End of training
Browse files- README.md +45 -179
- all_results.json +21 -0
- config.json +1 -1
- eval_results.json +11 -0
- train_results.json +8 -0
- trainer_state.json +330 -0
- training_args.bin +3 -0
README.md
CHANGED
@@ -1,199 +1,65 @@
|
|
1 |
---
|
2 |
library_name: transformers
|
3 |
-
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
4 |
---
|
5 |
|
6 |
-
|
|
|
7 |
|
8 |
-
|
9 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
10 |
|
|
|
11 |
|
12 |
-
|
13 |
|
14 |
-
|
15 |
|
16 |
-
|
17 |
|
18 |
-
|
19 |
|
20 |
-
|
21 |
-
- **Funded by [optional]:** [More Information Needed]
|
22 |
-
- **Shared by [optional]:** [More Information Needed]
|
23 |
-
- **Model type:** [More Information Needed]
|
24 |
-
- **Language(s) (NLP):** [More Information Needed]
|
25 |
-
- **License:** [More Information Needed]
|
26 |
-
- **Finetuned from model [optional]:** [More Information Needed]
|
27 |
|
28 |
-
|
29 |
|
30 |
-
|
31 |
|
32 |
-
|
33 |
-
-
|
34 |
-
-
|
|
|
|
|
|
|
|
|
|
|
|
|
35 |
|
36 |
-
|
37 |
|
38 |
-
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
|
39 |
|
40 |
-
### Direct Use
|
41 |
|
42 |
-
|
43 |
|
44 |
-
|
45 |
-
|
46 |
-
|
47 |
-
|
48 |
-
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
|
49 |
-
|
50 |
-
[More Information Needed]
|
51 |
-
|
52 |
-
### Out-of-Scope Use
|
53 |
-
|
54 |
-
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
|
55 |
-
|
56 |
-
[More Information Needed]
|
57 |
-
|
58 |
-
## Bias, Risks, and Limitations
|
59 |
-
|
60 |
-
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
|
61 |
-
|
62 |
-
[More Information Needed]
|
63 |
-
|
64 |
-
### Recommendations
|
65 |
-
|
66 |
-
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
|
67 |
-
|
68 |
-
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
|
69 |
-
|
70 |
-
## How to Get Started with the Model
|
71 |
-
|
72 |
-
Use the code below to get started with the model.
|
73 |
-
|
74 |
-
[More Information Needed]
|
75 |
-
|
76 |
-
## Training Details
|
77 |
-
|
78 |
-
### Training Data
|
79 |
-
|
80 |
-
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
|
81 |
-
|
82 |
-
[More Information Needed]
|
83 |
-
|
84 |
-
### Training Procedure
|
85 |
-
|
86 |
-
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
|
87 |
-
|
88 |
-
#### Preprocessing [optional]
|
89 |
-
|
90 |
-
[More Information Needed]
|
91 |
-
|
92 |
-
|
93 |
-
#### Training Hyperparameters
|
94 |
-
|
95 |
-
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
|
96 |
-
|
97 |
-
#### Speeds, Sizes, Times [optional]
|
98 |
-
|
99 |
-
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
|
100 |
-
|
101 |
-
[More Information Needed]
|
102 |
-
|
103 |
-
## Evaluation
|
104 |
-
|
105 |
-
<!-- This section describes the evaluation protocols and provides the results. -->
|
106 |
-
|
107 |
-
### Testing Data, Factors & Metrics
|
108 |
-
|
109 |
-
#### Testing Data
|
110 |
-
|
111 |
-
<!-- This should link to a Dataset Card if possible. -->
|
112 |
-
|
113 |
-
[More Information Needed]
|
114 |
-
|
