0.7560 | 0.5236 | 0.7277 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,808 | [
[
-0.038909912109375,
-0.034576416015625,
0.0181121826171875,
0.007904052734375,
-0.032806396484375,
-0.02423095703125,
-0.0034313201904296875,
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0.008941650390625,
0.01358795166015625,
-0.053436279296875,
-0.042083740234375,
-0.045135498046875,
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YakovElm/Qt20Classic_Balance_DATA_ratio_4 | 2023-05-31T07:17:18.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | YakovElm | null | null | YakovElm/Qt20Classic_Balance_DATA_ratio_4 | 0 | 2 | transformers | 2023-05-31T07:16:42 | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Qt20Classic_Balance_DATA_ratio_4
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Qt20Classic_Balance_DATA_ratio_4
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.3856
- Train Accuracy: 0.8202
- Validation Loss: 0.5670
- Validation Accuracy: 0.7607
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.5112 | 0.7845 | 0.4737 | 0.7929 | 0 |
| 0.4574 | 0.7976 | 0.4595 | 0.7929 | 1 |
| 0.3856 | 0.8202 | 0.5670 | 0.7607 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,808 | [
[
-0.0394287109375,
-0.03289794921875,
0.0177459716796875,
0.006870269775390625,
-0.03271484375,
-0.02264404296875,
-0.0037078857421875,
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0.00923919677734375,
0.01285552978515625,
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-0.042144775390625,
-0.045440673828125,
-... |
pradeepiisc/distilbert-base-uncased-finetuned-emotion | 2023-05-31T08:53:27.000Z | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | text-classification | pradeepiisc | null | null | pradeepiisc/distilbert-base-uncased-finetuned-emotion | 0 | 2 | transformers | 2023-05-31T08:27:49 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
config: split
split: validation
args: split
metrics:
- name: Accuracy
type: accuracy
value: 0.9245
- name: F1
type: f1
value: 0.924530571560491
---
<!-- 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/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2262
- Accuracy: 0.9245
- F1: 0.9245
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.845 | 1.0 | 250 | 0.3265 | 0.9035 | 0.9010 |
| 0.253 | 2.0 | 500 | 0.2262 | 0.9245 | 0.9245 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.1+cu117
- Datasets 2.10.1
- Tokenizers 0.13.3
| 1,847 | [
[
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-0.040924072265625,
0.01441192626953125,
0.021636962890625,
-0.02655029296875,
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0.0105133056640625,
0.008331298828125,
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-0.0... |
MJ03/distilbert-base-uncased-finetuned-clinc | 2023-05-31T08:48:25.000Z | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:clinc_oos",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | text-classification | MJ03 | null | null | MJ03/distilbert-base-uncased-finetuned-clinc | 0 | 2 | transformers | 2023-05-31T08:40:30 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- clinc_oos
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased-finetuned-clinc
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: clinc_oos
type: clinc_oos
config: plus
split: validation
args: plus
metrics:
- name: Accuracy
type: accuracy
value: 0.9180645161290323
---
<!-- 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-clinc
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the clinc_oos dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7720
- Accuracy: 0.9181
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 48
- eval_batch_size: 48
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 4.2896 | 1.0 | 318 | 3.2887 | 0.7419 |
| 2.6282 | 2.0 | 636 | 1.8753 | 0.8371 |
| 1.548 | 3.0 | 954 | 1.1570 | 0.8961 |
| 1.0148 | 4.0 | 1272 | 0.8573 | 0.9129 |
| 0.7952 | 5.0 | 1590 | 0.7720 | 0.9181 |
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu118
- Datasets 1.16.1
- Tokenizers 0.13.3
| 1,932 | [
[
-0.03466796875,
-0.041015625,
0.0126190185546875,
0.00701141357421875,
-0.0272216796875,
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-0.01291656494140625,
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0.0030231475830078125,
0.02191162109375,
-0.04644775390625,
-0.048309326171875,
-0.057861328125,
-0.01176... |
gokuls/hBERTv2_new_pretrain_cola | 2023-06-06T06:27:33.000Z | [
"transformers",
"pytorch",
"tensorboard",
"hybridbert",
"text-classification",
"generated_from_trainer",
"en",
"dataset:glue",
"model-index",
"endpoints_compatible",
"region:us"
] | text-classification | gokuls | null | null | gokuls/hBERTv2_new_pretrain_cola | 0 | 2 | transformers | 2023-05-31T09:32:57 | ---
