File size: 11,501 Bytes
291ed0b
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
b4ba2d0
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
291ed0b
 
 
 
b4ba2d0
 
 
291ed0b
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
---
license: mit
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
- precision
- recall
- accuracy
model-index:
- name: xlm-roberta-base-finetuned-panx-en
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: xtreme
      type: xtreme
      config: PAN-X.en
      split: validation
      args: PAN-X.en
    metrics:
    - name: F1
      type: f1
      value: 0.8236654056326187
    - name: Precision
      type: precision
      value: 0.8163449520899875
    - name: Recall
      type: recall
      value: 0.8311183373391772
    - name: Accuracy
      type: accuracy
      value: 0.8236654056326187
---

<!-- 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. -->

# xlm-roberta-base-finetuned-panx-en

This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2487
- F1: 0.8237
- Precision: 0.8163
- Recall: 0.8311
- Accuracy: 0.8237

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | F1     | Precision | Recall | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:------:|:---------:|:------:|:--------:|
| 1.2644        | 0.03  | 24   | 0.8175          | 0.4212 | 0.3696    | 0.4897 | 0.4212   |
| 0.7209        | 0.06  | 48   | 0.5633          | 0.4817 | 0.4190    | 0.5665 | 0.4817   |
| 0.5951        | 0.09  | 72   | 0.4670          | 0.6059 | 0.5588    | 0.6617 | 0.6059   |
| 0.4475        | 0.12  | 96   | 0.4425          | 0.6659 | 0.6336    | 0.7016 | 0.6659   |
| 0.4978        | 0.14  | 120  | 0.4469          | 0.6375 | 0.5930    | 0.6892 | 0.6375   |
| 0.4383        | 0.17  | 144  | 0.4093          | 0.7003 | 0.6668    | 0.7374 | 0.7003   |
| 0.4148        | 0.2   | 168  | 0.3688          | 0.7122 | 0.6877    | 0.7387 | 0.7122   |
| 0.4513        | 0.23  | 192  | 0.3700          | 0.7236 | 0.7081    | 0.7397 | 0.7236   |
| 0.3786        | 0.26  | 216  | 0.3666          | 0.7304 | 0.7125    | 0.7493 | 0.7304   |
| 0.425         | 0.29  | 240  | 0.3652          | 0.7046 | 0.6874    | 0.7227 | 0.7046   |
| 0.4014        | 0.32  | 264  | 0.3438          | 0.7246 | 0.6964    | 0.7552 | 0.7246   |
| 0.3789        | 0.35  | 288  | 0.3533          | 0.7208 | 0.6922    | 0.7519 | 0.7208   |
| 0.4032        | 0.37  | 312  | 0.3567          | 0.7252 | 0.7125    | 0.7383 | 0.7252   |
| 0.371         | 0.4   | 336  | 0.3282          | 0.7433 | 0.7255    | 0.7620 | 0.7433   |
| 0.3397        | 0.43  | 360  | 0.3304          | 0.7522 | 0.7312    | 0.7745 | 0.7522   |
| 0.3871        | 0.46  | 384  | 0.3244          | 0.7427 | 0.7160    | 0.7715 | 0.7427   |
| 0.3461        | 0.49  | 408  | 0.3284          | 0.7520 | 0.7298    | 0.7756 | 0.7520   |
| 0.3504        | 0.52  | 432  | 0.3049          | 0.7574 | 0.7418    | 0.7737 | 0.7574   |
| 0.3387        | 0.55  | 456  | 0.3178          | 0.7717 | 0.7537    | 0.7906 | 0.7717   |
| 0.3259        | 0.58  | 480  | 0.3026          | 0.7738 | 0.7636    | 0.7843 | 0.7738   |
| 0.3473        | 0.6   | 504  | 0.3254          | 0.7324 | 0.7090    | 0.7574 | 0.7324   |
| 0.2893        | 0.63  | 528  | 0.3102          | 0.7689 | 0.7571    | 0.7810 | 0.7689   |
| 0.3669        | 0.66  | 552  | 0.3119          | 0.7631 | 0.7528    | 0.7737 | 0.7631   |
| 0.312         | 0.69  | 576  | 0.2963          | 0.7818 | 0.7734    | 0.7905 | 0.7818   |
| 0.297         | 0.72  | 600  | 0.3217          | 0.7542 | 0.7332    | 0.7765 | 0.7542   |
| 0.3095        | 0.75  | 624  | 0.3038          | 0.7732 | 0.7580    | 0.7891 | 0.7732   |