115 |
-
#### Factors
|
116 |
-
|
117 |
-
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
|
118 |
-
|
119 |
-
[More Information Needed]
|
120 |
-
|
121 |
-
#### Metrics
|
122 |
-
|
123 |
-
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
|
124 |
-
|
125 |
-
[More Information Needed]
|
126 |
-
|
127 |
-
### Results
|
128 |
-
|
129 |
-
[More Information Needed]
|
130 |
-
|
131 |
-
#### Summary
|
132 |
-
|
133 |
-
|
134 |
-
|
135 |
-
## Model Examination [optional]
|
136 |
-
|
137 |
-
<!-- Relevant interpretability work for the model goes here -->
|
138 |
-
|
139 |
-
[More Information Needed]
|
140 |
-
|
141 |
-
## Environmental Impact
|
142 |
-
|
143 |
-
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
|
144 |
-
|
145 |
-
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
|
146 |
-
|
147 |
-
- **Hardware Type:** [More Information Needed]
|
148 |
-
- **Hours used:** [More Information Needed]
|
149 |
-
- **Cloud Provider:** [More Information Needed]
|
150 |
-
- **Compute Region:** [More Information Needed]
|
151 |
-
- **Carbon Emitted:** [More Information Needed]
|
152 |
-
|
153 |
-
## Technical Specifications [optional]
|
154 |
-
|
155 |
-
### Model Architecture and Objective
|
156 |
-
|
157 |
-
[More Information Needed]
|
158 |
-
|
159 |
-
### Compute Infrastructure
|
160 |
-
|
161 |
-
[More Information Needed]
|
162 |
-
|
163 |
-
#### Hardware
|
164 |
-
|
165 |
-
[More Information Needed]
|
166 |
-
|
167 |
-
#### Software
|
168 |
-
|
169 |
-
[More Information Needed]
|
170 |
-
|
171 |
-
## Citation [optional]
|
172 |
-
|
173 |
-
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
|
174 |
-
|
175 |
-
**BibTeX:**
|
176 |
-
|
177 |
-
[More Information Needed]
|
178 |
-
|
179 |
-
**APA:**
|
180 |
-
|
181 |
-
[More Information Needed]
|
182 |
-
|
183 |
-
## Glossary [optional]
|
184 |
-
|
185 |
-
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
|
186 |
-
|
187 |
-
[More Information Needed]
|
188 |
-
|
189 |
-
## More Information [optional]
|
190 |
-
|
191 |
-
[More Information Needed]
|
192 |
-
|
193 |
-
## Model Card Authors [optional]
|
194 |
-
|
195 |
-
[More Information Needed]
|
196 |
-
|
197 |
-
## Model Card Contact
|
198 |
-
|
199 |
-
[More Information Needed]
|
|
|
1 |
---
|
2 |
library_name: transformers
|
3 |
+
license: apache-2.0
|
4 |
+
base_model: distilbert/distilbert-base-uncased
|
5 |
+
tags:
|
6 |
+
- generated_from_trainer
|
7 |
+
metrics:
|
8 |
+
- accuracy
|
9 |
+
- f1
|
10 |
+
- precision
|
11 |
+
- recall
|
12 |
+
model-index:
|
13 |
+
- name: DistriBert_v10-1
|
14 |
+
results: []
|
15 |
---
|
16 |
|
17 |
+
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
|
18 |
+
should probably proofread and complete it, then remove this comment. -->
|
19 |
|
20 |
+
# DistriBert_v10-1
|
21 |
|
22 |
+
This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on the None dataset.
|
23 |
+
It achieves the following results on the evaluation set:
|
24 |
+
- Accuracy: 0.9135
|
25 |
+
- F1: 0.9115
|
26 |
+
- Precision: 0.9122
|
27 |