language:
- en
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- matthews_correlation
model-index:
- name: hBERTv2_new_pretrain_cola
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE COLA
type: glue
config: cola
split: validation
args: cola
metrics:
- name: Matthews Correlation
type: matthews_correlation
value: 0.0
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# hBERTv2_new_pretrain_cola
This model is a fine-tuned version of [gokuls/bert_12_layer_model_v2_complete_training_new](https://huggingface.co/gokuls/bert_12_layer_model_v2_complete_training_new) on the GLUE COLA dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6173
- Matthews Correlation: 0.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 4e-05
- train_batch_size: 128
- eval_batch_size: 128
- seed: 10
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.6294 | 1.0 | 67 | 0.6236 | 0.0 |
| 0.6169 | 2.0 | 134 | 0.6312 | 0.0 |
| 0.6115 | 3.0 | 201 | 0.6173 | 0.0 |
| 0.6372 | 4.0 | 268 | 0.6201 | 0.0 |
| 0.6087 | 5.0 | 335 | 0.6217 | 0.0 |
| 0.6086 | 6.0 | 402 | 0.6248 | 0.0 |
| 0.6113 | 7.0 | 469 | 0.6283 | 0.0 |
| 0.6109 | 8.0 | 536 | 0.6200 | 0.0 |
### Framework versions
- Transformers 4.29.2
- Pytorch 1.14.0a0+410ce96
- Datasets 2.12.0
- Tokenizers 0.13.3
| 2,319 | [
[
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-0.046295166015625,
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0.0194091796875,
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0.026763916015625,
0.0197601318359375,
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-0.03082275390625,
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gokuls/hBERTv1_new_pretrain_w_init__cola | 2023-06-06T06:30:52.000Z | [
"transformers",
"pytorch",
"tensorboard",
"hybridbert",
"text-classification",
"generated_from_trainer",
"en",
"dataset:glue",
"model-index",
"endpoints_compatible",
"region:us"
] | text-classification | gokuls | null | null | gokuls/hBERTv1_new_pretrain_w_init__cola | 0 | 2 | transformers | 2023-05-31T10:08:33 | ---
language:
- en
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- matthews_correlation
- accuracy
model-index:
- name: hBERTv1_new_pretrain_w_init__cola
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE COLA
type: glue
config: cola
split: validation
args: cola
metrics:
- name: Matthews Correlation
type: matthews_correlation
value: 0.0
- name: Accuracy
type: accuracy
value: 0.6912751793861389
---
<!-- 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. -->
# hBERTv1_new_pretrain_w_init__cola
This model is a fine-tuned version of [gokuls/bert_12_layer_model_v1_complete_training_new_wt_init](https://huggingface.co/gokuls/bert_12_layer_model_v1_complete_training_new_wt_init) on the GLUE COLA dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6171
- Matthews Correlation: 0.0
- Accuracy: 0.6913
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 4e-05
- train_batch_size: 128
- eval_batch_size: 128
- seed: 10
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|:--------:|
| 0.6355 | 1.0 | 67 | 0.6239 | 0.0 | 0.6913 |
| 0.6177 | 2.0 | 134 | 0.6211 | 0.0 | 0.6913 |
| 0.6142 | 3.0 | 201 | 0.6231 | 0.0 | 0.6913 |
| 0.6145 | 4.0 | 268 | 0.6171 | 0.0 | 0.6913 |
| 0.6102 | 5.0 | 335 | 0.6199 | 0.0 | 0.6913 |
| 0.6126 | 6.0 | 402 | 0.6184 | 0.0 | 0.6913 |
| 0.6127 | 7.0 | 469 | 0.6206 | 0.0 | 0.6913 |
| 0.6107 | 8.0 | 536 | 0.6185 | 0.0 | 0.6913 |
| 0.6086 | 9.0 | 603 | 0.6260 | 0.0 | 0.6913 |
### Framework versions
- Transformers 4.29.2
- Pytorch 1.14.0a0+410ce96
- Datasets 2.12.0
- Tokenizers 0.13.3
| 2,650 | [
[
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0.029144287109375,
0.0175323486328125,
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... |
Anmol0130/brand_bottle_prediction_v2 | 2023-05-31T10:52:27.000Z | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | Anmol0130 | null | null | Anmol0130/brand_bottle_prediction_v2 | 0 | 2 | transformers | 2023-05-31T10:15:09 | ---
tags:
- image-classification
- pytorch
- huggingpics
metrics:
- accuracy
model-index:
- name: brand_bottle_prediction_v2
results:
- task:
name: Image Classification
type: image-classification
metrics:
- name: Accuracy
type: accuracy
value: 1.0
---
# brand_bottle_prediction_v2
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/huggingpics).
## Example Images
#### bacardi_black
#### bacardi_carta_blanca
#### bombay_sapphire
#### coka_cola
#### martini
 | 971 | [
[
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0.0100860595703125,
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0.01486968994140625,
-0.033782958984375,
-0.0360107421875,
-0.04248046875,
0.00005... |
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