| 0.3514        | 0.78  | 648  | 0.2913          | 0.7794 | 0.7669    | 0.7924 | 0.7794   |
| 0.2824        | 0.81  | 672  | 0.3008          | 0.7813 | 0.7752    | 0.7876 | 0.7813   |
| 0.3203        | 0.83  | 696  | 0.2915          | 0.7807 | 0.7641    | 0.7980 | 0.7807   |
| 0.3089        | 0.86  | 720  | 0.2941          | 0.7838 | 0.7755    | 0.7923 | 0.7838   |
| 0.3174        | 0.89  | 744  | 0.2986          | 0.7770 | 0.7609    | 0.7937 | 0.7770   |
| 0.3264        | 0.92  | 768  | 0.2783          | 0.7788 | 0.7630    | 0.7951 | 0.7788   |
| 0.2815        | 0.95  | 792  | 0.2861          | 0.7848 | 0.7704    | 0.7998 | 0.7848   |
| 0.2895        | 0.98  | 816  | 0.2799          | 0.7842 | 0.7702    | 0.7988 | 0.7842   |
| 0.3023        | 1.01  | 840  | 0.2818          | 0.7876 | 0.7722    | 0.8038 | 0.7876   |
| 0.2358        | 1.04  | 864  | 0.2924          | 0.7836 | 0.7750    | 0.7925 | 0.7836   |
| 0.2819        | 1.06  | 888  | 0.2861          | 0.7761 | 0.7696    | 0.7828 | 0.7761   |
| 0.2692        | 1.09  | 912  | 0.2924          | 0.7756 | 0.7680    | 0.7833 | 0.7756   |
| 0.2478        | 1.12  | 936  | 0.2963          | 0.7833 | 0.7599    | 0.8082 | 0.7833   |
| 0.2557        | 1.15  | 960  | 0.2960          | 0.7783 | 0.7814    | 0.7751 | 0.7783   |
| 0.3003        | 1.18  | 984  | 0.2656          | 0.7862 | 0.7727    | 0.8002 | 0.7862   |
| 0.2254        | 1.21  | 1008 | 0.2791          | 0.8007 | 0.7890    | 0.8129 | 0.8007   |
| 0.2496        | 1.24  | 1032 | 0.2702          | 0.7877 | 0.7701    | 0.8062 | 0.7877   |
| 0.2124        | 1.27  | 1056 | 0.2888          | 0.7952 | 0.7895    | 0.8011 | 0.7952   |
| 0.2841        | 1.29  | 1080 | 0.2761          | 0.7946 | 0.7870    | 0.8023 | 0.7946   |
| 0.2517        | 1.32  | 1104 | 0.2659          | 0.8026 | 0.7909    | 0.8146 | 0.8026   |
| 0.2355        | 1.35  | 1128 | 0.2681          | 0.8003 | 0.7876    | 0.8134 | 0.8003   |
| 0.2402        | 1.38  | 1152 | 0.2701          | 0.7991 | 0.7892    | 0.8093 | 0.7991   |
| 0.2296        | 1.41  | 1176 | 0.2753          | 0.7946 | 0.7819    | 0.8077 | 0.7946   |
| 0.2453        | 1.44  | 1200 | 0.2696          | 0.8029 | 0.7912    | 0.8149 | 0.8029   |
| 0.2689        | 1.47  | 1224 | 0.2700          | 0.7936 | 0.7819    | 0.8056 | 0.7936   |
| 0.2362        | 1.5   | 1248 | 0.2705          | 0.8028 | 0.8005    | 0.8051 | 0.8028   |
| 0.226         | 1.53  | 1272 | 0.2642          | 0.8042 | 0.7910    | 0.8180 | 0.8042   |
| 0.2139        | 1.55  | 1296 | 0.2690          | 0.8013 | 0.7942    | 0.8084 | 0.8013   |
| 0.2744        | 1.58  | 1320 | 0.2619          | 0.7999 | 0.7841    | 0.8163 | 0.7999   |
| 0.2015        | 1.61  | 1344 | 0.2640          | 0.8066 | 0.8035    | 0.8098 | 0.8066   |
| 0.1949        | 1.64  | 1368 | 0.2750          | 0.8075 | 0.8023    | 0.8129 | 0.8075   |
| 0.2259        | 1.67  | 1392 | 0.2669          | 0.8092 | 0.7997    | 0.8189 | 0.8092   |
| 0.1884        | 1.7   | 1416 | 0.2729          | 0.8061 | 0.7990    | 0.8133 | 0.8061   |
| 0.1868        | 1.73  | 1440 | 0.2679          | 0.8083 | 0.8007    | 0.8161 | 0.8083   |
| 0.2292        | 1.76  | 1464 | 0.2658          | 0.8055 | 0.7954    | 0.8158 | 0.8055   |
| 0.22          | 1.78  | 1488 | 0.2610          | 0.8066 | 0.8006    | 0.8126 | 0.8066   |
| 0.2335        | 1.81  | 1512 | 0.2613          | 0.7997 | 0.7816    | 0.8185 | 0.7997   |
| 0.2379        | 1.84  | 1536 | 0.2495          | 0.8081 | 0.7975    | 0.8190 | 0.8081   |
| 0.2394        | 1.87  | 1560 | 0.2619          | 0.8063 | 0.7951    | 0.8177 | 0.8063   |
| 0.2526        | 1.9   | 1584 | 0.2502          | 0.8116 | 0.8032    | 0.8202 | 0.8116   |