+
- Recall: 0.9128
|
28 |
+
- Loss: 0.3513
|
29 |
|
30 |
+
## Model description
|
31 |
|
32 |
+
More information needed
|
33 |
|
34 |
+
## Intended uses & limitations
|
35 |
|
36 |
+
More information needed
|
37 |
|
38 |
+
## Training and evaluation data
|
39 |
|
40 |
+
More information needed
|
|
|
|
|
|
|
|
|
|
|
|
|
41 |
|
42 |
+
## Training procedure
|
43 |
|
44 |
+
### Training hyperparameters
|
45 |
|
46 |
+
The following hyperparameters were used during training:
|
47 |
+
- learning_rate: 2e-05
|
48 |
+
- train_batch_size: 32
|
49 |
+
- eval_batch_size: 32
|
50 |
+
- seed: 42
|
51 |
+
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
|
52 |
+
- lr_scheduler_type: cosine_with_restarts
|
53 |
+
- lr_scheduler_warmup_steps: 100
|
54 |
+
- num_epochs: 20
|
55 |
|
56 |
+
### Training results
|
57 |
|
|
|
58 |
|
|
|
59 |
|
60 |
+
### Framework versions
|
61 |
|
62 |
+
- Transformers 4.44.2
|
63 |
+
- Pytorch 2.5.0+cu121
|
64 |
+
- Datasets 3.1.0
|
65 |
+
- Tokenizers 0.19.1
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
all_results.json
ADDED
@@ -0,0 +1,21 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"epoch": 10.0,
|
3 |
+
"eval_accuracy": 0.9134920634920635,
|
4 |
+
"eval_f1": 0.9115252835945801,
|
5 |
+
"eval_loss": 0.3512510657310486,
|
6 |
+
"eval_precision": 0.9122290522908707,
|
7 |
+
"eval_recall": 0.9127652441899627,
|
8 |
+
"eval_runtime": 61.9253,
|
9 |
+
"eval_samples_per_second": 61.041,
|
10 |
+
"eval_steps_per_second": 1.922,
|
11 |
+
"total_flos": 1.16919589957632e+16,
|
12 |
+
"train_eval_accuracy": 0.9672335600907029,
|
13 |
+
"train_eval_f1": 0.9673423681503462,
|
14 |
+
"train_eval_loss": 0.11652734130620956,
|
15 |
+
"train_eval_precision": 0.967673238789642,
|
16 |
+
"train_eval_recall": 0.9673335278419569,
|
17 |
+
"train_loss": 0.7689362014549366,
|
18 |
+
"train_runtime": 6188.3292,
|
19 |
+
"train_samples_per_second": 28.505,
|
20 |
+
"train_steps_per_second": 0.892
|
21 |
+
}
|
config.json
CHANGED
@@ -1,5 +1,5 @@
|
|
1 |
{
|
2 |
-
"_name_or_path": "
|
3 |
"activation": "gelu",
|
4 |
"architectures": [
|
5 |
"DistilBertForSequenceClassification"
|
|
|
1 |
{
|
2 |
+
"_name_or_path": "distilbert/distilbert-base-uncased",
|
3 |
"activation": "gelu",
|
4 |
"architectures": [
|
5 |
"DistilBertForSequenceClassification"
|
eval_results.json
ADDED
@@ -0,0 +1,11 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"epoch": 10.0,
|
3 |
+
"eval_accuracy": 0.9134920634920635,
|
4 |
+
"eval_f1": 0.9115252835945801,
|
5 |
+
"eval_loss": 0.3512510657310486,
|
6 |
+
"eval_precision": 0.9122290522908707,
|
7 |
+
"eval_recall": 0.9127652441899627,
|
8 |
+
"eval_runtime": 61.9253,
|
9 |
+
"eval_samples_per_second": 61.041,
|
10 |
+
"eval_steps_per_second": 1.922
|
11 |
+
}
|
train_results.json
ADDED
@@ -0,0 +1,8 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"epoch": 10.0,
|
3 |
+
"total_flos": 1.16919589957632e+16,
|
4 |
+
"train_loss": 0.7689362014549366,
|
5 |
+
"train_runtime": 6188.3292,
|
6 |
+
"train_samples_per_second": 28.505,
|
7 |
+
"train_steps_per_second": 0.892
|
8 |
+
}
|
trainer_state.json
ADDED
@@ -0,0 +1,330 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"best_metric": null,
|
3 |
+
"best_model_checkpoint": null,
|
4 |
+
"epoch": 10.0,
|
5 |
+
"eval_steps": 500,
|
6 |
+
"global_step": 2760,
|
7 |
+
"is_hyper_param_search": false,
|
8 |
+
"is_local_process_zero": true,
|
9 |
+
"is_world_process_zero": true,
|
10 |
+
"log_history": [
|
11 |
+
{
|
12 |
+
"epoch": 1.0,
|
13 |
+
"step": 276,
|
14 |
+
"train_eval_accuracy": 0.4954648526077097,
|
15 |
+
"train_eval_f1": 0.424904604475739,
|
16 |
+
"train_eval_loss": 2.101224660873413,
|
17 |
+
"train_eval_precision": 0.4856527073524001,
|
18 |
+
"train_eval_recall": 0.4926378954436191,
|
19 |
+
"train_loss": 2.101224660873413,
|
20 |
+
"train_runtime": 144.8838,
|
21 |
+
"train_samples_per_second": 60.876,
|
22 |
+
"train_steps_per_second": 1.905
|
23 |
+
},
|
24 |
+
{
|
25 |
+
"epoch": 1.0,
|
26 |
+
"eval_accuracy": 0.4835978835978836,
|
27 |
+
"eval_f1": 0.4135688868128075,
|
28 |
+
"eval_loss": 2.1065914630889893,
|
29 |
+
"eval_precision": 0.4476136879464602,
|
30 |
+
"eval_recall": 0.48953657594776134,
|
31 |
+
"eval_runtime": 62.0697,
|
32 |
+
"eval_samples_per_second": 60.899,
|
33 |
+
"eval_steps_per_second": 1.917,
|
34 |
+
"step": 276
|
35 |
+
},
|
36 |
+
{
|
37 |
+
"epoch": 2.0,
|
38 |
+
"step": 552,
|
39 |
+
"train_eval_accuracy": 0.7447845804988662,
|
40 |
+
"train_eval_f1": 0.7128821278125044,
|
41 |
+
"train_eval_loss": 1.0038776397705078,
|
42 |
+
"train_eval_precision": 0.7489107215212647,
|
43 |
+
"train_eval_recall": 0.7458421207363585,
|
44 |
+
"train_loss": 1.0038776397705078,
|
45 |
+
"train_runtime": 145.0361,
|
46 |
+
"train_samples_per_second": 60.812,
|
47 |
+
"train_steps_per_second": 1.903
|
48 |
+
},
|
49 |
+
{
|
50 |
+
"epoch": 2.0,
|
51 |
+
"eval_accuracy": 0.7428571428571429,
|
52 |
+
"eval_f1": 0.7089773330055067,
|
53 |
+
"eval_loss": 1.0131804943084717,
|
54 |
+
"eval_precision": 0.7421511367537219,
|
55 |
+
"eval_recall": 0.7406592004709303,
|
56 |
+
"eval_runtime": 62.0776,
|
57 |
+
"eval_samples_per_second": 60.892,
|
58 |
+
"eval_steps_per_second": 1.917,
|
59 |
+
"step": 552
|
60 |
+
},
|
61 |
+
{
|
62 |
+
"epoch": 3.0,
|
63 |
+
"step": 828,
|
64 |
+
"train_eval_accuracy": 0.8630385487528345,
|
65 |
+
"train_eval_f1": 0.8603167863727107,
|
66 |
+
"train_eval_loss": 0.5634090304374695,
|
67 |
+
"train_eval_precision": 0.8737026471608341,
|
68 |
+
"train_eval_recall": 0.8625907729727306,
|
69 |
+
"train_loss": 0.5634090304374695,
|
70 |
+
"train_runtime": 144.9979,
|
71 |
+
"train_samples_per_second": 60.828,
|
72 |
+
"train_steps_per_second": 1.903
|
73 |
+
},
|
74 |
+
{
|
75 |
+
"epoch": 3.0,
|
76 |
+
"eval_accuracy": 0.8481481481481481,
|
77 |
+
"eval_f1": 0.8456142462826861,
|
78 |
+
"eval_loss": 0.5928145051002502,
|
79 |
+
"eval_precision": 0.8571463898409206,
|
80 |
+
"eval_recall": 0.8489798133243263,
|
81 |
+
"eval_runtime": 62.0895,
|
82 |
+
"eval_samples_per_second": 60.88,