| 0.2167        | 1.93  | 1608 | 0.2528          | 0.8134 | 0.8000    | 0.8273 | 0.8134   |
| 0.2354        | 1.96  | 1632 | 0.2449          | 0.8099 | 0.8013    | 0.8188 | 0.8099   |
| 0.2808        | 1.99  | 1656 | 0.2469          | 0.8067 | 0.7938    | 0.8201 | 0.8067   |
| 0.1924        | 2.01  | 1680 | 0.2487          | 0.8077 | 0.7930    | 0.8229 | 0.8077   |
| 0.1498        | 2.04  | 1704 | 0.2619          | 0.8127 | 0.8015    | 0.8242 | 0.8127   |
| 0.2           | 2.07  | 1728 | 0.2590          | 0.8133 | 0.8044    | 0.8224 | 0.8133   |
| 0.151         | 2.1   | 1752 | 0.2623          | 0.8066 | 0.7949    | 0.8186 | 0.8066   |
| 0.1646        | 2.13  | 1776 | 0.2632          | 0.8186 | 0.8137    | 0.8236 | 0.8186   |
| 0.1659        | 2.16  | 1800 | 0.2561          | 0.8188 | 0.8096    | 0.8281 | 0.8188   |
| 0.1888        | 2.19  | 1824 | 0.2549          | 0.8136 | 0.8038    | 0.8237 | 0.8136   |
| 0.2084        | 2.22  | 1848 | 0.2557          | 0.8141 | 0.8087    | 0.8197 | 0.8141   |
| 0.1571        | 2.24  | 1872 | 0.2697          | 0.8150 | 0.8053    | 0.8249 | 0.8150   |
| 0.1541        | 2.27  | 1896 | 0.2605          | 0.8191 | 0.8121    | 0.8262 | 0.8191   |
| 0.1586        | 2.3   | 1920 | 0.2742          | 0.8109 | 0.8073    | 0.8144 | 0.8109   |
| 0.1641        | 2.33  | 1944 | 0.2679          | 0.8148 | 0.8104    | 0.8193 | 0.8148   |
| 0.1914        | 2.36  | 1968 | 0.2596          | 0.8159 | 0.8056    | 0.8265 | 0.8159   |
| 0.1441        | 2.39  | 1992 | 0.2644          | 0.8183 | 0.8139    | 0.8226 | 0.8183   |
| 0.1672        | 2.42  | 2016 | 0.2652          | 0.8180 | 0.8081    | 0.8281 | 0.8180   |
| 0.1852        | 2.45  | 2040 | 0.2576          | 0.8205 | 0.8101    | 0.8313 | 0.8205   |
| 0.192         | 2.47  | 2064 | 0.2459          | 0.8179 | 0.8063    | 0.8298 | 0.8179   |
| 0.1698        | 2.5   | 2088 | 0.2482          | 0.8213 | 0.8149    | 0.8277 | 0.8213   |
| 0.1802        | 2.53  | 2112 | 0.2519          | 0.8155 | 0.8066    | 0.8247 | 0.8155   |
| 0.1619        | 2.56  | 2136 | 0.2582          | 0.8175 | 0.8036    | 0.8319 | 0.8175   |
| 0.1974        | 2.59  | 2160 | 0.2535          | 0.8184 | 0.8108    | 0.8261 | 0.8184   |
| 0.1655        | 2.62  | 2184 | 0.2514          | 0.8229 | 0.8165    | 0.8295 | 0.8229   |
| 0.1844        | 2.65  | 2208 | 0.2536          | 0.8208 | 0.8152    | 0.8264 | 0.8208   |
| 0.1601        | 2.68  | 2232 | 0.2531          | 0.8194 | 0.8104    | 0.8286 | 0.8194   |
| 0.161         | 2.71  | 2256 | 0.2508          | 0.8226 | 0.8145    | 0.8310 | 0.8226   |
| 0.1672        | 2.73  | 2280 | 0.2527          | 0.8216 | 0.8137    | 0.8296 | 0.8216   |
| 0.2053        | 2.76  | 2304 | 0.2482          | 0.8208 | 0.8112    | 0.8306 | 0.8208   |
| 0.1776        | 2.79  | 2328 | 0.2486          | 0.8215 | 0.8143    | 0.8288 | 0.8215   |
| 0.1559        | 2.82  | 2352 | 0.2495          | 0.8233 | 0.8156    | 0.8312 | 0.8233   |
| 0.1509        | 2.85  | 2376 | 0.2472          | 0.8231 | 0.8142    | 0.8322 | 0.8231   |
| 0.1695        | 2.88  | 2400 | 0.2465          | 0.8229 | 0.8134    | 0.8326 | 0.8229   |
| 0.1523        | 2.91  | 2424 | 0.2466          | 0.8234 | 0.8154    | 0.8315 | 0.8234   |
| 0.1525        | 2.94  | 2448 | 0.2478          | 0.8241 | 0.8165    | 0.8319 | 0.8241   |
| 0.1386        | 2.96  | 2472 | 0.2486          | 0.8236 | 0.8164    | 0.8309 | 0.8236   |
| 0.1532        | 2.99  | 2496 | 0.2487          | 0.8237 | 0.8163    | 0.8311 | 0.8237   |


### Framework versions

- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.0
- Tokenizers 0.13.3