|
83 |
+
"eval_steps_per_second": 1.917,
|
84 |
+
"step": 828
|
85 |
+
},
|
86 |
+
{
|
87 |
+
"epoch": 4.0,
|
88 |
+
"step": 1104,
|
89 |
+
"train_eval_accuracy": 0.9109977324263039,
|
90 |
+
"train_eval_f1": 0.9104882432709309,
|
91 |
+
"train_eval_loss": 0.3656206429004669,
|
92 |
+
"train_eval_precision": 0.9163348905783797,
|
93 |
+
"train_eval_recall": 0.9110465902769607,
|
94 |
+
"train_loss": 0.3656206727027893,
|
95 |
+
"train_runtime": 144.8557,
|
96 |
+
"train_samples_per_second": 60.888,
|
97 |
+
"train_steps_per_second": 1.905
|
98 |
+
},
|
99 |
+
{
|
100 |
+
"epoch": 4.0,
|
101 |
+
"eval_accuracy": 0.8962962962962963,
|
102 |
+
"eval_f1": 0.8935631085204128,
|
103 |
+
"eval_loss": 0.4208849370479584,
|
104 |
+
"eval_precision": 0.8990702798318864,
|
105 |
+
"eval_recall": 0.8957702000784453,
|
106 |
+
"eval_runtime": 62.1675,
|
107 |
+
"eval_samples_per_second": 60.803,
|
108 |
+
"eval_steps_per_second": 1.914,
|
109 |
+
"step": 1104
|
110 |
+
},
|
111 |
+
{
|
112 |
+
"epoch": 5.0,
|
113 |
+
"step": 1380,
|
114 |
+
"train_eval_accuracy": 0.9232426303854875,
|
115 |
+
"train_eval_f1": 0.9229682041537378,
|
116 |
+
"train_eval_loss": 0.2874959707260132,
|
117 |
+
"train_eval_precision": 0.9273281861728557,
|
118 |
+
"train_eval_recall": 0.9227952922309719,
|
119 |
+
"train_loss": 0.28749600052833557,
|
120 |
+
"train_runtime": 145.0525,
|
121 |
+
"train_samples_per_second": 60.806,
|
122 |
+
"train_steps_per_second": 1.903
|
123 |
+
},
|
124 |
+
{
|
125 |
+
"epoch": 5.0,
|
126 |
+
"eval_accuracy": 0.9029100529100529,
|
127 |
+
"eval_f1": 0.9011946016504275,
|
128 |
+
"eval_loss": 0.37733063101768494,
|
129 |
+
"eval_precision": 0.9049954124488648,
|
130 |
+
"eval_recall": 0.9036354818715393,
|
131 |
+
"eval_runtime": 62.1416,
|
132 |
+
"eval_samples_per_second": 60.829,
|
133 |
+
"eval_steps_per_second": 1.915,
|
134 |
+
"step": 1380
|
135 |
+
},
|
136 |
+
{
|
137 |
+
"epoch": 6.0,
|
138 |
+
"step": 1656,
|
139 |
+
"train_eval_accuracy": 0.9370748299319728,
|
140 |
+
"train_eval_f1": 0.9368912367928741,
|
141 |
+
"train_eval_loss": 0.23322050273418427,
|
142 |
+
"train_eval_precision": 0.939204184223903,
|
143 |
+
"train_eval_recall": 0.937322301823473,
|
144 |
+
"train_loss": 0.23322050273418427,
|
145 |
+
"train_runtime": 144.9845,
|
146 |
+
"train_samples_per_second": 60.834,
|
147 |
+
"train_steps_per_second": 1.904
|
148 |
+
},
|
149 |
+
{
|
150 |
+
"epoch": 6.0,
|
151 |
+
"eval_accuracy": 0.9105820105820106,
|
152 |
+
"eval_f1": 0.908396393828423,
|
153 |
+
"eval_loss": 0.3413718342781067,
|
154 |
+
"eval_precision": 0.9102975744779533,
|
155 |
+
"eval_recall": 0.9096675456615884,
|
156 |
+
"eval_runtime": 62.1169,
|
157 |
+
"eval_samples_per_second": 60.853,
|
158 |
+
"eval_steps_per_second": 1.916,
|
159 |
+
"step": 1656
|
160 |
+
},
|
161 |
+
{
|
162 |
+
"epoch": 7.0,
|
163 |
+
"step": 1932,
|
164 |
+
"train_eval_accuracy": 0.9435374149659864,
|
165 |
+
"train_eval_f1": 0.943453224205871,
|
166 |
+
"train_eval_loss": 0.20147815346717834,
|
167 |
+
"train_eval_precision": 0.9469882171165018,
|
168 |
+
"train_eval_recall": 0.9435449695196283,
|
169 |
+
"train_loss": 0.20147816836833954,
|
170 |
+
"train_runtime": 144.7624,
|
171 |
+
"train_samples_per_second": 60.927,
|
172 |
+
"train_steps_per_second": 1.907
|
173 |
+
},
|
174 |
+
{
|
175 |
+
"epoch": 7.0,
|
176 |
+
"eval_accuracy": 0.9121693121693122,
|
177 |
+
"eval_f1": 0.9103139018247439,
|
178 |
+
"eval_loss": 0.3457355499267578,
|
179 |
+
"eval_precision": 0.9132402827313028,
|
180 |
+
"eval_recall": 0.9120503817924005,
|
181 |
+
"eval_runtime": 62.1165,
|
182 |
+
"eval_samples_per_second": 60.853,
|
183 |
+
"eval_steps_per_second": 1.916,
|
184 |
+
"step": 1932
|
185 |
+
},
|
186 |
+
{
|
187 |
+
"epoch": 8.0,
|
188 |
+
"step": 2208,
|
189 |
+
"train_eval_accuracy": 0.9518140589569161,
|
190 |
+
"train_eval_f1": 0.9517198736909416,
|
191 |
+
"train_eval_loss": 0.1689794510602951,
|
192 |
+
"train_eval_precision": 0.9547315428415143,
|
193 |
+
"train_eval_recall": 0.9519285064221675,
|
194 |
+
"train_loss": 0.16897942125797272,
|
195 |
+
"train_runtime": 145.0413,
|
196 |
+
"train_samples_per_second": 60.81,
|
197 |
+
"train_steps_per_second": 1.903
|
198 |
+
},
|
199 |
+
{
|
200 |
+
"epoch": 8.0,
|
201 |
+
"eval_accuracy": 0.9119047619047619,
|
202 |
+
"eval_f1": 0.9099505788900774,
|
203 |
+
"eval_loss": 0.3478126525878906,
|
204 |
+
"eval_precision": 0.9135423252017797,
|
205 |
+
"eval_recall": 0.9116997470551864,
|
206 |
+
"eval_runtime": 62.1667,
|
207 |
+
"eval_samples_per_second": 60.804,
|
208 |
+
"eval_steps_per_second": 1.914,
|
209 |
+
"step": 2208
|
210 |
+
},
|
211 |
+
{
|
212 |
+
"epoch": 9.0,
|
213 |
+
"step": 2484,
|
214 |
+
"train_eval_accuracy": 0.9597505668934241,
|
215 |
+
"train_eval_f1": 0.9598126383463436,
|
216 |
+
"train_eval_loss": 0.13835597038269043,
|
217 |
+
"train_eval_precision": 0.9614206068146748,
|
218 |
+
"train_eval_recall": 0.9599367994432203,
|
219 |
+
"train_loss": 0.13835598528385162,
|
220 |
+
"train_runtime": 144.9973,
|
221 |
+
"train_samples_per_second": 60.829,
|
222 |
+
"train_steps_per_second": 1.903
|
223 |
+
},
|
224 |
+
{
|
225 |
+
"epoch": 9.0,
|
226 |
+
"eval_accuracy": 0.9132275132275133,
|
227 |
+
"eval_f1": 0.9114074718878633,
|
228 |
+
"eval_loss": 0.3454839885234833,
|
229 |
+
"eval_precision": 0.9133521030992556,
|
230 |
+
"eval_recall": 0.91269076612755,
|
231 |
+
"eval_runtime": 62.1786,
|
232 |
+
"eval_samples_per_second": 60.793,
|
233 |
+
"eval_steps_per_second": 1.914,
|
234 |
+
"step": 2484
|
235 |
+
},
|
236 |
+
{
|
237 |
+
"epoch": 10.0,
|
238 |
+
"step": 2760,
|
239 |
+
"train_eval_accuracy": 0.9672335600907029,
|
240 |
+
"train_eval_f1": 0.9673423681503462,
|
241 |
+
"train_eval_loss": 0.11652734130620956,
|
242 |
+
"train_eval_precision": 0.967673238789642,
|
243 |
+
"train_eval_recall": 0.9673335278419569,
|
244 |
+
"train_loss": 0.11652734875679016,
|
245 |
+
"train_runtime": 145.0683,
|
246 |
+
"train_samples_per_second": 60.799,
|
247 |
+
"train_steps_per_second": 1.903
|
248 |
+
},
|
249 |
+
{
|
250 |
+
"epoch": 10.0,
|
251 |
+
"eval_accuracy": 0.9134920634920635,
|
252 |
+
"eval_f1": 0.9115252835945801,
|
253 |
+
"eval_loss": 0.3512510657310486,
|
254 |
+
"eval_precision": 0.9122290522908707,
|
255 |
+
"eval_recall": 0.9127652441899627,
|
256 |
+
"eval_runtime": 62.1606,
|
257 |
+
"eval_samples_per_second": 60.81,
|
258 |
+
"eval_steps_per_second": 1.914,
|
259 |
+
"step": 2760
|
260 |
+
},
|
261 |
+
{
|
262 |
+
"epoch": 10.0,
|
263 |
+
"step": 2760,
|
264 |
+
"total_flos": 1.16919589957632e+16,
|
265 |
+
"train_loss": 0.7689362014549366,
|
266 |
+
"train_runtime": 6188.3292,
|
267 |
+
"train_samples_per_second": 28.505,
|
268 |
+
"train_steps_per_second": 0.892
|
269 |
+
},
|
270 |
+
{
|
271 |
+
"epoch": 10.0,
|
272 |
+
"eval_accuracy": 0.9134920634920635,
|
273 |
+
"eval_f1": 0.9115252835945801,
|
274 |
+
"eval_loss": 0.3512510657310486,
|
275 |
+
"eval_precision": 0.9122290522908707,
|
276 |
+
"eval_recall": 0.9127652441899627,
|
277 |
+
"eval_runtime": 62.1566,
|
278 |
+
"eval_samples_per_second": 60.814,
|
279 |
+
"eval_steps_per_second": 1.915,
|
280 |
+
"step": 2760
|
281 |
+
},
|
282 |
+
{
|
283 |
+
"epoch": 10.0,
|
284 |
+
"step": 2760,
|
285 |
+
"train_en_eval_accuracy": 0.9672335600907029,
|
286 |
+
"train_en_eval_f1": 0.9673423681503462,
|
287 |
+
"train_en_eval_loss": 0.11652734130620956,
|
288 |
+
"train_en_eval_precision": 0.967673238789642,
|
289 |
+
"train_en_eval_recall": 0.9673335278419569,
|
290 |
+
"train_en_loss": 0.11652734875679016,
|
291 |
+
"train_en_runtime": 145.0476,
|
292 |
+
"train_en_samples_per_second": 60.808,
|
293 |
+
"train_en_steps_per_second": 1.903
|
294 |
+
},
|
295 |
+
{
|
296 |
+
"epoch": 10.0,
|
297 |
+
"step": 2760,
|
298 |
+
"test_en_eval_accuracy": 0.9134920634920635,
|
299 |
+
"test_en_eval_f1": 0.9115252835945801,
|
300 |
+
"test_en_eval_loss": 0.3512510657310486,
|
301 |
+
"test_en_eval_precision": 0.9122290522908707,
|
302 |
+
"test_en_eval_recall": 0.9127652441899627,
|
303 |
+
"test_en_loss": 0.3512510657310486,
|
304 |
+
"test_en_runtime": 62.1457,
|
305 |
+
"test_en_samples_per_second": 60.825,
|
306 |
+
"test_en_steps_per_second": 1.915
|
307 |
+
}
|
308 |
+
],
|
309 |
+
"logging_steps": 500,
|
310 |
+
"max_steps": 5520,
|
311 |
+
"num_input_tokens_seen": 0,
|
312 |
+
"num_train_epochs": 20,
|
313 |
+
"save_steps": 500,
|
314 |
+
"stateful_callbacks": {
|
315 |
+
"TrainerControl": {
|
316 |
+
"args": {
|
317 |
+
"should_epoch_stop": false,
|
318 |
+
"should_evaluate": false,
|
319 |
+
"should_log": false,
|
320 |
+
"should_save": true,
|
321 |
+
"should_training_stop": true
|
322 |
+
},
|
323 |
+
"attributes": {}
|
324 |
+
}
|
325 |
+
},
|
326 |
+
"total_flos": 1.16919589957632e+16,
|
327 |
+
"train_batch_size": 32,
|
328 |
+
"trial_name": null,
|
329 |
+
"trial_params": null
|
330 |
+
}
|
training_args.bin
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:bcb42ec07bcecc4b0588222416d41db3233f3b190bcd4fad447cda42b4a96fd0
|
3 |
+
size 5176
|